DETAILED ACTION
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 .
This action is in response to the original application filed on Feb. 15th, 2024.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 9 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation, “determine the machine learning model having a maximum accuracy from the set of tested machine learning models to:” (Emphasis added). The term “maximum accuracy” is indefinite because the claims and specification fail to define what a “maximum accuracy” is and fails to disclose defined metrics for determining the maximum accuracy of a machine learning model. Therefore, since the claim uses indefinite language for failing to set definable bounds of the claim, this claim is rejected under 35 U.S.C. 112(b) for being indefinite. For examination purposes the claim limitation, “determine the machine learning model having a maximum accuracy from the set of tested machine learning models to:”, will be interpreted by the examiner to disclose, determining a model which provides an appropriate output from a set of training models. Appropriate action and correction is required.
Claim Rejections - 35 USC § 101
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 1-15 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 recites, “A non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor of a computing device, cause the processor to:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine a deviation associated with a component of the electronic device by applying a machine learning model to the device usage data and the sensor data, wherein the deviation is associated with the first characteristic, the second characteristic, or both; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use generic computing devices to apply abstract functions to evaluate data from sensors to determine outliers or deviations in data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor of a computing device, cause the processor to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receive device usage data associated with an electronic device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“receive sensor data indicative of an internal state of the electronic device, the sensor data comprising first data associated with a first characteristic of the internal state and second data associated with a second characteristic of the internal state;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“generate an alert notification based on the determined deviation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor of a computing device, cause the processor to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“receive device usage data associated with an electronic device;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“receive sensor data indicative of an internal state of the electronic device, the sensor data comprising first data associated with a first characteristic of the internal state and second data associated with a second characteristic of the internal state;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“generate an alert notification based on the determined deviation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine the deviation of the component by comparing a difference between the received first data related to the first characteristic of the component and first reference data based on the device usage data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use generic computing devices to evaluate and compare data from different sensors and determine deviations in the data compared to reference values. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine the deviation of the component by comparing a difference between the received second data related to the second characteristic of the component and second reference data based on the device usage data; or a combination thereof.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use generic computing devices to evaluate and compare data from different sensors and determine deviations in the data compared to reference values. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein instructions to determine the deviation associated with the component comprise instructions to: apply the machine learning model to the device usage data, the first data and the second data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein instructions to determine the deviation associated with the component comprise instructions to: apply the machine learning model to the device usage data, the first data and the second data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed data, device usage time data, device age data, or any combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed data, device usage time data, device age data, or any combination thereof.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein instructions to determine the deviation associated with the component comprise instructions to: correlate the device usage data with the first data and the second data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data using a generic computing device to find patterns in a systems workload as it relates to sensor data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine the deviation associated with the component by applying the machine learning model to the device usage data, the first data, and the second data based on the correlation.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able use a generic computer and generic functions to evaluate data to identify deviations or anomalies in data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the alert notification is to include a recommended action to reduce the deviation associated with the component replace the component, or a combination thereof, and wherein the alert notification is generated when the determined deviation exceeds a threshold.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the alert notification is to include a recommended action to reduce the deviation associated with the component replace the component, or a combination thereof, and wherein the alert notification is generated when the determined deviation exceeds a threshold.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 6 recites, "A non-transitory machine-readable storage medium storing instructions executable by a processor of a computing device to:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“process the historical device usage data and the historical sensor data to generate a train dataset and a test dataset;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate datasets and produce training data from the datasets. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine a machine learning model from the set of tested machine learning models to estimate, in real-time, the sound deviation, temperature deviation, or both of the component.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computing device evaluate machine learning models and select a model based on an evaluation. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “obtain historical device usage data and historical sensor data of an electronic device, the historical device usage data comprising processor usage data, device charging data device usage time or any combination thereof, and the historical sensor data comprising device sound data and device temperature data;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“train a set of machine learning models to estimate a sound deviation, temperature deviation, or both of a component of the electronic device using the train dataset;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“test the trained set of machine learning models with the test dataset; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “obtain historical device usage data and historical sensor data of an electronic device, the historical device usage data comprising processor usage data, device charging data device usage time or any combination thereof, and the historical sensor data comprising device sound data and device temperature data;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“train a set of machine learning models to estimate a sound deviation, temperature deviation, or both of a component of the electronic device using the train dataset;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“test the trained set of machine learning models with the test dataset; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Further, the process of testing trained machine learning model(s) with test data, is well-understood, routine and conventional as it is described in MPEP 2106.05(d) per Brownlee, (Brownlee, “Train-Test Split for Evaluating Machine Learning Algorithms”, Aug. 26th, 2020, Train-Test Split for Evaluating Machine Learning Algorithms - MachineLearningMastery.com; “The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small data set and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced. In this tutorial, you will discover how to evaluate machine learning models using the train-test split.”. This internet article is from a popular machine learning education website and discloses a basic tutorial on how to partition a dataset into training and test datasets. The test datasets are applied to the trained machine learning models to test the training accuracy. This is a routine and well understood machine learning practice, especially for models which use supervised learning and/or have large labeled datasets.)
