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 .
Response to Amendment
According to paper filed February 2nd 2026, claims 1-20 are pending for examination with an August 30th 2020 priority date under 35 USC §371 & 35 USC §119(a)-(d) or (f).
By way of the present Amendment, claims 1, 5-6, 8, 10, and 12-15 are amended. Claims 16-20 are newly added, no claim is canceled. Claim rejections under 35 USC 112(b) are withdrawn.
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.
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.
Claims 1, 3, 6-7, 10-11, and 16-17 are rejected under 35 U.S.C. §103 as being unpatentable over Sastinsky (US 2020/0055421), hereinafter Sastinsky, and further in view of Steingart et at. (US 2019/0064123), hereinafter Steingart.
Claim 1
“A server comprising: a receiving unit to obtain a set of battery attributes associated with a battery of a client device” Sastinsky [0019][0030] teaches servers and desktop computers, and a power cell optimization system that indicates the usable capacity of the battery (i.e., one of battery attributes), number of cycles used, and number of remaining cycles;
“a prediction unit to predict a battery condition by applying a plurality of different first machine learning models to different subsets of the set of battery attributes, wherein the battery condition comprises battery swelling, battery memory effect, battery performance degradation, or any combination thereof” Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells (i.e., the battery capacity/attribute/memory effect) and protecting the battery from degradation;
Sastinsky fails to spelled out the battery swelling, which is taught in Steingart, Steingart [0033] teaches the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc., and the physical state of one or more parts of the battery with correlated analysis can evaluate or predict the SOC and SOH of a similar battery;
“a recommendation unit to apply a second machine learning model to the predicted battery condition to: predict a remaining life of the battery” Sastinsky [0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells;
“recommend an action to be performed based on the predicted remaining life of the battery” Sastinsky [0073] teaches providing optimization recommendations to increase or maximize approximate battery end-of-life (ABEL) for batteries or battery-powered devices.
Sastinsky and Steingart disclose analogous art. Steingart is analogous art because it is in the field of determining state of change and state of health of electrical cells. Sastinsky does not spell out the “battery swelling” as recited above. Said feature is taught in Steingart. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Steingart into Sastinsky (Steingart [0033]: the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc.) to enhance its battery end of life and condition prediction functions.
Claim 3
“wherein the recommended action comprises: a remedy to manage a lifecycle, a swell rate, and/or a runtime of the battery based on the predicted remaining life; or a replacement or upgradation of the battery based on the predicted remaining life” Sastinsky [0073][0074] teaches providing optimization recommendations to increase or maximize approximate battery end-of-life for batteries, a set of optimal battery performance metrics includes battery-type specific, battery-chemistry specific, battery-powered device specific, location specific, time-specific such as temperature, weather conditions, or current season, or any
combination of the forgoing.
Claim 6
“obtain a set of battery attributes associated with a battery of a client device” Sastinsky [0019][0030] teaches servers and desktop computers, and a power cell optimization system that indicates the usable capacity
of the battery, number of cycles used, and number of remaining cycles;
“predict battery swelling by applying a first machine learning model to a first subset of the battery attributes” Steingart [0033] teaches the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc.;
“predict battery memory effect by applying a second machine learning model to a second subset of the battery attributes” Sastinsky [0028][0045] teaches applying machine learning models to determine ABELs for power cells, and batteries/energy storage systems of usable capacity for secondary use, i.e., approximated battery end of life (ABEL) of a second life battery, referring to an estimated or calculated time between the primary end of life and the real end-of-life;
“predict battery performance degradation by applying a third machine learning model to a third subset of the battery attributes” Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells and protecting the battery from degradation;
“wherein the first, second, and third machine learning models are different from one another and wherein the first, second, and third subsets of the battery attributes are different from one another” Steingart [0033] teaches the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including swelling, dissolution etc., Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine with a battery management system that control and manage charging and discharging of the power cells and protecting the battery from degradation, and Sastinsky [0028] teaches batteries/energy storage systems of usable capacity for secondary use, i.e., approximated battery end of life (ABEL) of a second life battery, referring to an estimated or calculated time between the primary end of life and the real end-of-life; all these machine learning models and applications are different from each other;
“predict a remaining life of the battery by applying a fourth machine learning model to the predicted battery swelling, battery memory effect, and battery performance degradation” Sastinsky [0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells;
“send a notification including a recommendation to the client device based on the predicted remaining life” Sastinsky [0073] teaches providing optimization recommendations to increase or maximize approximate battery end-of-life (ABEL) for batteries or battery-powered devices.
