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 .
Claim Rejections - 35 USC § 101
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“ A method for establishing a model of early warning of battery insulation fault, comprising: acquiring an insulation resistance value of a battery which changes over time; constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery, wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal; and establishing a prediction model for predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery”.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls 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 a statutory category (process).
Under the Step 2A, Prong One, we consider 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 the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes — concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery and establishing a prediction model” are treated as belonging to the mathematical process grouping while the step of “predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery” are treated as belonging to the mental process grouping. This mental step represents a process 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. In the context of this claim, it encompasses the user manually making a determination whether the insulation fault actually occurs in the battery.
Similar limitations comprise the abstract ideas of Claims 8 and 16.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A method for establishing a model of early warning of battery insulation fault, comprising: acquiring an insulation resistance value of a battery which changes over time; wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal
Claim 8: A method for early warning of battery insulation fault, comprising: acquiring an insulation resistance value that changes over time from a battery management system of a battery to be predicted and issuing an early warning if the probability exceeds a probability threshold
Claim 16: An apparatus for early warning of battery insulation fault, comprising: a data acquisition module, configured to acquire an insulation resistance value of a battery which changes over time, wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal
The above steps of a method for establishing a model of early warning of battery insulation fault, comprising: is generically recited, not meaningful, do not represent a particular machine and/or eligible transformation, they do not indicate a practical application, acquiring an insulation resistance value of a battery which changes over time are generically recited and represent mere data gathering steps (insignificant extra-solution activity) necessary to execute the abstract idea; and wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal are generically recited and represent outputting results (insignificant post-solution activity). Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references in the submitted IDS (12/31/2024) by the Applicant (Tian and Du).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7, 9-15, and 17-19 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical/mental/organizing human activity process steps (Claims 2-7, 9-15, and 17-19) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
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.
Claims 1-3, 6-11, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Tian et al. (US 20230109419), hereinafter referred to as ‘Tian’ and in further view of Du et al. (US 20230288490), hereinafter referred to as ‘Du’.
Regarding Claim 1, Tian discloses a method for establishing a model of early warning of battery insulation fault, comprising: acquiring an insulation resistance value of a battery which changes over time (According to a first aspect, an insulation monitoring method for a traction battery is provided, where the method includes: obtaining, by taking a current moment as a starting time, each insulation resistance value of the traction battery that is received within a first preset time period before the current moment [0006]); a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period, i.e. trend insulation feature, is much greater than a quantity of the insulation resistance values received within the first preset time period, i.e. transient insulation feature [0010]), wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period , i.e. trend insulation feature is much greater than the first preset time period , and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period, i.e. transient insulation feature [0010]; the method further includes: obtaining, by taking the current moment as the starting time, each insulation resistance value of the traction battery that is received within a second preset time period before the current moment, and arranging the insulation resistance values in chronological order of reception to obtain an insulation resistance value array [0010]); and at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]) and a label of whether the insulation fault actually occurs in the battery (…Therefore, it is necessary to make a further determination in a second stage to clarify the risk level of insulation deterioration, and to locate the faulty battery [0069]).
However, Tian does not explicitly disclose constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery and establishing a prediction model for predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery.
Nevertheless, Du discloses constructing feature engineering ( The first diagnostic data may include, for example, data obtained through deep diagnosis based on a part or all of data in the second original data set, which may be, for example, an eigenvalue extracted through feature engineering [0016]) and establishing a prediction model for predicting whether a fault occurs in the battery and a label of whether the fault actually occurs in the battery (In some embodiments, each fault type corresponds to one diagnostic model, and each diagnostic model is configured to preliminarily diagnose a battery of a corresponding fault type [0080]; In some embodiments, the method includes: deeply diagnosing the battery based on the second original data set and the first diagnostic data, to determine a fault level of the battery [0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to diagnose the battery more accurately and determining fault type while controlling costs.
Regarding Claim 2, Tian and Du disclose the claimed invention discussed in claim 1.
Tian discloses the transient insulation feature includes one or more of: a number of points in time with abnormal insulation resistance value in the time period (collecting data statistics on the obtained insulation resistance values, and analyzing, based on a data statistical result, whether the traction battery has a risk of insulation deterioration; and if the traction battery has the risk of insulation deterioration, outputting first alarm information [0006]), one or more time intervals between the points in time with abnormal insulation resistance value in the time period (The step of “collecting data statistics on the obtained insulation resistance values, and analyzing, based on a data statistical result, whether the traction battery has a risk of insulation deterioration” specifically includes: separately performing mean and variance calculations on the insulation resistance values received within the first preset time period [0007]), and one or more time intervals between one or more points in time with abnormal insulation resistance value in the time period closest to current time and the current time (where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]).
