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United States Patent6088722
Herz , ; et al.July 11, 2000
Title
System and method for scheduling broadcast of and access to video programs and other data using customer profiles
Abstract
A system and method for scheduling the receipt of desired movies and other forms of data from a network, which simultaneously distributes many sources of such data to many customers, as in a cable television system. Customer profiles are developed for the recipient describing how important certain characteristics of the broadcast video program, movie, or other data are to each customer. From these profiles, an "agreement matrix" is calculated by comparing the recipient's profiles to the actual profiles of the characteristics of the available video programs, movies, or other data. The agreement matrix thus characterizes the attractiveness of each video program, movie, or other data to each prospective customer. "Virtual" channels are generated from the agreement matrix to produce a series of video or data programming which will provide the greatest satisfaction to each customer. Feedback paths are also provided so that the customer's profiles and/or the profiles of the video programs or other data may be modified to reflect actual usage, and so that the data downloaded to the customer's set top terminal may be minimized. Kiosks are also developed which assist customers in the selection of videos, music, books, and the like in accordance with the customer's objective profiles.
Inventors:Herz; Frederick (Canaan Valley, Davis WV), Ungar; Lyle  (Philadephia, PA), Zhang; Jian  (Cherry Hill, NJ), Wachob; David  (Elkins Park, PA), Salganicoff; Marcos  (Phiadelphia, PA)
Appl. No.:849589
Filed:December 24, 1997
PCT 102e Date:December 24, 1997
PCT 371 Date:December 24, 1997
PCT File Date:November 29, 1995
PCT No:PCT/US95/15429
PCT Pub Date:June 6, 1996
PCT Pub No:WO96/17467
Current U.S. Class:709/217 725/46 
Field of Search:395/200.47-200.49 455/2,4.1,4.2,5.1,6.2 380/6-8,10,11-20,21 348/1,6,7,10,12,13,906,2
U.S. Patent Documents
4170782October 1979Miller
4264924April 1981Freeman
4381522April 1983Lambert
4694490September 1987Harvey et al.
4704725November 1987Harvey et al.
4706121November 1987Young
4745549May 1988Hashimoto
4751578June 1988Reiter et al.
4870579September 1989Hey
4965825October 1990Harvey et al.
4977455December 1990Young
4996642February 1991Hey
5003591March 1991Kauffman et al.
5075771December 1991Hashimoto
5109414April 1992Harvey et al.
5151789September 1992Young
5155591October 1992Wacob
5223924June 1993Strubbe
5230020July 1993Hardy et al.
5233654August 1993Harvey et al.
5237157August 1993Kaplan
5245420September 1993Harney et al.
5251324October 1993McMullan, Jr.
5351075September 1994Herz et al.
5353121October 1994Young et al.
5410344April 1995Graves et al.
5469206November 1995Strubbe et al.
5483278January 1996Strubbe et al.
5534911July 1996Levitan
5541638July 1996Story
5559549September 1996Hendricks et al.
5600364February 1997Hendricks et al.
5717923February 1998Dedrick
5758257May 1998Herz et al.
Other References
Lambert, "Personalized Mass Media Corp. Seeks Patent Partners," Cable Technologies, Nov. 30, 1993. .
Moss, "ADcom Starts Cable-Meter Rollout in California," Multichannel News, Apr. 25, 1994, p. 34. .
Otis Port, "Wonder Chips--How They'll Make Computing Power Ultrafast and Ultracheap," Business Week, Jul. 4, 1994, pp. 86-92. .
Jonathan Berry, "A Potent New Tool for Selling Database Marketing," Business Week, Sep. 5, 1994, pp. 34-40. .
William L. Thomas, "Electronic Program Guide Applications--The Basics of System Design," 1994 NCTA Technical Papers, pp. 15-20. .
Judith H. Irven et al., "Multi-Media Information Services: A Laboratory Study," IEEE Communications Magazine, vol. 26, No. 6, Jun., 1988, pp. 24-44..~
Primary Examiner: Miller; John W.
Attorney, Agent or Firm:Woodcock Wasburn Kurtz Mackiewicz & Norris LLP
Parent Case Text


CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part application of U.S. patent application Ser. No. 08/346,425, filed Nov. 29, 1994 now U.S. Pat. No. 5,758,257.
Claims

We claim:
1. A method of scheduling customer access to data of a plurality of data objects, comprising the steps of:
creating at least one customer profile for a customer of said data, said customer profile indicating the customer's preferences for data having predetermined characteristics;
creating content profiles for each of said data objects, said content profiles indicating at least one of the presence and the degree of content of said predetermined characteristics in data of each of said data objects;
relating said at least one customer profile with the content profiles for the data available from each data object to the customer at a particular time;
determining a subset of data having content profiles which are determined in said relating step to most closely match said at least one customer profile; and
presenting said subset of data to said customer for selection.

2. A method as in claim 1, wherein said relating step comprises the step of determining a distance between a customer profile and a content profile in a characteristic space by calculating an agreement scalar for common characteristics, ac, between said at least one customer profile, cv, and said content profiles, cp, in accordance with the relationship:
for i=a particular customer of a number of customers I, j=a particular data object of a number of data objects J, and k=a particular characteristic of a data object of a number of data object characteristics K, where cv.sub.ik is greater than or equal to 0 and w.sub.ik is customer i's normalized weight of characteristic k.

3. A method as in claim 1, comprising the further steps of:
monitoring which data objects are actually accessed by said customer; and
updating each customer profile in accordance with the content profiles of the data objects actually accessed by that customer to update that customer's actual preferences for said predetermined characteristics.

4. A method as in claim 1, comprising the further steps of:
monitoring which data objects are actually accessed by said customer; and
updating said customer profile to reflect the frequency of selection of the data objects by customers with customer profiles substantially similar to said customer profile.

5. A method as in claim 4, wherein said customer profile creating step comprises the steps of:
selecting a number of desired groups K into which said customers are divided whereby each customer in a group has a customer profile substantially similar to a customer profile of each other customer in said group;
grouping said customers into K groups so as to minimize: ##EQU25## where .vertline.v.sub.i -v.sub.k .vertline. is a distance between the vector of characteristics of the data objects accessed by customer i and the centroid of group k; and
determining an agreement matrix ac.sub.ij, where for each customer i, a jth row of the agreement matrix is a vector v.sub.k for a group k in which customer i belongs.

6. A method as in claim 1, wherein said step of creating at least one customer profile comprises the step of including in said at least one customer profile a profile of data previously accessed by said customer.

7. A method as in claim 1, wherein said step of creating content profiles comprises the step of including in said content profiles data reflecting the customer profiles of those customers who have previously accessed said data from each data object.

8. A method as in claim 1, wherein said data includes electronic program guide data, and said presenting step comprises the step of downloading, in a memory of a customer's electronic program guide receiving device, program descriptions in said electronic program guide only for those entries in said electronic program guide which have content profiles which are determined in said relating step to most closely match said at least one customer profile.

9. A method as in claim 1, wherein said customer is connected to a plurality of data sources over a data network, and wherein said presenting step comprises the step of downloading said subset of data to said customer from said data source prior to selection by said customer of a menu item identifying said subset of data.

10. A method as in claim 9, comprising the further steps of:
monitoring a customer's pattern in navigating through said data sources to access data having characteristics desirable to said customer; and
creating at least one customer profile for said customer indicating the customer's pattern in navigating through said data sources to access said data having characteristics desirable to said customer,
wherein said downloading step further comprises the step of downloading said data having characteristics desirable to said customer from said data sources prior to selection by said customer of menu items used on said data network for navigating to said data.

11. A data transmission system which schedules customer access to data of a plurality of data objects, comprising:
means for generating at least one customer profile for a customer of said data, said customer profile indicating the customer's preferences for data having predetermined characteristics;
means for generating content profiles for each data object, said content profiles indicating at least one of the presence and the degree of content of said predetermined characteristics in data of each data object;
means for relating said at least one customer profile with the content profiles for the data available from each data object to the customer at a particular time;
means for determining a subset of data having content profiles which most closely match said at least one customer profile; and
means for presenting said subset of data to said customer for selection.

12. A system as in claim 11, wherein said relating means comprises a processor programmed to determine a distance between a customer profile and a content profile in a characteristic space by calculating an agreement scalar for common characteristics, ac, between said at least one customer profile, cv, and said content profiles, cp, in accordance with the relationship:
for i=a particular customer of a number of customers I, j=a particular data object of a number of data objects J, and k=a particular characteristic of a data object of a number of data object characteristics K, where cv.sub.ik is greater than or equal to 0 and w.sub.ik is customer i's normalized weight of characteristic k.

13. A system as in claim 11, further comprising:
means for monitoring which data objects are actually accessed by each customer; and
means for updating each customer profile in accordance with the content profiles of the data objects actually accessed by that customer to update each customer's actual preferences for said predetermined characteristics.

14. A system as in claim 11, further comprising:
means for monitoring which data objects are actually accessed by each customer; and
means for updating each customer profile to reflect the frequency of selection of the data objects by customers with customer profiles substantially similar to said each customer profile.

15. A system as in claim 14, wherein said means for generating at least one customer profile comprises processing means which selects a number of desired groups K into which said customers are divided whereby each customer in a group has a customer profile substantially similar to the customer profile of each other customer in said group, groups said customers into K groups so as to minimize: ##EQU26## where .vertline.v.sub.i -v.sub.k .vertline. is a distance between the vector of characteristics of the data objects accessed by customer i and the centroid of group k, and determines an agreement matrix ac.sub.ij, where for each customer i, a jth row of the agreement matrix is a vector v.sub.k for a group k in which customer i belongs.

16. A system as in claim 11, wherein said means for generating at least one customer profile includes in said at least one customer profile a profile of data previously accessed by said customer.

17. A system as in claim 11, wherein said means for generating content profiles includes in said content profiles data reflecting the customer profiles of those customers who have previously accessed said data from each data object.

