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 . The rejections from the Office Action of 2/4/2026 are hereby withdrawn. New grounds for rejection are presented below.
Status of Claims
Claims 1-10 were amended with Applicant’s response dated 4/30/2026. Claims 1-10 are rejected.
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 non-statutory subject matter. See MPEP 2106 for details. The following is the two-prong analysis for subject matter eligibility:
Specifically, representative Claim 1 recites:
A method for predicting state of health of a battery, the method being applied to a vehicle, a control system in the vehicle, or a server communicated with the vehicle, the method comprising:
obtaining battery data of the vehicle;
performing feature extraction on the battery data by using a preset feature extractor to obtain a vehicle-using behavior feature corresponding to the vehicle,
wherein the vehicle-using behavior feature characterizes a battery working state and user driving behavior, and
comprises at least one of a mileage feature, a discharge feature, a remaining power feature, a vehicle current feature, a vehicle speed feature, a vehicle operating temperature feature, a usage time feature, a charging voltage difference feature, and a discharge voltage difference feature;
predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step by mapping, through a preset linear fitting model, the vehicle-using behavior feature to the predicted vehicle-using behavior feature of the vehicle in the next time step; and
normalizing the predicted vehicle-using behavior feature to obtain a processed predicted vehicle-using behavior feature, and
predicting and obtaining a health degree of the battery in the vehicle by mapping the processed predicted vehicle-using behavior feature to the health degree of the battery in the vehicle through a battery health state prediction model,
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 below; the remaining limitations are “additional elements.”
Step 1:
Claim 1 describes a method and falls under the four statutory categories.
Step 2A - Prong One:
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.
The claimed invention is directed to an abstract idea without significantly more. The underlined claim elements in Claim 1, above, all recite mathematical calculations to predict state of health of a battery. Specifically, “…a method for predicting state of health…” is a prediction for some later time that can be performed mentally or with the aid of pen and paper. “Performing feature extraction…using a preset feature extractor…” relies on the use of models to perform the extraction (See Specification, Paragraph [0120]), which are, by definition of a “model” in his art, mathematical calculations. Predicting and obtaining a predicted vehicle-using behavior feature…by mapping, through a preset linear fitting model…” similarly makes predictions based on a linear fitting model, which relies on mathematical calculations, while normalization is also a known mathematical calculation. The vehicle-using behavior features merely place limitations on what data is being used in the model and are part of the abstract idea. “Predicting and obtaining a health degree of the battery…by mapping…through a battery health state prediction model” relies on a model, which is also either a single or series of mathematical calculations. The feature type comprising a proportion or accumulation feature places a mathematical (proportion and accumulation) limitation on the data type. “Predicting and obtaining the predicted vehicle-using behavior feature of the vehicle…comprises: predicting and obtaining a predicted accumulation feature of the vehicle in the next time step according to a preset linear fitting model” similarly involves a mathematical calculation for the reasons described above. Finally, “aggregating the proportion feature and the accumulation feature…” is a form of data evaluation and grouping, which is a mental process.
Step 2A - Prong Two:
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.
Claim 1 does not amount to the recitation of a particular practical application because the abstract ideas that comprise the battery health state prediction are not used to improve the vehicle and/or vehicle battery.
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:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exceptions 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 the following additional elements: “the method being applied to a vehicle, a control system in the vehicle, or a server communicated with the vehicle,” and “obtaining battery data of the vehicle.”
Applying the battery health prediction method on a vehicle, control system in the vehicle, or server communicated with the vehicle, the vehicle itself amounts to implementing the prediction method, which is an abstract idea, on a general purpose computer. See MPEP 2106.05(f) as well as 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")”. Obtaining battery data of the vehicle is mere data gathering necessary to implement the battery health state prediction model and thus insignificant pre-solution activity that is further found to be well-understood, conventional, and routine in the art, 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). See also MPEP 2106.05(g) “Insignificant extra-solution activity.”
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.
