REGISTER  |  SIGN IN
United States Patent5550746
JacobsAugust 27, 1996
Title
Method and apparatus for storing and selectively retrieving product data by correlating customer selection criteria with optimum product designs based on embedded expert judgments
Abstract
A machine and method are provided for selecting product or service design, such as a social expression product. The machine and method each (i) stores a plurality of product or service designs and a plurality of descriptors for each of the plurality of product or service designs, each of the descriptors representing an application scale; (ii) stores an expert-predetermined optimum applicability value for each combination of the application scales and the product or service designs; (iii) presents, to a customer, selection criteria options for one or more application scales; (iv) stores customer preference values for one or more application scales used for describing the product/service design, the customer preference values to be predetermined by expert judgment and assigned to application scales where such values correspond to the selection criteria options chosen by the customer; (v) quantitatively correlates, by means of a correlation algorithm, each of the customer preference values with corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of the product or service designs based on the customer-chosen selection criteria options; and (vi) displays for the customer a group of identified product or service designs based on the average suitability ratings for those identified product or service designs. In the case of a product, the apparatus and method solicit the customer to select one of the identified product designs, verify the selection and possibly modify the selected product design. The selected or modified product design may then be dispensed to the customer.
Inventors:Jacobs; Herbert H. (LaJolla, CA)
Assignee:American Greetings Corporation (Cleveland, OH)
Appl. No.:349390
Filed:December 5, 1994
Current U.S. Class:700/231 705/27 706/50 706/934 
Field of Search:364/468,478,479,401-403,188,189 395/155-161,600,925,934,54
U.S. Patent Documents
2089563August 1937Luhrs
3454956July 1969Icenbice, Jr. et al.
3609250September 1971Morris
3637934January 1972Brewster
3669814June 1972Faltin
3688276August 1972Quinn
3704362November 1972Kolby et al.
3705384December 1972Wahlberg
3727038April 1973Ritter et al.
3757037September 1973Bialek
3800932April 1974Dana
3819854June 1974Kolb
3828904August 1974Naitou et al.
3829833August 1974Freeny, Jr.
3864708February 1975Allen
3898930August 1975Ikegami et al.
3911397October 1975Freeny, Jr.
3924065December 1975Freeny, Jr.
3932036January 1976Ueda et al.
3938190February 1976Semmlow et al.
3943335March 1976Kinker et al.
3946220March 1976Brobeck et al.
3949375April 1976Ciarlo
3970992July 1976Broothroyd et al.
3990710November 1976Hughes
4007362February 1977Sindermann
4023013May 1977Kinker
4034839July 1977Lee
4041467September 1977Cota et al.
4058056November 1977Rubin
4069896January 1978Rottmann et al.
4070223January 1978Stalzer
4070698January 1978Curtis et al.
4071911January 1978Mazur
4073368February 1978Mustapick
4085445April 1978Blevins et al.
4096933June 1978Massa
4112421September 1978Freeny, Jr.
4144656March 1979Podkopaev et al.
4160271July 1979Grayson et al.
4173024October 1979Miller
4190819February 1980Burgyan
4193114March 1980Benini
4209588June 1980Freeny, Jr.
4209787June 1980Freeny, Jr.
4216286August 1980Greene et al.
4220991September 1980Hamano et al.
4224601September 1980Davidson
4232317November 1980Freeny, Jr.
4232334November 1980Dyson
4239380December 1980Goldsholl
4247759January 1981Yuris et al.
4260229April 1981Bloomstein
4261012April 1981Maloomian
4265371May 1981Desai et al.
4270182May 1981Asija
4275449June 1981Aish
4300040November 1981Gould et al.
4305131December 1981Best
4308017December 1981Laughon et al.
4318121March 1982Taite et al.
4319336March 1982Anderson et al.
4328544May 1982Baldwin et al.
4329710May 1982Taylor
4339134July 1982Macheel
4354613October 1982Desai et al.
4358824November 1982Glickman et al.
4359631November 1982Lockwood et al.
4359634November 1982Saito et al.
4396307August 1983Shah et al.
4412292October 1983Sedam et al.
4414467November 1983Gould et al.
4414896November 1983Fischer
4417322November 1983Berry et al.
4417722November 1983Ishii et al.
4429385January 1984Cichelli et al.
4431323February 1984Kulow
4434467February 1984Scott
4435772March 1984Suzuki et al.
4438326March 1984Uchida
4441160April 1984Azcua et al.
4449186May 1984Kelly et al.
4458802July 1984Maciver et al.
4459676July 1984Oguchi
4460957July 1984Eggebrecht et al.
4463874August 1984Friedman et al.
4467349August 1984Maloomian
4481590November 1984Otten
4484304November 1984Anderson et al.
4486774December 1984Maloomian
4488244December 1984Freeman
4489389December 1984Beckwith et al.
4495582January 1985Dessert et al.
4498139February 1985Malinovsky
4517578May 1985Tazaki
4519037May 1985Brodeur et al.
4519522May 1985McElwee
4520451May 1985McLaughlin
4528643July 1985Freeny, Jr.
4546434October 1985Gioello
4553222November 1985Kurland et al.
4559598December 1985Goldwasser et al.
4567359January 1986Lockwood
4575813March 1986Bartlett et al.
4577206March 1986Hibino
4591281May 1986Howard et al.
4591983May 1986Bennett et al.
4596924June 1986Watanabe
4598376July 1986Burton et al.
4598810July 1986Shore et al.
4599598July 1986Komoda et al.
4602299July 1986Saito
4608662August 1986Watanabe et al.
4610200September 1986Metso
4616327October 1986Rosewarne et al.
4623292November 1986Suzuki et al.
4623964November 1986Getz et al.
4625275November 1986Smith
4627015December 1986Stephens
4644401February 1987Gaskins
4646250February 1987Childress
4649513March 1987Martin et al.
4650977March 1987Couch
4652998March 1987Koza et al.
4654799March 1987Ogaki et al.
4655026April 1987Wigoda
4664546May 1987Runzi
4668150May 1987Blumberg
4672554June 1987Ogaki
4674041June 1987Lemon et al.
4674055June 1987Ogaki et al.
4677565June 1987Ogaki et al.
4677570June 1987Taki
4683536July 1987Yamamoto
4699532October 1987Smith
4700181October 1987Maine et al.
4700317October 1987Watanabe et al.
4700318October 1987Ockman
4703465October 1987Parker
4710885December 1987Litteken
4712174December 1987Minkler, II
4714936December 1987Helinski et al.
4719885January 1988Nagano et al.
4722053January 1988Dubno et al.
4723212February 1988Mindrum et al.
4726697February 1988Maedge et al.
4727589February 1988Hirose et al.
4733362March 1988Haraguchi
4736306April 1988Christensen et al.
4740904April 1988Nagle
4745560May 1988Decker et al.
4750122June 1988Kaji et al.
4750131June 1988Martinez
4750151June 1988Baus
4764867August 1988Hess
