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 .
Status of the Claims
Claims 1, 4-8, 11-15 and 18-25 are pending for examinations.
Claims 1, 8 and 15 are independent Claims.
Claims 1, 4-8, 11-15 and 18-25 are rejected under 35 U.S.C. §101, §103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-8, 11-15 and 18-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a Judicial Exception without significantly more.
Independent Claims
As Claims 1, 8 and 15:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A method of predicting a health of a battery, comprising:
obtaining a battery data indicative of a parameter of the battery;
partitioning the battery data into a plurality of subsets;
determining a score for each of the plurality of subsets, wherein each score is related to the health of the battery;
generating an overall score from the scores from each of the subsets; and
predicting the health of the battery from the overall score.
wherein determining the score for each subset of the plurality of subsets includes inputting the subset into a plurality of machine learning models to generate a plurality of scores and generating the overall score further comprises generating a weighted sum of the scores that includes multiplying a score by a probabilistic coefficient associated with the machine learning model.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “partitioning the battery data into a plurality of subsets;
determining a score for each of the plurality of subsets, wherein each score is related to the health of the battery;
generating an overall score from the scores from each of the subsets; and
predicting the health of the battery from the overall score.” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
Limitations “generating the overall score further comprises generating a weighted sum of the scores that includes multiplying a score by a probabilistic coefficient associated with the machine learning model” is directed to mathematical calculation groups of abstract ideas.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “obtaining a battery data indicative of a parameter of the battery; ” are insignificant extra solution activity. See MPEP §2106.05(g).
Limitations “wherein determining the score for each subset of the plurality of subsets includes inputting the subset into a plurality of machine learning models to generate a plurality of scores” are Mere instructions to Apply an Exception. See MPEP §2106.05(f).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “obtaining a battery data indicative of a parameter of the battery; ” step was considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by MPEP 2106.05(d)(II)(i. Receiving or transmitting data over a network, e.g., using the Internet to gather data).
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As Claims 4, 11 and 18, the Claims recite “further comprising adjusting a probabilistic coefficient for the machine learning based on an evaluation metric associated with a machine learning model.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “further comprising adjusting a probabilistic coefficient for the machine learning based on an evaluation metric associated with a machine learning model.” is directed to mathematical calculations group of abstract idea. Prong 2: There are no additional limitations. Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. There are no additional limitations. The Claim is not patent eligible.
As Claim 5, 12, 19, the Claim recites “further comprising determining whether the plurality of subsets is at least one of: (i) non-overlapping; and (ii) obtained using a same partitioning method.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “further comprising determining whether the plurality of subsets is at least one of: (i) non-overlapping; and (ii) obtained using a same partitioning method” are insignificant Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 6, 13, 20, the Claim recites “further comprising fusing the scores to generate a plurality of subset scores and fusing the plurality of subset scores to generate the overall score.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “further comprising fusing the scores to generate a plurality of subset scores and fusing the plurality of subset scores to generate the overall score” are insignificant Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 7, 14, the Claim recites “further comprising partitioning the battery data into subsets based on a difference in a behavior of scores for the subsets.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “further comprising partitioning the battery data into subsets based on a difference in a behavior of scores for the subsets” are Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 21, 23, the Claim recites “wherein the battery data is obtained during a testing period after manufacture of the battery and before installation of the battery in a vehicle.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the battery data is obtained during a testing period after manufacture of the battery and before installation of the battery in a vehicle” are Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 22, 24, the Claim recites “wherein the same machine learning models are applied to each of the plurality of subsets.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the same machine learning models are applied to each of the plurality of subsets” are Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 25, the Claim recites “wherein the battery data is partitioned based on at least one of a voltage range, a time period, an initial voltage, a discharge rate, and a temperature range.”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There are no additional abstract idea(s). Prong 2: The limitation “wherein the battery data is partitioned based on at least one of a voltage range, a time period, an initial voltage, a discharge rate, and a temperature range” are Mere Instruction to Apply an Exception (See MPEP §2106.05(f)). Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible
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.
Claim(s) 1, 4-8, 11-15 and 18-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jindal et al. (U.S. 2023/0333166 hereinafter Jindal) in view of Garcia et al. (U.S. 2018/0143257 hereinafter Garcia).
