DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/19/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
101 Abstract idea Rejection Arguments
Applicant asserts:
Applicant argues, on page 14, that the office action improperly overgeneralizes the claim and ignores the claim as a whole. Regarding, the limitations "determining whether the trained machine learning model is valid or invalid..." and "analyzing the training dataset and/or the trained machine learning model to determine what additional training data to add..." as "mental processes" that can be performed in the human mind or with "pen and paper."
Examiner response:
Examiner respectfully disagrees. Examiner notes that the machine learning model is interpreted as applying an abstract idea on a computer. In addition, The claims do not describe what occurs to the determining and analyzing step in a way that would differentiate this process from the way a person could determine whether a machine is valid based on its output and analyzing a dataset to determine what to add. Applicant’s argument is therefore considered unpersuasive.
Applicant asserts:
Applicant argues, on page 14-16, claim 1 is not "directed to" that alleged exception because the claim integrates any such concept into a practical application. Claim 1 likewise does not merely "apply" some abstract idea. Instead, when the model in claim 1 is determined invalid, the claim requires determining what additional training data is needed and then transmitting signaling that configures network-served autonomous/automated mobile devices to help collect that additional training data, followed by retraining using the supplemented dataset.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the machine learning model is interpreted as a generic computer component; the collection of addition training data is interpreted as mere data gathering; the retraining step is interpreted as merely including instructions to implement an abstract idea on a computer. Regarding the technological improvement, MPEP 2106.04(d)(1) states if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Examiner notes that the claims and specification do not provide the detail necessary to be apparent to a person of ordinary skill in the art of the improvement. Applicant’s argument is therefore considered unpersuasive.
Applicant asserts:
Applicant argues, on page 16, that The rejection here similarly labels the training, signaling/configuring, and retraining steps as mere "apply it" / generic computer use without an evidentiary showing that the ordered combination was well-understood, routine, and conventional in the relevant technical field.
Examiner response:
Examiner respectfully disagrees. Examiner notes that the machine learning model is interpreted as a generic computer component and that training and retraining of the model merely including instructions to implement an abstract idea on a computer; the transmitting of a signal is interpreted as well understood and routine. Applicant’s argument is therefore considered unpersuasive.
102 Rejection Arguments
Applicant asserts:
Applicant argues, on page 17-18, that the prior art does not anticipate claim 1. Regarding the limitation "determining whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements," Bouziane does not disclose a validity/invalidity determination using a validation dataset.
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner notes that Bouziane talks about calculating a loss using real data and model output. Examiner interprets the test subset as the real data used in calculating the loss function.
Applicant asserts:
Applicant argues, on page 17-18, that the prior art does not anticipate claim 1. Regarding the limitation "based on the trained machine learning model being invalid, analyzing the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset."
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner interprets that analyzing the training machine learning model (global model) is determining if the model has reached a desired accuracy. “to determine what additional training data to add to the training dataset” is represented in Step 2 that collecting new data based on not reaching the desired accuracy is the additional training data to add to the training dataset. Examiner notes that “what additional data to add” can be interpreted as any new data can be added under BRI.
Applicant asserts:
Applicant argues, on page 19, that the prior art does not anticipate claim 1. Regarding the limitation "transmitting signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data."
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner interprets the claims as any signal/indication such that the mobile devices help in collecting new data. Examiner interprets that Step 2 shows that receiving the updated parameters is a signal/indication to collect additional data.
Applicant asserts:
Applicant argues, on page 19, that the prior art does not anticipate claim 1. Regarding the limitation "re- training the machine learning model with the training dataset as supplemented with the additional training data"
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner interprets the claims retraining the machine learning model with the training dataset with the additional new training data. Examiner interprets that local training and aggregation steps (2-3) is repeated until a desired accuracy with step 2 including collecting additional steps is retraining the machine learning model with training dataset with the additional new training data.
Applicant asserts:
Applicant argues, on page 19-20, that the prior art does not anticipate claim 4. Regarding the limitation "determining one or more locations ... at which to collect the additional training data."
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner interprets that “UAVs can adjust their location to serve ground users according to the predicted channel quality and hence adapt dynamically to the propagation environment.” Shows that the UAVs can determine one or more locations at which to collect the additional training data based on ground user’s needs. The locations chosen to collect data is the locations where the UAV can communicate and have good channel quality.
Applicant asserts:
Applicant argues, on page 21, that the prior art does not anticipate claim 4. Regarding the limitation "the signaling comprises signaling for configuring the ... mobile devices to help collect the additional training data at the one or more locations."
Examiner response:
Examiner respectfully disagrees. Examiner uses BRI when interpreting the claims. Examiner interprets the claims as any signal/indication such that the mobile devices help in collecting new data at the one or more locations. Bouziane notes that the data regarding the UAV’s altitude and type of propagation is data being collected for the FDL concept to predict the channel quality. Therefore, when the ANN model is updated from the propagated parameters, it is a signal/indication to collect additional data at the location that the UAV adjusted for based on the ANN’s prediction of channel quality.
Claim Rejections - 35 USC § 101 Abstract Idea
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determining whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine whether a trained machine learning model is valid or invalid based on whether predications or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements.
“based on the trained machine learning model being invalid, analyzing the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze the training dataset to determine what additional training data to add to the training dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A method performed by equipment supporting a non-public communication network, the method comprising: training a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmitting signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-training the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A method performed by equipment supporting a non-public communication network, the method comprising: training a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmitting signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-training the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, wherein said analyzing comprises analyzing how impactful different machine learning features represented by the training dataset are to the prediction or decision” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze how impactful different machine learning features represented by the training dataset are to the prediction or decision.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and selecting one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and selecting one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, wherein said analyzing comprises, for each of one or more machine learning features represented by the training dataset, analyzing a number of and/or a diversity of values in the training dataset for the machine learning feature,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze a number of and/or diversity of values in the training dataset for the machine learning feature.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and selecting one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and selecting one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 4:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, further comprising determining one or more locations, in a coverage area of the non-public communication network, at which to collect the additional training data,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine one or more locations at which to collect the additional training data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 5:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“and based on the score function, selecting one or more locations at which to collect additional training data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select one or more locations at which to collect additional training data based on the score function.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method of claim 4, wherein determining the one or more locations at which to collect the additional training data comprises: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method of claim 4, wherein determining the one or more locations at which to collect the additional training data comprises: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 10:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, further comprising solving an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints, wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data, wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the parameters of the situation and solve an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determine whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine whether a trained machine learning model is valid or invalid based on whether predications or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements.
