System and method for selecting relevant products to be transparently acquired for a consumer

• US 20030065520A1
• Filed: 09/28/2001
• Published: 04/03/2003
• Est. Priority Date: 09/28/2001
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Cooper, Jonathan H., Jutzi, Curtis E.
• Application Number:
  US09/967,829
• Publication Number:
  US 20030065520A1
• Time in Patent Office: Days
• US Class Current: 705/1
• CPC Class Codes: H04N 21/4334 Recording operations recording of a television signal H04N5/76; arrangements for recording or accumulating broadcast information or broadcast-related information H04H60/27
• 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 21/458 Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming stream; Updating operations, e.g. for OS modules ; time-related management operations arrangements for replacing or switching information during the broadcast or during the distribution H04H20/10
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73
• CPC Class Codes: H04N 7/163 by receiver means only

Abstract

System and method for selecting relevant products to be transparently delivered to a consumer. The method may include obtaining consumer preferences and deriving a plurality of predictive vectors based on the consumer preferences. After receiving a delivery schedule including schedule data and corresponding product description data regarding a plurality of products, a predicted group of products the consumer may prefer may be selected transparent to the consumer by comparing the product description data and the predictive vectors. Transparent to the consumer, the predicted group of products may be acquired for the consumer. In one embodiment, the method may be implemented on a system that includes a set-top box and may be stored as instructions on a machine readable medium within the set-top box.

Claims

1. A method comprising:

obtaining a plurality of consumer preferences to determine a plurality of ratings vectors;

deriving a plurality of predictive vectors based on the consumer preferences;

receiving a delivery schedule including a plurality of schedule data and corresponding plurality of product description data regarding a plurality of products;

selecting a predicted group of products of the plurality of products the consumer may prefer, transparent to the consumer, by comparing the plurality of product description data and the plurality of predictive vectors; and

acquiring, transparent to the consumer, the predicted group of products for the consumer.

2. The method of claim 1 wherein obtaining comprises:

extrapolating the plurality of consumer preferences implicitly based on those products accessed by the consumer.

3. The method of claim 2 wherein obtaining further comprises:

obtaining some of the plurality of consumer preferences explicitly from the consumer.

4. The method of claim 1 wherein deriving the plurality of predictive vectors comprises:

evaluating a reference magnitude, a preference magnitude and a standard deviation for each of a plurality of key/value pairs included in the ratings vectors.

5. The method of claim 4 wherein deriving the plurality of predictive vectors further comprises:

sorting each of the ratings vectors based on a relevance and a believability of each of the ratings vectors.

6. The method of claim 5 wherein:

the relevance of each of the ratings vectors is based on the reference magnitude of the ratings vectors; and

the believability of each of the ratings vectors is based on the standard deviation for each of the ratings vectors.

7. The method of claim 1 wherein selecting comprises:

choosing those of the plurality of products which most closely correspond to the predictive vectors to include in the predicted group of products.

8. The method of claim 7 wherein:

the choosing is based on a competence level and a predicted preference level of each of the plurality of products.

9. The method of claim 8 wherein:

the predicted preference level is based on a reference magnitude of all matching predictive vectors; and

the competence level is based on a standard deviation of all matching predictive vectors.

10. The method of claim 1 wherein:

the delivery schedule is received from a delivery center server and the acquiring is achieved by retrieving the predicted group of products from a plurality of channels specified in the schedule data and broadcast by the delivery center server at a plurality of corresponding times specified in the schedule data.

11. The method of claim 1 wherein the plurality of products comprise:

at least one of movies, computer games, music videos, audio files, raw data, computer programs, previews, television programs and news programs.

12. A system comprising:

a user input device to receive user input;

a television monitor;

a set-top box including a processor, a memory, a storage device, a communications interface, an output controller, and a user input controller coupled to a bus, the set-top box coupled to the television monitor via the output controller, the user input device coupled to the set-top box via the user input controller, and the set-top box coupled to a delivery center server via the communications interface; and

a consumer preference software program included on the storage device to enable the set-top box to obtain a plurality of consumer preferences to determine a plurality of ratings vectors, to derive a plurality of predictive vectors based on the consumer preferences, to receive a delivery schedule including a plurality of schedule data and corresponding plurality of product description data regarding a plurality of products from the delivery center server, to select a predicted group of products of the plurality of products the consumer may prefer transparent to the consumer by comparing the plurality of product description data and the plurality of predictive vectors, and to acquire, transparent to the consumer, the predicted group of products for the consumer.

