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 .
Response to Amendment
Claims 1, and 11 are amended.
Claims 7-8 and 17-18 are canceled.
Claims 1-6 9-16 and 19-20 are pending.
Claim Rejections - 35 USC § 112
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.
Claims 1-6, 9-16, and 19-20 are 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, 9, 11 and 19 recite “a feature”, notably multiple “a feature” in claims 1 and 11. It is unclear if these “a feature” are the same or different as they appear to refer to the same value. For the purposes of examining, they are considered to be the same.
Claims 4 and 14 recite “a minimum potential” It is unclear if these “a minimum potential” are the same or different as they appear to refer to the same value. For the purposes of examining, they are considered to be the same.
Claims 5 and 15 recite “a potential integral” It is unclear if these “a potential integral” are the same or different as they appear to refer to the same value. For the purposes of examining, they are considered to be the same.
Claims 2-3, 6, 10, 12-13, 16 and 20 are rejected based on their inherited issues.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 6, 9, 16, and 19 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
The claims 6 and 16 respectively recite “standardize the potential of each cell of the plurality of cells” and “standardizing, using the computing device, the potential of each cell of the plurality of cells” which is already recited in the respective independent claims, with the exception of “of the plurality of cells” which is a given, as each cell has to refer to the plurality of cells.
The claims 9 and 19 respectively recite “wherein the computing device is further configured to calculate at least a feature as a function of the potential of each cell of the plurality of cells” and “calculating, using the computing device, at least a feature as a function of potential of each cell of the plurality of cells” which already have narrower versions recited in their respective independent claims
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 9-16, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1, claim 1 and 11 belongs to a statutory category.
Under Step 2A prong 1, the claims as a whole are identified as being directed to a judicial exception as claim 1 and similarly 11 recite(s) “determining a weak battery cell”, “standardize the potential of each cell; determine at least a feature as a function of the standardized potential of each cell over the duration, wherein the at least a feature includes at least one of a minimum potential and a potential integral; generate, using a feature learning algorithm configured to perform clustering of potential data sets, generate a training data classifier as a function of unfiltered training data using a classification algorithm; filter, based on the at least feature, elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells; select at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier;”, “determine at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells wherein determining the at least an outlier cell comprises” and “training, iteratively, the machine-learning model using the selected at least one training data, wherein training the machine-learning model includes retraining the classifier with feedback from previous iterations of the machine-learning model; determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model; and filter the weak battery cell as a function of the at least an outlier cell” which are directed to mathematical concepts and/or mental processes per applicant’s specification, see applicants specification, for example Par. 30-31, 35, 37.
Under Step 2A prong 2, evaluating whether the claim as a whole integrates the exception into a practical application of that exception, the judicial exception is not integrated into a practical application because “the system comprising: a battery comprising a plurality of cells;” is considered to be generally link the abstract idea to a particular technological environment (see MPEP 2106.05(h)). The elements of “a plurality of sensors configured to detect a potential of each cell of the plurality of cells for a duration,” “receive the potential of each cell of the plurality of cells;” and “receiving the selected at least one filtered training data set;” are considered to be data gathering steps required to use the correlation that do not add a meaningful limitation to the method as they are insignificant extra-solution activity. The elements of “a computing device configured to:” are considered to be generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
Under Step 2B, evaluating additional elements to determine whether they amount to an inventive concept both individually and in combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “the system comprising: a battery comprising a plurality of cells;” are considered to be are well-understood, routine elements per MPEP 2106.05(d)(see US 20170141369 A1 US 20140232413 A1 CN 107091992 A). The elements of “a plurality of sensors configured to detect a potential of each cell of the plurality of cells for a duration;”, “receive the potential of each cell of the plurality of cells;”, and “receiving the selected at least one filtered training data set;” are considered to be adding insignificant extra-solution activity to the judicial exception Per MPEP 2106.05(g) and well-understood, routine elements per MPEP 2106.05(d)(i)(See US 20170141369 A1 US 20140232413 A1 CN 107091992 A). The elements of “a computing device configured to:” are considered to be are well-understood, routine elements per MPEP 2106.05(d).