As claim 1, Jindal teaches a method of predicting a health of a battery, comprising:
obtaining a battery data indicative of a parameter of the battery (Jindal (¶0037 line 4-9, fig. 2 item 202), system obtains a set of battery attributes);
partitioning the battery data into a plurality of subsets (Jindal (¶0037 line 4-9, fig. 2 item 206), battery attributes are classified into a first subset, a second subset and a third subset);
determining a score for each of the plurality of subsets, wherein each score is related to the health of the battery (Jindal (¶0038 line 1-10, fig. 2 item 208, 210, 212), machine learning is applied to each subset to generate different scores);
predicting the health of the battery from the overall score (Jindal (¶0044 line 1-5), system predict a remaining life of the battery).
Jindal may not explicitly disclose:
generating an overall score from the scores from each of the subsets; and
Garcia teaches:
generating an overall score from the scores from each of the subsets (Garcia (¶0123 line 1-3, ¶0125 line 4-10), Three health metrics are used to compute SOH. Decision fusion algorithm by taking average or using a weighted sum model); and
wherein determining the score for each subset of the plurality of subsets includes inputting the subset into a plurality of machine learning models (Garcia (¶0108 line 7-8), “the first path includes an approach integrating PF and NN 712”. Garcial (¶0111 line 1-5), “the second and third paths directly map estimated internal parameters to SOC calculations. In particular, the second path utilizes a NN algorithm 714 to directly translate parameters estimated by feature extraction into SOC values.”) to generate a plurality of scores (Garcia (¶0108 line 1-3, fig. 7 item 740), “The present disclosure utilizes three different algorithms, namely, particle filtering (PF), neural network (NN) 714, and autoregressive moving average (ARMA) 716.”, each algorithm provides a score for decision fusion algorithm 740) and generating the overall score further comprises generating a weighted sum of the scores that includes multiplying a score by a probabilistic coefficient associated with the machine learning model (Garcia (¶0113 line 1-8), “A decision fusion algorithm 740 is then used to integrate these calculations into a single stream of SOC(k) 797 combined estimates. In general, this decision fusion algorithm 740 weights these three SOC estimates based on a confidence measure constructed from using different factors such as information about these algorithms and observed performance”).
Jindal discloses a system/method to use machine learning model to partition battery attributes into multiple subsets. Each subset is processed by a different machine learning model. Garcia disclose a system/method to using multiple machine learning models to generates multiple scores and a weighted sum for predicting battery condition/health. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify overall score of Jindal instead be score fusion taught by Garcia, with a reasonable expectation of success. The motivation would be to “computes all these health metrics and combines two or more of them using a decision fusion algorithm to produce a more robust and comprehensive assessment of battery conditions”.
As Claim 4, besides Claim 1, Jindal in view of Garcia teaches further comprising adjusting a probabilistic coefficient for the machine learning based on an evaluation metric associated with a machine learning model (Garcia (¶101 last 5 lines), a decision fusion algorithm may weigh the three estimates associated with a given N, (e.g., Ne) based on a confidence measure constructed from using different factors such as information about these algorithms and observed performance.).
As Claim 5, besides Claim 1, Jindal in view of Garcia teaches further comprising determining whether the plurality of subsets is at least one of: (i) non-overlapping; and (ii) obtained using a same partitioning method (Jindal (¶0049 last 9 lines, fig. 4 item 304, 306, 308), the battery attributes may be grouped or classified into different subsets ( e.g., 304, 306, and 308) based on properties and/or characteristics of the battery attributes. Further, feature vectors may be selected/derived from the subsets 304, 306, and 308 and the selected/derived feature vectors may be fed into different machine learning models (e.g., a memory prediction model 310, a swelling prediction model 312, and a performance prediction model 314)).
As Claim 6, besides Claim 1, Jindal in view of Garcia teaches further comprising fusing the scores to generate a plurality of subset scores and fusing the plurality of subset scores to generate the overall score (Garcia (¶0123 line 1-3, ¶0125 line 4-10), Three health metrics are used to compute SOH. Decision fusion algorithm by taking average or using a weighted sum model).