4764880August 1988Pearl
4766548August 1988Cedrone et al.
4766581August 1988Korn et al.
4771401September 1988Kaufman et al.
4772882September 1988Mical
4773009September 1988Kucera et al.
4775935October 1988Yourick
4779080October 1988Coughlin et al.
4786229November 1988Henderson
4787050November 1988Suzuki
4789147December 1988Berger et al.
4789907December 1988Fischetti et al.
4794403December 1988Sleber et al.
4801375January 1989Padilla
4802170January 1989Trottier
4811240March 1989Ballou et al.
4811247March 1989Malady et al.
4814592March 1989Bradt et al.
4817005March 1989Kubota et al.
4817042March 1989Pintsov
4817043March 1989Brown
4818854April 1989Davies et al.
4821197April 1989Kenik et al.
4822365April 1989Walker et al.
4833307May 1989Gonzalez-Justiz
4835683May 1989Phillips et al.
4839505June 1989Bradt et al.
4839829June 1989Freedman
4845635July 1989Rosselli
4845653July 1989Conrad et al.
4847473July 1989Lee et al.
4847761July 1989Ferriter et al.
4852013July 1989Durst, Jr. et al.
4856653August 1989Ackeret
4862376August 1989Ferriter et al.
4864502September 1989Kucera et al.
4866661September 1989de Prins
4870576September 1989Tornetta
4873643October 1989Powell et al.
4882675November 1989Nichtberger et al.
4884199November 1989Boothroyd et al.
4887217December 1989Sherman et al.
4891660January 1990Biondo, Jr.
4896791January 1990Smith
4903815February 1990Hirschfeld et al.
4916637April 1990Allen et al.
4918604April 1990Baum
4924385May 1990Dote
4937439June 1990Wanninger et al.
4949257August 1990Orbach
4951203August 1990Halamka
4954956September 1990Yamakawa et al.
4954981September 1990Dehner, Jr. et al.
4961507October 1990Higgins
4964043October 1990Galvin
4970655November 1990Winn et al.
4972318November 1990Brown et al.
4974170November 1990Bouve et al.
4982337January 1991Burr et al.
4982343January 1991Hourvitz et al.
4982346January 1991Girouard et al.
4991108February 1991Hamilton
4992940February 1991Dworkin
4993587February 1991Abe
4999065March 1991Wilfert
5007084April 1991Materna et al.
5008810April 1991Kessel et al.
5016183May 1991Shyong
5017953May 1991Biondo, Jr.
5018085May 1991Smith, Jr.
5020958June 1991Tuttobene
5021953June 1991Webber et al.
5025397June 1991Suzuki
5025399June 1991Wendt et al.
5029099July 1991Goodman
5032989July 1991Tornetta
5036472July 1991Buckley et al.
5038293August 1991Goodman
5040132August 1991Schuricht et al.
5047613September 1991Swegen et al.
5047959September 1991Phillips et al.
5053956October 1991Donald et al.
5056029October 1991Cannon
5061098October 1991Engelhardt et al.
5062147October 1991Pickett et al.
5084817January 1992Kumano et al.
5088052February 1992Spielman et al.
5088586February 1992Isobe et al.
5099422March 1992Foresman et al.
5109337April 1992Ferriter et al.
5111392May 1992Malin
5115504May 1992Belove et al.
5119475June 1992Smith et al.
5132915July 1992Goodman
5146403September 1992Goodman
5148370September 1992Litt et al.
5153825October 1992Yauk et al.
5163006November 1992Deziel
5172245December 1992Kita et al.
5187797February 1993Nielsen et al.
5195030March 1993White
5200888April 1993Hashimoto
5206804April 1993Thies et al.
5208745May 1993Quentin et al.
5235519August 1993Miura
5241671August 1993Reed et al.
5243174September 1993Veeneman et al.
5442567August 1995Small
5452221September 1995Tumidei
Re32115April 1986Lockwood et al.
Re33316August 1990Katsuta et al.
Foreign Patent Documents
1005827May., 1963GB
1278862May., 1986CA
1388942Apr., 1972GB
2033966Jan., 1991CA
2041298Apr., 1991CA
2050106AApr., 1980GB
2065412AOct., 1980GB
2092354Sep., 1981GB
2105075May., 1982GB
2119600AApr., 1983GB
2150728Nov., 1984GB
2177245AMay., 1986GB
2177245BMay., 1986GB
2208460AJul., 1988GB
2222341AJun., 1988GB
2228125AAug., 1990GB
2240897AAug., 1990GB
2246929ADec., 1992GB
3919726Jun., 1989DE
59-144078Feb., 1984JP
59-194263May., 1984JP
59-33573Feb., 1984JP
79104527Nov., 1979EP
79302164Oct., 1979EP
83108181Aug., 1983EP
83306220Oct., 1983EP
87 13494Sep., 1987FR
89 09224Jun., 1989FR
89819937Jan., 1989EP
90313917Dec., 1990EP
91309669Oct., 1991EP
PCT/AU82/00123Apr., 1982WO
PCT/EP90/02079Jun., 1991WO
PCT/FR91/00650Aug., 1991WO
PCT/NO81/00028Jul., 1981WO
PCT/US82/0115.3Aug., 1982WO
PCT/US83/00628May., 1983WO
PCT/US89/05364Nov., 1989WO
PCT/US90/06283Oct., 1989WO
PCT/US92/02603Oct., 1992WO
Other References
"Compositing Digital Images"; Porter, Thomas; Duff, Tom; Computer Graphics; vol. 18, No. 3; Jul. 1984; pp. 253-259. .
"Now PC Users Say it With `Voice Notes`"; William M. Bulkeley Wall Street Journal; pp. B1, B5. Date Unknown. .
"Touch-Screens For Intuitive Input", M. David Stone, PC Magazine, Aug. 1987, pp. 183-192. .
"Touch Screens: A Viable Mouse Alternative", PC Week, p. 90; Date Unknown. .
"Man to Machine via Screen",PC Week, p. 41; Date Unknown. .
"Autoclerk" ABA Journal, Apr. 1992. .
"Gay groups put their cards on the table, so Hallmark lifts ban on using `lesbian`"; Hodges, Michael, Detroit News, Oct. 28, 1991, p. 1C. .
"Toys, gift for upcoming holiday shopping lists", Magid, Judy, Salt Lake Tribune, Oct. 14, 1991. .
"If you care enough to keyboard the best", Bombeck, Erma, Star-Ledger, Oct. 17, 1991. .
"Software Tools for Writers", Holder, Wayne, Byte Publication Inc., Jul. 1992, pp. 128-163. .
"Pioneering Perspective", Yeazel, Lynn A. Optical Information Systems, Feb. 1986, pp. 36, 37. .
"Retailers Beginning to Tune in Video Displays", Advertising Age, Nov. 1985, p. 66. .
"Computers, Fiction, and Poetry", McKean, Kevin Byte Publications, Jul. 1992, pp. 50-53. .
"Creative Writing with Computers", Owens, Peter, Popular Computing, Jan. 1984, pp. 128-132. .
"Interactive Fiction: Six Micro Stories", Liddil, Bob, BYTE Publications, Inc. Sep. 1981, p. 436. .
"GOSUB: Poet: Return Computers and the Poetic Muse", Marcus, Stephen, Educational Technology, Aug., 1984 pp. 15-20. .
"Comp-U-Store System Could Change Retail Economics", Direct Marketing, Jul. 1983, pp. 101-107. .
"Setting Up Shop on Computer Screens", Gatty, Bob, Nation's Business, Mar. 1984, pp. 57-58. .
"EDR puts Auto Insurance Policy `On Line`", Mooney, Barbara, Crain's Cleveland Business, Mar. 30, 1992, p. 23. .
"TouchSelect Turns Ordinary Monitors Into Touch-Screens", Zelnick, Nate, PC Magazine, Dec. 17, 1991, p. 42. .
"Maintenance by Modem: Processing Problems Can Be Solved Remotely"; Kreisher, Keith; Modern Plastics; Jan. 1990; p. 44. .
"The Touchables"; Informationweek; Apr. 13, 1992; p. 44. .
"Card Compete in New Outlets"; Toth, Debora; Graphic Arts Monthly; Dec. 1991; pp. 48-50. .
"How Computerized Cards Make This Shop 'Truly a Treat"; Hohman, Edward J.; Greetings Magazine; Jan. 1987; pp. 12-13 and 24. .
"Ryhme is Money with The Magical Poet"; The Computer Poet Corp. Brochure; Date Unknown. .