18. A system as in claim 11, wherein said data includes electronic program guide data, and said presenting means comprises means for downloading, in a memory of a customer's electronic program guide receiving device, program descriptions of an electronic program guide in said electronic program guide data only for those entries in said electronic program guide which have content profiles which are determined by said relating means to most closely match said at least one customer profile.

19. A system as in claim 11, wherein said customer is connected to a plurality of data sources over a data network, and wherein said presenting means includes means for downloading said subset of data to said customer prior to selection by said customer of a menu item identifying said subset of data.

20. A system as in claim 19, further comprising:
means for monitoring a customer's pattern in navigating through said data sources to access data having characteristics desirable to said customer; and
means for creating at least one customer profile for said customer indicating the customer's pattern in navigating through said data sources to access said data having characteristics desirable to said customer,
wherein said downloading means downloads said data having characteristics desirable to said customer prior to selection by said customer of menu items used on said data network for navigating to said data.

21. A system as in claim 11, further comprising means for identifying a customer and for providing customer profiles for said customer to said determining means for determining said subset of data for said customer.

22. A system as in claim 11, further comprising means for providing different customer profiles to said determining means in accordance with the time of day and day of the week.

23. A system as in claim 11, wherein said presenting means presents said subset of data to the customer as a virtual data channel.

24. A system as in claim 11, further comprising means for storing an electronic program guide at a customer's set top terminal, wherein said presenting means highlights programs within said electronic program guide which correspond to said subset of data.

25. A system as in claim 24, wherein said electronic program guide storing means stores program descriptions of said electronic program guide only for those entries in said electronic program guide which have content profiles which are determined by said determining means to most closely match said at least one customer profile.

26. A system as in claim 11, further comprising an interface which through which said determining means provides upstream data messages.

27. A system as in claim 26, further comprising encrypting means for encrypting said upstream data messages.
Description
BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and method for controlling broadcast of and/or customer access to data such as video programs in accordance with objective profile data indicative of the customer's preferences for that data. More particularly, a preferred embodiment of the invention relates to a system and method for determining from objective profile data of the customers which data or video programming is most desired by each customer so that the customers may receive data or video programming customized to their objective preferences. The objective profile data is updated on a continuing basis to reflect each customer's changing preferences so that the content of the data channels or video programming may be updated accordingly.

2. Description of the Prior Art

The so-called "Information Super Highway" is expected to bring wondrous technological changes to society. Data of all kinds will become readily available to the public in quantities never before imaginable. Recent breakthroughs in video compression technologies are expected to extend the "Information Super Highway" right into the video realm by allowing customers to receive literally hundreds of video channels in their homes. While the prospects of opening a whole new world of information to the average person are exciting, there is much concern that the average person will simply be overwhelmed by the quantity of data piped into their homes. Some techniques must be developed which permit the travelers of the Information Super Highway to navigate through the plethora of available information sources without getting hopelessly lost.

For example, in the home video context, it is desired to provide mechanisms which present the available video information to the customers in a comprehensible way. Such mechanisms should eliminate the necessity of "channel surfing" to find a program suitable for viewing out of the hundreds of video programming alternatives which are expected to be made available. The present invention is thus designed to help the customer of video and other data services to receive, with minimal effort, the information he or she is most interested in.

Numerous systems are available which assist customers in determining which video programs to watch. For example, electronic program guides and the like are available which give customers on-screen access to the upcoming programming whereby the desired programming may be selected in advance for later recording. An early system described in U.S. Pat. No. 4,170,782 to Miller allows the viewer to preselect a television viewing schedule of desired television channels to be viewed during subsequent time periods. Miller also monitors the television programs actually watched by the television viewer and relays this information to a central data processing center over a communication link. Subsequent interactive cable systems, such as that described by Freeman in U.S. Pat. No. 4,264,924, permit the viewer to select the information to be received on particular channels. The cable system described by Freeman also provides individually tailored messages to the individual viewers. Similarly, Young disclosed in U.S. Pat. No. 4,706,121 a system which permits the viewer to select programs from schedule information by controlling a programmable television tuner

to provide the broadcast signals for the selected programs to the television receiver at the time of broadcast. This system can also be used to control a VCR for unattended recording of the selected programs. Further details of such a VCR recording system is provided by Young in U.S. Pat. Nos. 4,977,455 and 5,151,789. Other systems, such as that described by Reiter et al. in U.S. Pat. No. 4,751,578, provide updatable television programming information via telephone link, magnetic cards, floppy disks, television or radio sub-carrier, and the like, to the viewer's television screen in such a manner that the viewer may selectively search this information.

Unfortunately, in each of the aforementioned prior art systems, the customer must actively select the desired programming. In other words, these systems facilitate access to programming designated by the customer but provide no assistance to the customer in determining what programming to select for subsequent viewing. With the possibility of several hundred video channels soon becoming available to video customers, additional systems are desirable which assist the customer in selecting the desired programming.

The system described by Herz et al. in U.S. Pat. No. 5,351,075 partially addresses the above problems, at least with respect to the provision of movies over cable television. As described therein, members of a "Home Video Club" select the video programs they would like to see in the following week. A scheduling computer receives the members' inputs for the current week and determines the schedule for the following week based upon the tabulated preferences. This schedule is then made available to the members of the Home Video Club. If, when, and how often a particular video program is transmitted is determined by the customer preferences received by the scheduling computer. Prime time viewing periods are used to make certain that the most popular video programs are broadcast frequently and at the most desirable times. As with the aforementioned systems, the "Home Video Club" system does not automatically broadcast the most desired video programs to the customers but instead requires the active participation of the customers to "vote" for the most desired video programs for subsequent viewing.

It is desired to extend a customer preference system such as the "Home Video Club" to include general cable programming offerings and to minimize active customer involvement in the determination of the desired programming. Unlike the movie scheduling system described in the "Home Video Club" application, the number and content of general cable programming channels is scheduled in advance and typically cannot be changed by the customer through a simple voting system. As a result, the customer can only vary his or her video programming by changing channels. In other words, the customer typically illustrates his or her programming preferences by changing channels. Indeed, such changes are monitored by Nielsen, Arbitron, and other ratings agencies in setting the rates for advertising. In U.S. Pat. No. 5,155,591, one of the present inventors carried this concept a step further by obtaining information about the customers and then demographically targeting television commercials to the customers most likely to respond favorably to such advertising. Unfortunately, however, this demographic and customer preference information has not been specifically described for providing customized channels which better reflect the customers' preferences for the programming itself.

The present inventors have found that the aforementioned problems may be overcome by creating customized programming channels from all of the programming available at any time and broadcasting the customized programming channels to groups of customers. The customer's set top multimedia terminal then creates "virtual channels" as a collection of the received programming data from one or more of the customized programming channels at any point in time for receipt on the customer's television. These virtual channels are received as an additional offering to the regular broadcast transmission and are customized to the customer's preferences. Thus, as used herein, a "virtual channel" is a channel formed as a composite of several source materials or programs which may or may not change during respective time periods to reflect the programming most desirable to the customer during that time period. The creation of such "virtual channels" is intended to minimize the amount of "channel surfing" necessary to find the most preferred video program at a particular time.

Previous attempts at providing such selective access to programming have required active customer participation. For example, in U.S. Pat. No. 4,381,522, Lambert disclosed a system in which the customer is permitted to specify which television signal source is to be connected to the video switch for broadcasting of a desired television program to the customer. The desired program is selected from a program schedule channel provided to the customer. Hashimoto discloses more elaborate systems in U.S. Pat. Nos. 4,745,549 and 5,075,771 in which programs suitable to individual customer's tastes are selected from all of the available television programs in accordance with the customer preferences specified on a customer questionnaire or provided from the customer over a telephone link or the like. The viewer preference data provided using the questionnaires, the telephone lines, and the like is then statistically processed by linear programming to provide an individual subscriber television program list which may be used by the video provider to select which programs to broadcast to particular individuals. Subscriber complaints about the program list are used to "tune" the television program list to better match the individual's tastes. An automatic controller is also used to automatically control a television or video cassette recorder in accordance with the subscriber's specified tastes. However, the system disclosed by Hashimoto works from limited objective data provided by the customer in response to a questionnaire and provides no mechanism for validating the accuracy of the profile of that customer other than through the use of a complaint system. In addition, the system disclosed by Hashimoto does not determine the desirability of particular video programs but merely allows the customer to characterize those types of programs to which he or she may be most interested.

For the reasons noted above, feedback regarding the customer programming and purchasing preferences is highly desirable. It is highly desirable to develop a technique for better acquiring and quantifying such customer video programming and purchasing preferences. Along these lines, Strubbe recently described a system in U.S. Pat. No. 5,223,924 which provides an interface for automatically correlating the customer preferences with the television program information and then creating and displaying a personalized customer program database from the results of the correlation. In the Strubbe system, the customer specifies whether he or she "likes" a particular video program and the database is updated accordingly. Then, from the video programs "liked" by the customer, a second, personalized, database is created. However, as with each of the systems described above, the Strubbe system does not develop customer profiles and automatically update the database of "liked" videos using feedback. Also, Strubbe does not teach that the preference information may be used to predict what new video programs the customer may like and then schedule those new video programs for viewing.

Those in the technical press have fantasized about so-called "smart" televisions which will keep track of past viewing preferences and suggest new programs that match the customer's personal tastes so that the customer need not "channel surf" through the 500 channel video system of the future. However, prior to the present invention, no one known to the present inventors has been able to make such "smart" televisions a reality. Indeed, the present invention is believed to be the first system to create "virtual channels" of recommended programming for each customer of a video or other data service.

SUMMARY OF THE INVENTION

The present invention relates to a system and method for making available the video programming and other data most desired by the customer by developing an "agreement matrix" characterizing the attractiveness of each available source of video programming or data to each customer. From the agreement matrix, one or more "virtual channels" of data, customized to each customer, are determined. At any given time, the one or more virtual channels include the video programming or other data which is predicted to be most desirable to the customer based on the customer's preferences. The virtual channels are determined by selecting from the available alternatives only those video programs or other data which most closely match the customer's objective preferences.

