DETAILED ACTION
Remarks
This non-final office action is in response to the application filled on 04/25/2024. Claims 1-20 are pending and examined below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
As of date of this action, IDS filled has been annotated and considered.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 16 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2013/0091083 (“Feisch”).
Regarding claim 1 (and similarly claim 16), Feisch discloses a system for detecting and classifying outlier battery cells operating abnormally in a storage battery of an energy storage system (BESS) (see at least [0005], where “By using the battery health diagnostics system described herein, an over reactive or under active charging system, as well as an unhealthy cell within a secondary battery”; unhealthy cell is interpreted as outlier battery cells operating abnormally), the system comprising:
a controller for controlling the operation of the BESS (see at least [0040], where “controller”; fig 4);
a battery management system (BMS) coupled to the storage battery configured to collect battery operational data from the storage battery (see at least [0024] and [0026], sensors 414);
a battery data repository coupled to the BMS for receiving and storing the storage battery operational data (see at least [0041-45], memory 406); and
a prognostic agent coupled to the battery data repository that uses the stored battery operational data to train a prognostics and fault detection model (see at least [0036-39]),
wherein the controller receives and uses the prognostics and fault detection model to detect at least one outlier battery cell (see at least [0039]).
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.
Claim(s) 2 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and further in view of US 2023/0384393 (“Lee”).
Regarding claim 2 (and similarly claim 17), Feisch further discloses a system wherein the prognostics and fault detection model further includes: (see at least [0033], where “The measurement logic 408 is coupled to the sensors 414, and is operable to receive real-time sensed data from the sensors 414, and measure, at least one battery property of the battery 416 when the real-time sensed data is received.”; see also [0038]);
calculate a (see at least [0045]); and
(see at least [0053]).
Feisch does not disclose the following limitations:
an instantaneous average stage that receives…data…to calculate an average measurement;
an instantaneous standard deviation stage that receives…data…and calculate a standard deviation measurement; and
a normalization stage that receives the average measurement, the standard deviation measurement…to calculate normalized data measurements.
However, Lee discloses a system wherein an instantaneous average stage that receives…data…to calculate an average measurement (see at least [0016], [0009], [0020], [0095] and [0148]);
an instantaneous standard deviation stage that receives…data…and calculate a standard deviation measurement (see at least [0184]); and
a normalization stage that receives the average measurement, the standard deviation measurement…to calculate normalized data measurements (see at least [0199]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch to incorporate the teachings of Lee by including the above feature for tracking cell inconsistency and aging variability.
Claim(s) 3-5 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and further in view of US 2023/0368054 (“Nikolie”).
Regarding claim 3 (and similarly claim 18), Feisch further discloses a system to identify a potential outlier battery cell (see citation above in claim 1).
Feisch in view of Lee does not disclose the following limitation:
an unsupervised autoencoder (AE) neural network stage is connected to the normalization stage and arranged to receive the normalized data measurements and using the normalized data measurements to discover anomalies in the normalized data measurements and to output discovered anomalous errors to an AE comparator that compares the anomalous errors from the AE with the normalized data measurements.
However, Nikolie discloses a system wherein an unsupervised autoencoder (AE) neural network stage is connected to the normalization stage and arranged to receive the normalized data measurements and using the normalized data measurements to discover anomalies in the normalized data measurements and to output discovered anomalous errors to an AE comparator that compares the anomalous errors from the AE with the normalized data measurements (see at least fig 4, fig 5, [0016] and [0087]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee to incorporate the teachings of Nikole by including the above feature for avoiding false positives.
Regarding claim 4 (and similarly claim 18), Nikole further discloses a system wherein a supervised principal component analysis (PCA) stage is connected to the normalization stage and is arranged to receive the normalized data measurements and using the normalized data measurements to generate an inverse PCA transform and a threshold data output, wherein the inverse PCA transform and the threshold data is input to a PCA comparator that compares the inverse PCA transform and threshold data to the normalized data measurements to identify a potential outlier battery cell (see at least [0034], [0091] and [0178]). Rejection relied on Feisch for battery outlier identification.