“based on the trained machine learning model being invalid, analyze the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze the training dataset to determine what additional training data to add to the training dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“Equipment configured to support a non-public communication network, the equipment comprising processing circuitry configured to: train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-train the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“Equipment configured to support a non-public communication network, the equipment comprising processing circuitry configured to: train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-train the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 16:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The equipment of claim 15, wherein the processing circuitry is configured to analyze how impactful different machine learning features represented by the training dataset are to the prediction or decision” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze how impactful different machine learning features represented by the training dataset are to the prediction or decision.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and select one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and select one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 17:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The equipment of claim 15, wherein the processing circuitry is configured to, for each of one or more machine learning features represented by the training dataset, analyze a number of and/or a diversity of values in the training dataset for the machine learning feature,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze a number of and/or diversity of values in the training dataset for the machine learning feature.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and select one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and select one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 18:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The equipment of claim 15, wherein the processing circuitry is further configured to determine one or more locations, in a coverage area of the non-public communication network, at which to collect the additional training data,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine one or more locations at which to collect the additional training data.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 19:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“and based on the score function, selecting one or more locations at which to collect additional training data” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select one or more locations at which to collect additional training data based on the score function.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The equipment of claim 18, wherein the processing circuitry is configured to determine the one or more locations at which to collect the additional training data by: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The equipment of claim 18, wherein the processing circuitry is configured to determine the one or more locations at which to collect the additional training data by: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 24:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The equipment of claim 15, wherein the processing circuitry is further configured to solve an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints, wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data, wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could evaluate the parameters of the situation and solve an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 29:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“determine whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could determine whether a trained machine learning model is valid or invalid based on whether predications or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements.
“based on the trained machine learning model being invalid, analyze the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could analyze the training dataset to determine what additional training data to add to the training dataset.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer readable storage medium on which is stored instructions that, when executed by at least one processor of equipment configured to support a non-public communication network, causes the equipment to: train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-train the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer readable storage medium on which is stored instructions that, when executed by at least one processor of equipment configured to support a non-public communication network, causes the equipment to: train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“and re-train the machine learning model with the training dataset as supplemented with the additional training data.” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4, 13, 15, 18, 27, and 29 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bouziane Brik; “Federated Learning for UAVs-Enabled Wireless Networks; Use Cases, Challenges, and Open Problems” (Hereinafter “Bouziane”).
Regarding claim 1, Bouziane anticipates A method performed by equipment supporting a non-public communication network, the method comprising: training a machine learning model with a training dataset to make a prediction or decision in the non-public communication network; (Bouziane Section II Subsection C Paragraph 1; "In UAV-based networks, UAVs may collaboratively build a learning model based on FDL concept. As we mentioned before, this will not only preserve privacy of UAVs' data but also improve the use of UAVs' resources including energy and computing resources… Step 1 (Training Initialization):…Step 2 (UAVs' Models Training):" Examiner notes that federated learning makes the network nonpublic because it preserves privacy of data and it is training a machine learning model with a training dataset to make a prediction as shown in step 1 and 2.)
determining whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that using the desired accuracy or loss function is determining whether the trained ML model is valid or invalid based on the predictions or decisions that the trained ML model makes from a validation dataset satisfy performance requirements; to find a desired accuracy, a validation dataset must be used)
based on the trained machine learning model being invalid, analyzing the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that based on if the desired accuracy is achieved or the loss function converges/analyzing the trained ML model it is determined what additional training data/additional data from all UAVs to add to the training dataset)
transmitting signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data; (Bouziane Section II Subsection C Paragraph 1; "the FL server aggregates them and sends back the updated model parameters to the UAVs." Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that the FL server transmit signal/broadcasts for configuring one or more autonomous or automated mobile devices/UAVs served by the nonpublic network to help collect the additional training data)
and re-training the machine learning model with the training dataset as supplemented with the additional training data. (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training):" Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that step two is repeated means re-training the ML model with the training dataset as supplemented with the additional training data)
Regarding claim 4, Bouziane anticipates The method of claim 1, further comprising determining one or more locations, in a coverage area of the non-public communication network, at which to collect the additional training data, (Bouziane Section II Subsection C Paragraph 1; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Bouziane Section III Subsection A Paragraph 2; "Using an ANN as reinforcement learning [22], UAVs can adjust their location to serve ground users according to the predicted channel quality and hence adapt dynamically to the propagation environment." Examiner notes that based on if the desired accuracy is achieved or the loss function converges, it is determined that one or more locations/location of all UAVs at which to collect the additional training data)
and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations. (Bouziane Section II Subsection C Paragraph 1; "the FL server aggregates them and sends back the updated model parameters to the UAVs." Bouziane Section II Subsection C Paragraph 1; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that the FL server transmit signal/broadcasts for configuring one or more autonomous or automated mobile devices/UAVs served by the nonpublic network to help collect the additional training data at the one or more locations)
Regarding claim 13, Bouziane anticipates The method of claim 1, wherein the non-public communication network is an industrial internet-of-things network, (Bouziane Section I Paragraph 1; "In such context, UAVs, known also as drones, are quickly growing by admitting many key applications in wireless networks, ranging from surveillance, monitoring, and delivery of medical supplies, to military and telecommunications" Examiner notes that non-public network uses drones to accomplish industrial processes/surveillance, monitoring, and delivery making the network an industrial internet of things network)
wherein the autonomous or automated mobile devices are each configured to perform a task of an industrial process, (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training): "Step 2 (UAVs' Models Training): Each UAV i starts to collect new data and update parameters of its local model" Examiner notes that the autonomous or automated mobile devices/UAV's are configured to perform a task of an industrial process/collect data)
and wherein the autonomous or automated mobile devices include one or more automated guided vehicles, one or more autonomous mobile robots, and/or one or more unmanned aerial vehicles. (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training): "Step 2 (UAVs' Models Training): Each UAV i starts to collect new data and update parameters of its local model" Examiner notes that the autonomous or automated mobile devices include one or more unmanned aerial vehicles/UAVs)
Regarding claim 15, Bouziane anticipates Equipment configured to support a non-public communication network, the equipment comprising processing circuitry configured to: (Bouziane Section Abstract; “UAVs are resource-constrained devices especially in terms of computing and power resources” Examiner notes that equipment/UAV comprise processing circuitry/computing resources that will perform the actions claimed)
train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network; (Bouziane Section II Subsection C Paragraph 1; "In UAV-based networks, UAVs may collaboratively build a learning model based on FDL concept. As we mentioned before, this will not only preserve privacy of UAVs' data but also improve the use of UAVs' resources including energy and computing resources… Step 1 (Training Initialization):…Step 2 (UAVs' Models Training):" Examiner notes that federated learning makes the network nonpublic because it preserves privacy of data and it is training a machine learning model with a training dataset to make a prediction as shown in step 1 and 2.)
