DETAILED ACTION
Claims 1-22 are presented for examination.
This office action is in response to submission of application on 06-JAN-2023.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/06/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
determining … a first dataset based on a cycle of one or more cells of a battery;
The determination of the “first dataset” is an observation of the “cycle of one or more cells of a battery”. No specifics are provided on how the cycle data is collected or how it is presented.
extracting … applying a first model to the first dataset, a second dataset comprising variables associated with the cycle of the one or more cells of the battery;
The “first model” is interpreted as principal component analysis because no additional information is provided on how to create the “first model”. The “extracting” is an evaluation of the “first dataset” using principal component analysis. Principal component analysis is using a mathematical relationship while evaluating the data. The “variables associated” with the “second dataset” is a result of the evaluation.
extracting … a third dataset based on applying a second model to the second dataset, wherein the second model is trained using a plurality of reference datasets associated with a plurality of battery cells, each of the plurality of reference datasets annotated with at least one cell feature or reference operational history aspects; and
The “second model” is interpreted as linear discriminate analysis. Linear discriminate analysis is a using a mathematical relationship to perform the evaluation of the data. The linear discriminate analysis uses the previous datasets to interpret the perform of the “third dataset” being evaluated. This works by creating groups of past performance characteristics. For example, if cells are in different temperature conditions and performed in a certain way, future cells exposed to those same temperature conditions will perform in that previous certain way.
determining … an operational history aspect indicative of the at least one cell feature of the battery extracted based on applying the second model to the third dataset.
The “operational history aspect” based on the applying linear discriminate analysis. [0035] of the specification as published describes the feature in additional detail [0035] “The datasets referred to herein include data that corresponds to an operational history aspect of the battery. As such, the various embodiments described herein provide for comparing the operational history aspects to determine a similarity between the candidate battery having an unknown operational history and reference batteries having one or more known operational history aspects. The operational history aspects may be representative of whether the candidate battery includes one or more cell features indicative of whether the candidate battery experienced abuse during the first use operation of the candidate battery.” The “operational history aspect” is comparing past performance of a battery to determine the future performance based on operating conditions.
Therefore, the claim recites a mental process and mathematical concepts.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
, by one or more processors of one or more computing devices,
, by the one or more processors by
, by the one or more processors,
, by the one or more processors,
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is a general-purpose computer and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
Claim 2. The method according to claim 1, wherein the first dataset comprises a plurality of first feature pairs indicative of an unknown operation history, the second dataset comprises a plurality of second feature pairs for the at least one cell feature, and the third dataset comprises a plurality of third feature pairs.
The “feature pairs” of the “first dataset” are determined based on the observed cycles (claim 1). Further defining the observation produces “feature pairs” is only additional steps in the abstract idea. Step 2A Prong 1.
Claim 3. The method according to claim 2, wherein extracting the second dataset based on applying the first model to the first dataset further comprises: extracting, by the one or more processors, the plurality of second feature pairs based on a principal component analysis including an unsupervised algorithm of the first dataset for the at least one cell feature.
The “first model” is defined as performing “principal component analysis”. As noted in the claim 1 analysis, “principal component analysis” is using a mathematical relationship while evaluating the data. The “unsupervised algorithm” is only specifying further how the mathematical relationship is established and the evaluation is performed. Step 2A Prong 1.
The processor is a generic computer under Step 2A Prong 2.
Claim 4. The method according to claim 3, wherein each feature pair of the plurality of second feature pairs comprises: a first vector based on a respective first feature pair of the plurality of first feature pairs and a first eigenvalue corresponding to the respective first feature pair, and a second vector based on the respective first feature pair and a second eigenvalue corresponding to the respective first feature pair.
When performing the linear discriminant analysis, the “features pairs” will be in a subspace. These features in the subspace will have an “eigenvector” spanning the subspace. The “eigenvector” allows for feature reduction, particularly when used with principal component analysis. The “vector” and “eigenvector” are mathematical operations part of the previous known models. This is further evaluation of the observed data. Step 2A Prong 1.
The processor is a generic computer under Step 2A Prong 2.
Claim 5. The method according to claim 2, wherein extracting the third dataset based on applying the second model to the second dataset further comprises: extracting, by the one or more processors, the plurality of third feature pairs based on a linear discriminant analysis including a supervised algorithm of the second dataset for the at least one cell feature.
The “second model” is claimed as performing “linear discriminant analysis”. As noted in claim 1, “linear discriminant analysis” is using a mathematical relationship while evaluating the data. The “unsupervised algorithm” is only specifying further how the mathematical relationship is established and the evaluation is performed. Step 2A Prong 1.
The processor is a generic computer under Step 2A Prong 2.
Claim 6. The method according to claim 5, wherein each feature pair of the plurality of third feature pairs comprises: a third vector based on a respective second feature pair of the plurality of second feature pairs and a third eigenvalue corresponding to the respective second feature pair, and a fourth vector based on the respective second feature pair and a fourth eigenvalue corresponding to the respective second feature pair.
The additional “third vector” and “fourth vector” are performing the mathematical function additional times. The “eigenvalue” determination is the determination of the feature that is differentiating the group. This allows for a grouping to characterize the effects of the different groupings. The additional vectors are different features, such as temperature and the battery discharge. Step 2A Prong 1.
Claim 7. The method according to claim 2, wherein each respective first feature pair of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage.
The “feature pair” is observed for a “voltage and a capacity”. The “voltage range” for the “first voltage to a second voltage” is an observation of the provided data. The condition of what portion of the “cycle” does not change the evaluation of the observed data. Step 2A Prong 1.
