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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/7/2024, 9/9/2024, and 10/28/2025 comply with the provisions of 37 CFR 1.97 and are being considered.
Drawings
The drawings are objected to because:
- Figs. 4 and 8 are labeled with “full discharge circles” rather than, presumably, “full discharge cycles”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Para. [0146] uses a Chinese character in the subscript for Q, where the subscript should, presumably, be ‘rated’
Para. [0146] uses ‘dt’ as the differential which should be ‘dt’
The use of the term “Bluetooth” in Para. [0084], which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM, or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
The “obtaining module,”
the “extraction module,”
the “feature predicting module,” and
the “health degree predicting module” in Claim 8.
Enough corresponding structure is disclosed in the instant Specification for one having ordinary skill in the art to understand that these “modules” correspond to general-use computer processing hardware elements and machine executable code.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recite sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106.
Specifically, Claim 1 recites:
A method for predicting state of health of a battery, comprising:
obtaining battery data of a vehicle;
performing feature extraction on the battery data to obtain a vehicle-using behavior feature corresponding to the vehicle;
predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature; and
predicting and obtaining a health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and a battery health state prediction model.
The claim limitations in the abstract idea have been underlined above; the remaining limitations are “additional elements.”
Step 1:
Under Step 1 of the analysis, Claim 1 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, Claim 1 is found to recite at least one judicial exception (i.e. abstract idea), that being a mental process. The claim limitations “predicting the state of health…,” “performing feature extraction…,” “predicting and obtaining a vehicle use behavior feature…,” and “predicting and obtaining a health degree…” are mental processes to predict the battery health at some future time step. They are merely data observations, evaluations, and/or judgements performed to make a prediction about the battery health state and are capable of being performed mentally and/or with the aid of pen and paper.
Step 2A – Prong 2:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in Claim 1, the claimed method recites additional elements including “obtaining battery data of a vehicle,” the “battery,” and the “vehicle.” “Obtaining battery data of a vehicle” is merely a data gathering and output step recited at a high level of generality with no limitation on how the battery data is obtained, and thus amounts to “insignificant extra-solution” activity. See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,”. See para. [0101, 0102] and Figs. 6 and 7, which describe embodiments wherein the battery data is obtained via transmission over a network. The battery, vehicle, and obtainment of the vehicle-use behavior “corresponding to the vehicle” merely generically link the data extraction, prediction, and interpretation steps the technological environment of a battery in a vehicle. See MPEP 2106.05(h) “Field of Use and Technological Environment,”.
Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. For instance, the data gathering, feature extraction, and predictions made in Claim 1 are used merely to formulate the overall battery health state prediction, which is, as stated above, a judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to data gathering that generally links the data gathering, data extraction, and prediction to the technological environment of a vehicle battery, as well as insignificant extra-solution activity. Such insignificant extra-solution activity (obtaining the battery data), e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document).
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 1 amounts to significantly more than the abstract idea.
Claim 2 recites:
The method according to claim 1, wherein
a feature type of the vehicle-using behavior feature comprises a proportion feature and an accumulation feature, and
the predicting and obtaining the predicted vehicle-using behavior feature of the vehicle in the next time step according to the vehicle-using behavior feature comprises:
predicting and obtaining a predicted accumulation feature of the vehicle in the next time step according to a preset linear fitting model and the vehicle-using behavior feature; and
aggregating the proportion feature and the accumulation feature to obtain the predicted vehicle-using behavior feature of the vehicle in the next time step.
The claim limitations in the abstract idea have been underlined above; the remaining limitations are “additional elements.”
Step 1:
Under Step 1 of the analysis, Claim 2 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 2 is found to recite at least one judicial exception (i.e. abstract idea), mental processes and mathematical calculations. Claim limitations “predicting and obtaining the predicted vehicle-using behavior feature…” and “predicting and obtaining a predicted accumulation feature…according to a preset linear fitting model…,” are both mental processes and mathematical calculations, as predicting is a form of data extrapolation that can be performed mentally, on pen and paper, or using a variety of mathematical operations. Similarly, fitting to a model can be done with pen and paper or via a variety of mathematical operations. “Aggregating the proportion feature and the accumulation feature…” is a form of data evaluation and grouping, which is a mental process.
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 2, the claimed method recites one additional element: a vehicle; however, this element merely generally links the data gathering, evaluating, and manipulation elements to the technological environment of a vehicle. See MPEP 2106.05(h), as well as the Step 2A – Prong 2 analysis for the similar additional element in Claim 1.
No specific practical application is associated with the claimed method. This claim does no more than identify features of a dataset, then make predictions for the future state of the dataset based on those features and a model. Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, and merely amount to an attempt to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use.
Therefore, as with the similar limitation in Claim 1, the above identified additional element when analyzed under Step 2B also fails to necessitate a conclusion that Claim 2 amounts to significantly more than the abstract idea.