determine whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that using the desired accuracy or loss function is determining whether the trained ML model is valid or invalid based on the predictions or decisions that the trained ML model makes from a validation dataset satisfy performance requirements; to find a desired accuracy, a validation dataset must be used)
based on the trained machine learning model being invalid, analyze the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that based on if the desired accuracy is achieved or the loss function converges/analyzing the trained ML model it is determined what additional training data/additional data from all UAVs to add to the training dataset)
transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data; (Bouziane Section II Subsection C Paragraph 1; "the FL server aggregates them and sends back the updated model parameters to the UAVs." Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that the FL server transmit signal/broadcasts for configuring one or more autonomous or automated mobile devices/UAVs served by the nonpublic network to help collect the additional training data)
and re-train the machine learning model with the training dataset as supplemented with the additional training data. (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training):" Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that step two is repeated means re-training the ML model with the training dataset as supplemented with the additional training data)
Regarding claim 18, Bouziane anticipates The equipment of claim 15, wherein the processing circuitry is further configured to determine one or more locations, in a coverage area of the non-public communication network, at which to collect the additional training data, (Bouziane Section II Subsection C Paragraph 1; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Bouziane Section III Subsection A Paragraph 2; "Using an ANN as reinforcement learning [22], UAVs can adjust their location to serve ground users according to the predicted channel quality and hence adapt dynamically to the propagation environment." Examiner notes that based on if the desired accuracy is achieved or the loss function converges, it is determined that one or more locations/location of all UAVs at which to collect the additional training data)
and wherein the signaling comprises signaling for configuring the one or more autonomous or automated mobile devices to help collect the additional training data at the one or more locations. (Bouziane Section II Subsection C Paragraph 1; "the FL server aggregates them and sends back the updated model parameters to the UAVs." Bouziane Section II Subsection C Paragraph 1; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that the FL server transmit signal/broadcasts for configuring one or more autonomous or automated mobile devices/UAVs served by the nonpublic network to help collect the additional training data at the one or more locations)
Regarding claim 27, Bouziane anticipates The equipment of claim 15, wherein the non-public communication network is an industrial internet-of-things network, (Bouziane Section I Paragraph 1; "In such context, UAVs, known also as drones, are quickly growing by admitting many key applications in wireless networks, ranging from surveillance, monitoring, and delivery of medical supplies, to military and telecommunications" Examiner notes that non-public network uses drones to accomplish industrial processes/surveillance, monitoring, and delivery making the network an industrial internet of things network)
wherein the autonomous or automated mobile devices are each configured to perform a task of an industrial process, (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training): "Step 2 (UAVs' Models Training): Each UAV i starts to collect new data and update parameters of its local model" Examiner notes that the autonomous or automated mobile devices/UAV's are configured to perform a task of an industrial process/collect data)
and wherein the autonomous or automated mobile devices include one or more automated guided vehicles, one or more autonomous mobile robots, and/or one or more unmanned aerial vehicles. (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training): "Step 2 (UAVs' Models Training): Each UAV i starts to collect new data and update parameters of its local model" Examiner notes that the autonomous or automated mobile devices include one or more unmanned aerial vehicles/UAVs)
Regarding claim 29, Bouziane anticipates A non-transitory computer readable storage medium on which is stored instructions that, when executed by at least one processor of equipment configured to support a non-public communication network, causes the equipment to: (Bouziane Section Abstract; “UAVs are resource-constrained devices especially in terms of computing and power resources” Examiner notes that UAV comprise computer readable storage medium and processor/computing resources)
train a machine learning model with a training dataset to make a prediction or decision in the non-public communication network; (Bouziane Section II Subsection C Paragraph 1; "In UAV-based networks, UAVs may collaboratively build a learning model based on FDL concept. As we mentioned before, this will not only preserve privacy of UAVs' data but also improve the use of UAVs' resources including energy and computing resources… Step 1 (Training Initialization):…Step 2 (UAVs' Models Training):" Examiner notes that federated learning makes the network nonpublic because it preserves privacy of data and it is training a machine learning model with a training dataset to make a prediction as shown in step 1 and 2.)
