Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on application JP2022-096452 filed in Japan on 06/15/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
The Amendment filed 05/19/2026 has been entered. Claims 2-3, 8, 11, 14 are canceled. Claims 1, 4-7, 9-10, 12-13 and 15-20 remain pending in the application.
Allowable Subject Matter
Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Miklosovic et al. (US 20210341901 A1) hereinafter Miklosovic in view of Fathallah-Shaykh et al. (US 20220366223 A1) hereinafter Fathallah-Shaykh and GHOSH et al. (US 20210374500 A1) hereinafter Ghosh.
As to independent claim 1, Miklosovic teaches a data analysis system comprising: [environment for analyzing data ¶44]
a memory [storage memory ¶102] configured to store a data analysis model, wherein [stores an analytics engine ¶104, that uses machine learning (model) ¶33]
the data analysis model is trained in advance using training data such that when the data analysis model receives input data on a target to be analyzed, the data analysis model outputs an output value indicating whether the target is in a specific state, and [trained with historical data and detects faults (state) ¶45, ¶43 "historical data is used to build and train a model that is used for early detection of rotor and stator faults with high accuracy and repeatability."]
circuitry configured to: [drive circuitry ¶9]
acquire control data of a motor control apparatus for a motor that is configured to drive a mechanism of an industrial machine, [runtime signal data (control data) for driving a motor ¶7 " obtaining runtime signal data in a drive configured to control power supplied to the induction motor"]
repeatedly input the acquired control data to the data analysis model as the input data a plurality of times, [cycles through capturing data and detections continuously ¶60 " continuously sequence through the capture, metrics, and detection cycle, starting each task when the previous tasks that feed it are complete. When detection is complete, another capture is initiated after an optional pre-programmed wait time."]
estimate a cause of the specific abnormal phenomenon occurring in the motor using the calculated estimate and present the estimated cause to a user. [identifies status such as broken rotor bars (specific phenomenon) and outputs results ¶81" Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)."]
Miklosovic does not specifically teach the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies,
However, Fathallah-Shaykh teaches the data analysis model includes a parameter that includes a random variable; and [parameters and random variables ¶12 "Bayesian framework, model parameters, i.e., the weights and biases, are defined as random variables"]
calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, [calculates a degree (confidence) of predictions using mean, covariance, distribution ¶4-5 "compute a confidence in the prediction based at least in part on the mean matrix and the covariance matrix of the output vector"]
wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies, and [greater covariance leads to uncertainty and thus lowers confidence ¶14 "The covariance of the predictive distribution, which represents the uncertainty associated with the prediction, is the covariance of the distribution of the weights propagated through layers of the CNN"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the fault modeling disclosed by Miklosovic by incorporating the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies disclosed by Fathallah-Shaykh because both techniques address the same field of machine learning and by incorporating Fathallah-Shaykh into Miklosovic improve learning models to demonstrate confidence in predictions allowing users to better deploy models in real-world scenarios [Fathallah-Shaykh ¶4, ¶12]
Fathallah-Shaykh and Miklosovic do not specifically teach wherein the acquired control data is the same for each of the plurality of times.
However, Ghosh teaches wherein the acquired control data is the same for each of the plurality of times. [same input passed through the learner multiple time ¶43-44 " the same input passed through the trained learners will lead to different outputs if passed through a multiple number of times."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic and Fathallah-Shaykh by incorporating the wherein the acquired control data is the same for each of the plurality of times disclosed by Ghosh because all techniques address the same field of machine learning and by incorporating Ghosh into Miklosovic and Fathallah-Shaykh reduce equipment downtime and inconsistent decisions from models [Ghosh ¶4].
