System and method for scheduling broadcast of and access to video programs and other data using customer profiles

• US 5,758,257 A
• Filed: 11/29/1994
• Issued: 05/26/1998
• Est. Priority Date: 11/29/1994
• Status: Expired due to Fees

• Current Assignee: Pinpoint Incorporated (Altitude Capital Partners)
• Original Assignee: Fred Herz
• Inventors: Wachob, David, Herz, Frederick, Salganicoff, Marcos, Zhang, Jian, Ungar, Lyle
• Primary Examiner(s): Peng, John K.
• Assistant Examiner(s): Miller, John W.
• Application Number:
  US08/346,425
• Time in Patent Office: 1,274 Days
• Field of Search: 348/1, 348/6, 348/7, 348/10, 348/12, 348/13, 348/906, 348/2, 455/2, 455/4.1, 455/4.2, 455/5.1
• US Class Current: 725/116
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06Q 20/306 using TV related infrastructures external card used for payment with client device H04N21/4185
• CPC Class Codes: G06Q 20/383 Anonymous user system
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H01H 1/00 Contacts liquid contacts H01H29/04
• CPC Class Codes: H04H 60/06 Arrangements for scheduling broadcast services or broadcast-related services
• CPC Class Codes: H04H 60/31 Arrangements for monitoring the use made of the broadcast services
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04L 2209/60 Digital content management, e.g. content distribution
• CPC Class Codes: H04L 9/0662 with particular pseudorandom sequence generator
• CPC Class Codes: H04L 9/0825 using asymmetric-key encryption or public key infrastructure [PKI], e.g. key signature or public key certificates
• CPC Class Codes: H04N 21/222 Secondary servers, e.g. proxy server, cable television Head-end provisioning of proxy services in data packet switching networks H04L67/56
• CPC Class Codes: H04N 21/2223 being a public access point, e.g. for downloading to or uploading from clients arrangements specially adapted to plural spots in a confined site in broadcast systems H04H20/63
• CPC Class Codes: H04N 21/23106 involving caching operations prefetching while addressing of a memory level in which the access to the desired data or data block requires associative addressing means within memory systems or architectures G06F12/0862; caching at an intermediate stage in a data network H04L67/568
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/262 Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists scheduling strategies for dispatcher in multiprogramming arrangements G06F9/4881; arrangements for scheduling broadcast services or broadcast-related services H04H60/06; flow control in packet networks H04L47/10; establishing a time schedule or organising the servicing of application requests H04L67/62
• CPC Class Codes: H04N 21/26241 involving the time of distribution, e.g. the best time of the day for inserting an advertisement or airing a children program
• CPC Class Codes: H04N 21/26603 for automatically generating descriptors from content, e.g. when it is not made available by its provider, using content analysis techniques
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/4332 : by placing content in organized collections, e.g. local EPG data repository interfaces, Database management systems or updating for information retrieval G06F16/23; details of retrieval of video data and associated meta data in video database G06F16/739
• CPC Class Codes: H04N 21/44222 : Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/4532 : involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/454 : Content or additional data filtering, e.g. blocking advertisements
• CPC Class Codes: H04N 21/4622 : Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• CPC Class Codes: H04N 21/466 : Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4665 : involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/47 : End-user applications
• CPC Class Codes: H04N 21/4755 : for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4782 : Web browsing , e.g. WebTV
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/14 : Systems for two-way working H04N7/12, H04N7/173 take precedence
• CPC Class Codes: H04N 7/163 : by receiver means only
• CPC Class Codes: H04N 7/165 : Centralised control of user terminal subsequent to an upstream request signal H04N7/17345; Registering at central by two-way working H04N7/17309
• CPC Class Codes: H04N 7/1675 : Providing digital key or authorisation information for generation or regeneration of the scrambling sequence pseudo-random number generators in general G06F7/58
• CPC Class Codes: H04N 7/173 : with two-way working, e.g. subscriber sending a programme selection signal
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests
• CPC Class Codes: H04N 7/17354 : in an intermediate station common to a plurality of user terminals
• CPC Class Codes: Y10S 707/99936 : Pattern matching access
• CPC Class Codes: Y10S 707/99939 : Privileged access

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. 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.