Regarding claim 5, Nikole further discloses a system wherein a decision gate is connected to the AE comparator and the PCA comparator and is arranged to receive the potential outlier battery cells from the AE comparator and the PCA comparator and detect the at least one outlier battery cell (see at least [0087], [0152] and [0155]). Rejection relied on Feisch for battery outlier identification.
Claim(s) 6, 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and in view of US 2023/0368054 (“Nikolie”), as applied to claim 5 above, and further in view of US 2023/0194614 (“Negoita”).
Regarding claim 6 (and similarly claim 19), Feisch further discloses a system wherein the prognostics and fault detection model includes: a data classification battery cell and the outlier battery cell temperature, voltage, and current data (see at least [0005] and [0027-30]).
Feisch in view of Lee and Nikolie does not disclose the following limitation:
a data classification neural network connected to the decision gate…wherein the classification neural network is arranged to derive a physical adjacency of other battery cells contained in the battery storage system to the detected at least one outlier battery cell.
However, Negoita discloses a system wherein a data classification neural network connected to the decision gate…wherein the classification neural network is arranged to derive a physical adjacency of other battery cells contained in the battery storage system to the detected at least one outlier battery cell (see at least [0105], where “illustrated in FIG. 3C, in which the nodes correspond to battery modules in a battery pack, the adjacency matrix in the graph-structured data represents inter-cell relationships or (e.g., electrical, thermal, spatial, physical, etc.) connections between or among the battery modules.”; see also [0086] and [0040]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee and Nikolie to incorporate the teachings of Negoita by including the above feature for providing better handling of pack level thermal or electrical imbalances.
Regarding claim 7 (and similarly claim 19), Negoita further discloses a system wherein an adjacency weighted curve distance neural network is connected to data classification neural network and is arranged to compute curve distance measurements using the temperature and voltage data of the detected at least one outlier battery cell using a discrete Fréchet distance, a discrete Hausdorff distance and dynamic time warping (see at least [0105], where “the input neurons in the graph-structured data may be (e.g., linear, weighted, normalized, etc.) combinations of the input features extracted by the input feature extraction of FIG. 3D from battery module level history data and SoH estimations or predictions generated from graph neural networks (or instances of graph neural networks) respectively representing the battery modules.”; see also [0054], [0084] and [0116]).
Claim(s) 8 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and in view of US 2023/0368054 (“Nikolie”), as applied to claim 5 above, and in view of US 2023/0194614 (“Negoita”), as applied to claim 7 above, and further in view of US 2025/0237703 (“Hu”).
Regarding claim 8 (and similarly claim 20), Feisch in view of Lee, Nikolie and Negoita does not disclose claim 8. However, Hu discloses a system wherein a convolutional neural network connected to the adjacency weighted curve distance neural network is arranged to receive the curve distance measurements from the adjacency weighted curve distance neural network and calculate a cross correlation data output between the detected at least one outlier battery cell temperature and voltage measurements and a current, state of charge SOC and cycle count of the storage battery (see at least [0078]and [0063]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee, Nikolie and Negoita to incorporate the teachings of Hu by including the above feature for increasing safety by considering discrepancies.
Claim(s) 9 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and in view of US 2023/0368054 (“Nikolie”), as applied to claim 5 above, and in view of US 2023/0194614 (“Negoita”), as applied to claim 7 above, and in view of US 2025/0237703 (“Hu”), as applied to claim 8 above, and further in view of US 2021/0334656 (“Sjogren”).
