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 .
Specification
The title of invention is objected to because it contains a typographical error.
Specifically, the word “stotage” in the current title is misspelled.
Applicant is required to submit an amendment to the title to correct this misspelling.
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.
Claim 1-7 and 9-10 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, 3, and 4 recite “each remaining cells”; because each must be followed by a singular noun (i.e., each remaining cell) or properly prepositioned (i.e., each of the remaining cells), the current phrasing creates ambiguity. It is not clear whether the calculation applies to a single cell at a time or a plurality of cells simultaneously. For the purpose of the examination, we consider the phrase “each remaining cell”.
Claims 2-7 and 9-10 depend from claim 1 and are rejected for the same reasons as set forth with respect to rejection of claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) abstract idea as discussed below. This judicial exception is not integrated into a practical application because of the reasons discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the reasons discussed below.
Step 1 - Statutory Category: Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, claims 1-10 are directed to a process (method) and a machine (device/system) and a non-transitory computer readable storage medium (manufacture). Accordingly, claims 1-10 fall within at least one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) under 35 U.S.C. 101.
Claim 1 is reproduced below with the abstract idea underlined.
Claim 1: A method for monitoring an energy storage cell abnormality, comprising: obtaining valid state data of a first cell of a plurality of cells in a battery module at a preset time; obtaining a discrete statistical state characteristic of the first cell based on the valid state data of the first cell; calculating distances between the discrete statistical state characteristic of the first cell and discrete statistical state characteristics of each remaining cell of the plurality of cells in the battery module separately, averaging said distances to obtain a distance characteristic associated with the first cell; and determining whether the first cell is abnormal based on the distance characteristic associated with the first cell.
Under Step 2A, Prong 1, Claim 1’s underlined limitations recite collecting information, analyzing it using mathematical formulas, and determining a result. Specifically, obtaining a discrete statistical state characteristic, calculating point to point mathematical distances and averaging said distances are purely mathematical concepts. The final step of determining whether the first cell is abnormal is a mental process that consist of comparing the calculated mathematical result to a threshold. Accordingly, claim 1 recites a judicial exception in the form of mathematical concepts and mental processes.
Claim 8 is an apparatus whose modules perform the same functions of calculating statistical and distance characteristics, and determining abnormality as recited in claim 1. Accordingly Claim 8 recites the same abstract idea as claim 1.
Dependent claims 2-7 and 9-10 recite the same abstract idea as claim 1. Specifically, these claims merely define the mathematical techniques used to process the collected battery state data, including calculating discrete statistical values (claims 6 and 2), calculating and accumulating distance values (claim3), calculating Euclidean distance (claim 4), comparing statistical distance characteristics to a threshold to determine abnormality (claim 5). Accordingly, claims 2-7 and 9-10 recite the abstract idea in the form of mathematical concepts and mental processes.
Step 2A, Prong 2: examiner needs to determine if the claim(s) recite additional elements that integrate the exception into a practical application of the exception. The additional elements in the claim have been left in normal font. Claim 1-10 do not integrate the judicial exception into a practical application because of the following reasons:
Claim 1 recites obtaining valid state data at a preset time. Merely gathering data for an algorithm is considered pre-solution activity and does not transform an abstract idea into a practical application. The claim further recites applying the statistical distance-averaging algorithm specifically to cells in a battery module which merely establishes a broad field of use. Accordingly, the additional elements do not integrate the abstract idea into a practical application.
Claim 8 additional elements recite generic functional modules that merely gather data, perform the recited mathematical calculations, and determine whether a cell is abnormal. The claim does not recite any specific technological action based on the determination of abnormality. Accordingly, the additional elements amount to insignificant extra-solution activity and generic computer implementation of the abstract idea and therefore do not integrate the judicial exception into a practical application.
Dependent claims 2-6 do not recite any additional elements beyond those discussed for claim 1. Claim 7 additionally recites that the first cell comprises a battery submodule including a plurality of battery cores. This limitation merely specifies the environment in which the abstract idea is performed and amounts to a field-of-use limitation rather than an improvement to battery technology. Claims 9-10 additionally recite a generic memory, processor and a generic non-transitory computer readable medium. These additional elements merely implement the abstract idea using generic computing components and do not improve the functioning of a computer or battery technology.