As to dependent claim 4 the rejection of claim 1 is incorporated, Miklosovic, Fathallah-Shaykh and Ghosh further teach the memory is configured to store a plurality of data analysis models configured to output, respectively, a plurality of the output values indicating whether a plurality of abnormal phenomena that are mutually different occur, and [Miklosovic multiple algorithms and fault conditions (broken rotor bars and stator windings) ¶61, ¶81-82 “One or more machine learning techniques may be implemented in machine learning module 1020, including but not limited to an SVM classifier”]
the circuitry is configured to calculate an estimate indicating a degree of occurrence of each of the plurality of abnormal phenomena that are mutually different based on a plurality of distributions of, respectively, the plurality of output values, the plurality of output values being obtained by the circuitry repeatedly inputting the control data to each of the plurality of data analysis models a plurality of times. [Fathallah-Shaykh multiple models and confidence ¶4]
As to dependent claim 5 the rejection of claim 1 is incorporated, Miklosovic, Fathallah-Shaykh and Ghosh further teach identify a unit phenomenon that has occurred in the mechanism based on the control data, and [Miklosovic fault conditions (broken rotor bars and stator windings) ¶81-82]
input unit phenomenon data related to the unit phenomenon to the data analysis model repeatedly a plurality of times. [Ghosh input passed through multiple time ¶43-44 " the same input passed through the trained learners will lead to different outputs if passed through a multiple number of times."]
As to dependent claim 17, the rejection of claim 1 is incorporated, Miklosovic, Fathallah-Shaykh and Ghosh further teach wherein the circuitry is configured to: calculate an indicator related to a variation in the distribution and a mean of a plurality of the output values; and [Fathallah-Shaykh mean matrix and covariance ¶5]
calculate the estimate based on the indicator and the mean. [Fathallah-Shaykh mean matrix and covariance used in confidence calculation ¶5 " compute a confidence in the prediction based at least in part on the mean matrix and the covariance matrix of the output vector."]
As to dependent claim 18, the rejection of claim 1 is incorporated, Miklosovic, Fathallah-Shaykh and Ghosh further teach wherein the circuitry is configured to calculate, as the estimate, a value indicating a probability that a specific abnormal phenomenon has occurred in the target to be analyzed based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution. [Miklosovic fault threshold ¶56], [Fathallah-Shaykh confidence ¶4]
As to independent claim 19, Miklosovic teaches a data analysis method comprising: storing a data analysis model, wherein a data analysis system comprising: [environment for analyzing data ¶44]
a memory [storage memory ¶102] configured to store a data analysis model, wherein [stores an analytics engine ¶104, that uses machine learning (model) ¶33]
the data analysis model is trained in advance using training data such that when the data analysis model receives input data on a target to be analyzed, the data analysis model outputs an output value indicating whether the target is in a specific state, and [trained with historical data and detects faults (state) ¶45, ¶43 "historical data is used to build and train a model that is used for early detection of rotor and stator faults with high accuracy and repeatability."]
circuitry configured to: [drive circuitry ¶9]
acquire control data of a motor control apparatus for a motor that is configured to drive a mechanism of an industrial machine, [runtime signal data (control data) for driving a motor ¶7 " obtaining runtime signal data in a drive configured to control power supplied to the induction motor"]
repeatedly input the acquired control data to the data analysis model as the input data a plurality of times, [cycles through capturing data and detections continuously ¶60 " continuously sequence through the capture, metrics, and detection cycle, starting each task when the previous tasks that feed it are complete. When detection is complete, another capture is initiated after an optional pre-programmed wait time."]
estimate a cause of the specific abnormal phenomenon occurring in the motor using the calculated estimate and present the estimated cause to a user. [identifies status such as broken rotor bars (specific phenomenon) and outputs results ¶81" Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)."]
Miklosovic does not specifically teach the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies,
However, Fathallah-Shaykh teaches the data analysis model includes a parameter that includes a random variable; and [parameters and random variables ¶12 "Bayesian framework, model parameters, i.e., the weights and biases, are defined as random variables"]
calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, [calculates a degree (confidence) of predictions using mean, covariance, distribution ¶4-5 "compute a confidence in the prediction based at least in part on the mean matrix and the covariance matrix of the output vector"]
wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies, and [greater covariance leads to uncertainty and thus lowers confidence ¶14 "The covariance of the predictive distribution, which represents the uncertainty associated with the prediction, is the covariance of the distribution of the weights propagated through layers of the CNN"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the fault modeling disclosed by Miklosovic by incorporating the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies disclosed by Fathallah-Shaykh because both techniques address the same field of machine learning and by incorporating Fathallah-Shaykh into Miklosovic improve learning models to demonstrate confidence in predictions allowing users to better deploy models in real-world scenarios [Fathallah-Shaykh ¶4, ¶12]
Fathallah-Shaykh and Miklosovic do not specifically teach wherein the acquired control data is the same for each of the plurality of times.