Claims

1. A method of scheduling customer access to data from a plurality of data sources, comprising the steps of:

creating at least one customer profile for each eligible recipient of said data, said customer profile indicating the customer'"'"'s preferences for data having predetermined characteristics;

creating content profiles for each data source of said data, said content profiles indicating the degree of content of said predetermined characteristics in data from each data source;

inputting recipient identity information;

selecting a customer profile which corresponds to said recipient identity information;

relating said selected customer profile with the content profiles for the data available from each data source to the customer at a particular time by 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 selected customer profile, cv, and said content profiles, cp, in accordance with the relationship;

ac_ij=l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

for i=a particular customer of a number of customers i, j=a particular data source of a number of data sources J, and k=a particular characteristic of a data source of a number of data source characteristics K, where cv_ik is treater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k;

determining a subset of data having content profiles which are determined in said relating step to most closely match said selected customer profile; and

presenting said subset of data to said customer for selection.

2. A method of scheduling customer access to video programs, comprising the steps of:

creating at least one customer profile for each customer of said video programs, said customer profile indicating the customer'"'"'s preferences for predetermined characteristics of the video programs;

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

determining an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for viewing by said customer at a particular time;

determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

presenting said subset of video programs to the customer as at least one "virtual channel" for display on the customer'"'"'s television.

3. A method as in claim 2, wherein said customer profile creating step comprises the step of creating a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, and said 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.

4. A method as in claim 3, wherein said customer profile creating step comprises the step of creating combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and said agreement matrix determining step comprises the step of using different combined customer profiles in accordance with the time of the day and of the week.

5. A method as in claim 2, wherein said agreement matrix determining step comprises the step of comparing said at least one customer profile with the content profiles for each video program available for viewing in a predetermined time period.

6. A method as in claim 2, wherein said agreement matrix determining 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:

ac_ij=l/ 1+Σ

_k w_ik |cv_ik -cp_jk !|,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

7. A method as in claim 6, wherein said subset determining step comprises the steps of sorting said video programs in an order of ac indicating increasing correlation and selecting as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

8. A method as in claim 2, comprising the further steps of receiving customer identity information and determining from said customer identity information which customer profile to use in said agreement matrix determining step.

9. A method of scheduling transmission of video programs to a plurality of customers, comprising the steps of:

creating a plurality of customer profiles for each of said plurality of customers of said video programs, said plurality of customer profiles being representative of the customers'"'"' changing preferences for predetermined characteristics of the video programs in accordance with time of the day and of the week, at least one customer profile being available at a particular time of the day and of the week;

creating content profiles for each video program available for transmission to said customers, said content profiles indicating the degree of content of said predetermined characteristics in each video program;

determining an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for transmission to said customers at a particular time;

determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

scheduling said subset of video programs for transmission from a video head end to said plurality of customers for receipt on the customers'"'"' televisions.

10. A method as in claim 9, wherein said customer profiles creating step comprises the steps of creating combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and forming said at least one customer profile from the combined customer profiles available at a particular time of the day and of the week, said agreement matrix determining step comprising the step of using a customer profile created from combined customer profiles in accordance with the time of the day and of the week.

11. A method as in claim 9, wherein said agreement matrix determining step comprises the step of comparing said at least one customer profile with the content profiles for each video program available for transmission during a predetermined time period.

12. A method as in claim 9, wherein said agreement matrix determining 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:

ac_ij=l/ l+Σ

_k w_ik |cv_ik -cp_jk |!, 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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

13. A method as in claim 12, wherein said subset determining step comprises the steps of sorting said video programs in an order of ac indicating increasing correlation and selecting as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

14. A method as in claim 12, wherein said scheduling step comprises the steps of:

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

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

(c) when said number of channels available for transmission of video programs to a particular customer of said plurality of customers reaches zero, removing said particular customer from said 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.

15. A method as in claim 9, comprising the further steps of:

encoding communications between said video head end and said plurality of customers; and

transmitting said encoded video programs to said plurality of customers.