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“estimate the sound deviation, the temperature deviation, or both associated with the component by analyzing the real-time device usage data and the real-time sensor data using the determined machine learning model;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data using generic computing systems and generic functions to locate and estimate deviations in systems based on the provided sensor data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “receive real-time device usage data and real-time sensor data associated with the electronic device;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“generate an alert notification based on the sound deviation, the temperature deviation, or both; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“send the alert notification to the electronic device.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “receive real-time device usage data and real-time sensor data associated with the electronic device;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“generate an alert notification based on the sound deviation, the temperature deviation, or both; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“send the alert notification to the electronic device.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein instructions to train the set of machine learning models comprise instructions to: train the set of machine learning models to: classify the historical sensor data to identify data associated with the component; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computer system to evaluate and classify data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“estimate the sound deviation, the temperature deviation, or both associated with the component using the classified historical sensor data and the historical device usage data of the train dataset.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine outlier or anomalies in the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine the machine learning model having a maximum accuracy from the set of tested machine learning models to:” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate machine learning model and determine the best, worst and appropriate models for a specific application. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identify the component of the electronic device that generates sound, temperature, or both using real-time sensor data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate sensor data and determine which component of a device is generating deviant data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“estimate the sound deviation, the temperature deviation, or both associated with the component for real-time device usage data and the real- time sensor data, wherein the real-time device usage data is to indicate a load on the component that impacts the sound, the temperature, or both associated with the component.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computing system and generic functions to evaluate sensor data to determine how a system is impacted by different workloads. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein instructions to determine the machine learning model from the set of tested machine learning models to estimate the sound deviation, the temperature deviation, or both comprise instructions to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein instructions to determine the machine learning model from the set of tested machine learning models to estimate the sound deviation, the temperature deviation, or both comprise instructions to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “prior to testing the trained set of machine learning models, validate the trained machine learning models to tune an accuracy of the trained machine learning models based on a validation dataset of the processed historical device usage data and the historical sensor data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “prior to testing the trained set of machine learning models, validate the trained machine learning models to tune an accuracy of the trained machine learning models based on a validation dataset of the processed historical device usage data and the historical sensor data.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“further comprising instructions to process the historical device usage data and the historical sensor data to generate the train dataset and the test dataset comprises instructions to: correlate the historical device usage data with the historical sensor data; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a generic computing system to evaluate data and correlate system usage or performance and prior sensor data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “generate the train dataset and the test dataset based on the correlation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “generate the train dataset and the test dataset based on the correlation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 12 recites, "An electronic device comprising:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“classify the retrieved sensor data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and classify data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“identify a component of the electronic device that generates sound, temperature, or both using the classified sensor data;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and identify which component in a system produced the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determine that the sound, temperature, or both associated with the component is to impact a performance of the electronic device; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine a component which is harming, or helping, a system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“in response to the determination, determine a recommended action to reduce the sound, the temperature, or both; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and use general reasoning and judgement to determine an appropriate response to deviant sensor data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A storage device;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“An output device; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“a processor to: retrieve, from the storage device, sensor data for a period in response to receiving a trigger event, wherein the sensor data comprises device sound data and device temperature data;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“apply a machine learning model to the sensor data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“output an alert notification including the recommended action via the output device.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A storage device;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“An output device; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“a processor to: retrieve, from the storage device, sensor data for a period in response to receiving a trigger event, wherein the sensor data comprises device sound data and device temperature data;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”.
“apply a machine learning model to the sensor data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“output an alert notification including the recommended action via the output device.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “a sound sensor to record the device sound data associated with the electronic device, wherein the sound sensor comprises a microphone; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“a temperature sensor to record the device temperature data associated with the electronic device.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “a sound sensor to record the device sound data associated with the electronic device, wherein the sound sensor comprises a microphone; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“a temperature sensor to record the device temperature data associated with the electronic device.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“classify the filtered sensor data into a group of categories, wherein the sensor data associated with a category in the group of categories belongs to the component of the electronic device.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and classify data based on the identified sensor data types. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “Apply the machine learning model to the sensor data to: filter the sensor data to remove ambient sound and ambient temperature from the retrieved sensor data; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, Apply the machine learning model to the sensor data to: filter the sensor data to remove ambient sound and ambient temperature from the retrieved sensor data; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determine that the sound, the temperature, or both associated with the component is to impact the performance of the electronic device.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and determine deviant data patterns. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “retrieve, from the storage device, device usage data for the period, wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed, device usage time, device age, or any combination thereof; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“apply the machine learning model to the sensor data and the device usage data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “retrieve, from the storage device, device usage data for the period, wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed, device usage time, device age, or any combination thereof; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“apply the machine learning model to the sensor data and the device usage data to:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Trinh et al, (Trinh et al, “MACHINE LEARNING BASED PREDICTIVE MAINTENANCE OF EQUIPMENT”, US 2020/0379454 A1, Filed 2020, hereinafter “Trinh”) in view of Sun et al, (Sun et al, “METHOD AND SYSTEMS FOR FAULT DETECTION AND IDENTIFICATION”, US 2020/0241514 A1, Filed 2020, hereinafter “Sun”).
Regarding claim 1, Trinh discloses, “A non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor of a computing device, cause the processor to:” (Computer Architecture, pp. 15, [0113]; “The storage device 2008 is a non-transitory computer- readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid state memory device. The non-transitory computer-readable storage medium may store computer code that includes instructions. The instructions, when executed by the processor 2002, cause the processor 2002 to perform various processes and methods described herein.” The model disclosed in this article contains multiple computing systems which use common programming instructions which are located memory devices. These instructions are used by the processing system to execute the disclosed functions.)
“receive device usage data associated with an electronic device;” (System Overview, pp. 4, [0044]; “The predictive maintenance server 110 receives and analyzes the data transmitted from various sensors 154 and settings 152.” This system will receive information from a variety of sensors. Some of the data gathered will be time and location data of the system.)
“determine a deviation associated with a component of the electronic device by applying a machine learning model to the device usage data and the sensor data, wherein the deviation is associated with the first characteristic, the second characteristic, or both; and” (Detailed Description, pp. 4, [0044]; “The predictive maintenance server 110 receives and analyzes the data transmitted from various sensors 154 and settings 152. The predictive maintenance server 110 may train one or more machine learning models that assign anomaly scores to a piece of equipment 150. The anomaly scores may include an overall anomaly score and individual anomaly scores each corresponding to a component, a measurement, or an aspect of the equipment 150.” This system will use machine learning methods to evaluate data and determine anomalies. This will take sensor data from one or more sensors and input the data into a machine learning model to produce a failure prediction.)
Trinh fails to explicitly disclose, “receive sensor data indicative of an internal state of the electronic device, the sensor data comprising first data associated with a first characteristic of the internal state and second data associated with a second characteristic of the internal state;” and “generate an alert notification based on the determined deviation.”.
However, Sun discloses, “receive sensor data indicative of an internal state of the electronic device, the sensor data comprising first data associated with a first characteristic of the internal state and second data associated with a second characteristic of the internal state;” (Detailed Description, pp. 5, [0056]; “In step 304, the central monitoring system can receive an initial observation (xt). The initial observation can comprise a measurement from each of the one or more sensors. In some aspects, the initial observation can be received in response to a request for observation transmitted from the central monitoring system to the one or more sensors.” The system in this article will also gather information from different sensors to predict a failure state of a component.) and (Detailed Description, pp. 5, [0052]; “The model can comprise a plurality of states for the industrial system. In some aspects, the states can be represented by values for measurements at each of one or more sensors (e.g., the one or more sensors 202).” This system can gather and evaluate data from one or more sensors. The sensors indicate a state of a given system)
“generate an alert notification based on the determined deviation.” (Detailed Description, pp. 6, [0068]; “In some aspects, the determination of a fault state can cause one or more alarms to occur. Example alarms can comprise, for example, an aural alert, a visual alert, such as a flashing light, transmission of an electronic message (e.g., an email, a text message, a pop-up window on a computer screen, etc.) to one or more system administrators, and/or other similar alerts.” This system will also evaluate sensor data and use machine learning to determine a failure state. When a threshold is met, this system will output an alert based on the output of the model)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Trinh and Sun. Trinh teaches a system that uses machine learning models to evaluate sensor data from other systems and predict anomalies and provide recommendations for maintenance or replacement of components. Sun teaches a system that uses machine learning to evaluate sensor data and detect and determine system faults of system components. One of ordinary skill would have motivation to combine these articles since provide similar methods to produce similar results because both articles disclose systems that use of machine learning to evaluate sensor data to monitor a system for component faults, anomalies, or deviant behavior. Further, both articles provide a similar result of alerting the user of system faults, “In one embodiment, a computer-implemented predictive maintenance process is described. The process may include receiving a set of sensor data generated from sensors associated with equipment, one of the sensors being a target sensor, the set of sensor data comprising measured values of the target sensor. The process may also include selecting a subset of sensor data, the subset of sensor data comprising data generated from the sensors and excluding the measured values of the target sensor. The process may further include inputting the subset of sensor data into a machine learning model to generate predicted sensor values of the target sensor. The process may further include determining differences between the predicted sensor values of the target sensor and the measured values of the target sensor. The process may further include generating an anomaly score for the equipment based on the differences. The process may further include generating, based on anomaly score, an alert for the equipment.” (Trinh, Summary, pp. 1, [0004]) and “In one aspect, an example method can comprise creating a non-linear neural network based model of a system based on historical operational data of the system and receiving first sensor data from a plurality of sensors associated with the system. Predicted next sensor data can be determined based on the received first sensor data and the non-linear network model. Second sensor data can be received from the plurality of sensors, and a measure of deviation between the predicted next sensor data and the received second sensor data is calculated. In response to the measured deviation exceeding a predefined threshold; it can be determined that a fault has occurred.” (Sun, Summary, pp. 1, [0009]).
