CTNF 18/640,704 CTNF 96262 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-26 AIA Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Information Disclosure Statement The information disclosure statement (IDS) submitted on -4/19/2024- is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 112 07-30-02 AIA The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim 1-8 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 2, 4-8 recite a degree of abnormality. It is unclear from the claim languages what or which criteria defines the degree of abnormality. The purpose of examination, the abnormality is used to address the degree of abnormality. Applicant is suggested to define degree of abnormality or include the intended meaning of degree of abnormality in the claims for the purpose of clarity. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-8 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. Specifically, claim 1-8 recites: An abnormality detection device comprising: a voltage measurer that measures a voltage of a secondary battery; a voltage holder that holds a voltage value of at least one of a maximum value or a minimum value, of the voltage measured by the voltage measurer, in every fixed time period; a feature calculator that calculates a feature that has sensitivity to a voltage spike waveform by processing the voltage value held in the voltage holder; a data set holder that holds a data set obtained from a normal secondary battery; and a degree-of-abnormality calculator that calculates, based on the data set read from the data set holder, a degree of abnormality of the feature calculated by the feature calculator. The claim limitations in the abstract idea have been highlighted in bold above. Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall within the four statutory categories of patentable subject matter identified by 35 U.S.C 101: process, machine, manufacture, or composition of matter. The above claim is considered to be in the statutory category of (machine). Under the step 2A, prong one, it is considered whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion. For example, a step of a feature calculator that calculates a feature that has sensitivity to a voltage spike waveform by processing the voltage value held in the voltage holder; a data set holder that holds a data set obtained from a normal secondary battery (is considered to be mathematical step) ; and a degree-of-abnormality calculator that calculates, based on the data set read from the data set holder, a degree of abnormality of the feature calculated by the feature calculator (is considered to be mathematical step) . These mental/mathematical steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind. Similar limitations comprise the abstract ideas of the independent claims 7 and 8. Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application. In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception. In claim 1 , the additional elements/steps are: voltage measurer, voltage holder, calculators. The above additional elements/steps (hardware) are recited in generality and represent extra solution activity to the judicial exception. The additional element in the preamble of “An abnormality detection…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “a voltage measurer that measures…” and “a voltage holder…” are also recited in generality which seem to merely be gathering, storing data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it. In claim 7, the additional elements/steps recite the similar additional elements/steps as of claim 1. The additional elements/steps are recited in generality and represent extra- solution activity to the judicial exception. The additional element in the preamble of “An electric power source system…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “a voltage measurer that measures…” and “a voltage holder…” are also recited in generality which seem to merely be gathering, storing data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it. In claim 8, the additional elements/steps recite the similar additional elements/steps as of claim 1. The additional elements/steps are recited in generality and represent extra- solution activity to the judicial exception. The additional element in the preamble of “An abnormality detection method…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. The additional elements/steps “measuring a voltage…” and “holding a voltage…” are also recited in generality which seem to merely be gathering, storing data and not really performing any kind of inventive step to provide any meaningful additional element. Also, it represents an extra-solution activity to the judicial exception. All uses of judicial exception require it. In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. The independent claims, therefore, are not patent eligible. With regards to the dependent claims, the claims 2-6 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claim 1, 7 and 8 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to independent claims. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takahashi et al US 20200355749 A1 herein after “Takahashi” in view of Paul US 20200116522 A1 . Regarding claim 1 Takahashi teaches, an abnormality detection device comprising (abstract: A secondary battery control system that conducts abnormality detection while predicting other parameters (internal resistance, SOC, and the like) with high accuracy is provided): a voltage measurer that measures a voltage of a secondary battery (para [0047] The abnormality detection system includes a first sensing means that senses a voltage value of a secondary battery that is to be a first observation value.) Examiner views voltage sensor as voltage measurer for measuring voltage of a secondary battery. a voltage holder that holds a voltage value of at least one of a maximum value or a minimum value, of the voltage measured by the voltage measurer, in every fixed time period (Abstract: A difference between an observation value (voltage) at a certain point in time and a voltage that is estimated using a prior-state variable is sensed.) [0168] In the value of voltage difference denoted by the reference numeral 401 in FIG. 7, −0.0631 V is set as the minimum value and +0.0324 V is set as the maximum value. In addition, a peak point that is close to 0 in a point at which a micro-short circuit is generated is −0.0386 V on the negative side and +0.0186 V on the positive side. Hence, in order to sense all micro-short circuits, the sensing may be performed using a comparator or the like with the threshold value set at −0.0386 V on the negative side and +0.0186 V on the positive side.; Examiner views comparator (i.e., voltage holder) holds value of minimum or maximum voltage in every fixed time period. Takahashi does not clearly teach a feature calculator that calculates a feature that has sensitivity to a voltage spike waveform by processing the voltage value held in the voltage holder; a data set holder that holds a data set obtained from a normal secondary battery and a degree-of-abnormality calculator that calculates, based on the data set read from the data set holder, a degree of abnormality of the feature calculated by the feature calculator. Paul teaches a feature calculator that calculates a feature that has sensitivity to a voltage spike waveform by processing the voltage value held in the voltage holder (para [0033] the features to be extracted by the preprocessor 2 may be waveform amplitude, state level, undershoot and overshoot, reference plane, transition time, etc., of the time-series waveform data… Data created by extracting the features from the time-series waveform data become tabular data. Data created by the preprocessor 2 may be stored in the sensor data holder 6 of FIG. 1.). Here examiner views the feature is extracted (i.e., calculated) that has amplitude, over or undershoot (i.e., sensitivity) to waveform (i.e., voltage spike) held in the senor data holder 6. Paul’s invention is generally used for wider field of arts where sensors are used. Examiner views the Paul invention is also applicable to the instant application where battery is inspected using sensors. a data set holder that holds a data set obtained from a normal secondary battery (para [0033] The sensor data may include time-series waveform data incrementally created by each sensor or tabular data of statistical values into which the time-series waveform data are converted. The sensor data include training data to be utilized in learning an anomaly detection model and test data to be utilized in detecting unknown anomalies. The training data include at least either of normal data and abnormal data of each sensor); and Here examiner views the sensor dataset holder 6 holds data for normal data (i.e., for normal secondary battery) a degree-of-abnormality calculator that calculates, based on the data set read from the data set holder, a degree of abnormality of the feature calculated by the feature calculator ( para [0033] Data created by extracting the features from the time-series waveform data become tabular data. Data created by the preprocessor 2 may be stored in the sensor data holder 6 of FIG. 1. para [0083] FIG. 5 is a figure showing a specific example in which the anomaly detection apparatus 1 according to the second embodiment creates an anomaly detection model. In the example of FIG. 5, the sensor data holder 6 supplies, at time t1, initial training data composed of normal data 1 and abnormal data 1, and supplies, at time t2, training data composed of normal data 2 and abnormal data 2, and supplies, at time t3, training data composed of normal data 3 and abnormal data 3, and supplies, at time t4, training data composed of normal data 4 and abnormal data 4, and incrementally supplies, at time t5, training data composed of normal data 5 and abnormal data 5, to the preprocessor 2. para [0100] Here, model learning using the k-nearest neighbor algorithm and the management of training data will be explained.) In Fig. 5, Examiner views the anomaly detection calculates the degree of abnormality based on the sensor data holder 6 using K-nearest neighbor algorithm, where the features extracted/calculated from waveform data (i.e., amplitude, under/overshoot) from sensor data are used anomaly or abnormality detection. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Paul into Takahashi for the purpose of calculating a feature data from by extracting data from sensor data so that abnormality in the data can be accurately calculated. Regarding claim 2, the combination of Takahashi and Paul teach the abnormality detection device according to claim 1, Paul teaches wherein the degree-of-abnormality calculator uses the data set each time the degree-of-abnormality calculator calculates the degree of abnormality (See above in paragraph [0083] and Fig 1-5, where the degree-of-abnormality (k-nearest neighbor algorithm) calculator uses the sensor data set each time t1, t2, t3… calculator calculates the degree of abnormality.) Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Paul into Takahashi for the purpose of calculating a degree of abnormality from sensor data set for each time so that abnormality in the data can be accurately calculated for each time the data set is collected. Regarding claim 3, the combination of Takahashi and Paul teaches the abnormality detection device according to claim 1, wherein the voltage holder includes at least one of a peak hold circuit that holds a peak value of a voltage spike having a peak in a positive direction included in the voltage measured by the voltage measurer, or a peak hold circuit that holds a peak value of a voltage spike having a peak in a negative direction included in the voltage measured by the voltage measurer (para [0168] In the value of voltage difference denoted by the reference numeral 401 in FIG. 7, −0.0631 V is set as the minimum value and +0.0324 V is set as the maximum value. In addition, a peak point that is close to 0 in a point at which a micro-short circuit is generated is −0.0386 V on the negative side and +0.0186 V on the positive side. Hence, in order to sense all micro-short circuits, the sensing may be performed using a comparator or the like with the threshold value set at −0.0386 V on the negative side and +0.0186 V on the positive side.). Examiner views the comparator (i.e., peak hold circuit) that holds a peak value of a voltage spike having a +ve or -ve side (see in fig. 8 and 9 waveforms) of the measured voltage. Regarding claim 4, the combination of Takahashi and Paul teaches the abnormality detection device according to claim 1, wherein the feature calculator calculates, as the feature, m-number of kinds of features, the m-number being greater than or equal to 2, the data set includes m-number of kinds of features obtained from the normal secondary battery, and (para [0032] The training data include at least either of normal data and abnormal data of each sensor para [0033] The features to be extracted from the time-series waveform data are statistical values. In more specifically, the statistical values include a maximum value, a median value, a minimum value, an average value, a standard deviation value, kurtosis, skewness, autocorrelation, etc. Or, the features to be extracted by the preprocessor 2 may be waveform amplitude, state level, undershoot and overshoot, reference plane, transition time, etc., of the time-series waveform data.), Examiner views the voltage waveform data set include features likes (maximum value, median, minimum, average, standard deviation etc). These features (i.e., sensor data) are greater or equal to 2 also include normal data (applicant uses the normal battery data). The combination does not clearly teach the degree-of-abnormality calculator calculates, by a k-nearest neighbor algorithm (para [0100] Here, model learning using the k-nearest neighbor algorithm and the management of training data will be explained.), a degree of abnormality of the m-number of kinds of features of the secondary battery, the k-nearest neighbor algorithm being based on the m-number of kinds of features of the normal secondary battery that are included in the data set and the m-number of kinds of features of the secondary battery calculated by the feature calculator (para [0063] In the supervised learning, the model creator 8 uses training data composed of normal data and abnormal data to create a plurality of candidate models {B1(t5), B2(t5), B3(t5), B4(t5), B5(t5)} with the plurality of techniques {B1, B2, B3, B4, B5}. The decision accuracies of these candidate models are {0.7, 0.5, 0.6, 0.8. 0.3}. para [0071]. Therefore, when the kind of sensor data changes in the course of creation of an anomaly detection model, a new anomaly detection model can be created. Moreover, the model-group learner/updater 3 and the data classifier 5 can perform their operations after preprocessing is performed to each sensor data. Therefore, even if the length in time and the feature are different per sensor data, an anomaly detection model with a high anomaly-detection decision accuracy can be created without depending on sensor data.). From Fig. 1-5 and above paragraph examiner views the K-nearest neighbor algorithm uses the different feature (i.e., m-number feature data) per sensor data that include normal data. Applicant uses the normal data from a battery. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Paul into Takahashi for the purpose of calculating a degree of abnormality from sensor data set for each time so that abnormality in the data can be accurately calculated for each time the data set is collected for each features using a K-nearest neighbor algorithm. Regarding claim 6, the combination of Takahashi and Paul teaches The abnormality detection device according to claim 1, Takahashi teaches wherein the degree-of-abnormality calculator calculates a degree of abnormality of the feature calculated by the feature calculator (para [0168] Since these values vary depending on the secondary battery that is used, simulation is performed as appropriate in advance using the characteristics data of the secondary battery that is used, and the threshold value may be determined on the basis of the results.), Examiner views the characteristics data as the feature data for calculating degree of abnormality of the feature of data. the degree-of-abnormality calculator calculating the degree of abnormality by any one of a subspace method, a recurrent neural network, an autoencoder, or a one-class support vector machine method that are based on the data set read from the data set holder (para [0023] In addition, correction by feedback is provided using an AI (Artificial Intelligence) system (neural network) to perform sensing of an abnormality in a secondary battery.). Claim 7 and 8 are rejected as claim 1 having same/similar claim limitations . 