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 .
Response to Arguments
Applicant's arguments filed February 2nd, 2026 have been fully considered but they are not persuasive. Claims 1, 7, 11, 18, are amended. Claims 8, 9, 12, 13, 15, 16, 19, 20, are cancelled. Claims 21-28 are new. Claims 1-7, 10-11, 14, 17-18, 21-28 are pending and presented for examination.
Applicant’s arguments, see page 10-11, filed 02/23/2026, with respect to the rejection(s) of claim(s) 1, 4-6, 10, 11, 17, 18, 21-25, 27 and 28 under US 20210400324 A1 (Lewis, et. al) have been fully considered and are not persuasive.
Regarding claim 1, applicant claims that Lewis fails to teach “sharing by the processing device, recommendations of digital content to the cohort of devices based on a calculated score for each resource in at least one said respective bucket, the calculated scores based on the filtered resource scores across said different devices in the cohort.” Lewis teaches, paragraph 27, 31, and 32, a media viewer to allow the user to see their recommendations based off the type of device, giving them a choice to cancel a stream depending on the type of device. Based off this determination, the model using a new calculated score for each device in the cohort, paragraph 38, 41, 73, 87. Therefore, based on the teachings provided by the reference the argument is not persuasive.
Applicant’s arguments, see page 10-12, filed 02/23/2026, with respect to the rejection(s) of claim(s) 1, 4-6, 10, 11, 17, 18, 21-25, 27 and 28 under US 20210400324 A1 (Lewis, et. al) and further in view of US 20220070504 A1 (Hartnett, et. al) have been fully considered and are not persuasive.
Regarding claim 7, applicant claims that Lewis and further in view of Hartnett fails to teach weighting devices to determine relationships between devices for each user. Harnett teaches, paragraph 416, 419, 427, a selection of devices in a graph, and each device is weighted to determine relationships between the devices for each user. 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 its technique to create a model that uses user data to suggest specific resources to a user, 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. Therefore, based on the teachings provided by the reference the argument is not persuasive.
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, 10, 11, 17, 18, 21-25, 27, and 28 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, the method comprising: receiving, by a 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 collective 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 (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); and sharing, by the processing device, recommendations of digital content to the cohort of devices based on a calculated score for each resource in at least one said respective bucket, the calculated score based on the filtered resource scores across said different devices in the cohort (paragraph 27, 31, and 32, a media viewer to allow the user to see their recommendations based off the type of device, giving them a choice to cancel a stream depending on the type of device, the model using a calculated score for each device in the cohort, paragraph 38, 41, 73, 87).
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 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 70 and 71, multiple user profiles associated with a shared account for sharing number of streams; 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, 70-73, 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), the calculated score based on the filtered resource scores across aid different user IDs in the cohort (paragraph 27, 31, 32, 70-71, a media viewer to allow the user to see their recommendations based off the type of device, giving them a choice to cancel a stream depending on the type of device, the model using a calculated score for each device in the cohort, paragraph 38, 41, 73, 87).
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 collective 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 recommendations of digital content with one or more devices associated with the cohort of user IDs based on a calculated score for each resource in at least one said respective bucket, the calculated score based on the filtered resource scores across said different user IDs in the cohort. (paragraphs 18, 23, 49-53 62, 72-73, delivering content information to the selected device; see also par. 83-87).
Regarding claim 21, Lewis recites, The method as described in claim 1, wherein the assembling includes: generating metadata for the respective buckets, the metadata indicating whether each resource score in a respective bucket corresponds to an individual device or the cohort of devices (paragraph 31, metadata for a bucket, associated with the individual resource, i,e., data about the individual resources, and if they are important to a user, using resources including subscription, current access to the resources, paragraph 39-41).
Regarding claim 22, Lewis recites, The method as described in claim 1, wherein the filtering includes: comparing tags associated with the resources against a list indicative of resources related to shared involvement (paragraph 23, 38, using the current user device to determine if a filtering is needed to cancel a stream across another device).
Regarding claim 23, Lewis recites, The method as described in claim 1, wherein the sharing includes: providing a user interface that visually distinguishes between the recommendations of digital content based on the user's device and the recommendations of digital content based on the cohort (paragraph 27, 31, and 32, a media viewer to allow the user to see their recommendations based off the type of device, giving them a choice to cancel a stream depending on the type of device, paragraph 38, 41, 73, 87).
Regarding claim 24, Lewis recites, The method as described in claim 1, wherein the sharing includes: providing an option to mark a resource as of interest to the cohort, wherein marking the resource causes the resource to be included in the recommendations of digital content provided to other devices within the cohort (paragraph 38, 41, 42, 73, 87, user is able to mark that a specific device is of use to the cohort using a preference, and thus is included in further analysis to determine if another device’s streams needs to be cancelled).
Regarding claim 25, Lewis recites The method as described in claim 1, wherein the calculated score for each resource is based on aggregating the filtered resource scores from at least two different devices in the cohort (paragraph 60-61, taking a variety of user information from multiple devices, paragraph 29, for example, from a single set of individual users set in a community, and sharing it to the model, and then, creating cohort scores, paragraph 17, 44, 49).
Regarding claim 27, Lewis recites The method as described in claim 1, wherein the recommendations of digital content include at least one of digital books, digital movies, or webpages (paragraph 27, 31, and 32, the recommendations can be books, websites, or digital movies).
Regarding claim 28, Lewis recites The method as described in claim 1, further comprising: predicting resource utilization by the cohort based on the recommendations of digital content; and provisioning hardware or software resources of a service provider system based on the predicted resource utilization (paragraph 23, 40, 41, 43, provisioning hardware based off assumed client usage, and paragraph 17, 27, 49-53, and 96, controlling resources provided to the user using a content service provider system).
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 and 4-6, 10, 11, 17, 18, 21-25, 27 and 28 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 and 14 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, 10, 11, 17, 18, 21-25, 27 and 28 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 for each resource category, respectively, and wherein the resource scores associated with respective said devices are weighted per resource category ybased at least in part on this impact, an influence of devices within the cohort, and collective influence of the plurality of devices.
Hartnett teaches, further comprising determining, by the processing device, an amount of impact of the devices included in the cohort on the resource utilization for each resource category, respectively, and wherein the resource scores associated with respective said devices are weighted per resource category based at least in part on this impact, an influence of devices within the cohort, and collective influence of the plurality of devices (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 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 for each resource category, respectively, and wherein the resource scores associated with respective said user IDs are weighted based at least in part on this impact, the influence of devices within the cohort, and collective influence of the plurality of devices.
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 for each resource category, respectively, and wherein the resource scores associated with respective said user IDs are weighted based at least in part on this impact, the influence of devices within the cohort, and collective influence of the plurality of devices (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.
Claim(s) 26 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, 10, 11, 17, 18, 21-25, 27 and 28 above, and further in view of US 20100169300 A1 (Liu et. al).
Regarding claim 26, Lewis teaches, the method as described in claim 1.
However, Lewis fails to teach, wherein the sharing includes: generating a search result comprising the recommendations of digital content to be provided to the devices within the cohort based on the at least one said respective bucket.
Liu teaches, wherein the sharing includes: generating a search result comprising the recommendations of digital content to be provided to the devices within the cohort based on the at least one said respective bucket (paragraph 17, 25, 27, and 48, creating an index of similar content, and providing it to a user when they search for it).
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 searching system as taught by Liu, as it allows for lower computational costs of indexing a library of media, by predetermining what media is likely desired by the user.
Conclusion
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/C.M.B./Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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