With regards to the dependent claims, Claims 3-7, 9, and 10, merely further expand upon the algorithm/abstract idea and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent Claim 1. Specifically:
Claim 3 recites mental processes and mathematical operations, such as modeling a dataset, feature extraction, predicting and obtaining, and iteratively optimizing, which are all data judgements and evaluations, and calculating and normalizing, which are mathematical calculations. The only additional element, the “battery in the vehicle” is similar to the additional elements in Claim 1, and amounts to no more than generally linking.
Claim 4 recites feature content of the training vehicle-using behavior features and selecting training battery data that satisfies a preset condition, which places limitations on the data being used in the battery health prediction model and thus are part of the mathematical calculations in the abstract idea. The data selection also constitutes a data judgement and therefore is a mental process. Performing the feature extraction and obtaining a cut-off time to calculate the real health degree both recite mathematical calculations. The only additional element is the battery, which has previously been addressed.
Claim 5 similarly recites calculations, data selections according to a preset condition, and determinations which are all mental processes (data judgements and mathematical calculations). A combination of abstract ideas is an abstract idea [See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"].
Claim 6 recites data selection for data satisfying preset conditions and thresholds, which is mental activity, while determining data based on a difference in remaining battery power and comparison to a threshold are mathematical calculations. A combination of abstract ideas is an abstract idea [See MPEP 2106.05(I) – "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"].
Claim 7 recites performing normalization processing, obtaining a preset feature threshold, and obtaining a normalized feature according to a ratio. These are mathematical calculations and data judgements based on observations and thus abstract ideas.
Claim 9 recites predicting the state of health of the battery according to claim one. Claim 1 has already been demonstrated to be mental processes/mathematical calculations that do not have a practical application or amount to significantly more than the abstract idea. The electronic equipment, processor, memory, and instructions executed by the processor are all elements recited with a high level of generality, with insufficient detail to necessitate the conclusion that the components are specialized to perform the state of battery health prediction, and are found to be elements of a general-purpose computer with instructions to apply the judicial exception [See Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014)] and do not integrate the abstract idea into practical application, nor do they amount to significantly more.
Claim 10 recites predicting the state of health of a battery, which has already been shown to be abstract. The non-transitory computer-readable storage medium is a general-purpose computer component.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Rangel et. al. (US 20220153166 A1) in view of Beyer et. al. (US 20050177337 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”], the method being applied to a vehicle, a control system in the vehicle, or a server communicated with the vehicle [Paragraph [0020] – “According to another aspect of the present disclosure, an electric vehicle is also provided. The electric vehicle includes the system for predicting battery health with machine learning model in above-mentioned embodiments.”], the method comprising:
obtaining battery data of the 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 [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"] by using a preset feature extractor [Paragraph [0019] – ”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 steps of methods in above-mentioned embodiments.“ - computer and program are configured to execute step 102 which includes the extraction] 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…”], wherein the vehicle-using behavior feature characterizes a battery working state and user driving behavior [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 comprises at least one of a mileage feature, a discharge feature, a remaining power feature, a vehicle current feature, a vehicle speed feature, a vehicle operating temperature feature, a usage time feature, a charging voltage difference feature, and a discharge voltage difference 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…” – cumulative distance is mileage feature, mean vehicle speed is vehicle speed feature, mean battery temperature is vehicle operating temperature feature, and mean cell-voltage difference is discharge voltage difference feature].
Rangel fails to disclose predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step by mapping, through a preset model, the vehicle-using behavior feature to the predicted vehicle-using behavior feature of the vehicle in the next time step.
However, Beyer discloses predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step by mapping, through a preset model, the vehicle-using behavior feature to the predicted vehicle-using behavior feature of the vehicle in the next time step [Paragraph [0062] – “In a linear regression analysis for forecasting future vehicle usage in accordance with the invention, a formula of the form Y=a+bX is used, where Y is a future odometer, clock, or other instrument or gauge reading, X is a future date or time period designated by a user for purposes of the analysis”; Paragraph [0064] – “FIGS. 10a-10c show individual data points (X.sub.i and Y.sub.i) used to perform a linear regression analyses plotted with curves of the form Y=a+bX determined using the data”; 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 to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply the preset linear regression model of Beyer to the vehicle-using behavior feature and preset model of Rangel in order to predict vehicle-using behavior feature values at future times.
