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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/02/2026 has been entered.
Remarks
This Office Action is responsive to Applicants' Amendment filed on February 2, 2026, in which claims 1, 8, and 14 are amended. Claims 3-7, 9, and 17 have been newly cancelled. Claims 20-23 have been newly added. Claims 1, 2, 8, 10-16, and 18-22 are currently pending.
Response to Arguments
With regards to the rejections of claims 1-19 under 35 U.S.C 101 as being directed towards abstract ideas, Applicant’s arguments that the claims as amended overcome the rejections have been considered, but have not been found persuasive. Applicant argues primarily with respect to claim 1.
Applicant first argues that claim 1 would not be considered to be directed towards abstract ideas at Step 2A, Prong One of the subject matter eligibility analysis, asserting that, in light of the specification, the limitations recited in claim 1 cannot be practically performed within the human mind.
Examiner respectfully disagrees that claim 1 is eligible over 35 U.S.C 101 for this reason. Examiner concedes that certain limitations within claim 1, such as what is mentioned by Applicant on page 7 of the Remarks (“extrapolating using at least one of random forest linear regression, K-nearest neighbor, support vector machine, Naive Bayes, or logistic regression, and using the usage data and training set data collected for other electronic devices”) and page 8 of the Remarks (“executing a preventative action at the electronic device based on the state of the electronic device”), in light of the specification, are not practically performed within the human mind. However, these limitations are analyzed later at Step 2A, Prong Two and Step 2B.
Applicant further argues on page 7 of the Remarks: “Applicant respectfully asserts that a human mind is not capable of performing extrapolation of data using the above methods for hundreds devices using at least one of the methods described in independent claim 1”, and additionally states on page 8 of the Remarks: “the predictions ultimately rely on newly created (extrapolated) data, extrapolated by the processor using the model generator and specific technique, which can include quantities of data that are not practically able to be used by a human mind to generate the claimed predictions”. Examiner respectfully notes that there is no particular limit on the number of devices extrapolation is performed for within claim 1.
Applicant further argues that claim 1 of the instant application differs from Example 47, Claim 2 of the July 2024 Subject Matter Eligibility Examples, used by the Examiner as an analogous example of the mental process of data analysis that could practically be performed within a human mind. Applicant states on pages 7 and 8 of the Remarks:
“in the USPTO analysis of Example 47, Claim 2, the ‘analyzing’ step is discounted because ‘[t]he claim does not limit how the analysis (evaluation) is performed, and there is nothing about a detected anomaly itself that would limit how it can be analyzed.’ In contrast, claim 1, as amended, specifies that ‘the model generator extrapolates the additional usage data using at least one of random forest linear regression, K-nearest neighbor, support vector machine, Naive Bayes. or logistic regression, and using the usage data and training set data collected for other electronic devices.’ Extrapolation using such specific techniques cannot be practically performed in the human mind”.
Examiner notes that the relevant limitation within Example 47, Claim 2, reads in full: “(e) analyzing the one or more detected anomalies using the trained ANN to generate anomaly data;”. The additional limitation amended into claim 1 that Applicant cites here is analogous both in that there is also nothing about the additional usage data itself that would limit how it can be extrapolated, and that the analysis in Example 47, Claim 2 is done by “the trained ANN”, which is not practically performed within the human mind but nevertheless is just mere instructions to apply a trained ANN to perform a mental process. Likewise, although Examiner concedes use of models such as random forest linear regression, K-nearest-neighbor, etc. are not practically performed within the human mind, extrapolation itself remains a mental process and the use of the models to perform the extrapolation is mere instructions to apply the models, which is analyzed at later steps but nevertheless is not grounds for 101 eligibility, MPEP 2106.05(f).
Applicant further argues on pages 10-11 of the Remarks that any recited abstract ideas are integrated into a practical application via improvement to technology or a technical field, however Examiner respectfully disagrees. Although Examiner does not dispute that the invention provides the improvements described on page 10 of the Remarks, Examiner notes that MPEP 2106.04(d).III. states “Because a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application”, i.e. that an improvement provided by an abstract idea, or solely by improving an abstract idea itself is ineligible. Examiner further notes that the limitations within claim 1 that have not been identified as reflecting an abstract idea merely recite instructions to apply the exception with generic computer components or amount to mere data gathering, as described in the 101 rejections below.
Applicant additionally argues that the claims of the instant application are analogous to those in Ex Parte Desjardins, which were found by the PTAB to be 101-eligible at Step 2A, Prong Two, due to addressing the technical problem of catastrophic forgetting within continual learning systems.
Examiner disagrees that the findings of Ex Parte Desjardins are analogous to the invention as claimed within claim 1 of the instant application. The new examples that may show an improvement to computer functionality, as stated on page 4 of “Advance notice of change to the MPEP in light of Ex Parte Desjardins” are the following:
“xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential)”.
Within claim 1 of the instant application, neither an improved way of training a machine learning model, nor improvements to computer components or system performance based on adjusted parameters of a machine learning model are disclosed. The technical problem Applicant purports to solve is a lack of data, and the machine learning models disclosed within claim 1 are used to produce this data. No improvements to machine learning model training are recited within claim 1.
Applicant further argues on pages 11-12 of the Remarks that any recited abstract ideas are integrated into a practical application via applying judicial exceptions in a meaningful way. Applicant argues that the limitation “execute a preventative action at the electronic device based on the state of the electronic device predicted using the model generator” within claim 1 “sufficiently limits the use of the alleged abstract idea to the practical application of executing preventive action based on the improved state prediction and extrapolated additional usage data”, Remarks Pg. 12. Applicant asserts Dimond v. Diehr, MPEP 2106.05(e), is analogous.
Examiner respectfully disagrees. Examiner considers Example 49, Claim 1 of the July 2024 Subject Matter Eligibility Examples to be analogous. The final limitation of Example 49, Claim 1 is:
“(c) administering an appropriate treatment to the glaucoma patient at high risk of PI after microstent implant surgery”.
