DETAILED ACTION
This office action is in response to the amendment filed on 4/30/2025. Claims 31-34 have been added.
Claims 1-34 are pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/26/2025 is in compliance with the provisions of 37 CFR 1.97 and has been considered by the examiner.
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.
Claims 1, 4-9, and 11-27, and 29-37 are rejected under 35 U.S.C. 103 as being unpatentable over Sotosek (US 20210271964 A1, filed 2/28/2020), in view of MOSTAFA, "Robust Federated Learning through Representation Matching and Adaptive Hyper-parameters," dated December 30, 2019.
Regarding claim 1, Sotosek teaches:
A client device for wireless communication, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: receive, from a server device, a reporting configuration that indicates one or more reporting conditions, wherein the reporting configuration further indicates that, based at least in part on the one or more reporting conditions being satisfied, the client device is to report an update associated with a machine learning component (Sotosek teaches AME server an analyzer 110-- a server device-- related to machine learning model, transmits instructions to an example meter(s) 102-- a client device(fig.1, 0028). The instructions are used to perform diagnostics and/or transmit instructions, patches, update, etc to mitigate a problem (31). Additionally, the instructions are triggered by the AME indicate to the meters to transmit data periodically according to a schedule-“ (e.g., daily, weekly, etc.),” (33).
Moreover, Sotosek teaches that the meters transmit periodically, based on a schedule, such as daily, weekly, etc. , using or collecting training data from the meters (33, 22-23, 11).
and selectively transmit the update associated with the machine learning component to the server device based at least in part on whether the one or more reporting conditions are satisfied, -- The meters transmit or fail to transmit amount of data to the AME periodically according to or within a scheduled period- and/or according to a trigger for a certain period of time sent from the server “ (e.g., daily, weekly, one-time events, etc.),” (33, 11, 42).
Sotosek fails to explicitly teach – wherein the one or more reporting conditions comprises a data quantity threshold; determine an amount of training data collected by the client device during a collection period; transmit the update associated with the machine learning component to the server device based at least in part on the amount of training data collected by the client device satisfying the data quantity threshold. Mostafa teaches each client receiving hyperparameters ht from a server during a training round, and running a number of SGD iterations-- a data quantity threshold-- as described in the hyperparameters ht, and minimizing two component training loss (page 6, algorithm 1). Then each client sends back the updated model parameters to the server (, page 3, section 3, fig.1)-- transmit the update associated with the machine learning component to the server device based at least in part on the amount of training data collected by the client device satisfying the data quantity threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sotosek and Mostafa, because Mostafa teaches the minimization of the losses in the models(fig.1b).
Regarding claim 4, Sotosek teaches that the meters transmit periodically, based on a schedule, such as daily, weekly, etc. The data from the meters is then used to train or retrain the model (33, 22-23). --The one or more processors are further configured to: train the
machine learning component based at least in part on determining that the amount of training data collected by the client device satisfies the data quantity threshold.
Regarding claim 5, Sotosek teaches using data from meters to train and retrain ML models until an acceptable amount of error is achieved. The models are then used to estimate meters In-tab totals (22, 24)--the one or more reporting condition correspond to a performance of the machine learning component.
Regarding claim 6, Sotosek teaches analyzing an accuracy of the deployed model against a threshold, and based on feedback output by the model (26)--the one or more reporting conditions correspond to a loss function value of the machine learning component.
Regarding claim 7, Sotosek teaches analyzing an accuracy of the deployed model against a threshold-- the loss function difference, and based on feedback output by the model (26, 35)--the one or more reporting conditions correspond to a loss function difference, wherein the loss function difference comprises a difference between a first loss function value associated with the machine learning component and a second loss function value associated with the machine learning component.
Regarding claim 8, Sotosek teaches analyzing an accuracy of the deployed model’s output against a threshold or ground truth of validated meter output data, and based on feedback output by the model. The model is then trained using the validated meter data (26, 35)--the first loss function value corresponds to an initial instance of the machine learning component,
and wherein the second loss function value corresponds to an updated instance of the machine learning component.
Regarding claim 9, Sotosek teaches metering data is validated to generate ground truth data which is then used to train the model (35, 26,)--the one or more processors are further configured to: receive initial machine learning component information; and determine the initial instance of the machine learning component based at least in part on the initial machine learning component information.
Regarding claim 11, Sotosek discloses the in tab analyzer 110 from the AME, transmit to the meter(s) 102 instructions to identify and mitigate a problem. The problem identified is technical, environmental, and geosocial(31, 33)--the one or more reporting conditions correspond to a use case associated with the machine learning component.
Regarding claim 12, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a problem-- channel state information derivation. The problem identified is technical, environmental, and geosocial(31, 33)--the use case comprises at least one of: a channel state information derivation, a positioning measurement derivation, demodulation of a data channel, or decoding of a data channel.
Regarding claim 13, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a technical problem. The problem identified is technical, environmental, and geosocial(31, 33)-- The one or more reporting conditions correspond to a data type associated with a set of collected data.
Regarding claim 14, Sotosek discloses that the meters (individually or independently) transmit metering data—identical-- over the network to the server --distributed data. The problem identified is technical, environmental, and geosocial(31, 33, 11)-- the data type comprises identical independent distributed data, and wherein the one or more processors to: selectively transmit the update is based at least in part on a determination that the set of collected data comprises independent.
Regarding claim 15, Sotosek teaches the meters transmit data over a network to the AME periodically according to a schedule-“ (e.g., daily, weekly, etc.),” (33). --the reporting configuration indicates at least one communication resource to be used for reporting the update.
Regarding claim 16, Sotosek teaches the meters transmit data over a network to the AME periodically according to a schedule-“ (e.g., daily, weekly, etc.),” (33). --the at least one communication resource comprises at least one of a time resource or a frequency resource.
Regarding claim 17, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a transmission problem. The problem identified by the meter(s) is transmitted to the AME based on the instruction (31, 33) basically indicating the reason for the failure in transmission--The one or more processors are further configured to transmit, to the server device, an indication that the client device is refraining from transmitting the update.
Regarding claim 18, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a transmission problem. The problem identified by the meter(s) is transmitted to the AME based on the instruction (31, 33) basically indicating the reason for the failure in data transmission--The one or more processors, to selectively transmit the update to the server device, are configured to selectively transmit a report of a first type, and wherein the one or more processors, to transmit, to the server device, the indication that the client device is refraining from transmitting the update, are configured to transmit a report of a second type.
Regarding claim 19, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a transmission problem. The problem identified by the meter(s) is transmitted to the AME based on the instruction (31, 33) basically indicating the reason for the failure in data transmission-- wherein the report of the second type indicates a reporting delay.
Regarding claim 20, Sotosek discloses the in tab analyzer 110 from the AME, transmits to the meter(s) 102 instructions to identify and mitigate a transmission problem. The problem identified by the meter(s) is transmitted to the AME based on the instruction (33, 31) basically indicating the reason for the failure in the periodic data transmission-- the reporting delay comprises at least one time resource or frequency resource during which the client device will refrain from reporting an additional update.
Regarding claim 21, Sotosek discloses validation of meter data to be used as input to train the Machine learning model (35, 37) --The report of the second type indicates a current instance of the machine learning component.
Regarding claim 22, Sotosek discloses comparing the actual in tab data with model-estimated data and determine if they are within a certain threshold (31, 33) --The report of the second type indicates at least one of a loss function value associated with a set of training data or a loss function value associated with a set of validation data.
Regarding claim 23, Sotosek discloses the in tab analyzer 110 from the AME-- a base station, transmits to the meter(s) 102 instructions to identify and mitigate a transmission problem. (31, 33) --the client device comprises a user equipment and wherein the server device comprises a base station.
Regarding claim 24, A server device for wireless communication, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: transmit, to a client device, a reporting configuration that indicates one or more reporting conditions, wherein the reporting configuration further indicates that, based at least in part on the one or more reporting conditions being satisfied, the client device is to report an update associated with a machine learning component (Sotosek teaches AME server an analyzer 110-- a server device-- related to machine learning model, transmits instructions to an example meter(s) 102-- a client device(fig.1, 0028). The instructions are used to perform diagnostics and/or transmit instructions, patches, update, etc to mitigate a problem (31). Additionally, the instructions are triggered by the AME indicate to the meters to transmit data periodically according to a schedule-“ (e.g., daily, weekly, etc.),” (33).
Moreover, Sotosek teaches that the meters transmit periodically, based on a schedule, such as daily, weekly, etc. , using or collecting training data from the meters (33, 22-23, 11)--and receive the update associated with the machine learning component from the client device based at least in part on the one or more reporting conditions being satisfied, wherein the one or more processors, to receive the update associated with the machine learning component from the client device, are configured to: receive the update associated with the machine learning component from the client device based at least in part on an amount of training data collected by the client device during a collection period -- The meters transmit or fail to transmit amount of data to the AME periodically according to or within a scheduled period- and/or according to a trigger for a certain period of time sent from the server “ (e.g., daily, weekly, one-time events, etc.),” (33, 11, 42).
Further, Sotosek fails to explicitly teach wherein the one or more reporting conditions comprises a data quantity threshold; receive the update associated with the machine learning component from the client device based at least in part on an amount of training data collected by the client device satisfying the data quantity threshold. Mostafa teaches each client receiving hyperparameters ht from a server during a training round, and running a number of SGD iterations-- a data quantity threshold-- as described in the hyperparameters ht, and minimizing two component training loss (page 6, algorithm 1). Then each client sends back the updated model parameters to the server (, page 3, section 3, fig.1. receive the update associated with the machine learning component from the client device based at least in part on an amount of training data collected by the client device satisfying the data quantity threshold. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sotosek and Mostafa, because Mostafa teaches the minimization of the losses in the models(fig.1b).
Regarding claim 25, Sotosek teaches that the meters transmit data, such as information corresponding to media exposure-- wherein the one or more reporting conditions correspond to at least one of: a use case associated with the machine learning component, or a data type associated with a set of collected data., to the AME periodically according to or within a scheduled period- and/or according to a trigger for a certain period of time sent from the server “ (e.g., daily, weekly, one-time events, etc.),” (33, 11, 42).
Regarding claim 26, Sotosek teaches that the meters are to transmit data-- wherein the reporting configuration indicates at least one communication resource to be used for reporting the update, such as information corresponding to media exposure, to the AME periodically according to or within a scheduled period- and/or according to a trigger for a certain period of time sent from the server “ (e.g., daily, weekly, one-time events, etc.),” (33, 11, 42).
Regarding claims 27, 29, the present claim recites similar limitations as corresponding claims 1, 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Claim 30 recites similar limitations as corresponding claim 24 and is rejected for similar reasons, using similar teachings and rationale.
Claims 31-34 recite similar limitations as corresponding claim 5 and are rejected for similar reasons, using similar teachings and rationale.
Regarding claim 35, Sotosek teaches training a model based on the collected intab information (15, 33)-- training the machine learning component based at least in part on the amount of training data collected by the client device satisfying the data quantity threshold.
Regarding claim 36, Sotosek discloses comparing the actual in tab data with model-estimated data and determine if they are within a certain threshold (31, 33). However, Sotosek fails to explicitly teach wherein the first loss function value corresponds to an initial instance of the machine learning component, and wherein the second loss function value corresponds to an updated instance of the machine learning component. Mostafa teaches training using SGD, and minimizing two component training loss (page 3, section 3, fig.1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Sotosek and Mostafa, because Mostafa teaches the minimization of the losses in the models(33).
Regarding claim 37, Sotosek discloses comparing the actual in tab data with model-estimated data and determine if they are within a certain threshold (31, 33)-- receiving initial machine learning component information; and determining the initial instance of the machine learning component based at least in part on the initial machine learning component information.
Response to Arguments
Applicant’s arguments with respect to claims 1, 4-27, and 29-37 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding claim 1, the Applicant indicates that “Accordingly, since SOTOSEK discloses meter 102 transmitting metering data periodically, aperiodically, and/or based on a trigger, SOTOSEK does not disclose meter 102 transmitting metering data based on an amount of metering data collected during a collection period satisfying a data quantity threshold. In fact, transmitting metering data periodically (e.g., daily, weekly, etc.) does not take into account whether an amount of metering data collected during a collection period satisfies a data quantity threshold. For example, if metering data is transmitted daily, the collection period is one day and the metering data is transmitted every day regardless of how much metering collected each day. In other words, SOTOSEK does not consider if an amount of metering data collected on a particular day (i.e., the collection period) satisfies a data quantity threshold…”. The Applicant is directed towards the new grounds of rejection as necessitated by the amendment, and included above.
Additionally, the Applicant indicates “that DE BROUWER and O'SHEA do not disclose each and every feature recited in amended claim 1.”. The Applicant is directed towards the new grounds of rejection included above as necessitated by the amendment.
Further, the Applicant states that “Independent claims 24, 27, and 30, as amended, recite similar features. Therefore, independent claims 1, 24, 27, and 30, and the claims that depend thereon, are patentable over the cited sections of DE BROUWER and O'SHEA.”. Regarding claims 24, and 30, De Brouwer teaches an FL aggregator using protocols or tensorial handshakes, containing model admission criteria, to receive model updates from flea devices(123). The protocols are transmitted to the devices by the central FL aggregator to facilitate the collection of the model updates(115)-- a reporting configuration that indicates one or more reporting conditions, wherein the reporting configuration further indicates that, based at least in part on the one or more reporting conditions being satisfied, the client device is to report an update associated with a machine learning component. The Applicant is directed towards the new grounds of rejection included above as necessitated by the amendment.
Additionally, the Applicant states that “Applicant submits that new claim 31 is patentable over DE BROUWER and O'SHEA. New claims 32-34 recite similar features.”. Applicant is directed towards the grounds of rejection above addressing the limitations of the newly introduced claims.
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mostafa US 20220284353--a model aggregator to, in response to obtaining at least one model trained using a first set of hyper-parameters of a probability distribution, generate a loss reduction, a hyper-parameter generator to, when the loss reduction satisfies a loss threshold, update the probability distribution and generate a second set of hyper-parameters using the updated probability distribution, and an interface to transmit the second set of hyper-parameters to a client.
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/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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