IP Library › Granted Patent US 12,361,049
Granted Patent B2
US 12,361,049 · App. 18/155,542 · Granted Jul 15, 2025

Dynamic content recommendations

Inventors: George Thomas Des Jardins (Washington, DC); Erik M. Schwartz (Kensington, MD)
Assignee: Comcast Cable Communications, LLC
G06F16/435
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Quick Facts
Patent No.
US 12,361,049
App. No.
18/155,542
Granted
Jul 15, 2025
Kind
B2
Abstract

According to some aspects, disclosed methods and systems may include determining, by a device and based on historical data associated with a first user, a first user profile comprising one or more content recommendation periods each associated with a time period and a content classification, and in response to detecting a user interaction, selecting a first content recommendation period of the one or more content recommendation periods. The methods and system may also include determining one or more content candidates corresponding to the content classification from a plurality of content assets based on an amount of remaining time in the time period associated with the first content recommendation period and a correlation between the historical data associated with the first user and one or more contextual features associated with the plurality of content assets, and transmitting, to a client device, an indication of the one or more content candidates.

Claims (136)

1. A method comprising:

determining, by a computing device based on data indicative of historical user interaction with one or more content assets, a plurality of profiles,

wherein each one of the plurality of profiles indicates a plurality of content recommendations, each one of the plurality of content recommendations being associated with a time period and a content type;

selecting a profile of the plurality of profiles;

determining a user interaction with a first device for outputting a content asset, the user interaction with the first device being via a second devices;

determining, based on the selected profile and based on a type of the second device, a content type;

determining, based on the determined content type, a content recommendation of the plurality of content recommendations of the selected profile;

determining a content asset based on a comparison of the determined content type of the content recommendation, to content types of a plurality of content assets; and

streaming the content asset to the first device in accordance with a communication protocol in response to the content asset being retrieved from a content source to cause the first device to automatically play the content asset.

2. The method of claim 1 , wherein determining the content asset is further based on a current time of day.

3. The method of claim 1 , further comprising receiving a request, wherein determining the first-candidate-content recommendation period-comprises determining the content recommendation based on a type of user interaction indicated by the request.

4. The method of claim 1 , further comprising causing display of a name of the content asset.

5. The method of claim 1 , further comprising ranking a plurality of content candidates, comprising the content asset, based on at least one of the plurality of profiles, wherein the determining the content asset is further based on the ranking.

6. The method of claim 1 , wherein the determining the content asset is further based on a remaining time in the content recommendation.

7. The method of claim 1 , wherein the determining the content recommendation period is further based on one or both of:

a user interaction associated with one of the plurality of profiles, or

a location of a user associated with one of the plurality of profiles.

8. The method of claim 1 , further comprising adjusting, based on weather, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

9. The method of claim 1 , further comprising adjusting, based on traffic, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

10. The method of claim 1 , further comprising adjusting, based on a command to stop playback of another content asset, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

11. The method of claim 1 , wherein the plurality of content recommendations, of the selected profile, are based on historical data that is associated with content previously accessed using the selected profile.

12. The method of claim 1 , wherein the selecting the profile comprises selecting the profile based on a frequency of use of the plurality of profiles on the first device by selecting, of the plurality of profiles, a profile having a highest frequency of device usage.

13. The method of claim 1 , wherein a time period of the content recommendation includes a time when the user interaction with the first device was detected.

14. An apparatus comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to perform:

determining, based on data indicative of historical user interaction with one or more content assets, a plurality of profiles, wherein each of the plurality of profiles indicates a plurality of content recommendations, each one of the plurality of content recommendations being associated with a time period and a content type;

selecting a profile of the plurality of profiles;

determining a user interaction with a first device for outputting a content asset, the user interaction with the first device being via a second device;

determining, based on the selected profile and based on a type of the second device, a content type;

determining, based on the determined content type, a content recommendation of the plurality of content recommendations of the selected profile;

determining a content asset based on a comparison of the determined content type of the content recommendation to a plurality of content types of a plurality of content assets; and

streaming the content asset to the first device in accordance with a communication protocol in response to the content asset being retrieved from a content source to cause the first device to automatically play the content asset.

15. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, cause the apparatus to determine the content asset further based on a current time of day.

16. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform:

receiving a request, wherein determining the content recommendation comprises determining the content recommendation period-based on a type of user interaction indicated by the request.

17. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, cause the apparatus to cause display of a name of the content asset.

18. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform:

ranking a plurality of content candidates, comprising the content asset, based on at least one of the plurality of profiles, wherein the determining the content asset is further based on the ranking.

19. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, cause the apparatus to determine the content asset further based on a remaining time in the content recommendation.

20. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, cause the apparatus to determine the content recommendation period further based on one or both of:

a user interaction associated with one of the plurality of profiles, or

a location of a user associated with one of the plurality of profiles.

21. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform:

adjusting, based on weather, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

22. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform:

adjusting, based on traffic, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

23. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform:

adjusting, based on a command to stop playback of another content asset, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

24. The apparatus of claim 14 , wherein the plurality of content recommendations, of the selected profile, are based on historical data that is associated with content previously accessed using the selected profile.

25. The apparatus of claim 14 , wherein the instructions, when executed by the one or more processors, cause the apparatus to select the profile, of the plurality of profiles, having a highest frequency of device usage.

26. A non-transitory computer-readable medium storing instructions that, when executed, configure a computing device to perform:

determining, based on data indicative of historical user interaction with one or more content assets, a plurality of profiles, wherein each of the plurality of profiles indicates a plurality of content recommendations, each one of the plurality of content recommendations being associated with a time period and a content type;

selecting a profile of the plurality of profiles;

determining a user interaction with a first device for outputting a content asset, the user interaction with the first device being via a second devices;

determining, based on the selected profile and based on a type of the second device, a content type;

determining, based on the determined content type, a content recommendation of the plurality of content recommendations of the selected profile;

determining a content asset based on a comparison of the determined content type of the content recommendation, to a content types of a plurality of content assets; and

streaming the content asset to the first device in accordance with a communication protocol in response to the content asset being retrieved from a content source to cause the first device to automatically play the content asset.

27. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, configure the computing device to determine the content asset further based on a current time of day.

28. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, further configure the computing device to perform:

receiving a request, wherein determining the content recommendation comprises determining the content recommendation based on a type of user interaction indicated by the request.

29. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, configure the computing device to cause display of a name of the content asset.

30. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, further configure the computing device to perform:

ranking a plurality of content candidates, comprising the content asset, based on at least one of the plurality of profiles, wherein the determining the content asset is further based on the ranking.

31. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, configure the computing device to determine the content asset further based on a remaining time in the content recommendation.

32. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, configure the computing device to determine the content recommendation further based on one or both of:

a user interaction associated with one of the plurality of profiles, or

a location of a user associated with one of the plurality of profiles.

33. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, further configure the computing device to perform:

adjusting, based on weather, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

34. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, further configure the computing device to perform:

adjusting, based on traffic, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

35. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, further configure the computing device to perform:

adjusting, based on a command to stop playback of another content asset, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

36. The non-transitory computer-readable medium of claim 26 , wherein the plurality of content recommendations, of the selected profile, are based on historical data that is associated with content previously accessed using the selected profile.

37. The non-transitory computer-readable medium of claim 26 , wherein the instructions, when executed, cause configure the computing device select the profile, of the plurality of profiles, having a highest frequency of device usage.

38. A system comprising:

a computing device comprising:

one or more processors; and

memory storing instructions; and

a first device comprising:

one or more processors; and

memory storing instructions,

wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, cause the computing device to perform:

determining, based on data indicative of historical user interaction with one or more content assets, a plurality of profiles, wherein each one of the plurality of profiles indicates a plurality of content recommendations, each one of the plurality of content recommendations being associated with a time period and a content type;

selecting a profile of the plurality of profiles;

determining a user interaction with the first device, the user interaction with the first device being via a second device;

determining, based on the selected profile and based on a type of the second device, a content type;

determining, based on the determined content type, a content recommendation of the plurality of content recommendations of the selected profile;

determining a content asset based on a comparison of the determined content type of the content recommendation to content types of a plurality of content assets; and

streaming the content asset to the first device in accordance with a communication protocol in response to the content asset being retrieved from a content source, and

wherein the instructions stored in the memory of the first device, when executed by the one or more processors of the first user-device, cause the user device to perform:

receiving the streamed content asset; and

performing one or both of automatically playing the content asset or recording the content asset.

39. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, cause the computing device to determine the content asset further based on a current time of day.

40. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, further cause the computing device to perform:

receiving a request, wherein determining the content recommendation comprises determining the content recommendation based on a type of user interaction indicated by the request.

41. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, further cause the computing device to perform:

ranking a plurality of content candidates, comprising the content asset, based on at least one of the plurality of profiles, wherein the determining the content asset is further based on the ranking.

42. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, cause the computing device to determine the content asset further based on a remaining time in the content recommendation.

43. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, cause the computing device to determine the content recommendation further based on one or both of:

a user interaction associated with one of the plurality of profiles, or

a location of a user associated with one of the plurality of profiles.

44. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, further cause the computing device to perform:

adjusting, based on weather, one or both of:

a start time of the content recommendation, or

an end time of the recommendation.

45. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, further cause the computing device to perform:

adjusting, based on traffic, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

46. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, further cause the computing device to perform:

adjusting, based on a command to stop playback of another content asset, one or both of:

a start time of the content recommendation, or

an end time of the content recommendation.

47. The system of claim 38 , wherein the plurality of content recommendations, of the selected profile, are based on historical data that is associated with content previously accessed using the selected profile.

48. The system of claim 38 , wherein the instructions stored in the memory of the computing device, when executed by the one or more processors of the computing device, cause the computing device to select the profile, of the plurality of profiles, having a highest frequency of user device usage.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 27, 2024
From: DES JARDINS, GEORGE THOMAS; SCHWARTZ, ERIK M.
To: COMCAST CABLE COMMUNICATIONS, LLC
Reel/Frame 067859/0362 →
Continuity (3)
Continuation 16993789 · Aug 14, 2020
Continuation 14310327 · Jun 20, 2014
Related Publication 20230229687A1 · Jul 20, 2023
References Cited (209)
US 6477123B1 · Hutter · 2002 [cited by applicant]
US 6782551B1 · Entwistle · 2004 [cited by applicant]
US 6925610B2 · Thurston et al. · 2005 [cited by applicant]
US 7509663B2 · Maynard et al. · 2009 [cited by applicant]
US 7624416B1 · Vandermolen et al. · 2009 [cited by applicant]
US 7680959B2 · Svendsen · 2010 [cited by applicant]
US 7757250B1 · Horvitz et al. · 2010 [cited by applicant]
US 8095432B1 · Berman et al. · 2012 [cited by applicant]
US 8180804B1 · Narayanan et al. · 2012 [cited by applicant]
US 8291321B2 · Matsubayashi · 2012 [cited by applicant]
US 8302127B2 · Klarfeld et al. · 2012 [cited by applicant]
US 8397257B1 · Barber · 2013 [cited by applicant]