The combination of Rangel and Beyer discloses normalizing [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." – normalized vehicle-using behavior feature] the predicted vehicle-using behavior feature to obtain a processed predicted vehicle-using behavior feature [Rangel, as modified would disclose normalizing “predicted vehicle-using behavior feature”, per use of linear regression of Beyer to predict linear values of the scaled distance driven.], and predicting and obtaining a health degree of the battery in the vehicle by mapping the processed predicted vehicle-using behavior feature to the health degree of the battery in the vehicle through 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)), and mean cell-voltage difference V.sub.diff(t)." per use of the predicted cumulative distance of Beyer.].
wherein 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 [See scaling of vehicle using behaviors in Rangel Paragraph [0053]; See linear regression of Beyer for predicted values] comprises:
predicting and obtaining a predicted accumulation feature [Paragraph [0036] of Rangel – “cumulative distance”] 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 accumulation 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, as modified by Beyer, discloses 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 [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.”]; and
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 [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.” – outcome of first battery health prediction is used to train subsequent predictions];
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 9, Rangel as modified by 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 as modified by 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.”],
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 as modified by Beyer discloses the method according to Claim 3, wherein 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.”].
The combination 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.
However, 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].
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 the combination of Rangel and Beyer in view of Beyer, in order to select battery data under ideal conditions to train the model.
The combination of Rangel, Beyer, 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].
Regarding Claim 5, Rangel, as modified by Beyer discloses the method according to Claim 4.
The combination 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.
However, 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, as modified by 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.
However, 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 as modified by 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.
However, 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.
Response to Arguments
Applicant argues:
PNG
media_image1.png
261
646
media_image1.png
Greyscale
Examiner’s Response:
Objections to the drawings are hereby withdrawn.
Applicant Argues:
PNG
media_image2.png
177
635
media_image2.png
Greyscale
PNG
media_image3.png
218
668
media_image3.png
Greyscale
Examiner’s Response
Objections to the specification are hereby withdrawn.
Applicant Argues:
PNG
media_image4.png
219
642
media_image4.png
Greyscale
Examiner’s Response
The corresponding interpretation under 35 USC 112(f) is hereby withdrawn.
Applicant Argues:
PNG
media_image5.png
388
639
media_image5.png
Greyscale
PNG
media_image6.png
54
731
media_image6.png
Greyscale
PNG
media_image7.png
272
640
media_image7.png
Greyscale
PNG
media_image8.png
224
639
media_image8.png
Greyscale
Examiner Response:
The Examiner agrees that instant Claim 1, for example, is not directed to mere mental activity. However, the Examiner respectfully disagrees that Claim 1 recites eligible subject matter because the claim recites the abstract idea of a mathematical algorithm for predicting and obtaining a health degree of a battery (see, for example, the equation recited in Paragraph [0146] of the instant Specification). New grounds of rejection are presented above. Specifically, predicting the state of health of a battery and the extraction, normalization, and aggregation performed in order to make the prediction can all be performed, under Broadest Reasonable Interpretation, via mathematical calculation.
Applicant Argues:
PNG
media_image9.png
308
798
media_image9.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. While this predictive model is linked to the technological environment of a vehicle having a battery, there are no claimed elements that recite any improvements to the vehicle or vehicle battery through the prediction of battery state of health. As such, the model only generally links to the technological environment of a vehicle. The remaining claim elements are data gathering steps necessary to perform the abstract idea.
Applicant Argues:
PNG
media_image10.png
60
645
media_image10.png
Greyscale
PNG
media_image11.png
227
730
media_image11.png
Greyscale
Examiner’s Response
The Examiner respectfully disagrees. The use of the feature extractor, preset linear fitting model, normalization processing, and battery health state prediction model are within the abstract idea and not additional elements. The recited vehicle-using behavior features merely specify the nature of the mathematical algorithm and do not serve to amount to significantly more than the recitation of the abstract idea itself.