Claim 3 recites:
The method according to claim 1, wherein
before the predicting and obtaining the health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and the battery health state prediction model, the method further comprises:
obtaining a battery health state prediction model to be trained and training sample data of the battery in the vehicle, and calculating a real health degree of the battery based on the training sample data;
performing feature extraction on the training sample data to obtain a training vehicle-using behavior feature corresponding to the battery;
performing normalization processing on the training vehicle-using behavior feature to obtain normalized vehicle-using behavior feature;
predicting and obtaining a training health degree of the battery according to the normalized vehicle-using behavior feature and the battery health state prediction model to be trained; and
iteratively optimizing the battery health state prediction model to be trained to obtain the battery health state prediction model according to the training health degree and the real health degree.
Step 1:
Under Step 1 of the analysis, Claim 3 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 3 is found to recite at least one judicial exception (i.e. abstract idea), and is both a mental process and mathematical calculation. The claim limitations “obtaining a…model and training sample data…,” “performing feature extraction…,” “predicting and obtaining a health degree…,” and “iteratively optimizing the battery health state prediction model…to obtain the battery health state prediction model…” are all both mental processes and mathematical operations. Modeling a dataset, regardless of the methods used to develop the model, feature extraction, predicting and obtaining, and iteratively optimizing a model, are data judgements and evaluations, capable of being performed mentally or on pen and paper. The limitations “calculating a real health degree…” and “performing normalization processing…,” are mathematical calculations. The real health degree calculation (see para. [0146]) and, as shown in the instant Specification, is accomplished via integration over a time interval while the normalization processing is a ratio of a feature to a predetermined threshold (see para. [0130]).
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 3, the claimed method recites one additional element, the “battery in the vehicle,” however this element, similar to similar additional elements in Claim 1, merely generally links the data gathering, evaluating, and manipulation elements to the technological environment of a vehicle. See MPEP 2106.05(h).
This claim does no more than gather data and develop a model from it. No specific practical application is associated with the claimed method. Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, this additional element does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to an attempt to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activities.
Therefore, as with the similar limitation in Claim 1, the above identified additional elements when analyzed under Step 2B also fail to necessitate a conclusion that Claim 3 amounts to significantly more than the abstract idea.
Claim 4 recites:
The method according to claim 3, wherein
a feature content of the training vehicle-using behavior feature comprises a user behavior feature and a battery performance feature, and
the performing the feature extraction on the training sample data to obtain the training vehicle-using behavior feature corresponding to the battery comprises:
obtaining a cut-off time of battery charging data for calculating the real health degree in the training battery data;
selecting training battery data satisfying a preset health state prediction condition before the cut-off time from the training sample data; and
extracting the user behavior feature and the battery performance feature according to the training battery data.
Step 1:
Under Step 1 of the analysis, Claim 4 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 4 is found to recite at least one judicial exception (i.e. abstract idea), mental processes. The claim limitations, “a feature content of the training vehicle-using behavior feature comprises a user behavior feature and a battery performance feature,” “performing the feature extraction on the training sample data,” “obtaining a cut-off time of battery charging data for calculating the real health degree,” “selecting training battery data satisfying a preset health state prediction condition…,” and “extracting the user behavior feature and the battery performance feature,” are mental processes because these limitations are merely data observations, selection, and evaluations in order to obtain a feature of the training vehicle-using behavior data: the user behavior feature, and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, “obtaining a cut-off time of battery charging data for calculating the real health degree” recites a mathematical calculation, as discussed in greater detail above.
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 4, the claimed method recites one additional element: “corresponding to the battery,” which is similar to an additional element in Claim 1. This element, however, merely generally links the calculation and data observation, selection, and evaluation elements to the technological environment of the battery. See MPEP 2106.05(h).
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, this additional element does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. This claim does no more than select and refine data to develop a training dataset. No specific practical application is associated with the claimed method.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to an attempt to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activities.
Therefore, as with the similar limitation in Claim 1, the above identified additional element when analyzed under Step 2B also fails to necessitate a conclusion that Claim 4 amounts to significantly more than the abstract idea.
Claim 5 recites:
The method according to claim 3, wherein
the calculating the real health degree of the battery based on the training sample data comprises:
selecting battery charging data satisfying a preset charging working condition from the training sample data, wherein the battery charging data comprises
a training battery current of the battery during a charging process, a first remaining power of the training battery to start the charging process, a second remaining power of the training battery after the charging process and a rated battery capacity of the battery; and
determining the real health degree of the battery according to the training battery current, the first remaining power, the second remaining power and the rated battery capacity.
Step 1:
Under Step 1 of the analysis, Claim 5 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 5 is found to recite at least one judicial exception (i.e. abstract idea) and is both a mental process and mathematical calculation. The claim limitations, “calculating the real health degree of the battery based on the training sample data…,” “selecting battery charging data satisfying a preset charging working condition wherein the battery charging data comprises a training battery current of the battery during a charging process, a first remaining power of the training battery to start the charging process, a second remaining power of the training battery after the charging process and a rated battery capacity…,” and “determining the real health degree of the battery according to the training battery current, the first remaining power, the second remaining power and the rated battery capacity,” are mental processes. They are merely data observations, evaluations, and/or judgements made in order to select data and identify the parameters and limits to be used in the real health degree calculation, which again, is an integral and thus a mathematical calculation.