However, Example 49, Claim 1 was found to be 101 ineligible. Page 33 of the July 2024 Subject Matter Eligibility provides the relevant analysis: “Limitation (c) may thus be understood as no more than an attempt to generally link the judicial exception to a field of use. See MPEP 2106.05(h). Therefore, limitation (c) fails to meaningfully limit the claim because it does not require any particular application of the abstract idea and therefore amounts only to a generic instruction to “apply” the exception or to a mere indication of the field of use or technological environment in which the abstract idea is performed”. Likewise, the limitation of “execute a preventative action at the electronic device based on the state of the electronic device” in the instant application does not require any particular application of the abstract idea of extrapolation, but rather amounts to mere generic instructions to apply the judicial exception, MPEP 2106.05(f).
Applicant further argues on pages 12 and 13 of the Remarks that the claims recite significantly more than any recited abstract ideas, however Examiner respectfully disagrees. Within claim 1, the limitations that are not identified as abstract ideas are well-understood, routine, and conventional, and mere instructions to apply the exception or amount to mere data gathering, respectfully. According to MPEP 2106.05.I.A, limitations such as these do not qualify as significantly more.
With regards to the rejections of claims 1-3, 8, 9, 16, 17, and 19 under 35 U.S.C. 103 as unpatentable over Sethi et al. (U.S. Patent Application Publication No. 2022/0391722), in view of Hofer et al. (U.S. Patent Application Publication No. 2018/0090127), further in view of Crowe et al. (U.S. Patent No. 7,251,589), Examiner finds Applicant’s arguments that the claims as amended overcome the rejections are persuasive, however the arguments are moot in view of a new grounds of rejection, as presented below.
However, with regards to the rejections of claims 14 and 15 under 35 U.S.C. 103 as being unpatentable over Sethi et al. (U.S. Patent Application Publication No. 2022/0391722), in view of Hofer et al. (U.S. Patent Application Publication No. 2018/0090127), further in view of Crowe et al. (U.S. Patent No. 7,251,589), further in view of Vijayaraghavan (U.S. Patent Application Publication No. 2021/0165708), Examiner does not find Applicant’s arguments that the claims as amended overcome the rejections persuasive.
Applicant argues on page 15 of the Remarks that: “Vijayaraghavan provides a prediction value based on performance metrics alone, wherein the binary value simply predicts if a system will fail, and if the system is predicted to fail, a remedial action is performed. In contrast, the claimed processor predicts a state based on (extrapolated) additional usage data, and then based on the [predicted] state, the processor predicts a health metric. That is, Vijayaraghavan does not teach or suggest predicting one item (e.g., a state of an electronic device), and then use of that predicted item to predict a second item (e.g., a health metric of the electronic device)”.
Examiner concurs with this assessment of Vijaraghavan, that Vijayaraghavan teaches prediction of a health metric of an electronic device based on its state and a subsequent recommended action, but not prediction of the state based on specifically extrapolated usage data. However, Examiner maintains it would be obvious to use the predicted health metric and preventative action of electronic devices of Vijayaraghavan with the extrapolated data of electronic devices of the combination of Sethi, Hofer, and Crowe, as it would provide the predictable benefit of increasing the resilience of the electronic devices against failure. Although Vijayaraghavan does not teach all limitations of the claim, the combination of Sethi, Hofer, Crowe, and Vijayaraghavan does, and thus a rejection under 35 U.S.C. 103 is appropriate, see MPEP 2124.
Claim Objections
Claim 8 objected to because of the following informality: A method, comprising:…predict, using a model generator, a health metric should read “A method, comprising:…predicting, using a model generator, a health metric”. Appropriate correction is required.
Claim 22 objected to because of the following informality: wherein the training set data including training set usage data should read “wherein the training set data includes training set usage data”. Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 18 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.
Regarding claim 18,
Claim 18 recites the term the first period similarity metric. A “first period similarity metric” is not recited earlier in claim 18, nor in any parent claims of claim 18. Therefore the term lacks antecedent basis. For examination purposes, the claim will be interpreted as reading “The system of claim 1, wherein a similarity metric is based on respective geographic locations of the electronic device and the plurality of electronic devices”.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Regarding claim 18,
Claim 18 is in dependent form, however, the parent claim referred to within claim 18, claim 4, has been cancelled and no longer exists. Therefore claim 18 does not contain a reference to a claim previously set forth.
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 No therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 8, 10-16, and 18-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding claim 1,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a machine.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of extrapolate, using a model generator, additional usage data for the electronic device over a second period of time that is longer than the first period of time, wherein the additional usage data relates to a past usage of the electronic device that occurred prior to collection of the usage data over the first period of time; recites an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of predict, using the model generator, a state of the electronic device based on the additional usage data for the electronic device over the second period of time recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of predict, using the model generator, a health metric for the electronic device based on the state of the electronic device; recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of a processor to: recites mere instructions to apply the exceptions with generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
The limitation of collect usage data for hardware components of an electronic device over a first period of time; recites the mere extra-solution activity of data gathering, which is necessary for effective extrapolation and prediction, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of wherein the model generator extrapolates the additional usage data using at least one of random forest linear regression, K-nearest neighbor, support vector machine, Naive Bayes, or logistic regression, and using the usage data and training set data collected for other electronic devices recites mere instructions to apply the exceptions with generic machine learning models, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
The limitation of execute a preventative action at the electronic device based on the state of the electronic device predicted using the model generator recites mere instructions to generically apply the exception, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of a processor to: recites mere instructions to apply the exceptions with generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(f).
The limitation of collect usage data for hardware components of an electronic device over a first period of time; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II.i.
The limitation of wherein the model generator extrapolates the additional usage data using at least one of random forest linear regression, K-nearest neighbor, support vector machine, Naive Bayes, or logistic regression, and using the usage data and training set data collected for other electronic devices recites mere instructions to apply the exceptions with generic machine learning models, which does not integrate the exception into a practical application, MPEP 2106.05(f).
The limitation of execute a preventative action at the electronic device based on the state of the electronic device predicted using the model generator recites mere instructions to generically apply the exception, which does not integrate the exception into a practical application, MPEP 2106.05(f).
Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 2,
Claim 2 adds the additional limitation of wherein the usage data includes at least one of:
processor usage;
applications executed;
application usage;
battery state;
electronic device state;
hard drive state;
thermal state;
anomalous operation of the electronic device;
login time stamps;
or logout time stamps
to claim 1, which recites mere additional details on the usage data, without changing that collect usage data for hardware components of an electronic device over a first period of time, as recited in claim 1, is mere data gathering and data retrieval over a network, which does not integrate any recited exceptions into a practical application, nor is it significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(g), and MPEP 2106.05(d).II.i.
Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 8,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a process.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of extrapolating, with the processor using backwards forecasting, based on the usage data over the first period of time, additional usage data over a second period of time that is longer than the first period of time, wherein the additional usage data relates to a past usage of the electronic device that occurred prior to collection of the usage data over the first period of time; recites an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of determining, with the processor, based on the additional usage data over the second period of time, a state of the electronic device; recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of predict, using a model generator, a health metric for the electronic device based on the state of the electronic device; recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of collecting, with a processor, usage data for hardware components of an electronic device over a first period of time; recites the mere extra-solution activity of data gathering, which is necessary for effective extrapolation and prediction, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of and executing a preventative action at the electronic device based on the state of the electronic device determined based on the additional usage data recites mere instructions to generically apply the exception, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of collecting, with a processor, usage data for hardware components of an electronic device over a first period of time; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II.i.
The limitation of and executing a preventative action at the electronic device based on the state of the electronic device determined based on the additional usage data recites mere instructions to generically apply the exception, which does not integrate the exception into a practical application, MPEP 2106.05(f).
Therefore, claim 8 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 10,
Claim 10 adds the additional limitation of wherein the additional usage data over the second period of time is sourced from at least two of:
a linear extrapolation of the observed usage data over the first period of time;
usage data over the second period of time from another electronic device;
or usage data over the second period of time from a training set of usage data to claim 8, which recites mere additional details on the information used to make an evaluation of extrapolating additional usage data, without changing that extrapolating, with the processor using backwards forecasting, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 8, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer.
Therefore, claim 10 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 11,
Claim 11 adds the additional limitation of combining, with the processor, usage data from different sources to claim 10, which recites mere additional details on the information used to make an evaluation of extrapolating additional usage data, without changing extrapolating, with the processor using backwards forecasting, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 8, the parent claim of claim 10, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer.
Therefore, claim 11 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 12,
Claim 12 adds the additional limitation of weighting, with the processor, usage data from different sources to claim 11, which recites mere additional details on the information used to make an evaluation of extrapolating additional usage data, without changing that extrapolating, with the processor using backwards forecasting, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 8, a parent claim of claim 11, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer.
Therefore, claim 12 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 13,
Claim 13 adds the additional limitation of averaging, with the processor, usage data from different sources to claim 10, which recites mere additional details on the information used to make an evaluation of extrapolating additional usage data, without changing that extrapolating, with the processor using backwards forecasting, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 8, the parent claim of claim 10, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer.
Therefore, claim 13 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 14,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a manufacture.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of extrapolate linearly, based on the usage data over the first period of time, additional usage data over a second period of time that is longer than the first period of time, wherein the additional usage data relates to a past usage of the electronic device that occurred prior to collection of the usage data over the first period of time; recites an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of determine a current state of the electronic device based on the additional usage data; recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of predict, using a model generator, a health metric for the electronic device based on the current state; recites a judgement on the state of the electronic device, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of and provide a recommendation on an action to execute at the electronic device based on the predicted health metric recites a judgement on the state of the electronic device and an action in relation to it, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of collect usage data for hardware components of the electronic device over a first period of time; recites the mere extra-solution activity of data gathering, which is necessary for effective extrapolation and prediction, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of collect usage data for hardware components of the electronic device over a first period of time; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II.i.
Therefore, claim 14 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 15,
Claim 15 adds the additional limitation of wherein the second period of time is ten times longer than the first period of time to claim 14, which recites mere additional details on the period of time additional usage data is extrapolated for, without changing that extrapolate linearly, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 14, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 15 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 16,
Claim 16 adds the additional limitation of wherein the second period of time occurred prior to the first period of time to claim 1, which recites mere additional details on the period of time additional usage data is extrapolated for, without changing that extrapolate linearly, based on the usage data over the first period of time, additional usage data over a second period of time, as recited in claim 1, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 16 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 18,
Claim 18 adds the additional limitation of wherein the first period similarity metric is based on respective geographic locations of the electronic device and the plurality of electronic devices to claim 4, which recites a judgement of the similarity between geographic locations, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 18 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 19,
Claim 19 adds the additional limitation of wherein the additional usage data represents unrecorded data related to the past usage of the electronic device to claim 1, which recites mere additional details on the additional usage data, without changing that extrapolate, using a model generator, additional usage data for the electronic device over a second period of time, as recited in claim 1, is an evaluation of how the usage data extrapolates, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 19 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 20,
Claim 20 adds the additional limitations to claim 1:
wherein the health metric is a stage of battery swell of a battery of the electronic device recites an observation of a stage of battery swell, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
and, in response to predicting, using the model generator, the state of the electronic device, the processor is to generate a recommendation to replace the battery of the electronic device recites a judgement on the state of the electronic device and an action in relation to it, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 20 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 21,
Claim 21 adds the additional limitation of wherein, to execute the preventative action at the electronic device, the processor is to change a setting to increase a life of a battery of the electronic device to claim 1, which recites mere instructions to generically apply the exception, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 21 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 22,
Claim 22 adds the additional limitation of wherein the training set data including training set usage data collected over the second period of time for the other electronic devices to claim 1, which recites mere additional details on training set data, without changing that using the usage data and training set data collected for other electronic devices, as recited in claim 1, recites mere instructions to generically apply the exceptions, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 22 is found to be ineligible subject matter under 35 U.S.C. 101.