16. A method as in claim 15, wherein said encoding step comprises the steps of:

generating a seed random number N at either the video head end or a customer'"'"'s set top terminal for seeding a pseudo random number generator of the customer'"'"'s set top terminal and a pseudo random number generator of the video head end;

encrypting seed random number N using a public key algorithm and a public key P known to the customer'"'"'s set top terminal and the video head end to yield encrypted seed random number E(N,P);

providing the encrypted seed random number E(N,P) to the other of the customer'"'"'s set top terminal and the video head end at which the seed random number N was not generated;

decrypting the encrypted seed random number E(N,P) at the other of the customer'"'"'s set top terminal and the video head end at which the seed random number N was not generated using a private key of the other of the customer'"'"'s set top terminal and the video head end at which the seed random number N was not generated to yield seed random number N;

initializing the pseudo random number generator of the customer'"'"'s set top terminal and the pseudo random number generator of the video head end with seed random number N to generate pseudo random sequence K_i at the customer'"'"'s set top terminal and the video head end; and

for each number i in random sequence K_i, logically exclusive-ORing K_i with a data stream P_i to be transmitted to the customer'"'"'s set top terminal, thereby forming a data stream C_i to be transmitted from the video head end to the customer'"'"'s set top terminal in said transmitting step, and decrypting data stream C_i at the customer'"'"'s set top terminal to yield a decrypted data stream P_i by logically exclusive-ORing sequence K_i with data stream C_i.

17. A method of scheduling customer access to data from a plurality of data sources, comprising the steps of:

creating at least one customer profile for each eligible recipient of said data, said customer profile indicating the customer'"'"'s preferences for data having predetermined characteristics;

creating content profiles for each data source of said data, said content profiles indicating the degree of content of said predetermined characteristics in data from each data source;

monitoring which data sources are actually accessed by each recipient; and

updating, without input from each customer, each customer profile in accordance with the content profiles of the data sources actually accessed by that customer to automatically update each customer'"'"'s actual preferences for said predetermined characteristics.

18. A method of scheduling customer access to video programs, comprising the steps of:

creating at least one customer profile for each customer of said video programs, said customer profile indicating the customer'"'"'s preferences for predetermined characteristics of the video programs;

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

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

updating, without input from each customer, each customer profile in accordance with the content profiles of the video programs actually viewed by that customer to automatically update each customer'"'"'s actual preferences for said predetermined characteristics.

19. A method as in claim 18, wherein said customer profile creating step comprises the step of creating a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, and said updating step comprises the step of updating different customer profiles for each customer in accordance with the time of the day and of the week.

20. A method as in claim 19, wherein said customer profile creating step comprises the step of creating combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and said updating step comprises the step of updating different combined customer profiles in accordance with the time of the day and of the week.

21. A method as in claim 18, comprising the further steps of receiving customer identity information and determining from said customer identity information which customer profile to update in said updating step.

22. A method as in claim 18, further comprising the steps of:

determining an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for viewing by said customer at a particular time;

determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

presenting said subset of video programs to the customer as a virtual channel for display on the customer'"'"'s television.

23. A method as in claim 22, wherein said agreement matrix determining step comprises the step of comparing said at least one customer profile with the content profiles for each video program available for viewing in a predetermined time period.

24. A method as in claim 22, wherein said agreement matrix determining 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:

ac_ij=l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

25. A method as in claim 24, wherein said subset determining step comprises the steps of sorting said video programs in an order of ac indicating increasing correlation and selecting as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

26. A method as in claim 18, wherein said updating step comprises the steps of adjusting said at least one customer profile, cv_ik, for customer i and video program characteristic k, to a new customer profile, cv_ik '"'"', in accordance with the equation:

cv_ik =cv_ik -Δ

(cv_ik -cp_jk),

where cp_jk represents the degree of video program characteristic k in video program j.