Claim 7
“wherein the set of battery attributes is classified into the first subset, the second subset, and the third subset based on properties and/or characteristics of the battery attributes” Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells and protecting the battery from degradation; wherein the charging of power cells is considered one of the battery attributes (the first subset of attributes) and the discharging is another attribute (the second subset of attributes), the remaining capacity of the battery is a third attribute.
Claim 10
“extract at least one first feature vector from the first subset, at least one second feature vector from the second subset, and at least one third feature vector from the third subset; assign a weightage to each of the at least one first feature vector, at least one second feature vector, and at least one third feature vector” Sjogren [0255]
[0256] teaches weight matrices and activation vectors of observation that may be considered as an intermediate output from respective nodes of layers; and Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system;
“predict the battery swelling, the battery memory effect, and the battery performance degradation by inputting the at least one first feature vector and associated weightage into the first machine learning model, at least one second feature vector and associated weightage into the second machine learning model, at least one third feature vector and associated weightage into the third machine learning model, wherein the battery swelling, the battery memory effect, and the battery performance degradation are predicted based on corresponding benchmark data” Steingart [0033]-[0035] teaches battery change state including swelling, degradation, and the numerical values of the limits of the battery, such as charging and discharging.
Claim 11
“wherein instructions to predict the remaining life of the battery comprise instructions to: extract a feature vector by combining the predicted battery swelling, the predicted battery memory effect, and the predicted battery performance degradation; and predict the remaining life of the battery by inputting the feature vector, a domain expert feed, and device information of the client device into the fourth machine learning model” Sastinsky [0045] teaches execute one or more machine learning models to determine ABELs for power cells, and a battery management system.
Claim 11 is also rejected for the similar rationale given for claim 1.
Claim 16
“wherein the plurality of first machine learning models include a swelling prediction model to predict the battery swelling, a memory prediction model to predict the battery memory effect, and a performance prediction model to predict the battery performance degradation” Steingart [0033] teaches the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc.; Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, and a battery management system that control and manage charging and discharging of the power cells (i.e., the battery capacity/attribute/memory effect) and protecting the battery from degradation.
Claim 17
“wherein the recommendation unit is to receive the predicted battery swelling, the predicted battery memory effect, and the predicted battery performance degradation as a single combined feature vector” Sastinsky [0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells (i.e., receiving predictions), and Sastinsky [0073][0074] teaches providing optimization recommendations to increase or maximize approximate battery end-of-life for batteries, a set of optimal battery performance metrics includes battery-type specific, battery-chemistry specific, battery-powered device specific, location specific, time-specific such as temperature, weather conditions, or current season, or any combination of the forgoing.
Claims 2, 5, 8-9, 12, 14-15, and 18-20 are rejected under 35 U.S.C. §103 as being unpatentable over Sastinsky (US 2020/0055421), hereinafter Sastinsky, and further in view of Steingart et at. (US 2019/0064123), hereinafter Steingart, and Sjogren et al. (US 2021/0334656), hereinafter Sjogren.
Claim 2
“wherein the recommendation unit is to: retrieve device information associated with the client device; retrieve a
domain expert feed corresponding to the battery from a knowledge base” Sjogren [0004] teaches historical data has been collected and faults that have occurred historically have been investigated by domain experts;
“predict the remaining life of the battery by applying the second machine learning model to the device information, determine the predicted battery condition, and the domain expert feed” Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells and protecting the battery from degradation.