Regarding claim 2, Trinh discloses, “wherein instructions to determine the deviation associated with the component comprise instructions to: apply the machine learning model to the device usage data, the first data and the second data to: determine the deviation of the component by comparing a difference between the received first data related to the first characteristic of the component and first reference data based on the device usage data;” (Predictive Power Parity Anomaly, Detection Model, pp. 8, [0071]; “The objective function of the machine learning model may measure the difference between the predicted historical measurements of the vital (e.g. outputs of the machine learning model) and the actual historical measurements of the vital.” The system in this article will use machine learning models to determine anomalies in the given data from one or more sensors.) and (Predictive Power Parity Anomaly Detection Model, pp. 9, [0075]; “The predictive maintenance server 110 may generate 560 an anomaly score for the equipment 150 based on the dissimilarity metrics associated with one or more vitals. The anomaly score may be the overall anomaly score for the equipment 150.” This model can evaluate data and compare the data to reference data to determine anomalies.)
“determine the deviation of the component by comparing a difference between the received second data related to the second characteristic of the component and second reference data based on the device usage data;” (Predictive Power Parity Anomaly, Detection Model, pp. 8, [0071]; “The objective function of the machine learning model may measure the difference between the predicted historical measurements of the vital (e.g. outputs of the machine learning model) and the actual historical measurements of the vital.” The system in this article will use machine learning models to determine anomalies in the given data from one or more sensors.) and (Predictive Power Parity Anomaly Detection Model, pp. 9, [0075]; “The predictive maintenance server 110 may generate 560 an anomaly score for the equipment 150 based on the dissimilarity metrics associated with one or more vitals. The anomaly score may be the overall anomaly score for the equipment 150.” This model can evaluate data and compare the data to reference data to determine anomalies.)
“or a combination thereof.” (This limitation is a conditional limitation. The examiner would like to note that this condition is not mapped because both of the data items, the first and second, are taught by the system in Trinh. This teaches the combination of the first and second data items.)
Regarding claim 3, Trinh discloses, “wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed data, device usage time data, device age data, or any combination thereof.” (System Overview, pp. 4, [0043]; “In another case, a setting value may be dynamic. For example, a target temperature of a particular component or location of the equipment 150 may be dynamically changed based on other conditions of the equipment 150. The values of the settings 152 may also be reported as a time series and transmitted to the data store 120 through the controller 160 or directly. For simplicity, in this disclosure and claims, sensor data may be used to collectively refer to both the data generated by the sensors 154 and the data from the settings 152.” The system in Trinh is able to evaluate location and time data from the sensors and the given setting values. These values can be dynamic and change, which requires the system to evaluate the setting data and the sensor data together.)
Regarding claim 4, Trinh discloses, “wherein instructions to determine the deviation associated with the component comprise instructions to: correlate the device usage data with the first data and the second data; and” (Predictive Power Parity Anomaly Detection model, pp. 9, [0073]; “For the set of scoring sensor data, the predictive maintenance server 110 may input 530 the first subset of scoring sensor data to the trained machine learning model to generate predicted values of the vital measurements. The predictive maintenance server 110 may determine 540 differences between the predicted sensor values and the measured values of the target sensor.” This model is able to evaluate data and compare the data to reference data.)
“determine the deviation associated with the component by applying the machine learning model to the device usage data, the first data, and the second data based on the correlation.” (Predictive Power Parity Anomaly Detection Model, pp. 9, [0074]; “The machine learning model may be trained for a particular vital (sensor channel). According to an embodiment, the PPP model may determine the anomaly of a piece of equipment 150 using one or more vitals.” This model is able to apply a machine learning model to the given senor data to determine anomalies in the system.) and (Predictive Power Parity Anomaly Detection Model, pp. 9, [0075]; “The predictive maintenance server 110 may generate 560 an anomaly score for the equipment 150 based on the dissimilarity metrics associated with one or more vitals. The anomaly score may be the overall anomaly score for the equipment 150.” The anomaly score can be generated using the dissimilarity value generated by comparing the reference information and input sensor data.)
Regarding claim 5, Trinh discloses, “wherein the alert notification is to include a recommended action to reduce the deviation associated with the component, replace the component, or a combination thereof, and” (Example Predictive Maintenance Server, pp. 5, [0052]; “The maintenance recommendation engine 270 may provide one or more alerts (e.g., in the form of recommendations) for inspecting or repairing of pieces of equipment 150.” This system will generate an alert as well and will provide recommendations to alleviate or minimize the anomaly in the given component.)
“wherein the alert notification is generated when the determined deviation exceeds a threshold.” (Example Predictive Maintenance Server, pp. 5, [0052]; “Based on the anomaly scores such as by comparing the scores to one or more threshold values or some predetermined ranges, the maintenance recommendation engine 270 may select an appropriate alert or recommendation. For example, if an overall anomaly score exceeds a predetermined threshold value, the maintenance recommendation engine 270 may recommend an inspection of the particular equipment 150.” This alert is generated after a threshold is met or exceeded.)
Regarding claim 6, Trinh discloses, “A non-transitory machine-readable storage medium storing instructions executable by a processor of a computing device to:” (Computer Architecture, pp. 15, [0113]; “The storage device 2008 is a non-transitory computer- readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid state memory device. The non-transitory computer-readable storage medium may store computer code that includes instructions. The instructions, when executed by the processor 2002, cause the processor 2002 to perform various processes and methods described herein.” Trinh discloses a system which contains multiple computing systems which contain processors which are coupled to memory which contain machine instructions to perform the given actions.)