"This Computer Can Ryhme . . . for Three Bucks Each Time"; Changing Times; Jan. 1986. .
"Cardmarketing . . . Your Way to a Powerful Database"; Corliss, Paul W., Jr.; Dynamic Controls, Inc.; Date Unknown. .
"These Instant-Win Games Talk Back"; ADWeek's Promote; p. 4; Date Unknown. .
"CASPIR, Computer Aided Searching and Patent Image Retrieval", pp. 1, 8, 9, 10, 12, 13.; Date Unknown. .
"Point-of-Sale Terminal System with Clerical Facilities/Functions", G. Allmendinger, A. Bette, K. H. Engler, B. Haeusser and R. Muehlenbrock, IBM Technical Disclosure Bulletin, Jan. 1976, pp. 2554, 2555. .
"Knowledge-Assisted Document Retrieval: I. The Natural-Language Interface"; Biswas, Bezdek, Marques, Subramanian; Journal of The American Society For Information Science; pp. 83-96; Mar. 1987. .
"Knowledge-Assisted Document Retrieval: II. The Retrieval Process"; Biswas, Bezdek, Subramanian, Marques, Journal of the American Society For Information Science; pp. 97-110; Mar. 1987. .
"I.sup.3 R: A New Approach to the Design of Document Retrieval Systems"; W. B. Croft and R. H. Thompson; Journal of the American Society For Information Science; pp. 389-404; Nov. 1987. .
"Integrating Mechanisms for Storage and Retrieval of Land Data"; Andrew Frank; Surveying and Mapping, vol. 46, No. 2, pp. 107-121.; June 1986. .
"Retrieval of Help Information for Novice Users of Interactive Computer Systems"; Andrew M. Cohill; Human Factors; Jun. 1985; pp. 335-343. .
"Automatic Tex Generation"; R. Marsh; Br Telecom TechnolJ vol. 6 No. 4; Oct. 1988; pp. 84-88. .
"Advanced Feedback Methods in Information Retrieval"; G. Salton, E. A. Fox, E. Voorhees; Journal of the American Society for Information Science; May 1985, pp. 200-210. .
"Producing Geological Illustrations Using PC-Based Computer-Aided Drafting"; K. Cameron, D. Cameron, P. Kelleher; Computers & Geosciences, vol. 14, No. 3 pp. 291-297; 1988. .
"Adapting of String Indexing Systems For Retrieval Using Proximity Operators"; Timothy C. Craven, Information Processing & Management; vol. 24, No. 2, pp. 133-140, 1988. .
"A language for simple interactive retrieval from a database system"; Perry Jones and Michael Shave; Data & Knowledge Engineering 2, 1987, pp. 303-321. .
"Automated Storage and Retrieval of Work Standards"; Richard A. Bihr; Computers ind. Engng, vol. 13, No. 1-14, pp. 312-316, 1987. .
"A Novice User's Interface to Information Retrieval Systems"; R. G. Crawford and H. S. Becker; Information Processing & Management, vol. 22, No. 4, pp. 287-298, 1986. .
"Novel Filing Systems Applicable To An Automted Office: A State-Of-The-Art Study"; F. Mark Restorick; Information Processing & Management; vol. 22, No. 2, pp. 151-172, 1986. .
"Thesaurus For End-User Indexing and Retrieval; Gary W. Strong, M. Carl Drott; Information Processing & Management; Vol. 22, No. 6, .
"OAKDEC, A Program For Studying The Effects on Users of a Procedural Expert System for Database Searching"; Charles T. Meadow, Information Processing & Management, vol. 24, No. 4, pp. 449-457, 1988. .
"WYSIWIS Revised: Early Experiences with Multiuser Interfaces"; M. Stefik, D. G. Bobrow, G. Foster, S. Lanning, and D. Tatar; ACM Transactions on Office Information Systems, vol. 5, No. 2, Apr. 1987 pp. 147-167. .
"Criteria For the Evaluation of Text Storage and Retrieval Software"; Paul Nieuwenhuysen, The Electronic Library, Jun. 1988, vol. 6, No. 3; pp. 160-166. .
"A Visual User Interface for Map Information Retrieval Based on Semantic Significance"; M. Tanaka and T. Ichikawa; IEEE Transactions On Software Engineering, vol. 14, No. 5, pp. 666-670, May 1988. .
"Interactive Document Display and Its Use In Information Retrieval; J. D. Bovey and P. J. Brown; Journal of Documentation, vol. 43, No. 2, Jun. 1987, pp. 125-137. .
"Image Retrieval Systems Based on Object Features"; Akio Yamamoto and Mikio Takagi, IEEE Montech 1987, pp. 131-134. .
"Retrieval Techniques"; Nicholas J. Belkin, W. Bruce Croft; Annual Review of Information Science and Technology, vol. 22, 1987, pp. 108-145. .
"Video-Graphic Query Facility For Database Retrieval"; Nancy H. McDonald, Visual Computer, 1966; pp. 72-77. .
"Targeted Marketing", Paul corliss, Foodmarketing; vol. 1. No. 1, Jan./Feb. 1989. .
"Melpas-1 Ticket-Issuing Apparatus" by Ken 'Ichi Murato, Tadahiro Okura, Eliji Suzuki & Masataka Kobayashi; Mitsubishi Denki Giho, vol. 55, No. 4 pp. 35-39 (1981). .
"Point-Of-Sale Terminal System With Clerical Facilities/Functions"; by G. Alimendinger, A. Bette, J. H. Engler, B. Haeusser and R. Muehlenbrock; IBM Technical Disclosure Bulletin; vol. 18, No. 8, Jan. 1976; pp. 2554-2555. .
"Filing and Retrieval of Unstructured Information: Some Systems Considerations; J. Bansler, et al.; Espirit '84 Brochure; pp. 295-313. .
"The Interactive Display Design Tool: An Application Program for Human Factors Part Task Simulation Development; Mark W. Smith, et al. 1899 IEEE; pp. 856-859. .
"A Frame-based Interface for Question-Answering Systems"; Toshihisa Takagi, et al. 1985 IEEE; pp. 388-393. .
"The Use of Adaptive Mechanisms For Selection of Search Strategies in Document Retrieval Systems"; W. Bruce Croft, et al.; Department of Computer and Information Science, University of Massachusetts, pp. 95-110; date unknown. .
"An Interactive Database End User Facility For the Definition and Manipulation of Forms"; A. H. F. Laender, et al.; School of Computing Studies and accountancy, University of East Anglia, Norwich, NR 4 71J, England; pp. 40-54; date unknown. .
"CAD Generation of Accident Diagrams For Local Agencies"; Willard Lyman, et al.; Microcomputer Within Transportation Brochure; pp. 657-664; date unknown. .
"Which Way to Go With Interactive Video"; 1987 Interac Corporation Brochure. .
"Interactive Video Merchandising"; By Video Inc. Brochure, May 1988. .
"Vision 1000 The Total Promotion Delivery Vehicle"; Advanced Promotion Technologies-Brochure; date unknown. .
"Food Chain Employees Scanning Technology In Instant Win/Prize Drawing Promotion"; Incentives in Action Brochure; date unknown. .
"Direct Marketing Goes Electronic", Larry Riggs, Sales & Marketing Management, vol. 134, No. 1, Jan. 14, 1985, pp. 59-60. .
Videodisc: Product Search System Launched for Architects and Interior Designers, Videodisc and Optical Disk, vol. 5, no. 4, Jul. 1985, pp. 244-247. .
Current Spring 1987 Sale Catalog. .
Current Christmas 1987 Catalog. .
Current Birthday 1987 Catalog. .
Current Valentine 1987 Catalog. .
Photo enlargements via vending machine; Kodak Create-A-Print 35mm enlargment center; Machine Design, vol. 60; No. 9; p. 12; 1988..~
Primary Examiner: Ruggiero; Joseph
Attorney, Agent or Firm:Calfee, Halter & Griswold
Claims

We claim:
1. A method for storing and selectively retrieving product/seryice data, comprising the steps of:
storing in a design data file a plurality of product/service designs;
storing in a selection criteria data file a plurality of descriptors, each of said descriptors representing an application scale associated with each of said plurality of product/service designs;
storing in a design applicability data file an expert-predetermined optimum applicability value for each combination of said application scales and said product/service designs;
presenting, to a customer, selection criteria options for one or more application scales;
storing in said selection criteria data file customer preference values for one or more application scales used for describing the product/service designs, said customer preference values to be predetermined by expert judgment and assigned to application scales where such values correspond to said selection criteria options chosen by the customer;
quantitatively correlating, by means of a correlation algorithm, each of said customer preference values with corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said product/service designs based on said customer-chosen selection criteria options; and
displaying for the customer a group of identified product/service designs based on said average suitability ratings for those identified product/service designs.

2. The method of claim 1, further comprising the steps of (i) requesting the customer to select one of said identified product/service designs and to verify the selection and (ii) displaying said selected product/service design.

3. The method of claim 2, further comprising the step of storing said selected product/service design on a suitable storage medium.

4. The method of claim 2, further comprising the step of printing said selected product/service design and dispensing said printed selected product/service design to the customer.

5. The method of claim 2, further comprising the steps of requesting the customer to modify said selected product/service design and receiving modification instructions from the customer after said selected product/service design is displayed.

6. The method of claim 2, wherein said step of storing customer preference values in said selection criteria data file comprises the steps of translating said selection criteria options chosen by the customer into a plurality of associated application scales and preference values.

7. The method of claim 2, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said product/service designs includes the steps of (i) calculating the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values for each of said application scales in which one or more corresponding pairs exist; (ii) squaring each of the calculated differences; (iii) summing the squared differences; (iv) determining the square root of the summed squared differences to obtain a gross suitability rating, and (v) averaging the gross suitability rating by the number of calculated differences to obtain the average suitability rating.

8. The method of claim 7, wherein said step of quantitatively correlating each of said customer preference values with corresponding expert-predetermined optimum applicability values involves constructing a matrix of corresponding customer preference values and said expert-predetermined optimum applicability values in a correlation data file.

9. The method of claim 7, wherein said customer preference values and said corresponding expert-predetermined optimum applicability values may be assigned either positive or negative values.

10. The method of claim 7, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said product/service designs further includes the step of multiplying each of the calculated differences by a scaling factor prior to squaring the calculated differences.

11. The method of claim 7, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values on said application scales to calculate an average suitability rating for each of said product/service designs further includes the step of multiplying each of the squared differences by a weighting factor prior to summing the squared differences.

12. The method of claim 7, wherein the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values are calculated for all but a select group of application scales in which one or more corresponding pairs exist, if said average suitability rating does not meet a predetermined minimum threshold value, and wherein the applicability values of substitute components are retrieved directly from an auxiliary file and employed in subsequent correlation calculations.

13. The method of claim 7, wherein said selection criteria options chosen by the customer do not correspond identically to said application scales.

14. The method of claim 4, further comprising the steps of requesting and verifying payment from the customer prior to printing said selected product/service design and dispensing said printed selected product/service design to the customer.

15. The method of claim 7, wherein said descriptors representing application scales relate to (i) occasion for sending the product/service, (ii) sender-receiver relationship, (iii) sender-receiver traits, and (iv) product/service design themes and styles.

16. The method of claim 7, wherein said step of storing in a design data file a plurality of product/service designs involves the further step of storing in a component design data file a plurality of product/service design components.

17. The method of claim 7, wherein said product/service design is a travel service design.

18. The method of claim 7, wherein said product/service design is a social expression product design.

19. A method for storing and selectively retrieving a social expression product design, comprising the steps of:
storing in a design data file a plurality of social expression product designs;
storing in a selection criteria data file a plurality of descriptors, each of said descriptors representing an application scale associated with each of said plurality of social expression product designs;
storing in a design applicability data file an expert-predetermined optimum applicability value for each combination of said application scales and said social expression product designs;
presenting, to a customer, selection criteria options for one or more application scales;
storing in said selection criteria data file customer preference values for one or more application scales used for describing the social expression product designs, said customer preference values to be predetermined by expert judgment and assigned to application scales where such values correspond to said selection criteria options chosen by the customer;
quantitatively correlating, by means of a correlation algorithm, each of said customer preference values with corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said social expression product designs based on said customer-chosen selection criteria options; and
displaying for the customer a group of identified social expression product designs based on said average suitability ratings for those identified social expression product designs.

20. The method of claim 19, further comprising the steps of (i) requesting the customer to select one of said identified social expression product designs and to verify the selection and (ii) displaying said selected social expression product design.

21. The method of claim 20, further comprising the step of storing said selected social expression product design on a suitable storage medium.

22. The method of claim 20, further comprising the step of printing said selected social expression product design and dispensing said printed selected social expression product design to the customer.

23. The method of claim 20, further comprising the steps of requesting the customer to modify said selected social expression product design and receiving modification instructions from the customer after said selected social expression product design is displayed.

24. The method of claim 20, wherein said step of storing customer preference values in said selection criteria data file comprises the steps of translating said selection criteria options chosen by the customer into a plurality of associated application scales and preference values.

25. The method of claim 20, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said social expression product designs includes the steps of (i) calculating the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values for each of said application scales in which one or more corresponding pairs exist; (ii) squaring each of the calculated differences; (iii) summing the squared differences; (iv) determining the square root of the summed squared differences to obtain a gross suitability rating, and (v) averaging the gross suitability rating by the number of calculated differences to obtain the average suitability rating.

26. The method of claim 25, wherein said step of storing in a design data file a plurality of social expression product designs involves the further step of storing in a component design data file a plurality of social expression product design components.

27. The method of claim 25, wherein said customer preference values and said corresponding expert-predetermined optimum applicability values may be assigned either positive or negative values.

28. The method of claim 25, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said social expression product designs further includes the step of multiplying each of the calculated differences by a scaling factor prior to squaring the calculated differences.

29. The method of claim 25, wherein said step of quantitatively correlating said customer preference values with said corresponding expert-predetermined optimum applicability values on said application scales to calculate an average suitability rating for each of said social expression product designs further includes the step of multiplying each of the squared differences by a weighting factor prior to summing the squared differences.

30. The method of claim 25, wherein the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values are calculated for all but a select group of application scales in which one or more corresponding pairs exist, if said average suitability rating does not meet a predetermined minimum threshold value, and wherein the applicability values of substitute components are retrieved directly from an auxiliary file and employed in subsequent correlation calculations.

31. The method of claim 30, wherein said select group of application scales includes a scale representing sending occasion.

32. The method of claim 23, wherein said selection criteria options chosen by the customer do not correspond identically to said application scales.

33. The method of claim 22, further comprising the steps of requesting and verifying payment from the customer prior to printing said selected social expression product design and dispensing said printed selected social expression product design to the customer.

34. The method of claim 23, wherein said descriptors representing application scales relate to (i) occasion for sending the social expression product, (ii) sender-receiver relationship, (iii) sender-receiver traits, and (iv) social expression product design themes and styles.

35. The method of claim 23, wherein said selected social expression product design is stored on a suitable storage medium at a first location and printed at a second remote location.

36. The method of claim 23, wherein said expert-predetermined optimum applicability values are adjusted by the time of day.

37. An apparatus for storing and selectively retrieving product/service data, comprising:
a design data file for storing a plurality of product/service designs;
a selection criteria data file for storing a plurality of descriptors, each of said descriptors representing an application scale associated with each of said plurality of product/service designs;
a design applicability data file for storing an expert-predetermined optimum applicability value for each combination of said application scales and said product/service designs;
a display for presenting, to a customer, selection criteria options for one or more application scales;
means to store in said selection criteria data file customer preference values for one or more application scales used for describing the product/service designs, said customer preference values to be predetermined by expert judgment and assigned to application scales where such values correspond to said selection criteria options chosen by the customer; and
a correlation algorithm for quantitatively correlating each of said customer preference values with corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said product/service designs based on said customer-chosen selection criteria options; wherein
said display displays for the customer a group of identified product/service designs based on said average suitability ratings for those identified product/service designs.

38. The apparatus of claim 37, wherein said display (i) requests the customer to select one of said identified product/service designs and to verify the selection and (ii) displays said selected product/service design.

39. The apparatus of claim 38, further comprising a suitable storage medium on which said selected product/service design may be stored.

40. The apparatus of claim 38, further comprising a printer for printing said selected product/service design and a dispenser for dispensing said printed selected product/service design to the customer.

41. The apparatus of claim 38, further comprising means for requesting the customer to modify said selected product/service design and means for receiving modification instructions from the customer after said selected product/service design is displayed.

42. The apparatus of claim 38, further comprising means for translating said selection criteria options chosen by the customer into a plurality of associated application scales and preference values.

43. The apparatus of claim 38, wherein said correlation algorithm (i) calculates the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values for each of said application scales in which one or more corresponding pairs exist; (ii) squares each of the calculated differences; (iii) sums the squared differences; (iv) determines the square root of the summed squared differences to obtain a gross suitability rating, and (v) averages the gross suitability rating by the number of calculated differences to obtain the average suitability rating.

44. The apparatus of claim 43, further comprising means for constructing a matrix of corresponding customer preference values and said expert-predetermined optimum applicability values in a correlation data file.

45. The apparatus of claim 43, wherein said customer preference values and said corresponding expert-predetermined optimum applicability values may be assigned either positive or negative values.

46. The apparatus of claim 43, wherein said correlation algorithm additionally multiplies each of the calculated differences by a scaling factor prior to squaring the calculated differences.

47. The apparatus of claim 43, wherein said correlation algorithm additionally multiplies each of the squared differences by a weighting factor prior to summing the squared differences.

48. The apparatus of claim 40, wherein the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values are calculated for all but a select group of application scales in which one or more corresponding pairs exist, if said average suitability rating does not meet a predetermined minimum threshold value, and wherein the applicability values of substitute components are retrieved directly from an auxiliary file and employed in subsequent correlation calculations.

49. The apparatus of claim 41, wherein said selection criteria options chosen by the customer do not correspond identically to said application scales.

50. The apparatus of claim 40, further comprising a payment mechanism for requesting and verifying payment from the customer prior to printing said selected product/service design and dispensing said printed selected product/service design to the customer.

51. The apparatus of claim 41, wherein said descriptors representing application scales relate to (i) occasion for sending the product/service, (ii) sender-receiver relationship, (iii) sender-receiver traits, and (iv) product/service design themes and styles.

52. The apparatus of claim 41, further comprising a component design data file in which is stored a plurality of product/service design components.

53. The apparatus of claim 41, wherein said product/service design is a travel service design.

54. The apparatus of claim 41, wherein said product/service design is a social expression product design.

55. An apparatus for storing and selectively retrieving a social expression product design, comprising:
a design data file for storing a plurality of social expression product designs;
a selection criteria data file for storing a plurality of descriptors, each of said descriptors representing an application scale associated with each of said plurality of social expression product designs;
a design applicability data file for storing an expert-predetermined optimum applicability value for each combination of said application scales and said social expression product designs;
a display for presenting, to a customer, selection criteria options for one or more application scales;
means to store in said selection criteria data file customer preference values for one or more application scales used for describing the social expression product designs, said customer preference values predetermined by expert judgment and assigned to application scales where such values correspond to said selection criteria options chosen by the customer;
a correlation algorithm for quantitatively correlating each of said customer preference values with corresponding expert-predetermined optimum applicability values to calculate an average suitability rating for each of said social expression product designs based on said customer-chosen selection criteria options; wherein
said display displays for the customer a group of identified social expression product designs based on said average suitability ratings for those identified social expression product designs.

56. The apparatus of claim 55, wherein said display (i) requests the customer to select one of said identified social expression product designs and to verify the selection and (ii) displays said selected social expression product design.

57. The apparatus of claim 56, further comprising a suitable storage medium for storing said selected social expression product design.

58. The apparatus of claim 56, further comprising a printer for printing said selected social expression product design and a dispenser for dispensing said printed selected social expression product design to the customer.

59. The apparatus of claim 56, further comprising means for requesting the customer to modify said selected social expression product design and means for receiving modification instructions from the customer after said selected social expression product design is displayed.

60. The apparatus of claim 56, further comprising means for translating said selection criteria options chosen by the customer into a plurality of associated application scales and preference values.

61. The apparatus of claim 56, wherein said correlation algorithm (i) calculates the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values for each of said application scales in which one or more corresponding pairs exist; (ii) squares each of the calculated differences; (iii) sums the squared differences; (iv) determines the square root of the summed squared differences to obtain a gross suitability rating, and (v) averages the gross suitability rating by the number of calculated differences to obtain the average suitability rating.

62. The apparatus of claim 61, further comprising a component design data file in which is stored a plurality of social expression product design components.

63. The apparatus of claim 61, wherein said customer preference values and said corresponding expert-predetermined optimum applicability values may be assigned either positive or negative values.

64. The apparatus of claim 61, wherein said correlation algorithm additionally multiplies each of the calculated differences by a scaling factor prior to squaring the calculated differences.

65. The apparatus of claim 61, wherein said correlation algorithm additionally multiplies each of the squared differences by a weighting factor prior to summing the squared differences.

66. The apparatus of claim 61, wherein the differences between each pair of said customer preference values and said corresponding expert-predetermined optimum applicability values are calculated for all but a select group of application scales in which one or more corresponding pairs exist, if said average suitability rating does not meet a predetermined minimum threshold value, and wherein the applicability values of substitute components are retrieved directly from an auxiliary file and employed in subsequent correlation calculations.

67. The apparatus of claim 66, wherein said select group of application scales includes a scale representing sending occasion.

68. The apparatus of claim 67, wherein said selection criteria options chosen by the customer do not correspond identically to said application scales.

69. The apparatus of claim 58, further comprising a payment mechanism for requesting and verifying payment from the customer prior to printing said selected social expression product design and dispensing said printed selected social expression product design to the customer.

70. The apparatus of claim 59, wherein said descriptors representing application scales relate to (i) occasion for sending the social expression product, (ii) sender-receiver relationship, (iii) sender-receiver traits, and (iv) social expression product design themes and styles.

71. The apparatus of claim 59, wherein said selected social expression product design is stored on a suitable storage medium at a first location and printed at a second remote location.

72. The apparatus of claim 59, wherein said expert-predetermined optimum applicability values are adjusted by the time of day.
Description
FIELD OF THE INVENTION

This invention relates generally to machine ends methods for storing and selectively retrieving product data by correlating multiple customer selection criteria with optimum application judgments for product designs, and more particularly to such machines and methods wherein optimum product design applications are identified based on embedded expert judgments, and wherein identified product designs may be optionally modified by a customer.

1. Related Applications

The following U.S. patent application is incorporated herein by reference as if it had been fully set out:

Application Ser. No. 08/299,499, filed Sep. 1, 1994, entitled "METHOD AND APPARATUS FOR STORING AND SELECTIVELY RETRIEVING AND DELIVERING PRODUCT DATA BASED ON EMBEDDED EXPERT JUDGMENTS".