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 5, the claimed method recites an additional element, the “battery,” however this element is found to merely generally link the calculation and data observation, selection, and evaluation elements to the technological environment of the battery. See MPEP 2106.05(h).
This claim does no more than select and refine data to develop a training dataset. No specific practical application is associated with the claimed method. Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, this additional element does not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to an attempt to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely performs insignificant extra-solution activities.
Therefore, as with the similar limitation in Claim 1, the above identified additional element when analyzed under Step 2B also fails to necessitate a conclusion that Claim 5 amounts to significantly more than the abstract idea.
Claim 6 recites:
The method according to claim 5, wherein
the selecting the battery charging data satisfying the preset charging working condition from the training sample data comprises:
selecting a first battery data from the training sample data, wherein the first battery data is battery data whose resting duration after the charging process is completed is longer than a preset duration threshold; and
determining that a second battery data in the first battery data is the battery charging data, wherein the second battery data is battery data in which a difference between the second remaining power and the first remaining power is greater than a preset power threshold and a current at the end of the charging process is less than a preset current threshold.
Step 1:
Under Step 1 of the analysis, Claim 6 belongs to a statutory category, namely it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 6 is found to recite at least one judicial exception (i.e. abstract idea), that being a mental process and mathematical calculation. This can be seen in the claim limitations of “selecting the battery charging data satisfying the preset charging working condition…,” “selecting a first battery data from the training sample data, wherein the first battery data is battery data whose resting duration after the charging process is completed is longer than a preset duration threshold,” and “determining that a second battery data in the first battery data is the battery charging data, wherein the second battery data is battery data in which a difference between the second remaining power and the first remaining power is greater than a preset power threshold and a current at the end of the charging process is less than a preset current threshold.” The above limitations are merely data observations, selections, evaluations, and/or judgements in order to define the data and parameters to be used in the real battery health degree calculation and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, the aforementioned limitations recite mathematical calculations such as determining differences and whether the values of some features (first battery data, second battery data, etc.) exceed predetermined thresholds.
Step 2A – Prong 2:
Claim 6 does not recite additional elements and thus the judicial exception is not integrated into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements, therefore, the claim does not amount to significantly more than the abstract idea.
Claim 7 recites:
The method according to claim 3, wherein
the performing the normalization processing on the training vehicle-using behavior feature to obtain the normalized vehicle-using behavior feature comprises:
obtaining a preset feature threshold corresponding to each training vehicle-using behavior feature; and
obtaining the normalized vehicle-using behavior feature according to a ratio of each training vehicle-using behavior feature to the preset feature threshold.
Step 1:
Under Step 1 of the analysis, Claim 7 belongs to a statutory category, namely, it is a method claim.
Step 2A – Prong 1:
In the instant case, Claim 7 is found to recite at least one judicial exception (i.e. abstract idea), and is a mental process and mathematical calculation. These claim limitations recite “performing the normalization processing on the training vehicle-using behavior feature to obtain the normalized vehicle-using behavior feature,” “obtaining a preset feature threshold corresponding to each training vehicle-using behavior feature,” and “obtaining the normalized vehicle-using behavior feature according to a ratio of each training vehicle-using behavior feature to the preset feature threshold.” The normalization and obtaining the normalized feature are calculated the ratios of the data to a predetermined threshold, making them mathematical calculations. As obtaining a feature threshold is no defined in the specification, under Broadest Reasonable Interpretation, obtaining the threshold requires data observation, which is a mental process and is capable of being performed mentally and/or with the aid of pen and paper. The limitations recited in these claims are performed to make a calculation and develop a model and thus are no more than mathematical calculations and mental processes.
Step 2A – Prong 2:
Claim 7 does not recite additional elements and thus the recited judicial exception is not integrated into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements, therefore, the claim does not amount to significantly more than the abstract idea.
Claim 8 recites:
A device for predicting state of health of a battery, comprising:
an obtaining module for obtaining battery data of a vehicle;
an extraction module for performing feature extraction on the battery data to obtain a vehicle-using behavior feature corresponding to the vehicle;
a feature predicting module for predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature; and
a health degree predicting module for predicting and obtaining a health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and a battery health state prediction model.
Step 1:
Under Step 1 of the analysis, 8 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
Under Step 2A, prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In the instant case, Claim 8 is found to recite at least one judicial exception (i.e. abstract idea), and is a mental process and mathematical calculation. The claim limitations “obtaining battery data of a vehicle,” “performing feature extraction on the battery data to obtain a vehicle-using behavior feature,” and “predicting and obtaining a health degree… according to the predicted vehicle-using behavior feature and a battery health state prediction model” are merely data observations, evaluations, and/or judgements in order to make a prediction and are capable of being performed mentally and/or with the aid of pen and paper. Additionally, under Broadest Reasonable Interpretation, claim limitations involving feature extraction, predicting, and predicting according to a model recite mathematical calculations as well, as the methods used to perform the extraction, predicting, and predicting according to a model are recited with a high level of generality in the claims and the instant Specification.