27. A method as in claim 18, wherein said updating step comprises the steps of adjusting customer i'"'"'s weighting of video program characteristic k, w_ik, in said at least one customer profile, cv_ik, to a new weighting, w_ik '"'"', in accordance with the equation:

w_ik '"'"'=(w_ik -Δ

|cv_ik -cp_jk |)/Σ

_k (w_ik -Δ

|cv_ik -cp_jk |)

where cp_jk represents the degree of video program characteristic k in video program j.

28. A method as in claim 18, comprising the further step of updating the content profiles, cp_jk, of certain video programs j having video program characteristics k to new content profiles, cp_jk '"'"', to update the customer profiles of customers i who actually watch video program j, in accordance with the equation:

space="preserve" listing-type="equation">cp.sub.jk '"'"'=cp.sub.jk -Δ

(cv.sub.ik -cp.sub.jk),

where cv_ik represents the customer profile of customer i for video program characteristic k.

29. A method of scheduling transmission of video programs to a plurality of customers, comprising the steps of:

creating at least one clustered customer profile for each of said plurality of customers of said video programs, said clustered customer profile indicating said plurality of customers'"'"' combined preferences for predetermined characteristics of the video programs;

creating content profiles for each video program available for transmission to said customers, said content profiles indicating the degree of content of said predetermined characteristics in each video program;

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

updating, without input from each customer, each clustered customer profile in accordance with the content profiles of the video programs actually viewed by said plurality of customers to automatically update the actual preferences of said plurality of customers for said predetermined characteristics.

30. A method as in claim 29, wherein said clustered customer profile creating step comprises the steps of creating a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, and forming said at least one clustered customer profile from the customer profiles available at a particular time of the day and of the week, said updating step comprising the step of updating different clustered customer profiles in accordance with the time of the day and of the week.

31. A method as in claim 30, wherein said clustered customer profile creating step comprises the steps of creating combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and forming said at least one clustered customer profile from the combined customer profiles available at a particular time of the day and of the week, said updating step comprising the step of updating different clustered customer profiles in accordance with the time of the day and of the week.

32. A method as in claim 29, further comprising the steps of:

determining an agreement matrix which relates said at least one clustered customer profile with the content profiles for certain video programs available for transmission to said customers at a particular time;

determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

scheduling said subset of video programs for transmission from a video head end to said plurality of customers for receipt on the customers'"'"' televisions.

33. A method as in claim 32, wherein said agreement matrix determining step comprises the step of comparing said at least one customer profile with the content profiles for each video program available for transmission during a predetermined time period.

34. A method as in claim 32, wherein said agreement matrix determining 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:

ac_ij=l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

35. A method as in claim 34, wherein said subset determining step comprises the steps of sorting said video programs in an order of ac indicating increasing correlation and selecting as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

36. A method as in claim 34, wherein said scheduling step comprises the steps of:

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

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

(c) when said number of channels available for transmission of video programs to a particular customer of said plurality of customers reaches zero, removing said particular customer from said 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.

37. A method as in claim 29, wherein said monitoring step comprises the steps of storing, at each customer'"'"'s set top terminal, a record of the video programs actually watched by the customer at the customer'"'"'s location, and polling said set top terminals to retrieve said records of the video programs actually watched by the customers at each of the customer locations.

38. A method as in claim 29, wherein said updating step comprises the steps of adjusting said at least one customer profile, cv_ik, for customer i and video program characteristic k, to a new customer profile, cv_ik '"'"', in accordance with the equation:

cv_ik '"'"'=cv_ik -Δ

(cv_ik -cp_jk),

where cp_jk represents the degree of video program characteristic k in video program j.

39. A method as in claim 29, wherein said updating step comprises the steps of adjusting customer i'"'"'s weighting of video program characteristic k, w_ik, in said at least one customer profile, cv_ik, to a new weighting, w_ik '"'"', in accordance with the equation:

w_ik '"'"'=(w_ik -Δ

|cv_ik -cp_jk |)/Σ

_k (w_ik -Δ

|cv_ik -cp_jk |)

where cp_jk represents the degree of video program characteristic k in video program j.