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, 2, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sethi et al. (U.S. Patent Application Publication No. 2022/0391722), hereinafter Sethi, in view of Hofer et al. (U.S. Patent Application Publication No. 2018/0090127), hereinafter Hofer, further in view of Crowe et al. (U.S. Patent No. 7,251,589), hereinafter Crowe, further in view of Vijayaraghavan (U.S. Patent Application Publication No. 2021/0165708), hereinafter Vijayaraghavan, further in view of Aljuaid and Sasi “Proper Imputation Techniques for Missing Values in Data sets”, hereinafter Aljuaid.
Regarding claim 1,
Sethi teaches A system, comprising:
a processor to: ((Sethi [0057]) “The computer 60 includes a central processing unit (‘CPU’)”)
collect usage data for hardware components ((Sethi [0035]) “the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components”) of an electronic device over a first period of time; ((Sethi [0031]) “The workload predictor 21 predicts the workload of remote devices (e.g. remote devices 16) by analyzing the historical performance of the remote devices and configuration information of the remote devices for a given period, e.g. the last 365 days. Historical performance of the remote devices may be represented, for example, as time series data indicating various metrics that are relevant to respective components of the remote devices, and stored in the workload history database 22 using techniques known in the art”, the workload history database that stores collected performance data for a remote device for a given period corresponds to a data collector that collects usage data)
extrapolate, using a model generator, additional usage data for the electronic device over a second period of time [that is longer than the first period of time], ((Sethi [0035]) “To predict the future workload for a particular device, the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components over a future period, e.g. the next 24 hours”, the workload predictor is a model generator, predicted relevant performance metrics correspond to additional usage data, Sethi does not teach that the second period of time is longer than the first period of time)
wherein the model generator extrapolates the additional usage data…using the usage data and training set data collected for other electronic devices; ((Sethi [0038]) “In some cases, when a selected electronic device exactly shares a configuration with another deployed electronic device for which historical workload data are available in sufficient quantities, then the electronic device that is most similar to a selected electronic device (for purposes of applying machine learning) is simply that other electronic device. That is, embodiments predict the future workload on a particular device by analyzing historical workload data of another device having the same configuration”, (Sethi [0037]) “If a particular device has been deployed for long enough to train the machine learning algorithm on its past workloads, then predicting its future workloads should be performed by analyzing its own past performance data. Otherwise, the prediction should be based on analyzing historical workload data for an electronic device that is most similar to a selected electronic device”)
predict, using the model generator, a state of the electronic device based on the additional usage data for the electronic device over the second period of time; ((Sethi Abstract) “These data are then mapped together to predict future overall idle periods of each device”, a state of idleness of the device in the future corresponds to a state of the device over a second period of time)
Hofer teaches the following further limitation that Sethi does not teach:
[extrapolate, using a model generator, additional usage data for the electronic device over] a second period of time that is longer than the first period of time, ((Hofer [0030]) “The values in the database may then be utilized to derive a new time threshold to determine the end of a phrase. As an example, the adapted new time threshold could be set to be ten percent (10%) larger than the longest pause between words stored in the database”, a threshold to determine the end of the time a phrase has been spoken that is longer than any previously observed corresponds to extrapolating data over a second period of time longer than a first recorded period of time, Sethi teaches a model generator that extrapolates usage data for an electronic device)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi and Hofer by taking the system for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data, taught by Sethi, and having the period of time of the extrapolated additional usage data be longer than the period of time of the collected data, taught by Hofer, as it is well known within the art to use a predictive system in situations not completely analogous to those found in historical data, which imparts the predictable benefit of more flexible and applicable predictions. Such a combination would be obvious.
Crowe teaches the following further limitation that neither Sethi nor Hofer teaches:
wherein the additional [usage] data relates to a past [usage of the electronic device] that occurred prior to collection of the [usage] data over the first period of time; ((Crowe Cols. 7-8, lines 62-3) “FIG. 7 illustrates that not only can there be predictions of future values but also there can be predictions of values that may have occurred before the time period of the time series. Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost. Backcasts 200 can provide a view into how that historical data may have looked”, predicted historical data corresponds to additional data, Sethi teaches usage data related to usage of electronic devices)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, and Crowe by taking the system for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period, taught jointly by Sethi and Hofer, and having the extrapolated additional usage data be for a time period before the collection period, i.e. a backcast or imputation, taught by Crowe, as Crowe teaches: (Crowe Cols. 7-8, lines 65-1) “Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost”. Such a combination would be obvious.
Vijayaraghavan teaches the following further limitations that neither Sethi, nor Hofer, nor Crowe teaches:
predict, using the model generator, a health metric for the electronic device based on the state of the electronic device; ((Vijayaraghavan [0004]) “The present application is directed to systems, methods, and computer-readable storage media configured to predict future system failures using machine learning techniques. Performance metrics (e.g., key performance indicators (KPIs)) of a system may be monitored and compiled into datasets. The datasets may be used as inputs to a machine learning engine that executes a trained model against information included in the datasets to identify trends in the performance metrics indicative of future failures of the monitored system”, performance metrics indicative of future failures of the monitored system correspond to health metrics for an electronic device based on a current state)
and execute a preventative action at the electronic device based on the state of the electronic device predicted using the model generator ((Vijayaraghavan [0055]) “In addition to providing recommendations and tools for taking action to mitigate the occurrence of a predicted system failure, systems configured in accordance with aspects of the present disclosure may be configured to automatically execute operations to mitigate the occurrence of a predicted system failure. For example, where a system failure is predicted based on a set of KPIs that includes negative trends associated with a lock wait metric (e.g., a Lock wait BOT in an SAP system), the system may automatically remove one or more of the locks contributing that that KPI. Automatically performing such actions may eliminate the impact of the corresponding KPIs on overall system health and performance and reduce the likelihood that a system failure occurs”, an automatically performed action based on a lock state to reduce the likelihood of system failure is a preventative action)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, and Vijayaraghavan by taking the system for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period and in the past, taught jointly by Sethi, Hofer, and Crowe, and adding that the state indicates a health of the electronic device, and executing a preventative action based on the health metric, taught by Vijayaraghavan, as monitoring the health of computer systems and performing actions to maintain the health of computer systems is well-known within the art, providing the predictable benefit of increasing the resilience of computer systems against failure. Such a combination would be obvious.
Aljuaid teaches the following further limitation more explicitly than Sethi, Hofer, Crowe, or Vijayaraghavan:
wherein the model generator extrapolates the additional usage data using at least one of random forest linear regression, K-nearest neighbor, support vector machine, Naive Bayes, or logistic regression, ((Aljuaid Pg. 2) “K-Nearest Neighbor is a pre-replace method that replaces the missingness before the data mining process as presented in [6]. It classifies the data into groups and then it replaces the missing values with the corresponding value from the nearest-neighbor. The nearest-neighbor is the closest value based on the Euclidean distance [7]. The missing values are imputed considering a given number of instances that are mostly similar to the instance of interest. The similarity of two instances is determined using Euclidean distance”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid by taking the system for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period and in the past, then using the predicted state to predict a health metric and execute a preventative action, taught jointly by Sethi, Hofer, and Crowe, and Vijayaraghavan, and adding extrapolation of the past usage data using a k-nearest neighbors model, taught by Aljuaid, as k-nearest neighbors is a well-known machine learning model that provides adequate results when used for imputation, i.e. prediction of past values, as Aljuaid states: (Aljuaid Pg. 5) “both EM and KNN can be effective”. Such a combination would be obvious.
Regarding claim 2,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
Sethi further teaches:
wherein the usage data includes at least one of:
processor usage; ((Sethi [0033]) “Moreover, each component of a remote device has one or more relevant performance metrics that may be measured. Thus, a relevant metric for a central processing unit (CPU) of a remote device may be its percentage utilization; other components have a variety of other relevant performance metrics. The historical performance of each such component (i.e., values representing its performance metrics) may be stored in workload history database 22 in association with their collection times”, a central processing unit’s percentage utilization corresponds to processor usage)
applications executed;
application usage;
battery state;
electronic device state;
hard drive state;
thermal state;
anomalous operation of the electronic device;
login time stamps;
or logout time stamps
At the time of filing, one of ordinary skill in the art would have motivation to combine the system jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid for the parent claim of claim 2, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 16,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
Crowe further teaches:
wherein the second period of time occurred prior to the first period of time. ((Crowe Col. 7, lines 62-64) “FIG. 7 illustrates that not only can there be predictions of future values but also there can be predictions of values that may have occurred before the time period of the time series”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the system jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid for the parent claim of claim 16, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 19,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
Crowe further teaches:
wherein the additional usage data represents unrecorded data related to the past [usage of the electronic device]. ((Crowe Cols. 7-8, lines 63-1) “there can be predictions of values that may have occurred before the time period of the time series. Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded”, Sethi teaches usage data related to usage of electronic devices)
At the time of filing, one of ordinary skill in the art would have motivation to combine the system jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid for the parent claim of claim 19, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Claims 8, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan.
Regarding claim 8,
Sethi teaches A method, comprising:
collecting, with a processor, usage data for hardware components ((Sethi [0035]) “the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components”) of an electronic device over a first period of time; ((Sethi [0031]) “The workload predictor 21 predicts the workload of remote devices (e.g. remote devices 16) by analyzing the historical performance of the remote devices and configuration information of the remote devices for a given period, e.g. the last 365 days. Historical performance of the remote devices may be represented, for example, as time series data indicating various metrics that are relevant to respective components of the remote devices, and stored in the workload history database 22 using techniques known in the art”, collecting performance data for a remote device for a given period corresponds to collecting usage data over a first period of time)
extrapolating, with the processor [using backwards forecasting], based on the usage data over the first period of time, additional usage data over a second period of time [that is longer than the first period of time]; ((Sethi [0035]) “To predict the future workload for a particular device, the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components over a future period, e.g. the next 24 hours”, predicted relevant performance metrics correspond to extrapolated usage data, Sethi does not teach that the second period of time is longer than the first period of time or backwards forecasting)
determining, with the processor, based on the additional usage data over the second period of time, a state of the electronic device; ((Sethi Abstract) “These data are then mapped together to predict future overall idle periods of each device”, a state of idleness of the device in the future corresponds to a state of the device over a second period of time)
Hofer teaches the following further limitation that Sethi does not teach:
[extrapolating, with the processor, based on the usage data over the first period of time, additional usage data over] a second period of time that is longer than the first period of time ((Hofer [0030]) “The values in the database may then be utilized to derive a new time threshold to determine the end of a phrase. As an example, the adapted new time threshold could be set to be ten percent (10%) larger than the longest pause between words stored in the database”, a threshold to determine the end of the time a phrase has been spoken that is longer than any previously observed corresponds to extrapolating data over a second period of time longer than a first recorded period of time, Sethi teaches extrapolating usage data for an electronic device)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi and Hofer by taking the method for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data and recommending a corresponding action, taught by Sethi, and having the period of time of the extrapolated additional usage data be longer than the period of time of the collected data, taught by Hofer, as it is well known within the art to use a predictive method in situations not completely analogous to those found in historical data, which imparts the predictable benefit of more flexible and applicable predictions. Such a combination would be obvious.
Crowe teaches the following further limitation that neither Sethi nor Hofer teaches:
extrapolating, with the processor using backwards forecasting…wherein the additional [usage] data relates to a past [usage of the electronic device] that occurred prior to collection of the [usage data] over the first period of time; ((Crowe Cols. 7-8, lines 62-3) “FIG. 7 illustrates that not only can there be predictions of future values but also there can be predictions of values that may have occurred before the time period of the time series. Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost. Backcasts 200 can provide a view into how that historical data may have looked”, predicted historical data corresponds to additional data, Sethi teaches usage data related to usage of electronic devices)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, and Crowe by taking the method for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period and recommending a corresponding action, taught jointly by Sethi and Hofer, and having the extrapolated additional usage data be for a time period before the collection period, i.e. a backcast or imputation, taught by Crowe, as Crowe teaches: (Crowe Cols. 7-8, lines 65-1) “Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost”. Such a combination would be obvious.
Vijayaraghavan teaches the following further limitations that neither Sethi, nor Hofer, nor Crowe teaches:
predict, using a model generator, a health metric for the electronic device based on the state of the electronic device; ((Vijayaraghavan [0004]) “The present application is directed to systems, methods, and computer-readable storage media configured to predict future system failures using machine learning techniques. Performance metrics (e.g., key performance indicators (KPIs)) of a system may be monitored and compiled into datasets. The datasets may be used as inputs to a machine learning engine that executes a trained model against information included in the datasets to identify trends in the performance metrics indicative of future failures of the monitored system”, performance metrics indicative of future failures of the monitored system correspond to health metrics for an electronic device based on a current state)
and executing a preventative action at the electronic device based on the state of the electronic device determined based on the additional usage data ((Vijayaraghavan [0055]) “In addition to providing recommendations and tools for taking action to mitigate the occurrence of a predicted system failure, systems configured in accordance with aspects of the present disclosure may be configured to automatically execute operations to mitigate the occurrence of a predicted system failure. For example, where a system failure is predicted based on a set of KPIs that includes negative trends associated with a lock wait metric (e.g., a Lock wait BOT in an SAP system), the system may automatically remove one or more of the locks contributing that that KPI. Automatically performing such actions may eliminate the impact of the corresponding KPIs on overall system health and performance and reduce the likelihood that a system failure occurs”, an automatically performed action based on a lock state to reduce the likelihood of system failure is a preventative action)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, and Vijayaraghavan by taking the method for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period and in the past, taught jointly by Sethi, Hofer, and Crowe, and adding that the state indicates a health of the electronic device, and executing a preventative action based on the health metric, taught by Vijayaraghavan, as monitoring the health of computer systems and performing actions to maintain the health of computer systems is well-known within the art, providing the predictable benefit of increasing the resilience of computer systems against failure. Such a combination would be obvious.
Regarding claim 14,
Sethi teaches:
collect usage data for hardware components ((Sethi [0035]) “the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components”) of an electronic device over a first period of time; ((Sethi [0031]) “The workload predictor 21 predicts the workload of remote devices (e.g. remote devices 16) by analyzing the historical performance of the remote devices and configuration information of the remote devices for a given period, e.g. the last 365 days. Historical performance of the remote devices may be represented, for example, as time series data indicating various metrics that are relevant to respective components of the remote devices, and stored in the workload history database 22 using techniques known in the art”, collecting performance data for a remote device for a given period corresponds to collecting usage data over a first period of time)
extrapolate linearly, based on the usage data over the first period of time, additional usage data over a second period of time [that is longer than the first period of time]; ((Sethi [0035]) “To predict the future workload for a particular device, the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components over a future period, e.g. the next 24 hours”, predicted relevant performance metrics correspond to extrapolated usage data, Sethi does not teach that the second period of time is longer than the first period of time)
determine a current state of the electronic device based on the additional usage data; ((Sethi Abstract) “These data are then mapped together to predict future overall idle periods of each device”, a state of idleness of the device in the future corresponds to a state of the device over a second period of time)
Hofer teaches A non-transitory machine-readable storage medium encoded with instructions executable by a processor of an electronic device to, when executed by the processor, cause the processor to: ((Hofer [0013]) “these components may be implemented in one or more modules as a set of logic instructions stored in a machine- or computer-readable storage medium…to be executed by a processor or computing device”), as well as the following further limitation that Sethi does not teach:
[extrapolate, based on the usage data over the first period of time, additional usage data over] a second period of time that is longer than the first period of time ((Hofer [0030]) “The values in the database may then be utilized to derive a new time threshold to determine the end of a phrase. As an example, the adapted new time threshold could be set to be ten percent (10%) larger than the longest pause between words stored in the database”, a threshold to determine the end of the time a phrase has been spoken that is longer than any previously observed corresponds to extrapolating data over a second period of time longer than a first recorded period of time, Sethi teaches extrapolating usage data for an electronic device)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi and Hofer by taking the method for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data, taught by Sethi, and having the method be implemented as instructions on a computer-readable medium and the period of time of the extrapolated data be longer than the period of time of the collected data, taught by Hofer, as it is well known within the art to implement computer methods on computer-readable media, and use a predictive method in situations not completely analogous to those found in historical data, which imparts the predictable benefit of more flexible and applicable predictions. Such a combination would be obvious.
Crowe teaches the following further limitation that neither Sethi nor Hofer teaches:
wherein the additional [usage] data relates to a past [usage of the electronic device] that occurred prior to collection of the [usage] data over the first period of time; ((Crowe Cols. 7-8, lines 62-3) “FIG. 7 illustrates that not only can there be predictions of future values but also there can be predictions of values that may have occurred before the time period of the time series. Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost. Backcasts 200 can provide a view into how that historical data may have looked”, predicted historical data corresponds to additional data, Sethi teaches usage data related to usage of electronic devices)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, and Crowe by taking the medium with instructions for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period, taught jointly by Sethi and Hofer, and having the extrapolated additional usage data be for a time period before the collection period, i.e. a backcast or imputation, taught by Crowe, as Crowe teaches: (Crowe Cols. 7-8, lines 65-1) “Such predictions can be termed backcasts 200 or imputations and can be useful in many different situations, such as if early historical data of a data generating process activity was not recorded or has been lost”. Such a combination would be obvious.
Vijayaraghavan teaches the following further limitations that neither Sethi, nor Hofer, nor Crowe teaches:
predict, using a model generator, a health metric for the electronic device based on the current state; ((Vijayaraghavan [0004]) “The present application is directed to systems, methods, and computer-readable storage media configured to predict future system failures using machine learning techniques. Performance metrics (e.g., key performance indicators (KPIs)) of a system may be monitored and compiled into datasets. The datasets may be used as inputs to a machine learning engine that executes a trained model against information included in the datasets to identify trends in the performance metrics indicative of future failures of the monitored system”, performance metrics indicative of future failures of the monitored system correspond to health metrics for an electronic device based on a current state)
and provide a recommendation on an action to execute at the electronic device based on the health metric ((Vijayaraghavan [0005]) “Upon identifying a predicted system failure, operations of embodiments may perform operations to mitigate the potential failure before it actually occurs. For example, a user interface may be presented that provides interactive tools that enable a user (e.g., a system administrator, etc.) to initiate operations to mitigate the predicted failure, such as to free up resources of the system or other operations”, presenting a user interface to a user to initiate operations to mitigate a failure corresponds to providing a recommendation of action based on a predicted health metric)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, and Vijayaraghavan by taking the medium with instructions for collecting and extrapolating additional usage data for an electronic device, then predicting a state of the electronic device with the extrapolated additional usage data over a period of time longer than the collection period and in the past, taught jointly by Sethi, Hofer, and Crowe, and adding that the state indicates a health if the electronic device, and recommending an action to execute based on the health metric, taught by Vijayaraghavan, as monitoring the health of computer systems and performing actions to maintain the health of computer systems is well-known within the art, providing the predictable benefit of increasing the resilience of computer systems against failure. Such a combination would be obvious.
Regarding claim 15,
Sethi, Hofer, Crowe, and Vijayaraghavan jointly teach The non-transitory machine-readable storage medium of claim 14,
Hofer further teaches:
wherein the second period of time is ten times longer than the first period of time ((Hofer [0030]) “The values in the database may then be utilized to derive a new time threshold to determine the end of a phrase. As an example, the adapted new time threshold could be set to be ten percent (10%) larger than the longest pause between words stored in the database”, Hofer discloses a second time period to extrapolate data that is longer than a first time period with stored data, with a specific example being a second time period that is 10% longer than the first time period, a longer time period such as ten times as long is a matter of design choice)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Sethi, Hofer, Crowe, and Vijayaraghavan for the parent claim of claim 15, claim 14. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Claims 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Weber et al. “Data-Driven Copy-Paste Imputation for Energy Time Series”, hereinafter Weber.
Regarding claim 10,
Sethi, Hofer, Crowe, and Vijayaraghavan jointly teach The method of claim 8,
Sethi further teaches:
wherein the additional usage data over the second period of time is sourced from at least two of: a linear extrapolation of the observed usage data over the first period of time; ((Sethi [0035]) “To predict the future workload for a particular device, the workload predictor 21 may apply linear time series forecasting to historical workload data stored in the workload history database 22 to predict each relevant performance metric for the device components over a future period, e.g. the next 24 hours”, linear time series forecasting of historical workload data corresponds to linear extrapolation of usage data)
Weber teaches the following further limitation that neither Sethi nor Hofer teaches and more explicitly than Crowe teaches:
usage data over the second period of time [from another electronic device]; ((Weber Abstract) “the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series”, data from a time series similar to gaps in a time series corresponds to data over a second period of time, electronic devices taught by Sethi)
or usage data over the second period of time from a training set of usage data
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, and Weber by taking the method of claim 8, and performing extrapolation of usage data with linear time series forecasting, taught jointly by Sethi, Hofer, Crowe, and Vijayaraghavan, and adding the use of data over a period of time where data is not present for a selected electronic device, taught by Weber, as data imputation, or filling in missing fields within a dataset in order to make it usable for prediction methods that require all fields to be filled, is well-known within the art. Such a combination would be obvious.
Regarding claim 11,
Sethi, Hofer, Crowe, Vijayaraghavan, and Weber jointly teach The method of claim 10, further comprising:
Sethi further teaches:
combining, with the processor, usage data from different sources ((Sethi [0010]) “a first embodiment is a method of collecting performance data from a plurality of electronic devices”, collecting usage data from multiple electronic devices is a combination of data from different sources)
At the time of filing, one of ordinary skill in the art would have motivation to combine the medium jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Weber for the parent claim of claim 11, claim 10. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Weber, further in view of Hui et al. (Chinese Patent Application Publication No. 111651341), hereinafter Hui.
Regarding claim 12,
Sethi, Hofer, Crowe, Vijayaraghavan, and Weber jointly teach The method of claim 11, further comprising:
Hui teaches the following further limitation that neither Sethi, nor Hofer, nor Crowe, nor Vijayaraghavan, nor Weber teaches:
weighting, with the processor, usage data from different sources ((Hui [0007]) “This method divides the program into program fragments of equal length and selects some program fragments that can represent the program characteristics as test samples. These test samples are executed on a simulator or emulator platform, and the data of all test samples are weighted to obtain the performance data of the entire program”, weighting data from several test samples to obtain performance data corresponds to weighting usage data from different sources)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Weber, and Hui by taking the method of claim 11, jointly taught by Sethi, Hofer, Vijayaraghavan, and Weber, and adding the weighting of usage data from several sources, as taught by Hui, as creating a weighted average from several data sources is well-known within the art and results in the predictable benefit of increasing the saliency or relevance of more useful parts of the data. Such a combination would be obvious.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Weber, further in view of Brewer et al. (U.S. Patent No. 10,073,754), hereinafter Brewer.
Regarding claim 13,
Sethi, Hofer, Crowe, Vijayaraghavan, and Weber jointly teach The method of claim 10, further comprising:
Brewer teaches the following further limitation that neither Sethi, nor Hofer, nor Crowe, nor Vijayaraghavan, nor Weber teaches:
averaging, with the processor, usage data from different sources ((Brewer Col. 2, lines 14-17) “the computer-implemented method further comprises: determining, by a processor, an average performance metric for the plurality of computing devices”, an average performance metric for a plurality of devices corresponds to an average of usage data from different sources)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Weber, and Brewer by taking the method of claim 10, jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Weber, and averaging the usage data from several different sources, as taught by Brewer, as averaging data from several sources is well-known within the art and results in the predictable benefit of reducing the negative effects of outliers within data. Such a combination would be obvious.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Aljuaid, further in view of Sevce et al. (U.S. Patent No. 11,178,014), hereinafter Sevce.