In accordance with the invention, a method of scheduling customer access to data from a plurality of data sources is provided. Although the technique of the invention may be applied to match customer profiles for such disparate uses as computerized text retrieval, music and music video selection, home shopping selections, infomercials, and the like, in the presently preferred embodiment, the method of the invention is used for scheduling customer access to video programs and other broadcast data. In accordance with the preferred method, objective customer preference profiles are obtained and compared with content profiles of the available video programming. The initial customer profiles are determined from customer questionnaires, customer demographics, relevance feedback techniques, default profiles, and the like, while the initial content profiles are determined from questionnaires completed by "experts" or some sort of customer's panel, are generated from the text of the video programs themselves, and/or are determined by adopting the average of the profiles of those customers who actually watch the video program. Based on the comparison results, one or more customized programming channels are created for transmission, and from those channels, each customer's set top multimedia terminal may further determine "virtual channels" containing a collection of only those video programs having content profiles which best match the customer's profile and hence are most desirable to the customer during the relevant time frame.

Preferably, one or more customer profiles are created for each customer of the video programs. These customer profiles indicate the customer's preferences for predetermined characteristics of the video programs and may vary in accordance with time of day, time of the week, and/or customer mood. Such "characteristics" may include any descriptive feature suitable in describing particular video programs, such as classification category; directors; actors and actresses; degree of sex and/or violence; and the like. Corresponding content profiles are created for each video program available for viewing and generally indicate the degree of content of the predetermined characteristics in each video program. An agreement matrix relating the customer profiles with the content profiles is then generated. Preferably, the agreement matrix enables the system to determine a subset of the available programs at a particular point in time which is most desirable for viewing by the customer. The determined subset of video programs is then presented to the customer for selection in the conventional manner, except that each "virtual channel" includes a collection of the offerings available on all of the originally broadcast channels from the cable system. The "virtual channels" are then generated by the customer's set top multimedia terminal for display on the customer's television. The customer may then select the desired video programming, which may or may not include the programming offered on the "virtual channels." Similar techniques are used at the video head end to determine which video programs to transmit to each node for use in the creation of the "virtual channels" at each customer's set top multimedia terminal.

Preferably, the customer profile creating step comprises the step of creating a plurality of customer profiles for each customer, where the plurality of customer profiles are representative of the customer's changing preferences for the predetermined characteristics in accordance with time of the day and of the week. In such an embodiment, the agreement matrix determining step comprises the step of using different customer profiles for each customer in accordance with the time of the day and of the week, thereby reflecting changes in the customer's preferences or "moods" during the course of the week. In addition, the customer profile creating step preferably comprises the step of clustering customer profiles for combinations of customers expected to view the video programs at a particular customer location at particular times on particular days. For example, the clustered profiles for a customer's residence may contain the combined profiles of Mom and Dad in the evening and the combined profiles of the children in the afternoon. In this embodiment, the agreement matrix determining step comprises the step of using the different clustered customer profiles in accordance with the time of the day and of the week. Alternatively, the appropriate customer profiles for use in calculating the agreement matrix may be determined directly from identity information received from the customer or assigned to the customer in accordance to the cluster of customers to which that customer belongs. In the latter technique, it will be appreciated that customer profiles are not strictly necessary since each customer is assigned an initial customer profile determined from the clustered profiles of the other customers in his or her cluster of customers.

In the presently preferred embodiment of the invention, the agreement matrix determining step comprises the step of comparing the customer profiles with the content profiles for each video program available for viewing in a predetermined time period. In particular, the agreement matrix determining step preferably comprises the step of determining a distance in multidimensional characteristic space between a customer profile and a content profile by calculating an agreement scalar for common characteristics, ac, between the customer profile, cv, and the content profiles, cp, in accordance with the relationship:

for i=a particular customer of a number of customers I, j=a particular video program of a number of video programs J, and k=a particular video program characteristic of a number of video program characteristics K, where W.sub.ik is customer i's weight of characteristic k. As will be appreciated by those skilled in the art, an agreement matrix so defined is the reciprocal of the distance d (=1/ac) in multi-dimensional space between the customer profile vector and the content profile vector and that many different distance measurement techniques may be used in determining the distance d. In such an embodiment, the subset determining step preferably comprises the steps of sorting the video programs in an order of ac indicating increasing correlation and selecting as the subset a predetermined number of the video programs having the values for ac indicating the most correlation.

When scheduling video programs at a head end using the techniques of the invention, the agreement matrix is preferably determined from customer profiles of a plurality of customers and the video programming is scheduled using the steps of:

(a) determining a video program j which most closely matches the customer profiles of the plurality of customers of the video programs;

(b) scheduling the video program j for receipt by the plurality of customers and decrementing a number of channels available for transmission of video programs to said customers;

(c) when the number of channels available for transmission of video programs to a particular customer of the plurality of customers reaches zero, removing the particular customer from the plurality of customers for scheduling purposes; and

(d) repeating steps (a)-(c) until the number of video programs scheduled for transmission equals the number of channels available for transmission of video programs.

In accordance with a currently preferred embodiment of the invention, a passive feedback technique is provided for updating the customer profiles in accordance with the video programming actually watched by the customer. Such a method in accordance with the invention preferably comprises the steps of:

creating at least one customer profile for each customer of the video

programs, the customer profile indicating the customer's preferences for predetermined characteristics of the video programs;

creating content profiles for each video program available for viewing, the content profiles indicating the degree of content of the predetermined characteristics in each video program;

monitoring which video programs are actually watched by each customer; and

updating each customer profile in accordance with the content profiles of the video programs actually watched by that customer to update each customer's actual preferences for the predetermined characteristics.

Preferably, the monitoring function is accomplished by storing, at each customer's set top multimedia terminal, a record of the video programs actually watched by the customer at the customer's location and, in the case of a system with a two-way communication path to the head end, polling the set top multimedia terminals of all customers to retrieve the records of the video programs actually watched by the customers at each customer location. Also, from the retrieved records, combined customer profiles may be determined which reflect the customer profiles of a plurality of customers. Then, by determining the agreement matrix using the combined customer profiles for each node, programming channels containing the video programming which are collectively most desired by the customers making up the combined customer profiles may be determined for transmission from the head end to each of the customers connected to the same node.

When a predicted video program is not selected by the customer, it is desirable to update the agreement matrix to better reflect the customer's tastes. The updating of the agreement matrix may be accomplished in a variety of ways. For example, the customer profile, cv.sub.ik, for customer i and video program characteristic k may be adjusted to a new customer profile, cv.sub.ik ', in accordance with the equation:

where cp.sub.jk represents the degree of video program characteristic k in video program j and .DELTA. is a small constant which can vary in accordance with the desired accuracy for the profiles. On the other hand, customer i's weighting of video program characteristic k, w.sub.ik, in the customer profile, cv.sub.ik may be adjusted to a new weighting, w.sub.ik ', in accordance with the equation:

In addition, the content profiles, cp.sub.jk, of certain video programs j having video program characteristics k may be adjusted to new content profiles, cp.sub.jk ', to update the customer profiles of customers i who actually watch video program j, in accordance with the equation:

where cv.sub.ik represents the customer profile of customer i for video program characteristic k. Of course, other updating techniques are also possible within the scope of the invention.

Since the data passing from the set top multimedia terminal to the head end contains data which the customers may consider to be confidential, the two-way transmission system of the invention may be modified to encrypt the transmissions from the set top multimedia terminals to the head end. Similarly, as in the case of pay-per-view programming, it is often desirable to encrypt the transmissions from the head end to the set top multimedia terminals. In accordance with the invention, a secure transmission system from the head end to the set top multimedia terminal is obtained by performing the steps of:

(1) At the set top multimedia terminal, generate a seed random number N to be used for the random number generator.

(2) Retrieve the public key P from the head end and encrypt the seed random number N as E(N,P) at the set top multimedia terminal using a public key algorithm such as RSA which is known to be difficult to break.

(3) Send the encrypted seed N (E(N,P)) to the head end where E(N,P) is received and decrypted to yield N using the head end's private key Q.

(4) The head end and set top multimedia terminals then initialize their respective pseudo-random number generators with N as a seed.

(5) Begin the encryption at the head end by generating the first number in the sequence K.sub.i and logically exclusive-ORing it with the first data word in the stream P.sub.i, thereby forming C.sub.i (i.e., C.sub.i =EOR(K.sub.i,P.sub.i)).

(6) Send the result C.sub.i from the encryptor at the head end to the set top.

(7) Form K.sub.i at the synchronized random number generator of the set top multimedia terminal, which has also been initialized with seed N, by decrypting the received C.sub.i to yield P.sub.i. This is done by exclusive-ORing K.sub.i with C.sub.i to yield P.sub.i (i.e., P.sub.i =EOR(K.sub.i,C.sub.i)), generating the next pseudo-random K.sub.i in the sequence at the head end and the set top multimedia terminal, determining whether all words i in the sequence have been decrypted, and repeating steps (5)-(6) until all words in the digital video stream have been decrypted. Normal processing of the digital video stream continues from that point. Secure transmission from the set top multimedia terminal to the head end is obtained in the same manner by reversing the set top multimedia terminal and the head end in steps (1)-(7) above.

Those skilled in the art will appreciate that the techniques described herein are applicable to numerous other areas of technology in which it is desirable to assist the customer in the selection of a data service which best meets that customer's needs. For example, the agreement matrix may be used at a head end to minimize the amount of data, such as electronic program guide data, which is to be stored at the user's terminal, thereby minimizing memory requirements of the user's terminal. As another example, the agreement matrix of the invention may be used to facilitate text retrieval in a computer database system and may be implemented in a kiosk or personal computer designed to assist in the selection of movies, music, books, and the like. All such embodiments will become apparent to those skilled in the art from the following description of the preferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the invention will become more apparent and more readily appreciated from the following detailed description of the presently preferred exemplary embodiments of the invention taken in conjunction with the accompanying drawings, of which:

FIG. 1 is a flow chart illustrating the flow of processing of the customer and content profile data in accordance with a preferred embodiment of the invention.