Claim 8. The method according to claim 7, further comprising: determining, by the one or more processors, a fourth dataset based on the first dataset, wherein the fourth dataset comprises a plurality of fourth feature pairs for the at least one cell feature; wherein each respective fourth feature pair of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle.
The term “dQ-dV” is interpreted as “incremental capacity” interpreted in view of [0051] of the specification as published. The “mean voltage” and “dQ-dV” are an evaluation of the “feature pairs” previously observed and evaluated. Step 2A Prong 1.
Claim 9. The method according to claim 1, further comprising: training, by the one or more processors, the first model and the second model based on a training dataset; and training, by the one or more processors, the plurality of reference datasets based on the training dataset; wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminate analysis performed by the second model to determine the at least one cell feature.
The “training dataset” is used in the “principal component analysis” and “linear discriminate analysis”. The “principal component analysis” and “linear discriminate analysis” are known mathematical operations. When performing these methods, a grouping of the data is completed based on the features. The “training data” is being used to determine the eigenvectors/eigenvalues. Performing this function is observing the data placed on a subspace and then evaluating which datapoints are closest to each other based on features. Fig. 5C demonstrates an example of this being performed. Step 2A Prong 1.
The processor is a generic computer under Step 2A Prong 2.
Claim 10. The method according to claim 2, wherein each reference dataset of the plurality of reference datasets further comprises a plurality of historical feature pairs annotated with a cell feature of the at least one cell feature.
The “feature pairs annotated” is done by observing the “reference datasets” and applying the annotation based on an evaluation the data. Step 2A Prong 1.
Claim 11. The method according to claim 1, wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range.
The “cell feature” that is determined from the evaluation of the observed cycling data is further defined by one of five features. The “cell feature” selected during the evaluation remains a part of the abstract idea. Prong 2A Step 1.
Claim 12 (Statutory Category – System)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
determine a first dataset based on a cycle of one or more cells of a battery having an unknown operation history;
The determination of the “first dataset” is an observation of the “cycle of one or more cells of a battery”. No specifics are provided on how the cycle data is collected or how it is presented. The feature of an “unknown operation history” does not specify how the battery data is collected and the determination is based on the observation.
extract, based on applying a first model to the first dataset, a second dataset comprising variables associated with the cycle of the one or more cells of the battery;
The “first model” is interpreted as principal component analysis because no additional information is provided on how to create the “first model”. The “extracting” is an evaluation of the “first dataset” using principal component analysis. Principal component analysis is using a mathematical relationship while evaluating the data. The “variables associated” with the “second dataset” is a result of the evaluation.
extract a third dataset based on applying a second model to the second dataset, wherein the second model is trained using a plurality of reference datasets associated with a plurality of battery cells, each of the plurality of reference datasets annotated with at least one cell feature or reference operational history aspects;
The “second model” is interpreted as linear discriminate analysis. Linear discriminate analysis is a using a mathematical relationship to perform the evaluation of the data. The linear discriminate analysis uses the previous datasets to interpret the perform of the “third dataset” being evaluated. This works by creating groups of past performance characteristics. For example, if cells are in different temperature conditions and performed in a certain way, future cells exposed to those same temperature conditions will perform in that previous certain way.
determine an operational history aspect indicative of the at least one cell feature of the battery extracted based on applying the second model to the third dataset; and
The “operational history aspect” based on the applying linear discriminate analysis. [0035] of the specification as published describes the feature in additional detail [0035] “The datasets referred to herein include data that corresponds to an operational history aspect of the battery. As such, the various embodiments described herein provide for comparing the operational history aspects to determine a similarity between the candidate battery having an unknown operational history and reference batteries having one or more known operational history aspects. The operational history aspects may be representative of whether the candidate battery includes one or more cell features indicative of whether the candidate battery experienced abuse during the first use operation of the candidate battery.” The “operational history aspect” is comparing past performance of a battery to determine the future performance based on operating conditions.
send data … indicative of the operational history aspect of the battery.
The “data” for the “operational history” is from the evaluation of the observed models. One could reasonably observe the model and determine by judgement the “operational history”, such as batteries exposed to a particular temperature have these characteristics based on the clustering in the model.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
at least one processor; and
a non-transitory computer readable medium having instructions stored thereon that, when executed by the at least one processor, cause the system to perform operations comprising:
send data to a display
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is a general-purpose computer and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
Claim 13. The system according to claim 12, wherein the first dataset comprises a plurality of first feature pairs indicative of the unknown operation history, the second dataset comprises a plurality of second feature pairs for the at least one cell feature, and the third dataset comprises a plurality of third feature pairs.
The “feature pairs” of the “first dataset” are determined based on the observed cycles (claim 1). Further defining the observation produces “feature pairs” is only additional steps in the abstract idea. Step 2A Prong 1.
Claim 14. The system according to claim 13, wherein the data sent to the display is indicative of the third dataset matching the plurality of reference datasets.
The display is a generic computer under Step 2A Prong 2.
The “third dataset” is evaluated against the “plurality of reference datasets” to determine if the data set matches. Matching is performing an observation and evaluation/judgement. Step 2A Prong 1.
Claim 15. The system according to claim 14, wherein the data sent to the display further comprises scatter plot data configured to cause the display to show a representation of the third dataset; wherein the third dataset is indicative of a linear discriminant analysis including a supervised algorithm for the at least one cell feature.
The “first model” is defined as performing “principal component analysis”. As noted in the claim 1 analysis, “principal component analysis” is using a mathematical relationship while evaluating the data. The “unsupervised algorithm” is only specifying further how the mathematical relationship is established and the evaluation is performed. Step 2A Prong 1.
The display is a generic computer under Step 2A Prong 2.