Claims 1 and similarly 11 recites the additional element(s) of using generic AI/ML technology, i.e. “generate, using a feature learning algorithm configured to perform clustering of potential data sets, a training data classifier as a function of unfiltered training data using a classification algorithm;” and “a machine-learning model using the selected at least one training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model;”, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “generate, using a feature learning algorithm configured to perform clustering of potential data sets, a training data classifier as a function of unfiltered training data using a classification algorithm;” and “a machine-learning model using the selected at least one training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model;” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “generate, using a feature learning algorithm configured to perform clustering of potential data sets, a training data classifier as a function of unfiltered training data using a classification algorithm;” and “a machine-learning model using the selected at least one training data set, wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the machine-learning model; and determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model;” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
Claims 2, 4-6, 9, 12, 14-16 and 19 are considered to further describe the abstract ideas above.
Claims 3 and 13 are not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging” is considered to be data gathering steps required to use the correlation that do not add a meaningful limitation to the method as they are insignificant extra-solution activity and adding insignificant extra-solution activity to the judicial exception Per MPEP 2106.05(g) and well-understood, routine elements per MPEP 2106.05(d)(i)(See US 20170141369 A1 US 20140232413 A1 CN 107091992 A).
Claims 10 and 20 are not integrated into a practical application or include additional elements that are sufficient to amount to significantly more than the judicial exception because “wherein at least a cell of the plurality of cells comprises a pouch cell” is considered to be generally link the abstract idea to a particular technological environment (see MPEP 2106.05(h)) and are well-understood, routine elements per MPEP 2106.05(d)(See US 20140232413 A1 US 20140232413 A1 CN 107091992 A).
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) 1, 4, 6, 9, 11, 14, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kitahara (US 20140232413 A1) in view of Keene (US 11283114 B1) and in further view of Park (US 20170126027 A1).
In claim 1, Kitahara discloses a system for determining a weak battery cell (Fig. 5 y4-y10), the system comprising: a battery comprising a plurality of cells (see Fig. 1); a plurality of sensors (Fig. 1 monitor ICs) configured to detect a potential of each cell of the plurality of cells for a duration (Fig. 2 X1 X2, examiner considers the time which these measurements are taken to be a duration); and a computing device (Fig. 1, 10) configured to: receive the potential of each cell of the plurality of cells (Fig. 1 connection from 10 to monitor ICs); determine at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells (Fig. 2, x4); and filter the weak battery cell as a function of the at least an outlier cell (Par. 44 Fig. 5 y4 “prioritizes charging for the battery cell of low voltage”).
Kitahara does not explicitly disclose standardize the potential of each cell; determine at least a feature as a function of the standardized potential of each cell over the duration, wherein the at least a feature includes at least one of a minimum potential and a potential integral; generate, using a feature learning algorithm configured to perform clustering of potential data sets, a training data classifier as a function of unfiltered training data using a classification algorithm; filter, based on the at least feature, elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells; select at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier; determine at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells wherein determining the at least an outlier cell: receiving the selected at least one filtered training data set, training, iteratively, a machine-learning model using the training data, wherein training the a machine-learning model includes retraining the a machine-learning model with feedback from previous iterations of the a machine-learning model; determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model; (Emphasis added).
Keene teaches generate a training data classifier (Column 11 Lines 10-39 “classification” “classified”) as a function of unfiltered training data (Column 11 Lines 10-39 “measuring a plurality of parameters of the cell;”) using a classification algorithm (Column 11 Lines 10-39 see algorithms on lines 14-19, “classified based on the determined cell performance and similarly performing cells may be placed in the same bin”); determine at least a feature as a function of the potential of each cell over the duration (Column 5 Lines 10-26 “features taken/measured from the formation cycles such as voltage values”); filter, based on the at least feature, elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells (Columns 9-10 Line 61-5 “one or more cells in a battery pack are behaving abnormally and flag the cell or cells for removal”); select at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier (Column 9 Lines 6-20 “duration of a constant”, Column 11 Lines 10-23 “gradient boosted decision tree algorithm”); determine at least an outlier cell (Column 9 Lines 61-67 “abnormal cell”) from the plurality of cells as a function of the potential of each cell of the plurality of cells (Column 9 Lines 61-67 “model”) wherein determining the at least an outlier cell comprises: receiving the selected at least one filtered training data set (Column 7 Line 56 – Column 8 Line 11, Column 8 Lines 12-45 “train” “data used to train the model”); training, iteratively, a machine-learning model (Column 11 Lines 10-23) using selected at least one training data set (Column 7 Line 56 – Column 8 Line 11), wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the classifier (Column 7 Line 56 – Column 8 Line 11); and determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine-learning model (Column 10 Lines 1-5 and 28-36).