Step 2A – Prong 2:
Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the abstract ideas recited in Claim 2, the claimed apparatus recites additional elements including “an obtaining module,” an “extraction module,” a “feature predicting module,” and a “health degree predicting module,” however these elements are found to be merely data gathering and evaluation steps that are performed by the “modules,” which are recited at a high level of generality, with no additional structural detail. This is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general-purpose computer does not integrate the abstract idea into practical application. See MPEP 2106.05(f).
The generic data gathering, processing, and output steps, are recited at such a high level of generality (e.g. using an “obtaining module to obtain” data, an “extraction module” to extract data, a “feature predicting module for predicting” data, and a “health degree predicting module for predicting” a quantity derived from data) that they represent no more than mere instructions to apply the judicial exceptions on a computer. This can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general-purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use.
Therefore, the combination of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 8, amounts to significantly more than the abstract idea.
Claim 9 recites:
An electronic equipment, comprising:
at least one processor; and
a memory communicated with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and
when the instructions are executed by the at least one processor, the at least one processor implements the method for predicting the state of health of the battery according to claim 1.
Step 1:
Under Step 1 of the analysis, Claim 9 belongs to a statutory category, namely it is an apparatus.
Step 2A – Prong 1:
In the instant case, Claim 9 is found to recite at least one judicial exception (i.e. abstract idea), a mental process and mathematical calculation. Refer to the analysis for Claim 1, which has a similar claim limitation. The claim limitation, “predicting the state of health of the battery according to claim 1” is a combination of data observations, evaluations, and judgements in order to make a prediction and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, as stated in Claims 1-7, the functions and operations that could be performed to make the prediction involve mathematical calculations.
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 9, the claimed apparatus recites additional elements including “An electronic equipment, comprising,” “at least one processor,” “a memory communicated with the at least one processor, wherein the memory stores instructions executable by the at least one processor,” and “when the instructions are executed by the at least one processor, the at least one processor implements the method.” These elements are recited with a high level of generality, with insufficient detail to necessitate the conclusion that the electronic equipment, memory, and processor(s) are specialized to perform the state of battery health prediction, and are found to be mere instructions to apply a judicial exception on a general-purpose computer and do not integrate the abstract idea into a practical application. See MPEP 2106.05(f).
Using this equipment to “implement the method” of Claim 1 can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions of Claim 1 to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general-purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use.
Claim 9 amounts to no more than the implementation of Claim 1, which has already been identified as being directed to a judicial exception, on generically recited structure. Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 9 amounts to significantly more than the abstract idea.
Claim 10 recites:
A non-transitory computer-readable storage medium, wherein
the non-transitory computer-readable storage medium stores a program for realizing a method for predicting state of health of a battery, and
when the program for realizing the method for predicting the state of health of the battery is executed by a processor,
the method for predicting the state of health of the battery according to claim 1 is implemented.
Step 1:
Under Step 1 of the analysis, Claim 10 belongs to a statutory category, namely it is an apparatus claim.
Step 2A – Prong 1:
In the instant case, Claim 10 is found to recite one judicial exception (i.e. abstract idea), that being a mental process and mathematical calculation. The claim limitation, “predicting the state of health of the battery,” as stated for similar claim limitations, is a combination of data observations, evaluations, and judgements in order to make a prediction and is capable of being performed mentally and/or with the aid of pen and paper. Additionally, as stated in Claims 1-7, the functions and operations that could be performed (as interpreted under BRI) to make the prediction involve mathematical calculations.
Step 2A – Prong 2:
In addition to the abstract ideas recited in Claim 10, the claimed apparatus recites additional elements, including “A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores a program,” “the program for realizing the method,” and “executed by a processor.” These elements, however, are recited at such a high level of generality that they do not integrate the abstract idea into a practical application and merely amount to instructions to implement the abstract ideas of Claim 1 on a computer. This is found to be equivalent to adding the words “apply it” and mere instructions to apply a judicial exception on a general-purpose computer does not integrate the abstract idea into a practical application. See MPEP 2106.05(f). This can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer.
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general-purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use.
The additional elements of Claim 10 amount to no more than the execution of the judicial exceptions of Claim 1 via code on general use computer components. Therefore, similarly, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 10 amounts to significantly more than the abstract idea.
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 non-obviousness.
Claims 1-3 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Rangel et. al (US 2022/0153166 A1) in view of Beyer et. al. (US 2005/0177337 A1).
Regarding Claim 1, Rangel discloses a method for predicting state of health of a battery [Paragraph [0023] – “FIG. 1 is a flowchart of a method for predicting battery health according to an embodiment of the present disclosure”], comprising:
obtaining battery data of a vehicle [Paragraph [0032] – “At S101, obtaining historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery state-of-charge (SOC), vehicle speed, battery-module temperatures, and battery-cell voltages;”];
and performing feature extraction on the battery data to obtain a vehicle-using behavior feature corresponding to the vehicle [Paragraph [0036] – “In the present embodiment, at the step of S102, the method may further comprise: … extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance”].
Rangel fails to disclose predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature.
However, Beyer discloses predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature [See the linear regression of cumulative vehicle distance relative to daily use in Fig. 10A. The determined function amounting to a prediction of future values.].