Accordingly, claims 2-7 and 9-10 do not integrate the judicial exception into a practical application.
Step 2B: Claims 1-10: the additional elements, considered individually and in combination, do not amount to significantly more than the abstract idea for the same reasons set forth with respect to Step 2A, Prong 2 analysis of claims 1-10.
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.
Claims 1-4 and 8-10 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Xie Hui et. al (CN115327417 A) hereinafter Xie, See attached English Translation.
Regarding claim 1, Xie teaches a method for monitoring an energy storage cell abnormality (a method for early warning of abnormality in a single power battery cell, ¶ [5]). Xie further teaches a method comprising obtaining valid state data of a first cell (acquiring recent historical data of a vehicle's power battery ¶ [6]. Xie further recites filtering out cell data frames with abnormal voltage and retaining cell data frames with normal voltage, ¶ [14]) of a plurality of cells in a battery module (Xie teaches that the number of cells in each frame of cell data is (n) and it calculates the characteristics for each cell, ¶ [16]) at a preset time (Xie discloses acquiring recent historical data of a vehicle power battery and it further details this as selecting continuous national standard historical data of the vehicle power battery for a recent sampling period, such as historical data of continuous one week ¶ [6 & 60]).
Xie teaches obtaining a discrete statistical state characteristic of the first cell (difference value (ΔSOC ij) calculated between the real SOC and the median real SOC ¶ [7 & 23]) based on the valid state data of the first cell (Xie further discloses that it may obtain the voltage value of each cell in each frame of cell data … and query the real SOC of each cell through the voltage value of each cell, ¶ [17]; this calculation requires inputting preprocessed voltage of that specific individual cell to find its true SOC, so the resulting statistical characteristic is based on the true state data of the first individual cell).
Xie further teaches calculating distances between (calculating the Euclidean distance Djk between each cell and all cells ¶ [24] and formula (3)) the discrete statistical state characteristic of the first cell (the difference value of the jth cell, ΔSOCij , ¶ [26]) and discrete statistical state characteristics of each remaining cells (the difference value of the kth cell, ΔSOCik ,¶ [26]) of the plurality of cells (in formula (3) the remaining cells are represented by the k-th cell, where k includes all n cells in the pack (k
∈
[
1
,
n
]
), ¶ [28]) in the battery module separately (in formula (3) ¶ [28] because the algorithm must iterate through (K=1, 2,…n) to find the individual distance between the target cell and each specific other single cell in the pack, the distance are calculated separately (point to point)).
Xie teaches averaging said distances (Calculating the average Euclidean distance D_AV Gj … using formula (4), ¶ [27]) to obtain a distance characteristic (D_AV Gj is the baseline metric that is subsequently compared against a threshold to determine if there is an abnormality, ¶ [29-32]). associated with the first cell ( the subscript j in D_AV Gj ties the final value to the j-th cell being evaluated, ¶ [28]).
Xie further teaches determining whether the first cell is abnormal based on the distance characteristic associated with the first cell (The obtained average Euclidean distance D_AV Gj is compared with a preset threshold; if D_AV Gj is greater than the preset threshold, the corresponding cell can be judged as abnormal, and an early warning prompt is generated, ¶ [85]).
Regarding claim 2, Xie teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Xie further teaches wherein obtaining the discrete statistical state characteristic of the first cell (the difference value of the j-th cell, ΔSOCij , ¶ [26]) based on the valid state data of the first cell (SOC_Realij for the j-th cell and for each individual frame i (where
i
∈
[
1
,
m
]
), ¶ [23]) comprises: calculating a plurality of first discrete statistical values of the first cell (the system calculates difference values ΔSOCij for each of the m frames using formula (2),¶ [23]. This statistical difference calculation is repeated for every valid frame i from 1 to m, which means the system calculates a plurality of difference values (i.e., ΔSOC1j, ΔSOC2j, …,ΔSOCmj) for the j-th cell ) based on the valid state data of the first cell (the system obtains preprocessed state data over m frames (e.g., representing data collected over a continuous week, ¶ [60]), then for a given individual cell (the j-th cell (first cell)), the system calculates real SOC and median SOC for each individual frame i ¶ [23]);
Xie teaches using the plurality of first discrete statistical values of the first cell as the discrete statistical state characteristic of the first cell (Formula (3) calculates the Euclidean distance between the j-th cell and k-th cell, ¶ [25]. Because the formula applies a summation over all m frames, it requires inputting the entire plurality of the first cell difference’s values (ΔSOC1j …ΔSOCmj) to execute the distance calculation against the remaining cell).
