DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-8 are presented for examination on the merits.
Claim Rejections - 35 USC§ 101
2. 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.
3. Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 7 recites “computer program stored in a medium that is coupled with hardware …” the "medium" fails to fall within a statutory category because the original disclosure does not provide a clear definition for "medium", thus, the "medium" could be directed to "transmission medium" (i.e., signal/transitory medium). Therefore, claim 7 is rejected under 35 USC 101 as failing to be limited to embodiments which fall within a statutory category. To overcome this rejection, it is suggested to applicant to replace “computer program stored in a medium that is coupled with hardware …” to “--- non transitory computer-readable medium - storing computer-executable software/instructions -. Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary or customary meaning that includes signals per se.
4. Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim s 1-6 and 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without providing significantly more to transform the exception into patent-eligible subject matter. According to 2025 Update guidelines and USPTO 2019 PEG, the claims failed:
II. Subject Matter Eligibility Analysis (Step 2A / Step 2B Framework)
Step 2A, Prong 1: Determination of Judicial Exception:
Claims 1-8 recite data processing operations comprising receiving real-time state information from a battery management system (BMS), applying state information to a "digital twin" reflecting mathematical/simulation models (electrochemical-thermal, ECM, NTGK, Newman P2D, machine learning, CNNs), and transmitting virtual measurement values.
These claim limitations set forth mathematical concepts (mathematical algorithms, models, and calculation formulas) and abstract data collection/ transmission routines. The core operation of simulating battery behavior via a digital twin consists of processing data through mathematical models.
Step 2A, Prong 2: Integration into a Practical Application:
The claims as a whole do not integrate the judicial exception into a practical application. The additional limitations (a generic BMS, receiving unit, transmitting unit, generic processor/hardware, and storage media) perform generic, high-level computer operations (gathering data and transmitting calculated outputs).
The claims do not recite any technical improvement to the physical operational parameters or hardware performance of the battery itself (e.g., actively adjusting cooling systems, dynamically modifying charging rates, or tripping protection circuitry based on the virtual temperature).
The digital twin model execution is not tied to a specific physical transformation or technological improvement in computer/hardware architecture. Merely generating and outputting virtual measurement values constitutes generic pre-solution and post-solution data activity.
Accordingly, Claims 1-8 are directed to an abstract idea under Step 2A.
Step 2B: Inventive Concept Analysis (“Significantly More”)
The claim elements evaluated individually and as an ordered combination fail to recite “significantly more” than the judicial exception itself. The physical/hardware elements (BMS, receiving unit, digital twin unit, transmitting unit, and hardware storage medium) are described in functional, generic terms.
Executing conventional algorithms, machine learning models (CNNs), or mathematical evaluations on generic computer components is well-understood, routine, and conventional in the art. Adding limitations directed to sample image processing or standard network structures (activation functions, hidden layers) does not transform the abstract algorithm into patent-eligible subject matter.
Claim Rejections - 35 USC § 102
5. 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.
6. 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.
7. Claims 1-3 and 7-8 rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (CN 111027165 A)
As to claim 1, Yang discloses in a power battery pack management system and method based on digital twin of having claimed:
a. digital twin-based battery temperature monitoring method comprising: receiving real-time state information about a battery unit from a battery management system (BMS), read on Page 3, Para. 1, (when the BMS system terminal performs information interaction with the cloud computing system through the wireless signal transmission, obtaining the control strategy and analyzing to obtain from the cloud computing system parameter information to realize the real-time status updates of the BMS system terminal itself with the control policy update, the BMS system terminal comprises a data collecting terminal, a balanced system terminal, heat management system terminal and/or security service terminal);
b. analyzing internal temperature distribution of the battery unit by applying the real- time state information to a digital twin corresponding to the battery unit read on Page 3, Para. 6, (the physical element attribute data comprises relationship data between each part in system specification, function, performance and system; the dynamic process data comprises system operation state, real time performance, environmental parameters and/or burst disturbance data; the collecting data comprises system obtained via the data collecting terminal of the real-time data, the data collecting terminal comprises a sensor, an embedded system and/or data collecting card; the simulation data comprises using process simulation virtual entity simulation obtained by simulation. behavior simulation, process verification, evaluation, analysis and/or prediction data, the algorithm data comprises an algorithm, model, data processing method involved in the analysis process. The BMS system terminal of data collecting terminal processing power battery group for acquiring voltage, current collecting, temperature acquisition and/or sensor accuracy adjustment, state update of the equalization system terminal processing power battery group. equalizing judging, topological efficiency and/or policy update, dynamic environment in the thermal management system terminal processing system and working condition analysis, time-varying flow field and/or policy optimization, insulation monitoring, high voltage interlock in the security service terminal processing system; security services and/or fault analysis);
c. transmitting, to the BMS, a virtual temperature value at a virtual measurement point requested by the BMS read on Page 6, Para. 1, (twin cloud data platform respectively for data interaction to the physical entity and the virtual entity, and the data for storage, transmission, analysis, at the same time, transmits the information to the cloud computing system for real-time updating the power battery group of full life cycle states. when the cloud computing system for physical entity state information analysis obtained from the twin cloud data platform of the virtual entity state information, when the cloud calculated in real time and for generating a control strategy and real-time based on full life cycle of the power battery group parameter information, the cloud computing system can use cloud big data calculating method, it can further use the smart prediction processing system based on deep learning for realizing cloud computing process. when terminal BMS system performs information interaction with the cloud computing system through the wireless signal transmission, obtaining the control strategy and analyzing to obtain from the cloud computing system parameter information, real-time status updates and control strategy for the updated terminal BMS system).