It would have been obvious prior to the effective filing date to one having ordinary skill in the art to apply the linear regression technique of Beyer to the vehicle-using behavior feature of Rangel in order to predict vehicle-using behavior feature values at future times.
Rangel, as modified, would disclose predicting and obtaining a health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and a battery health state prediction model [Paragraph [0065] – “In the model of the present embodiment, the loss of distance driven with time (degradation D(t)) is proportional to cumulative usage (cumulative distance driven on battery U(t)),” per use of the predicted cumulative distance of Beyer.].
Regarding Claim 2, Rangel in view of Beyer, would disclose that a feature type of the vehicle-using behavior feature comprises a proportion feature [Paragraph [0036] of Rangel – “positive/negative acceleration counts”] and an accumulation feature [Paragraph [0036] of Rangel – “cumulative distance”], and the predicting and obtaining the predicted vehicle-using behavior feature of the vehicle in the next time step according to the vehicle-using behavior feature comprises:
predicting and obtaining a predicted accumulation feature of the vehicle in the next time step according to a preset linear fitting model and the vehicle-using behavior feature [Applying the linear regression technique of Beyer to the context of Rangel in order to predict vehicle-using behavior feature values at future times]; and
aggregating the proportion feature and the accumulation feature [Paragraph [0036] of Rangel – “In the present embodiment, at the step of S102, the method may further comprise: … extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance”] to obtain the predicted vehicle-using behavior feature of the vehicle in the next time step [Applying the linear regression technique of Beyer to the vehicle-using behavior features of Rangel in order to predict vehicle-using behavior feature values at future times].
Regarding Claim 3, Rangel discloses, before the predicting and obtaining the health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and the battery health state prediction model [See Fig. [1], S101 and S102, which both precede the predicting step], the method further comprises:
obtaining a battery health state prediction model to be trained and training sample data of the battery in the vehicle, and calculating a real health degree of the battery based on the training sample data [Paragraph [0076] – “Raw data is collected from vehicles at a predetermined frequency through vehicle telematics. The data is stored in a centralized platform. Data is cleaned and processed to build the model's input. Model hyper parameters are tuned automatically using cross-validation performance scores. After the model is trained, battery health can be predicted, which provides insights on performance loss due to battery degradation or seasonal effects. The model is updated in real-time as new data is collected and so are the model predictions.”];
Rangel discloses performing feature extraction on battery sample data to obtain a training vehicle-using behavior feature corresponding to the battery [Paragraph [0036] – “In the present embodiment, at the step of S102, the method may further comprise: … extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance”];
and performing normalization processing on the vehicle-using behavior feature to obtain normalized vehicle-using behavior feature [Paragraph [0053] – “Then scale distance driven, positive/negative acceleration counts and regenerated energy for each trip to 100 SOC, by multiplying a normalization constant of 100/ΔSOC, where ΔSOC is the net change in SOC in the trip.”].
Rangel fails to disclose performing feature extraction on the training sample data to obtain a training vehicle-using behavior feature corresponding to the battery; and performing normalization processing on the training vehicle-using behavior feature to obtain normalized vehicle-using behavior feature because Rangel fails to disclose performing the extraction and normalization as part of the training process. However, it would have been obvious to perform the extraction and normalization as part of the training process because Rangel teaches that such steps are useful in the determination of battery SOH.
Rangel, as modified, would further disclose predicting and obtaining a training health degree of the battery according to the normalized vehicle-using behavior feature and the battery health state prediction model to be trained; and iteratively optimizing the battery health state prediction model to be trained to obtain the battery health state prediction model [Paragraph [0076] – “After the model is trained, battery health can be predicted, which provides insights on performance loss due to battery degradation or seasonal effects. The model is updated in real-time as new data is collected and so are the model predictions.”] according to the training health degree and the real health degree [Paragraph [0073] – “To validate the model in the present embodiment, the SOH predicted with the models can be compared with a reference value”].
Regarding Claim 8, Rangel teaches a device for predicting state of health of a battery, comprising: an obtaining module for obtaining battery data of a vehicle [Paragraph [0079] – “The first obtaining module 10 is configured to obtain historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery state-of-charge (SOC), vehicle speed, battery-module temperatures, and battery-cell voltages .” - see also Fig. [5]];
an extraction module for performing feature extraction on the battery data [Paragraph [0079] – see above, specifically “odometer readings” and “battery state-of-charge (SOC)”] to obtain a vehicle-using behavior feature corresponding to the vehicle [Paragraph [0036] – “In the present embodiment, at the step of S102, the method may further comprise: identifying trips on battery from the historical vehicle telematics of vehicles; extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance; modeling distance driven according to the relationship between the distance driven on a full battery load and the model features.” – features during trips on battery are extracted and used in the distance driven model, which is used to predict overall battery health state];
and a health degree predicting module for predicting and obtaining a health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and a battery health state prediction model [Paragraph [0082] – “The prediction module 40 is configured to predict battery health of the vehicle by comparing the obtained distance with a reference distance value.”; see also Paragraph [0065], above].
While Rangel discloses a feature predicting module [Paragraph [0081] – “The second obtaining module 30 is configured to obtain a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input;””, Fig. [5], 30 – the “creation module”], Rangel does not disclose predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature.
However, Beyer does discloses predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature. [See the linear regression of cumulative vehicle distance relative to daily use in Fig. 10A. The determined function amounting to a prediction of future values.].
It would have been obvious prior to the effective filing date to one having ordinary skill in the art use the second obtaining module of Rangel to apply the linear regression technique of Beyer to predict vehicle-using behavior feature values at future times.
Regarding Claim 9, Rangel in view of Beyer, discloses an electronic equipment, comprising: at least one processor [Paragraph [0090] - “each module or each step of the present disclosure may be implemented by a universal computing device”]),
and a memory communicated with the at least one processor, wherein the memory stores instructions executable by the at least one processor,” [Paragraph [0090] - “It is apparent that those skilled in the art should know that each module or each step of the present disclosure may be implemented by a universal computing device, and the modules or steps may be concentrated on a single computing device or distributed on a network formed by a plurality of computing devices, and may in an embodiment be implemented by program codes executable for the computing devices, so that the modules or the steps may be stored in a storage device for execution with the computing devices, the shown or described steps may be executed in sequences different from those described here in some circumstances, or may form individual integrated circuit module respectively, or multiple modules or steps therein may form a single integrated circuit module for implementation....”],
and when the instructions are executed by the at least one processor, the at least one processor implements the method for predicting the state of health of the battery according to claim 1 [Paragraph [0077] – “In the present embodiment, a system for predicting battery health with machine learning models is also provided. The system can be applied to a cloud-based server or an on-board computing device and is configured to implement the abovementioned embodiments with preferred implementation modes. What has been described will not be elaborated. For example, term “module”, used below, may be a combination of software and/or hardware realizing a predetermined function. Although the device described in the following embodiment is preferably implemented by the software, implementation by the hardware or the combination of the software and the hardware is also possible and conceivable.”, refer also to Fig. [5]].
Regarding Claim 10, Rangel in view of Beyer discloses a non-transitory computer-readable storage medium [Paragraphs [0083], [0088] – “According to the present embodiment, a non-volatile computer readable storage medium is provided, a program is stored in the non-volatile computer readable storage medium, and the program is configured to be executed by a computer to perform the following steps… In an example embodiment, the storage medium may include, but not limited to, various media capable of storing program codes such as a U disk, a ROM, a RAM, a mobile hard disk, a magnetic disk or an optical disk.”],
wherein the non-transitory computer-readable storage medium stores a program for realizing a method for predicting state of health of a battery,”[Paragraph [0084]-[0087] – “At S1, obtaining historical vehicle telematics of vehicles, wherein the historical vehicle telematics comprise at least one of the following: odometer readings, battery state-of-charge (SOC), vehicle speed, battery-module temperatures, and battery-cell voltages; At S2, creating a distance driven model according to a relationship between a distance driven on a full battery load of vehicles and the historical vehicle telematics; At S3, obtaining a distance driven on a full battery load of a vehicle based on the distance driven model by using real-time vehicle telematics of the vehicle as model input; At S4, predicting battery health of the vehicle by comparing the obtained distance with a reference distance value. (para. [0087])”],
and when the program for realizing the method for predicting the state of health of the battery is executed by a processor, the method for predicting the state of health of the battery according to claim 1 is implemented [Paragraph [0083] – “configured to be executed by a computer to perform”].
Claims 4-7 are rejected under 35 U.S.C. 103 as being unpatentable over Rangel et. al in view of Beyer in further view of Lee et. al. (US 20200386819 A1).
Regarding Claim 4, Rangel in view of Beyer, discloses that a feature content of the training vehicle-using behavior feature [Paragraph [0076] – “Raw data is collected from vehicles at a predetermined frequency through vehicle telematics. The data is stored in a centralized platform. Data is cleaned and processed to build the model's input. Model hyper parameters are tuned automatically using cross-validation performance scores. After the model is trained, battery health can be predicted, which provides insights on performance loss due to battery degradation or seasonal effects. The model is updated in real-time as new data is collected and so are the model predictions.”] comprises a user behavior feature [Paragraph [0036] – “In the present embodiment, at the step of S102, the method may further comprise: … extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance”] and a battery performance feature [Paragraph [0036] – “In the present embodiment, at the step of S102, the method may further comprise: … extracting at least one of the following model features from the historical vehicle telematics: positive/negative acceleration counts, mean vehicle speed, mean battery temperature, battery temperature imbalance, regenerated energy, mean cell-voltage difference, and cumulative distance”].
and performing the feature extraction on the training sample data to obtain the training vehicle-using behavior feature corresponding to the battery [Paragraphs [0049]-[0050] – “In the bottom panel of FIG. 3, trips are extracted, defined as segments for a vehicle that is driving on battery. With the above definition of trips-on-battery, key metrics are utilized to describe battery performance per trip on a full battery load, as, described next.”]
Rangel, in view of Beyer, does not disclose obtaining a cut-off time of battery charging data for calculating the real health degree in the training battery data; selecting training battery data satisfying a preset health state prediction condition before the cut-off time from the training sample data; and extracting the user behavior feature and the battery performance feature according to the training battery data.