Park teaches standardize the potential of each cell (Par. 69 “an average of respective voltage data V.sub.1 through V.sub.n of n battery cells”); determine at least a feature as a function of the standardized potential of each cell over the duration (Par. 756 “to extract a feature of balance data, i.e., data representing a normal operation of the battery”), wherein the at least a feature includes at least one of a minimum potential and a potential integral (Par. 70 “a minimum value, for example, V.sub.min”); generate, using a feature learning algorithm configured to perform clustering of potential data sets (Par. 78 “Based on the feature distribution model, a normal area, for example, a normal range can be defined. For example, the feature distribution model may learn an area or range of operation of the same normal battery used for the unbalance feature model, for example, thereby being able more likely recognize or retrieve the area or range of operation from the calculated feature information”).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filled generate a training data classifier as a function of unfiltered training data using a classification algorithm; filter elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells; select at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier; determine at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells wherein determining the at least an outlier cell: receiving the selected at least one filtered training data set, training, iteratively, a machine-learning model using the training data, wherein training the a machine-learning model includes retraining the a machine-learning model with feedback from previous iterations of the a machine-learning model; determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model; as taught by Keene to the outlier determination of Kitahara in order to detect one or more cells in a battery pack are behaving abnormally (Keene Column 10 Lines 1-5) thus leading to improved performance and safety. Further, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have standardize the potential of each cell; determine at least a feature as a function of the standardized potential of each cell over the duration, wherein the at least a feature includes at least one of a minimum potential and a potential integral: generate, using a feature learning algorithm configured to perform clustering of potential data sets, as taught by Park and Keene in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 4, as cited in claim 1, Kitahara does not explicitly disclose wherein the computing device is further configured to calculate a minimum potential for each cell of the plurality of cells.
Park teaches wherein the computing device is further configured to calculate a minimum potential for each cell of the plurality of cells (Par. 70 “a minimum value, for example, V.sub.min”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have wherein the computing device is further configured to calculate a minimum potential for each cell of the plurality of cells, as taught by Park in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 6, as cited in claim 1, Kitahara does not explicitly disclose wherein the computing device is further configured to standardize the potential of each cell of the plurality of cells.
Park teaches wherein the computing device is further configured to standardize the potential of each cell of the plurality of cells (Par. 69 “an average of respective voltage data V.sub.1 through V.sub.n of n battery cells”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have wherein the computing device is further configured to standardize the potential of each cell of the plurality of cells, as taught by Park in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 9, Kitahara discloses calculate at least a feature as a function of the potential of each cell of the plurality of cells (Fig. 5 y7, y8, y9).
In claim 11, Kitahara discloses a method of determining at least a weak cell of at least a battery (Fig. 5 y4), the method comprising: detecting, using a plurality of sensors (Fig. 1 monitor ICs), a potential of each cell of a plurality of cells of a battery (see Fig. 1) for a duration (Fig. 2 X1 X2, examiner considers the time which these measurements are taken to be a duration); receiving, using a computing device (Fig. 1, 10), the potential of each cell of the plurality of cells (Fig. 1 connection from 10 to monitor ICs); determining, using the computing device, at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells (Fig. 2, x4); and filtering, using the computing device, the at least a weak cell as a function of the at least an outlier cell (Par. 44 Fig. 5 y4 “prioritizes charging for the battery cell of low voltage”).
Kitahara does not explicitly disclose standardize the potential of each cell; determine at least a feature as a function of the standardized potential of each cell over the duration, wherein the at least a feature includes at least one of a minimum potential and a potential integral; generating, using the computing device, using a feature learning algorithm configured to perform clustering of potential data sets, a training data classifier as a function of unfiltered training data using a classification algorithm; filter, based on the at least feature elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells; selecting, using the computing device, at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier; determining, using the computing device, at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells wherein determining the at least an outlier cell: receiving the selected at least one filtered training data set, training, iteratively, a machine-learning model using the training data, wherein training the a machine-learning model includes retraining the a machine-learning model with feedback from previous iterations of the a machine-learning model; determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model; (Emphasis added).