Claim 16. The system according to claim 13, further comprising: determine a fourth dataset based on the first dataset, wherein the fourth dataset includes a plurality of fourth feature pairs for the at least one cell feature; wherein each respective first feature pair of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage; and wherein each respective fourth feature pair of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle.
The “feature pair” is observed for a “voltage and a capacity”. The “voltage range” for the “first voltage to a second voltage” is an observation of the provided data. The condition of what portion of the “cycle” does not change the evaluation of the observed data. Step 2A Prong 1.
The term “dQ-dV” is interpreted as “incremental capacity” interpreted in view of [0051] of the specification as published. The “mean voltage” and “dQ-dV” are an evaluation of the “feature pairs” previously observed and evaluated. Step 2A Prong 1.
Claim 17. The system according to claim 13, wherein each respective second feature pair of the plurality of second feature pairs comprises: a first vector based on a respective first feature pair of the plurality of first feature pairs and a first eigenvalue corresponding to the respective first feature pair, and a second vector based on the respective first feature pair and a second eigenvalue corresponding to the respective first feature pair; wherein each respective third feature pair of the plurality of third feature pairs comprises: a third vector based on a respective second feature pair of the plurality of second feature pairs and a third eigenvalue corresponding to the respective second feature pair, and a fourth vector based on the respective second feature pair and a fourth eigenvalue corresponding to the respective second feature pair.
When performing the linear discriminant analysis, the “features pairs” will be in a subspace. These features in the subspace will have an “eigenvector” spanning the subspace. The “eigenvector” allows for feature reduction, particularly when used with principal component analysis. The “vector” and “eigenvector” are mathematical operations part of the previous known models. This is further evaluation of the observed data. The additional “third vector” and “fourth vector” are performing the mathematical function additional times. The “eigenvalue” determination is the determination of the feature that is differentiating the group. This allows for a grouping to characterize the effects of the different groupings. The additional vectors are different features, such as temperature and the battery discharge. Step 2A Prong 1.
Claim 18. The system according to claim 13, wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range.
The “cell feature” that is determined from the evaluation of the observed cycling data is further defined by one of five features. The “cell feature” selected during the evaluation remains a part of the abstract idea. Prong 2A Step 1.
Claim 19. The system according to claim 13, further comprising: train the first model and the second model based on a training dataset; and train the plurality of reference datasets based on the training dataset; wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminant analysis performed by the second model to determine the at least one cell feature.
The “training dataset” is used in the “principal component analysis” and “linear discriminate analysis”. The “principal component analysis” and “linear discriminate analysis” are known mathematical operations. When performing these methods, a grouping of the data is completed based on the features. The “training data” is being used to determine the eigenvectors/eigenvalues. Performing this function is observing the data placed on a subspace and then evaluating which datapoints are closest to each other based on features. Fig. 5C demonstrates an example of this being performed. Step 2A Prong 1.
Claim 20 (Statutory Category – Manufacture)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claim recites a mental process, specifically:
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.”
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation.”
2106.04(a)(2)(I)(A) “Mathematical Relationships A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A.”
2106.04(a)(2)(I)(B) “Mathematical Formulas or Equations A claim that recites a numerical formula or equation will be considered as falling within the "mathematical concepts" grouping. In addition, there are instances where a formula or equation is written in text format that should also be considered as falling within this grouping. For example, the phrase "determining a ratio of A to B" is merely using a textual replacement for the particular equation (ratio = A/B). Additionally, the phrase "calculating the force of the object by multiplying its mass by its acceleration" is using a textual replacement for the particular equation (F= ma).”
2106.04(a)(2)(I)(C) “Mathematical Calculations A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
receive a plurality of reference datasets annotated with a cell feature of at least one cell feature;
The “feature pairs” of the “first dataset” are determined based on the observed cycles (claim 1). Further defining the observation produces “feature pairs” is only additional steps in the abstract idea.
determine a first dataset based on a cycle of one or more cells of a battery, wherein the first dataset comprises a plurality of first feature pairs indicative of an unknown operation history;
The determination of the “first dataset” is an observation of the “cycle of one or more cells of a battery”. No specifics are provided on how the cycle data is collected or how it is presented. The feature of an “unknown operation history” does not specify how the battery data is collected and the determination is based on the observation.
extract a second dataset including a plurality of second feature pairs based on applying a first model to the first dataset, wherein the plurality of second feature pairs comprises:
The “first model” is interpreted as principal component analysis because no additional information is provided on how to create the “first model”. The “extracting” is an evaluation of the “first dataset” using principal component analysis. Principal component analysis is using a mathematical relationship while evaluating the data. The “variables associated” with the “second dataset” is a result of the evaluation.
a first vector based on a respective second feature pair of the plurality of second feature pairs and a first eigenvalue corresponding to the respective second feature pair, and a second vector based on the respective second feature pair and a second eigenvalue corresponding to the respective second feature pair; extract a third dataset including a plurality of third feature pairs based on applying a second model to the second dataset, wherein the plurality of third feature pairs comprises: a third vector based on a respective third feature pair of the plurality of third feature pairs and a third eigenvalue corresponding to the respective third feature pair, and a fourth vector based on the respective third feature pair and a fourth eigenvalue corresponding to the respective third feature pair; and
The “second model” is interpreted as linear discriminate analysis. Linear discriminate analysis is a using a mathematical relationship to perform the evaluation of the data. The linear discriminate analysis uses the previous datasets to interpret the perform of the “third dataset” being evaluated. This works by creating groups of past performance characteristics. For example, if cells are in different temperature conditions and performed in a certain way, future cells exposed to those same temperature conditions will perform in that previous certain way.