Lee discloses obtaining a cut-off time of battery charging data for calculating the real health degree in the training battery data [See Fig. [3]; Paragraph [0013] – “FIG. 3 illustrates a graph of a battery charge and discharge cycle for the prediction system 10 to obtain experimental data according to an embodiment.” – here the experimental data is the training battery data, the cut-off time is the end of the charging periods];
selecting training battery data [Paragraph [0042] – “In an implementation, the training unit 111 may be for training the AI model using experimental data and virtual data as training data.”] satisfying a preset health state prediction condition [See Fig. 3, data including rest periods] before the cut-off time from the training sample data [Paragraph [0056] – “In an implementation, the ADF may include, as factors, current C-rate, SOC, and temperature (T), and the value of the ADF may be an aging density. In this case, the aging density may refer to the amount of variation in relative capacity per unit time.” – note that ADF is aging density function; see also Fig. [3] for the cut-off current; Paragraph [0060] – “In Equation 3, A and B may be a function of parameters θ=(α.sub.0,α.sub.1,1˜.sub.2H.sub.1, α.sub.2,1˜.sub.3H.sub.2, α.sub.3,1˜.sub.3H.sub.3). of the ADF and a function of charge and discharge cycle conditions Q=(C-rate 1, C-rate 2, Cut-off voltage 1, Cut-off voltage 2, Rest time).”; refer also to cut-off voltages in Fig. [3] – these cut-off voltages define cut-off times during a charge-discharge cycle];
The combination of Rangel and Lee teaches extracting the user behavior feature and the battery performance feature according to the training battery data [Rangel Paragraph [0049]-[0050] and Fig. [3], bottom panel].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the current invention, to use the battery health state prediction condition and cut-off times of Lee in the data extraction disclosed in Rangel in view of Beyer, in order to select battery data under ideal conditions to train the model.
Regarding Claim 5, Rangel in view of Beyer does not disclose that calculating the real health degree of the battery based on the training sample data comprises: selecting battery charging data satisfying a preset charging working condition from the training sample data, wherein the battery charging data comprises a training battery current of the battery during a charging process, a first remaining power of the training battery to start the charging process, a second remaining power of the training battery after the charging process and a rated battery capacity of the battery; and determining the real health degree of the battery according to the training battery current, the first remaining power, the second remaining power and the rated battery capacity.
Lee discloses that calculating the real health degree of the battery based on the training sample data comprises: selecting battery charging data satisfying a preset charging working condition from the training sample data, wherein the battery charging data comprises a training battery current of the battery during a charging process [Paragraph [0075] – “The CC charge period may be a period for charging with a CC; the CV charge period may be a period for charging at a CV after reaching an upper cut-off voltage; the first rest period may be a period during which no current is applied after reaching a cut-off current; the CC discharge period may be a period for discharging with a CC; and the second rest period may be a period during which no current is applied after reaching a lower cut-off voltage. In an implementation, the first rest period and the second rest period may have the same length.”; Fig. [3], see cut-off current and note that ‘CC’ is an abbreviation for ‘constant current’],
a first remaining power of the training battery to start the charging process, a second remaining power of the training battery after the charging process [Paragraph [0061] – “In Equation 3, the cut-off voltages are assumed to be the maximum SOC and the minimum SOC, and the capacity value in one cycle is assumed to be a constant in the cycle.” – max SOC is 2nd remaining power and min SOC is 1st remaining power],
and a rated battery capacity of the battery [Paragraph [0057], [0058], Eq. [2] – “To implement the ADFM, the prediction system 10 may acquire a relative capacity variation amount by integrating the relative capacity variation amount per unit time obtained using the ADF with respect to time. This may be expressed by Equation 2 below. Equation 2 expresses a relative capacity variation amount (relative capacity loss) from a time t.sub.1 to a time t.sub.2. Here, Cap.sub.fresh refers to the capacity value of a secondary battery that has not undergone any charge and discharge cycle.”];
and determining the real health degree of the battery according to the training battery current [Paragraph [0056] – “In an implementation, the ADF may include, as factors, current C-rate, SOC, and temperature (T), and the value of the ADF may be an aging density. In this case, the aging density may refer to the amount of variation in relative capacity per unit time.” – note that ADF is aging density function; see also Fig. [3] for the cut-off current],
the first remaining power, the second remaining power [Paragraph [0061] – “In Equation 3, the cut-off voltages are assumed to be the maximum SOC and the minimum SOC, and the capacity value in one cycle is assumed to be a constant in the cycle.” – max SOC is 2nd remaining power and min SOC is 1st remaining power]
and the rated battery capacity [Paragraph [0057], [0058], Eq. [2] – “To implement the ADFM, the prediction system 10 may acquire a relative capacity variation amount by integrating the relative capacity variation amount per unit time obtained using the ADF with respect to time. This may be expressed by Equation 2 below. Equation 2 expresses a relative capacity variation amount (relative capacity loss) from a time t.sub.1 to a time t.sub.2. Here, Cap.sub.fresh refers to the capacity value of a secondary battery that has not undergone any charge and discharge cycle.” – fresh capacity is the rated capacity].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the charging and discharging currents, first remaining power, second remaining power, and the rated capacity of Lee in the battery charging data selection of Rangel, in view of Beyer.
