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 .
Claim Objections
Claim 15 objected to because of the following informalities:
The phrase rearranging a timestamps, should be “rearranging timestamps”. Appropriate 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A method comprising:
collecting, by a processor, telemetry data from a component of an information handling system;
determining health of the component of the information handling system based on the telemetry data;
abstracting the health of the component prior to transmitting information on the health of the component to a telemetry framework; and
encrypting the telemetry data prior to storing the encrypted telemetry data in a data store.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “determining health of the component of the information handling system based on the telemetry data (determination/inference based on observed data);
abstracting the health of the component prior to transmitting information on the health of the component to a telemetry framework (translating/organizing data); and
encrypting the telemetry data prior to storing the encrypted telemetry data in a data store (encoding transmission data before storing, i.e. through hand ciphers or manual cryptography)” are treated by the Examiner as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 8 and 14.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: collecting, by a processor, telemetry data from a component of an information handling system;
Claim 8: a processor; and a memory storing instructions that when executed cause the processor to perform operations including: collecting telemetry data from a component of an information handling system;
Claim 14: A non-transitory computer-readable medium to store instructions that are executable to perform operations comprising: collecting telemetry data from a component of an information handling system.
The additional element of “collecting telemetry data from a component of an information handling system” is not qualified for a meaningful limitation because it represents a mere data gathering step and only adds an insignificant extra-solution activity to the judicial exception. A non-transitory computer-readable medium or memory (generic memory) and a processor (generic processor) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 9-13, and 15-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3-6, 8, 10-12, 14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dinh et al. (US 20210126845 A1), hereinafter “Dinh”, in view of Hardwood et al. (US 20230216666 A1), hereinafter “Hardwood”.
Regarding Claim 1, Dinh teaches a method comprising:
collecting, by a processor, telemetry data from a component of an information handling system (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices.);
determining health of the component of the information handling system based on the telemetry data (Dinh [0057] Step 602 includes automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data. In at least one embodiment, applying the machine learning model includes implementing one or more linear regression techniques (such as, for example, one or more single variate regression techniques and/or one or more multi-variate regression techniques). Such an embodiment can also include applying one or more sigmoid functions to at least a portion of an output of the one or more linear regression techniques.); and
abstracting the health of the component prior to transmitting information on the health of the component to a telemetry framework (Ding [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life.).
Dinh is not relied upon to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data in a data store.
Hardwood teaches encrypting the telemetry data prior to storing the encrypted telemetry data in a data store (Hardwood [0210] In step 546, the aggregated telemetry data is encrypted based on telemetry distribution information associated with the group […] The aggregated telemetry data may be encrypted based on the telemetry distribution information associated with the group and the composed information handling system via other and/or additional methods without departing from the invention. Also see [0145] (ii) identifying a telemetry intent associated with the telemetry request, (iii) aggregate telemetry data obtained from system control processor and/or data collectors that satisfy the telemetry intent, (iv) encrypt the aggregated telemetry data using appropriate group encryption keys. Specific data can be encrypted as desired).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data in a data store, to ensure that only those intended to have access to certain parts of the data can (i.e. some users only get a subsection of the data, while others can view all of the data).
Regarding Claim 3, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining an expected remaining life of the component (Dinh [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life. Also see [0039] Accordingly, at least one embodiment of the invention includes providing an IoT infrastructure (with respect to both client devices and a server) with one or more machine learning plug-ins that can proactively predict the end of life of a client device and/or a consumable part thereof. Such an embodiment includes acquiring device telemetry data (from the client device) and applying at least one machine learning model to predict the end of life information and proactively take action to mitigate outage risks. Predicting the end of life inherently involves determining the remaining life).
Regarding Claim 4, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining usage of the component (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices. Also see [0037] More specifically, in at least one example embodiment, the IoT-enabled dongle 209 periodically polls a client device 207 such as a projector using HEX code (in start-of-text (STX) command end-of-text (ETX) format) for serial interface data related to, for example, lamp life, brightness, errors, heat and power information. And [0046] The pseudocode 300 illustrates importing libraries and loading a projector data set (pd.read_csv(path, header=header)). Additionally, as detailed in the pseudocode 300, the values (X and Y axis) are read from the data set and a scatterplot (lamp hours as the X axis and brightness of lamp as the Y axis) is generated. Usage data such as power over time is collected for analysis).
Regarding Claim 5, Dinh in view of Hardwood (as stated above) further teaches wherein the determining of the health of the component is performed by using a deep learning technique (Dinh [0038] an EOL prediction generated machine learning model 238. Also see [0040] As also detailed herein, examples of the machine learning models utilized in accordance with one or more embodiments include one or more supervised learning models.).