determine whether the trained machine learning model is valid or invalid based on whether predictions or decisions that the trained machine learning model makes from a validation dataset satisfy performance requirements; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that using the desired accuracy or loss function is determining whether the trained ML model is valid or invalid based on the predictions or decisions that the trained ML model makes from a validation dataset satisfy performance requirements; to find a desired accuracy, a validation dataset must be used)
based on the trained machine learning model being invalid, analyze the training dataset and/or the trained machine learning model to determine what additional training data to add to the training dataset; (Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that based on if the desired accuracy is achieved or the loss function converges/analyzing the trained ML model it is determined what additional training data/additional data from all UAVs to add to the training dataset)
transmit signaling for configuring one or more autonomous or automated mobile devices served by the non-public communication network to help collect the additional training data; (Bouziane Section II Subsection C Paragraph 1; "the FL server aggregates them and sends back the updated model parameters to the UAVs." Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that the FL server transmit signal/broadcasts for configuring one or more autonomous or automated mobile devices/UAVs served by the nonpublic network to help collect the additional training data)
and re-train the machine learning model with the training dataset as supplemented with the additional training data. (Bouziane Section II Subsection C Paragraph 1; "Step 2 (UAVs' Models Training):" Bouziane Section II Subsection C Paragraph 2; "We note that the local training and aggregation steps (Steps 2-3) are repeated until a desired accuracy is achieved or the loss function converges." Examiner notes that step two is repeated means re-training the ML model with the training dataset as supplemented with the additional training data)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bouziane Brik; “Federated Learning for UAVs-Enabled Wireless Networks; Use Cases, Challenges, and Open Problems” (Hereinafter “Bouziane”) in view of Jason Brownlee; “How to Choos a Feature Selection Method For Machine Learning” (hereinafter “Jason”).
Regarding claim 2, Bouziane does not teach The method of claim 1, wherein said analyzing comprises analyzing how impactful different machine learning features represented by the training dataset are to the prediction or decision
and selecting one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.
However, Jason does teach The method of claim 1, wherein said analyzing comprises analyzing how impactful different machine learning features represented by the training dataset are to the prediction or decision (Jason Paragraph 2; "Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics" Examiner notes that feature selection analyzes how impactful different ML features/input variable represented by the training dataset are to the prediction or decision of target variable)
and selecting one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision. (Jason Paragraph 2; "and selecting those input variables that have the strongest relationship with the target variable." Examiner notes that based on the analyzing, the input variables that have the strongest relationship/how impactful are selected for which to collect additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Jason. Bouziane teaches using UAVs in federated learning. Jason teaches using feature selection for machine learning. One of ordinary skill would have motivation to combine Bouziane and Jason to use feature selection to reduce the number of input variables to reduce the computational cost and improve the performance of the model “It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model.” (Jason Paragraph 2).
Regarding claim 16, Bouziane does not teach The equipment of claim 15, wherein the processing circuitry is configured to analyze how impactful different machine learning features represented by the training dataset are to the prediction or decision
and select one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision.
However, Jason does teach The equipment of claim 15, wherein the processing circuitry is configured to analyze how impactful different machine learning features represented by the training dataset are to the prediction or decision (Jason Paragraph 2; "Statistical-based feature selection methods involve evaluating the relationship between each input variable and the target variable using statistics" Examiner notes that feature selection analyzes how impactful different ML features/input variable represented by the training dataset are to the prediction or decision of target variable)
and select one or more machine learning features for which to collect additional training data, based on how impactful the one or more machine learning features are to the prediction or decision. (Jason Paragraph 2; "and selecting those input variables that have the strongest relationship with the target variable." Examiner notes that based on the analyzing, the input variables that have the strongest relationship/how impactful are selected for which to collect additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Jason. Bouziane teaches using UAVs in federated learning. Jason teaches using feature selection for machine learning. One of ordinary skill would have motivation to combine Bouziane and Jason to use feature selection to reduce the number of input variables to reduce the computational cost and improve the performance of the model “It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model.” (Jason Paragraph 2).
Claim(s) 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bouziane Brik; “Federated Learning for UAVs-Enabled Wireless Networks; Use Cases, Challenges, and Open Problems” (Hereinafter “Bouziane”) in view of Weichan Zhong et al; “Selection of diverse features with a diverse regularization” (Hereinafter “Weichan”).
Regarding claim 3, Bouziane does not teach The method of claim 1, wherein said analyzing comprises, for each of one or more machine learning features represented by the training dataset, analyzing a number of and/or a diversity of values in the training dataset for the machine learning feature,
and selecting one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.
However, Weichan does teach The method of claim 1, wherein said analyzing comprises, for each of one or more machine learning features represented by the training dataset, analyzing a number of and/or a diversity of values in the training dataset for the machine learning feature, (Weichan Section 3.1 Paragraph 1; "We propose to partition the features into feature clusters such that the features in the different feature clusters are weakly nonlinearly correlated." Weichan Section 3.1 Paragraph 3; "To select the features that are highly informative and weakly correlated, we define a diverse regularization(DR)," Examiner notes that clustering the features so that the clusters are weakly nonlinearly correlated is analyzing a diversity of values in the training dataset for the machine learning feature)
and selecting one or more machine learning features for which to collect additional training data, based on said number and/or said diversity. (Weichan Section 3.1 Paragraph 3; "To select the features that are highly informative and weakly correlated, we define a diverse regularization(DR)," Examiner notes that the features that are highly informative and weakly correlated/diversity are selected for which to collect additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Weichan. Bouziane teaches using UAVs in federated learning. Weichan teaches diverse feature selection. One of ordinary skill would have motivation to combine Bouziane and Weichan to use diverse feature selection to reduce linear and nonlinear correlations among important features “we proposed to simultaneously perform feature clustering and feature selection, where feature clustering is used to group correlated features and a diverse regularization (DR) is introduced to reduce the linear and nonlinear correlations among important features. Therefore, DFS is able to select both informative and diverse features that are weakly linearly and nonlinearly correlated.” (Weichan Section 1 Paragraph 4).
Regarding claim 17, Bouziane does not teach The equipment of claim 15, wherein the processing circuitry is configured to, for each of one or more machine learning features represented by the training dataset, analyze a number of and/or a diversity of values in the training dataset for the machine learning feature,
and select one or more machine learning features for which to collect additional training data, based on said number and/or said diversity.