When performing the linear discriminant analysis, the “features pairs” will be in a subspace. These features in the subspace will have an “eigenvector” spanning the subspace. The “eigenvector” allows for feature reduction, particularly when used with principal component analysis. The “vector” and “eigenvector” are mathematical operations part of the previous known models. This is further evaluation of the observed data. The additional “third vector” and “fourth vector” are performing the mathematical function additional times. The “eigenvalue” determination is the determination of the feature that is differentiating the group. This allows for a grouping to characterize the effects of the different groupings. The additional vectors are different features, such as temperature and the battery discharge.
determine an operational history aspect of the battery extracted based on applying the second model and the third dataset;
The “operational history aspect” based on the applying linear discriminate analysis. [0035] of the specification as published describes the feature in additional detail [0035] “The datasets referred to herein include data that corresponds to an operational history aspect of the battery. As such, the various embodiments described herein provide for comparing the operational history aspects to determine a similarity between the candidate battery having an unknown operational history and reference batteries having one or more known operational history aspects. The operational history aspects may be representative of whether the candidate battery includes one or more cell features indicative of whether the candidate battery experienced abuse during the first use operation of the candidate battery.” The “operational history aspect” is comparing past performance of a battery to determine the future performance based on operating conditions.
wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range.
The “cell feature” that is determined from the evaluation of the observed cycling data is further defined by one of five features. The “cell feature” selected during the evaluation remains a part of the abstract idea.
Therefore, the claim recites a mental process and mathematical concepts.
Step 2A – Prong 2: Integrated into a Practical Solution?
MPEP 2106.05(f) Mere Instructions To Apply An Exception has found simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
A non-transitory computer readable medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising:
The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application.
Therefore, no meaningful limits are imposed on practicing the abstract idea.
The claim is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitation is a general-purpose computer and does not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
The additional elements have been considered both individually and as an ordered combination in the significantly more consideration.
The claim is ineligible.
Claim 21. The non-transitory computer readable medium of claim 20, wherein the computing device performs operations further comprising: determine a fourth dataset based on the first dataset, wherein the fourth dataset includes a plurality of fourth feature pairs for a respective cell feature; wherein each of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage; and wherein each of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle.
The “feature pair” is observed for a “voltage and a capacity”. The “voltage range” for the “first voltage to a second voltage” is an observation of the provided data. The condition of what portion of the “cycle” does not change the evaluation of the observed data. Step 2A Prong 1.
The term “dQ-dV” is interpreted as “incremental capacity” interpreted in view of [0051] of the specification as published. The “mean voltage” and “dQ-dV” are an evaluation of the “feature pairs” previously observed and evaluated. Step 2A Prong 1.
Claim 22. The non-transitory computer readable medium of claim 20, further comprising: train the first model and the second model based on a training dataset; and train the plurality of reference datasets based on the training dataset; wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminate analysis performed by the second model to determine the at least one cell feature.
The “training dataset” is used in the “principal component analysis” and “linear discriminate analysis”. The “principal component analysis” and “linear discriminate analysis” are known mathematical operations. When performing these methods, a grouping of the data is completed based on the features. The “training data” is being used to determine the eigenvectors/eigenvalues. Performing this function is observing the data placed on a subspace and then evaluating which datapoints are closest to each other based on features. Fig. 5C demonstrates an example of this being performed. Step 2A Prong 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over
Lai et al., “Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range” [2020] (hereinafter ‘Lai’) in view of
PARK, U.S. Patent Application Publication 2016/0161567 A1 (hereinafter ‘PARK’).
Regarding Claim 1: A method comprising:
Lai teaches determining … a first dataset based on a cycle of one or more cells of a battery; (Fig. 3 Lai and Pg. 2 right col section 3.2 Lai “…Fig. 3(b). The study of dQ/dV-V curve generally focuses on the positions of peaks and valleys because they represent a phase transformation in cathode or intercalation of lithium into a graphite anode that is highly-related to cell aging behavior. Hence, four peaks (P1, P2, P3, P4) and three valleys (V1, V2, V3) are marked in Fig. 3(b), and their x-values and y-values are used as features for algorithms to make grouping or classification later in the sections 2.4 and 2.5…”)
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Lai teaches extracting … by applying a first model to the first dataset, a second dataset comprising variables associated with the cycle of the one or more cells of the battery; (Fig. 8 Lai, first model = Principle component analysis)
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Lai teaches extracting … a third dataset based on applying a second model to the second dataset, wherein the second model is trained using a plurality of reference datasets associated with a plurality of battery cells, each of the plurality of reference datasets annotated with at least one cell feature or reference operational history aspects; and (Fig. 10 Lai, second model = linear discriminant analysis)
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Lai teaches determining … an operational history aspect indicative of the at least one cell feature of the battery extracted based on applying the second model to the third dataset. (Pg. 6 left col conclusion Lai “…By applying a supervised LDA algorithm, cells cycled under different temperatures (-10oC, 25oC, and 60oC) can be well separated into three blocks in a 2-D projected plane. According to an evaluation test, the identified accuracy reaches 89% in a test set ratio of 0.15…”)
Lai does not appear to explicitly disclose
, by one or more processors of one or more computing devices,
However, PARK teaches by one or more processors of one or more computing devices ([0170] PARK “…processing device may be implemented using one or more general-purpose or special purpose computers, such as, for example, a processor, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a field programmable array, a programmable logic unit, a microprocessor or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications that run on the OS. The processing device also may access, store, manipulate, process, and create data in response to execution of the software. For purpose of simplicity, the description of a processing device is used as singular; however, one skilled in the art will appreciated that a processing device may include multiple processing elements and multiple types of processing elements. For example, a processing device may include multiple processors or a processor and a controller. In addition, different processing configurations are possible, such a parallel processors…”)
Lai and PARK are analogous art because they are from the same field of endeavor, battery prediction modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method as disclosed by Lai by the one or more processors as disclosed by PARK.
