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 .
Contents of this Office Action:
35 U.S.C. 101 rejections
35 U.S.C. 112(b) rejections
35 U.S.C. 102 rejections
35 U.S.C. 103 rejections
Allowable subject matter
Prior art not relied on but relevant section
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-20 are held to claim an unpatentable abstract idea, and are therefore rejected as ineligible subject matter under 35 U.S.C. § 101.
The limitations of the independent claims of receiving vehicle condition data prior to operating the vehicle; generating, based on the vehicle condition data, input data for a machine learning model, wherein the ML model is configured to output at least one of predicted vehicle performance or vehicle parameters based on the vehicle condition data; obtaining, based on the ML model, optimized vehicle parameters based on the input data; and sending, based on the optimized vehicle parameters, instructions to tune the vehicle covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the application of the steps by a generic processor (though note that claim 1 does not recite a device at all) nothing is being recited that could not be performed mentally.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the ‘Mental Processes’ grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the element of a processor to perform the listed steps. The processor in all steps is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Please note that although the independent claims recite that data is for a machine learning model, and based on the ML model, there is nothing in the claim that actually defines creating or refining the ML model, or even what the ML model is.
Turning to the dependent claims, claim 2 simply recites generating a report, which can be done mentally, and while the claim recites an LLM, again, as in the independent claim, it only broadly and generally recites using an LLM without any specifics. Claim 3 recites a chat interface, which can be performed mentally, and claims 4, 7, and 8 recite either sending and receiving data, what a file includes, and generic evaluation of an ML model.
However, claims 5 and 6 do recite specifics of training the ML model, which is compliant with 35 U.S.C. 101 in accordance with the August 2025 USPTO memo on eligibility. As such, these claims are rejected for their dependency on claim 1, but would otherwise be compliant if they were in the independent claim.
The other dependent claims are mirror claims and are rejected for the same reason. Claim 19 is slightly modified and would also be compliant with 35 U.S.C. 101 for the reasons above, as well as the specific use of sensor feedback.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the listed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
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.
The term “optimized” in each independent claim is a relative term which renders the claim indefinite. The term “optimized” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree of optimization, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
All claims are rejected because of this.
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.
Claim(s) 1, 9, and 17 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Barfield US9881428, hereinafter “Barfield.”
Regarding claims 1, 9, and 17, Barfield discloses a method, apparatus, and non-transitory computer-readable medium for optimizing performance of a vehicle (Abstract discloses vehicle data may be analyzed to predict potential component failures, diagnostic trouble codes (DTCs), or other mechanical failures relating to the vehicle. In one implementation the vehicle data may be received from a number of vehicles, the vehicle data including DTCs generated by on-board diagnostic (OBD) systems of the vehicles. The vehicle data may be evaluated using a predictive model to output predictions of DTCs that are likely to occur for a particular vehicle), the method comprising:
receiving vehicle condition data prior to operating the vehicle (Col. 4, lines 25-45 disclose that vehicle operational data may include either data received when the vehicle is in operation or not related to operation (i.e. prior to operation). Col. 5 lines 50-65 disclose vehicle operational data 222 may further include information relating to reliability (e.g., DTCs, engine data, breakdown data, etc.) and/or environmental data (e.g., emissions information, the number of miles per gallon of fuel (MPG), etc.). Vehicle operational data 222 may further include information relating to calls placed by an operator of vehicle 210, such as emergency calls from vehicle 210. Vehicle operational data is discussed at length throughout the reference);
generating, based on the vehicle condition data, input data for a machine learning (ML) model, wherein the ML model is configured to output at least one of predicted vehicle performance or vehicle parameters based on the vehicle condition data (Col. 6, lines 53-65 disclose FIG. 4 is a flowchart illustrating an example process 400 relating to the generation of predictive models, to predict the occurrence of vehicle issues, using supervised and/or unsupervised machine learning techniques. Process 400 may be performed by, for example, model generation server 220);
obtaining, based on the ML model, optimized vehicle parameters based on the input data (See limitation above); and
sending, based on the optimized vehicle parameters, instructions to tune the vehicle (Col. 6, lines 10-20 disclose specific repair information which is the same as “tuning” the vehicle).