40. A method as in claim 29, comprising the further step of updating the content profiles, cp_jk, of certain video programs j having video program characteristics k to new content profiles, cp_jk '"'"', to update the customer profiles of customers i who actually watch video program j, in accordance with the equation:

cp_jk '"'"'=cp_jk -Δ

(cv_ik -cp_jk),

where cv_ik represents the customer profile of customer i for video program characteristic k.

41. A method of scheduling customer access to data from a plurality of data sources, comprising the steps of:

creating a customer profile for each customer of said plurality of data sources, said customer profile indicating said customer'"'"'s preferences for predetermined characteristics of the data sources;

monitoring which data sources are actually accessed by each customer; and

updating each customer profile to reflect the frequency of selection of the data sources by customers with customer profiles substantially similar to said each customer profile.

42. A method as in claim 41, wherein said 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;

##EQU18## where |v_i -v_k | is a distance between the vector of characteristics of the data sources accessed by customer i and the centroid of group k; and

determining an agreement matrix ac_ij, where for each customer i, a jth row of the agreement matrix is a vector v_k for a group k in which customer i belongs.

43. A method of scheduling customer access to data from a plurality of data sources, comprising the steps of:

creating at least one customer profile for each eligible recipient of said data, said customer profile including a profile of data previously accessed by said customer;

creating content profiles for each data source of said data, said content profiles reflecting the customer profiles of those customers who have previously accessed said data from each data source;

relating said at least one customer profile with the content profiles for the data available from each data source to the customer;

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.

44. A data transmission system which schedules customer access to data from a plurality of data sources, comprising:

at least one customer profile for each eligible recipient of said data, said customer profile indicating the customer'"'"'s preferences for data having predetermined characteristics;

content profiles for each data source of said data, said content profiles indicating the degree of content of said predetermined characteristics in data from each data source;

means for inputting recipient identity information;

means for selecting a customer profile which corresponds to said recipient identity information;

a processor which relates said selected customer profile with the content profiles for the data available from each data source to the customer at a particular time and which determines a subset of data having content profiles which most closely match said selected customer profile, said processor being 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 selected customer profile, cv, and said content profiles, cp, in accordance with the relationship;

ac_ij =l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

for i=a particular customer of a number of customers i, j=a particular data source of a number of data sources J, and k=a particular characteristic of a data source of a number of data source characteristics K, where cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k; and

means for presenting said subset of data to said customer for selection.

45. A data transmission system which schedules customer access to video programs received from a video head end, comprising:

at least one customer profile for each customer of said video programs, said customer profile indicating the customer'"'"'s preferences for predetermined characteristics of the video programs;

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

an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for viewing by said customer at a particular time;

means for determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

means for presenting said subset of video programs to the customer as at least one "virtual channel" for display on the customer'"'"'s television.

46. A system as in claim 45, wherein said at least one customer profile comprises a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, and said agreement matrix uses different customer profiles for each customer in accordance with the time of the day and of the week.

47. A system as in claim 46, wherein said customer profile comprises combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and said agreement matrix uses different combined customer profiles in accordance with the time of the day and of the week.

48. A system as in claim 45, further comprising means for transmitting said content profiles to each customer along with electronic program guide data for upcoming television viewing periods.

49. A system as in claim 45, wherein said agreement matrix compares said at least one customer profile with the content profiles for each video program available for viewing in a predetermined time period.

50. A system as in claim 45, wherein said determining 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:

ac_ij =l/ l+Σ

_k w_ik |cv_jk -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

51. A system as in claim 50, wherein said determining means sorts said video programs in an order of ac indicating increasing correlation and selects as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

52. A system as in claim 45, comprising means for inputting customer identity information and for determining from said customer identity information which customer profile to use in calculating said agreement matrix.

53. A system of scheduling transmission of video programs from a video head end to a plurality of customers, comprising the steps of:

a plurality of customer profiles for each of said plurality of customers of said video programs, said plurality of customer profiles being representative of the customers'"'"' changing preferences for predetermined characteristics of the video programs in accordance with time of the day and of the week, at least one customer profile being available at a particular time of the day and of the week;

content profiles for each video program available for transmission to said customers, said content profiles indicating the degree of content of said predetermined characteristics in each video program;

an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for transmission to said customers at a particular time;

means for determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

means for scheduling said subset of video programs for transmission from said video head end to said plurality of customers for receipt on the customers'"'"' televisions.