However, Weichan does teach The equipment of claim 15, wherein the processing circuitry is configured to, for each of one or more machine learning features represented by the training dataset, analyze a number of and/or a diversity of values in the training dataset for the machine learning feature, (Weichan Section 3.1 Paragraph 1; "We propose to partition the features into feature clusters such that the features in the different feature clusters are weakly nonlinearly correlated." Weichan Section 3.1 Paragraph 3; "To select the features that are highly informative and weakly correlated, we define a diverse regularization(DR)," Examiner notes that clustering the features so that the clusters are weakly nonlinearly correlated is analyzing a diversity of values in the training dataset for the machine learning feature)
and select one or more machine learning features for which to collect additional training data, based on said number and/or said diversity. (Weichan Section 3.1 Paragraph 3; "To select the features that are highly informative and weakly correlated, we define a diverse regularization(DR)," Examiner notes that the features that are highly informative and weakly correlated/diversity are selected for which to collect additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Weichan. Bouziane teaches using UAVs in federated learning. Weichan teaches diverse feature selection. One of ordinary skill would have motivation to combine Bouziane and Weichan to use diverse feature selection to reduce linear and nonlinear correlations among important features “we proposed to simultaneously perform feature clustering and feature selection, where feature clustering is used to group correlated features and a diverse regularization (DR) is introduced to reduce the linear and nonlinear correlations among important features. Therefore, DFS is able to select both informative and diverse features that are weakly linearly and nonlinearly correlated.” (Weichan Section 1 Paragraph 4).
Claim(s) 5-11 and 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bouziane Brik; “Federated Learning for UAVs-Enabled Wireless Networks; Use Cases, Challenges, and Open Problems” (Hereinafter “Bouziane”) in view of Richard Wang et al; “Active Sensing Data Collection with Autonomous Mobile Robots” (hereinafter “Richard”).
Regarding claim 5, Bouziane does not teach The method of claim 4, wherein determining the one or more locations at which to collect the additional training data comprises: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;
based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;
and based on the score function, selecting one or more locations at which to collect additional training data.
However, Richard does teach The method of claim 4, wherein determining the one or more locations at which to collect the additional training data comprises: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network; (Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments. High priority locations (triangle) have not been visited in the previous three deployments versus low priority locations (square) have been recently visited. Start position indicated with the circle S." Richard Section IV.A Paragraph 2; "One possible representation of the environment would be an occupancy grid;" Examiner notes that occupancy grid is generated heatmap representing values of the machine learning feature/time since last visit at different location in the coverage area of the non-public communication network with triangles as location not recently visited and squares as recently
PNG
media_image1.png
378
818
media_image1.png
Greyscale
visited)
based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location; (Richard Section IV.C Paragraph 1; "Attached to each measurement location are cost and reward parameters (ei, ci, ri), which allows us to compute navigation paths using variants of the prize collecting traveling salesman problem. Reward adjustments between deployments will incentivize measurement collection from less visited locations in subsequent deployments.” Richard Section IV.C Paragraph 5; "With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Examiner notes that based on the heatmap/occupancy grid, reward and cost for each location in the coverage area of the nonpublic communication network is a score function; the score for a location quantifies a benefit of collecting additional training data at the location/collecting data from less visited location within a short time)
and based on the score function, selecting one or more locations at which to collect additional training data. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "In contrast, the prize collecting TSP also consider rewards collected. One variant is called the orienteering problem, which finds the maximum reward path within a fixed time limit MAX_COST." Examiner notes that solving the prize collecting TSP is selecting one or more locations at which to collect additional training data based on the score function/maximizing the reward path within a fixed time limit)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 6, Bouziane does not teach The method of claim 5, wherein the score function represents the score for a location as a function of one or more of: a number of and/or a diversity of values in the training dataset at the location; and/or an accuracy of the machine learning model at the location; and/or an uncertainty of the machine learning model at the location.
However, Richard does teach The method of claim 5, wherein the score function represents the score for a location as a function of one or more of: a number of and/or a diversity of values in the training dataset at the location; and/or an accuracy of the machine learning model at the location; and/or an uncertainty of the machine learning model at the location. (Richard Section IV.C Paragraph 5; "With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Richard Section IV.C Paragraph 7; "After each deployment, readjusting the rewards for recently visited measurement locations will ensure variability in the navigation paths for subsequent deployments." Examiner notes that the rewards and cost for each location is a score function to ensure diversity of values in training dataset within at the location within an allotted time; variability in paths means diversity of collected values)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 7, Bouziane does not teach The method of claim 4, wherein the signaling comprises, for each of at least one of the one or more autonomous or automated mobile devices, signaling for routing the autonomous or automated mobile device to at least one location of the one or more locations to help collect at least some of the additional training data.
However, Richard does teach The method of claim 4, wherein the signaling comprises, for each of at least one of the one or more autonomous or automated mobile devices, signaling for routing the autonomous or automated mobile device to at least one location of the one or more locations to help collect at least some of the additional training data. (Richard Section IV.B Paragraph 2; "With upto- date contextual data, the robot can compute a navigation path (x1, Y1), (x2, Y2)... E P at the beginning of each deployment starting from its current location (xi nit, Yi nit}· The robot executes the path while it is deployed, continuously updating the contextual data records as it collects measurements at different measurement locations emeas in the environment." Examiner notes that robot signals to itself for routing the autonomous or automated mobile device/the robot to at least one location of the one or more locations to help collect at least some of the additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 8, Bouziane does not teach The method of claim 7, wherein the signaling revises a route of the autonomous or automated mobile device to include the at least one location as a destination or waypoint in the route.
However, Richard does teach The method of claim 7, wherein the signaling revises a route of the autonomous or automated mobile device to include the at least one location as a destination or waypoint in the route. (Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments." Examiner notes that robot revises a path to include the at least one location as a destination or waypoint in the route/high priority locations shown in fig 3)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 9, Bouziane does not teach The method of claim 4, wherein, for each of at least one of the one or more autonomous or automated mobile devices, the signaling comprises signaling for configuring the autonomous or automated mobile device to: perform one or more transmissions of test traffic at one or more of the one or more locations; and/or perform one or more measurements at one or more of the one or more locations and to collect the results of the one or more measurements as at least some of the additional training data.