Regarding Claim 3, Tian and Du disclose the claimed invention discussed in claim 2.
Tian discloses a corresponding transient insulation feature is marked as abnormal if the battery meets one or more of transient conditions as follows: the number of the points in time with abnormal insulation resistance value in the time period for the battery being more than a number threshold (Before the step of “collecting data statistics on the obtained insulation resistance values”, the method further includes: step S1: determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold, performing the step of “collecting data statistics on the obtained insulation resistance values”; or if the quantity of the obtained insulation resistance values does not reach the preset quantity threshold [0009]), and the one or more time intervals between the closest one or more points in time with abnormal insulation resistance value in the time period and the current time for the battery (Before the step of “collecting data statistics on the obtained insulation resistance values”, the method further includes: step S1: determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold, performing the step of “collecting data statistics on the obtained insulation resistance values”; or if the quantity of the obtained insulation resistance values does not reach the preset quantity threshold [0009]…outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]).
However, Tian does not explicitly the time interval between two points in time with abnormal insulation resistance value in the time period for the battery being less than a first time interval threshold, and the one or more time intervals between the closest one or more points in time with abnormal insulation resistance value in the time period and the current time for the battery being less than a second time interval threshold.
Nevertheless, Du discloses the time interval between two points in time (In an embodiment, the risk level is used to determine a degree of emergency of the deep diagnosis, and different risk levels correspond to different degrees of emergency. In an embodiment, different time limits are designed for different risk levels. For example, a time limit corresponding to a high risk level is shorter than a time limit corresponding to a low risk level. The time limit is specifically a time limit for delivering the vehicle to a vehicle vendor for deep diagnosis [0076]), and the one or more time intervals between the closest one or more points in time (In an embodiment, the risk level is used to determine a degree of emergency of the deep diagnosis, and different risk levels correspond to different degrees of emergency. In an embodiment, different time limits are designed for different risk levels. For example, a time limit corresponding to a high risk level is shorter than a time limit corresponding to a low risk level. The time limit is specifically a time limit for delivering the vehicle to a vehicle vendor for deep diagnosis [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to determine a degree of emergency of the deep diagnosis while increasing accuracy of fault detection.
Regarding Claim 6, Tian and Du disclose the claimed invention discussed in claim 5.
Tian discloses a corresponding trend insulation feature is marked as abnormal if the battery meets one or more of trend conditions as follows: the slope of the straight line derived by linear fitting the insulation resistance values in the time period for the battery being lower than a slope threshold, the intercept value at the midpoint of the time period of the straight line derived by linear fitting the insulation resistance values in the time period for the battery being lower than an intercept value threshold (In this implementation, regression algorithms such as a tree regression (Tree Regression) algorithm, a ridge regression (Ridge Regression or Tikhonov Regularization) algorithm, a linear regression (Linear Regression) algorithm, and the like may be used to perform a regression fitting calculation on each insulation resistance value received within the second preset time period before the current moment [0074]; determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold [0009]), and the insulation resistance values in the time period and for the battery being lower than an area threshold (…determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold [0009]).
However, Tian and Du do not explicitly disclose the area in the time period between the curve derived by polynomial fitting the insulation resistance values in the time period and the horizontal axis for the battery being lower than an area threshold.
Nevertheless, Schreiber discloses the area in the time period between the curve derived by polynomial fitting in the time period (Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to provide a best predicted output/actual output fit is sought and to minimize error functions.
Regarding Claim 7, Tian and Du disclose the claimed invention discussed in claim 1.
Tian discloses the battery is a power battery of a vehicle, and the insulation resistance value of the battery which changes over time is obtained by a battery management system of the vehicle (As an example, a battery management system of an electric vehicle detects an insulation resistance value of a traction battery in real time, and sends detected insulation resistance values to a backend server connected to the electric vehicle via a network [0022]).