One of ordinary skill in the art would have been motivated to make this modification in order to understand battery life estimations as discussed in [0009] by PARK “…In accordance with an embodiment, there is provided a battery life estimation apparatus, including a time information accumulator configured to partition sensing data of a battery into sections, and to accumulate time information corresponding to the sections; a time information extractor configured to extract time information corresponding to a period from the accumulated time information; and a life estimator configured to extract expected time information based on the accumulated time information, the time information corresponding to the period, and learning information, and configured to estimate an end of life (EOL) of the battery based on the expected time information…”
Regarding Claim 2: Lai and PARK teach The method according to claim 1, wherein the first dataset comprises
Lai teaches a plurality of first feature pairs indicative of an unknown operation history, the second dataset comprises a plurality of second feature pairs for the at least one cell feature, and the third dataset comprises a plurality of third feature pairs. (Pg. 2 right col section 3.2 Lai “…Fig. 3(b). The study of dQ/dV-V curve generally focuses on the positions of peaks and valleys because they represent a phase transformation in cathode or intercalation of lithium into a graphite anode that is highly-related to cell aging behavior. Hence, four peaks (P1, P2, P3, P4) and three valleys (V1, V2, V3) are marked in Fig. 3(b), and their x-values and y-values are used as features for algorithms to make grouping or classification later in the sections 2.4 and 2.5…”)
Regarding Claim 3: Lai and PARK teach The method according to claim 2, wherein extracting the second dataset based on applying the first model to the first dataset further comprises:
Lai teaches extracting, by the one or more processors, the plurality of second feature pairs based on a principal component analysis including an unsupervised algorithm of the first dataset for the at least one cell feature. (Pg. 4 left col section 3.3 Lai “…Principle component analysis (PCA) is an unsupervised algorithm used to keep maximum data variation when reducing data dimensions to provide a visualized data distribution…”)
Regarding Claim 4: Lai and PARK teach The method according to claim 3, wherein each feature pair of the plurality of second feature pairs comprises:
Lai teaches a first vector based on a respective first feature pair of the plurality of first feature pairs and a first eigenvalue corresponding to the respective first feature pair, and a second vector based on the respective first feature pair and a second eigenvalue corresponding to the respective first feature pair. (Table 2 Lai and Pg. 5 left col section 3.5 “…In both PCA and LDA, the axes are also called eigenvectors, which are linear vectors composed of each feature multiplied by each corresponding eigenvalues. The higher the eigenvalue, the more important the corresponding feature in contributing explained variation. Generally in LDA, when a feature has a high eigenvalue, it represents the feature has more power to classify samples according to their labels…”)
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Regarding Claim 5: Lai and PARK teach The method according to claim 2, wherein extracting the third dataset based on applying the second model to the second dataset further comprises:
Lai teaches extracting, by the one or more processors, the plurality of third feature pairs based on a linear discriminant analysis including a supervised algorithm of the second dataset for the at least one cell feature. (Pg. 5 right col section 3.4 Lai “… “…Linear discriminant analysis (LDA) is a supervised algorithm used to maximize the gap between groups but minimize internal differences within a group…”)
Regarding Claim 6: Lai and PARK teach The method according to claim 5, wherein each feature pair of the plurality of third feature pairs comprises:
Lai teaches a third vector based on a respective second feature pair of the plurality of second feature pairs and a third eigenvalue corresponding to the respective second feature pair, and a fourth vector based on the respective second feature pair and a fourth eigenvalue corresponding to the respective second feature pair. (Table 2 Lai and Pg. 5 left col section 3.5 “…In both PCA and LDA, the axes are also called eigenvectors, which are linear vectors composed of each feature multiplied by each corresponding eigenvalues. The higher the eigenvalue, the more important the corresponding feature in contributing explained variation. Generally in LDA, when a feature has a high eigenvalue, it represents the feature has more power to classify samples according to their labels…”)
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Regarding Claim 7: Lai and PARK teach The method according to claim 2,
Lai teaches wherein each respective first feature pair of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage. (Pg. 1 right col 2nd paragraph Section 2 Lai “…Each cell corresponded to one of combinations composed of different temperature (-10oC, 25oC, 60oC) and working SOC ranges (0-10%, 25-75%, 90-100%, 0-100%). The cells were cycled by CC discharge mode and CC-CV charge mode with 0.2C within a voltage range between 2.5V and 4.2V and a cutoff current of 0.02C. For 0-10% SOC, cells were fully discharged before charge/discharge cycles. For 25-75% SOC, cells were discharged to 25% SOC and then followed by charge/discharge cycles. For 90-100% and 0-100% SOC, cells were directly cycled from a fully charged state. To ensure all cells are cycled under proper SOC ranges, the maximum capacity at each cycling temperature was measured as a baseline to estimate charged/discharge time…”)
Regarding Claim 8: Lai and PARK teach The method according to claim 7, further comprising:
Lai teaches determining, by the one or more processors, a fourth dataset based on the first dataset, wherein the fourth dataset comprises a plurality of fourth feature pairs for the at least one cell feature; wherein each respective fourth feature pair of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle. (Pg. 2 right col section 3.2 Lai “…(i) slice original discharge data into multiple segments with a voltage interval of 0.011V from 4.2V to 2.5V; (ii) calculate the capacity difference (dQ) in each segment and divided by 0.011V (dV) to obtain dQ/dV value; (iii) calculate mean voltage of each segment; (iv) take dQ/dV as y-axis and mean voltage as x-axis to plot dQ/dV-V curve…”)
Regarding Claim 9: Lai and PARK teach The method according to claim 1, further comprising:
Lai teaches training, by the one or more processors, the first model and the second model based on a training dataset; and training, by the one or more processors, the plurality of reference datasets based on the training dataset; (Abstract Lai “…Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range…”)
