Response to Arguments
Applicant's arguments filed August 22, 2025 have been fully considered but they are not persuasive. Claims 1, 9, 10 - 14, 16 - 18, have been amended. Claims 1-20 are pending and presented for examination.
Applicant’s arguments regarding 35 U.S.C. 101 rejection have been fully considered and are persuasive.
Applicant modifications has been determined to have overcome the 101 rejection, and it has been dropped.
Applicant’s arguments regarding 35 U.S.C. 102 rejection have been fully considered but they are not persuasive.
Regarding claim 1, Applicant highlights this quotation from Claim 1:
"Obtaining … resource utilization data defining resource categories…. And indicative of which resources likely pertain to shared involvement" (page 12)
Regarding the argument that Lewis does not recite the proper structure, Examiner respectfully disagrees. First, the claims do not illustrate the interpretation of how the resource utilization data / resource categories and the resource scores are utilized in determining its influence of devices within the cohort. Lewis recites obtaining groups of devices that are assigned to a specific user. Further Lewis removes devices whose relevance is not associated with the specific user and those that are – develops cohorts of those devices based on their usage / user activity / other relevant information that deems whether the device is active and keeps its feed or removed and loses its feed. (paragraph 27, splitting the devices into groups according to the specific client associated, paragraph 18, 23, 49-53 62, 72-73, removing devices that that user does not want to be streaming, removing it from the bucket of active resources, paragraph 31, the user device being put into a bucket according to their desired feed). This reasonably reads on the recited limitation as articulated above – the claim language does not provide a BRI on how the scores influence the devices within the cohort.
Further, Applicant recites: "Receiving… an input involving user interaction with an application executed on a user’s device… in at least one said respective bucket" (page 13).
Regarding the argument that Lewis does not recite the proper structure, Examiner respectfully disagrees. Lewis recites obtaining groups of devices that are assigned to a specific user, depending on the scores for use of the cohort bucket. (paragraph 44-46, each device having a confidence level, which is influenced by the behaviors of other devices in the cohort, then, paragraph 18, 23, 49-53 62, 72-73, removing devices that that user does not want to be streaming, removing it from the bucket of active resources, paragraph 31, the user device being put into a bucket according to their desired feed)
The complete rejection can be found below and claims 1-20 are found to not be patentable over the recited art. The full detail of the analysis is in the amended 35 U.S.C. 102 and 35 U.S.C. 103 rejection below.
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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 4-6, 9-13, 16- 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by over US 20210400324 A1 (Lewis, et. al).
Regarding claim 1, Lewis recites, A method implemented by a processing device, the method comprising: receiving, by the processing device, an input involving user interaction with an application executed on a user’s device (paragraph 60-61, obtaining user input streams for training the model); detecting, by the processing device, a cohort of devices from a plurality of devices, the cohort having a shared characteristic that defines membership in the cohort (paragraph 27, splitting the devices into groups according to the specific client associated); obtaining, by the processing device, resource utilization data defining resource categories and including resource scores based on an influence of devices within the cohort and overall influence of the plurality of devices (paragraph 17, 44, 49, a resource score associated with the device that the user wants to use, keeping devices that the user intends to use connected, using a confidence score); filtering, by the processing device, the resource scores by removing resource scores of resources that are not identified as pertaining to shared involvement within the cohort of devices and indicative of which resources likely pertain to shared involvement (paragraph 18, 23, 49-53 62, 72-73, removing devices that that user does not want to be streaming, removing it from the bucket of active resources, paragraph 31, the user device being put into a bucket, e.g. determination of whether to automatically keep feed / automatically delete feed / request whether to keep feed category); assembling, by the processing device, the filtered resource scores into respective buckets based on the resources; and sharing, by the processing device, the user interaction with the application as executed by the user’s device for output by the cohort of devices based on resource scores included in at least one said respective bucket (paragraph 60-61, taking user information, and sharing it to the model, and then, after creating the buckets, paragraph 17,44, 49, paragraph 19, 32, 43, delivering content information to the selected device).
Regarding claim 4, Lewis recites, the method as described in claim 1, wherein the detecting includes detecting the shared characteristic that defines membership in the cohort using a machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model; see also paragraphs 18, 23, 49-53 62, 72-73).
Regarding claim 5, Lewis recites, the method as described in claim 1, further comprising generating the resource utilization data defining the resource categories and including the resource scores (paragraph 49, scores associated with the device, which include data associated with the user information; ; see also paragraphs 18, 23, 49-53 62, 72-73).