Applicant Argues:
PNG
media_image12.png
331
645
media_image12.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. Rangel was not relied upon as disclosing the predicted vehicle-using behavior feature. Rather, the linear regression of Beyer is used in combination with Rangel to predict the feature in the next time step.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., intermediate feature-feature construction process) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant Argues:
PNG
media_image13.png
193
641
media_image13.png
Greyscale
PNG
media_image14.png
48
631
media_image14.png
Greyscale
Examiner’s Response
The Examiner respectfully disagrees. Beyer discloses applying linear regression, which is “a linear regression analysis for forecasting future vehicle usage in accordance with the invention, a formula of the form Y=a+bX is used, where Y is a future odometer, clock, or other instrument or gauge reading, X is a future date or time period designated by a user for purposes of the analysis” (See Beyer, Paragraph [0062]). A “next time step” is a future date or time period. Beyer is not relied upon to disclose the proportion and accumulation features or the aggregation of different feature categories. Rather, Rangel discloses these limitations.
Applicant Argues:
PNG
media_image15.png
143
630
media_image15.png
Greyscale
Examiner’s Response:
The Examiner agrees. However, Beyer is not relied on as disclosing such limitations.
Applicant Argues:
PNG
media_image16.png
162
636
media_image16.png
Greyscale
Examiner’s Response:
The Examiner respectfully disagrees. The ratio of positive to negative acceleration counts is a proportion.
Applicant Argues:
PNG
media_image17.png
158
636
media_image17.png
Greyscale
Examiner’s Response:
Examiner respectfully disagrees. The cumulative distance disclosed by Rangel is the accumulation feature, which is a feature-type distinction. Rangel is not relied upon for the prediction of a predicted accumulation feature for the next time step. This is disclosed by Beyer, as discussed above. The aggregation with the proportion feature, which is recited with a high level of generality, and the feature-processing architecture are both disclosed by Rangel, as per the explanation above.
Applicant Argues:
PNG
media_image18.png
336
638
media_image18.png
Greyscale
Examiner’s Response
The Examiner respectfully disagrees. Beyer discloses use of a linear fitting model for extrapolating parameter values [Paragraph [0062] – “In a linear regression analysis for forecasting future vehicle usage in accordance with the invention, a formula of the form Y=a+bX is used, where Y is a future odometer, clock, or other instrument or gauge reading, X is a future date or time period designated by a user for purposes of the analysis”; Paragraph [0064] – “FIGS. 10a-10c show individual data points (X.sub.i and Y.sub.i) used to perform a linear regression analyses plotted with curves of the form Y=a+bX determined using the data”; 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, which encompasses a prediction in a next time step.] It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply the preset linear regression model of Beyer to the vehicle-using behavior feature and preset model of Rangel in order to predict vehicle-using behavior feature values at future times. No impermissible hindsight is present as the use of the linear fitting model was taught by Beyer, evidencing that the use of such a model was known within the prior art.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., proportion or ratio within a preset range; aggregation logic) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant Argues:
PNG
media_image19.png
57
646
media_image19.png
Greyscale
PNG
media_image20.png
185
719
media_image20.png
Greyscale
Examiner’s Response
The Examiner respectfully disagrees. Normalized predicted vehicle-using behavior feature is disclosed by the combination of Rangel and Beyer, as detailed in the rejection of Claim 1 above. Rangel discloses the use of cumulative vehicle usage to predict battery state of health (See Rangel, Paragraph [0065]) by using loss in distance driven with time as a proxy for battery state of health. The combination would read on the recitations of Claim 1 per the explanation given in the rejection section above.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230306803 A1, METHOD AND APPARATUS FOR THE USER-DEPENDENT SELECTION OF A BATTERY OPERATED TECHNICAL DEVICE DEPENDING ON A USER USAGE PROFILE
US 20220276317 A1, BATTERY FAILURE PREDICTION
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANELLE A HOLMES whose telephone number is (571)272-4336. The examiner can normally be reached Monday - Friday 8:00 am - 5 pm.
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, Arleen M Vazquez can be reached at (571) 272-2619. 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.
/J.A.H./Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights