Interactive user-prediction of content
A method including providing at least one of a television service or an Internet access service to a customer; generating predictive options pertaining to content delivered to the customer, wherein the predictive options correspond to possible future events that could occur with respect to the content; receiving a customer selection of one of the predictive options; determining whether the customer-selected predictive option matches an outcome with respect to the content; and assigning a score to the customer based on whether the selected predictive option successfully or unsuccessfully predicted the outcome.
1. A method comprising:
providing at least one of a television service or an Internet access service to a user device associated with a customer;
generating predictive options pertaining to content delivered via the at least one of the television service or the Internet access service, wherein the predictive options correspond to possible future events that could occur with respect to the content;
determining multiple sets of the predictive options based on a different time granularity for each set of the predictive options, wherein the different time granularities are configured by the customer;
providing, at particular points in the content and for presentation via a user interface of the user device, a different set of the predictive options that share a common time-granularity corresponding to a particular future event with respect to the content;
receiving, from the user device, a customer selection of one of the presented predictive options;
determining an amount of correspondence of the customer-selected predictive option to an actual outcome with respect to the content; and
assigning a score to the customer based on the amount of correspondence of the customer-selected predictive option to the actual outcome.
2. The method of claim 1 , further comprising:
receiving at least one of data or information pertaining to the content; and
interpreting the at least one of data or information, and wherein the generating of the predictive options is based on the interpreted data or information.
3. The method of claim 2 , further comprising:
identifying the actual outcome based on the interpreted data or information.
4. The method of claim 1 , wherein the content includes television content including sports content, entertainment content, court show content, and political show content.
5. The method of claim 1 , further comprising:
identifying the customer when the customer uses the user device to receive the at least one of the television service or the Internet access service.
6. The method of claim 1 , further comprising:
receiving a customer-selection to post the score to a social network site.
7. The method of claim 1 , wherein when the content corresponds to sports content, the predictive options are directed to a player, a next play, or an outcome of a game.
8. The method of claim 1 , wherein the generating comprises performing at least one of quantitative data analysis or qualitative data analysis of data or information pertaining to the content.
9. A device comprising:
a memory storing instructions; and
one or more processors to execute the instructions to:
generate predictive options pertaining to television content delivered to a user device associated with a customer, wherein the predictive options correspond to events that could occur with respect to the television content;
determine multiple sets of the predictive options based on a different time granularity for each set of the predictive options, wherein the different time granularities are configured by the customer;
provide, at particular points in the television content and for presentation via a user interface of the user device, a different set of the predictive options that share a common time-granularity corresponding to a particular future event with respect to the television content;
receive, from the user device, a customer selection of one of the presented predictive options;
determine an amount of correspondence of the customer-selected predictive option to an actual outcome with respect to the television content; and
assign a score to the customer based on the amount of correspondence of the customer-selected predictive option to the actual outcome.
10. The device of claim 9 , wherein when generating the predictive options, the one or more processors are further configured to:
receive from a data or information source at least one of data or information characterizing the television content; and
interpret the at least one of data or information.
11. The device of claim 10 , wherein the data includes closed captioning for the hearing impaired data.
12. The device of claim 9 , wherein when determining, the one or more processors are further configured to:
identify the actual outcome; and
compare the customer-selected predictive option to the identified outcome.
13. The device of claim 9 , wherein the television content pertains to entertainment, politics, sports, news, or a television series.
14. The device of claim 9 , wherein the television content is a football game, and the predictive options are player-specific, team-specific, or play-specific.
15. The device of claim 9 , wherein the one or more processors comprise:
a quantitative data analyzer; and
a qualitative data analyzer.
16. A non-transitory computer-readable medium comprising executable instructions for execution by at least one processing system, the instructions causing the at least one processing system to:
generate predictive options pertaining to content delivered to a user device associated with a customer, wherein the predictive options correspond to events that could occur with respect to the content;
determine multiple sets of the predictive options based on a different time granularity associated with each set of the predictive options;
provide, at particular points in the content and for presentation via a user interface of the user device, a different set of the predictive options that share a common time-granularity corresponding to a particular point in the content, wherein the common time-granularity is user-configurable;
receive, from the user device, a customer selection of one of the presented predictive options;
determine an amount of correspondence of the customer-selected predictive option to an actual outcome associated with the content; and
provide a result for display to the customer based on the amount of correspondence of the customer-selected predictive option to the actual outcome.
17. The non-transitory computer-readable medium of claim 16 , wherein the instructions further cause the at least one processing system to:
interpret closed captioning for the hearing impaired data, and wherein the generating is based on the interpreted closed captioning for the hearing impaired data.
18. The non-transitory computer-readable medium of claim 16 , wherein the content includes television content.
19. The non-transitory computer-readable medium of claim 18 , wherein the television content is a live broadcast of a sporting event.
20. The non-transitory computer-readable medium of claim 16 , the instructions further causing the at least one processing system to:
receive a selection, by the customer, to post the result to a social network site.