US 8554640B1 · Dykstra et al. · 2013 [cited by applicant]
US 8555177B1 · Junee et al. · 2013 [cited by applicant]
US 8677415B2 · Angiolillo et al. · 2014 [cited by applicant]
US 8692763B1 · Kim · 2014 [cited by applicant]
US 8782121B1 · Chang · 2014 [cited by applicant]
US 8782122B1 · Chang · 2014 [cited by applicant]
US 8793735B2 · VanDuyn et al. · 2014 [cited by applicant]
US 8811685B1 · Bowers · 2014 [cited by examiner]
US 8856833B2 · Conness et al. · 2014 [cited by applicant]
US 8943529B2 · Kardatzke · 2015 [cited by applicant]
US 9147000B2 · Vasudevan et al. · 2015 [cited by applicant]
US 9183523B2 · Cudak et al. · 2015 [cited by applicant]
US 9246613B2 · McKee et al. · 2016 [cited by applicant]
US 9247300B2 · Oddo et al. · 2016 [cited by applicant]
US 9277472B1 · Price et al. · 2016 [cited by applicant]
US 9342576B2 · Saito et al. · 2016 [cited by applicant]
US 9407751B2 · Hansen et al. · 2016 [cited by applicant]
US 9426509B2 · Ellis et al. · 2016 [cited by applicant]
US 9554163B2 · Ellis et al. · 2017 [cited by applicant]
US 9626875B2 · Gal et al. · 2017 [cited by applicant]
US 9826034B2 · Chang · 2017 [cited by applicant]
US 9832501B2 · Ruffini et al. · 2017 [cited by applicant]
US 10034051B2 · Card, II · 2018 [cited by applicant]
US 11308157B1 · Rackley, III et al. · 2022 [cited by applicant]
US 11449901B1 · Rackley, III et al. · 2022 [cited by applicant]
US 11553251B2 · Leech et al. · 2023 [cited by applicant]
US 11570521B2 · Perez · 2023 [cited by applicant]
US 11589111B2 · Brenner et al. · 2023 [cited by applicant]
US 11605097B2 · Stephens et al. · 2023 [cited by applicant]
US 11688377B2 · Packouz · 2023 [cited by applicant]
US 11758212B2 · Gordon et al. · 2023 [cited by applicant]
US 11816703B1 · Rackley, III et al. · 2023 [cited by applicant]
US 11861561B2 · Foley et al. · 2024 [cited by applicant]
US 11887056B2 · Foley et al. · 2024 [cited by applicant]
US 20020004742A1 · Willcocks et al. · 2002 [cited by applicant]
US 20020004748A1 · Koga et al. · 2002 [cited by applicant]
US 20020085024A1 · White et al. · 2002 [cited by applicant]
US 20020152224A1 · Roth et al. · 2002 [cited by applicant]
US 20020152459A1 · Bates et al. · 2002 [cited by applicant]
US 20020199188A1 · Sie et al. · 2002 [cited by applicant]
US 20030084448A1 · Soundararajan · 2003 [cited by applicant]
US 20030145338A1 · Harrington · 2003 [cited by applicant]
US 20030233278A1 · Marshall · 2003 [cited by applicant]
US 20040031046A1 · Weinblatt et al. · 2004 [cited by applicant]
US 20040122736A1 · Strock et al. · 2004 [cited by applicant]
US 20040203901A1 · Wilson et al. · 2004 [cited by applicant]
US 20040230484A1 · Greenlee · 2004 [cited by applicant]
US 20040244030A1 · Boyce et al. · 2004 [cited by applicant]
US 20050049858A1 · Busayapongchai · 2005 [cited by applicant]
US 20050114340A1 · Huslak et al. · 2005 [cited by applicant]
US 20050192000A1 · Lloyd · 2005 [cited by applicant]
US 20050285739A1 · Velhal · 2005 [cited by examiner]
US 20060031882A1 · Swix et al. · 2006 [cited by applicant]
US 20060041478A1 · Zheng · 2006 [cited by applicant]
US 20060047678A1 · Miyazaki et al. · 2006 [cited by applicant]
US 20060048184A1 · Poslinski et al. · 2006 [cited by applicant]
US 20060143236A1 · Wu · 2006 [cited by applicant]
US 20060143653A1 · Suh · 2006 [cited by applicant]
US 20060149617A1 · Yamashita et al. · 2006 [cited by applicant]
US 20060156329A1 · Treese · 2006 [cited by applicant]