Regarding Claim 8, Tian discloses a method for early warning of battery insulation fault, comprising: acquiring an insulation resistance value that changes over time from a battery management system of a battery to be predicted (According to a first aspect, an insulation monitoring method for a traction battery is provided, where the method includes: obtaining, by taking a current moment as a starting time, each insulation resistance value of the traction battery that is received within a first preset time period before the current moment [0006]; As an example, a battery management system of an electric vehicle detects an insulation resistance value of a traction battery in real time, and sends detected insulation resistance values to a backend server connected to the electric vehicle via a network [0022 at least one of a transient insulation feature and a trend insulation feature of the battery to be predicted (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]),); at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]; ); and issuing an early warning if … exceeds a probability threshold (determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold, performing the step of “collecting data statistics on the obtained insulation resistance values”; or if the quantity of the obtained insulation resistance values does not reach the preset quantity threshold [0009]).
However, Tian does not explicitly disclose constructing feature engineering for the insulation resistance value of the battery to be predicted in a time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery to be predicted; deriving a probability of abnormality of the battery to be predicted utilizing a prediction model, based on at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted; and issuing an early warning if the probability exceeds a probability threshold.
Nevertheless, Du discloses constructing feature engineering for the insulation resistance value of the battery to be predicted in a time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery to be predicted ( The first diagnostic data may include, for example, data obtained through deep diagnosis based on a part or all of data in the second original data set, which may be, for example, an eigenvalue extracted through feature engineering [0016]) deriving a probability of abnormality of the battery to be predicted utilizing a prediction model, based on at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted; and issuing an early warning if the probability exceeds a probability threshold (In some embodiments, each fault type corresponds to one diagnostic model, and each diagnostic model is configured to preliminarily diagnose a battery of a corresponding fault type [0080]; In some embodiments, the method includes: deeply diagnosing the battery based on the second original data set and the first diagnostic data, to determine a fault level of the battery [0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to diagnose the battery more accurately and determining fault type while controlling costs.
Regarding Claim 9, Tian and Du disclose the claimed invention discussed in claim 8.
Tian discloses acquiring an insulation resistance value of a battery which changes over time (as discussed above); a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery (as discussed above), wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal (as discussed above); and whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery (as discussed above) and a label of whether the insulation fault actually occurs in the battery (as discussed above).
However, Tian does not explicitly disclose the prediction model is established by a method comprising the following steps: acquiring an insulation resistance value of a battery which changes over time; constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery, wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal; and establishing a prediction model for predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery.
Nevertheless, Du discloses the prediction model is established by a method comprising the following steps (as discussed above): constructing feature engineering for a set of insulation resistance values of each battery within a predetermined time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery (as discussed above), and establishing a prediction model for predicting whether an insulation fault occurs in the battery (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to diagnose the battery more accurately and determining fault type while controlling costs.
Regarding Claim 10, Tian and Du disclose the claimed invention discussed in claim 8.
Tian discloses the transient insulation feature includes one or more of: a number of points in time with abnormal insulation resistance value in the time period (collecting data statistics on the obtained insulation resistance values, and analyzing, based on a data statistical result, whether the traction battery has a risk of insulation deterioration; and if the traction battery has the risk of insulation deterioration, outputting first alarm information [0006]), one or more time intervals between the points in time with abnormal insulation resistance value in the time period (The step of “collecting data statistics on the obtained insulation resistance values, and analyzing, based on a data statistical result, whether the traction battery has a risk of insulation deterioration” specifically includes: separately performing mean and variance calculations on the insulation resistance values received within the first preset time period [0007]), and one or more time intervals between one or more points in time with abnormal insulation resistance value in the time period closest to current time and the current time (where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]).
Regarding Claim 11, Tian and Du disclose the claimed invention discussed in claim 10.
Tian discloses a corresponding transient insulation feature is marked as abnormal if the battery meets one or more of transient conditions as follows: the number of the points in time with abnormal insulation resistance value in the time period for the battery being more than a number threshold (Before the step of “collecting data statistics on the obtained insulation resistance values”, the method further includes: step S1: determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold, performing the step of “collecting data statistics on the obtained insulation resistance values”; or if the quantity of the obtained insulation resistance values does not reach the preset quantity threshold [0009]), and the one or more time intervals between the closest one or more points in time with abnormal insulation resistance value in the time period and the current time for the battery (Before the step of “collecting data statistics on the obtained insulation resistance values”, the method further includes: step S1: determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold, performing the step of “collecting data statistics on the obtained insulation resistance values”; or if the quantity of the obtained insulation resistance values does not reach the preset quantity threshold [0009]…outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]).