54. A system as in claim 53, wherein said customer profiles comprise combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, said at least one customer profile being formed from the combined customer profiles available at a particular time of the day and of the week, and said agreement matrix uses a customer profile created from combined customer profiles in accordance with the time of the day and of the week.

55. A system as in claim 53, further comprising means for transmitting said content profiles to each customer along with electronic program guide data for upcoming television viewing periods.

56. A system as in claim 53, wherein said agreement matrix compares said at least one customer profile with the content profiles for each video program available for transmission during a predetermined time period.

57. A system as in claim 53, wherein said determining 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 characteristic s, ac, between said at least one customer profile, cv, and said content profiles, cp, in accordance with the relationship:

ac_ij =l l+Σ

_k w_ik |cv_ik -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

58. A system as in claim 57, wherein said subset determining means sorts said video programs in an order of ac indicating increasing correlation and selects as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

59. A system as in claim 57, wherein said scheduling means comprises a processor programmed to perform the steps of:

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

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

(c) when said number of channels available for transmission of video programs to a particular customer of said plurality of customers reaches zero, removing said particular customer from said 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.

60. A system as in claim 53, further comprising:

means for encoding communications between said video head end and said plurality of customers; and

means for transmitting said encoded video programs to said plurality of customers.

61. A system as in claim 60, wherein said encoding means comprises at said video head end:

means for receiving encrypted seed random number E(N,P), where N is an unencrypted seed random number and P is a public key known to a customer'"'"'s set top terminal and the video head end;

means for decrypting the encrypted seed random number E(N,P) using a private key of the video head end to yield seed random number N; and

a pseudo random number generator initialized with seed random number N to generate pseudo random sequence K_i at the video head end; and

means for logically exclusive-ORing sequence K_i with data stream P_i to be transmitted to the customer'"'"'s set top terminal, thereby forming a data stream C_i for transmission from the video head end to the customer'"'"'s set top terminal by said transmitting means; and

said encoding means comprises at said customer'"'"'s set top terminal;

a pseudo random number generator initialized with seed random number N to generate pseudo random sequence K_i at the customer'"'"'s set top terminal;

means for receiving data stream C_i from the video head end; and

means for decrypting data stream C_i, to yield a decrypted data stream P_i by logically exclusive-ORing sequence K_i with data stream C_i.

62. A system for scheduling customer access to data from a plurality of data sources, comprising:

at least one customer profile for each eligible recipient of said data, said customer profile indicating the customer'"'"'s preferences for data having predetermined characteristics;

content profiles for each data source of said data, said content profiles indicating the degree of content of said predetermined characteristics in data from each data source;

means for monitoring which data sources are actually accessed by each recipient; and

means for updating, without input from each customer, each customer profile in accordance with the content profiles of the data sources actually accessed by that customer to automatically update each customer'"'"'s actual preferences for said predetermined characteristics.

63. A system for scheduling customer access to video programs received from a video head end, comprising:

at least one customer profile for each customer of said video programs, said customer profile indicating the customer'"'"'s preferences for predetermined characteristics of the video programs;

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

means for monitoring which video programs are actually viewed by each customer; and

means for updating, without input from each customer, each customer profile in accordance with the content profiles of the video programs actually viewed by that customer to automatically update each customer'"'"'s actual preferences for said predetermined characteristics.

64. A system as in claim 63, wherein said at least one customer profile comprises a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, and said updating means updates different customer profiles for each customer in accordance with the time of the day and of the week.

65. A system as in claim 64, wherein said at least one customer profile comprises combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, and said updating means updates different combined customer profiles in accordance with the time of the day and of the week.

66. A system as in claim 63, further comprising means for transmitting said content profiles to each customer along with electronic program guide data for upcoming television viewing periods.

67. A system as in claim 63, further comprising means for inputting customer identity information and for determining from said customer identity information which customer profile to update with said updating means.