“obtain historical device usage data and historical sensor data of an electronic device, the historical device usage data comprising processor usage data, device charging data device usage time or any combination thereof, and” (Example Training and Scoring Pipelines, pp. 7, [0062]; “The predictive maintenance server 110 may collect the sensor data of the first period of time (e.g., a month of sensor data) as training data 422. In the training data pre-processing 430, the predictive maintenance server 110 may process the training data 422 and extract features used for the machine learning models from the training data 422.” The system in Trinh can use stored or retrieved data or previous, historical data over a period of time, as training, testing and validation datasets.)
“the historical sensor data comprising device sound data and device temperature data;” (Detailed Description, pp. 3, [0040]; “A piece of equipment 150 may include one or more settings 152 and one or more sensors 154 that are equipped to monitor one or more measures of the equipment 150, articles on which the equipment 150 operate, or the environment of the equipment 150. Example measurements that are monitored by various sensors 154 may include temperature, pressure, force, acceleration, tension, light, motion, rotation, magnetic field, electrical field, capacitance, current, voltage, inductance, gravity, humidity, moisture, vibration, sound, and other physical aspects.” This system contains sensors which collect various forms of data including temperature and sound data.)
“train a set of machine learning models to estimate a sound deviation, temperature deviation, or both of a component of the electronic device using the train dataset;” (Example Predictive Maintenance Server, pp. 4, [0047]; “The training dataset may be used to train one or more models that are used to determine anomaly scores of pieces of equipment 150. The testing dataset may be used to validate the performance of the trained models.” This model use the training datasets to train sets of machine learning models to detect anomalies in systems which contain sensors able to record sounds and temperature data.)
“test the trained set of machine learning models with the test dataset; and” (Example Predictive Maintenance Server, pp. 4, [0047]; “The testing dataset may be used to validate the performance of the trained models.” This system will use the testing datasets test the machine learning models after they have been trained using the training datasets.)
“determine a machine learning model from the set of tested machine learning models to estimate, in real-time, the sound deviation, temperature deviation, or both of the component.” (Example Predictive Maintenance Server, pp. 5, [0050]; “The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150.” This system is able to use multiple machine learning models to evaluate sensor data.) and (Example Predictive Maintenance Server, pp. 5, [0052]; “For example, for a particular equipment 150 that newly generates a set of sensor data, the predictive maintenance server 110 may retrieve one or more machine learning models stored in the anomaly detection model store 250 and/or in the failure classification and prediction model store 260. One or more anomaly scores may be determined for the particular equipment 150.” The different models are stored and can be used by the system to make predictions on sensor data.) and (System Overview, pp. 4, [0041]; “Alternatively, or additionally, the controller 160 (or the sensor 154 itself) may also transmit generated data in real-time (with or without a slight delay) to the data store 120 or directly to the predictive maintenance server 110 in the form of a data stream with the predictive maintenance server 110.” This system can evaluate live, or close to real time sensor data.)
Trinh fails to explicitly disclose, “process the historical device usage data and the historical sensor data to generate a train dataset and a test dataset;”.
However, Sun discloses, “process the historical device usage data and the historical sensor data to generate a train dataset and a test dataset;” (Detailed Description, pp. 8, [0083]; “Step 401: Historical data is collected, normalized and divided into training/validation sets.” This system is able to process the historical data into training and validation datasets.)
Regarding claim 7, Trinh discloses, “receive real-time device usage data and real-time sensor data associated with the electronic device;” (System Overview, pp. 4, [0041]; “Alternatively, or additionally, the controller 160 (or the sensor 154 itself) may also transmit generated data in real-time (with or without a slight delay) to the data store 120 or directly to the predictive maintenance server 110 in the form of a data stream with the predictive maintenance server 110.” This system ca evaluate data in real time, or near real time.)
“estimate the sound deviation, the temperature deviation, or both associated with the component by analyzing the real-time device usage data and the real-time sensor data using the determined machine learning model;” (System Overview, pp. 4, [0041]; “If a data stream is established, the predictive maintenance server 110 may continuously receive data from the sensors 154 as new data are generated.” This system can use and transmit real time data from the sensors.) and (System Overview, pp. 4, [0044]; “The predictive maintenance server 110 receives and analyzes the data transmitted from various sensors 154 and settings 152. The predictive maintenance server 110 may train one or more machine learning models that assign anomaly scores to a piece of equipment 150. The anomaly scores may include an overall anomaly score and individual anomaly scores each corresponding to a component, a measurement, or an aspect of the equipment 150.” This system will receive sensor data and evaluate it using machine learning models. This is used to identify potential anomalies in the given systems.)
“generate an alert notification based on the sound deviation, the temperature deviation, or both; and” (Example Predictive Maintenance Server, pp. 5, [0052]; “One or more anomaly scores may be determined for the particular equipment 150. Based on the anomaly scores such as by comparing the scores to one or more threshold values or some predetermined ranges, the maintenance recommendation engine 270 may select an appropriate alert or recommendation.” This system can also generate an alert based on the sensor data.)
Trinh fails to explicitly disclose, “send the alert notification to the electronic device.”.
However, Sun discloses, “send the alert notification to the electronic device.” (Detailed Description, pp. 6, [0068]; “In some aspects, the determination of a fault state can cause one or more alarms to occur. Example alarms can comprise, for example, an aural alert, a visual alert, such as a flashing light, transmission of an electronic message (e.g., an email, a text message, a pop-up window on a computer screen, etc.) to one or more system administrators, and/or other similar alerts.” This system is able to send an alert to a given system after a fault or anomaly is found using machine learning models.)
Regarding claim 8, Trinh discloses, “wherein instructions to train the set of machine learning models comprise instructions to: train the set of machine learning models to: classify the historical sensor data to identify data associated with the component; and” (Example Predictive Maintenance Server, pp. 5, [0049]; “Based on the trained model, an anomaly score may be generated using the sensor and setting data from the equipment 150 as the input of the trained model. In some cases, the trained model may be a classifier or a regression model. For example, a classifier may be trained to determine which component of the equipment 150 may need an inspection, repair or general follow up.” This system is able to evaluate the sensor data and determine which component in a system that needs further evaluation. This can include historical data stored in the data storage unit.)
“estimate the sound deviation, the temperature deviation, or both associated with the component using the classified historical sensor data and the historical device usage data of the train dataset.” (Example Predictive Maintenance Server, pp. 5, [0050]; “The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150. Different types of pieces of equipment 150 may be associated with different anomaly detection models. Also, there can be multiple anomaly detection models that determine the anomaly scores of various aspects of a single equipment 150.” This system will produce an output with the given sensor data after the model has been trained on historical data.)
Regarding claim 9, Trinh discloses, “wherein instructions to determine the machine learning model from the set of tested machine learning models to estimate the sound deviation, the temperature deviation, or both comprise instructions to: determine the machine learning model having a maximum accuracy from the set of tested machine learning models to:” (Example Predictive Maintenance Server, pp. 5, [0050]; “The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150.” This system is able to use multiple machine learning models to evaluate sensor data.) and (Example Predictive Maintenance Server, pp. 5, [0052]; “For example, for a particular equipment 150 that newly generates a set of sensor data, the predictive maintenance server 110 may retrieve one or more machine learning models stored in the anomaly detection model store 250 and/or in the failure classification and prediction model store 260. One or more anomaly scores may be determined for the particular equipment 150.” The different models are stored and can be used by the system to make predictions on sensor data. The models are selected based on which model corresponds to the input data. The examiner would like to note that the “maximum accuracy” is interpreted to be the machine learning model that is designed for the given data and the component.)
“identify the component of the electronic device that generates sound, temperature, or both using real-time sensor data; and” (System Overview, pp. 4, [0044]; “When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair. The predictive maintenance server 110 may also train additional models such as classifiers and regressors that can identify a specific component of the equipment 150 that may need an inspection, repair and/or replacement.” This system is able to evaluate the senor data and determine which component is having issues and need attention.)
“estimate the sound deviation, the temperature deviation, or both associated with the component for real-time device usage data and the real- time sensor data,” (Example Predictive Maintenance Server, pp. 5, [0050]; “The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150. Different types of pieces of equipment 150 may be associated with different anomaly detection models. Also, there can be multiple anomaly detection models that determine the anomaly scores of various aspects of a single equipment 150.” This system is able to evaluate real time data and determine anomalies or component fails of systems.)
“wherein the real-time device usage data is to indicate a load on the component that impacts the sound, the temperature, or both associated with the component.” (System Overview, pp. 4, [0041]; “The data generated by a sensor 154 may be in any suitable format such as a time-series format. For example, a sensor 154 may monitor the temperature of a particular component of a piece of equipment. For every predetermined period of time (e.g., a second, a few seconds, a minute, an hour, etc.), the sensor 154 generates a data point. The generated data points may be associated with timestamps or may be collected presented as a time series. Different sensors 154 in a piece of equipment or the same type of sensors 154 among different pieces of equipment may have the same frequency in generating data points or may have different frequencies.” This system is able to evaluate sensor data from multiple sensors in real time. This data is temporal and indicates the component loads at given times.)
Regarding claim 10, Trinh discloses, “prior to testing the trained set of machine learning models, validate the trained machine learning models to tune an accuracy of the trained machine learning models based on a validation dataset of the processed historical device usage data and the historical sensor data.” (Example Predictive Maintenance Server, pp. 4, [0046]; “When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair. The predictive maintenance server 110 may also train additional models such as classifiers and regressors that can identify a specific component of the equipment 150 that may need an inspection, repair and/or replacement.” This system is able to use historical sensor data from the data store to generate training and validation sets. These datasets can be used to train the machine learning models to be more accurate, i.e. the purpose of training machine learning models.)