2. Background of the Invention

In a conventional retail, catalogue or library environment, customers are able to browse quickly and conveniently through large physical displays of products, while they inspect images, read words, listen to music and/or engage in other reviewing activities, until they find the specific product most suitable for their needs, interests or tastes. Under these conventional circumstances, customers can and do exercise their discriminating judgments and mental processes to make selections.

Recently, machines have been introduced that replace these large physical product displays by storing data relating to the products in magnetic or optical storage devices. An example of such machines are the social expression card machines which have become popular in recent years because they eliminate many of the problems associated with displaying numerous categories and sub-categories of social expression products. Some of these problems include the space required for displaying such a variety of social expression products, the resulting inventory requirements, and potential customer confusion resulting from the wide variety of social expression products from which to choose.

Social expression card machines typically comprise a computer operated vending machine, a display screen and a keyboard input terminal. A variety of available social expression product designs are stored in the computer. By means of the display screen, the computer prompts a customer to provide design criteria, or to select from a menu of computer-provided design criteria, indicative of appropriate social expression product designs for that customer. The keyboard input terminal is used to select or present the design criteria.

The computer uses the provided or selected design criteria to identify appropriate social expression product designs from the variety of available social expression product designs stored therein, generally by techniques which search for and identify those designs whose specified properties are exactly matched to customer input selection criteria. From these identified designs, the customer is directed to select one design, which the computer-driven vending machine prints on blank card stock and dispenses to the customer. In this manner, the customer can retrieve and review portions of the data on a video screen and audio system, by giving instructions on a keyboard or touchscreen that is connected by a programmed computer to the storage devices holding the data.

In simple situations involving such machines, the retrieval of the data is easily managed by conventional methods. For example, in the case of inputting or selecting a title, an object image or a few descriptive words can communicate to a machine all of the information required to specify the data file or files containing information that a customer wants to retrieve and display. Product characteristics are identified by allowable combinations of customer entered data. The computer can be programmed to retrieve the file or files that the user specifies, either by accessing known locations in a data storage device or by searching a data base to find the files whose identities match the descriptive words input by the customer. An example of a machine and method that accesses data from known storage locations is shown in U.S. Pat. No. 3,757,037 to Norman Bialek.

An example of a machine and method that searches a data base to find files whose identities match descriptive words is shown in U.S. Pat. No. 5,056,029 to Thomas G. Cannon. Cannon discloses a method wherein a customer is queried to elicit responses, in the form of occasion parameters, each of which relates to the customer's intended communication purpose. Greeting cards which may be selected for manufacture are stored, not physically, but in the form of design data held in high density magnetic or optical storage. The design data is identifiable by some unique combination of occasion parameters. Following the entry of customer responses, the computer retrieves and displays a set of product files which includes all of the stored product designs having occasion parameters which identically match those entered by the customer.

While the card vending machine shown in the Cannon patent provides an efficient means for storing many different types of social expression cards and for retrieving and displaying those card designs which match a customer's criteria, that machine, as well as other known machines, suffers from several drawbacks. One drawback is that the present machines can retrieve and display only those card designs that are identified by labels or descriptors that match exactly the criteria specified by the customer. However, some card designs can convey messages so broad in scope that they cannot be defined exclusively with selected descriptors. Because the present card vending machines are limited in this respect, they cannot use a large database of card designs to its fullest potential in meeting customer needs.

Indeed the number of card designs that must be stored in the database of one of the presently available machines is extremely large in relation to the number of different combinations of customer needs that it can meet. Because of the exact correspondence that is required between the card descriptors and the customer criteria, the number of stored card designs must be equal to the number of possible combinations of the various criteria that a customer can specify, multiplied by the average number of card designs that a vendor would want to display in response to a particular criteria combination. For instance, if the customer were given five possible criteria options to choose from within each of four card descriptors, 625 (=5.sup.4) combinations of customer-selected criteria would be possible. If an average of ten card designs were made available for each combination, then a total of 6,250 card designs would be required in the database.

Another drawback is that such machines restrict the identities of product data files to fixed combinations of customer entry data. Many buyers of products and users of information cannot easily provide the exact word or words necessary for retrieving data either from known storage locations or by data base searching. The suitability of products, especially those that have rich aesthetic, intellectual or entertainment values, often cannot be described by single combinations of descriptive words. Thus, it may be necessary to provide the capability for several different forms or contents of customer data entry to access and retrieve a given product data file. Sometimes, a customer will be able to specify only a few criteria for products that he wants to view, while those products are identified by many descriptive words. Sometimes, a customer's specific criteria should be considered as suggestive only and a wide range of product files should be shown to him, some of which have very few, if any, of the exact criteria specified by the customer. Conversely, some data files may apply to and ought to be retrievable in response to many different sets of customer purposes, interests, needs or tastes.

But most important, on many occasions, a given product design may possess a very high degree of applicability with respect to one selection criterion input by a customer, but lower or very low degrees of applicability with respect to other criteria. In the general case where customer inputs comprise multiple selection criteria, these will possess varying degrees of closeness to the set of optimum application judgements used to describe the properties of stored product designs. The problem to be solved is to identify for retrieval some subset of designs whose overall suitability is judged to be the best.

In this sense, these files may have varying degrees of applicability or suitability for a particular set of customer criteria, rather than being designated as either suitable or not suitable. In such cases, the customer might prefer to see files of such varying suitability in the order of their anticipated suitabilities, from the highest to the lowest. Also, different customers may prefer to see different numbers of products having a range of suitabilities.

All of the aforementioned circumstances and needs can best be served by a system which, rather than seeking to identify products whose characteristics exactly match customer specifications, embodies one or more kinds of expert judgment data for the purpose of selectively retrieving some subset of best fitting or most appropriate products or product data files in response to customer data entry. It is therefore an object of the present invention to provide a method and machine for selecting products or services by correlating customer selection criteria with optimum product application judgments or designations to identify those products where the fit between specifications and optimum applications is best. It is a further object of the invention to provide a method and machine, such as a social expression card machine, for storing and identifying card designs, receiving customer selection criteria, correlating the customer selection criteria with optimum product design application designations, identifying and displaying product designs most likely to satisfy the customer selection criteria on an overall basis, modifying the displayed designs, and delivering the displayed designs, either modified or unmodified, in some tangible form.

These and other objects of the invention will become evident to those skilled in the art in view of the following description of the invention.

SUMMARY OF THE INVENTION

The present invention provides an improved method and machine by which a product or service, such as a social expression product, may store, retrieve, display, personalize, print and deliver to a customer a wide range of social expression product designs suitable for a broad spectrum of customer interests. The method for identifying and retrieving product designs to be displayed for customer selection follows the input of customer-related selection criteria and is based on the quantitative degree of correlation of product design characteristics (as represented by multiple optimum application designations) with the customer-entered selection criteria. This method permits individual product designs to be identified and retrieved for multiple applications to a wide range of customer needs and desires on a best fit basis, rather than on the basis of an exact match to a single or unique combination of customer needs.

Thus, given the limited library of stored product designs, a vending machine may retrieve subsets of designs from the library which are suitable for application to a much larger number of combinations of customer selection criteria than would otherwise be possible. In addition, the machine may respond to any given combination of customer-entered selection criteria by displaying many product designs in descending order of applicability as determined by the correlation method, thereby providing a large and diverse selection of applicable product designs for customer examination and choice.

The inventive machine of the present invention stores a plurality of product or service designs in a design data file, and a plurality of descriptors are stored in a selection criteria data file for each of the plurality of product or service designs. Each of the descriptors represents an application scale. An expert-predetermined optimum applicability value is stored in a design applicability data file for each combination of the application scales and the product or service designs.