Regarding Claim 6, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes using telemetry data from another component (Dinh [0057] Step 602 includes automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data. In at least one embodiment, applying the machine learning model includes implementing one or more linear regression techniques (such as, for example, one or more single variate regression techniques and/or one or more multi-variate regression techniques). Such an embodiment can also include applying one or more sigmoid functions to at least a portion of an output of the one or more linear regression techniques. Also see 0059] Additionally, the techniques depicted in FIG. 6 can also include generating the machine learning model based at least in part on device telemetry data collected from multiple devices analogous to the one or more IoT-enabled devices within the IoT network. The analysis is based on the combination of multiple variables from multiple devices.).
Regarding Claim 8, Dinh teaches an information handling system, comprising:
a processor (Dinh [0023] Additionally, the IoT server 105 in the FIG. 1 embodiment is assumed to be implemented using at least one processing device.); and
a memory storing instructions that when executed cause the processor to perform operations (Dinh [0023] Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of the IoT server 105.) including:
collecting telemetry data from a component of an information handling system (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices.);
determining health of the component of the information handling system based on the telemetry data (Dinh [0057] Step 602 includes automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data. In at least one embodiment, applying the machine learning model includes implementing one or more linear regression techniques (such as, for example, one or more single variate regression techniques and/or one or more multi-variate regression techniques). Such an embodiment can also include applying one or more sigmoid functions to at least a portion of an output of the one or more linear regression techniques.); and
abstracting the health of the component prior to transmitting information on the health of the component to a telemetry framework (Ding [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life.).
Dinh is not relied upon to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data at a data store.
Hardwood teaches encrypting the telemetry data prior to storing the encrypted telemetry data at a data store (Hardwood [0210] In step 546, the aggregated telemetry data is encrypted based on telemetry distribution information associated with the group […] The aggregated telemetry data may be encrypted based on the telemetry distribution information associated with the group and the composed information handling system via other and/or additional methods without departing from the invention. Also see [0145] (ii) identifying a telemetry intent associated with the telemetry request, (iii) aggregate telemetry data obtained from system control processor and/or data collectors that satisfy the telemetry intent, (iv) encrypt the aggregated telemetry data using appropriate group encryption keys. Specific data can be encrypted as desired).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data at a data store, to ensure that only those intended to have access to certain parts of the data can (i.e. some users only get a subsection of the data, while others can view all of the data).
Regarding Claim 10, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining an expected remaining life of the component (Dinh [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life. Also see [0039] Accordingly, at least one embodiment of the invention includes providing an IoT infrastructure (with respect to both client devices and a server) with one or more machine learning plug-ins that can proactively predict the end of life of a client device and/or a consumable part thereof. Such an embodiment includes acquiring device telemetry data (from the client device) and applying at least one machine learning model to predict the end of life information and proactively take action to mitigate outage risks. Predicting the end of life inherently involves determining the remaining life).
Regarding Claim 11, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining usage of the component (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices. Also see [0037] More specifically, in at least one example embodiment, the IoT-enabled dongle 209 periodically polls a client device 207 such as a projector using HEX code (in start-of-text (STX) command end-of-text (ETX) format) for serial interface data related to, for example, lamp life, brightness, errors, heat and power information. And [0046] The pseudocode 300 illustrates importing libraries and loading a projector data set (pd.read_csv(path, header=header)). Additionally, as detailed in the pseudocode 300, the values (X and Y axis) are read from the data set and a scatterplot (lamp hours as the X axis and brightness of lamp as the Y axis) is generated. Usage data such as power over time is collected for analysis).
Regarding Claim 12, Dinh in view of Hardwood (as stated above) further teaches wherein the determining of the health of the component is performed by using a deep learning technique (Dinh [0038] an EOL prediction generated machine learning model 238. Also see [0040] As also detailed herein, examples of the machine learning models utilized in accordance with one or more embodiments include one or more supervised learning models.).
Regarding Claim 14, Dinh teaches a non-transitory computer-readable medium (Dinh [0027] One or more embodiments include articles of manufacture, such as computer-readable storage media.) to store instructions that are executable to perform operations (Dinh [0023] Additionally, the IoT server 105 in the FIG. 1 embodiment is assumed to be implemented using at least one processing device. Each such processing device generally comprises at least one processor and an associated memory, and implements one or more functional modules for controlling certain features of the IoT server 105.) comprising:
collecting telemetry data from a component of an information handling system (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices.);
determining health of the component of the information handling system based on the telemetry data (Dinh [0057] Step 602 includes automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data. In at least one embodiment, applying the machine learning model includes implementing one or more linear regression techniques (such as, for example, one or more single variate regression techniques and/or one or more multi-variate regression techniques). Such an embodiment can also include applying one or more sigmoid functions to at least a portion of an output of the one or more linear regression techniques.); and
abstracting the health of the component prior to transmitting information on the health of the component to a telemetry framework (Ding [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life.).
Dinh is not relied upon to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data at a data store.
Hardwood teaches encrypting the telemetry data prior to storing the encrypted telemetry data at a data store (Hardwood [0210] In step 546, the aggregated telemetry data is encrypted based on telemetry distribution information associated with the group […] The aggregated telemetry data may be encrypted based on the telemetry distribution information associated with the group and the composed information handling system via other and/or additional methods without departing from the invention. Also see [0145] (ii) identifying a telemetry intent associated with the telemetry request, (iii) aggregate telemetry data obtained from system control processor and/or data collectors that satisfy the telemetry intent, (iv) encrypt the aggregated telemetry data using appropriate group encryption keys. Specific data can be encrypted as desired).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood to explicitly teach encrypting the telemetry data prior to storing the encrypted telemetry data at a data store, to ensure that only those intended to have access to certain parts of the data can (i.e. some users only get a subsection of the data, while others can view all of the data).
Regarding Claim 16, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining an expected remaining life of the component (Dinh [0058] Step 604 includes initiating at least one automated action in response to the determined lifespan-related information. In one or more embodiments, initiating the at least one automated action includes automatically generating an incident report pertaining to the at least a portion of the one or more IoT-enabled devices, and automatically submitting the incident report to a ticketing system. Also, in at least one embodiment, the determined lifespan-related information includes a binary classification between (i) within a predetermined proximity of end of life and (ii) not within the predetermined proximity of end of life. Also see [0039] Accordingly, at least one embodiment of the invention includes providing an IoT infrastructure (with respect to both client devices and a server) with one or more machine learning plug-ins that can proactively predict the end of life of a client device and/or a consumable part thereof. Such an embodiment includes acquiring device telemetry data (from the client device) and applying at least one machine learning model to predict the end of life information and proactively take action to mitigate outage risks. Predicting the end of life inherently involves determining the remaining life).
Regarding Claim 17, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes determining usage of the component (Dinh [0056] Step 600 includes automatically obtaining device telemetry data from one or more IoT-enabled devices within an IoT network. The device telemetry data can include measurements quantifying one or more variables pertaining to operation of at least a portion of the one or more IoT-enabled devices. Also see [0037] More specifically, in at least one example embodiment, the IoT-enabled dongle 209 periodically polls a client device 207 such as a projector using HEX code (in start-of-text (STX) command end-of-text (ETX) format) for serial interface data related to, for example, lamp life, brightness, errors, heat and power information. And [0046] The pseudocode 300 illustrates importing libraries and loading a projector data set (pd.read_csv(path, header=header)). Additionally, as detailed in the pseudocode 300, the values (X and Y axis) are read from the data set and a scatterplot (lamp hours as the X axis and brightness of lamp as the Y axis) is generated. Usage data such as power over time is collected for analysis).
Regarding Claim 18, Dinh in view of Hardwood (as stated above) further teaches wherein the determining of the health of the component is performed by using a deep learning technique (Dinh [0038] an EOL prediction generated machine learning model 238. Also see [0040] As also detailed herein, examples of the machine learning models utilized in accordance with one or more embodiments include one or more supervised learning models.).
Regarding Claim 19, Dinh in view of Hardwood (as stated above) further teaches wherein the determining the health of the component includes using telemetry data from another component (Dinh [0057] Step 602 includes automatically determining lifespan-related information pertaining to at least a portion of the one or more IoT-enabled devices by applying a machine learning model to the device telemetry data. In at least one embodiment, applying the machine learning model includes implementing one or more linear regression techniques (such as, for example, one or more single variate regression techniques and/or one or more multi-variate regression techniques). Such an embodiment can also include applying one or more sigmoid functions to at least a portion of an output of the one or more linear regression techniques. Also see 0059] Additionally, the techniques depicted in FIG. 6 can also include generating the machine learning model based at least in part on device telemetry data collected from multiple devices analogous to the one or more IoT-enabled devices within the IoT network. The analysis is based on the combination of multiple variables from multiple devices.).
Claim(s) 2, 9, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dinh in view of Hardwood (as stated above), further in view of Cella et al. (US 20190025813 A1), hereinafter “Cella”.
Regarding Claim 2, Dinh in view of Hardwood (as stated above) is not relied upon to further teach scrambling the telemetry data by rearranging a sequence of the telemetry data.
Cella teaches scrambling the telemetry data by rearranging a sequence of the telemetry data (Cella [0129] alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood (as stated above), further in view of Cella, to explicitly teach scrambling the telemetry data by rearranging a sequence of the telemetry data, so that the model use different combinations and permutations to produce better, more nuanced, outputs (Cella [0129]).
Regarding Claim 9, Dinh in view of Hardwood (as stated above) is not relied upon to further teach scrambling the telemetry data by rearranging a sequence of the telemetry data.
Cella teaches scrambling the telemetry data by rearranging a sequence of the telemetry data (Cella [0129] alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood (as stated above), further in view of Cella, to explicitly teach scrambling the telemetry data by rearranging a sequence of the telemetry data, so that the model use different combinations and permutations to produce better, more nuanced, outputs (Cella [0129]).
Regarding Claim 15, Dinh in view of Hardwood (as stated above) is not relied upon to further teach scrambling the telemetry data by rearranging a timestamps.
Cella teaches scrambling the telemetry data by rearranging a timestamps (Cella [0129] alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like. In embodiments, local machine learning may turn on or off one or more sensors in a multi-sensor data collector 102 in permutations over time, while tracking success outcomes such as contributing to success in predicting a failure, contributing to a performance indicator (such as efficiency, effectiveness, return on investment, yield, or the like). Different combinations of data and events are determined in different orders over time).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Dinh in view of Hardwood (as stated above), further in view of Cella, to explicitly teach scrambling the telemetry data by rearranging a timestamps, so that the model use different combinations and permutations to produce better, more nuanced, outputs (Cella [0129]).
Claim(s) 7, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dinh in view of Hardwood (as stated above), further in view of Kowol et al. (“A-Eye: Driving with the Eyes of AI for Corner Case Generation.” ArXiv abs/2202.10803. 22 Feb. 2022), hereinafter “Kowol”.
Regarding Claim 7, Dinh in view of Hardwood (as stated above) is not relied upon to teach wherein a model is used to filter a corner case in the telemetry data.
Kowol teaches wherein a model is used to filter a corner case in the telemetry data (Kowol p. 42 Col. 2 Para. 3, In summary, the term ”corner case” can encompass rare and unusual situations that may include anomalies, unknown objects or outliers which are outside of operating parameters. Outside the operating parameters, in the context of machine learning, means that these situations or objects were not part of the training data. The model is trained to recognize corner cases).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Dinh in view of Hardwood (as stated above), further in view of Kowol, to explicitly teach wherein a model is used to filter a corner case in the telemetry data, so that the machine learning model can recognize a corner case in the data and adjust its output accordingly (Kowol p. 41, Abstract, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms.).
Regarding Claim 13, Dinh in view of Hardwood (as stated above) is not relied upon to teach wherein a model is used to filter a corner case in the telemetry data.
Kowol teaches wherein a model is used to filter a corner case in the telemetry data (Kowol p. 42 Col. 2 Para. 3, In summary, the term ”corner case” can encompass rare and unusual situations that may include anomalies, unknown objects or outliers which are outside of operating parameters. Outside the operating parameters, in the context of machine learning, means that these situations or objects were not part of the training data. The model is trained to recognize corner cases).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Dinh in view of Hardwood (as stated above), further in view of Kowol, to explicitly teach wherein a model is used to filter a corner case in the telemetry data, so that the machine learning model can recognize a corner case in the data and adjust its output accordingly (Kowol p. 41, Abstract, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms.).
Regarding Claim 20, Dinh in view of Hardwood (as stated above) is not relied upon to teach wherein a model is used to filter a corner case in the telemetry data.
Kowol teaches wherein a model is used to filter a corner case in the telemetry data (Kowol p. 42 Col. 2 Para. 3, In summary, the term ”corner case” can encompass rare and unusual situations that may include anomalies, unknown objects or outliers which are outside of operating parameters. Outside the operating parameters, in the context of machine learning, means that these situations or objects were not part of the training data. The model is trained to recognize corner cases).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application to modify Dinh in view of Hardwood (as stated above), further in view of Kowol, to explicitly teach wherein a model is used to filter a corner case in the telemetry data, so that the machine learning model can recognize a corner case in the data and adjust its output accordingly (Kowol p. 41, Abstract, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gonzalez Cervantes et al. (US 20220052847 A1) discloses Secure Storage And Retrieval Of Sensitive Information.
Mahmood et al. (US 20210280287 A1) discloses Capacity Optimization Across Distributed Manufacturing Systems.
Goldberg et al. (US 20230410965 A1) discloses Customer Health Activity Based System For Secure Communication And Presentation Of Health Information.
Sethu et al. (US 20240168748 A1) discloses Continuous Integration And Continuous Delivery Of Artificial Intelligence Machine Learning Components Using Metamorphic Relations.
Breitenstein et al. (Corner cases for visual perception in automated driving: Some guidance on detection approaches. arXiv preprint arXiv:2102.05897. 2021 Feb 11) discloses systematization of corner cases on different levels by an extended set of examples for each level.
Rajamani et al. ("Toward Detecting and Addressing Corner Cases in Deep Learning Based Medical Image Segmentation," in IEEE Access, vol. 11, pp. 95334-95345, 2023, doi: 10.1109/ACCESS.2023.3311134.) discloses identifying corner-cases in the evaluation of a state-of-the-art model using a standardised heart image segmentation database.
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