However, Tian does not explicitly the time interval between two points in time with abnormal insulation resistance value in the time period for the battery being less than a first time interval threshold, and the one or more time intervals between the closest one or more points in time with abnormal insulation resistance value in the time period and the current time for the battery being less than a second time interval threshold.
Nevertheless, Du discloses the time interval between two points in time with abnormal insulation resistance value in the time period for the battery being less than a first time interval threshold (In an embodiment, the risk level is used to determine a degree of emergency of the deep diagnosis, and different risk levels correspond to different degrees of emergency. In an embodiment, different time limits are designed for different risk levels. For example, a time limit corresponding to a high risk level is shorter than a time limit corresponding to a low risk level. The time limit is specifically a time limit for delivering the vehicle to a vehicle vendor for deep diagnosis [0076]), and the one or more time intervals between the closest one or more points in time with abnormal insulation resistance value in the time period and the current time for the battery being less than a second time interval threshold (In an embodiment, the risk level is used to determine a degree of emergency of the deep diagnosis, and different risk levels correspond to different degrees of emergency. In an embodiment, different time limits are designed for different risk levels. For example, a time limit corresponding to a high risk level is shorter than a time limit corresponding to a low risk level. The time limit is specifically a time limit for delivering the vehicle to a vehicle vendor for deep diagnosis [0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to determine a degree of emergency of the deep diagnosis while increasing accuracy of fault detection.
Regarding Claim 15, Tian and Du disclose the claimed invention discussed in claim 8.
Tian discloses the battery is a power battery of a vehicle, and the insulation resistance value of the battery which changes over time is obtained by a battery management system of the vehicle (As an example, a battery management system of an electric vehicle detects an insulation resistance value of a traction battery in real time, and sends detected insulation resistance values to a backend server connected to the electric vehicle via a network [0022]).
Regarding Claim 16, Tian discloses an apparatus for early warning of battery insulation fault, comprising: a data acquisition module, configured to acquire an insulation resistance value of a battery which changes over time (According to a first aspect, an insulation monitoring method for a traction battery is provided, where the method includes: obtaining, by taking a current moment as a starting time, each insulation resistance value of the traction battery that is received within a first preset time period before the current moment [0006]); a set of insulation resistance values of each battery in a time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010])); at least one of the transient insulation feature and the trend insulation feature of the battery (analyzing a risk level of insulation deterioration of the traction battery based on a comparison result, and outputting corresponding second alarm information, where the second preset time period is much greater than the first preset time period, and a quantity of the insulation resistance values received within the second preset time period is much greater than a quantity of the insulation resistance values received within the first preset time period [0010]); and a label of whether the insulation fault actually occurs in the battery (…Therefore, it is necessary to make a further determination in a second stage to clarify the risk level of insulation deterioration, and to locate the faulty battery [0069]).
However, Tian does not explicitly disclose a feature extraction module, configured to construct feature engineering for a set of insulation resistance values of each battery in a time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery, and a model module, configured to establish a prediction model for predicting whether an insulation fault occurs in the battery at least based on the extracted at least one of the transient insulation feature and the trend insulation feature marked as normal or abnormal of each battery and a label of whether the insulation fault actually occurs in the battery.
Nevertheless, Du discloses a feature extraction module, configured to construct feature engineering for a set of insulation resistance values of each battery in a time period to extract at least one of a transient insulation feature and a trend insulation feature of the battery ( The first diagnostic data may include, for example, data obtained through deep diagnosis based on a part or all of data in the second original data set, which may be, for example, an eigenvalue extracted through feature engineering [0016]) and a model module, configured to establish a prediction model for predicting whether a fault occurs in the battery and a label of whether the insulation fault actually occurs in the battery (In some embodiments, each fault type corresponds to one diagnostic model, and each diagnostic model is configured to preliminarily diagnose a battery of a corresponding fault type [0080]; In some embodiments, the method includes: deeply diagnosing the battery based on the second original data set and the first diagnostic data, to determine a fault level of the battery [0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to diagnose the battery more accurately and determining fault type while controlling costs.
Regarding Claim 17, Tian and Du disclose the claimed invention discussed in claim 16.