Regarding claim 9 (and similarly claim 20), Feisch in view of Lee, Nikolie, Negoita and Hu does not disclose claim 9. However, Sjogren discloses a system wherein the convolutional neural network includes: a SoftMax layer that receives the cross correlation data output from the convolutional neural network and normalizes the output of the convolutional neural network to a probability distribution of a potential fault type for the detected at least one outlier battery cell (see at least [0215], where “FIG. 2 comprises four convolutional layers C1, C2, C3, C4, two max pool layers MP1, MP2 and an output layer with a softmax function as the activation function of nodes included in the output layer.”; see also [0101], [0127], [0248]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee, Nikolie, Negoita and Hu to incorporate the teachings of Sjogren by including the above feature for easier fault detection by using softmax in neural network models for battery outlier detection turns raw model scores into a clean probability distribution.
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and in view of US 2023/0368054 (“Nikolie”), as applied to claim 5 above, and further in view of US 2022/0140617 (“Wang”).
Regarding claim 10, Feisch in view of Lee and Nikolie does not disclose claim 10. However, Wang discloses a system wherein the system includes a thermal runaway and short circuit agent connected to the decision gate and arranged to receive the detected at least one outlier battery cell temperature measurement (see at least [0048], where “FIG. 6 is a flowchart of an algorithm 160 that can be used to identify thermal runaway conditions in a battery cell.”; see also [0054], where “If the algorithm reaches step 186, a short-circuited cell that may result in thermal runaway is suspected.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee and Nikolie to incorporate the teachings of Wang by including the above feature for increasing safety and performance advantages.
Regarding claim 13, Feisch in view of Lee and Nikolie does not disclose claim 13. However, Wang discloses a system wherein the system includes a thermal runaway and short circuit agent connected to the decision gate and arranged to receive the detected at least one outlier battery cell voltage measurement (see at least [0054]).
Claim(s) 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2013/0091083 (“Feisch”), as applied to claim 1 above, and in view of US 2023/0384393 (“Lee”), as applied to claim 2 above, and in view of US 2023/0368054 (“Nikolie”), as applied to claim 5 above, and in view of US 2022/0140617 (“Wang”), as applied to claim 10 above, and further in view of US 2008/0256398 (“Gross”).
Regarding claim 11, Feisch in view of Lee further discloses a system wherein the thermal runaway and short circuit agent further includes: an instantaneous average stage that receives the at least one outlier battery cell temperature and is arranged to calculate an average outlier battery cell temperature measurement (see citation on claim 2);
an instantaneous standard deviation stage that receives the at least one outlier cell temperature measurement, and is arranged to calculate an outlier cell standard deviation temperature measurement (see citation on claim 2); and
a normalization stage that receives the average temperature measurement, the standard deviation temperature measurement and the at least one outlier cell temperature to calculate a normalized zero mean data output (see citation on claim 2).
Feisch in view of Lee, Nikolie and Wang does not disclose the following limitation:
a time domain data stage connected to the normalization stage arranged to receive the normalized zero mean data output and calculate a time derivative data output.
However, Gross discloses a system wherein a time domain data stage connected to the normalization stage arranged to receive the normalized zero mean data output and calculate a time derivative data output (see at least [0078], [0082] and [0085]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Feisch in view of Lee, Nikolie and Wang to incorporate the teachings of Gross by including the above feature for improving signal to noise ratio.
Regarding claim 14, Feisch in view of Lee, Nikolie, Wang and Gross disclose a system wherein the thermal runaway and short circuit agent further includes:
an instantaneous average stage that receives the at least one outlier battery cell voltage and is arranged to calculate an average outlier battery cell voltage measurement (see citation on claim 2);
an instantaneous standard deviation stage that receives the at least one outlier cell voltage measurement, and is arranged to calculate an outlier cell standard deviation voltage measurement (see citation on claim 2); and
a normalization stage that receives the average voltage measurement, the standard deviation voltage measurement and the at least one outlier cell voltage to calculate a normalized zero mean data output (see citation on claim 2); and
a time domain data stage connected to the normalization stage arranged to receive the normalized zero mean data output and calculate a time derivative data output (see citation on claim 11).
Allowable Subject Matter
Claims 12 and 15 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657
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