US 20060176398A1 · Kang · 2006 [cited by applicant]
US 20060225088A1 · Gutta · 2006 [cited by applicant]
US 20060277100A1 · Parham · 2006 [cited by applicant]
US 20070061863A1 · Rajasekaran · 2007 [cited by applicant]
US 20070154168A1 · Cordray et al. · 2007 [cited by applicant]
US 20070192723A1 · Anzelde et al. · 2007 [cited by applicant]
US 20070196795A1 · Groff · 2007 [cited by applicant]
US 20070207780A1 · McLean · 2007 [cited by applicant]
US 20070294131A1 · Roman et al. · 2007 [cited by applicant]
US 20080055272A1 · Anzures et al. · 2008 [cited by applicant]
US 20080083001A1 · Peacock et al. · 2008 [cited by applicant]
US 20080177617A1 · Gupta · 2008 [cited by applicant]
US 20080209229A1 · Ramakrishnan et al. · 2008 [cited by applicant]
US 20080243733A1 · Black · 2008 [cited by applicant]
US 20080276273A1 · Billmaier et al. · 2008 [cited by applicant]
US 20080320139A1 · Fukuda et al. · 2008 [cited by applicant]
US 20090019375A1 · Garofalo · 2009 [cited by applicant]
US 20090021474A1 · Bentley · 2009 [cited by applicant]
US 20090083116A1 · Svendsen · 2009 [cited by applicant]
US 20090083779A1 · Shteyn et al. · 2009 [cited by applicant]
US 20090089352A1 · Davis et al. · 2009 [cited by applicant]
US 20090100463A1 · St. John-Larkin · 2009 [cited by applicant]
US 20090119294A1 · Purdy et al. · 2009 [cited by applicant]
US 20090132935A1 · Van Zwol · 2009 [cited by applicant]
US 20090144635A1 · Miyazaki et al. · 2009 [cited by applicant]
US 20090158326A1 · Hunt et al. · 2009 [cited by applicant]
US 20090172161A1 · Singh · 2009 [cited by applicant]
US 20090216577A1 · Killebrew · 2009 [cited by applicant]
US 20090234876A1 · Schigel et al. · 2009 [cited by applicant]
US 20090281888A1 · Zai et al. · 2009 [cited by applicant]
US 20090300670A1 · Barish · 2009 [cited by applicant]
US 20100023506A1 · Sahni et al. · 2010 [cited by applicant]
US 20100031162A1 · Wiser et al. · 2010 [cited by applicant]
US 20100058376A1 · Alhadeff et al. · 2010 [cited by applicant]
US 20100083115A1 · Park · 2010 [cited by applicant]
US 20100122207A1 · Kim et al. · 2010 [cited by applicant]
US 20100138867A1 · Wong et al. · 2010 [cited by applicant]
US 20100162289A1 · Sanders · 2010 [cited by applicant]
US 20100229212A1 · Liu et al. · 2010 [cited by applicant]
US 20100241699A1 · Muthukumarasamy et al. · 2010 [cited by applicant]
US 20100250336A1 · Selinger et al. · 2010 [cited by applicant]
US 20100250341A1 · Hauser · 2010 [cited by applicant]
US 20100251305A1 · Kimble et al. · 2010 [cited by applicant]
US 20100287033A1 · Mathur · 2010 [cited by applicant]
US 20100293034A1 · Olejniczak et al. · 2010 [cited by applicant]
US 20100306401A1 · Gilson · 2010 [cited by applicant]
US 20100318611A1 · Curtin et al. · 2010 [cited by applicant]
US 20100325544A1 · Alhadeff et al. · 2010 [cited by applicant]
US 20100325646A1 · Alhadeff et al. · 2010 [cited by applicant]
US 20100332330A1 · Goel et al. · 2010 [cited by applicant]
US 20110035445A1 · Eickhoff · 2011 [cited by applicant]
US 20110124408A1 · Ward · 2011 [cited by applicant]
US 20110154213A1 · Wheatley et al. · 2011 [cited by applicant]
US 20110161148A1 · Schmidt · 2011 [cited by applicant]
US 20110184792A1 · Butcher et al. · 2011 [cited by applicant]
US 20110231265A1 · Brown et al. · 2011 [cited by applicant]
US 20110246908A1 · Akram · 2011 [cited by applicant]
US 20110264494A1 · Lechowicz · 2011 [cited by applicant]
US 20110276882A1 · Buehler et al. · 2011 [cited by applicant]