Lai teaches wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminate analysis performed by the second model to determine the at least one cell feature. (Pg. 4 left col section 3.3 Lai “…Principle component analysis (PCA) is an unsupervised algorithm used to keep maximum data variation when reducing data dimensions to provide a visualized data distribution…” Pg. 5 right col section 3.4 Lai “…Linear discriminant analysis (LDA) is a supervised algorithm used to maximize the gap between groups but minimize internal differences within a group…”)
Regarding Claim 10: Lai and PARK teach The method according to claim 2,
Lai teaches wherein each reference dataset of the plurality of reference datasets further comprises a plurality of historical feature pairs annotated with a cell feature of the at least one cell feature. (Fig. 8 Lai)
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Regarding Claim 11: Lai and PARK teach The method according to claim 1, wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range. (Table 1 and 2 Lai. Pg. 1 right col 2nd paragraph section 2 Lai “…The charge/discharge time and cycles for 15 equivalent cycles under different temperature and SOC ranges are summarized in Table. 1…”)
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Regarding Claim 12: A system comprising:
Lai teaches determine a first dataset based on a cycle of one or more cells of a battery having an unknown operation history; (Fig. 3 Lai and Pg. 2 right col section 3.2 Lai “…Fig. 3(b). The study of dQ/dV-V curve generally focuses on the positions of peaks and valleys because they represent a phase transformation in cathode or intercalation of lithium into a graphite anode that is highly-related to cell aging behavior. Hence, four peaks (P1, P2, P3, P4) and three valleys (V1, V2, V3) are marked in Fig. 3(b), and their x-values and y-values are used as features for algorithms to make grouping or classification later in the sections 2.4 and 2.5…”)
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Lai teaches extract, based on applying a first model to the first dataset, a second dataset comprising variables associated with the cycle of the one or more cells of the battery; (Fig. 8 Lai, first model = Principle component analysis)
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Lai teaches extract a third dataset based on applying a second model to the second dataset, wherein the second model is trained using a plurality of reference datasets associated with a plurality of battery cells, each of the plurality of reference datasets annotated with at least one cell feature or reference operational history aspects; (Fig. 10 Lai, second model = linear discriminant analysis)
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Lai teaches determine an operational history aspect indicative of the at least one cell feature of the battery extracted based on applying the second model to the third dataset; and (Pg. 6 left col conclusion Lai “…By applying a supervised LDA algorithm, cells cycled under different temperatures (-10oC, 25oC, and 60oC) can be well separated into three blocks in a 2-D projected plane. According to an evaluation test, the identified accuracy reaches 89% in a test set ratio of 0.15…”)
Lai does not appear to explicitly disclose
at least one processor; and
a non-transitory computer readable medium having instructions stored thereon that, when executed by the at least one processor, cause the system to perform operations comprising:
send data to a display indicative of the operational history aspect of the battery.
However, PARK teaches at least one processor; and ([0170] PARK “…processing device may be implemented using one or more general-purpose or special purpose computers, such as, for example, a processor, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a field programmable array, a programmable logic unit, a microprocessor or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications that run on the OS. The processing device also may access, store, manipulate, process, and create data in response to execution of the software. For purpose of simplicity, the description of a processing device is used as singular; however, one skilled in the art will appreciated that a processing device may include multiple processing elements and multiple types of processing elements. For example, a processing device may include multiple processors or a processor and a controller. In addition, different processing configurations are possible, such a parallel processors…”)
PARK teaches a non-transitory computer readable medium having instructions stored thereon that, when executed by the at least one processor, cause the system to perform operations comprising: ([0172] PARK “…Program instructions to perform a method described in FIGS. 17-18, or one or more operations thereof, may be recorded, stored, or fixed in one or more non-transitory computer-readable storage medium. The non-transitory computer readable recording medium may include any data storage device that can store data which can be thereafter read by a computer system or processing device. Examples of the nontransitory computer readable recording medium include readonly memory (ROM), random-access memory (RAM), CDROMs, magnetic tapes, floppy disks, optical data storage devices. Also, functional programs, codes, and code segments that accomplish the examples disclosed herein can be easily construed by programmers skilled in the art to which the examples pertain based on and using the flow diagrams and block diagrams of the figures and their corresponding descriptions as provided herein…”)
PARK teaches send data to a display indicative of the operational history aspect of the battery ([0149] PARK “…For example, when an ignition of an EV including the battery and the battery control apparatus is turned on, an ECU displays a user interface 1510 including a battery management system (BMS) on a dashboard. The user interface 1510 includes an interface 1520 configured to receive a request from a user to verify battery life information and, in response, generate a trigger signal…” [0150] PARK “…The battery control apparatus transmits the estimated EOL to the ECU. The ECU displays the EOL received from the battery control apparatus…”)
Lai and PARK are analogous art because they are from the same field of endeavor, battery prediction modeling.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method as disclosed by Lai by the at least one processor; and a non-transitory computer readable medium having instructions stored thereon that, when executed by the at least one processor, cause the system to perform and send data to a display indicative of the operational history aspect of the battery as disclosed by PARK.