Regarding claim 3, Xie teaches the method of claim 1, wherein calculating the distances between the discrete statistical state characteristic of the first cell and the discrete statistical state characteristics of each remaining cells of the plurality of cells in the battery module separately, averaging said distances to obtain the distance characteristic associated with corresponding to the first cell to obtain the distance characteristic associated with the first cell, as set forth with respect to rejection of claim 1.
Xie further teaches that the method comprises: calculating the distances (Djk formula (3) ¶ [25]) between the discrete statistical state characteristic of the first cell (ΔSOCij ¶ [25]) and the discrete statistical state characteristics of all remaining cells (ΔSOCik ¶ [25]) of the plurality of cells separately (mathematically formula (3) requires system to iterate through each k-th cell from 1 to n to calculate an individual, point to point Euclidean distance between the target cell and each of the other individual cells ¶ [25 & 28]) to obtain a distance data set associated with the first cell (formula (4) sums the individual calculated distances, this group of separately calculated point-to-point distances (Dj1, Dj2, …, Djk) constitutes a distance data set, ¶ [28]. Because all these distances in the specific set share the subscript j, this data set is associated with the j-th cell (first cell)).
Xie teaches accumulating all data in the distance data set associated with the first cell.
(the numerator of Formula (4) is (Dj1+ Dj2+ …+ Djk) which requires the system to add together all of the point-to-point distances associated with the target j-th cell ¶ [28]) to obtain the distance characteristic associated with the first cell (after accumulating these individual distances, Formula (4), ¶ [28], divides the sum by n to obtain D_AV Gj (distance characteristic) value which serves as the overall baseline metric that is used to evaluate the j-th cell).
Regarding claim 4, Xie teaches the method of claim 3 wherein calculating the distances between the discrete statistical state characteristic of the first cell and the discrete statistical state characteristics of each remaining cells of the plurality of cells separately to obtain a distance data set associated with the first cell as set forth with respect to rejection of claim 3.
Xie further teaches the method comprises: obtaining the discrete statistical state characteristic of the first cell (difference value ΔSOCij of the j-th cell,¶ [23 & 25]) and the discrete statistical state characteristic of a second cell selected from the remaining cells of the plurality of cells in the battery module (to execute distance calculation shown in Formula (3), ¶ [25], the system must pull data from another specific cell in the module to compare against j-th cell. Xie identifies this comparison cell as the k-th cell and its difference value as ΔSOCik ,¶ [26] where
k
∈
[
1
,
n
]
).
Xie teaches calculating a Euclidean distance between the discrete statistical state characteristic of the first cell and the discrete statistical state characteristic of the second cell, to obtain a Euclidean distance value associated with the first cell (Formula (3) calculates the Euclidean distance using the calculated difference value (ΔSOC) (discrete statistical state characteristic) of the target j-th cell and the calculated difference value (ΔSOC) of the selected k-th cell ¶ [25 & 26]. Djk in Formula (3) has the subscript j which ties this measurement directly to the evaluation of the j-th cell.);
Xie further teaches recording the Euclidean distance value associated with the first cell (Xie teaches an electronic device with a memory that implements calculation of individual Euclidean distance (Djk) for the j-th cell using formula (3) ¶ [25-26 & 102-103]. In order for the system to utilize these individual calculated distances in its subsequent averaging step, Formula (4), the system must store each calculated Euclidean distance value (Djk) in its memory) as a datum point in the distance data set associated with the first cell (the system utilizes (Djk) as a single datum point within the larger set of distances (Dj1, Dj2, …, Djk) that are associated with the target j-th cell, ¶ [27-28]).