Keene teaches generate using the computing device (Column 11 Lines 55-60), a training data classifier (Column 11 Lines 10-39 “classification” “classified”) as a function of unfiltered training data (Column 11 Lines 10-39 “measuring a plurality of parameters of the cell;”) using a classification algorithm (Column 11 Lines 10-39 see algorithms on lines 14-19, “classified based on the determined cell performance and similarly performing cells may be placed in the same bin”); determine at least a feature as a function of the potential of each cell over the duration (Column 5 Lines 10-26 “features taken/measured from the formation cycles such as voltage values”); filter, using the computing device, based on the at least feature, elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells (Columns 9-10 Line 61-5 “one or more cells in a battery pack are behaving abnormally and flag the cell or cells for removal”); selecting, using the computing device, at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier (Column 9 Lines 6-20 “duration of a constant”, Column 11 Lines 10-23 “gradient boosted decision tree algorithm”); determining, using the computing device, at least an outlier cell (Column 9 Lines 61-67 “abnormal cell”) from the plurality of cells as a function of the potential of each cell of the plurality of cells (Column 9 Lines 61-67 “model”) wherein determining the at least an outlier cell comprises: receiving the selected at least one filtered training data set (Column 7 Line 56 – Column 8 Line 11, Column 8 Lines 12-45 “train” “data used to train the model”); training, iteratively, a machine-learning model (Column 11 Lines 10-23) using selected at least one training data set (Column 7 Line 56 – Column 8 Line 11), wherein training the machine-learning model includes retraining the machine-learning model with feedback from previous iterations of the classifier (Column 7 Line 56 – Column 8 Line 11); and determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine-learning model (Column 10 Lines 1-5 and 28-36).
Park teaches standardize the potential of each cell (Par. 69 “an average of respective voltage data V.sub.1 through V.sub.n of n battery cells”); determine at least a feature as a function of the standardized potential of each cell over the duration (Par. 756 “to extract a feature of balance data, i.e., data representing a normal operation of the battery”), wherein the at least a feature includes at least one of a minimum potential and a potential integral (Par. 70 “a minimum value, for example, V.sub.min”); generate, using a feature learning algorithm configured to perform clustering of potential data sets (Par. 78 “Based on the feature distribution model, a normal area, for example, a normal range can be defined. For example, the feature distribution model may learn an area or range of operation of the same normal battery used for the unbalance feature model, for example, thereby being able more likely recognize or retrieve the area or range of operation from the calculated feature information”).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filled to generating, using the computing device, a training data classifier as a function of unfiltered training data using a classification algorithm; filter elements of the unfiltered training data using the training data classifier to generate a plurality of filtered training data sets each containing a plurality of data entries classifying battery cell potential elements to categories of outlier cells; selecting, using the computing device, at least one filtered training data set of the plurality of training data sets as a function of the duration of detection using the training data classifier; determining, using the computing device, at least an outlier cell from the plurality of cells as a function of the potential of each cell of the plurality of cells wherein determining the at least an outlier cell: receiving the selected at least one filtered training data set, training, iteratively, a machine-learning model using the training data, wherein training the a machine-learning model includes retraining the a machine-learning model with feedback from previous iterations of the a machine-learning model; determining the at least an outlier cell from the plurality of cells as a function of the potential of each cell using the trained machine- learning model; as taught by Keene to the outlier determination of Kitahara in order to detect one or more cells in a battery pack are behaving abnormally (Keene Column 10 Lines 1-5) thus leading to improved performance and safety. Further, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have standardize the potential of each cell; determine at least a feature as a function of the standardized potential of each cell over the duration, wherein the at least a feature includes at least one of a minimum potential and a potential integral: generate, using a feature learning algorithm configured to perform clustering of potential data sets, as taught by Park and Keene in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 14, as cited in claim 11, Kitahara does not explicitly disclose calculating, using the computing device, a minimum potential for each cell of the plurality of cells.
Park teaches calculating, using the computing device, a minimum potential for each cell of the plurality of cells (Par. 70 “a minimum value, for example, V.sub.min”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to perform calculating, using the computing device, a minimum potential for each cell of the plurality of cells as taught by Park in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 16, as cited in claim 1, Kitahara does not explicitly disclose standardizing, using the computing device, the potential of each cell of the plurality of cells.
Park teaches standardizing, using the computing device, the potential of each cell of the plurality of cells (Par. 69 “an average of respective voltage data V.sub.1 through V.sub.n of n battery cells”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to perform standardizing, using the computing device, the potential of each cell of the plurality of cells, as taught by Park in combination with Kitahara in order to determine whether a battery state is a normal state or an abnormal state (Park Par. 78) thus increasing battery safety.
In claim 19, Kitahara discloses calculating at least a feature as a function of the potential of each cell of the plurality of cells (Fig. 5 y7, y8, y9).
Claim(s) 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kitahara in view of Keene and Park and in further view of NOR (EP 0659305 B1).
In claim 2, Kitahara does not explicitly disclose wherein the duration is no less than 1 second and no greater than 1,000 seconds.
