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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
Preliminary Amendment
Preliminary Amendment filed on 02/06/2023 noted by the examiner, claims 1-12 are pending.
Claim Rejections - 35 USC § 112
2. 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 1-12 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.
Regarding claim 1-9 the terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “end of life “ “minimum charging current “ “long short-term memory“ are vague and a relative term that renders the claim indefinite. The terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “end of life“ “minimum charging current“ “long short-term memory“ are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “end of life “ “minimum charging current“ “long short-term memory“ within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “end of life“ “minimum charging current“ “long short-term memory“ occur.
Regarding claim 10-11 the terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” are vague and a relative term that renders the claim indefinite. The terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” are vague and a relative term that renders the claim indefinite. The terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” are vague and a relative term that renders the claim indefinite. The terms “another measure value of state-of-health” “normal operation” “deep learning” ”degradation” “nominal capacity” “similar neural network” “similar cell” occur.
Regarding claim 12 the term “deep learning” is vague and a relative term that renders the claim indefinite. The term “deep learning” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “deep learning” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “deep learning” occurs.
Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood.
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 1-12 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.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an measured during normal operation, are supplied to a neural network, wherein the neural network is a network that is designed for sequence-to-sequence deep learning, obtainment of an indicator from the neural network, wherein the indicator is a further measure of the anticipated degradation of the cell, wherein the indicator is determined from the history of the nominal capacity of the battery cell which is an abstract idea of data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? The additional elements of a method for the local determination of at least one characteristic value of a battery cell, wherein time series of voltages and time, or another measure of a state-of-health indicator of a cell are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? There is no more additional element. Step 2B: No. claim 1 not eligible. Similar claims analysis for claim 2-9 as the dependent claims merely recite further data characterization and mathematical concepts that are part of the abstract idea, claims 2-9 not eligible as well.
Claim 10, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to an measured during normal operation, are supplied to a neural network, wherein the neural network is a network that is designed for sequence-to-sequence deep learning, obtainment of an indicator from the neural network, wherein the indicator is a further measure of the anticipated degradation of the cell, wherein the indicator is determined from the history of the nominal capacity of the battery cell which is an abstract idea of data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? The additional elements of a system comprising at least one device configured to make a local determination of at least one characteristic value of a battery cell, wherein the at least one device is further configured to supply to a neural network a time series of voltages and time, or another measure of a state-of-health indicator of a cell are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of
a remote computation device, wherein the remote computation device comprises a similar neural network, wherein the neural network obtains data from ageing tests of at least one similar cell. and from determined voltages and time stamps, wherein a model of the remote computation device, thereby trained, is made available to the neural network of the at least one device appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 10 not eligible. Similar claim analysis for claim 11 as the dependent claims merely recite further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible as well.
Claim 12, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to use of a neural network, which is designed for sequence-to-sequence deep learning. in a method for the determination of at least one characteristic value of a battery cell which is an abstract idea of data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? There is/are no more additional elements,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? There is/are no more additional element. Step 2B: No. claim 12 not eligible.
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.
Claim(s) 1-12 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Kumar et al. (US Patent Application Publication . 2019/0176639, Date Published: 2019-06-13).
Regarding claim 1:
Kumar described a method for the local determination of at least one characteristic value of a battery cell (0007, 0062, voltage and current of a battery cell), wherein time series of voltages and time (0007-0008, voltage and time), or another measure of a state-of-health indicator of a cell, measured during normal operation (0007, vehicle operation), are supplied to a neural network, wherein the neural network is a network that is designed for sequence-to-sequence deep learning (fig. 3), obtainment of an indicator from the neural network (fig. 3, step 322), wherein the indicator is a further measure of the anticipated degradation of the cell (0008, battery degradation due to corrosion), wherein the indicator is determined from the history of the nominal capacity of the battery cell (0006-0007, capacity may be monitored, the battery internal resistance over time).
Regarding claim 10:
Kumar described a system comprising at least one device configured to make a local determination of at least one characteristic value of a battery cell (0007, 0062, voltage and current of a battery cell), wherein the at least one device is further configured to supply to a neural network a time series of voltages and time (0007-0008, voltage and time), or another measure of a state-of-health indicator of a cell, measured during normal operation (0007, vehicle operation), wherein the neural network is a network that is designed for sequence-to-sequence deep learning (fig. 3), wherein the at least one device is further configured to obtain an indicator from the neural network (fig. 3, step 322), wherein the indicator is a further measure of the anticipated degradation of the battery cell, wherein the indicator is determined from the history of the nominal capacity of the battery cell (0006-0007, capacity may be monitored, the battery internal resistance over time); and a remote computation device (fig. 1A, unit 12), wherein the remote computation device comprises a similar neural network (fig. 4), wherein the neural network obtains data from ageing tests of at least one similar cell (0062, battery cell) and from determined voltages and time stamps (0008, timely notification may enable), wherein a model of the remote computation device, thereby trained, is made available to the neural network of the at least one device (0047, The BMS learns the battery state of the connected battery over time)
Regarding claim 12:
Kumar described use of a neural network, which is designed for sequence-to-sequence deep learning. in a method for the determination of at least one characteristic value of a battery cell (fig. 3, 0007, 0062, voltage and current of a battery cell, 0047, The BMS learns the battery state of the connected battery over time) .
Regarding claim 2, Kumar further described wherein determined voltages and time stamps of a charging process are in each case stored as a time series, wherein the multiplicity of time series of various charging processes are supplied to the further neural network (0006-0007,0047 capacity may be monitored, the battery internal resistance over time SOC).
Regarding claim 3, Kumar further described wherein the indicator is the kink point in the degradation (0119, the percentage of undiagnosed problems defined by the BISF-metric does not again become excessive).
Regarding claim 4, Kumar further described wherein the indicator is a measure of the anticipated end of life (0124, end of life is reached).
Regarding claim 5, Kumar further described the steps: during a charging process of the battery cell, repeated (0102, triggered repeatedly) determination of an applied voltage and assignment to a time stamp (0007-0008, charge voltage SOC, over time), supply of the determined voltages and time stamps to a neural network, obtainment of an indicator from the neural network (0007, 100% SOC?), wherein the indicator is a measure of the nominal capacity at the end of the last measured applied voltage (0007, charge OCV).
Regarding claim 6, Kumar further described wherein the charging process has a predetermined constant current (0106, non-zero calibratable threshold, voltage and current), wherein when a predetermined target voltage is reached, the charging process is continued at a predetermined voltage (0021, charge voltage), wherein the charging process terminates when the charging current falls below a predetermined minimum charging current (0106, non-zero calibratable threshold, voltage and current).
Regarding claim 7, Kumar further described wherein the determination of an applied voltage and assignment to a time stamp takes place during a constant charging current (0106, non-zero calibratable threshold, voltage and current).
Regarding claim 8, Kumar further described wherein the neural network is a long short-term memory network- based neural network. (0043, memory chip 110 )
Regarding claim 9, Kumar further described a device for the execution of a method (fig. 1A, unit 12 computer)
Regarding claim 11, Kumar further described use of a device according to Claim 10 with a lithium- based battery cell (0061, battery is a lithium-ion battery).
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TURNER SHELBY, can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
August 26, 2025