As Claim 7, besides Claim 1, Jindal in view of Garcia teaches further comprising partitioning the battery data into subsets based on a difference in a behavior of scores for the subsets (Jindal (¶0049 last 9 lines, fig. 4 item 304, 306, 308), the battery attributes may be grouped or classified into different subsets (e.g., 304, 306, and 308) based on properties and/or characteristics of the battery attributes).
As Claim 8, Jindal in view of Garcia teaches a system for predicting a health of a battery, comprising:
a sensor configured to obtain a battery data indicative of a parameter of the battery (Jindal (¶0037 line 2-5), using passive and active measurements collected from a set of distributed sensors in order to estimate and predict the health and performance of electrochemical cells, batteries, and battery systems.); and
a processor (Jindal (¶0035 line 5), processor 202) configured to:
The rest of the Claim is rejected for the same reasons as Claim 1.
As Claim 11-14, the Claim are rejected for the same reasons as Claim 4-7, respectively.
As Claim 15, 18--20, the Claims are rejected for the same reasons as Claims 1, 4-6, respectively.
As Claim 21, besides Claim 1, Jindal in view of Garcia teaches wherein the battery data is obtained during a testing period after manufacture of the battery and before installation of the battery in a vehicle (Garcia (¶0035 last 3 lines), “collect data about the energy storage device while in charging, discharging, and quiescent conditions.” Quiescent condition is construed as “a period after manufacture of the battery and before installation of the battery in a vehicle”).
As Claim 22, besides Claim 1, Jindal in view of Garcia teaches wherein the same machine learning models are applied to each of the plurality of subsets (Garcia (¶0108 line 1-3, fig. 7 item 740), “The present disclosure utilizes three different algorithms, namely, particle filtering (PF), neural network (NN) 714, and autoregressive moving average (ARMA) 716.”, each algorithm provides a score for decision fusion algorithm 740).
As Claim 23-24, the Claims are rejected for the same reason as Claims 21-22.
As Claim 25, besides Claim 15, Jindal in view of Garcia teaches wherein the battery data is partitioned (Jindal (¶0037 line 4-9, fig. 2 item 206), battery attributes are classified into a first subset, a second subset and a third subset based on battery attributes) based on at least one of a voltage range, a time period, an initial voltage, a discharge rate, and a temperature range (Jindal (¶0105 line 10-13), “battery attributes associated with battery serial number 562 such as design capacity 564, battery drain ratio 566, number of cells 568, cell voltage 570, warranty status 572, and the like.”).
Response to Arguments
Rejections under 35 U.S.C. §101:
Applicant argues that the Claims are not an abstract idea but rather rooted in computer-based battery diagnostics and predictive modeling for practical application of improving battery monitoring and management systems (second paragraph of page 8 in the remarks).
Applicant’s arguments are not persuasive because the specification does not disclose the “improving battery monitoring and management systems” benefit(s) or implementation(s).
Applicant argues that “partition battery data into subsets, processing each subset …, generating multiple scores … and computing probabilistically weighted overall score are not actions that can be performed by the mental processed” (second paragraph of page 11 in the remarks).
Applicant’s arguments are not persuasive because partitioning data into subsets, processing each subset and generating scores for each subset could be performed by a human mind. While “computing probabilistically weighted overall score” is directed to mathematical calculation groups of abstract idea.
Applicant argues that “partition battery data and applying multiple machine learning models to generate probabilistically weighted score” result in a concrete, technical improvement to battery health prediction systems (second paragraph of page 13 in the remarks).
Applicant’s arguments are not persuasive because the improvement is not discussed or disclosed in the Applicant’s Specification. There is no way to determine whether the claimed steps will cause the improvement or not.
Rejections under 35 U.S.C. §103:
Applicant argues that Jindal and Garcia fails to disclose “determining the score for a subset of the plurality of subsets ….” (first paragraph of page 15 in the remarks).
Applicant’s arguments are not persuasive. Jindal discloses a system/method to use machine learning model to partition battery attributes into multiple subsets. Each subset is processed by a different machine learning model. Garcia disclose a system/method to using multiple machine learning models to generates multiple scores and a weighted sum for predicting battery condition/health. The combination of Jindal and Garcia teaches the claim limitation(s). See the current rejections for details.
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.
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/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147
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