Tian discloses the data acquisition module is further configured to acquire the insulation resistance value that changes over time from a battery management system of a battery to be predicted (as discussed above); at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted (as discussed above), wherein at least one of the transient insulation feature and the trend insulation feature is marked as normal or abnormal (as discussed above); and at least one of the transient insulation feature and the trend insulation feature of the battery (as discussed above); and issue a warning if the probability exceeds a probability threshold (as discussed above).
However, Tian does not explicitly disclose the feature extraction module is further configured to construct feature engineering for the insulation resistance values of the battery to be predicted in the time period to extract at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted; and the model module is further configured to derive a probability of abnormality of the battery to be predicted utilizing the prediction model, based on at least one of the transient insulation feature and the trend insulation feature of the battery to be predicted; and issue an early warning if the probability exceeds a probability threshold.
Nevertheless, Du discloses the feature extraction module is further configured to construct feature engineering (as discussed above), and the model module is further configured to derive a probability of abnormality of the battery (as discussed above).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian with the teachings of Du to diagnose the battery more accurately and determining fault type while controlling costs.
Regarding Claim 18, Tian and Du disclose the claimed invention discussed in claim 1.
Tian discloses a device for early warning of battery insulation fault, comprising: a memory having stored computer instructions thereon; and a processor, wherein the instructions, when executed by the processor, cause the processor to perform the method (In order to overcome the foregoing defects, the disclosure proposes an insulation monitoring method and system for a traction battery and an apparatus, to solve or at least partially solve the problem of how to accurately predict a risk of insulation deterioration of the traction battery before the insulation deterioration occurs on the traction battery, so as to provide an early warning about failures in the traction battery [0005]; According to a fourth aspect, a control apparatus including a processor and a storage apparatus is provided, where the storage apparatus is adapted to store a plurality of pieces of program codes, and the program codes are adapted to be loaded and run by the processor to perform the insulation monitoring method for a traction battery described in any one of the above [0020]).
Regarding Claim 19, Tian and Du disclose the claimed invention discussed in claim 1.
Tian discloses a non-transitory computer-readable storage medium storing instructions that cause a processor to perform (A non-transitory computer-readable storage medium includes any suitable medium that can store program code [0034]).
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Tian and Du, and further in view of Yamamoto et al. (US20100315096) hereinafter referred to as ‘Yamamoto’.
Regarding Claim 4, Tian and Du disclose the claimed invention discussed in claim 2.
Tian discloses a point in time with an insulation resistance value of zero is the point in time with abnormal insulation resistance value.
However, Tain and Du do not explicitly disclose a point in time with an insulation resistance value of zero is the point in time with abnormal insulation resistance value.
Nevertheless, Yamamoto discloses a point in time with an insulation resistance value of zero is the point in time with abnormal insulation resistance value (A curve L1 shown in FIG. 7 is a characteristic view showing a relationship between the insulation resistance Ri and the wave height value of the reference signal V1, and the microcomputer 55 determines the insulation resistance Ri of the load circuit 10 on the basis of the reference signal V1 outputted from the waveform shaping circuit 54…the wave height value of the reference signal V1 is sharply reduced; and when the insulation resistance Ri reaches the vicinity of a zero point, the wave height value of the reference signal V1 becomes almost zero [0008]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian and Du with the teachings of Yamamoto to determine that the insulation resistance and issue an alarm or the like to notify the user of the reduction.
Regarding Claim 12, Tian and Du disclose the claimed invention discussed in claim 10.
Tian discloses a point in time with an insulation resistance value is the point in time with abnormal insulation resistance value (as discussed above).
However, Tain and Du do not explicitly disclose a point in time with an insulation resistance value of zero is the point in time with abnormal insulation resistance value.
Nevertheless, Yamamoto discloses a point in time with an insulation resistance value of zero is the point in time with abnormal insulation resistance value (A curve L1 shown in FIG. 7 is a characteristic view showing a relationship between the insulation resistance Ri and the wave height value of the reference signal V1, and the microcomputer 55 determines the insulation resistance Ri of the load circuit 10 on the basis of the reference signal V1 outputted from the waveform shaping circuit 54…the wave height value of the reference signal V1 is sharply reduced; and when the insulation resistance Ri reaches the vicinity of a zero point, the wave height value of the reference signal V1 becomes almost zero [0008]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian and Du with the teachings of Yamamoto to determine that the insulation resistance and issue an alarm or the like to notify the user of the reduction.