Regarding Claim 6, Rangel, in view of Beyer, does not disclose the method according to claim 5, wherein the selecting the battery charging data satisfying the preset charging working condition from the training sample data comprises: selecting a first battery data from the training sample data, wherein the first battery data is battery data whose resting duration after the charging process is completed is longer than a preset duration threshold; and determining that a second battery data in the first battery data is the battery charging data, wherein the second battery data is battery data in which a difference between the second remaining power and the first remaining power is greater than a preset power threshold and a current at the end of the charging process is less than a preset current threshold.
Lee discloses method according to claim 5, wherein the selecting the battery charging data satisfying the preset charging working condition from the training sample data comprises: selecting a first battery data from the training sample data, wherein the first battery data is battery data whose resting duration after the charging process is completed is longer than a preset duration threshold [Paragraph [0050] – “The CC charge period may be a period for charging with a CC; the CV charge period may be a period for charging at a CV after reaching an upper cut-off voltage; the first rest period may be a period during which no current is applied after reaching a cut-off current; the CC discharge period may be a period for discharging with a CC; and the second rest period may be a period during which no current is applied after reaching a lower cut-off voltage.” Fig. [3] – see rest period following charge period and note slight voltage dip],
and determining that a second battery data in the first battery data is the battery charging data, wherein the second battery data is battery data in which a difference between the second remaining power and the first remaining power is greater than a preset power threshold [Paragraph [0008] – “Collecting the at least one first piece of data may include performing a reference performance test (RPT) on the battery every preset number of charge and discharge cycles; and obtaining an open circuit voltage-state of charge lookup table (OCV-SOC LUT) based on results of the RPT, the first calculation equation is for calculating the relative capacity variation value by calculating combinations with repetition based on a charge rate, a discharge rate, a maximum state of charge (SOC) per cycle, a minimum SOC per cycle, and a temperature of the battery, and the optimizing of the first calculation equation may include determining the maximum SOC per cycle and the minimum SOC per cycle of the battery based on the OCV-SOC LUT.”],
and a current at the end of the charging process is less than a preset current threshold [Paragraph [0050] – “The CC charge period may be a period for charging with a CC; the CV charge period may be a period for charging at a CV after reaching an upper cut-off voltage; the first rest period may be a period during which no current is applied after reaching a cut-off current; the CC discharge period may be a period for discharging with a CC; and the second rest period may be a period during which no current is applied after reaching a lower cut-off voltage.” – see also the minimum cut-off current at the end of the CV charge period in Fig. [3]].
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to define a minimum and maximum power threshold, a minimum power threshold range, a maximum current at the end of the charging process, and a rest period after charging as defined in Lee et. al in the data extraction of Rangel, in view of Beyer, in order to ensure consistent quality in the selected training battery data.
Regarding Claim 7, Rangel in view of Beyer does not disclose the method according to claim 3, wherein the performing the normalization processing on the training vehicle-using behavior feature to obtain the normalized vehicle-using behavior feature comprises: obtaining a preset feature threshold corresponding to each training vehicle-using behavior feature; and obtaining the normalized vehicle-using behavior feature according to a ratio of each training vehicle-using behavior feature to the preset feature threshold.
Lee discloses the method according to claim 3, wherein the performing the normalization processing on the training vehicle-using behavior feature to obtain the normalized vehicle-using behavior feature comprises: obtaining a preset feature threshold corresponding to each training vehicle-using behavior feature [Paragraph [0061], [0064] – “maximum SOC and the minimum SOC,” “cut-off voltages,” and “fresh capacity”];
and obtaining the normalized vehicle-using behavior feature according to a ratio of each training vehicle-using behavior feature to the preset feature threshold [Eq. [2] – ratio of starting and ending capacities to predetermined capacity value of a “fresh” battery].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to normalize the parameters to predetermined thresholds, as done with capacity in Lee, as part of the normalization processing and feature extraction described in Rangel, in view of Beyer, in order to ensure that the battery data used to train the model is optimal.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230306803 A1, Simonis, C., Method and Apparatus for the User-Dependent Selection of a Battery Operated Technical Device Depending on a User Usage Profile, 2023.
US 20090243556 A1, Lu, W., System and Method for Monitoring the State of Charge of a Battery, 2009.
US 20210218073 A1, Duan, X., Intelligent Vehicle Battery Charging for High Capacity Batteries, 2021
US 2021116513 A1, Du, M., Method for Correcting SOH, Apparatus, Battery management System, and Storage Medium, 2021.
US 20230196846 A1, Braunstein, M., Machine and Battery System Prognostics, 2023.
CN 112666464 A, Huang, Z., Method For Predicting Health State Of Battery In Target Electric Car, Involves Inputting Current Battery Health State, Current Driving Mileage And Current Battery Parameter To Pre-trained Machine Learning Model To Determine Health State, 2021.
CN 111257779 B, Chen, Y., SOH Determining Method And Device Of Battery System, 2022.
Conclusion
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/J.A.H./ Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857