One of ordinary skill in the art would have been motivated to make this modification in order to understand battery life estimations as discussed in [0009] by PARK “…In accordance with an embodiment, there is provided a battery life estimation apparatus, including a time information accumulator configured to partition sensing data of a battery into sections, and to accumulate time information corresponding to the sections; a time information extractor configured to extract time information corresponding to a period from the accumulated time information; and a life estimator configured to extract expected time information based on the accumulated time information, the time information corresponding to the period, and learning information, and configured to estimate an end of life (EOL) of the battery based on the expected time information…”
Regarding Claim 13: Lai and PARK teach The system according to claim 12, wherein the first dataset comprises
Lai teaches a plurality of first feature pairs indicative of the unknown operation history, the second dataset comprises a plurality of second feature pairs for the at least one cell feature, and the third dataset comprises a plurality of third feature pairs. (Pg. 2 right col section 3.2 Lai “…Fig. 3(b). The study of dQ/dV-V curve generally focuses on the positions of peaks and valleys because they represent a phase transformation in cathode or intercalation of lithium into a graphite anode that is highly-related to cell aging behavior. Hence, four peaks (P1, P2, P3, P4) and three valleys (V1, V2, V3) are marked in Fig. 3(b), and their x-values and y-values are used as features for algorithms to make grouping or classification later in the sections 2.4 and 2.5…”)
Regarding Claim 14: Lai and PARK teach The system according to claim 13,
PARK teaches wherein the data sent to the display is indicative of the third dataset matching the plurality of reference datasets. ([0149] PARK “…For example, when an ignition of an EV including the battery and the battery control apparatus is turned on, an ECU displays a user interface 1510 including a battery management system (BMS) on a dashboard. The user interface 1510 includes an interface 1520 configured to receive a request from a user to verify battery life information and, in response, generate a trigger signal…” [0150] PARK “…The battery control apparatus transmits the estimated EOL to the ECU. The ECU displays the EOL received from the battery control apparatus…”)
Regarding Claim 15: Lai and PARK teach The system according to claim 14, wherein the data sent to the display further comprises
Lai teaches scatter plot data configured to cause the display to show a representation of the third dataset; wherein the third dataset is indicative of a linear discriminant analysis including a supervised algorithm for the at least one cell feature. (Lai Fig. 10)
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Regarding Claim 16: Lai and PARK teach The system according to claim 13, further comprising:
Lai teaches determine a fourth dataset based on the first dataset, wherein the fourth dataset includes a plurality of fourth feature pairs for the at least one cell feature; (Table 2 Lai)
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Lai teaches wherein each respective first feature pair of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage; and (Pg. 1 right col 2nd paragraph Section 2 Lai “…Each cell corresponded to one of combinations composed of different temperature (-10oC, 25oC, 60oC) and working SOC ranges (0-10%, 25-75%, 90-100%, 0-100%). The cells were cycled by CC discharge mode and CC-CV charge mode with 0.2C within a voltage range between 2.5V and 4.2V and a cutoff current of 0.02C. For 0-10% SOC, cells were fully discharged before charge/discharge cycles. For 25-75% SOC, cells were discharged to 25% SOC and then followed by charge/discharge cycles. For 90-100% and 0-100% SOC, cells were directly cycled from a fully charged state. To ensure all cells are cycled under proper SOC ranges, the maximum capacity at each cycling temperature was measured as a baseline to estimate charged/discharge time…”)
Lai teaches wherein each respective fourth feature pair of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle. (Pg. 2 right col section 3.2 Lai “…(i) slice original discharge data into multiple segments with a voltage interval of 0.011V from 4.2V to 2.5V; (ii) calculate the capacity difference (dQ) in each segment and divided by 0.011V (dV) to obtain dQ/dV value; (iii) calculate mean voltage of each segment; (iv) take dQ/dV as y-axis and mean voltage as x-axis to plot dQ/dV-V curve…”)
Regarding Claim 17: Lai and PARK teach The system according to claim 13, wherein each respective second feature pair of the plurality of second feature pairs comprises:
Lai teaches a first vector based on a respective first feature pair of the plurality of first feature pairs and a first eigenvalue corresponding to the respective first feature pair, and a second vector based on the respective first feature pair and a second eigenvalue corresponding to the respective first feature pair; (Fig. 8 and Table 2 Lai)
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Lai teaches wherein each respective third feature pair of the plurality of third feature pairs comprises: a third vector based on a respective second feature pair of the plurality of second feature pairs and a third eigenvalue corresponding to the respective second feature pair, and a fourth vector based on the respective second feature pair and a fourth eigenvalue corresponding to the respective second feature pair. (Lai Fig. 10)
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Regarding Claim 18: Lai and PARK teach The system according to claim 13, wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range. (Table 1 and 2 Lai. Pg. 1 right col 2nd paragraph section 2 Lai “…The charge/discharge time and cycles for 15 equivalent cycles under different temperature and SOC ranges are summarized in Table. 1…”)
Regarding Claim 19: Lai and PARK teach The system according to claim 13, further comprising:
train the first model and the second model based on a training dataset; and train the plurality of reference datasets based on the training dataset; (Abstract Lai “…Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range…”)
Lai teaches wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminant analysis performed by the second model to determine the at least one cell feature. (Pg. 4 left col section 3.3 Lai “…Principle component analysis (PCA) is an unsupervised algorithm used to keep maximum data variation when reducing data dimensions to provide a visualized data distribution…” Pg. 5 right col section 3.4 Lai “…Linear discriminant analysis (LDA) is a supervised algorithm used to maximize the gap between groups but minimize internal differences within a group…”)
