Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This Office Action is in response to an AMENDMENT entered on January 8, 2026 for patent application 18/141,727 filed on May 1, 2023.
Claims 1-20 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,678,001. Although the claims at issue are not identical, they are not patentably distinct from each other because both disclose collecting usage data information, determining a data cap for the locations, training a machine learning model to associate the usage data information with one of the one or more users based on how the one of the one or more users handles a remote control device to control the plurality of media playback devices, predicting, using the machine learning model, whether the location is going to exceed its associated data cap, and determining whether to modulate a quality of media content transmitted to the location based on the prediction.
Instant Application:
1. (Currently Amended) A computer-implemented method, comprising: collecting usage data information from one or more locations, the usage data comprising an indication of how frequent a first media playback device, of a plurality of media playback devices that are located at the one or more locations, that is associated with relatively high consumption of data than compared to a second media device of the plurality of media playback devices is utilized by one or more users; determining a data cap for the one or more locations; training a machine learning model using the usage data information and the data cap to associate the usage data information with at least one of the one or more users based on how the at least one of the one or more users handles a remote control device to control the first media playback device; predicting, using the machine learning model, whether at least one of the one or more locations is going to exceed its associated data cap based on the usage data information associated with the at least one of the one or more users, and determining to modulate a quality of media content transmitted to the second at least one of the one or more locations based on the prediction.
2. (Currently Amended) The method of claim 1, wherein determining to modulate the quality of the media content comprises automatically modulating the quality of the media content in response to the machine learning model predicting that the at least one of the one or more locations is going to exceed the data cap.
3. (Original) The method of claim 2, wherein automatically modulating the quality of the media content comprises automatically modulating the quality of the media content when a current consumption exceeds a percentage of the data cap.
4. (Currently Amended) The method of claim 1, further comprising: notifying the at least one of the one or more users at the at least one of the one or more locations that the at least one of the one or more locations is predicted to exceed the data cap; and prompting the at least one of the one or more users to select whether or not to modulate the quality of the media content, wherein determining to modulate the quality of the media content is based on a selection of the at least one of the one or more users.
5. (Original) The method of claim 1, wherein collecting the usage data information comprises continuously collecting usage data information, and wherein the method further comprises training the machine learning model based on the continuously collected usage data information.
6. (Currently Amended) The method of claim 1, wherein predicting that the at least one of the one or more locations is going to exceed the data cap is further based on an identified cluster in which the at least one of the one or more locations is included.
7. (Currently Amended) The method of claim 1, wherein predicting that the at least one of the one or more locations is going to exceed the data cap is further based on a determined increase of users at the at least one of the one or more locations.
8. (Currently Amended) The method of claim 1, wherein predicting that the at least one of the one or more locations is going to exceed the data cap is further based on historical usage at other locations having a comparable profile as the at least one of the one or more locations.
9. (Currently Amended) A system, comprising: a memory; and at least one processor, communicatively coupled to the memory, configured to execute the instructions, the instructions causing the at least one processor to: collect usage data information from one or more locations, the usage data comprising an indication of how frequent a media playback device, of a plurality of media playback devices that are located at the one or more locations, that is associated with relatively high consumption of data than compared to a second media device of the plurality of media playback devices is utilized by one or more users; determine a data cap for the one or more first locations;
train a machine learning model using the usage data information and the data cap to associate the usage data information with at least one of the one or more users based on how the at least one of the one or more users handles a remote control device to control the first media playback device;
predict, using the machine learning model, whether at least one of the one or more locations is going to exceed its associated data cap based on the usage data information associated with the at least one of the one or more user; and determine to modulate a quality of media content transmitted to the at least one of the one or more locations based on the prediction.
10. (Currently Amended) The system of claim 9, wherein to determine to modulate the quality of the media content, the instructions cause the at least one processor to automatically modulate the quality of the media content in response to the machine learning model predicting that the at least one of the one or more locations is going to exceed the data cap.
11. (Previously Presented) The system of claim 10, wherein to automatically modulate the quality of the media content, the instructions cause the at least one processor to automatically modulate the quality of the media content when a current consumption exceeds a percentage of the data cap.
12. (Currently Amended) The system of claim 9, wherein the instructions further cause the at least one processor to: notify the at least one of the one or more users at the at least one of the one or more locations that the at least one of the one or more locations is predicted to exceed the data cap; and cause a prompt to be displayed to the at least one of the one or more users, the prompt including a selection as to whether or not to modulate the quality of the media content, wherein to determine to modulate the quality of the media content is based on the selection.
13. (Previously Presented) The system of claim 9, wherein to collect the usage data information, the instructions cause the at least one processor to continuously collect usage data information, and wherein the instructions further cause the at least one processor to train the machine learning model based on the continuously collected usage data information.
14. (Currently Amended) The system of claim 9, wherein to predict that the at least one of the one or more locations is going to exceed the data cap is further based on an identified cluster in which the at least one of the one or more locations is included.
15. (Currently Amended) The system of claim 14, wherein to predict that the at least one of the one or more locations is going to exceed the data cap is further based on a determined increase of users at the at least one of the one or more locations.
16. (Previously Presented) The system of claim 9, wherein to predict that at least one of the one or more second locations is going to exceed the data cap is further based on historical usage at other locations having a comparable profile as the at least one of the one or more second locations.
17. (Currently Amended) A non-transitory, tangible computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: collecting usage data information from one or more locations, the usage data comprising an indication of how frequent a media playback device, of a plurality of media playback devices that are located at the one or more locations, that is associated with relatively high consumption of data than compared to a second media device of the plurality of media playback devices is utilized by one or more users; determining a data cap for the one or more first locations; training a machine learning model using the usage data information and the data cap to associate the usage data information with at least one of the one or more users based on how the at least one of the one or more users handles a remote control device to control the first media playback device;
predicting, using the machine learning model, whether at least one of the one or more locations is going to exceed its associated data cap based on the usage data information associated with the at least one of the one or more users,; and determining to modulate a quality of media content transmitted to the at least one of the one or more locations based on the prediction.
18. (Currently Amended) The non-transitory, tangible computer-readable medium of claim 17, wherein determining to modulate the quality of the media content comprises automatically modulating the quality of the media content when a current consumption exceeds a percentage of the data cap in response to the machine learning model predicting that the at least one of the one or more locations is going to exceed the data cap.
19. (Currently Amended) The non-transitory, tangible computer-readable medium of claim 17, wherein predicting that the at least one of the one or more locations is going to exceed the data cap is further based on an identified cluster in which the at least one of the one or more locations is included.
20. (Currently Amended) The non-transitory, tangible computer-readable medium of claim 17, wherein predicting that the at least one of the one or more locations is going to exceed the data cap is further based on historical usage at other locations having a comparable profile as the at least one of the one or more locations.
Patent 11,678,001:
1. A computer-implemented method comprising: collecting, by at least one server, usage data information from a plurality of locations, wherein at least a location of the plurality of locations includes a plurality of media playback devices accessible by one or more users and coupled to a network device located at the location that is further coupled to the at least one server through a network; determining, by the at least one server, a data cap for each of the plurality of locations including the location based on a notification received through the network from the network device located at the location that is coupled to the plurality of media playback devices at the location; training a machine learning model using multiple usage data information and data caps including the usage data information and the data cap received from each of the plurality of locations to associate the usage data information with one of the one or more users based on how the one of the one or more users handles a remote control device to control the plurality of media playback devices; predicting, using the machine learning model, based on the usage data information associated with the one of the one or more users, whether the location is going to exceed its associated data cap; and determining whether to modulate a quality of media content transmitted to the location based on the prediction.
2. The method of claim 1, wherein determining whether to modulate the quality of the media content comprises automatically modulating the quality of the media content in response to the machine learning model predicting that the location is going to exceed the data cap.
3. The method of claim 2, wherein automatically modulating the quality of the media content comprises automatically modulating the quality of the media content when a current consumption exceeds a percentage of the data cap.
4. The method of claim 1, further comprising: notifying a user at the location that the location is predicted to exceed the data cap; and prompting the user to select whether or not to modulate the quality of the media content, wherein determining whether to modulate the quality of the media content is based on the user selection.
5. The method of claim 1, wherein collecting the usage data information comprises continuously collecting usage data information, and wherein the method further comprises training the machine learning model based on the continuously collected usage data information.
6. The method of claim 1, wherein predicting whether the location is going to exceed the data cap is based on a historical usage at the location.
7. The method of claim 6, wherein predicting whether the location is going to exceed the data cap is based on the historical usage at the location and one or more circumstances of data usage.
8. The method of claim 1, wherein predicting whether the location is going to exceed the data cap is based on historical usage at other locations from among the plurality of locations having a comparable profile as the location.
9. A system, comprising: at least one processor configured to execute instructions, the instructions causing the at least one processor to: collect usage data information from a plurality of locations, wherein at least a location of the plurality of locations includes a plurality of media playback devices accessible by one or more users and coupled to a network device located at the location that is further coupled to the system through a network; determine a data cap for each of the plurality of locations including the location based on a notification received through the network from the network device located at the location that is coupled to the plurality of media playback devices at the location; train a machine learning model using multiple usage data information and data caps including the usage data information and the data cap received from each of the plurality of locations to associate the usage data information with one of the one or more users based on how the one of the one or more users handles a remote control device to control the plurality of media playback devices; predict, using the machine learning model, based on the usage data information associated with the one of the one or more users, whether the location is going to exceed its associated data cap; and determine whether to modulate a quality of media content transmitted to the location based on the prediction.
10. The system of claim 9, wherein to determine whether to modulate the quality of the media content, the instructions cause the at least one processor to automatically modulate the quality of the media content in response to the machine learning model predicting that the location is going to exceed the data cap.
11. The system of claim 10, wherein to automatically modulate the quality of the media content, the instructions cause the at least one processor to automatically modulate the quality of the media content when a current consumption exceeds a percentage of the data cap.
12. The system of claim 9, wherein the instructions further cause the at least one processor to: notify a user at the location that the location is predicted to exceed the data cap; and cause a prompt to be displayed to the user, the prompt including a selection as to whether or not to modulate the quality of the media content, wherein to determine whether to modulate the quality of the media content is based on the user selection.
13. The system of claim 9, wherein to collect the usage data information, the instructions cause the at least one processor to continuously collect usage data information, and wherein the instructions further cause the at least one processor to train the machine learning model based on the continuously collected usage data information.
14. The system of claim 9, wherein the location is one of the plurality of locations, and wherein to predict whether the location is going to exceed the data cap is based on a historical usage at the location.
15. The system of claim 14, wherein to predict whether the location is going to exceed the data cap is based on the historical usage at the location and one or more circumstances of data usage.
16. The system of claim 9, wherein to predict whether the location is going to exceed the data cap is based on historical usage at other locations from among the plurality of locations having a comparable profile as the location.
17. A non-transitory, tangible computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: collecting usage data information from a plurality of locations, wherein at least a location of the plurality of locations includes a plurality of media playback devices accessible by one or more users and coupled to a network device located at the location that is further coupled to the at least one computing device through a network; determining a data cap for each of the plurality of locations including the location based on a notification received through the network from the network device located at the location that is coupled to the plurality of media playback devices at the location; training a machine learning model using multiple usage data information and data caps including the usage data information and the data cap received from each of the plurality of locations to associate the usage data information with one of the one or more users based on how the one of the one or more users handles a remote control device to control the plurality of media playback devices; predicting, using the machine learning model, based on the usage data information associated with the one of the one or more users, whether the location is going to exceed its associated data cap; and determining whether to modulate a quality of media content transmitted to the location based on the prediction.
18. The non-transitory, tangible computer-readable medium of claim 17, wherein, in response to the machine learning model predicting that the location is going to exceed the data cap, determining whether to modulate the quality of the media content comprises automatically modulating the quality of the media content when a current consumption exceeds a percentage of the data cap.
19. The non-transitory, tangible computer-readable medium of claim 17, wherein the location is one of the plurality of locations, and wherein predicting whether the location is going to exceed the data cap is based on a historical usage at the location.
20. The non-transitory, tangible computer-readable medium of claim 17, wherein predicting whether the location is going to exceed the data cap is based on historical usage at other locations from among the plurality of locations having a comparable profile as the location.
Conclusion
Claims 1-20 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Joshua D Taylor/Primary Examiner, Art Unit 2426 March 6, 2026