However, Ghosh teaches wherein the acquired control data is the same for each of the plurality of times. [same input passed through the learner multiple time ¶43-44 " the same input passed through the trained learners will lead to different outputs if passed through a multiple number of times."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic and Fathallah-Shaykh by incorporating the wherein the acquired control data is the same for each of the plurality of times disclosed by Ghosh because all techniques address the same field of machine learning and by incorporating Ghosh into Miklosovic and Fathallah-Shaykh reduce equipment downtime and inconsistent decisions from models [Ghosh ¶4].
As to independent claim 20, Miklosovic teaches non-transitory computer-readable recording medium containing a program for causing circuitry to implement processing, the processing comprising: storing a data analysis model [environment for analyzing data ¶44], [stores an analytics engine ¶104, that uses machine learning (model) ¶33]
the data analysis model is trained in advance using training data such that when the data analysis model receives input data on a target to be analyzed, the data analysis model outputs an output value indicating whether the target is in a specific state, and [trained with historical data and detects faults (state) ¶45, ¶43 "historical data is used to build and train a model that is used for early detection of rotor and stator faults with high accuracy and repeatability."]
circuitry configured to: [drive circuitry ¶9]
acquire control data of a motor control apparatus for a motor that is configured to drive a mechanism of an industrial machine, [runtime signal data (control data) for driving a motor ¶7 " obtaining runtime signal data in a drive configured to control power supplied to the induction motor"]
repeatedly input the acquired control data to the data analysis model as the input data a plurality of times, [cycles through capturing data and detections continuously ¶60 " continuously sequence through the capture, metrics, and detection cycle, starting each task when the previous tasks that feed it are complete. When detection is complete, another capture is initiated after an optional pre-programmed wait time."]
estimate a cause of the specific abnormal phenomenon occurring in the motor using the calculated estimate and present the estimated cause to a user. [identifies status such as broken rotor bars (specific phenomenon) and outputs results ¶81" Machine learning module 1020 may use the fault signature information provided by drive 1010 as input to produce output 1030, in which a status is identified. The status may include a number of broken rotor bars (i.e., 1 BRB, 2 BRB, 3 BRB, 4 BRB, or healthy)."]
Miklosovic does not specifically teach the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies,
However, Fathallah-Shaykh teaches the data analysis model includes a parameter that includes a random variable; and [parameters and random variables ¶12 "Bayesian framework, model parameters, i.e., the weights and biases, are defined as random variables"]
calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, [calculates a degree (confidence) of predictions using mean, covariance, distribution ¶4-5 "compute a confidence in the prediction based at least in part on the mean matrix and the covariance matrix of the output vector"]
wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies, and [greater covariance leads to uncertainty and thus lowers confidence ¶14 "The covariance of the predictive distribution, which represents the uncertainty associated with the prediction, is the covariance of the distribution of the weights propagated through layers of the CNN"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the fault modeling disclosed by Miklosovic by incorporating the data analysis model includes a parameter that includes a random variable; and calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies disclosed by Fathallah-Shaykh because both techniques address the same field of machine learning and by incorporating Fathallah-Shaykh into Miklosovic improve learning models to demonstrate confidence in predictions allowing users to better deploy models in real-world scenarios [Fathallah-Shaykh ¶4, ¶12]
Fathallah-Shaykh and Miklosovic do not specifically teach wherein the acquired control data is the same for each of the plurality of times.
However, Ghosh teaches wherein the acquired control data is the same for each of the plurality of times. [same input passed through the learner multiple time ¶43-44 " the same input passed through the trained learners will lead to different outputs if passed through a multiple number of times."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic and Fathallah-Shaykh by incorporating the wherein the acquired control data is the same for each of the plurality of times disclosed by Ghosh because all techniques address the same field of machine learning and by incorporating Ghosh into Miklosovic and Fathallah-Shaykh reduce equipment downtime and inconsistent decisions from models [Ghosh ¶4].
Claims 7 and 9-10, 12-16 are rejected under 35 U.S.C. 103 as being unpatentable over Miklosovic, Fathallah-Shaykh and Ghosh, as applied in the rejection of claim 1 above, and further in view of Harper et al. (US 20210015417 A1 hereinafter Harper).
As to dependent claim 7, Miklosovic, Fathallah-Shaykh and Ghosh, teach the rejection of claim 1 that is incorporated.
Miklosovic, Fathallah-Shaykh and Ghosh do not specifically teach wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution.
However, Harper teaches wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution. [Harper confidence threshold at output distribution ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic, Fathallah-Shaykh and Ghosh by incorporating the wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution disclosed by Harper because all techniques address the same field of machine learning systems and by incorporating Harper into Miklosovic, Fathallah-Shaykh and Ghosh develop more efficient and effective ways of assisting humans [Harper ¶3-4]
As to dependent claim 9, Miklosovic, Fathallah-Shaykh, and Ghosh, teach the rejection of claim 4 that is incorporated.
Miklosovic, Fathallah-Shaykh, and Ghosh do not specifically teach wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution.
However, Harper teaches wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution. [Harper confidence threshold at output distribution ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic, Fathallah-Shaykh, and Ghosh by incorporating the wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution disclosed by Harper because all techniques address the same field of machine learning systems and by incorporating Harper into Miklosovic, Fathallah-Shaykh, and Ghosh develop more efficient and effective ways of assisting humans [Harper ¶3-4]
As to dependent claim 10, the rejection of claim 7 is incorporated, Miklosovic, Fathallah-Shaykh, Ghosh and Harper further teach input the training data to the data analysis model that is trained repeatedly a plurality of times; and [Miklosovic trained, cycles ¶43, ¶56, ¶62]
determine the threshold based on a distribution of a plurality of the output values output from the data analysis model that is trained for the plurality of times. [Harper confidence threshold at output distribution ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
As to dependent claim 12, the rejection of claim 9 is incorporated, Miklosovic, Fathallah-Shaykh, Ghoshl and Harper further teach input the training data to the data analysis model that is trained repeatedly a plurality of times; and [Miklosovic trained, cycles ¶43, ¶56, ¶62]
determine the threshold based on a distribution of a plurality of the output values output from the data analysis model that is trained for the plurality of times. [Harper confidence threshold at output distribution ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
As to dependent claim 13, the rejection of claim 10 is incorporated, Miklosovic, Fathallah-Shaykh, Ghosh and Harper further teach input a plurality of pieces of the training data, respectively, to the data analysis model that is trained repeatedly a plurality of times, [Simonis trained and sent at regular intervals ¶21 "The model parameters of the anomaly detection model trained in this way can then be transmitted to the motor vehicle once or at regular intervals."]
calculate candidates for the threshold for each of the plurality of pieces of the training data based on the distribution obtained from the plurality of pieces of the training data, and [Harper confidence threshold at various levels ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
determine a largest candidate out of the candidates as the threshold. [Harper threshold set to make model less risky ¶91 "the model behaviour moves from risky to cautious but with less likelihood that a classification will be output (but with more certainty for classifications that are output). For binary classifications, there will always be at least 50% of the output distribution that will be within one of the two prediction zones, thus when α=0.5 the classification is determined by the median of the output distribution and a classification will always be made."]
As to dependent claim 14, the rejection of claim 11 is incorporated, Miklosovic, Fathallah-Shaykh, Ghoshl and Harper further teach input a plurality of pieces of the training data, respectively, to the data analysis model that is trained repeatedly a plurality of times, [Miklosovic trained, cycles ¶43, ¶56, ¶62] calculate candidates for the threshold for each of the plurality of pieces of the training data based on the distribution obtained from the plurality of pieces of the training data, and [Harper confidence threshold at various levels ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
determine a largest candidate out of the candidates as the threshold. [Harper threshold set to make model risky ¶91 "the model behaviour moves from risky to cautious but with less likelihood that a classification will be output (but with more certainty for classifications that are output). For binary classifications, there will always be at least 50% of the output distribution that will be within one of the two prediction zones, thus when α=0.5 the classification is determined by the median of the output distribution and a classification will always be made."]