FIG. 2 is a flow chart illustrating the method of selecting the contents of channels which are to be transmitted from a CATV head end to a plurality of customers in accordance with the invention.

FIG. 3 is a flow chart illustrating the method of passively updating customer profiles in accordance with the invention.

FIG. 4 is a generalized system overview of a one-way customer profile system in accordance with the invention in which customized virtual channels are created at the set top multimedia terminals from the channels received from the CATV head end.

FIG. 5 is a generalized system overview of a two-way customer profile system which expands upon the embodiment of FIG. 4 by feeding back data representative of the customers' viewing habits from the customers' set top multimedia terminals to the CATV head end for purposes of optimally scheduling the channels for transmission from the head end in accordance with the recorded customer preferences.

FIG. 6 is a block diagram of a cable television distribution system, including an optional two-way return path, which has been modified to transmit video programs determined from the fed back customer profile data in accordance with the techniques of the invention.

FIG. 7 is a flow diagram of an upstream encryption technique for encrypting data sent from the set top multimedia terminal to the head end in accordance with the techniques of the invention.

FIG. 8 is a flow diagram of a downstream encryption technique for encrypting data sent from the head end to the set top multimedia terminal in accordance with the techniques of the invention.

FIG. 9 is a block diagram of the software used in the set top multimedia terminals in a preferred embodiment of the invention.

FIG. 10 is a block diagram of a preferred hardware implementation of a set top multimedia terminal in accordance with the invention.

FIG. 11 is a simplified block diagram of a computer kiosk or personal computer which uses the profile and clustering techniques of the invention to assist a customer in the selection of videos for rental, music or books for purchase, and the like.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

The present invention will be described in detail below with respect to FIGS. 1-11. Those skilled in the art will appreciate that the description given herein is for explanatory purposes only and is not intended to limit the scope of the invention. For example, while the invention is described with respect to a cable television broadcasting system, those skilled in the art will appreciate that the system described herein may also be used for selecting receipt of desired data services and shop at home services, and for selecting from available music and multimedia offerings. Accordingly, the scope of the invention is only to be limited by the scope of the appended claims.

I. Overview

The present invention relates to a customer profile system in which the characteristics of a data source are quantified in some objective manner and stored as content profiles and the customer's preferences for those characteristics are stored in the form of one or more customer profiles. In the following detailed description, the present inventors will describe how the techniques of the invention are used for creating content profiles which characterize the data sources in accordance with their degree of content of predetermined characteristics. Techniques will also be described for creating, weighting, and updating customer profiles which reflect the customer's affinity for those predetermined characteristics. From the content profiles and the customer profiles, an agreement matrix will be described which matches the customers' preferences and the contents of the data sources available at any point in time. As will be described in detail below, the agreement matrix is used at the customer's set top multimedia terminal to create "virtual" data channels from the available data sources, or, alternatively, the agreement matrix may be used by the data provider to determine which data sources of those available will have the most appeal to his or her customers.

A preferred embodiment of the invention will be described in the context of a one-way CATV transmission system and a two-way transmission CATV transmission system with feedback for adjusting the agreement matrix. Several of the numerous possible alternative embodiments for application of the techniques of the invention will then be described.

II. Content Profiles and Customer Profiles

As noted above, a preferred embodiment of the invention will be described for application in a CATV distribution system for aiding a customer in the selection of video programming for viewing by matching the available video programming to each customer's objective preferences. Accordingly, the content and customer profiles will include characteristics which are useful in defining the characteristics of video programming. Of course, when the present invention is used to assist in the selection of data from other data sources, the content and customer profiles will include completely different characteristics.

In accordance with the preferred embodiment of the invention, the content profiles describe the contents of video programs and are compared mathematically in a computer to customer profiles to generate an agreement matrix which establishes the degree of correlation between the preferences of the customer or customers and the video programming available during each video programming time slot. The content profiles and the customer profiles are thus described as a collection of mathematical values representing the weighted significance of several predetermined characteristics of the video programming. For ease of description, the present inventors will describe the mathematical basis for the content profiles and the customer profiles in this section and will describe the generation of the agreement matrix and the uses of the agreement matrix in the next section.

A. Terminology

The following subscription indices will be used throughout this specification:

______________________________________ i customers (i = 1,2, . . ., I); j programs (j = i,2, . . ., J); k characteristics (k = 1,2, . . ., K); l categories (l = 1,2, . . ., L); ______________________________________

and the following variables will be used throughout this specification:

______________________________________ cv.sub.ik : customer i's rating for characteristic k; CV.sub.i : the vector {cv.sub.ik .vertline.k.epsilon.K} which forms customer i's profile for all characteristics k; sv.sub.ik : spread (flexibility) in viewer i's rating for characteristic k; wv.sub.ik : customer i's weight of characteristic k; cp.sub.jk : objective weighting of program j for characteristic k; CP.sub.j : the vector {cp.sub.jk .vertline.k.epsilon.K} which forms program j's profile for all characteristics k; sp.sub.jk : spread (flexibility) in program j's rating for characteristic k; and ac.sub.ij : agreement scalar representing similarity between CV.sub.i and CP.sub.j. ______________________________________

It should be noted at the outset that cv.sub.ik indicates customer i's preferred level for characteristic k, while cp.sub.jk indicates the level of presence of a characteristic in the program. sv.sub.ik and sp.sub.jk, on the other hand, respectively represent customer is flexibility in accepting different levels of characteristic k and the flexibility in the determination of the degree of content of characteristic k in program j.

cv and cp may have values between 0 and +10, where the actual range number indicates the relevance of that characteristic. In other words, a video programming having a value of +10 for a given characteristic has the highest degree of content for that variable. The values of cv and cp should always be non-negative, since both are related to the level of characteristics, the former being the desired level and the latter being the actual level. Naturally, zero in cv means the customer's rejection of a characteristic, while zero in cp indicates the absence of a characteristic in a program. Those skilled in the art may wish to allow cv to become negative so that the magnitude of negativity could indicate the level of the customer's aversion to a characteristic. However, there are

drawbacks in using negative values in this manner. First, a negative value will blur the meaning of cv and sv and weaken the statistical basis for their calculation. Second, cv has been defined as the desired level of a characteristic, which should be non-negative. Thus, the level of aversion is preferably expressed as a point value, instead of a range of values.

Preferably, the level of aversion is expressed by the combination of a zero-value in cv and a certain value in the corresponding wv. For example, when customer i totally rejects characteristic k, cv.sub.ik can be set to -1, which means prohibition. Any program k for which cv.sub.ik <0 will be excluded from the recommendation list for customer i. Of course, as in the Strubbe system, the values could simply be "0" or "1" to indicate the presence or absence of a characteristic.

wv.sub.ik illustrates the importance of characteristic k to customer i. Typically, different characteristics bear different levels of importance for a customer, and the introduction of this variable catches the variation. Although any scaling system may be used, the weight variable, wv, may simply weight the associated characteristic on a scale of 0-5, where 5 indicates the highest affinity for the associated characteristic. On the other hand, as in the Strubbe system, the weights could simply indicate a "like" or "dislike" value for each characteristic.

Finally, as will be described in more detail below, the agreement scalar for characteristics, ac, is the weighted average of the values of a variant of the two-sample t test for significance between CV and CP.

B. Creating Initial Customer and Content Profiles

A profile, either of a customer (Customer Profile) or of a program (Content Profile), is composed of arrays of characteristics which define the customer profile vector CV.sub.i and the program profile vector CP.sub.j. To increase the accuracy in statistical estimation, the selection of the characteristics should follow the following guidelines:

______________________________________ .smallcircle. The characteristics should be descriptive of the features of the programs; .smallcircle. The list should be fairly inclusive, i.e., include all the common features of the programs; and .smallcircle. There should be no synonyms, nor much overlapping in meaning between two or more characteristics. In other words, the correlations between the characteristics are desirably minimized. ______________________________________

For example, characteristics currently in use for characterizing video programming include film genres such as westerns, comedies, dramas, foreign language, etc. as defined by the American Film Institute and/or as provided via existing television database services; directors; actors/actresses; attributes such as amount and degree of sex, violence, and profanity; MPAA rating; country of origin; and the like. Of course, many other characteristics may be used, such as those characteristics used in the Minnesota Psychological Test or other more particularized categories based on life experiences and emotions. Such characteristics may also be value based by indicating the scientific, socio-political, cultural, imagination evoking, or psychological content as well as the maturity level to which the program appeals.

In accordance with the invention, there are several ways to develop the initial customer and content profiles for such characteristics. For example, the initial customer profile may be assigned on the basis of the customer's zip code or other characteristic demographic information. In other words, the profile may be set to a profile typical of the customer's zip code area or to a typical profile determined by interviews or empirically by monitoring what customers watch. Similarly, each customer may be assigned a generic customer profile which is personalized over time through the profile adjustment techniques to be described below. Alternatively, a customer may be asked to name several of his or her favorite movies and television shows so that an initial customer profile may be determined by combining or averaging the content profiles of the selected movies and television shows. In addition, each customer may complete a ballot for each viewing mood. This latter approach builds upon the technique described by Hashimoto in U.S. Pat. Nos. 4,745,549 and 5,075,771 and will be described in some detail here.

For explanation purposes, it will be assumed that the initial customer profile is determined from an initial customer questionnaire or ballot. When completing the initial questionnaire, the customer may choose between two voting schemes, one (Scheme A) by characteristics, and the other (Scheme B) by categories. Scheme A is straight-forward. In Scheme A, the customer gives acceptable ranges for all the characteristics which identify a video program. The customer's profile {cv.vertline.cv.epsilon.CV} is immediately obtained by simply calculating the means of these ranges. In Scheme B, however, the customer gives a specific rating for each of the categories. If ccv.sub.il is the rating for customer i for category l, with a scale of 10 in which zero means least satisfaction with the category and 10 means the greatest satisfaction, the customer's profile {cv.sub.ik .vertline.cv.sub.ik .epsilon.CV.sub.i } may be calculated as:

where L' is the set of categories in which ccv.sub.il =max.sub.l ccv.sub.il' and N.sub.L' is the cardinality of L'. In other words, a customer's rating of a characteristic equals the objective content rating of that characteristic in the customer's most preferred category. If there are multiple most-preferred categories, indicated as ties in ccv, the objective content ratings can be used.

