Predictive tuning of unscheduled streaming digital content

• US 20060067296A1
• Filed: 08/01/2005
• Published: 03/30/2006
• Est. Priority Date: 09/03/2004
• Status: Abandoned Application

• Current Assignee: University of Washington
• Original Assignee: University of Washington
• Inventors: Bhaya, Gaurav Ravindra, Bershad, Brian Nathan
• Application Number:
  US11/195,089
• Publication Number:
  US 20060067296A1
• Time in Patent Office: Days
• US Class Current: 370/351
• CPC Class Codes: H04L 12/2854 Wide area networks, e.g. public data networks

Abstract

A predictive tuning system enables a user to easily and efficiently find desired digital content among a plurality of content streams. Using a data collector, analyzer, and distributed tuning service, users may specify one or more particular items of interest, and the system, through the use of predictive algorithms, determines a subset of the plurality of content streams that should be monitored in order to optimize along one or more dimensions, such as the length of time that the user must wait in order to receive their desired digital content. Various strategies can be employed to find the desired content in the data streams, and a combination of strategies can provide the most efficient approach to achieving the desired content. Once found, a desired content can be accessed contemporaneously, stored for later access, or can be input to another application.

Claims

1. A method for finding desired labeled data within a plurality of streams of labeled data that are accessible over a network, comprising the steps of:

(a) identifying a plurality of sources of the labeled data accessible over the network;

(b) providing a history indicating specific labeled data that have been included in streams provided by the plurality of sources over a period of time;

(c) determining a subset of the plurality of streams of labeled data that are likely to include the desired labeled data;

(d) monitoring the subset of the plurality of streams of labeled data to detect when any of the desired data are included therein; and

(e) providing an indication when any portion of the desired labeled data is detected in the subset of the plurality of streams of labeled data.

2. The method of claim 1, further comprising the step of providing a list of the desired labeled data for use in the step of monitoring the subset of the plurality of the streams of labeled data.

3. The method of claim 2, further comprising the steps of:

(a) revising the list of the desired labeled data to exclude all portions of the desired labeled data that have already been detected; and

(b) successively repeating steps (c) through (e) of claim 1 to detect another portion of the desired labeled data that has not yet been detected, until no more desired labeled data remains to be detected.

4. The method of claim 1, wherein the step of providing a history comprises the step of creating a database that indicates the specific labeled data that have been included in the streams provided by the plurality of sources.

5. The method of claim 1, wherein the step of providing a history comprises the step of sampling the plurality of streams of labeled data over the period of time, to develop the history.

6. The method of claim 1, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the step of determining the subset of the plurality of streams of labeled data that are monitored comprises the step of selecting streams of labeled data that most quickly convey a maximum number of labeled data objects included in the different labeled data objects that are desired.

7. The method of claim 6, wherein after monitoring the streams of labeled data selected as most quickly conveying the maximum number of the labeled object included in the different labeled data objects that are desired for a period of time, the method further comprises the step of instead monitoring streams of labeled data selected as most likely to include any labeled object of the different labeled data objects that are desired.

8. The method of claim 7, wherein a change in the streams of labeled data that are monitored occurs when an expected coverage of the different labeled data objects that are desired has been maximized.

9. The method of claim 1, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the step of determining the subset of the plurality of streams of labeled data that are monitored comprises the step of selecting streams of labeled data that most frequently play a subset of more preferred desired labeled data objects from the plurality of different desired labeled data objects.

10. The method of claim 1, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the step of determining the subset of the plurality of streams of labeled data that are monitored comprises the step of selecting streams of labeled data that are most likely to include any of the different labeled data objects that are desired.

11. The method of claim 1, wherein the streams of labeled data comprise steams of audio data, and wherein the labels identify the audio data.

12. The method of claim 11, further comprising the step of enabling a user to store the desired labeled data that are detected, so that the desired labeled data that are thus stored may subsequently be played.

13. The method of claim 1, further comprising the step of enabling a user to selectively set a scope for monitoring the plurality of streams of labeled data so as to efficiently cover the plurality of streams of labeled data.

14. A medium having machine instructions for carrying out the steps of claim 1.

15. A system for finding desired labeled data within a plurality of streams of labeled data that are accessible over a network, comprising:

(a) a network interface for communication over the network;

(b) a memory in which machine instructions are stored;

(c) a processor that is coupled to the network interface and the memory, the processor executing the machine instructions that are stored in the memory to carry out a plurality of functions, including;

(i) identifying a plurality of sources of the labeled data accessible over the network;

(ii) providing a history indicating specific labeled data that have been included in streams provided by the plurality of sources over a period of time;

(iii) determining a subset of the plurality of streams of labeled data that are likely to include the desired labeled data;

(iv) monitoring the subset of the plurality of streams of labeled data to detect when any of the desired data are included therein; and

(v) providing an indication when any portion of the desired labeled data is detected in the subset of the plurality of streams of labeled data.

16. The system of claim 15, wherein the machine instructions further cause the processor to enable a user to provide a list of the desired labeled data for use in the step of monitoring the subset of the plurality of the streams of labeled data.

17. The system of claim 15, wherein the machine instructions further cause the processor to:

(a) automatically revise the list of the desired labeled data to exclude all portions of the desired labeled data that have already been detected; and

(b) successively repeat functions (iii) through (v) of claim 15 to detect another portion of the desired labeled data that has not yet been detected, until no more desired labeled data remains to be detected.

18. The system of claim 15, wherein the machine instructions further cause the processor to provide the history by creating a database that indicates the specific labeled data that have been included in the streams provided bye the plurality of sources.

19. The system of claim 15, wherein the machine instructions further cause the processor to provide the history by sampling the plurality of streams of labeled data over the period of time, to develop the history.

20. The system of claim 15, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the step of determining the subset of the plurality of streams of labeled data that are monitored comprises the step of automatically selecting streams of labeled data that most quickly convey a maximum number of labeled data objects included in the different labeled data objects that are desired.

21. The system of claim 20, wherein after monitoring the streams of labeled data selected as most quickly conveying the maximum number of the labeled object included in the different labeled data objects that are desired for a period of time, the machine instructions further cause the processor to instead monitor streams of labeled data selected by the processor as most likely to include any labeled object of the different labeled data objects that are desired.

22. The system of claim 21, wherein a change in the streams of labeled data that are monitored by the processor occurs when an expected coverage of the different labeled data objects that are desired has been maximized.

23. The system of claim 15, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the processor determines the subset of the plurality of streams of labeled data that are monitored selecting streams of labeled data that most frequently play a subset of more preferred desired labeled data objects from the plurality of different desired labeled data objects.

24. The system of claim 15, wherein the desired labeled data comprise a plurality of different desired labeled data objects, and wherein the processor determines the subset of the plurality of streams of labeled data that are monitored by selecting streams of labeled data that are most likely to include any of the different labeled data objects that are desired.

25. The system of claim 15, wherein the streams of labeled data comprise steams of audio data, and wherein the labels identify the audio data.

26. The system of claim 25, wherein the machine instructions further cause the processor to enable a user to store the desired labeled data that are detected, so that the desired labeled data that are thus stored may subsequently be played.

27. The system of claim 15, wherein the machine instructions further cause the processor to enable a user to selectively set a scope for monitoring the plurality of streams of labeled data so as to efficiently cover the plurality of streams of labeled data.