07-21-aia AIA Claim (s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Takahashi and Paul in view of Huang et al US 20180027004 A1 herein after “Huang” . Regarding claim 5, the combination of Takahashi and Paul teaches the abnormality detection device according to claim 1, wherein the feature calculator calculates, as the feature, m-number of kinds of features, the m-number being greater than or equal to 2, the data set includes a degree of abnormality of each of multiple specific points, the degree of abnormality being derived by a k-nearest neighbor algorithm, the k-nearest neighbor algorithm being based on m-number of kinds of features obtained from the normal secondary battery and coordinates of the specific points in a m-number-dimensional feature space having the m-number of kinds of features as respective dimensions (para [0049] For purposes of anomaly detection in a network, a learning machine may construct a model of normal network behavior, to detect data points that deviate from this model…Example machine learning techniques that may be used to construct and analyze such a model may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.)…, or the like. Fig. 7, 8. para [0110] By way of example, FIG. 8 illustrates an example of performing topological analysis using persistent homology techniques. As shown, assume that there are data points 802 (e.g., measured metrics from the system) that exist in a multi-dimensional space. By selecting a resolution (e.g., a value for r), points 802 within r distance of one another are connected, thereby forming topology 804. A barcode of topology 904 would then be its topological features, such as its clusters and holes. This process may be repeated any number of times computationally with different values of r, to identify the persistent features of the resulting topologies.), and From above paragraphs and Figures 7 and 8 examiner views the k-nearest neighbor algorithm being based on different types of features obtained from the normal data (applicant uses for secondary battery) and the data points 802 (i.e., coordinates of the specific points) in a m-number-dimensional feature space having the holes and cluster (i.e., m-number of kinds of features) as in respective dimensions. the degree-of-abnormality calculator calculates, based on the degree of abnormality of each of the specific points included in the data set, a degree of abnormality of the m-number of kinds of features calculated by the feature calculator (para [0111] To detect anomalies using persistent homology-based techniques, the anomaly detector may operate first in a learning phase and then in a detection phase. In the learning phase, the anomaly detector may take a time series from a sliding window and maintain a persistence diagram using the above techniques. The anomaly detector may store such a diagram as a set of 2-D points, e.g., {birth, death}, of each topological feature. This also allows the anomaly detector to compute an asymptotic confidence set for the bottleneck distance.). From above paragraphs and figures examiner views the m-number features spaces are divided into their multiple locality sensitive (i.e. specific points) to determine the abnormality or anomaly of the point in the feature space. The point’s result is used to determine the abnormality of the m-feature space. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Paul into Huang for the purpose of calculating a degree of abnormality for each point so that abnormality in the data can be accurately calculated for each feature space is collected for each features using a K-nearest neighbor algorithm . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Maeda et al US 20120166142 A1 discusses anomaly diagnosis using a K-nearest neighbor algorithm. Isa et al US 20200278398 A1 discusses anomaly detection for secondary battery. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHARAD TIMILSINA whose telephone number is (571)272-7104. The examiner can normally be reached Monday-Friday 9:00-5:00. 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, Catherine Rastovski can be reached at 571-270-0349. 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. /SHARAD TIMILSINA/Examiner, Art Unit 2857 /Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857 Application/Control Number: 18/640,704 Page 2 Art Unit: 2857 Application/Control Number: 18/640,704 Page 3 Art Unit: 2857 Application/Control Number: 18/640,704 Page 4 Art Unit: 2857 Application/Control Number: 18/640,704 Page 5 Art Unit: 2857 Application/Control Number: 18/640,704 Page 6 Art Unit: 2857 Application/Control Number: 18/640,704 Page 7 Art Unit: 2857 Application/Control Number: 18/640,704 Page 8 Art Unit: 2857 Application/Control Number: 18/640,704 Page 9 Art Unit: 2857 Application/Control Number: 18/640,704 Page 10 Art Unit: 2857 Application/Control Number: 18/640,704 Page 11 Art Unit: 2857 Application/Control Number: 18/640,704 Page 12 Art Unit: 2857 Application/Control Number: 18/640,704 Page 13 Art Unit: 2857 Application/Control Number: 18/640,704 Page 14 Art Unit: 2857 Application/Control Number: 18/640,704 Page 15 Art Unit: 2857 Application/Control Number: 18/640,704 Page 16 Art Unit: 2857 Application/Control Number: 18/640,704 Page 17 Art Unit: 2857
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