Regarding claim 6, Lewis recites, the method as described in claim 5, wherein the generating is performed using a machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model; ; see also paragraphs 18, 23, 49-53 62, 72-73).
Regarding claim 9, Lewis recites, the method as described in claim 1, wherein the sharing includes controlling transmission of digital content to the cohort of devices via a network (paragraphs 18-19, 23, 32, 49-53 62, 72-73, delivering content information to the selected device).
Regarding claim 10, Lewis recites, the method as described in claim 1, wherein the sharing includes provisioning hardware and software resources of computing devices of a service provider system (paragraphs 17, 27, 49-53 and 96, controlling resources provided to the user, using a content service provider system).
Regarding claim 11, Lewis recites, A system comprising: a processor; and a computer-readable storage medium storing instructions that, responsive to execution by the processor, causes the processor to perform operations including: receiving an input involving user interaction with an application via a user’s device (paragraph 60-61, obtaining user input streams for training the model); detecting, by the processing device, a cohort of devices from a plurality of devices, the cohort having a shared characteristic that defines membership in the cohort (paragraph 27, splitting the devices into groups according to the specific client associated); detecting a cohort of devices from a plurality of user identifiers (IDs) (paragraph 27, splitting the devices into groups according to the specific client associated), the cohort having a shared characteristic that defines membership in the cohort; generating resource utilization data including resource scores describing respective amounts of resource utilization by the cohort of user IDs based on an influence of devices within the cohort and overall influence of the plurality of devices (paragraphs 18, 23, 49-53 62, 72-73, a resource score associated with the device that the user wants to use, keeping devices that the user intends to use connected, paragraph 53-63, 69); filtering the resource scores by removing resource scores of resources that are not identified as pertaining to shared involvement within the cohort of user IDs (paragraph 18, 23, 49-53 62, 72-73, removing devices that that user does not want to be streaming, removing it from the bucket of active resources, paragraph 31, the user device being put into a bucket according to their desired feed); assembling the filtered resource scores into respective buckets based on the resources; and sharing execution of the application as a subject of the user interaction with one or more devices associated with the cohort of user IDs based on resource scores included in at least one said respective bucket (paragraphs 18, 23, 49-53 62, 72-73, delivering content information to the selected device; see also par. 83-87).
Regarding claim 12, Lewis recites, the system as described in claim 11, wherein the detecting utilizes a machine-learning model to detect the shared characteristic, the machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model).
Regarding claim 13, Lewis recites, the system as described in claim 11, wherein the generating utilizes a machine-learning model to generate the resource scores, the machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model, said model providing confidence scores).
Regarding claim 16, Lewis recites, the system as described in claim 11, wherein the sharing is configured to control transmission of digital content to the cohort of user IDs via a network (paragraph 19, 32, 43, delivering content information to the selected device, said device connected via a network, 24; see also paragraphs 18, 23, 49-53 62, 72-73).
Regarding claim 17, Lewis recites, the system as described in claim 11, wherein the sharing is configured to control provisioning hardware and software resources of computing devices of a service provider (paragraphs 17, 27, 49-53 and 96, delivering content information to the selected device using a service provider).
Regarding claim 18, Lewis recites, A non-transitory computer-readable storage medium storing instruction that, responsive to execution by a processing device, causes the processing device to perform operations including: receiving an input involving user interaction with an application at a user’s device (paragraph 60-61, obtaining user input streams for training the model); detecting a cohort of devices from a plurality of devices, the cohort having a shared characteristic that defines membership in the cohort (paragraph 27, splitting the devices into groups according to the specific client associated); obtaining resource utilization data including resource scores based on an influence of devices within the cohort and overall influence of the plurality of devices (paragraph 17, 44, 49, a resource score associated with the device that the user wants to use, keeping devices that the user intends to use connected, using a confidence score; see also paragraphs 18, 23, 49-53 62, 72-73); filtering the resource scores by removing resource scores of resources that are not identified as pertaining to shared involvement within the cohort of devices and indicative of which resources likely pertain to shared involvement assembling the resource scores into respective buckets based on the resources (paragraph 18, 23, 49-53 62, 72-73, removing devices that that user does not want to be streaming, removing it from the bucket of active resources, paragraph 31, the user device being put into a bucket according to their desired feed); and sharing the user interaction with the application with the cohort of devices based on resource scores (paragraphs 18, 23, 49-53 62, 72-73, delivering content information to the selected device; see also par. 83-87).
Regarding claim 19, Lewis recites, the computer-readable storage medium as described in claim 18, the detecting utilizes a machine-learning model to detect the shared characteristic, the machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model; paragraphs 18, 23, 49-53 62, 72-73).
Regarding claim 20, Lewis recites, the computer-readable storage medium as described in claim 18, wherein the obtaining utilizes a machine-learning model to generate the resource scores, the machine-learning model trained using training data as part of machine learning (paragraph 15, 17, 46 a machine learning model that combines members into one cohort -an account- and uses training data to train the model, said model providing confidence scores; paragraphs 18, 23, 49-53 62, 72-73).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over 20210400324 A1 (Lewis, et. al) as applied to claim 1, 4 - 20 above, and further in view of US 20200134497 A1 (Salomon, et. al).
Regarding claim 2, Lewis teaches, the method as described in claim 1.
However, Lewis fails to teach, wherein the detecting includes generating a cohort graph defining relationships of the devices within the cohort.
Salomon teaches, wherein the detecting includes generating a cohort graph defining relationships of the devices within the cohort (paragraph 41, a graph of devices associated within the same cohort).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the generation of a cohort graph of the devices as taught by Salomon, as it allows for tracking of the user trends across devices.
Regarding claim 3, Lewis does not teach, the method as described in claim 2, wherein the cohort graph is a probabilistic cohort graph or a deterministic cohort graph.
However, Salomon teaches, the method as described in claim 2, wherein the cohort graph is a probabilistic cohort graph or a deterministic cohort graph (paragraph 41, the graph is created probabilistically, creating connections between devices in a cohort).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the generation of a cohort graph of the devices as taught by Salomon, as it allows for tracking of the user trends across devices.
Claim(s) 7, 8, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over 20210400324 A1 (Lewis, et. al) as applied to claim 1 and 4-6, 9-13, 16- 20 above, and further in view of US 20220070504 A1 (Hartnett, et. al).
Regarding claim 7, Lewis teaches, the method as described in claim 1.
However, Lewis fails to teach, further comprising determining, by the processing device, an amount of impact of the devices included in the cohort on the resource utilization, respectively, and wherein the resource scores associated with respective said devices are weighted based at least in part on this impact.
Hartnett teaches, further comprising determining, by the processing device, an amount of impact of the devices included in the cohort on the resource utilization, respectively, and wherein the resource scores associated with respective said devices are weighted based at least in part on this impact (paragraph 416, 419, 427, each device is weighted to determine relationships between the devices for each user).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the relationship of users according to a weighting system as taught by Hartnett, as it allows for better determination of the relationship between devices.
Regarding claim 8, Lewis does not teach, The method as described in claim 7, wherein the resource scores associated with respective said devices are weighted based on an influence of devices within the cohort and overall influence of the plurality of devices.
However, Hartnett teaches, The method as described in claim 7, wherein the resource scores associated with respective said devices are weighted based on an influence of devices within the cohort and overall influence of the plurality of devices (paragraph 416, 419, 427, each device is weighted to determine relationships between the devices for each user, and each device impacts the total influence on the user).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the relationship of users according to a weighting system as taught by Hartnett, as it allows for better determination of the relationship between devices.
Regarding claim 14, Lewis teaches, the method as described in claim 11.
However, Lewis fails to teach, The system as described in claim 11, further comprising determining an amount of impact of the user IDs included in the cohort on the resource utilization, respectively, and wherein the resource scores associated with respective said user IDs are weighted based at least in part on this impact .
Hartnett teaches, The system as described in claim 11, further comprising determining an amount of impact of the user IDs included in the cohort on the resource utilization, respectively, and wherein the resource scores associated with respective said user IDs are weighted based at least in part on this impact (paragraph 416, 419, 427, each device is weighted to determine relationships between the devices for each user).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the relationship of users according to a weighting system as taught by Hartnett, as it allows for better determination of the relationship between devices.
Regarding claim 15, Lewis does not teach, The system as described in claim 14, wherein the resource scores associated with respective said user IDs are weighted based on an influence of devices within the cohort and overall influence of the plurality of user IDs.
However, Hartnett teaches, The system as described in claim 14, wherein the resource scores associated with respective said user IDs are weighted based on an influence of devices within the cohort and overall influence of the plurality of user IDs (paragraph 416, 419, 427, each device is weighted to determine relationships between the devices for each user, and each device impacts the total influence on the user).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lewis with the relationship of users according to a weighting system as taught by Hartnett, as it allows for better determination of the relationship between devices.
Conclusion
THIS ACTION IS MADE FINAL. 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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/CHRISTIAN M BAKHIT/Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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