Regarding claim 11, Trinh discloses, “further comprising instructions to process the historical device usage data and the historical sensor data to generate the train dataset and the test dataset comprises instructions to:” (Example Training and Scoring Pipelines, pp. 7, [0062]; “The predictive maintenance server 110 may generate training data 422 for the training of the machine learning models. The predictive maintenance server 110 may collect the sensor data of the first period of time (e.g., a month of sensor data) as training data 422. In the training data pre-processing 430, the predictive maintenance server 110 may process the training data 422 and extract features used for the machine learning models from the training data 422.” This system is able to use historical temporal data to generate new training datasets.)
“correlate the historical device usage data with the historical sensor data; and” (Predictive Power Parity Anomaly Detection model, pp. 9, [0073]; “For the set of scoring sensor data, the predictive maintenance server 110 may input 530 the first subset of scoring sensor data to the trained machine learning model to generate predicted values of the vital measurements. The predictive maintenance server 110 may determine 540 differences between the predicted sensor values and the measured values of the target sensor.” This system is able to use historical data for training and is able to correlate the sensor data and the setting data for a given system.)
“generate the train dataset and the test dataset based on the correlation.” (Example Training and Scoring Pipelines, pp. 7, [0062]; “The predictive maintenance server 110 may generate training data 422 for the training of the machine learning models. The predictive maintenance server 110 may collect the sensor data of the first period of time (e.g., a month of sensor data) as training data 422.” This system is able to generate training datasets from historical data.) and (Example Predictive Maintenance Server, pp4, [0047]; “The training dataset may be used to train one or more models that are used to determine anomaly scores of pieces of equipment 150. The testing dataset may be used to validate the performance of the trained models.” The training data set is used to train the machine learning models)
Regarding claim 12, Trinh discloses, “An electronic device comprising: A storage device; An output device; and a processor to:” (Figures 1 and 20, This system contains a data store which is a repository of data. The system in Trinh discloses multiple systems which contain processors and output the data to another device as seen in figure 20.) and (Detailed Description, pp. 3-4, [0035]; “For example, parts of the predictive maintenance server 110 may be a computer, a distributed computing system, or any computing machines capable of executing instructions that specify actions to be taken by the equipment. Parts of the predictive maintenance server 110 may include one or more processors such as a CPU (central processing unit), a GPU (graphics processing unit), a TPU (tensor processing unit), a DSP (digital signal processor), a system on a chip (SOC), a controller, a state machine, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or any combination of these.” This system contains generic computing devices which contain processors that use machine instructions stored in memory.)
“retrieve, from the storage device, sensor data for a period in response to receiving a trigger event,” (Example Training and Scoring Pipelines, pp. 8, [00658]; “The PPP model detects instances or periods when the correlation among different measurements of sensor data breaks down. An overall anomaly score may be generated based on the comparison between the predicted measurements and the actual measurements of sensor data.” This system is able to retrieve information and sensor data from a data store. This data can be observed from events where the anomaly was detected.)
“wherein the sensor data comprises device sound data and device temperature data;” (Detailed Description, pp. 3, [0040]; “A piece of equipment 150 may include one or more settings 152 and one or more sensors 154 that are equipped to monitor one or more measures of the equipment 150, articles on which the equipment 150 operate, or the environment of the equipment 150. Example measurements that are monitored by various sensors 154 may include temperature, pressure, force, acceleration, tension, light, motion, rotation, magnetic field, electrical field, capacitance, current, voltage, inductance, gravity, humidity, moisture, vibration, sound, and other physical aspects.” As stated above, this system is able to evaluate sensor data from multiple sensor sources.)
“apply a machine learning model to the sensor data to: classify the retrieved sensor data;” (Example Predictive Maintenance Server, pp. 5, [0051]; “Another trained classifier model may also determine the type of defect of an identified component. The models that are trained to classify failures may estimate failure probabilities of the equipment 150 or of a particular component of the equipment 150. The models may also provide priority rankings among different pieces of equipment 150 and among different components of a piece of equipment 150.” The system in Trinh is able to apply a machine learning model to the input sensor data.)
“identify a component of the electronic device that generates sound, temperature, or both using the classified sensor data;” (System Overview, pp. 4, [0044]; “The predictive maintenance server 110 may also train additional models such as classifiers and regressors that can identify a specific component of the equipment 150 that may need an inspection, repair and/or replacement.” This system is able to identify which component has an anomaly.)
“determine that the sound, temperature, or both associated with the component is to impact a performance of the electronic device; and” (System Overview, pp. 4, [0044]; “The anomaly scores may include an overall anomaly score and individual anomaly scores each corresponding to a component, a measurement, or an aspect of the equipment 150. When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair.” This system is able to detect anomalies in a system which would require maintenance or replaced. This would affect the performance of the given machine.)
“in response to the determination, determine a recommended action to reduce the sound, the temperature, or both; and” (Example Predictive Maintenance Server, pp. 5, [0052]; “Based on the anomaly scores such as by comparing the scores to one or more threshold values or some predetermined ranges, the maintenance recommendation engine 270 may select an appropriate alert or recommendation. For example, if an overall anomaly score exceeds a predetermined threshold value, the maintenance recommendation engine 270 may recommend an inspection of the particular equipment 150.” This system will provide recommendations to mitigate the anomaly or system failure.)
Trinh fails to explicitly disclose, “output an alert notification including the recommended action via the output device.”.
However, Sun discloses, “output an alert notification including the recommended action via the output device.” (Detailed Description, pp. 6, [0068]; “In some aspects, the determination of a fault state can cause one or more alarms to occur. Example alarms can comprise, for example, an aural alert, a visual alert, such as a flashing light, transmission of an electronic message (e.g., an email, a text message, a pop-up window on a computer screen, etc.) to one or more system administrators, and/or other similar alerts.” This system is able to output an alert based on using a machine learning model to evaluate sensor data from a machine.)
Regarding claim 13, Trinh discloses, “a sound sensor to record the device sound data associated with the electronic device, wherein the sound sensor comprises a microphone; and” (System Overview, pp. 3, [0040]; “A piece of equipment 150 may include one or more settings 152 and one or more sensors 154 that are equipped to monitor one or more measures of the equipment 150, articles on which the equipment 150 operate, or the environment of the equipment 150. Example measurements that are monitored by various sensors 154 may include temperature, pressure, force, acceleration, tension, light, motion, rotation, magnetic field, electrical field, capacitance, current, voltage, inductance, gravity, humidity, moisture, vibration, sound, and other physical aspects.” This system is able to evaluate different forms of sensor data. In order to evaluate the sensor data, the sensors are required acquire the data using known means, i.e. the system uses a components such as a thermometer to gather temperature data and/or microphones to capture audio data.)
“a temperature sensor to record the device temperature data associated with the electronic device.” (System Overview, pp. 3, [0040]; “A piece of equipment 150 may include one or more settings 152 and one or more sensors 154 that are equipped to monitor one or more measures of the equipment 150, articles on which the equipment 150 operate, or the environment of the equipment 150. Example measurements that are monitored by various sensors 154 may include temperature, pressure, force, acceleration, tension, light, motion, rotation, magnetic field, electrical field, capacitance, current, voltage, inductance, gravity, humidity, moisture, vibration, sound, and other physical aspects.” This system is able to evaluate different forms of sensor data. In order to evaluate the sensor data, the sensors are required acquire the data using known means, i.e. the system uses a components such as a thermometer to gather temperature data and/or microphones to capture audio data.)
Regarding claim 14, Trinh discloses, “Apply the machine learning model to the sensor data to: filter the sensor data to remove ambient sound and ambient temperature from the retrieved sensor data; and” (Example Predictive Maintenance Server, pp. 4, [0046]; “The data processing may also include extracting features for various machine learning models. The data processing may further include other data processing techniques such as filtering (e.g., finite impulse response FIR filter, high-pass filter, band-pass filter, low-pass filter), applying one or more kernels, up-sampling, and down-sampling.” This system uses a data processing engine. This processes and formats the input data for the machine learning models. As disclosed, this system is able to filter data.)
“classify the filtered sensor data into a group of categories, wherein the sensor data associated with a category in the group of categories belongs to the component of the electronic device.” (Example Predictive Maintenance Server, pp. 4, [0046]; “The data processing engine 210 retrieves data from various pieces of equipment 150 and processes the data for the predictive maintenance server 110 to perform further data analysis and machine learning. The sensor and setting data from a piece of equipment 150 may be stored in a cloud storage system such as a data store 120. The data processing engine 210 retrieves the data and converts the data into formats that are compatible with the equipment leaning models used by the predictive maintenance server 110.” This system is able to use the filtered, processed data, as input to the machine learning models to produce a prediction.)
Regarding claim 15, Trinh discloses, “retrieve, from the storage device, device usage data for the period, wherein the device usage data comprises central processing unit (CPU) usage data, application usage data, device charging data, device location data, fan speed, device usage time, device age, or any combination thereof; and” Trinh (Predictive Power Parity Anomaly, Detection Model, pp. 8, [0071]; “The objective function of the machine learning model may measure the difference between the predicted historical measurements of the vital (e.g. outputs of the machine learning model) and the actual historical measurements of the vital.” This system is able to use specific machine learning models with different objectives to evaluate the sensor data. As stated above the sensor data is from multiple sources and is temporal and is able have dynamic settings able to disclose a systems location.)
“apply the machine learning model to the sensor data and the device usage data to: determine that the sound, the temperature, or both associated with the component is to impact the performance of the electronic device.” (System Overview, pp. 4, [0044]; “The anomaly scores may include an overall anomaly score and individual anomaly scores each corresponding to a component, a measurement, or an aspect of the equipment 150. When the anomaly scores are determined to be beyond a specific range such as above a predetermined threshold, the predictive maintenance server 110 identifies a particular facility site 140 and a particular piece of equipment 150 and provides an indication that the equipment 150 may need an inspection and possible repair.” This system is able to evaluate sensor data to determine anomalies in a system. This will use machine learning models to evaluate the sensor data.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL MICHAEL GALVIN-SIEBENALER whose telephone number is (571)272-1257. The examiner can normally be reached Monday - Friday 8AM to 5PM.
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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