13. The system of claim 12 wherein:

the consumer preference software further enables the set-top box to extrapolate the plurality of consumer preferences implicitly based on those products accessed by the consumer.

14. The system of claim 13 wherein:

the consumer preference software further enables the set-top box to obtain some of the plurality of consumer preferences explicitly from the consumer.

15. The system of claim 12 wherein:

the consumer preference software further enables the set-top box to evaluate a reference magnitude, a preference magnitude and a standard deviation for each of a plurality of key/value pairs included in the ratings vectors.

16. The system of claim 15 wherein:

the consumer preference software further enables the set-top box to sort each of the ratings vectors based on a relevance and a believability of each of the ratings vectors.

17. The system of claim 16 wherein:

the consumer preference software further enables the set-top box to evaluate the relevance of each of the ratings vectors based on the reference magnitude of the ratings vectors and to evaluate the believability of each of the ratings vectors based on the standard deviation for each of the ratings vectors.

18. The system of claim 12 wherein:

the consumer preference software further enables the set-top box to choose those of the plurality of products which most closely correspond to the predictive vectors to include in the predicted group of products, the choosing based on a predictive preference level and a competence level of all matching predictive vectors.

19. The system of claim 18 wherein:

the predicted preference level is based on a reference magnitude of all matching predictive vectors; and

the competence level is based on a standard deviation of all matching predictive vectors.

20. The system of claim 12 wherein:

the consumer preference software further enables the set-top box to receive the delivery schedule from a delivery center server and to acquire the predicted products from the delivery center server at a plurality of times specified in the schedule data on a plurality of corresponding delivery center channels specified in the schedule data.

21. A machine readable medium including instructions which when executed by a processor cause the processor to perform operations comprising:

obtaining a plurality of consumer preferences to determine a plurality of ratings vectors;

deriving a plurality of predictive vectors based on the consumer preferences;

receiving a delivery schedule including a plurality of schedule data and corresponding plurality of product description data regarding a plurality of products;

selecting a predicted group of products of the plurality of products the consumer may prefer, transparent to the consumer, by comparing the plurality of product description data and the plurality of predictive vectors; and

acquiring, transparent to the consumer, the predicted group of products for the consumer.

22. The machine readable medium of claim 21 wherein obtaining comprises:

extrapolating the plurality of consumer preferences implicitly based on those products accessed by the consumer.

23. The machine readable medium of claim 22 wherein obtaining further comprises:

obtaining some of the plurality of consumer preferences explicitly from the consumer.

24. The machine readable medium of claim 21 wherein deriving the plurality of predictive vectors comprises:

evaluating a reference magnitude, a preference magnitude and a standard deviation for each of a plurality of key/value pairs included in the ratings vectors.

25. The machine readable medium of claim 24 wherein deriving the plurality of predictive vectors further comprises:

sorting each of the ratings vectors based on a relevance of each of the ratings vectors and a believability of each of the ratings vectors.

26. The machine readable medium of claim 25 wherein:

the relevance of each of the ratings vectors is based on the reference magnitude of the ratings vectors; and

the believability of each of the ratings vectors is based on the standard deviation for each of the ratings vectors.

27. The machine readable medium of claim 21 wherein selecting comprises:

choosing those of the plurality of products which most closely correspond to the predictive vectors to include in the predicted group of products, the choosing based on a predictive preference level and a competence level of all matching predictive vectors.

28. The machine readable medium of claim 27 wherein:

the predicted preference level is based on a reference magnitude of all matching predictive vectors; and

the competence level is based on a standard deviation of all matching predictive vectors.

29. The machine readable medium of claim 21 wherein:

the delivery schedule is received from a delivery center server and the acquiring is achieved by retrieving the predicted group of products at a plurality of times specified in the schedule data via a plurality of corresponding delivery center channels specified in the schedule data.