Claims 5 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Tian and Du, and further in view of Schreiber et al. (US20220416333) hereinafter referred to as ‘Schreiber’.
Regarding Claim 5, Tian and Du disclose the claimed invention discussed in claim 1.
Tian discloses the trend insulation feature includes one or more of: a slope of a straight line derived by linear fitting the insulation resistance values in the time period, an intercept value at the midpoint of the time period of the straight line derived by linear fitting the insulation resistance values in the time period (In this implementation, regression algorithms such as a tree regression (Tree Regression) algorithm, a ridge regression (Ridge Regression or Tikhonov Regularization) algorithm, a linear regression (Linear Regression) algorithm, and the like may be used to perform a regression fitting calculation on each insulation resistance value received within the second preset time period before the current moment [0074]), and an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis.
However, Tian and Du does not explicitly disclose an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis.
Nevertheless, Schreiber discloses an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis (Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian and Du with the teachings of Schreiber to provide a best predicted output/actual output fit is sought and to minimize error functions.
Regarding Claim 13, Tian and Du disclose the claimed invention discussed in claim 8.
Tian discloses the trend insulation feature includes one or more of: a slope of a straight line derived by linear fitting the insulation resistance values in the time period, an intercept value at the midpoint of the time period of the straight line derived by linear fitting the insulation resistance values in the time period (In this implementation, regression algorithms such as a tree regression (Tree Regression) algorithm, a ridge regression (Ridge Regression or Tikhonov Regularization) algorithm, a linear regression (Linear Regression) algorithm, and the like may be used to perform a regression fitting calculation on each insulation resistance value received within the second preset time period before the current moment [0074]), and an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis.
However, Tian and Du does not explicitly disclose an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis.
Nevertheless, Schreiber discloses an area, in the time period, between a curve derived by polynomial fitting the insulation resistance values in the time period and a horizontal axis (Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian and Du with the teachings of Schreiber to provide a best predicted output/actual output fit is sought and to minimize error functions.
Regarding Claim 14, Tian and Du disclose the claimed invention discussed in claim 13.
Tian discloses a corresponding trend insulation feature is marked as abnormal if the battery meets one or more of trend conditions as follows: the slope of the straight line derived by linear fitting the insulation resistance values in the time period for the battery being lower than a slope threshold, the intercept value at the midpoint of the time period of the straight line derived by linear fitting the insulation resistance values in the time period for the battery being lower than an intercept value threshold (In this implementation, regression algorithms such as a tree regression (Tree Regression) algorithm, a ridge regression (Ridge Regression or Tikhonov Regularization) algorithm, a linear regression (Linear Regression) algorithm, and the like may be used to perform a regression fitting calculation on each insulation resistance value received within the second preset time period before the current moment [0074]; determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold [0009]), and the area in the time period between the curve derived by polynomial fitting the insulation resistance values in the time period and the horizontal axis for the battery being lower than an area threshold (…determining whether a quantity of the obtained insulation resistance values reaches a preset quantity threshold; and if the quantity of the obtained insulation resistance values reaches the preset quantity threshold [0009]).
However, Tian and Du do not explicitly disclose the area in the time period between the curve derived by polynomial fitting the insulation resistance values in the time period and the horizontal axis for the battery being lower than an area threshold.
Nevertheless, Schreiber discloses the area in the time period between the curve derived by polynomial fitting the insulation resistance values in the time period and the horizontal axis for the battery being lower than an area threshold (Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tian and Du with the teachings of Schreiber to provide a best predicted output/actual output fit is sought and to minimize error functions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ki Seung Baek (US11366167) discloses the controller is configured to control the components in the vehicle to be in a power-off (IG OFF) state and turning off the main relay when receiving a vehicle power-off input, and to determine whether or not the battery is abnormal by calculating a parameter for determining a state of the battery and comparing the calculated parameter with a plurality of reference values that are preset.
Richard Braatz (US20190113577) discloses a method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling.
Taichiro Tamida (US20160377670) discloses an insulation detector for highly accurately detecting or measuring, with a simple configuration, insulation resistance of a load or an apparatus to a ground or a housing and connected to an electric apparatus including one or both of an intra-apparatus capacitor and a battery.
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/SHARAH ZAAB/Examiner, Art Unit 2857
/ALEXANDER SATANOVSKY/Primary Examiner, Art Unit 2857
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