Sastinsky, Steingart, and Sjogren disclose analogous art. Steingart is analogous art because it is in the field of determining state of change and state of health of electrical cells. Sjogren is analogous art because it is in the field of detecting anomaly and predictive maintenance of electrical cells. Sastinsky fails to spell out the “battery swelling” as recited above. Said feature is taught in Steingart. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Steingart into Sastinsky (Steingart [0033]: the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc.) to enhance its battery end of life and condition prediction functions. Sastinsky also fails to spell out the “retrieve device information associated with the client device and retrieve a domain expert feed corresponding to the battery” as recited above. Said feature is taught in Sjogren. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Sjogren into Sastinsky (Sjogren [0004]: historical data has been collected and faults that have occurred historically and investigated by domain experts) to enhance its battery end of life and prediction functions by bringing in domain expert feed.
Claim 5
“wherein different ones of the plurality of first machine learning models are trained on input data using machine learning and data mining methods to predict battery swelling, battery memory effect, and/or battery performance degradation, and wherein the input data is selected from a set of time-series historical battery attributes associated with a plurality of batteries” Sjogren [0004] teaches historical data has been collected and faults that have occurred historically have been investigated by domain experts, and Sjogren [0193][0303] teaches using historical data, machine learning based classifiers trained to classify a transaction or an actor or to identify a tendency, and the cell remember values over arbitrary time intervals, and Sjogren [0117][0118] teaches principal component analysis (PCA) with variants include data mining.
Claim 8
“wherein the first machine learning model is trained on input data using machine learning and data mining methods to predict battery swelling, the second machine learning model is trained on input data using machine learning and data mining methods to predict battery memory effect, and the third machine learning model is trained on input data using machine learning and data mining methods to predict battery performance degradation” prediction data swelling and degradation is taught in Steingart [0033] and Sjogren [0117][0118] teaches principal component analysis (PCA) with variants include data mining,
“wherein the input data is selected from a set of time-series historical battery attributes associated with a plurality of batteries” Sjogren [0004] teaches historical data has been collected and faults that have occurred historically have been investigated by domain experts.
Claim 9
Claim 9 is rejected for the similar rationale given for claim 2.
Claim 12
Claim 12 is rejected for the similar rational given for claims 1-2 and 5.
Claim 14
“classify the time-series historical battery attributes into a first subset, a second subset, and a third subset based on properties and/or characteristics of the battery attributes” Sjogren [0193][0303] teaches using historical data, machine learning based classifiers trained to classify a transaction or an actor or to identify a tendency, and the cell remember values over arbitrary time intervals;
“train, validate, and test a swelling prediction model using the first subset to predict the battery swelling of the batteries; train, validate, and test a memory prediction model using the second subset to predict the battery memory effect of the batteries; and train, validate, and test a performance prediction model using the third subset to predict the battery performance degradation of the batteries” Sastinsky [0045] teaches execute one or more machine learning models to determine ABELs for power cells, and a battery management system. Claim 14 is rejected for the similar rationale given for claim 1.
Claim 15
“wherein instructions to build the second machine learning model comprise instructions to: train, validate, and test the second machine learning model using an outcome of the first plurality of machine learning models and the expert feeds to predict the remaining life of the batteries and generate the remediation actions” Sastinsky [0045] teaches execute one or more machine learning models to determine ABELs for power cells, and a battery management system.
Claim 15 is also rejected for the similar rationale given for claims 1 and 2.
Claim 18
“wherein the plurality of first machine learning models and the second machine learning model are supervised machine learning models” Sjogren [0004] teaches historical data has been collected and faults that have occurred historically have been investigated by domain experts, and Sjogren [0193][0303] teaches using historical data, machine learning based classifiers trained (i.e., supervised machine learning) to classify a transaction or an actor or to identify a tendency, and the cell remember values over arbitrary time intervals.
Claim 19
“wherein the first, second, third, and fourth machine learning models are supervised machine learning models” Sjogren [0004] teaches historical data has been collected and faults that have occurred historically have been investigated by domain experts, and Sjogren [0193][0303] teaches using historical data, machine learning based classifiers trained (i.e., supervised machine learning) to classify a transaction or an actor or to identify a tendency, and the cell remember values over arbitrary time intervals; and various machine learning models is taught in Sastinsky,
Sastinsky [0037][0045] teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells (i.e., the battery capacity/attribute/memory effect).