A customer is presented with selection criteria options for one or more application scales. Based on the selection criteria options chosen by the customer, customer preference values for one or more application scales for each product or service design are stored in the selection criteria data file. These customer preference values are assigned to application scales where such values correspond to the selection criteria options chosen by the customer. The selection criteria options chosen by the customer need not correspond identically with particular application scales. Instead, the selection criteria options chosen by the customer may be translated into either one or a plurality of preference values on one or more associated application scales for each product or service design.

A correlation algorithm is utilized to quantitatively correlate each of the customer preference values with corresponding expert-predetermined optimum applicability values to calculate an overall or average suitability rating for each of the product or service designs based on the customer-chosen selection criteria options. A group of identified product or service designs is displayed for the customer based on the average suitability ratings for those identified product or service designs.

The correlation algorithm quantitatively correlates the customer preference values with the corresponding expert-predetermined optimum applicability values to calculate an overall or average suitability rating for each of the product or service data files in storage by first calculating the differences between each pair of the customer preference values and the corresponding expert-predetermined optimum applicability values for each of the application scales in which a corresponding pair exists. Then each of the calculated differences is squared, because the differences between the customer preference values and the corresponding expert-predetermined optimum applicability values may be calculated as either positive or negative values and to cause an exponential effect of difference magnitudes on the goodness of fit calculation. The squared differences are then summed, and the square root of the summed squared differences is calculated to obtain a gross suitability rating for each product design. This gross suitability rating is averaged by the number of calculated differences to obtain the average suitability rating for each product design.

The operation of the algorithm may be modified by the introduction of scaling factors for each of the application scales by which each of the calculated differences on a given scale is multiplied prior to squaring the calculated differences. These scaling factors used to multiply the calculated differences may be used to control the magnitude of exponential effect associated with calculated differences on any scale. Further modification of the algorithm may include the introduction of weighting factors by which each of squared differences is multiplied prior to summing the squared differences. These weighting factors may be used to control the impact of any scale on the overall goodness of fit calculations.

A predetermined minimum threshold value may be established for the average suitability rating. If the above calculations result in an average suitability rating which does not meet the minimum threshold value, the differences between each pair of the customer preference values and the corresponding expert-predetermined optimum applicability values may be re-calculated using all but a select group of application scales in which a corresponding pair exists. In this manner, application scales which may disproportionately skew the average suitability rating may be ignored when carrying out the required calculations. In effect, the goodness of fit algorithm can be constructed to ignore successively those application scales considered to be least important to customer interests while searching the product files to find potentially suitable items.

In the case of product designs, the machine and method solicit the customer to select one of the identified product designs and verify the selection, and then display the selected design. The selected design may then be modified by the customer. The selected or modified product design is then dispensed to the customer in the form of a printed product, or stored on a suitable storage medium for later delivery.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of one embodiment of a machine, for selecting products or services by correlating customer selection criteria with optimum product and service designs, constructed according to the principles of the present invention;

FIG. 2A is a system block diagram of the machine of FIG. 1;

FIG. 2B is a system block diagram of another type of system, not confined to a kiosk, for selecting products or services by correlating customer selection criteria with optimum product and service designs, constructed according to the principles of the present invention;

FIG. 3 is a block diagram of the data storage devices shown in the block diagram of FIG. 2A;

FIGS. 4, 5A, 6A, and 7 are block diagrams of select data files which make up the data storage devices of FIG. 3;

FIGS. 5B1-5B2 and 6B shows examples of data contained in the data files of FIGS. 5A and 6A, respectively;

FIG. 6C lists summaries of examples of card designs which are stored in the data files and to which the optimum applicability values of FIG. 6B apply;

FIGS. 8 and 9 are examples of algorithms which may be used by the machine of FIG. 1 for correlating customer selection criteria with optimum product and service designs;

FIG. 10 is a flow chart representing the operating programs stored in the computer residing in the machine of FIG. 1;

FIGS. 11 and 12 are flow charts representing operation of the machine of FIG. 1 to facilitate customer entry of data, correlation of the entered data with predetermined product design applicability values, and identification of suitable card designs based on the result of the correlation process;

FIG. 13 is a flow chart representing operation of the machine of FIG. 1 to facilitate modification of the suitable card designs identified by the process of FIGS. 11 and 12;

FIG. 14 is a flow chart representing the operation of one of the operating programs of FIG. 10;

FIG. 15 is a flow chart representing one of the programming modules shown in the flow chart of FIG. 14;

FIGS. 16, 17, 18, 19A/19B, and 20A/20B are examples of display screens presented to a customer during operation of the process of

FIGS. 11 and 12 (the scales and values shown represent data associated with customer selected criterion options and are not visible on the display screens, but are stored in memory as shown in FIGS. 4-7);

FIGS. 21A/21B are is an example of an alternate simplified set of display screens presented to a customer during operation of the process of FIGS. 11 and 12;

FIGS. 22A/22B show an example of the calculations performed by the computer using the algorithm of FIG. 9, as applied to a specific set of customer selection criteria and to designs 1 and 6 of the illustrative set of design applicability values shown in FIG. 6B;

FIG. 23 illustrates a table of correlation values calculated in accordance with the algorithm of FIG. 9 for the various designs listed in FIG. 6C in response to a customer data entry set; and

FIG. 24 is a flow chart representing an alternate modification program performed by the machine of FIG. 1 to facilitate modification of the suitable card designs identified by the process of FIGS. 11 and 12.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

A. System Components

A machine 10 for storing and selectively retrieving product data by correlating customer selection criteria with optimum product design applicabilities based on embedded expert judgments is shown in FIG. 1. The machine 10, which is merely one embodiment constructed according to the principles of the present invention, is used to store and selectively retrieve social expression products (e.g. greeting cards) by correlating customer selection criteria with optimum greeting card design application values stored therein. It will be understood by others skilled in the art, however, that the principles of the present invention may be applied to other types of machines for selecting other types of products or services. The following detailed description, however, will relate to the greeting card machine 10 shown in FIG. 1.

The machine 10 assumes the form of a kiosk designed for on-site storage, retrieval, modification and delivery of greeting cards in a retail store. For illustration purposes, a single machine 10 is shown for performing all of these functions at one location. However, various parts of the system, such as data storage devices and printers, may be placed at locations remote from the machine 10, either within the retail store or at a distant control center.

The greeting cards may be delivered from the kiosk in printed form. Alternatively, only the retrieval and modification of the card design may take place at the kiosk. The retrieved or modified card designs may then be stored on a magnetic disk and either delivered to the customer, or the stored design data may be sent directly to the customer's home computer, allowing him to produce the card on his own printer or plotter. In general, the method which characterizes this invention does not require that the various components such as data entry device, the monitor, the computer, and the printer be located within the same housing. Any of the components may be remote from the others with data flow between them carried by any appropriate form of telecommunications.

The machine 10 includes an enclosure 12 in which is housed a computer 14. The computer 14 is provided with memory or data storage 15 associated therewith (see FIG. 2A) and is electrically connected by means of wiring 16 (shown in phantom in FIG.
1) to an input/output (I/O) terminal 18, a printer device 20, an audio system or loudspeaker 22 and a payment device 24. A bin or dispensing tray 26 provides means for delivering a selected or modified greeting card to a customer. A paper tray 28 (see FIG. 2A) provides a supply of paper to the printer device 20.

The I/O terminal 18 in the embodiment of the invention is preferably a video monitor 30 provided with a touch screen overlay 32. The video monitor 30 provides the means to query the customer to obtain customer selection criteria, and the touchscreen overlay 32 provides the means for the customer to enter responses to these computer-generated queries. The video monitor 30 is also used to display optimum greeting card designs and greeting card component designs to the customer which are identified after the computer correlates the customer selection criteria with stored card designs. Other forms of data input devices are contemplated in place of the touch screen overlay 32, for example, a keyboard, a stylus in combination with a screen which recognizes contact thereof, or a mouse. These alternative forms of input devices may also be used in addition to, instead of in lieu of, the touch screen overlay 32. Input and display hardware and software 31 (see FIG. 2A) provide means for communications between the computer 14, the video monitor 30 and the touchscreen 32.