US 20110312375A1 · Kim et al. · 2011 [cited by applicant]
US 20120047008A1 · Alhadeff et al. · 2012 [cited by applicant]
US 20120064820A1 · Bemmel · 2012 [cited by applicant]
US 20120089705A1 · French et al. · 2012 [cited by applicant]
US 20120120094A1 · Oiwa et al. · 2012 [cited by applicant]
US 20120243850A1 · Basra et al. · 2012 [cited by applicant]
US 20130046772A1 · Gu et al. · 2013 [cited by applicant]
US 20130057765A1 · Zeleznikar · 2013 [cited by applicant]
US 20130103628A1 · Skelton · 2013 [cited by examiner]
US 20130125161A1 · Herby et al. · 2013 [cited by applicant]
US 20130268966A1 · Foote · 2013 [cited by applicant]
US 20130297447A1 · Sakata · 2013 [cited by applicant]
US 20130322850A1 · Chang et al. · 2013 [cited by applicant]
US 20130325655A1 · Wouhaybi et al. · 2013 [cited by applicant]
US 20140005814A1 · Hwang · 2014 [cited by examiner]
US 20140006399A1 · Vasudevan · 2014 [cited by examiner]
US 20140019902A1 · DeLuca et al. · 2014 [cited by applicant]
US 20140033240A1 · Card, II · 2014 [cited by examiner]
US 20140046660A1 · Kamdar · 2014 [cited by applicant]
US 20140075352A1 · Hansen · 2014 [cited by examiner]
US 20140075385A1 · Wan et al. · 2014 [cited by applicant]
US 20140089980A1 · Alexander et al. · 2014 [cited by applicant]
US 20140123160A1 · van Coppenolle et al. · 2014 [cited by applicant]
US 20140130076A1 · Moore et al. · 2014 [cited by applicant]
US 20140132524A1 · Lee · 2014 [cited by applicant]
US 20140149424A1 · Kalmes et al. · 2014 [cited by applicant]
US 20140173435A1 · Arnold · 2014 [cited by applicant]
US 20140201766A1 · Stepanov et al. · 2014 [cited by applicant]
US 20140208362A1 · Alexander et al. · 2014 [cited by applicant]
US 20140215506A1 · Kalmes et al. · 2014 [cited by applicant]
US 20140331260A1 · Gratton · 2014 [cited by applicant]
US 20140344861A1 · Berner · 2014 [cited by examiner]
US 20150003595A1 · Yaghi et al. · 2015 [cited by applicant]
US 20150012416A1 · Woods · 2015 [cited by examiner]
US 20150066652A1 · Collins et al. · 2015 [cited by applicant]
US 20150089517A1 · Ruffini et al. · 2015 [cited by applicant]
US 20150121408A1 · Jacoby et al. · 2015 [cited by applicant]
US 20150133164A1 · Song et al. · 2015 [cited by applicant]
US 20150143394A1 · Hijikata · 2015 [cited by examiner]
US 20150148005A1 · Chau et al. · 2015 [cited by applicant]
US 20150169189A1 · Want et al. · 2015 [cited by applicant]
US 20150195621A1 · Harron et al. · 2015 [cited by applicant]
US 20150199708A1 · Ying et al. · 2015 [cited by applicant]
US 20150287219A1 · Liang et al. · 2015 [cited by applicant]
US 20150304718A1 · Cui et al. · 2015 [cited by applicant]
US 20150319468A1 · Park et al. · 2015 [cited by applicant]
US 20150350709A1 · Tomita · 2015 [cited by applicant]
US 20160019017A1 · Walker et al. · 2016 [cited by applicant]
US 20160063998A1 · Krishnamoorthy et al. · 2016 [cited by applicant]
US 20210250656A1 · Perez · 2021 [cited by applicant]
US 20230308700A1 · Perez · 2023 [cited by applicant]
EP 1454484B1 · 2004 [cited by applicant]
EP 1901475A2 · 2008 [cited by applicant]
WO 2007143562A2 · 2007 [cited by applicant]
WO 2009012048A2 · 2009 [cited by applicant]
WO 2011002572A1 · 2011 [cited by applicant]
Liu, Ning-Han, Szu-Wei Lai, Chien-Yi Chen, and Shu-Ju Hsieh. “Adaptive music recommendation based on user behavior in time slot.” International Journal of Computer Science and Network Security 9, No. 2 (2009): 219-227. … [cited by examiner]