As to claim 2, Yang further discloses:
a. Digital twin generated by electrochemical-thermal model, cell module arrangement, heat dissipation structure read on Page 6, Para. 2, (the physical entity-of battery modules-by "battery monomer battery pack system" level is established, wherein the battery monomer entity content comprises a monomer battery positive and negative electrode material and a defect structure, electrolyte concentration and ion concentration distribution. diaphragm porosity reduced or migration, the SEI membrane thickening or thinning; battery module entity on the single battery solid foundation, comprising coupling battery heat generating heat transfer mechanism, combined battery pack radiating system and other electrical accessory elements such as establishing battery module-level entity; battery pack system entity comprises battery group standard structure and so on. The virtual entity "monomer system" level-module-is established, all coupling model for a single battery constructed single stage model. The electrochemical heat - - machine so as to realize the monitoring of a single battery, fault prediction and maintenance, for module level model, establishing dynamic boundary based on multi-dimensional Ulti-scale thermal heat dissipation battery module model, so the module battery group consistency characteristics, aging characteristics, etc. for analysis and optimization, and for the whole battery pack system, it can construct all life cycle dynamic system model);
As to Claim 3, Yang further discloses:
a. real-time info comprises current, voltage, temperature, SOC, SOH read on (Collects real-time battery voltage, current, and temperature, and updates dynamic battery states like SOC and degradation/SOH read on Page 5, Para. 3, virtual data generated by the physical data and the virtual entity running the plurality of data combines the physical entity generated by running of the types of the plurality of data comprises physical element attribute data, dynamic process data, collecting data, simulation data, algorithm data, standard data, several combination in history data; when physical entity state information for the cloud computing system analyzes obtained from twin cloud data platform and virtual entity status information, and using the big cloud data calculating method in the cloud and real-time calculating control strategy and real-time to generate full life cycle based on the power battery group of parameter information , the BMS system terminal performs information interaction with the cloud computing system through the wireless signal transmission, acquiring control policy and real-time from the cloud computing system obtained by analyzing the time parameter information to realize the real-time status updates of the BMS system terminal itself with the control policy update. the BMS system terminal comprises a data collecting terminal, balance the system terminal, the thermal management system terminal and/or security service terminal).
As to claim 7, Yang further discloses:
a. a computer program stored in a medium that is coupled with hardware to perform the digital twin-based battery temperature monitoring read on Page 6, Para. 1, (As shown in FIG. 2 is one embodiment of schematic diagram of the present invention system structure, wherein the physical entity and the virtual entity by the system structure of the invention forms the rolling optimization process. twin cloud data platform respectively for data interaction to the physical entity and the virtual entity, and the data for storage, transmission, analysis, at the same time, transmits the information to the cloud computing system for real-time updating the power battery group of full life cycle states).
As to claim 8, the claim is interpreted and rejected as to claim 1.
Claim Rejections - 35 USC § 103
8. 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.
9. 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 of this title, 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.
10. Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Wu et al., (Evaluation of “Battery Digital Twins” 2020).
As to claim 4, Yang does not explicitly recite wherein the digital twin is generated by applying the electrochemical-thermal model of any one of an equivalent circuit model (ECM), a Newman-Tiedemann-Gu-Kim (NTGK) model, and a Newman pseudo 2-dimensional (Newman P2D) model.
However, Wu cures this deficiency by teaching that it may be beneficial wherein discloses in asset tracking system having claimed:
a. the digital twin is generated by applying the electrochemical-thermal model of any one of an equivalent circuit model (ECM), a Newman-Tiedemann-Gu-Kim (NTGK) model, and a Newman pseudo 2-dimensional (Newman P2D) model read on Wu Page 1-12 (the paper explicitly reviews and defines: Equivalent Circuit Models (ECM) Continuum-level physics models based on Doyle, Fuller, and Newman, specifically detailing the Pseudo-2D (P2D) model; Reduced-order and physics-based variants).
Yang discloses the overarching digital twin cloud platform and real-time BMS feedback loop. Wu et al. explicitly teach implementing P2D electro-thermal continuum models and using spatial temperature distributions to train neural network surrogate models.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the battery digital twins perspective on the fusion of models, data and artificial intelligence for smart battery management system of Wo into Yang in order to increase simulation fidelity and state estimation accuracy without incurring heavy computational overhead. As to claim 5, Wu further teaches:
a. wherein the digital twin is generated by machine learning of sample data representing 2D or 3D temperature distribution of the battery unit (20) generated using the NTGK model, and the sample data is a 2D or 3D image visually representing the 2D or 3D temperature distribution of the battery unit (20) read on Wu Page 1-12, (The integration of multi-scale physics models (P2D/electro-thermal) with machine learning algorithms to train surrogate models and artificial neural networks (ANNs). Spatial thermal variations and temperature distribution fields (in-plane and through-plane thermal gradients) used for machine learning inputs and diagnostic feature extraction.
11. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Wu et al., and further in view of Wang (CN 111832220 A).
As to claim 6, Yang further teaches the primary digital twin platform for a battery management system (BMS) with cloud-based real-time state estimation, and Wu et al. teaches integrating electro-thermal multi-scale physics models with machine learning algorithms using spatial thermal distributions (2D/3D temperature maps) to train surrogate machine learning models for battery state estimation. Neither Yang nor Wu et al. explicitly details configuring the surrogate machine learning model as a Convolutional Neural Network (CNN) that incorporates an exponential activation function as recited.
Wang et al. teaches in Page 6, Para. 3-9 and Page , Para. 1, a machine learning state estimation model for lithium-ion batteries that employs a Convolutional Neural Network (CNN) in an encoder-decoder architecture, wherein the neural network pipeline incorporates an exponential activation function (specifically, within an attention/Softmax layer) to process structural feature arrays for calculating battery state of health (SOH). The collected voltage and current data and the SOH reference value corresponding to each group of data are respectively normalized; the final output SOH reference value needs to be inversely normalized. the expression is as follows: Xmax, Xmin is the maximum value and the minimum value in the data, is the result after normalization, is the result after the inverse normalization.
Therefore, It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the surrogate machine learning model of Yang and Wu et al. in order to incorporate the CNN architecture and exponential activation function operations taught by Wang et al. Extraction of High-Dimensional Spatial Features: Wu et al. teaches generating multi-dimensional spatial thermal distribution maps for surrogate model training. A person with ordinary skill in the art looking to implement this would naturally adopt the CNN feature-extraction pipeline of Wang et al., which is specifically optimized to process 2D spatial grid structures times 4 arrays of battery parameters. Wang et al. teaches that applying an exponential activation function within the network decoder enables precise, normalized weight assignment over non-linear feature sequences. Integrating this into the digital twin framework of Yang and Wu et al. enhances prediction accuracy and numerical stability during cloud-based battery state estimation. Therefore, combining these references to implement a CNN with exponential activation functions for battery state prediction would have been an obvious combination of well-known machine learning techniques.
Citation of pertinent Prior Arts
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i. Kechmir (US 20200132779 A1) discloses in methods, systems, and devices that include improvements to determining properties of a battery are described. For example, a method may include measuring one or more properties of a battery by a device communicatively coupled to the battery; transmitting the measured one or more properties to a computing device located remotely from the battery; detecting, by the computing device and based on the measured one or more properties, one or more anomalous conditions within the battery; and transmitting, by the computing device, an indication of the one or more anomalous conditions, and
ii. Park (US 20190361411 A1) discloses in a building management system of a building includes one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to generate agents, each agent of the agents paired with one entity of a plurality of entities of an entity database, wherein the entity database includes relationships between the entities, wherein the entities represent physical building entities of the building comprising building equipment or building spaces. The instructions cause the one or more processors to communicate, by the plurality of agents, data of the physical building entities via a plurality of agent communication channels and perform, by the plurality of agents, one or more operations for the plurality of entities based on the data.
Conclusion
9. If the claimed invention is amended, Applicant is respectfully requested to indicate the portion(s) of the specification, which dictate(s) the structure/description relied upon to assist the Examiner in proper interpretation of the amended language and also to verify and ascertain the metes and bounds of the claimed invention. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fekadeselassie Girma whose telephone number is (571) 270-5886. The examiner can normally be reached on Monday thru Friday, 8:30 – 5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Davetta Goins can be reached on (571) 272-2957. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Fekadeselassie Girma/
Primary Examiner Art Unit 2689
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