FIG. 2A represents a system block diagram of the machine of FIG. 1. However, as explained above, although the present invention is described in terms of a machine for dispensing social expression products, and greeting cards in particular, other uses for the present invention are contemplated. A machine represented by the system block diagram of FIG. 2B, for example, may be used to store and retrieve a variety of other products, such as photographs, motion pictures, television programs, musical recordings, gift products, literary works or reference data, or services such as travel services.

In addition, the machine represented by the system block diagram of FIG. 2B is not restricted to the on-site storage, retrieval and delivery of these products or services. Accordingly, a machine constructed according to the system block diagram of FIG. 2B includes a first data communications system 34 that is connected between the computer 14 and input and display hardware and software 31, so that the hardware and software 31 and connected video monitor 30, audio system 22 and data input devices 32 may be placed at a location remote from the computer 14 and data storage devices 15. Also, a second data communications system 36 connects the computer 14 to one of a variety of remote reception, display, production and product ordering devices 38. An example of one such device would be the home computer and attached printer of a customer or a recipient to whom the customer wishes to send a product or service, with the video monitor 40 and audio system 42 being the corresponding parts of the home computer of the customer or recipient. Thus, the home computer might receive a data file of a product selected by the customer through an input device 32 located at a retail store. After selecting a product data file at the retail store, the customer could have the file sent to the home computer for storage on an associated data storage device and/or printing on an associated printer.

Alternatively, the input and display hardware and software 31 and input devices 32 could also be parts of the home computer and the video monitors 30 and 42 as well as the audio systems 22 and 40 could be one and the same parts of the home computer. The customer could then send data relating to the kind of product he desires to a remote computer 14 and data storage device 15, which would in turn retrieve data files responsive to those desires and send them back to the customer's computer. The customer would then select the product he wants and, depending on the type of product, either have the product printed on his or some recipient's printer, order the product by E-mail or other transmission means, or if the product is a still or motion picture, have it displayed on his or another recipient's television screen. He could also have the product file stored on a read/write CD-ROM disc or other media for recording pictures and/or sound.

The machine 10 of FIG. 1, designed for the on-site storage, retrieval and delivery of greeting cards, will now be described in detail. The video monitor 30 is preferably a CTX 5468A Super VGA color monitor with a 0.28 dot pitch. Preferably the data input device 32 is a touchscreen that covers the monitor 30. The touchscreen 32 is a transparent, pressure sensitive plate capable of sensing a location where it is touched by a customer. One touchscreen that may be utilized with the present invention is a model E-274 from Elographics Company of Oak Ridge, Tenn.

Preferably, the printer 20 is a Hewlett-Packard 7550B (plus) plotter that is capable of detecting its paper loading status and automatically reloading paper from the paper tray 28 to prepare for the next operation without receiving control instructions from the computer 14. This plotter has a one megabyte RAM upgrade with 70 ns chips and a "B" size card stock loading tray. The printer 20 should also have a four layer plotter control board, an Intel based 12 kHz 8031 micro-controller with a programmable EPROM, a 26 pin DC input/output, and a 7400 based chip set digital logic.

An optional part of the machine 10 is the payment device 24 that is designed to receive money from customers in payment for printed cards. The payment device 24 is connected to the computer 14, which instructs the device 24 concerning the amount of money to collect. The payment device 24 is also connected to the printer 20 and prevents the printer from operating until it has received the amount of money specified by the computer 14. The payment device 24 may include a coin acceptor that has a Model C-120 electronic validator with a standard (S10 compatible) body, available from Coin Controls Inc., 1859 Howard Street, Elk Grove Village, Ill. 60007. The device 24 may also include a Mars VFM4 electronic bill acceptor with an upstacker body, available from Mars Electronics International, 1301 Wilson Drive, West Chester, Pa. 19380. In addition, device 24 may have a vending controller board for accepting credit cards, including a thermal printer, a cutter mechanism and a magnetic stripe reader, per Standard Industries specification dated May 23, 1993, available from Standard Industries, Kontrolle Division, 14250 Gannet Street, La Mirada, Calif. 90638.

The audio system 22 allows the computer 14 to send verbal operating instructions to the customer. The computer 14 may also be equipped to send messages through the speakers to potential customers, encouraging them to use the machine. The audio system 22 preferably includes two speakers, each with a 3 to 4 watt output and equipped with their own individual power supply and a one amp transformer.

The computer 14 displays card designs, card design components and card design criteria on the monitor 30, inviting a customer to make selections. The customer makes selections by pressing the locations of the touchscreen 32 that cover the portions of the monitor 30 that display the desired designs, components and criteria. The touchscreen 32 then sends those selections to the computer 14.

The computer 14 preferably has mini-tower chassis, a 486/33 mhz DX Intel chip upgradable processing system, a 16 megabyte random access memory (RAM) (70 ns), a sound blaster compatible sound board with midi capacity, a Sony internal CD-ROM (CDU-535-01), a Sony bus adapter OPA-461 with a custom "pre-fetch cache" that includes dealer integration of a component level circuit bypass jumper, a Sony custom pre-fetch cache driver, an ATI Mach 32 video accelerator card with a one megabyte Vram, an Elographics touchscreen board, a non bootable 1.44 megabyte Teac or Sony floppy disk drive, a 128 k cache, a 200 watt power supply, three parallel printer ports and two serial printer ports. The computer 14 is preferably loaded with Microsoft DOS 5.0
software and Fastlynx 2.0 transfer software.

The data storage device 15 connected to the computer 14 may include any combination of replaceable, remote, or built-in digital or analog data storage systems. The digital data storage systems may include magnetic disks or tapes, magnetic or electromagnetic storage media, or optical storage media and these storage media may be capable of temporary and/or permanent data storage.

As shown in the block diagram of FIG. 3, the data storage device 15 includes a high density storage unit 50 and other data storage 52. The storage unit 50 preferably comprises optical disc devices that use CD-ROM or other high density storage means, which contain product design data files 54, product component design data files 56, auxiliary product design data files 58, component assembly program files 60, and data modification program files 62. The component assembly program files 60
operate to assemble various component designs to form complete products. The data modification program files 62 enable the customer and/or the computer to modify a selected product data file 54 or component data file 56 prior to display or printing.

The files for each product or product component may be duplicated, with one compact version designed for the display of the product on a video monitor and the other designed for printing the product. In addition, the files 54 for displaying complete products may be stored separately from the files 56 for displaying product components, and the printing files may be likewise separated. If the storage device 50 comprises CD-ROM optical disc devices, the product data files 54 and 56 may be changed periodically simply by substituting new discs for old discs. If the CD-ROM memory is of the read-only type, no product data file and or its product code can be changed except by replacing the disc on which it is stored.

The design data files 54, 56, 58 contain all of the information necessary to display or print social expression product designs contained therein. Product codes which identify products and product components are stored in the product design data files 54, the product component design data files 56, and the auxiliary product design data files 58 to identify the product designs contained therein. In the preferred embodiment, the product codes consist of simple alphanumeric character strings. However, they may be titles, names or any other identifying symbols.

The storage unit 50 also includes selection criteria data files 64, design applicability data files 66, auxiliary design applicability data files 68, and correlation data files 70. As explained below, these files are used to (i) store expertly predetermined information relating to the suitability or applicability of given card designs for a variety of customer-dependent situations, (ii) store customer entered criteria, and (iii) correlate the predetermined information with that currently entered by the customer to identify suitable card designs for that customer.

The data storage devices 15 also includes the other data storage 52. Some or all of the data files in the unit 52 may be stored on the same CD-ROM discs that contain the product data, on othe