Regarding claim 8, Xie teaches an apparatus for monitoring an energy storage cell abnormality ( the power battery cell abnormality early warning method, system, electronic device,¶ [44]) , comprising :a data obtaining module (A pre-processing module, ¶ [39]), configured to obtain valid state data of cells in a battery module (for acquiring recent historical data of a vehicle power battery and performing pre-processing to obtain data in a battery quiescent state; ¶ [39]) at a preset time (Xie discloses acquiring recent historical data of a vehicle power battery and it further details this as selecting continuous national standard historical data of the vehicle power battery for a recent sampling period, such as historical data of continuous one week ¶ [6 & 60]); a state characteristic obtaining module (a data calculation module ¶ [40]), configured to obtain a discrete statistical state characteristic of each of the cells in the battery module based on the valid state data of the cell (for calculating the real SOC of each cell in each frame of cell data, calculating a real SOC median for each frame based on the real SOCs of all cells, calculating a difference between the real SOC of each cell in a current frame and the real SOC median, ¶ [40]); a distance characteristic calculation module (a data calculation module, ¶ [40]), configured to calculate distances between the discrete statistical state characteristic (ΔSOC, ¶ [23]) of each of the cells in the battery module and discrete statistical state characteristics of each of other cells in the battery module to obtain a distance characteristic (D_AV Gj, ¶ [28]) of each of the cells in the battery module (calculating a Euclidean distance (Formula (3), ¶ [25]) between each cell and all cells based on said differences, and calculating an average Euclidean distance (Formula (4), ¶ [28]) between each cell and all cells based on the Euclidean distances; ¶ [40]); and a monitoring module (result detection module, ¶ [41]) , configured to determine whether each of the cells in the battery module is abnormal based on the distance characteristic of the cell (The obtained average Euclidean distance D_AV Gj is compared with a preset threshold; if D_AV Gj is greater than the preset threshold, the corresponding cell can be judged as abnormal, and an early warning prompt is generated, ¶ [85]).
Regarding claim 9, Xie teaches an electronic device (electronic device, ¶ [44]), comprising: a memory (memory, ¶ [42]), storing instructions (computer management program stored in the memory. ¶ [42]) ; and a processor (processor, ¶ [42]), configured to load the instructions from the memory to perform the method for monitoring the energy storage cell abnormality ( is configured to implement the steps of the aforementioned power battery cell abnormality early warning method when executing a computer management program stored in the memory¶ [42]) as in claim 1 (The method of claim 1 as set forth with respect to rejection of claim 1).
Regarding claim 10, Xie teaches a non-transitory computer-readable storage medium (computer-readable storage medium ¶ [43]), storing a computer program, wherein when the computer program is executed by an electronic device, the method for monitoring the energy storage cell abnormality (having a computer management program stored thereon, wherein the computer management program is executed by a processor to implement the steps of the aforementioned power battery cell abnormality early warning method.¶ [43]) as in claim 1 is implemented (The method of claim 1 as set forth with respect to rejection of claim 1).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Xie as applied to claim 1 above, and further in view of Sung Keun Kim et.al (US20220077514A1) hereinafter Kim.
Regarding claim 5, Xie teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Xie further teaches calculating the Euclidean distance average value, D_AV Gj (distance characteristic) associated with j-th cell where
j
∈
[
1
,
n
]
and n is the number of cells in each frame of cell data in batter pack (associated with the first cell in the battery module), ¶ [17 & 28]. Xie also teaches calculation of a mean
μ
and standard deviation
σ
from distance characteristics, ¶ [31 & 32].
However, Xie applies these statistical calculations exclusively to a historical sample dataset of known abnormal cells to formulate a static preset threshold (
λ
), ¶ [35].
Xie does not teach determining whether the first cell is abnormal based on the distance characteristic associated with the first cell comprises obtaining a discrete statistical distance characteristic associated with the first cell in the battery module based on the distance characteristic associated with the first cell and determining that the first cell is abnormal when the discrete statistical distance characteristic associated with the first cell is greater than a preset threshold.
Kim teaches a battery management apparatus that monitors the risk of failure and inconsistency in battery cells by calculating a standardized score (Z-score) for each battery cell, ¶ [29]. Kim teaches obtaining an average value (
μ
) and standard deviation (
σ
) of a cell characteristic across the module, and then calculating a Z-score for each individual cell based on its specific value, the average value, and the standard deviation, ¶ [29 & 239-240]. Z-score indicates the degree of dispersion or deviation of that specific cell from the pack’s average, ¶ [242-243] (obtaining a discrete statistical characteristic (Z-score) associated with the first cell in the battery module based on the characteristic associated with the first cell)
Kim further teaches generating an identification signal to monitor the risk of failure of each battery cell based on whether this calculated Z-score exceeds a reference Z-score threshold (e.g., a Z-score of 3), ¶ [32 & 248-249] (determining that the first cell is abnormal when the discrete statistical characteristic associated with the first cell is greater than a preset threshold).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify abnormality detection method of Xie by applying the Z-score calculation taught by Kim to distance characteristics (D_AV Gj) of the battery cells monitored by Xie system. By doing so, the system would achieve a more dynamic and robust abnormality judgment based on the real-time standard deviation of the current module, rather than relying solely on the static historical (
λ
) taught by Xie. This combination yields predictable results using known statistical methods to improve the accuracy of isolating inconsistent battery cells.
Regarding claim 6, Xie in view of Kim teaches the method of claim 5 as set forth with respect to rejection of claim 5.
Xie teaches calculating the Euclidean distance average value (D_AV Gj) (distance characteristic) associated with the j-th cell (associated with the first cell), ¶ [28]. Xie also teaches calculation of a mean
μ
and standard deviation
σ
from distance characteristics, ¶ [31 & 32]. However, Xie applies these statistical calculations exclusively to a historical sample dataset of known abnormal cells to formulate a static preset threshold (
λ
), ¶ [35].
Xie does not teach obtaining a discrete statistical distance characteristic associated with the first cell in the battery module based on the distance characteristic associated with the first cell comprises calculating a plurality of second discrete statistical values associated with the first cell based on the distance characteristic associated with the first cell; and using the plurality of second discrete statistical values associated with the first cell as the discrete statistical distance characteristic associated with the first cell.
Kim teaches a battery management apparatus that monitors the risk of failure and inconsistency in battery cells by calculating a standardized score (Z-score) for each battery cell, ¶ [29]. Kim teaches calculating an average value (
μ
) and standard deviation (
σ
) (calculating a plurality of second discrete statistical values associated with the cell) of a cell characteristic across the module, and then calculating a Z-score (discrete statistical characteristic) for each individual cell based on its specific value, the average value (
μ
), and the standard deviation (
σ
) (using the plurality of second discrete statistical values associated with the cell), ¶ [29 & 239-240].
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify abnormality detection method of Xie by incorporating the specific statistical calculation steps taught by Kim. Because calculating a plurality of second discrete statistical values (the mean and the standard deviation) based on the distance characteristics (D_AV Gj) derived by Xie, and using those values to obtain a normalized standard score (Z-score) for the monitored cell would replace a static, historical threshold with a dynamic statistical metric. As taught by Kim, the Z-score provides a value that accurately indicates the degree of dispersion (Kim, ¶ [242]) of a specific cell’s state from the average, serving as a relative metric regardless of the absolute magnitude of the underlying values.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Xie as applied to claim 1 above, and further in view of Tian Peigen (CN114675190A) hereinafter Tian, See attached English Translation.
Regarding claim 7, Xie teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Xie focuses its statistical calculation in individual single cells (battery monomers) and does not teach applying this specific calculation to higher-level battery submodule comprising a plurality of battery cores.
Tian teaches a battery safety assessment method that evaluates the health of a battery system hierarchically at both the single-cell level and the module level. Specifically, Tian teaches determining a module indicator and a module score for a battery module (battery submodule), ¶ [36]. It states that a battery system generally includes a plurality of battery modules, and each battery module includes multiple cells (plurality of battery cores), ¶ [26]
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify the abnormality detection method of Xie by applying the statistical evaluation to a higher level battery submodule containing a plurality of battery cores or cells as taught by Tian. By combining the teaching of Xie with Tian, the system would be able to reliably identify inconsistencies and abnormalities between larger physical sub-assemblies, rather than only at the individual base cell level, thereby improving the overall safety management and maintenance of the battery system.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang Jing-jing (CN 107340475B) teaches using a data mining algorithm to identify faulty battery cells. It specifically teaches calculating a voltage change value and a temperature change value, and combining these with voltage and temperature to form characteristic elements.
Conclusion
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/SAEEDE NAFOOSHE/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
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