Regarding claim 18,
Claim 18 is stated to depend on The system of claim 4, however claim 4 has been cancelled. Claim 18 is thus assumed to depend on the system of claim 1, which is jointly taught by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid.
Sevce teaches the following further limitation that neither Sethi, nor Hofer, nor Crowe, nor Vijayaraghavan, nor Aljuaid teaches:
wherein the first period similarity metric is based on respective geographic locations of the electronic device and the plurality of electronic devices. ((Sevce Col. 17, lines 14-18) “In some other embodiments, the similarity metric is selected based on geographic locations of the network devices, i.e., a switch on a host network may have a closer similarity metric to a client computer based on geographical proximity”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Aljuaid, and Sevce by taking the system of claim 1, taught jointly by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid, and adding computing a similarity metric based on geographic locations of electronic devices, taught by Sevce, as doing so provides the predictable benefit of having region differences be taken into consideration when comparing electronic device usage, making it less likely that the usage data of regions with many electronic devices will skew predicted data for regions with fewer electronic devices. Such a combination would be obvious.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Aljuaid, further in view of Han et al. (International Patent Application Publication No. 2021/049800), hereinafter Han.
Regarding claim 20,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
HanHa teaches the following further limitations that neither Sethi, nor Hofer, nor Crowe, nor Vijayaraghavan, nor Aljuaid teaches:
wherein the health metric is a stage of battery swell of a battery of the electronic device ((Han [0063]) “the processor (220) can detect a swelling state of the battery (289) among abnormal states inside the battery while monitoring the state due to deterioration of the life of the battery (289)”)
and, in response to predicting, using the model generator, the state of the electronic device, the processor is to generate a recommendation to replace the battery of the electronic device ((Han [0122]) “an electronic device may provide a notification regarding battery life, indicating that the battery life is decreasing and a replacement is recommended”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Aljuaid, and Han by taking the system of claim 1, taught jointly by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid, including prediction of a health metric, and adding that the health metric is a stage of battery swell and subsequently recommending the battery be replaced, taught by Han, as Han teaches: (Han [0010]) “by detecting various battery states in real time, such as swelling due to overcharging, the progress of swelling can be prevented and battery damage leading to battery fire can be prevented in advance”. Such a combination would be obvious.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Aljuaid, further in view of Keene et al. (U.S. Patent Application Publication No. 2023/0187710), hereinafter Keene.
Regarding claim 21,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
KeeneHa teaches the following further limitation that neither Sethi, nor Hofer, nor Crowe, nor Vijayaraghavan, nor Aljuaid teaches:
wherein, to execute the preventative action at the electronic device, the processor is to change a setting to increase a life of a battery of the electronic device ((Keene [0049]) “an algorithm that utilizes enhanced SOH model(s) generated or configured as described herein may be used to adjust the operating parameters of batteries, such as to prolong useful life of the batteries. The adjustments effectuated by the algorithm may comprise, but are not limited to changes to maximum and minimum voltage, changes to maximum rate, and changes to maximum and minimum temperature”)
At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Aljuaid, and Keene by taking the system of claim 1, taught jointly by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid, including executing a preventative action, and adding that the action is to change a setting to increase battery life, taught by Keene, as doing so confers the predictable benefit of making more efficient use of an existing battery by increasing the time until it must be replaced, as well as reducing the risk of battery damage or death, which would interfere with the operation of an electronic device. Such a combination would be obvious.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Hofer, further in view of Crowe, further in view of Vijayaraghavan, further in view of Aljuaid, further in view of Kok and Ozdemir “DeepMDP: A Novel Deep-Learning-Based Missing Data Prediction Protocol for IoT”, hereinafter Kok.
Regarding claim 22,
Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid jointly teach The system of claim 1,
Kok Hateaches the following further limitation that neither Sethi, nor Hofer, nor Vijayaraghavan teaches and more explicitly than Crowe or Aljauid teaches:
wherein the training set data including training set usage data collected over the second period of time for the other electronic devices (Kok Pg. 6, Fig. 4 shows that other sensors with data that isn’t missing for a given time period are part of the data set, sensors are electronic devices, (Kok Pg. 5) “We use the dataset given in Section IV-B during the training process of the proposed models”, (Kok Pg. 7) “we use IntelLabData collected from sensors in the Intel Berkeley Research Lab between February 28, 2004, and April 5, 2004 [71] for our DL models and testbed scenarios. This data was collected by placing 54 Mica2Dot sensor nodes at different points in the Lab (40 m × 30 m). The sensor nodes are set to read time-stamped topology information, humidity, temperature, light intensity (lux), and voltage values…most of the nodes in the data set have a large number of missing or corrupted readings (roughly 50% of data)”)
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At the time of filing, one of ordinary skill in the art would have motivation to combine Sethi, Hofer, Crowe, Vijayaraghavan, Aljuaid, and Kok by taking the system of claim 1, taught jointly by Sethi, Hofer, Crowe, Vijayaraghavan, and Aljuaid, including training set data, and adding that the training set data includes usage data collected over a second, past period of time for other electronic devices, taught by Kok, as Kok teaches: (Kok Pg. 1) “When predicting missing data, neighboring IoT devices may be a great help as they probably sense highly correlated data”. Such a combination would be obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rehman et al. “SimFiller: Similarity-Based Missing Values Filling Algorithm” teaches a method of creating artificial data for fields within a dataset having missing values by finding a most similar field and copying the corresponding data.
Cazzanti et al. (U.S. Patent No. 10,353,764) teaches a method of determining and executing corrective actions to avert imminent device failure.
Yang et al. (U.S. Patent Application Publication No. 2022/0206898) teaches a method for predicting hard disk failure.
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/V.A.N./Examiner, Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124