As to dependent claim 15, the rejection of claim 12 is incorporated, Miklosovic, Fathallah-Shaykh, Ghoshl and Harper further teach input a plurality of pieces of the training data, respectively, to the data analysis model that is trained repeatedly a plurality of times, [Miklosovic trained, cycles ¶43, ¶56, ¶62]
calculate candidates for the threshold for each of the plurality of pieces of the training data based on the distribution obtained from the plurality of pieces of the training data, and [Harper confidence threshold at various levels ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
determine a largest candidate out of the candidates as the threshold. [Harper threshold set to make model risky ¶91 "the model behaviour moves from risky to cautious but with less likelihood that a classification will be output (but with more certainty for classifications that are output). For binary classifications, there will always be at least 50% of the output distribution that will be within one of the two prediction zones, thus when α=0.5 the classification is determined by the median of the output distribution and a classification will always be made."]
As to dependent claim 16, the rejection of claim 1 is incorporated, Miklosovic, Fathallah-Shaykh, and Ghosh further teach input a plurality of pieces of the training data, respectively, to the data analysis model that is trained repeatedly a plurality of times, [Miklosovic trained, cycles ¶43, ¶56, ¶62]
Miklosovic, Fathallah-Shaykh, and Ghosh do not specifically teach calculate candidates for the threshold for each of the plurality of pieces of the training data based on the distribution obtained from the plurality of pieces of the training data and determine a largest candidate out of the candidates as the threshold
However, Harper teaches calculate candidates for the threshold for each of the plurality of pieces of the training data based on the distribution obtained from the plurality of pieces of the training data, and [Harper confidence threshold at various levels ¶91 "Next a confidence threshold parameter a is used to tune predictions to a specified level of model uncertainty. For example, when α=0.95, at least 95% of the output distribution must lie in a given class zone in order for the input sample to be classified as belonging to that class (see FIG. 6). If this is not the case, then no prediction is made"]
determine a largest candidate out of the candidates as the threshold. [Harper threshold set to make model risky ¶91 "the model behaviour moves from risky to cautious but with less likelihood that a classification will be output (but with more certainty for classifications that are output). For binary classifications, there will always be at least 50% of the output distribution that will be within one of the two prediction zones, thus when α=0.5 the classification is determined by the median of the output distribution and a classification will always be made."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the predictions disclosed by Miklosovic, Fathallah-Shaykh, and Ghosh by incorporating the wherein the circuitry is configured to calculate the estimate based on a percentage of a plurality of the output values equal to or greater than a threshold or a plurality of the output values equal to or less than the threshold, in the distribution disclosed by Harper because all techniques address the same field of machine learning systems and by incorporating Harper into Miklosovic, Fathallah-Shaykh, and Ghosh develop more efficient and effective ways of assisting humans [Harper ¶3-4]
Response to Arguments
Applicant's arguments filed 06/05/2026, with respect to 101, these rejections have been withdrawn
Applicant's arguments filed 06/05/2026. In the remark, applicant argues that:
(1) Fathallah-Shaykh, Simons, Adamsand Harper fail to teach "acquire control data of a motor control apparatus for a motor that is configured to drive a mechanism of an industrial machine, repeatedly input the acquired control data to the data analysis model as the input data a plurality of times, wherein the acquired control data is the same for each of the plurality of times,
calculate an estimate indicating a degree of occurrence of a specific abnormal phenomenon as the specific state based on a distribution of a plurality of output values output from the data analysis model in response to the repeated inputting of the acquired control data, wherein the estimate is calculated such that the degree of occurrence of the specific abnormal phenomenon becomes lower as the distribution varies, and estimate a cause of the specific abnormal phenomenon occurring in the motor using the calculated estimate and present the estimated cause to a user." as recited in amended claim 1 and discussed in interview.
As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Miklosovic in view of Fathallah-Shaykh and Ghosh.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Munoz Delgado (US 20210089895 A1) teaches sampling techniques for machine learning with confidence calculations (see ¶12-13).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEAU SPRATT whose telephone number is (571)272-9919. The examiner can normally be reached M-F 8:30-5 PST.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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