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
DETAILED ACTION
The instant action is a non final office action on the merits. Claims 1-12 are pending and rejected.
Claim Objections
Claims 1, 12 are objected because “blocking a battery from being charted” should likely be charging?
“an artificial intelligence” should be “artificial intelligence”
“the battery” lacks antecedent basis
For the purposes of examination the examiner will presume that the literal meaning of the claims.
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 because they are directed to an abstract idea without significantly more.
The claims are directed to statutory classes of invention, Claim 7 “method” and claim 1 system, both are statutory, step 1 yes
Claim 7 is the statutory claim that will be analyzed.
The invention is directed to evaluating battery status using financial information and machine learning to determine a vehicle battery residual value.
These limitations under their broadest reasonable interpretation cover performance of the limitation as certain methods of organizing human activity. The use of generic computing equipment to conduct the evaluation of battery residual value does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore claims 1, 7 and the dependent claims are directed to an abstract idea without a practical application. Step 2A prong 2 No the additional elements are not integrated into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more;
For example claim 7 contains;
evaluating a value of a … of a … vehicle based on at least one of a chemical value evaluation and a driving data-based value evaluation; generating a plurality of financial product matrices for the … … - based financial product and adjust a … residual value variable of the plurality of financial product matrices based on the … residual value in consideration of a financial risk fluctuation of the plurality of financial product matrices; dynamically determining, based on a first artificial intelligence engine, a second artificial intelligence engine and a third artificial intelligence engine, the … residual value applied based on the financial product; managing, by using the first artificial intelligence engine, the second artificial intelligence engine and the third artificial intelligence engine, a risk for the financial product after a financial company sells the … -based financial product; pre-purchasing the … related to the financial product sold by the financial company; generating at least one non-fungible token (NFT) of a battery history based on driving data, charging data, discharging data and ownership change history data; training a fourth artificial intelligence engine based on a predetermined .. history sample and a selected training algorithm to generate a trained fourth artificial intelligence engine, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; detecting a violation of a preset condition by using the trained fourth artificial intelligence engine and the at least one NFT of the … history, the present condition including whether the … is replaced, whether a penalty option set for the … is violated, and whether tampering with the … has occurred; determining the detected violation is associated with a fraudulent use of the …; blocking the … from being charted; and disabling a … for the …, wherein the … vehicle residual value is determined to be one of a strategic residual value, a market residual value, or a standard residual value, wherein the market residual value is determined by the first artificial intelligence engine trained on input data including … depreciation factor data and … vehicle depreciation rate data, wherein the standard residual value is determined by the second artificial intelligence engine trained on market residual value data related to a previously sold standard residual value- based financial product and second training data, wherein the strategic residual value is determined by the third intelligence engine trained on market residual value data and standard residual value data corresponding to a previously sold strategic residual value-based financial product and third training data, wherein the financial product matrix is generated based on the … residual value variable and a …. return option variable, wherein each of the plurality of financial product matrices is determined to be a first type financial product generation matrix or a second type financial product generation matrix, the first type financial product generation matrix is a financial product matrix in which a financial risk is greater than or equal to a threshold value, and the second type financial product generation matrix is a financial product matrix in which the financial risk is less than the threshold value, wherein each of the plurality of financial product matrices is set to a default financial product generation matrix to perform sales of an initial product, and is changed to the first type financial product generation matrix or the second type financial product generation matrix according to an increase rate of an actual sales volume of the financial product and a reserved sales volume of the financial product, and wherein a financial product generation matrix is determined based on a set …. residual value and a … return option, the …. residual value having been determined based on … residual value.
Here the non abstract elements include battery, battery electric vehicle and other derivations of battery history, power supply etc.
If a claim limitation under its’ broadest reasonable interpretation covers performance of the limitations as a fundamental economic practice or commercial or legal interactions, then it falls within the certain methods or organizing human activity, grouping of abstract ideas. Accordingly the claim recites an abstract idea.
The system and method include generic computing elements as described above. The judicial exception is not integrated into the practical application. In particular the claims recite the additional elements (see the previous mentioned). These elements such generic processor, battery history etc. are recited as a high level of generality, ie a generic processor performing a generic computer function such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly there additional elements when considered separately and as an ordered combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore claims 1, 7 are directed to an abstract idea without a practical application. Step 2 A prong 2 No the additional elements are not integrated into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and as an ordered combination. The additional element of using general computer equipment and batteries amounts to no more than mere instructions to apply the exception using a generic component.
Applicant specification does contain battery controls and various other technical concepts. Thus claims 1, 7 are not patent eligible as presented. Step 2B no the claims do not provide significantly more.
The dependent claims 2-6 and 8-12 are rejected because they do not include any additional elements that integrate the abstract idea into a practical application.
Thus claims 1-12 are not patent eligible.
Potentially Allowable Subject matter;
The most relevant art of record includes 12210990 to Lothman and US Patent Publication 20230305587 to Thirumurthy.
The art of record does not teach or suggest the totality of claim 7, for example,
“generating a plurality of financial product matrices for the battery electric vehicle- based financial product and adjust a battery electric vehicle residual value variable
of the plurality of financial product matrices based on the battery electric vehicle residual value in consideration of a financial risk fluctuation of the plurality of financial product matrices; dynamically determining, based on a first artificial intelligence engine, a second artificial intelligence engine and a third artificial intelligence engine, the battery electric vehicle residual value applied based on the financial product; managing, by using the first artificial intelligence engine, the second artificial intelligence engine and the third artificial intelligence engine, a risk for the financial product after a financial company sells the battery electric vehicle-based financial product; pre-purchasing the battery electric vehicle related to the financial product sold by the financial company; generating at least one non-fungible token (NFT) of a battery history based on driving data, charging data, discharging data and ownership change history data; training a fourth artificial intelligence engine based on a predetermined battery history sample and a selected training algorithm to generate a trained fourth artificial intelligence engine, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; “
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. From IP.com
TokenGreen: A Versatile NFT Framework for Peer-to-Peer Energy Trading and Asset Ownership of Electric Vehicles, IEEE 2024
Blockchain and Nested Tokens for Tracking, Reusing, and Recycling Batteries, IEEE 2023
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/BRUCE I EBERSMAN/ Primary Examiner, Art Unit 3693