68. A system as in claim 63, further comprising:

means for determining an agreement matrix which relates said at least one customer profile with the content profiles for certain video programs available for viewing by said customer at a particular time and for determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

means for presenting said subset of video programs to the customer as a virtual channel for display on the customer'"'"'s television.

69. A system as in claim 68, wherein said agreement matrix determining means compares said at least one customer profile with the content profiles for each video program available for viewing in a predetermined time period.

70. A system as in claim 68, wherein said agreement matrix determining means determines 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:

ac_ij =l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

71. A system as in claim 70, wherein said agreement matrix determining means sorts said video programs in an order of ac indicating increasing correlation and selects as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

72. A system as in claim 63, wherein said updating means adjusts said at least one customer profile, cv_ik, for customer i and video program characteristic k, to a new customer profile, cv_ik '"'"', in accordance with the equation:

cv_ik '"'"'=cv_ik -Δ

(cv_ik -cp_jk),

where cp_jk represents the degree of video program characteristic k in video program j.

73. A system as in claim 63, wherein said updating means adjusts customer i'"'"'s weighting of video program characteristic k, w_ik, in said at least one customer profile, cv_ik, to a new weighting, w_ik '"'"', in accordance with the equation:

w_ik '"'"'=(w_ik -Δ

|cv_ik -cp_jk |)/Σ

_k (w_ik -Δ

|cv_ik -cp_jk |)

where cp_jk represents the degree of video program characteristic k in video program j.

74. A system as in claim 63, wherein said updating means updates the content profiles, cp_jk, of certain video programs j having video program characteristics k to new content profiles, cp_jk '"'"', and updates the customer profiles of customers i who actually watch video program j, in accordance with the equation:

cp_jk '"'"'=cp_jk -Δ

(cv_ik -cp_jk),

where cv_ik represents the customer profile of customer i for video program characteristic k.

75. A system for scheduling transmission of video programs from a video head end to a plurality of customers, comprising:

at least one clustered customer profile for each of said plurality of customers of said video programs, said clustered customer profile indicating said plurality of customers'"'"' combined preferences for predetermined characteristics of the video programs;

content profiles for each video program available for transmission to said customers, said content profiles indicating the degree of content of said predetermined characteristics in each video program;

means for monitoring which video programs are actually viewed by each customer; and

means for updating, without input from each customer, each clustered customer profile in accordance with the content profiles of the video programs actually viewed by said plurality of customers to automatically update the actual preferences of said plurality of customers for said predetermined characteristics.

76. A system as in claim 75, wherein said clustered customer profile comprises a plurality of customer profiles for each customer, said plurality of customer profiles being representative of the customer'"'"'s changing preferences for said predetermined characteristics in accordance with time of the day and of the week, said at least one clustered customer profile being formed from the customer profiles available at a particular time of the day and of the week, and said updating means updates different clustered customer profiles in accordance with the time of the day and of the week.

77. A system as in claim 76, wherein said clustered customer profile comprises combined customer profiles for combinations of customers at a particular customer location at particular times on particular days, said at least one clustered customer profile being formed from the combined customer profiles available at a particular time of the day and of the week, and said updating means updates different clustered customer profiles in accordance with the time of the day and of the week.

78. A system as in claim 75, further comprising means for transmitting said content profiles to each customer along with electronic program guide data for upcoming television viewing periods.

79. A system as in claim 75, further comprising:

means for determining an agreement matrix which relates said at least one clustered customer profile with the content profiles for certain video programs available for transmission to said customers at a particular time, and for determining from said agreement matrix a subset of said video programs having content profiles which most closely match said at least one customer profile; and

means for scheduling said subset of video programs for transmission from said video head end to said plurality of customers for receipt on the customers'"'"' televisions.

80. A system as in claim 79, wherein said determining means compares said at least one customer profile with the content profiles for each video program available for transmission during a predetermined time period.

81. A system as in claim 79, wherein said determining 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:

ac_ij =l/ l+Σ

_k w_ik |cv_ik -cp_jk |!,

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 cv_ik is greater than or equal to 0 and w_ik is customer i'"'"'s normalized weight of characteristic k.