Alternatively, the customer may be required to input an upper limit cvu.sub.ik and a lower limit cvl.sub.ik for characteristic k to show his/her acceptable range for characteristic k, where cv.sub.ik is calculated as:

i.e., the middle point of the range. If customer i wants to indicate his/her indifference to characteristic k, i.e., that he/she can accept any level of characteristic k, then he/she may either let cvu.sub.ik =10 and cvl.sub.ik =0, or let wv.sub.ik =0.

The initial value for cp.sub.jk is calculated as the mean of all votes on that characteristic by "experts" or other viewers used to characterize the video programs. As will be noted below, initial profiles for video programs may be obtained by using a panel of experts or customers to assign content profiles or by assigning the customer profiles to the video programs on the basis of those who liked the video program during a test screening. On the other hand, the initial value for sv.sub.ik is calculated as:

where Z preferably equals 2. The calculation simulates that for standard deviation, where the cutting point for rejection in normal distribution is usually when the divisor Z=2. Accordingly, cv is of point value, and sv is of range value. Thus, while the value of the divisor Z in Equation (3) may be altered to tighten/loosen the cutting point, the divisor in Equation (2) may not be changed. sp, on the other hand, may be calculated as the standard deviation in the experts' or test group's votes on cp.

The major advantage of Scheme B is that much burden will be taken away from the customer in the ballot completion process, since as a rule, the number of characteristics well exceed the number of categories. The disadvantage, however, is the inaccuracies due to the fact that not all the characteristics in the customer's favorite category are on the customer's most preferred level. The inaccuracy may be reduced by expanding the most-preferred categories to those with ccv values in a certain upper percentile, rather than with the maximum ccv values. The value of sv can be derived from the deviation of ccv's in the customer's most-preferred categories. To help the customers vote more consistently, each category is preferably accompanied by a list of keywords (i.e., characteristics that are relevant to the category).

Similarly, the content profile may be determined using questionnaires completed by a panel of experts or customers who determine the content of all video programming available for broadcast. The same scaling systems would be used as for the customer profiles. For statistical purposes, it is desired that the expert or customer panel be as large as possible. As will be noted below, once the system of the invention is in operation, the customer profiles of those who actually watch a particular video program during an initial screening by a sample group may be used to assign a content profile to that program for subsequent viewings. In such a system, those customers who watch the program from beginning to end will be presumed to have "liked" the program. The customer profiles of those who "liked" the program would then be combined to create the initial profile for that program. That program would then be ready for broadcast to all members of the network.

Alternatively, in the presently preferred embodiment, more sophisticated techniques are used to generate the initial content profiles. In the preferred embodiment, the content profile of a program is determined automatically from the word frequency of certain words in the text or on-line description of a program or the frequency of certain words in the closed captions of a television show, where such words are chosen as representative of certain categories. Of course, other simpler techniques such as one which simply determines the presence or absence of particular characteristics may be used within the scope of the invention.

The weighting of the characteristics in the customer and content profiles somewhat depends on how the profiles were determined initially. For example, the weighting of the customer profiles may be obtained directly from a questionnaire by asking the customer to appropriately scale (from 1-10) his or her preference for each characteristic. On the other hand, if the customer profile is assigned based on demographics, zip code, and the like, the average weights for other customers with the same demographics, zip code, and the like may be used. When more statistical techniques are used for creating the initial customer and content profiles, the weights may be determined mathematically as, for example, the reciprocal of the standard deviation of the characteristics. Of course, other weighting techniques may also be used.

C. Adjusting Customer Profiles

As noted above, the data from which the initial customer profile is derived may be obtained through ballot filling, whereby a number of characteristics are listed and the customer gives his/her preference rating (cv) and flexibility range (sv) for each characteristic. However, people often do not provide all of the necessary responses or the correct responses to such ballots or questionnaires. Similarly, when the initial customer profiles are assigned to new customers on the basis of demographics and the like, there is a substantial likelihood that the initial customer profile will need considerable adjustment. Moreover, the system should account for the fact that many people's tastes change over time. Thus, to ensure accuracy of the profiles, there must be some way to correct errors in the initial customer profiles and to adjust the customer profiles over time.

In accordance with the invention, a passive feedback technique is provided whereby the programming viewed by the customers are automatically monitored and used to adjust the customer profiles. That technique will be described in more detail in Section V below. This section will instead refer to an active feedback mechanism which will be referred to as a "rave review."

As noted above, one way to establish an initial customer profile is to show an unrated program or portion of a program to a target audience and to assign to the unrated program a combination of the customer profiles of those who actually watched the program or portion of the program from beginning to end or to assign ratings inputted by those who completed a survey. A similar technique may be used for error correction or for creating initial customer profiles. In particular, the customer is exposed to a series of short sections of different video programs. Each section is characterized by a few characteristics, and the assigned characteristic level of each of the characteristics is presented to the customer. The customer is then asked to state his/her most preferred level for the characteristic given the assigned characteristic level for the viewed section of the video program. For instance, if the level of "action" in a section of the movie "First Blood" is assigned a value of 8, the customer may give 4-6 as his/her acceptance range. On the other hand, if the customer strongly disagrees with the assigned characteristic level, he/she may provide his/her own estimation of the level of the characteristic in the presented program section and give his/her acceptance accordingly. Of course, the major advantage of such a "rave review" procedure over ballot completion is that instead of voting on an abstract concept, the customer now makes estimations based on concrete examples.

Since the customer may not be able to remember exactly his/her preferred level of a characteristic that he/she indicated in a previous review or in an original ballot or may not have known his or her assigned initial value for a characteristic, he/she may intentionally or involuntarily repeat the same level for the same characteristic in one review if that characteristic appears in more than one program section, even if that characteristic should not have the same level. Therefore, in a "rave review" a program section with a large variety of characteristics should be selected to avoid repetitions of the same characteristic across several video clips. In addition, the customer should be advised that while making level estimations he/she should concentrate on the features of the current program sections and forget his/her previous ratings.

As mentioned above, changes in a customer profile should be expected over time. In other words, the values of cv and sv obtained from the rave reviews typically will be different from the values specified in the original ballot and during previous rave reviews. In order to avoid dramatic changes in the values, the setting of the new values should take into consideration both the old and the new data. Thus, if x represents either cv or sv, x.sup.n is the value of x after period n (n "rave review" changes), and y.sup.n is the value obtained during time n, then:

and

Each y is obtained either from the customer's ballot or rave review, but typically y.sup.1 is from the ballot data, and y.sup.1, 1>1, is from the rave review data. From the above equations, it follows that:

i.e., the new value of x after the n iterations is equal to the averace of all the n-1 previous data. The method is formally termed MSA, or Method of Successive Average. With this method, data at each iteration is equally accounted for in the final value. Any dramatic changes will be damped down, especially at later iterations (when n is large). This approach agrees with intuition, since a customer's profile should stabilize over time.

However, customers may have systematic bias in estimating their preferences. For instance, a customer may constantly underestimate or overestimate his/her preference rating for a characteristic. One way to detect the inaccuracy is to check if there are some customers who, when viewing various programs in rave reviews, constantly disagree with the content profile value for a particular characteristic and suggest disproportionately higher/lower ratings. If frequently the t values in the t significance test turn out to be insignificant despite their agreement, then it may be necessary to adjust the customers' ratings of the characteristic in question in the direction opposite to their suggestions.

Another way to make adjustments to the customers' combined ratings is through the clustering of customers. Customers are asked to give overall ratings for various programs. If a group of customers come up with very similar ratings for most of the programs in a category, it is assumed that the actual acceptance ranges for these customers for each characteristic relevant to the category forms a narrow distribution, i.e., their values are close to each other. However, if in the distribution of stated ratings, some outside values which are far away from the majority are seen, then the indication is that these outside values need to be adjusted.

There are are many algorithms to find outsiders in a population. For instance, all the values may be sorted in the ascending order of their absolute distances from the mean, and gaps searched for at the lower end. Those values that are located below the largest gap would be outsiders. For statistical validity, the mean and standard deviation of the population less the outsiders may be calculated and a t significance test conducted to determine if any of the outsiders belong to the population. Only those that do not belong will be subject to adjustment.

D. Adjusting Content Profiles

As discussed above, in a rave review, the customer may state his/her disagreement with the rating of a characteristic in a video program and put forward his/her own rating for each characteristic in the program. This provides a mechanism for adjustment of the content profiles.

In general, the present invention may use the ratings of experts or test groups as the reference base.

Generally, the calculation of the agreement scalar ac is based on the values of cp and sp. Since the values of cp and sp are used in calculating ac for all customers, any inaccuracy in their values will affect the final results for all customers. (By contrast, an inaccuracy in the value of a customer's cv and sv only affects the results for that customer.) By definition, customers collectively make ratings relatively closer to reality than any experts or test groups. In other words, the customer's rating is reality. For instance, if all the customers on the average tend to overestimate a particular characteristic (for one or all programs), then the experts' or test groups' objective ratings for that characteristic (for one or all programs) should be raised to agree with the customers' perceptions.

Again, MSA may be used for the content profile adjustment, letting x be either cp or sp and letting y.sup.n be the value collectively suggested in period n by the customers for the variable, where y.sup.n is defined as:

where y.sup.n.sub.i is the value suggested by customer i during period n. By substituting y in Equation (5) into Equation (4), the x.sup.n+1 value may be calculated. For customers who do not state disagreement, their y.sup.n.sub.i may be set to x.sup.n, i.e., the original content profile. Therefore, y.sup.n is the average of the customers' suggested value at period n. The resultant x.sup.n+1 is the adjusted content profile after period n.

This method would be less useful if only the content profiles of a characteristic for individual programs may be adjusted. Often, the relative bias is systematic, i.e., as seen from the customer's side, the characteristic values may underestimate or overestimate the significance of a characteristic for programs of certain or all categories. This problem can also be addressed as follows.

For clarity in discussion, subscripts again will be used. y.sub.jk is the customers' average suggested rating for characteristic k for program j. The distribution of the customers' ratings is assumed to be normal. For simplicity, time subscripts are dropped. A t value significance test is then conducted as: ##EQU1## where: ##EQU2## and where: t.sub.jk is the t value for significance of difference between the customers' suggested rating of characteristic k for program j and the corresponding assigned objective rating;

s.sub.Yjk-CPjk is the standard deviation between the distribution of y.sub.jk and that of cp.sub.jk ;

I is the total number of customers; and

M is the total number of "experts."

If t.sub.jk is significant for a pre-defined level (say 0.05) with degree of freedom of I+M-2, then cp.sub.jk is determined to be significantly different from y.sub.jk. In that case, an adjustment in cp.sub.jk is necessary, and MSA is calculated to obtain the new cp.sub.jk from y.sub.jk.

With the above method, only the assigned objective rating of a characteristic for individual programs is adjusted. In order to adjust the assigned objective ratings of a characteristic for all the programs in a category, the following is used:

where:

T.sub.lk is the average of the t values for characteristic k in all the programs for category l;

t.sub.j-lk is the t value for significance of the difference between the customers' suggested rating of characteristic k for program j.sub.l, and the corresponding objective rating; and J.sub.l is the number of programs in category l.

If an adjustment of the assigned objective rating of a characteristic over several or all categories is desired, the t values of an even wider range are averaged. For instance, if it is necessary to make an adjustment for all categories, calculate:

where:

T.sub.k is the average of the t values for characteristic k in all programs.

When a content profile value cp is changed to cp', it is also necessary to change the corresponding sp (deviation in cp) to sp'. Because there is a distribution in cp, there must be some expert(s) whose rating(s) is below or above the mean (cp). If through the above calculation cp overestimates/underestimates reality, it is assumed that only those experts whose ratings are above/below the mean made an overestimation/underestimation. Therefore, after the adjustment, the new deviation sp' should be smaller than sp.

One possible calculation of sp' is:

Since cp>0, sp'<sp (i.e., sp always declines after adjustment). If .alpha.=1, cp=3, cp'=4, and sp=1, then sp'=0.75. The parameter a thus determines the rate of decreasing in sp with the rate of change in cp.

It should be pointed out that before making actual changes to content profiles determined by experts or test groups, it is desirable to consult with the experts or test groups for the proposed changes. That will not only preclude any unreasonable changes, but also will reduce possible future bias by the experts or test groups.

E. Customer Moods and Time Windows

Few people are purely single minded, especially when enjoying entertainment. Besides a generic propensity, it is therefore reasonable to assume that each customer could have one or more viewing moods, and in each of the moods he/she would like to watch a particular set of program categories. For normal and not highly capricious people, the moods should be time-specific, i.e., each mood has a time window, within which the mood is effective.

On the other hand, people are not free all the time. The time when they can enjoy entertainment is limited. The time window concept can be used to represent this temporal limitation as well. Thus, each time window can be expressed as a pair of time variables, l and u, where l is the starting point of the window and u is the ending point of the window. Customer profiles used in accordance with the preferred embodiment of video scheduling preferably incorporate this concept of moods and time windows.

In the present invention, each customer preferably has a generic mood and may also have some specific moods. Both generic moods and specific moods may or may not be time-specific. In fact, a non time-specific mood can also have a time window, only with l=u, i.e., its window covers the whole day. Typically, for a particular customer, the time window for his/her generic mood will have the greatest width, and the width of the windows for his/her specific moods will decrease with an increase in specification. In this sense, all the moods of a customer form a tree, in which the generic mood is the root, and a specific mood becomes the child of another mood if the former's window is contained in the latter's window. For example, if a customer has four moods: generic, peaceful, violent, speculative, the generic mood may cover all times, the violent mood may cover 6 a.m. to noon, the peaceful mood 6 p.m. to midnight, and the speculative mood from 8 p.m. to midnight. Thus, the speculative mood is a child (subset) of the peaceful mood. The mood at the lowest (most specific) level of the hierarchy is generally used to develop the program list for the customer (described below).

The definition of moods can be the responsibility of the customer. When ballots are used to create the initial customer profiles, each ballot may correspond to a mood. In other words, a mood may be equivalent to a customer profile. The generic mood or generic customer profile is required unless there is an automatic system default mood or profile. Beyond that, the customer can fill out as many ballots as he/she likes to establish specific moods.

A satisfaction factor, sf, is attached to each mood. For the generic mood, sf=1, which is the base. sf increases as the time window narrows since it is reasonable to believe that people get greater satisfaction as their more specific requirements are met. sf is either determined by the customer or takes a default value. For instance, the system may set a maximum value on sf for the most specific window, which is two hours wide, and do a linear interpolation to find the sf values for windows of greater widths. If the customer provides the sf values for his various mood windows, the values will be normalized in light of the base value of one and the above-mentioned system-set maximum value.

With the introduction of time windows, each customer i (or customer-mood i, to be more accurate) will take on time window superscripts as i.sup.l.sbsp.--.sup.i, u.sbsp.--.sup.i, while each program j will become j.sup.l.sbsp.--.sup.j, u.sbsp.--.sup.j, where l.sub.j is the starting time of program j and u.sub.j is the ending time of program j. The calculation of the agreement scalar ac then proceeds as will be described in the next section. However, the calculation of as, the final objective value, becomes:

where f(l.sub.i,u.sub.i,l.sub.j,u.sub.j) gives a punishment value expressing the customer's dissatisfaction due to the mismatch between the time window of customer-mood i and the broadcast time of program j, sf.sub.i is the normalized satisfaction factor of customer-mood i, wc.sub.i is the weight for the existing agreement scalar, and wf is the weight for f, which needs to be determined through practice.

The major issue here is the form of the punishment function f. Intuitively, f=0 when the mood window contains the broadcast window, i.e., l.sub.i .ltoreq.l.sub.j, and u.sub.i .gtoreq.u.sub.j, and f increases as the two windows move away from each other. Since u.sub.i -l.sub.i .gtoreq.u.sub.j -l.sub.j, i.e., the mood window is not narrower than the broadcast window, the time discrepancy d between the two windows may be expressed as:

So f=f(d).

It is reasonable to expect that the customer's dissatisfaction increases rather sharply when the mismatch of the time windows first begins, which means he/she will miss some part of the program. But when the time mismatch increases further, the customer's discontent will level off. For example, the customer will feel quite upset if he/she misses the beginning ten minutes of a program which he/she likes. However, if he/she has already missed the first one hour and a half, his/her dissatisfaction will not increase much if he/she misses the remaining half an hour. This non-linear relationship can be well expressed by the following negative exponential equation:

where .alpha. is the maximum dissatisfaction that a customer could have for missing a program, and .beta. is a parameter which determines how sharply f(d) increases with d. The greater the value of .beta., the steeper the curve would be. It can be seen through Equation (15) that f(d)=0 when d=0, and f(d)=.alpha. when d=.infin.. Thus, the punishment function becomes:

Given the form of Equation (13), a may be set to one since the extent of dissatisfaction can be adjusted by the weight parameter wf.

III. Calculation of Agreement Matrix

The calculated agreement scalars, ac, form an agreement matrix, AC, which provides measurements of the similarity between the customer profiles and the content profiles. Its calculation incorporates the desired amounts of the various characteristics used to define the programs, their importance (weights) to each customer, and the amounts of these characteristics present in each program as determined by experts or test groups. Assuming there are I customers, J programs, K characteristics, and M experts, then each cell in the initial agreement matrix (agreement scalar for cv.sub.ik and cp.sub.jk) may be calculated as: ##EQU3## where: ##EQU4## and: ##EQU5## and:

______________________________________ ac.sub.ij : agreement scalar between the profiles of customer i and that of program j; t.sub.ijk : t value for significance of difference between the rating of characteristic k in customer i and that in program j; s.sub.CVik-CPjk : standard deviation between the distribution of cv.sub.ik and that of cp.sub.jk ; W.sub.i : .SIGMA..sub.k wv.sub.ik, i.e., the sum of all weights for customer i; M: number of "experts" who rate program j; and N: number of times of consideration before the customer reaches a final decision on the rating for cv. ______________________________________

The magnitude of each of the t values shows the deviation of the customer's ratings of a characteristic from that of the video program given the distributions of the ratings by both the customer and the experts or test group. If the t value is significant at a predetermined level of significance, e.g., 0.05 for degree of freedom of 2M-2, then the two distributions could be regarded as belonging to the same population. The average of all the t values can serve as an indicator of the divergence (distance in characteristic space) between the profile of the customer and that of the program. Therefore, the variable ac, which is basically the reciprocal of the average of the t values (reciprocal of the distance), exhibits the level of agreement between the two profiles. Thus,

ac.epsilon.(0,1) and reaches its maximum value of 1 (perfect agreement) only when .SIGMA..sub.k wv.sub.ik *t.sub.ijk =0 or wv.sub.ik *t.sub.ijk =0 (i=1,2, . . . , I; j=1,2, . . . ,J; k=1,2, . . . ,K) since wv.sub.ik >=0 and t.sub.ijk >=0. In other words, perfect agreement will be met only when there is no difference between the customer profile and the content profile, or when there are differences only on certain characteristics and the customer ignores those characteristics. As a result, sorting all the programs in the ascending order of ac renders a recommendation list of programs for the customer.

In the original formulation of the t significance test, both M and N are the sample sizes. While M is the number of experts or members in the test group, N represents the number of times of consideration before the customer reaches his/her final decision on the rating for cv. N's value may be determined empirically through experiments. Generally speaking, the higher N's value, the lower the flexibility in the customer's acceptance for various characteristics on average. Preferably, N=M-1 so that dispersions in cv, which is interpreted as the customer's acceptance range, and in cp, which represents the difference in the experts' voting, are equally counted. This calculation is underpinned by the assumption that both the distributions of the experts' vote and the customer's rating are normal (Gaussian). Although the assumption is not guaranteed, that is the best that can be hoped for.

Once the initial customer profiles and initial content profiles have been established, a simpler form of Equation (17) may be used by combining wv and s into a single measure of importance: ##EQU6## where sv.sub.ik is the spread in customer i's rating for characteristic k (inversely correlated with the importance of k to i), and sp.sub.jk is the spread in the experts' ratings for characteristic k. Thus:

This simpler notation is preferred and will be used throughout the discussion below. However, the algorithms described below are all easily extended to the more complex model of Equation (17). Generally, the more complex form of the agreement matrix (Equation (17)) is only preferred when the customers are asked questions to build their customer profiles. The simpler form of the agreement matrix (Equation (21)) is preferred when the profiles are initialized using demographics and updated using passive monitoring, as in the presently preferred implementation of the present invention.

For purposes of illustration, a calculation of a simple agreement matrix will be described here.

It is assumed that there are only two customers: (1) John and (2) Mary. Their sample customer profiles are as follows:

______________________________________ romance high-tech violence ______________________________________ characteristic (cv) : 1 John 3.0 9.0 7.0 2 Mary 9.0 3.0 0.0 standard deviation (sv): 1 John 1.0 2.0 1.0 2 Mary 1.0 0.5 0.0 weight (wv) 1 John 2.0 9.0 5.0 2 Mary 8.0 3.0 7.0 ______________________________________

The available programs are as follows:

______________________________________ Program Titles ______________________________________ 1 Star Trek 2 Damnation Alley 3 Forever Young 4 Terminator II 5 Aliens 6 Fatal Attraction ______________________________________

The sample content profiles are as follows:

______________________________________ romance high-tech violence ______________________________________ .backslash. char. (cp): program.backslash. 1 Star Trek 2.0 9.0 4.0 2 Damnation Alley 5.0 0.0 1.0 3 Forever Young 8.0 3.0 0.0 4
Terminator II 0.0 10.0 8.0 5 Aliens 0.0 8.0 9.0 6 Fatal Attraction 7.0 0.0 8.0 .backslash. standard deviation (sp): program.backslash. 1 Star Trek 0.5 1.0 1.0 2 Damnation Alley 1.0 0.0 1.0 3 Forever Young 1.0 0.5 0.0 4 Terminator II 0.0 1.0
1.5 5 Aliens 0.0 1.0 1.0 6 Fatal Attraction 2.0 0.0 1.0 ______________________________________

After normalizing w using: ##EQU7## where s.sub.CVik-CPjk is defined in Equation (19), the above input data produces the following weight matrix (w):

______________________________________ .backslash. characteristic: customer .backslash. romance high-tech violence ______________________________________ 1 John .166 .425 .409 2 Mary .292 .192 .516 ______________________________________

Given the weight matrix and the characteristic profiles of the customers and the programs, the agreement matrix may be calculated. For example, the agreement scalar between customer 1 and program 2 is: ##EQU8##

The final agreement matrix (AC) is thus:

______________________________________ .backslash. Program Customer .backslash. 1 2 3 4 5 6 ______________________________________ 1 John .418 .131 .138 .429 .365 .170 2 Mary .160 .307 .774 .110 .108 .159 ______________________________________

From the agreement matrix, it is evident that John prefers "Star Trek", "Terminator II", and "Aliens", while Mary prefers "Forever Young" and "Damnation Alley". This is the results that would have been expected from the profiles, only here the preferences have been quantified.

Of course, in the simple case where merely the presence or absence of particular characteristics are measured, the agreement matrix would look for identity in the most categories rather than the distance between the customer profile vector and the content profile vector using the techniques described above.

IV. Scheduling Video Delivery in Accordance with Customer and Content Profiles

The introduction of time dimensions makes possible the scheduling of video programs, i.e., the assignment of programs to days and to time slots in accordance with each customer profile.

A. Scheduling Constraints

Solving the problem of assigning days and time slots simultaneously is often impractical because of the exponential increasing order in the number of possible combinations. Therefore, the two tasks are preferably performed separately through heuristic methods.

The first task, assigning programs to days, is simplified significantly by the fact that in the present system the customers' preferences are differentiated mostly by time slots (hours), rather than by days. When the customer defines weekday mood time windows, it is assumed that the window will apply to any weekday. Of course, the customer may define some weekend time windows, which apply to either Saturday or Sunday. Therefore, for a given set of programs available for a week, the major question is not on which day to broadcast them, but during which hours to broadcast them.

A possible approach to scheduling is that for each program its top n most-preferred broadcast windows are determined from the average of the objective values as calculated using Equation (13). The scheduler then uses some methods to find a solution in which the average objective value reaches a reasonably high value, and in which the time slots are covered. There are many such methods available, such as integer linear programming.

An extra complexity is the possible necessity of repeating some programs during a day or a week, because of their high popularity. A simple approach is to let the scheduler first determine the number of necessary repetitions, and then add the number in as constraints in the programming. However, it should be noted that the above approach is for just one channel. If there are multiple channels, then it is usually necessary to first categorize the channels, find their respective "target audience", and then run the scheduling procedure on the target audience of each channel.

Attention should also be paid to the mutual exclusion among the overlapping time windows of a customer. Although the customer may define time windows which conflict with each other, in terms of overlapping and containment, only one of the windows in the conflicting set can be used in the final assignment. This condition should be added to the constraints.

B. Scheduling Algorithm

With the above scheduling constraints in mind, the present inventors have developed an algorithm which uses customer profiles and content profiles for scheduling the broadcast of movies and other shows over a video distribution network which allows the simultaneous distribution of many channels from a head end to the set top multimedia terminals associated with many customers' television sets. The same approach is then used to develop "virtual channels" at the set top multimedia terminals based on domain or genre or tastes of individuals so that the customer can view the video programming predicted to be most desirable to that customer. The "virtual channels" may be displayed on dedicated channels, or the recommended programming may be highlighted directly on the electronic program guide or displayed on the customer's screen as recommended programming selections. Also, the channels may be reprioritized for presentation on the electronic program guide on the basis of the calculated "virtual channels." Similarly, video programming of a particular type, even if not part of the "virtual channels" may be highlighted on the electronic program guide as desired. The algorithm for determining the recommended programming is based on the above-mentioned "agreement matrix" which characterizes the attractiveness of each movie and video program to each prospective customer. In short, a broadcast schedule and/or virtual channel is generated which is designed to produce the greatest total customer satisfaction. The generation of the agreement matrix and the scheduling of programs in accordance with the generated agreement matrix will now be described in more detail.

As described above, the agreement matrix may be produced by comparing the customer profiles and the content profiles. In the following description, it is assumed that the agreement matrix is normalized so that all agreements between customers and movies lie between zero and one.

The basic problem of scheduling a cable television broadcast can be formulated as follows.

Given an agreement matrix A where ac.sub.ij is the agreement scalar between customer i and program j, find: ##EQU9## where J is; a set of programs to be broadcast drawn from a set Q of candidate programs available for broadcast, the first summation is over all customers i, and the second summation (of j) is over the n.sub.i programs in the set of programs K.sub.i that customer i would most desire to watch. In other words, given the agreement matrix between customers and programs, it is desired to pick the set of programs which maximizes the agreement between customers and those programs which the customers might watch. For example, if a hundred programs are being broadcast and a given customer would not consider more than five of them, it does not matter how much the customer likes the other ninety-five programs. However, as noted above, the actual problem can be much more complex, since different agreement matrices can depend on the time of day and since multiple time slots cannot be scheduled independently.

If only one program were to be broadcast using the method of the invention, the above optimization problem is trivially solved by summing each column of A (calculating .SIGMA..sub.i ac.sub.ij) and picking the program j which gives the largest value. When many programs are being selected, however, it is not possible to try all possible combinations; therefore, heuristic methods must be used.

The following algorithm is an example of a greedy algorithm which provides an efficient algorithm for approximately solving the above scheduling problem including the fact that it is desired to select ni programs for each customer i (the "viewing appetite"). In other words, n.sub.i represents the number of programs scheduled for broadcast to a particular customer at any time. In a preferred embodiment, n.sub.i corresponds to the number of "virtual" channels available to each customer.

In accordance with the invention, the "greedy" scheduling algorithm will function as follows. As illustrated in FIG. 2, at each step of the algorithm:

1) Pick the program j which yields the greatest satisfaction summed over the current customer population (i.e., those eligible to receive program j).

2) Decrement the viewing appetite n.sub.i of those customers who have the greatest agreement scalars with the currently selected program.

3) Remove from the customer population any customers whose viewing appetite has dropped to zero (n.sub.i =0), since they have all the shows they need and hence are not a factor in selecting further shows.

The scheduling process stops when the number of programs selected, m, equals the number of broadcast channels available, M, for the schedule time.

A more precise description of the process may be described in pseudocode as:

0) Initialize:

______________________________________ Let ap.sub.i = n.sub.i for all i

set all customers' appetites to n.sub.i Let V = {1} initialize the customer population to include all customers Let m = 0. start with no programs selected ______________________________________

As described below, different initializations are possible to account for programs which will always be broadcast. Also, if individual appetites are not available, all can be set to a single value, n.

1) Select the currently most popular program:

______________________________________ Select program j which gives max .SIGMA..sub.i in V ac.sub.ij ______________________________________ Let m = m + 1 increment number of programs selected If m = M, stop, stop if done ______________________________________

otherwise proceed to (2).

2) Decrement the appetite of those customers who like the currently selected program:

Select the customers i in V for which ac.sub.ij is above a threshold value .alpha.. Then decrement the appetites for selected customers by letting ap.sub.i =ap.sub.i -1.

3) Remove customers from the current customer population V who have no appetite left:

Remove from V customers j for whom ap.sub.ij =0. Go to (1).