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.
Claim(s) 2-4, 10-12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barfield US9881428, hereinafter “Barfield,” in view of Mai US20240419905, hereinafter “Mai.”
Regarding claims 2 and 10, Barfield discloses wherein the vehicle condition data comprises a first report from a vehicle driver indicating the vehicle driver’s condition (Claim discloses classifying, by the one or more computing devices, a driver associated with a particular one of the plurality of vehicles to obtain a driver archetype, of a plurality of possible driver archetypes, the driver archetypes indicating driving patterns of drivers), and a second report from a vehicle mechanic indicating the vehicle’s condition (Col. 12 lines 40-50 discloses mechanics or car dealerships may receive DTC prediction reports, to prepare parts or technicians for upcoming maintenance, recommend preventative services to customers, or take other appropriate actions. For example, repair shops or dealerships may create maintenance schedules adapted to specific vehicles. Repair shops or dealerships may be able to reduce costs by performance maintenance only when required. Accurate DTC prediction may allow dealerships and repair shops to avoid both over maintaining vehicles, and repairs resulting for under maintenance).
However, Barfield does not disclose:
wherein generating the input data for the ML model comprises converting, using a large language model, the first report and second report into machine-readable parameters.
However, Mai, P35, P36, and P41 disclose using an LLM to convert human natural language into machine-readable instructions to control a vehicle and driving parameters. P48 discloses a chat window for virtual conversation.
Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify the teachings of Barfield to include the LLM conversions of Mai so that the instructions from the mechanic can be used in an actionable way automatically by the vehicle.
Regarding claim 3 and 11, Barfield discloses wherein obtaining the optimized vehicle parameters include interacting with at least one of the driver and the vehicle mechanic (this is addressed in the claims above) to iteratively suggest optimized vehicle conditions and optimized vehicle parameters based on the predicted vehicle performance (this is also addressed in claim 2 above).
The only difference between these claims and Barfield is, similar to claim 2, that it does not use an LLM, and it does not use a chat interface.
However, as explicitly addressed in claim 2 above, Barfield discloses the mechanic communicating repairs with the vehicle, and Mai discloses using a chat interface for virtual conversation. It would therefore be obvious to combine these features for the intended purpose of expanding the scope of the chat function of Mai to extend to the mechanic so that repairs can be made.
Regarding claims 4 and 12, Barfield discloses sending a first instruction to an Electronic Control Unit (ECU) of the vehicle to tune software-related parameters of the vehicle (Col. 14, lines 25-40 disclose software processing in the vehicle); and sending a second instruction to the vehicle mechanic to tune hardware-related parameters of the vehicle wherein the second instruction is generated as an instruction manual using an LLM (the claims above discuss mechanics providing repairs or instructions for repairs. Col. 12, lines 40-42 specifically state that these are reports, which is equivalent to an instruction manual. Mai above discusses integrating an LLM).
Please note that claim 18 is a combination of the claims above.
Allowable Subject Matter
Claims 5, 13, and 19, as well as the claims that depend on them include allowable subject matter. Neither of the prior art references, discussed at length above, teach generating, based on the vehicle performance data, a Requirements as Code (RaC) using the LLM; generating, based on the RaC file, simulated vehicle data; and training the ML model based on simulated data.
Prior Art Cited but not Relied on
Ghosh US20200097921 which is directed to a computer system may receive historical repair data for first equipment, and may extract features from the historical repair data for the first equipment as training data including one or more of: free-text variables associated with comments related to the first equipment; usage attributes associated with the first equipment; equipment attributes associated with the first equipment; sensor data associated with the first equipment; or event data associated with the first equipment. The system may determine a repair hierarchy including a plurality of repair levels for the equipment. The system may use the training data to train a machine learning model as a multilayer model trained to perform multiple tasks for predicting individual levels of the repair hierarchy. The system may receive a repair request associated with second equipment and uses the machine learning model to determine at least one repair action based on the received repair request.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARYAN E WEISENFELD whose telephone number is (571)272-6602. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at 5712721206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ARYAN E. WEISENFELD
Primary Examiner
Art Unit 3689
/ARYAN E WEISENFELD/Primary Examiner, Art Unit 3663