82. A system as in claim 81, wherein said determining means sorts said video programs in an order of ac indicating increasing correlation and selects as said subset a predetermined number of said video programs having the values for ac indicating the most correlation.

83. A system as in claim 81, wherein said scheduling means comprises a processor programmed to perform the steps of:

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

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

(c) when said number of channels available for transmission of video programs to a particular customer of said plurality of customers reaches zero, removing said particular customer from said 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.

84. A system as in claim 75, wherein said monitoring means stores, at each customer'"'"'s set top terminal, a record of the video programs actually watched by the customer at the customer'"'"'s location, and polls said set top terminals to retrieve said records of the video programs actually watched by the customers at each of the customer locations.

85. A system as in claim 75, wherein said updating means adjusts said at least one customer profile, cv_ik, for customer i and video program characteristic k, to a new customer profile, cv_ik '"'"', in accordance with the equation:

cv_ik '"'"'=cv_ik -Δ

(cv_ik -cp_jk),

where cp_jk represents the degree of video program characteristic k in video program j.

86. A system as in claim 75, wherein said updating means adjusts customer i'"'"'s weighting of video program characteristic k, w_ik, in said at least one customer profile, cv_ik, to a new weighting, w_ik '"'"', in accordance with the equation:

w_ik '"'"'=(w_ik -Δ

|cv_ik -cp_jk |)/Σ

_k (w_ik -Δ

|cv_ik -cp_jk |)

where cp_jk represents the degree of video program characteristic k in video program j.

87. A system as in claim 75, wherein said updating means updates the content profiles, cp_jk, of certain video programs j having video program characteristics k to new content profiles, cp_jk '"'"', and updates the customer profiles of customers i who actually watch video program j, in accordance with the equation:

cp_jk '"'"'=cp_jk -Δ

(cv_ik -cp_jk)

where cv_ik represents the customer profile of customer i for video program characteristic k.

88. A system for scheduling customer access to data from a plurality of data sources, comprising:

at least one customer profile for each eligible recipient of said data, said customer profile including a profile of data previously accessed by said customer;

a content profile for each data source of said data, each content profile reflecting the customer profiles of those customers who have previously accessed said data from each data source;

means for relating said at least one customer profile with the content profiles for the data available from each data source to the customer;

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.

89. A system for scheduling customer access to data provided by a plurality of data sources, comprising:

means for creating a customer profile for each customer of said plurality of data sources, said customer profile indicating said customer'"'"'s preferences for predetermined characteristics of the data sources;

means for monitoring which data sources are actually accessed by each customer; and

means for updating each customer profile to reflect the frequency of selection of the data sources by customers with customer profiles substantially similar to said each customer profile.

90. A method as in claim 89, wherein said means for creating a 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:

##EQU19## where |v_i -v_k | is a distance between the vector of characteristics of the data sources accessed by customer i and the centroid of group k, and determines an agreement matrix ac_ij, where for each customer i, a jth row of the agreement matrix is a vector V_k for a group k in which customer i belongs.

91. A multimedia terminal for receiving data from a plurality of data sources, comprising:

means for storing at least one customer profile indicating a customer'"'"'s preferences for data having predetermined characteristics;

means for storing content profiles for each data source of said data, said content profiles indicating the degree of content of said predetermined characteristics in data from each data source;

means for inputting recipient identity information;

means for selecting different customer profiles which correspond to said recipient identity information in accordance with the time of day and day of the week;

processing means for relating said selected customer profiles with the content profiles for the data available from each data source to the customer at a particular time and for determining a subset of data having content profiles which most closely match said selected customer profile; and

a display guide for presenting said subset of data to said customer for selection.

92. A multimedia terminal as in claim 91, wherein said display guide presents said subset of data to the customer as a virtual data channel.

93. A multimedia terminal as in claim 91, further comprising means for storing an electronic program guide, wherein said display guide highlights programs within said electronic program guide which correspond to said subset of data.

94. A multimedia terminal as in claim 91, further comprising an interface which through which said processing means provides upstream data messages.

95. A multimedia terminal as in claim 94, further comprising encrypting means for encrypting said upstream data messages.