DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/05/2026 has been entered.
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.
Claim(s) 1-3, 5-14, 16-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1, 11 recites
A method of
estimating a state of health of a battery,
the method comprising:
preparing a pre-trained artificial neural network,
wherein
the pre-trained artificial neural network includes:
a first convolution layer that receives the input data; an inverted bottleneck network; a second convolution layer; and a global average pooling (GAP) layer,
and
wherein
the GAP layer outputs the health state estimation value and the attention map;
measuring at least one parameter of the battery, resulting in a at least one measured parameter of the battery;
inputting the at least one measured parameter of the battery into the pre- trained artificial neural network to obtain a health state estimation of the battery and an attention map;
and
determining whether to trust the health state estimation based on the attention map
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation].
Independent Claim(s) 12 recites
estimating a state of health of a battery,
storing a pre-trained artificial neural network and input data generated by measuring at least one parameter of the battery,
wherein
the pre-trained artificial neural network includes:
a first convolution layer that receives the input data; an inverted bottleneck network; a second convolution layer; and a GAP layer,
and
wherein
the GAP layer outputs the health state estimation value and the attention map;
measure at least one parameter of the battery, resulting in a measured at least one parameter of the battery;
input the at least one measured parameter of the battery into the pre-trained artificial neural network to obtain a health state estimation of the battery and an attention map;
and
determine whether to trust the health state estimation based on the attention map
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation].
In combination with Independent Claim(s) 1, 12, Claim(s) 2, 3, 5-10, 13, 14, 16-20 recite(s)
the at least one parameter includes
a voltage and a current of the battery.
the generating input data includes:
measuring a voltage value and a current value of the battery by using every preset sampling cycle;
and
generating normalized voltage values and normalized current values as the input data by normalizing voltage values and current values of the battery, respectively.
the inverted bottleneck network includes
a first pointwise convolution layer,
a depthwise convolution layer,
and
a second pointwise convolution layer.
the attention map indicates points on which the pre-trained artificial neural network concentrates
and
is generated by
PNG
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44
146
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wherein
h is a channel number of the second convolutional layer, fh(t) is an output of an hth channel of the second convolutional layer at time t, (oh is a weight of the hth channel used in the GAP layer, and M(t) is an attention value at time t.
the attention score is calculated by
PNG
media_image2.png
50
242
media_image2.png
Greyscale
wherein
Sattention is the attention score, H(-) is a high-pass filter function, V(t) is a voltage value at time t, M(t) is the attention value at time t, and T is a sampling period.
outputting the health state estimation value when the attention score exceeds a preset reference value.
calculating reliability of the health state estimation value based on the attention score.
the preparing a pre-trained artificial neural network includes:
estimating parameters of a pseudo-2-dimensional (P2D) model from actual charge/discharge measurement data of the battery;
generating the P2D model by changing over time at least one preset parameter related to aging from among the parameters;
generating synthetic data by using the P2D model;
and
training the artificial neural network by using the synthetic data.
calculating an attention score based on the attention map,
and
determining whether to trust the health state estimation value based on the attention score
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. being performed by at least one computing device; An apparatus for; the apparatus comprising: a memory; and at least one processor configured to:);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition and output); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. a battery).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
Allowable Subject Matter (over Prior Art)
See prior OA, mailed 08/12/2025, for the statement of reasons for the indication of allowable subject matter over prior art.
Response to Arguments
Applicant’s amendments, filed on 03/05/2026, have been entered and fully considered. In light of the applicant’s amendments changing the scope of the claimed invention, the rejection(s) have been withdrawn or updated. However, upon further consideration, a new or updated ground(s) of rejection(s) have been made, and applicant's argument(s)/remark(s) pertaining to the amended language have been rendered moot.
Applicant's argument(s)/remark(s), see page(s) 8-11, filed 03/05/2026, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“A. Rejection of Claims 1-3, 5-14, and 16-19 as Being Directed to an Abstract Idea
In the outstanding Office Action, claims 1-3, 5-14, and 16-19 were rejected under 35 U.S.C. § 101 as the claimed invention is directed to an abstract idea without significantly more. Applicant respectfully traverses the noted rejection as it applies to the amended claims.
As amended, claim 1 recites, among other things, "generating input data by measuring at least one parameter of the battery, resulting in at least one measured parameter of the battery; inputting the input data at least one measured parameter of the battery into the pre-trained artificial neural network to obtain a health state estimation value of the battery and an attention map; calculating an attention score based on the attention map; and determining whether to trust the health state estimation value based on the attention score map."
Claim 20 has been added to capture the subject matter removed from claim 1.
As amended, applicant submits that claim 1 does not include an abstract idea. In particular, "input data,""value,""score" and "calculating an attention score based on the attention map" have been removed from the claim. Applicant submits that what remains in claim 1 does not recite anything directly attributable to a mathematical concept or mental steps.
See, for example, USPTO official subject matter eligibility example 39. The claim of Example 39 recites "a computer-implemented method of training a neural network for facial detection comprising: collecting a set of digital facial images from a database; applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and digital non-facial images that are incorrectly detected as facial images after the first stage of training; and training the neural network in a second stage using the second training set."
While the claim in Example 39 is directed to a neural network training method, the concept of the example is applicable here. Like that example, amended claim 1 "does not recite any mathematical relationships, formulas or calculations. While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claim. Further, the claim does not recite a metal process because the steps are not practically performed in the human mind." The mental process aspect for the instant claim 1 was successfully addressed in the last response.
Therefore, applicant submits that claim 1 recites patentable subject matter, such that the noted rejection thereof is respectfully requested to be withdrawn.
In addition, claim 12 has been amended to include the same subject matter as claim 1. Thus, the reasoning for claim 1 applies equally to claim 12. Therefore, applicant submits claim 12 is also patentable subject matter, such that the rejection of claim 12 should be withdrawn. The remainder of the rejected claims all depend from claim 1 or claim 12. As such, the dependent claims are also allowable at least for their dependence, as well as their individual limitations, such that the rejection thereof should be withdrawn.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Applicant compares the claimed invention to Example 39. In contrast to the claimed invention, Example 39 is directed towards facial detection is a computer technology for identifying human faces in digital images. The USPTO found said example to not recite any judicial exceptions.
The examined claims are closer related to Example 47, claim 2, which was found to be patent ineligible.
Further, Examiner maintains previous response(s).
See updated rejection(s) above necessitated by amendment(s).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
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