However, Richard does teach The method of claim 4, wherein, for each of at least one of the one or more autonomous or automated mobile devices, the signaling comprises signaling for configuring the autonomous or automated mobile device to: perform one or more transmissions of test traffic at one or more of the one or more locations; and/or perform one or more measurements at one or more of the one or more locations and to collect the results of the one or more measurements as at least some of the additional training data. (Richard Section IV.B Paragraph 2; "The robot executes the path while it is deployed, continuously updating the contextual data records as it collects measurements at different measurement locations emeas in the environment." Examiner notes that each of the at least one of the one or more autonomous or automated mobile devices/the robot is signaled to perform one or more measurements at the one or more of the one or more locations and collect results/collects measurements at different measurements locations as at least some of the additional training data.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 10, Bouziane does not teach The method of claim 1, further comprising solving an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints,
wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data,
wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network.
However, Richard does teach The method of claim 1, further comprising solving an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints, (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "TSP for Computing Paths: With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Examiner notes that computing a navigation path using variants of the prize collecting traveling salesman problem is solving an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices/the robot, subject to one or more constraints)
wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data, (Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments." Examiner notes that navigation paths/data collection plan for an autonomous or automated mobile device/the robot includes a plan on what training data the mobile device will help to collect/high priority locations and what route the mobile device will take as a part of helping to collect the training data/the navigation order)
wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Examiner notes that how long it takes to navigate between different measurement locations is a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices/the robot)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 11, Bouziane does not teach The method of claim 10, wherein a score function represents scores for respective locations in the coverage area of the non-public communication network,
wherein the score for a location quantifies a benefit of collecting additional training data at the location,
and wherein solving the optimization problem comprises maximizing the score function over a planning time horizon, subject to the one or more constraints.
However, Richard does teach The method of claim 10, wherein a score function represents scores for respective locations in the coverage area of the non-public communication network, (Richard Section IV.C Paragraph 1; "Attached to each measurement location are cost and reward parameters (ei, ci, ri), which allows us to compute navigation paths using variants of the prize collecting traveling salesman problem." Examiner notes that reward and cost for each location in the coverage area of the nonpublic communication network is a score function)
wherein the score for a location quantifies a benefit of collecting additional training data at the location, (Richard Section IV.C Paragraph 1; "Reward adjustments between deployments will incentivize measurement collection from less visited locations in subsequent deployments." Examiner notes that the score for a location quantifies a benefit of collecting additional training data at the location/collect data from less visited locations)
and wherein solving the optimization problem comprises maximizing the score function over a planning time horizon, subject to the one or more constraints. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "In contrast, the prize collecting TSP also consider rewards collected. One variant is called the orienteering problem, which finds the maximum reward path within a fixed time limit MAX_COST." Examiner notes that prize collecting TSP/optimization problem comprises maximizing the score function/maximum reward path over a planning time horizon/within a fixed time limit, subject to the one or more constraints/constrains how long it takes to navigate between different measurement locations)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 19, Bouziane does not teach The equipment of claim 18, wherein the processing circuitry is configured to determine the one or more locations at which to collect the additional training data by: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;
based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location;
and based on the score function, selecting one or more locations at which to collect additional training data.
PNG
media_image1.png
378
818
media_image1.png
Greyscale
However, Richard does teach The equipment of claim 18, wherein the processing circuitry is configured to determine the one or more locations at which to collect the additional training data by: for each of one or more machine learning features, generating a heatmap representing values of the machine learning feature at different locations in the coverage area of the non-public communication network;
(Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments. High priority locations (triangle) have not been visited in the previous three deployments versus low priority locations (square) have been recently visited. Start position indicated with the circle S." Richard Section IV.A Paragraph 2; "One possible representation of the environment would be an occupancy grid;" Examiner notes that occupancy grid is generated heatmap representing values of the machine learning feature/time since last visit at different location in the coverage area of the non-public communication network with triangles as location not recently visited and squares as recently visited)
based on the one or more heatmaps, generating a score function representing scores for respective locations in the coverage area of the non-public communication network, wherein the score for a location quantifies a benefit of collecting additional training data at the location; (Richard Section IV.C Paragraph 1; "Attached to each measurement location are cost and reward parameters (ei, ci, ri), which allows us to compute navigation paths using variants of the prize collecting traveling salesman problem. Reward adjustments between deployments will incentivize measurement collection from less visited locations in subsequent deployments.” Richard Section IV.C Paragraph 5; "With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Examiner notes that based on the heatmap/occupancy grid, reward and cost for each location in the coverage area of the nonpublic communication network is a score function; the score for a location quantifies a benefit of collecting additional training data at the location/collecting data from less visited location within a short time)
and based on the score function, selecting one or more locations at which to collect additional training data. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "In contrast, the prize collecting TSP also consider rewards collected. One variant is called the orienteering problem, which finds the maximum reward path within a fixed time limit MAX_COST." Examiner notes that solving the prize collecting TSP is selecting one or more locations at which to collect additional training data based on the score function/maximizing the reward path within a fixed time limit)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 20, Bouziane does not teach The equipment of claim 19, wherein the score function represents the score for a location as a function of one or more of: a number of and/or a diversity of values in the training dataset at the location; and/or an accuracy of the machine learning model at the location; and/or an uncertainty of the machine learning model at the location.
However, Richard does teach The equipment of claim 19, wherein the score function represents the score for a location as a function of one or more of: a number of and/or a diversity of values in the training dataset at the location; and/or an accuracy of the machine learning model at the location; and/or an uncertainty of the machine learning model at the location. (Richard Section IV.C Paragraph 5; "With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Richard Section IV.C Paragraph 7; "After each deployment, readjusting the rewards for recently visited measurement locations will ensure variability in the navigation paths for subsequent deployments." Examiner notes that the rewards and cost for each location is a score function to ensure diversity of values in training dataset within at the location within an allotted time; variability in paths means diversity of collected values)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 21, Bouziane does not teach The equipment of claim 18, wherein the signaling comprises, for each of at least one of the one or more autonomous or automated mobile devices, signaling for routing the autonomous or automated mobile device to at least one location of the one or more locations to help collect at least some of the additional training data.
However, Richard does teach The equipment of claim 18, wherein the signaling comprises, for each of at least one of the one or more autonomous or automated mobile devices, signaling for routing the autonomous or automated mobile device to at least one location of the one or more locations to help collect at least some of the additional training data. (Richard Section IV.B Paragraph 2; "With upto- date contextual data, the robot can compute a navigation path (x1, Y1), (x2, Y2)... E P at the beginning of each deployment starting from its current location (xi nit, Yi nit}· The robot executes the path while it is deployed, continuously updating the contextual data records as it collects measurements at different measurement locations emeas in the environment." Examiner notes that robot signals to itself for routing the autonomous or automated mobile device/the robot to at least one location of the one or more locations to help collect at least some of the additional training data)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 22, Bouziane does not teach The equipment of claim 21, wherein the signaling revises a route of the autonomous or automated mobile device to include the at least one location as a destination or waypoint in the route.
However, Richard does teach The equipment of claim 21, wherein the signaling revises a route of the autonomous or automated mobile device to include the at least one location as a destination or waypoint in the route. (Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments." Examiner notes that robot revises a path to include the at least one location as a destination or waypoint in the route/high priority locations shown in fig 3)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 23, Bouziane does not teach The equipment of claim 18, wherein, for each of at least one of the one or more autonomous or automated mobile devices, the signaling comprises signaling for configuring the autonomous or automated mobile device to: perform one or more transmissions of test traffic at one or more of the one or more locations; and/or perform one or more measurements at one or more of the one or more locations and to collect the results of the one or more measurements as at least some of the additional training data.
However, Richard does teach The equipment of claim 18, wherein, for each of at least one of the one or more autonomous or automated mobile devices, the signaling comprises signaling for configuring the autonomous or automated mobile device to: perform one or more transmissions of test traffic at one or more of the one or more locations; and/or perform one or more measurements at one or more of the one or more locations and to collect the results of the one or more measurements as at least some of the additional training data. (Richard Section IV.B Paragraph 2; "The robot executes the path while it is deployed, continuously updating the contextual data records as it collects measurements at different measurement locations emeas in the environment." Examiner notes that each of the at least one of the one or more autonomous or automated mobile devices/the robot is signaled to perform one or more measurements at the one or more of the one or more locations and collect results/collects measurements at different measurements locations as at least some of the additional training data.)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 24, Bouziane does not teach The equipment of claim 15, wherein the processing circuitry is further configured to solve an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints,
wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data,
wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network.
However, Richard does teach The equipment of claim 15, wherein the processing circuitry is further configured to solve an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices, subject to one or more constraints, (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "TSP for Computing Paths: With the formulation of each measurement location as (ei, ci, Ti}, we can construct a tour to visit all locations and compute a navigation path using variants of the prize collecting traveling salesman problem." Examiner notes that computing a navigation path using variants of the prize collecting traveling salesman problem is solving an optimization problem that optimizes a data collection plan for each of the one or more autonomous or automated mobile devices/the robot, subject to one or more constraints)
wherein a data collection plan for an autonomous or automated mobile device includes a plan on what training data the autonomous or automated mobile device will help collect and what route the autonomous or automated mobile device will take as part of helping to collect that training data, (Richard Fig 3; "Fig. 3: Different navigation path choices across multiple deployments." Examiner notes that navigation paths/data collection plan for an autonomous or automated mobile device/the robot includes a plan on what training data the mobile device will help to collect/high priority locations and what route the mobile device will take as a part of helping to collect the training data/the navigation order)
wherein the one or more constraints include one or more of: a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices; and/or a constraint on allowed deviation from a production route of each of the one or more autonomous or automated mobile devices; and/or a constraint on an extent to which collection of additional training data is allowed to disturb the non-public communication network. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Examiner notes that how long it takes to navigate between different measurement locations is a constraint on movement dynamics of each of the one or more autonomous or automated mobile devices/the robot)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Regarding claim 25, Bouziane does not teach The equipment of claim 24, wherein a score function represents scores for respective locations in the coverage area of the non-public communication network,
wherein the score for a location quantifies a benefit of collecting additional training data at the location,
and wherein the processing circuitry is configured to solve the optimization problem by maximizing the score function over a planning time horizon, subject to the one or more constraints.
However, Richard does teach The equipment of claim 24, wherein a score function represents scores for respective locations in the coverage area of the non-public communication network, (Richard Section IV.C Paragraph 1; "Attached to each measurement location are cost and reward parameters (ei, ci, ri), which allows us to compute navigation paths using variants of the prize collecting traveling salesman problem." Examiner notes that reward and cost for each location in the coverage area of the nonpublic communication network is a score function)
wherein the score for a location quantifies a benefit of collecting additional training data at the location, (Richard Section IV.C Paragraph 1; "Reward adjustments between deployments will incentivize measurement collection from less visited locations in subsequent deployments." Examiner notes that the score for a location quantifies a benefit of collecting additional training data at the location/collect data from less visited locations)
and wherein the processing circuitry is configured to solve the optimization problem by maximizing the score function over a planning time horizon, subject to the one or more constraints. (Richard Section IV.C Paragraph 1; "In addition, the robot's underlying navigation graph (V, E) E G constrains how long it takes to navigate between different measurement locations." Richard Section IV.C Paragraph 5; "In contrast, the prize collecting TSP also consider rewards collected. One variant is called the orienteering problem, which finds the maximum reward path within a fixed time limit MAX_COST." Examiner notes that prize collecting TSP/optimization problem comprises maximizing the score function/maximum reward path over a planning time horizon/within a fixed time limit, subject to the one or more constraints/constrains how long it takes to navigate between different measurement locations)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Richard. Bouziane teaches using UAVs in federated learning. Richard teaches a system to compute paths for robots to aid them in data collection. One of ordinary skill would have motivation to combine Bouziane and Richard to use the system to compute paths for the UAVs for data collection that ensure high accuracy, automatic navigation, and reliable measurement collection for training a machine learning model “The major advantage of mobile robots as data collection devices is continuous localization with high accuracy, automatic navigation to numerous locations, and reliable measurement collection under similar mounting conditions” (Richard Section 1 Paragraph 2).
Claim(s) 12, 14, 26, 28 are rejected under 35 U.S.C. 103 as being unpatentable over Bouziane Brik; “Federated Learning for UAVs-Enabled Wireless Networks; Use Cases, Challenges, and Open Problems” (Hereinafter “Bouziane”) in view of Catriona Clarke; US 20250180408 A1 (hereinafter “Clarke”)
Regarding claim 12, Bouziane does not teach The method of claim 1, wherein the training data includes performance management data and/or configuration management data for the non-public communication network.
However, Clarke does teach The method of claim 1, wherein the training data includes performance management data and/or configuration management data for the non-public communication network. (Clarke Paragraph 0024; "a deep learning system trained from recorded environment parameter data to identify thermal anomalies;" Clarke Paragraph 0046; "A number of types of thermally related data are obtainable from the data centre. Energy consumption data 15 can be obtained from the energy source 5, and individual server load performance data 12 obtained from the servers, which can be used to provide information about heat generation (and its location) in the data centre." Examiner notes that training data/parameter data includes performance management data/load performance data for the non-public communication network)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Clarke. Bouziane teaches using UAVs in federated learning. Clarke teaches a system that collects environment parameter data to train a deep learning system to identify thermal anomalies. One of ordinary skill would have motivation to combine Bouziane and Clarke to use the system to collect training data to train a machine learning model for anomaly detection to keep the model’s effectiveness and productivity from reducing. “Thermal anomalies might lead to system operation in unsafe temperature regions. This can lead to accelerated degradation of servers or other apparatus—this can lead to reduced effectiveness and hence reduced productivity, unplanned down time, and safety issues. It is therefore important to identify and diagnose thermal anomalies in real time to take appropriate corrective actions before problems result.” (Clarke Paragraph 0008).
Regarding claim 14, Bouziane does not teach The method of claim 1, further comprising, after validating the re-trained machine learning model, using the re-trained machine learning model for root-cause analysis, anomaly detection, or network optimization in the non-public communication network.
However, Clarke does teach The method of claim 1, further comprising, after validating the re-trained machine learning model, using the re-trained machine learning model for root-cause analysis, anomaly detection, or network optimization in the non-public communication network. (Clarke Paragraph 0084; "this information can be used to retrain the model to perform better in future." Clarke Paragraph 0060; "This provides a thermal anomaly detection model, but this model now needs to be validated 230 before use." Examiner notes that the model can be retrained and after validation, the re-trained model is used for anomaly detection)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Clarke. Bouziane teaches using UAVs in federated learning. Clarke teaches a system that collects environment parameter data to train a deep learning system to identify thermal anomalies. One of ordinary skill would have motivation to combine Bouziane and Clarke to use the system to collect training data to train a machine learning model for anomaly detection to keep the model’s effectiveness and productivity from reducing. “Thermal anomalies might lead to system operation in unsafe temperature regions. This can lead to accelerated degradation of servers or other apparatus—this can lead to reduced effectiveness and hence reduced productivity, unplanned down time, and safety issues. It is therefore important to identify and diagnose thermal anomalies in real time to take appropriate corrective actions before problems result.” (Clarke Paragraph 0008).
Regarding claim 26, Bouziane does not teach The equipment of claim 15, wherein the training data includes performance management data and/or configuration management data for the non-public communication network.
However, Clarke does teach The equipment of claim 15, wherein the training data includes performance management data and/or configuration management data for the non-public communication network. (Clarke Paragraph 0024; "a deep learning system trained from recorded environment parameter data to identify thermal anomalies;" Clarke Paragraph 0046; "A number of types of thermally related data are obtainable from the data centre. Energy consumption data 15 can be obtained from the energy source 5, and individual server load performance data 12 obtained from the servers, which can be used to provide information about heat generation (and its location) in the data centre." Examiner notes that training data/parameter data includes performance management data/load performance data for the non-public communication network)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Clarke. Bouziane teaches using UAVs in federated learning. Clarke teaches a system that collects environment parameter data to train a deep learning system to identify thermal anomalies. One of ordinary skill would have motivation to combine Bouziane and Clarke to use the system to collect training data to train a machine learning model for anomaly detection to keep the model’s effectiveness and productivity from reducing. “Thermal anomalies might lead to system operation in unsafe temperature regions. This can lead to accelerated degradation of servers or other apparatus—this can lead to reduced effectiveness and hence reduced productivity, unplanned down time, and safety issues. It is therefore important to identify and diagnose thermal anomalies in real time to take appropriate corrective actions before problems result.” (Clarke Paragraph 0008).
Regarding claim 28, Bouziane does not teach The equipment of claim 15, wherein the processing circuitry is further configured to, after validating the re-trained machine learning model, using the re-trained machine learning model for root-cause analysis, anomaly detection, or network optimization in the non-public communication network.
However, Clarke does teach The equipment of claim 15, wherein the processing circuitry is further configured to, after validating the re-trained machine learning model, using the re-trained machine learning model for root-cause analysis, anomaly detection, or network optimization in the non-public communication network. (Clarke Paragraph 0084; "this information can be used to retrain the model to perform better in future." Clarke Paragraph 0060; "This provides a thermal anomaly detection model, but this model now needs to be validated 230 before use." Examiner notes that the model can be retrained and after validation, the re-trained model is used for anomaly detection)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Bouziane and Clarke. Bouziane teaches using UAVs in federated learning. Clarke teaches a system that collects environment parameter data to train a deep learning system to identify thermal anomalies. One of ordinary skill would have motivation to combine Bouziane and Clarke to use the system to collect training data to train a machine learning model for anomaly detection to keep the model’s effectiveness and productivity from reducing. “Thermal anomalies might lead to system operation in unsafe temperature regions. This can lead to accelerated degradation of servers or other apparatus—this can lead to reduced effectiveness and hence reduced productivity, unplanned down time, and safety issues. It is therefore important to identify and diagnose thermal anomalies in real time to take appropriate corrective actions before problems result.” (Clarke Paragraph 0008).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DUC TRAN whose telephone number is (571)272-6870. The examiner can normally be reached Mon-Fri 8:00-5:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at (571) 270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.D.T./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
Prosecution Insights