Entertainment Fiend: Keep Track of Watched TV Show Episodes and Movies and Plan What to Watch! dated Apr. 11, 2008, http://entertainmentfiend.blogspot.com/2008/04/keep-track-of-watched-tv-show-episodes.html, retrieved J… [cited by applicant]
Entertainment Fiend: Keep Track of Watched TV Show & Movies and Plan What to Watch—Updated for 2011 Edition, dated Oct. 13, 2011, http://entertainmentfiend.blogspot.com/2011/10/keeping-track-of-watched-tv-shows.html, re… [cited by applicant]
Alan Henry. How to Track All Your TV Shows So You Never Miss an Episode, Lifehacker, http://lifehacker/how-to-track-all-your-tv-shows-so-you-never-miss-an-epi-1180239026, 2013, 11 pages. [cited by applicant]
Thorin Klosowski. Netflix Profiles Ensure Roommates Won't Mess Up Your Recommendations, Lifehacker, http://lifehacker.com/netflix-profiles-ensure-roommates-wont-mess-up-your-re-987927584, dated Aug. 1, 2013, 11 pages. [cited by applicant]
European Office Action—EP Appl. 11166793.7—dated Oct. 8, 2015. [cited by applicant]
Canadian Office Action—CA Application No. 2740650—dated Feb. 28, 2017. [cited by applicant]
European Summons to Oral Proceedings—EP Appl. 11166793.7—dated Apr. 12, 2017. [cited by applicant]
Feb. 2, 2018—Canadian Office Action—CA 2,740,650. [cited by applicant]
EP 11166793.9-221 Search Report dated Aug. 5, 2011. [cited by applicant]
Feb. 21, 2019—Canadian Office Action—CA 2,740,650. [cited by applicant]
Mar. 12, 2020—Canadian Office Action—CA 2,740,650. [cited by applicant]
R. Gaglianello, L. Spergel and D. Liu, “Convergence of telephony and television: More than a computer on your TV,” 2009 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, Bilbao, 2009, pp. 1-… [cited by applicant]
J. B. Carlucci, “Social Media Television in Today's Cable Systems,” 2010 7th IEEE Consumer Communications and Networking Conference, Las Vegas, NV, 2010, pp. 1-5, doi: 10.1109/CCNC.2010.5421636. (Year: 2010). [cited by applicant]
D. C. A. Bulterman, p. Cesar, A. J. Jansen, H. Knoche and W. Seager, “Enabling Pro-Active User-Centered Recommender Systems: An Initial Evaluation,” Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007)… [cited by applicant]
P. Cesar, D. C. A. Bulterman and A. J. Jansen, “Social Sharing of Television Content: An Architecture,” Ninth IEEE International Symposium on Multimedia Workshops (ISMW 2007), Beijing, 2007, pp. 145-150, doi: 10.1109/IS… [cited by applicant]
E. Larson and S. Taulu, “Reducing Sensor Noise in MEG and EEG Recordings Using Oversampled Temporal Projection,” in IEEE Transactions on Biomedical Engineering, vol. 65, No. 5, pp. 1002-1013, May 2018, doi: 10.1109/TBME… [cited by applicant]
M. Schroder et al., “Building Autonomous Sensitive Artificial Listeners,” in IEEE Transactions on Affective Computing, vol. 3, No. 2, pp. 165-183, Apr.-Jun. 2012, doi: 10.1109/T-AFFC.2011.34. (Year: 2011). [cited by applicant]
Aug. 17, 2022—CA Office Action—CA App. No. 3122547. [cited by applicant]
N. Howard, “Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions,” 2013 12th Mexican International Conference on Artificial Intelligence, Mexico, Mexico, 2013, pp. 234-243, … [cited by applicant]
Wiggins, Grant. “7 Keys to Effective Feedback”, Educational Leadership / Sep. 2012, ASCD / www.ascd.org. [cited by applicant]
Xia, Weiwei, A new collaborative filtering approach utilizing item's popularity, Dec. 1, 2008, 2008 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1480-1484 (Year: 2008). [cited by applicant]