This method automatically produces a schedule in which a variety of programs are selected according to the spread of customer interests. Note that the simpler algorithm of selecting the most popular programs (those with highest agreement matrices) will not produce acceptable results, for if a majority of customers prefer action films, then only action films would be selected, leaving the minority of customers with no films that they find attractive.

FIGS. 1-3 summarize the above-mentioned procedures for establishing customized channels of preferred programs in accordance with the invention.

As illustrated in FIG. 1, a schedule of available shows and their characteristics (content profiles) is created and stored in a database at step 102. As noted above, the characteristics of the shows may be determined by "experts" or test groups by completing questionnaires and the like, or the content profiles may be generated from the frequency of usage of certain words in the text of the video programs (the on-line descriptions or the script). Alternatively, the content profiles may be determined by combining the customer profiles of those who "liked" the video program during a "rave review." Preferably, the content profiles are downloaded all at once for a given time period along with the corresponding scheduling data as part of the electronic program guide data and sent via a separate data channel. On the other hand, the content profiles may be transmitted as part of the bit stream of the video program (for digital transmission), in the vertical blanking intervals of the video program (for analog transmission), or by other appropriate means.

At step 104, the customers' preferred characteristics (customer profiles) are created and stored in a database. As noted above, the customer profiles represent the customers' preferences for the program characteristics and preferably differ in accordance with the time of day to account for different moods of the customer and different customers within each household. In a preferred embodiment, the customer profiles for each household are stored in the set top multimedia terminal for that customer's household.

The content profiles received with the electronic program guide data are preferably stored at the set top multimedia terminal and compared by the set top multimedia terminal to the customer profiles for each customer. An agreement matrix is then created at step 106 using the techniques described above. Once the agreement matrix has been generated, those programs with the highest values for ac, i.e., the closest distance (1/ac) and hence closest match to the customer's profile or profiles, are prioritized and selected for presentation as "virtual channels" (in the case of creating "virtual channels" at a set top multimedia terminal) or as the programming channels (in the case of scheduling video programming at the CATV head end) at step 108. This process is described in more detail herein with respect to FIG. 2.

In a simple embodiment of the invention in which no feedback is used to update the customer profiles, no further activity is necessary. However, it is preferred that the customer and/or content profiles be updated to allow for changes in the customers' preferences as well as to correct errors in the original determinations of the profiles. Accordingly, at step 110, the customers' set top multimedia terminals maintain a record of the video programs that are actually watched by the customer for a period of time (say, 10 minutes) sufficient to establish that the customer "liked" that program. Of course, the monitoring function may be selectively activated so that the profiles are not always updated, as when a guest or child takes control of the television at an unexpected time.

Finally, at step 112, the customers' profiles are updated to reflect the programs actually watched by the customers. Such updating techniques are described above and further below with respect to FIG. 3.

FIG. 2 illustrates a technique for selecting video programs for "virtual channels" at the customers' set top multimedia terminals or, alternatively, for scheduling video programming at the head end from the available video programming sources. As illustrated, the method is initialized at step 202 by determining which customer profile or profiles are active for the time period to be scheduled, by determining the customers' appetites (number of channels available for transmission), and by determining the database of video programming from which the schedule may be created. For example, at the head end, the video programming database may be any video programming available for transmission during the designated time frame, while at the set top multimedia terminal, the video programming database comprises only the video programming on those channels which the customer is authorized to receive.

Once the agreement matrix for the available video programs has been determined, at step 204 the most popular programs for a single customer (at the set top multimedia terminal) or a cluster of customers (at the head end) are selected and removed from the list of available programs during the relevant time interval. Of course, in the case of scheduling at the set top multimedia terminal, the video programs scheduled onto "virtual channels" are still received on their regular channels and the "virtual channels" are assigned to unused channels of the set top multimedia terminal. At step 206, it is then determined whether the customer's appetite is satisfied (at the set top multimedia terminal) and whether all the customers' appetites are satisfied (at the head end). If all relevant appetites are satisfied, the scheduling algorithm is exited at step 208. On the other hand, if all customer appetites are not satisfied, the appetites of the customers likely to watch the selected program are decremented at step 210. Hence, only those customers with preferences which relatively "match" the characteristics of a particular video program have their appetites decremented. At step 212, those with no appetites (channels for scheduling) left are removed from the scheduling list. The process is then repeated starting at step 204 for those with channels left to schedule.

When establishing "virtual channels" in accordance with the invention, it is important to know which customer profile or profiles to use in creating the agreement matrix. In a preferred embodiment, this is accomplished by using the customer profile or combination of customer profiles which are given priority during a particular time interval for a predicted customer mood. This determination is made independent of the person actually viewing the television. However, the system of the invention may be easily modified to permit the customer to identify himself or herself by providing a user ID to the set top multimedia terminal so that a particular profile of that customer may be selected in the determination of the agreement matrix. In other words, customer names may be matched to particular profiles based on selections made when that customer's user ID has control of the television. In addition, combined profiles may be created which best reflect the combined viewing tastes of several persons in the same household. On the other hand, the system may come with preselected profiles which the customer may select to use as his or her initial profile. After a certain amount of time, the system would recognize a particular profile as belonging to a particular viewer or combination of viewers so that it would eventually be unnecessary for the customers to input their user IDs. In other words, the system would "guess" which customers are viewing by noting which customer profile is closest to the shows being selected. Of course, this latter approach requires the customer profiles to be matched to individuals rather than just time slots as in the preferred embodiment.

FIG. 3 illustrates a preferred technique for updating customer profiles in accordance with the invention. As illustrated, the initial customer profiles are selected at step 302 using any of the techniques described above. At step 304, the agreement matrix is calculated to determine which video programs the customer might desire to view in the selected time period. Then, at step 306, the passive monitoring feature of the invention is invoked to determine if the customer actually watched the video program selected from by the agreement matrix. If the customer watched the predicted program, then the customer profile is presumed accurate at step 308 and no adjustment is made. Of course, the customer profile may be positively reinforced by varying the adjustment increment. However, if the customer did not watch the predicted video program, the customer profile for the appropriate time interval is selected at step 310 which has characteristics closest to those of the video program actually watched. That customer profile is then adjusted using the techniques described above. The adjusted profile is then considered valid until the next time slot is encountered at step 312. The agreement matrix is then recalculated at step 304 for the new customer profiles and video programs offered in the next time slot.

Particular hardware implementations of the invention in a set top multimedia terminal and/or a video head end will be described in Section VI below.

C. Scheduling Variations

Many variations to the above generalized scheduling scheme are possible within the scope of the invention. The following variations may be used by those skilled in the art, but, of course, this list is not comprehensive.

A. Special programs such as standard network broadcasts may be included in the scheduling. In this embodiment, when certain programs have already been scheduled for broadcast, such as standard network programs or specially selected popular movies, the above algorithm is modified to account for the effect of these programs on customer interest in the remaining video programming. This is easily done by initially running the above algorithm with step (2) modified to simply include the prescheduled programs rather than selecting new ones. When all prescheduled programs have been "scheduled" (i.e., customers likely to watch the prescheduled programs have been removed from the customer pool and the broadcast slots have been filled), then the scheduling algorithm proceeds as usual. As desired, this will lead to additional movies being selected which will appeal most to customers who will probably not be watching the standard network broadcasts.

B. The effect of recent broadcasts may be included in the scheduling. The above scheduling algorithm is presented for a single time slot. In actuality, the video programs selected must depend on which other video programs have been shown recently. This can be done in several ways. For example, one can remove recently shown movies from the list of movies available to broadcast. Alternatively, one can remove recently shown movies from the list of movies available to broadcast when their popularity (number of customers per broadcast) drops below a threshold. This approach is better in that it allows new hits to be broadcast multiple times. More complex models may explicitly include a saturation effect by shifting the agreement matrix based on the number of similar video programs recently viewed.

C. The effect of overlapping time slots, such as so-called "near video on demand" may be included in the scheduling. For this purpose, the above algorithm can be modified to account for the fact that popular video programs may occupy more than one time slot and that time slots may overlap. As an extreme example, it may be desirable to offer multiple overlapping broadcasts of a popular movie on vacant channels, e.g., at 15 minute intervals. In this example, the customer appetite concept set forth above would be augmented by a more sophisticated model which includes the fact that customers turn on the television at different, possibly random times, and only want to watch shows which are starting at times close to the time they turn on the television.

D. The effect of moods may be included in the scheduling. If one has different agreement matrices for different customer moods (either reflecting multiple customers with different tastes using the same television or reflecting one customer having different viewing preferences due to moods), the above algorithm can be trivially extended. The different moods are just treated as different customers, with the appetite for each mood selected to be proportional to how often that mood occurs in the time slot being scheduled. This will result in programs being scheduled for each of the potential moods; the customer can then pick his or her preferred show.

E. Programs may be matched to channels for scheduling content based channels. Customers may prefer to have channels which have a consistent content or style (e.g., sports or "happy" shows). The basic algorithm presented above could be modified so that some slots are reserved so that shows of a given type (e.g., close to a given set of characteristics typical of a channel) can be selected if they have not already been chosen in the main scheduling algorithm.

F. How the customer appetites are decremented may be selected for scheduling purposes. In the above scheduling algorithm, whenever a program is chosen, the viewing appetite of all its "audience" is decremented, and those who have used up their appetites are removed from the viewing population. Since the checking of viewing appetite is made only of the audience of the currently chosen program, the outcome of the scheduling process depends in part on the definition of audience--who will potentially watch the program. Above it was assumed, for simplicity, that customers should be included in the potential audience if their agreement with the program was above a threshold. However, many variations on this are possible. For example, one could pick a fixed audience size for each show. For example:

Select the n.sub.w customers i in V for which ac.sub.ij is maximum. n.sub.w can be calculated, for example, as n*I/M as the average customer appetite, n, times the number of customers, I, divided by the number of programs, M, to be broadcast. One could also make the threshold a variable, either by decreasing the threshold or the number of customers n.sub.w over the course of developing a schedule so that the first programs scheduled would have large audiences, while the last programs sc