Regarding Claim 20: A non-transitory computer readable medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform operations comprising:
Lai teaches receive a plurality of reference datasets annotated with a cell feature of at least one cell feature; (Fig. 3 Lai)
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Lai teaches determine a first dataset based on a cycle of one or more cells of a battery, wherein the first dataset comprises a plurality of first feature pairs indicative of an unknown operation history; (Pg. 2 right col section 3.2 Lai “…Fig. 3(b). The study of dQ/dV-V curve generally focuses on the positions of peaks and valleys because they represent a phase transformation in cathode or intercalation of lithium into a graphite anode that is highly-related to cell aging behavior. Hence, four peaks (P1, P2, P3, P4) and three valleys (V1, V2, V3) are marked in Fig. 3(b), and their x-values and y-values are used as features for algorithms to make grouping or classification later in the sections 2.4 and 2.5…”)
Lai teaches extract a second dataset including a plurality of second feature pairs based on applying a first model to the first dataset, wherein the plurality of second feature pairs comprises: a first vector based on a respective second feature pair of the plurality of second feature pairs and a first eigenvalue corresponding to the respective second feature pair, and a second vector based on the respective second feature pair and a second eigenvalue corresponding to the respective second feature pair; (Fig. 8 and Table 2 Lai and Pg. 5 left col section 3.5 “…In both PCA and LDA, the axes are also called eigenvectors, which are linear vectors composed of each feature multiplied by each corresponding eigenvalues. The higher the eigenvalue, the more important the corresponding feature in contributing explained variation. Generally in LDA, when a feature has a high eigenvalue, it represents the feature has more power to classify samples according to their labels…”)
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Lai teaches extract a third dataset including a plurality of third feature pairs based on applying a second model to the second dataset, wherein the plurality of third feature pairs comprises: a third vector based on a respective third feature pair of the plurality of third feature pairs and a third eigenvalue corresponding to the respective third feature pair, and a fourth vector based on the respective third feature pair and a fourth eigenvalue corresponding to the respective third feature pair; and (Lai Fig. 10)
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Lai teaches determine an operational history aspect of the battery extracted based on applying the second model and the third dataset; (Pg. 6 left col conclusion Lai “…By applying a supervised LDA algorithm, cells cycled under different temperatures (-10oC, 25oC, and 60oC) can be well separated into three blocks in a 2-D projected plane. According to an evaluation test, the identified accuracy reaches 89% in a test set ratio of 0.15…”)
Lai teaches wherein the at least one cell feature comprises one of: a normal operation, an ambient temperature, a working voltage range, a high-rate discharge parameter, or an abnormal operation voltage range. (Table 1 and 2 Lai. Pg. 1 right col 2nd paragraph section 2 Lai “…The charge/discharge time and cycles for 15 equivalent cycles under different temperature and SOC ranges are summarized in Table. 1…”)
Regarding Claim 21: Lai and PARK teach The non-transitory computer readable medium of claim 20, wherein the computing device performs operations further comprising:
Lai teaches determine a fourth dataset based on the first dataset, wherein the fourth dataset includes a plurality of fourth feature pairs for a respective cell feature; (Table 2 Lai)
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Lai teaches wherein each of the plurality of first feature pairs comprises a voltage and a capacity of the one or more cells over a voltage range of the cycle from a first voltage to a second voltage; and (Pg. 1 right col 2nd paragraph Section 2 Lai “…Each cell corresponded to one of combinations composed of different temperature (-10oC, 25oC, 60oC) and working SOC ranges (0-10%, 25-75%, 90-100%, 0-100%). The cells were cycled by CC discharge mode and CC-CV charge mode with 0.2C within a voltage range between 2.5V and 4.2V and a cutoff current of 0.02C. For 0-10% SOC, cells were fully discharged before charge/discharge cycles. For 25-75% SOC, cells were discharged to 25% SOC and then followed by charge/discharge cycles. For 90-100% and 0-100% SOC, cells were directly cycled from a fully charged state. To ensure all cells are cycled under proper SOC ranges, the maximum capacity at each cycling temperature was measured as a baseline to estimate charged/discharge time…”)
Lai teaches wherein each of the plurality of fourth feature pairs comprises a mean voltage and a dQ-dV of the battery of the one or more cells over the voltage range of the cycle. (Pg. 2 right col section 3.2 Lai “…(i) slice original discharge data into multiple segments with a voltage interval of 0.011V from 4.2V to 2.5V; (ii) calculate the capacity difference (dQ) in each segment and divided by 0.011V (dV) to obtain dQ/dV value; (iii) calculate mean voltage of each segment; (iv) take dQ/dV as y-axis and mean voltage as x-axis to plot dQ/dV-V curve…”)
Regarding Claim 22: Lai and PARK teach The non-transitory computer readable medium of claim 20, further comprising:
Lai teaches train the first model and the second model based on a training dataset; and train the plurality of reference datasets based on the training dataset; (Abstract Lai “…Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range…”)
Lai teaches wherein each reference dataset is indicative of results of a reference principal component analysis performed by the first model and a reference linear discriminate analysis performed by the second model to determine the at least one cell feature. (Pg. 4 left col section 3.3 Lai “…Principle component analysis (PCA) is an unsupervised algorithm used to keep maximum data variation when reducing data dimensions to provide a visualized data distribution…” Pg. 5 right col section 3.4 Lai “…Linear discriminant analysis (LDA) is a supervised algorithm used to maximize the gap between groups but minimize internal differences within a group…”)
Conclusion
Claims 1-22 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN E JOHANSEN whose telephone number is (571)272-8062. The examiner can normally be reached M-F 9AM-3PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emerson Puente can be reached at 5712723652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOHN E JOHANSEN/Examiner, Art Unit 2187
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