Claim 20
“wherein the second machine learning model is further built with device information associated with the batteries” Sastinsky [0074] teaches a set of optimal battery performance metrics includes battery-type specific, battery-chemistry specific, battery-powered device specific, location specific, time-specific such as temperature, weather conditions, or current season, or any combination of the forgoing; and machine learning model training is spelled out in Sjogren [0193] teaches using historical data, machine learning based classifiers trained.
Claim 4 is rejected under 35 U.S.C. §103 as being unpatentable over Sastinsky (US 2020/0055421), hereinafter Sastinsky, and further in view of Steingart et at. (US 2019/0064123), hereinafter Steingart, and Awiszus et al. (US 2019/0175961), hereinafter Awiszus.
Claim 4
“wherein the recommendation unit is to: generate an analytical report, on a dashboard of a user interface,
including a visualization of analytic or summary information related to the battery swelling, the battery memory effect, the battery performance degradation, the remaining life of the battery, an expected battery life based on the recommend action, or any combination thereof” Awiszus [0047] teaches a personal protection equipment management system (PPEMS) with dashboards and alert notifications.
Sastinsky, Steingart, and Awiszus disclose analogous art. Steingart is analogous art because it is in the field of determining state of change and state of health of electrical cells. Awiszus is analogous art because it is in the field of pixel optical sensing of visibly transparent object utilizing reflective materials for personal protective equipment. Sastinsky fails to spell out the “battery swelling” as recited above. Said feature is taught in Steingart. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Steingart into Sastinsky (Steingart [0033]: the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc.) to enhance its battery end of life and condition prediction functions. Sastinsky also fails to spell out the “generate an analytical report, on a dashboard of a user interface” as recited above. Said feature is taught in Awiszus. It would have been obvious to one ordinary skilled in the art at the time the present invention was made to incorporate said feature of Awiszus into Sastinsky (Awiszus [0047]: a personal protection equipment management system (PPEMS) with dashboards and alert notifications) to enhance its battery end of life and prediction functions by visually presenting analytical and summary information of electrical cells.
Allowable Subject Matter
Claim 13 is objected to as being dependent upon a rejected base claim, but would be allowable
if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant's arguments filed February 2nd 2026 have been fully considered but they are not persuasive.
Applicant argues that the “Office alleges that Sastinsky discloses a prediction unit applying at least one first machine learning model to a set of battery attributes.” Particularly, “Sastinsky’s mention of ‘one or more’ ML models does not teach this claim feature. For example, Sastinsky does not teach that individual ones of the one or more ML models are different from one another, and does not teach that individual ones of the one or more ML models are applied to different subsets of the battery data.” Said arguments are not persuasive because the argued feature is taught in the cited references Sastinsky in combination of Steingart.
Specifically, applicant amends the pending claim 1 feature to recite “a plurality of different first ML models to different subsets of a set of battery attributes. In other words, as amended, the prediction unit applies one ML model to one subset of battery attributes, another ML model to another subset of battery attributes, and so on.” Said argument is not persuasive either. Nevertheless, the amendments clarified the argued feature to be different ML model applies to different battery attribute.
In the cited references, Sastinsky teaches an end-of-life (EoL) machine learning engine, which can execute one or more machine learning models to determine ABELs for power cells, and a battery management system that control and manage charging and discharging of the power cells, i.e., the battery memory effect, one of the battery attributes and preventing degradation. Further, Sastinsky clearly spells out “one or more ML modes”, which indicates that either a single ML model or plural ML models can be applied to determine ABELs.
As cited above in the claim rejection citation with explanation, one of the battery attributes, battery swelling, is taught in Steingart. Steingart teaches the State of Charge (SOC) and the State of Health (SOH) of the battery with data set including the formation of a fracture in the material of an internal component, swelling, dissolution etc. (i.e., two of the battery attributes). Hence, Sastinsky in combination of Steingart teaches the newly amended feature in Claim 1.
With respect to the “different subsets” of a set of battery attributes that is emphasized by the applicant, said feature is construed as “a set of battery attributes classified into a first subset, a second subset, …” according to the given description in the Specification of the present application. Accordingly, Sastinsky [0037][0045] are cited for the battery memory effect and degradation attribute subsets, and Steingart [0033] is cited for the battery swelling and dissolution attribute subsets.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/Ruay Ho/Primary Patent Examiner, Art Unit 2126