Nor teaches wherein the duration is no less than 1 second and no greater than 1,000 seconds (See Fig. 18B and 19B).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have the duration is no less than 1 second and no greater than 1,000 seconds as taught by Nor in Kitahara in order to test for a fault (Nor Column 8 Lines 30-37) thus leading to a more accurate system.
In claim 12, Kitahara does not explicitly disclose wherein the duration is no less than 1 second and no greater than 1,000 seconds.
Nor teaches wherein the duration is no less than 1 second and no greater than 1,000 seconds (See Fig. 18B and 19B).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have the duration is no less than 1 second and no greater than 1,000 seconds as taught by Nor in Kitahara in order to test for a fault (Nor Column 8 Lines 30-37) thus leading to a more accurate method.
Claim(s) 3, 5, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kitahara in view of Keene and Park and in further view of Ji (CN 107091992 A).
In claim 3, Kitahara does not explicitly disclose wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging.
Ji teaches wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging (Fig. 2A page 5 last Par.).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging as taught by Ji in Kitahara as one of ordinary skill in the art would recognize that the state of charging vs discharging effects the expected results to compare to (Ji page 5) thus leading to a more accurate system.
In claim 5, Kitahara does not explicitly disclose calculate a potential integral for each cell of the plurality of cells.
Ji teaches calculate a potential integral for each cell of the plurality of cells (Page 2 “integration”).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to calculate a potential integral for each cell of the plurality of cells as taught by Ji in Kitahara as one of ordinary skill in the art would recognize that the state of charging vs discharging effects the expected results to compare to (Ji page 5) thus leading to a more accurate system.
In claim 13, Kitahara does not explicitly disclose wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging.
Ji teaches wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging (Fig. 2A page 5 last Par.).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have wherein the plurality of sensors is further configured to detect the potential of each cell starting when the battery begins discharging as taught by Ji in Kitahara as one of ordinary skill in the art would recognize that the state of charging vs discharging effects the expected results to compare to (Ji page 5) thus leading to a more accurate method.
In claim 15, Kitahara does not explicitly disclose calculate a potential integral for each cell of the plurality of cells.
Ji teaches calculate a potential integral for each cell of the plurality of cells (Page 2 “integration”).
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to calculate a potential integral for each cell of the plurality of cells as taught by Ji in Kitahara as one of ordinary skill in the art would recognize that the state of charging vs discharging effects the expected results to compare to (Ji page 5) thus leading to a more accurate method.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kitahara in view of Keene and Park and in further view of Burke (US 20170141369 A1)
In claim 10, Kitahara does not explicitly disclose wherein at least a cell of the plurality of cells comprises a pouch cell.
Burke teaches wherein at least a cell of the plurality of cells comprises a pouch cell (Par. 5)
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have wherein at least a cell of the plurality of cells comprises a pouch cell as taught by Burke in Kitahara in order to have a dense power source (Burke Par. 5) thus leading a more robust system.
In claim 20, Kitahara does not explicitly disclose wherein at least a cell of the plurality of cells comprises a pouch cell.
Burke teaches wherein at least a cell of the plurality of cells comprises a pouch cell (Par. 5)
Therefore, it would have been obvious to one of ordinary skill in the art before the time the invention was filed to have wherein at least a cell of the plurality of cells comprises a pouch cell as taught by Burke in Kitahara in order to have a dense power source (Burke Par. 5) thus leading a more robust method.
Response to Arguments
Applicant's arguments filed 12/30/2025 have been fully considered but they are not persuasive. Regarding applicant’s 101 arguments, on pages 6-15, the examiner respectfully disagrees. Regarding mental and mathematical concepts, as cited above the machine learning aspects in the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general-purpose computer. Regarding Step 2A prong 2, examiner notes that improving an abstract idea is still abstract and thus not eligible per MPEP 2106.05(a)(II) “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Thus the claims are not considered to be an improvement. Further the examiner does not consider Desjardins to be germane to the claims. Regarding Step 2B, as noted above, improving an abstract idea is still abstract, and further, per MPEP 2106.04(d)(1) “Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology”.
Regarding applicant’s 103 arguments, as cited above, the new art teaches the amended claim language in combination with the prior art of record.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON J BECKER whose telephone number is (571)431-0689. The examiner can normally be reached M-F 9:30-5:30.
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, Shelby Turner can be reached at (571) 272-6334. 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.
/B.J.B/ Examiner, Art Unit 2857
/SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights