DETAILED ACTION
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 .
Status of Claims
This office action is in response to the RCE filed on 11/26/205.
Claims 1, 11, and 17 have been amended.
Claims 10 and 16 have been canceled.
Claims 1-8, 11-14, and 17-20 are pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/26/2025 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.
Claims 1-8, 11-14, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 are directed to a method. Claims 11-14 are directed to a system. Claim 17-20 are directed to a non-transitory computer readable medium. Thus, on their face they fall within the four statutory categories of patentable subject matter.
Step 2A prong 1:
Claim 1 includes all of the limitations of claims 11 and 17. Claim 1 will be used as representative. Each claims additional elements will be addressed individually. The following limitations, when considered individually and as an ordered combination, are merely descriptive of abstract concepts:
Claims 1, 11, 17:
training a model using one or more prompts including historical driving behavior paired with one or more respective responses including feedback on the historical driving behavior to identify behavioral patterns in driving;
receiving driving behavior data associated with the driver;
inputting the driving behavior data associated with the driver into the model to generate a first response including feedback about the driving behavior of a driver, the model configured to:
correlate driving behavioral patterns with suggestions to improve driving,
analyze input driving behavior data associated with the driver to determine suggestions to improve the driving behavior of the driver,
and generate feedback regarding the driving behavior associated with the driver, wherein the feedback includes a potential impact of one or more suggested modifications the driver may make to improve driving behavior;
updating the first response to reduce a distance between the first response and a second response, wherein the updating is based on a first value indicating a preference for the first response, a second value indicating a preference for the second response, and a cost function;
presenting the first response including the feedback to the driver in paragraph form;
receiving additional driving behavior data; and
inputting the additional driving behavior into a model, wherein the model analyzes the additional driving behavior data to determine compliance with the feedback.
The following dependent claim limitations, when considered individually and as an ordered combination, are merely further descriptive of abstract concepts:
Claims 2, 12, 18:
wherein the driving behavior data includes one or more of: (i) acceleration data; (ii) braking data; (iii) cornering data; (iv) speed data; (v) location data and/or (vi) drive duration data.
Claim 3:
wherein the historical driving behavior includes time of day data and/or weather data.
Claims 4, 13, 19:
wherein receiving the driving behavior data comprises: receiving, from an entity associated with a vehicle operated by the driver, the driving behavior data.
Claims 5, 14, 20:
wherein receiving the driving behavior data comprises: receiving, from an entity, the driving behavior data.
Claim 6:
wherein the feedback further includes one or more of (i) effects of current driving behavior of the driver, and/or (ii) the one or more suggested modifications the driver may make to driving behavior improve driving behavior
The claims provide a manner of analyzing driver behavior data using a model and providing feedback to the driver based on the analyzed driver behavior data. Thus, when considered individually and as an ordered combination, the claims embody mental processes.
But for the inclusion of generic computing components, a human analog using pen and paper or in the mind would be able to receive driver behavior data, input the data into a model to analyze the driver behavior data, fine tune the model using historical driving behavior paired with responses to feedback on the historical driving data, correlate driving patterns with suggestions to improve driving, generate feedback including potential impacts of the suggestions, and present feedback to a driver based on the analysis in paragraph form. Thus, the claims fall within the mental process grouping of abstract ideas.
Step 2A prong 2: This judicial exception is not integrated into a practical application. The claims recite the following additional elements:
one or more processors (claims 1, 11); large language model (LLM) (claims 1, 8, 11, 17), wherein the LLM model includes at least one of: (i) an AI or machine learning (ML) chatbot and/or an AI or ML voice bot (claim 8); computing system associated with a vehicle operated by the driver (claims 4, 13, 19); mobile or other computing device (claims 5, 14, 20); wherein output presented to the driver is in the form of one or more of the following: (i) text; (ii) images; (iii) audio; (iv) video; (v) augmented reality and/or (vi) virtual reality (claim 7); machine learning model (claim 1, 11); one or more non-transitory memories storing processor-executable instructions (claim 11); non-transitory computer-readable medium storing processor-executable instructions (claim 17);
The one or more processors, computing system associated with a vehicle operated by the driver, mobile or other computing device, one or more non-transitory memories storing processor-executable instructions, and non-transitory computer-readable medium storing processor-executable instructions are recited at a high level of generality and amount to “applying” the abstract idea using generic computing components (see spec [0022]-[0027]). The devices merely send and receive data (receiving) and processes data (inputting, correlate, analyze, generate). Therefore, nothing in the claims improves upon computer themselves, technology, or a technical field (See MPEP 2106.05(f)).
The LLM model, wherein the LLM model includes at least one of: (i) an AI or machine learning (ML) chatbot and/or an AI or ML voice bot and the machine learning model are recited at a high level of generality. The claims do not discuss the actual algorithms used for the model and merely discuss the models functionality. Nothing in the claims improves LLM or machine learning technology or the technical field. Therefore, the LLM model and machine learning model do not go beyond the “apply it” level of implementation (See MPEP 2106.05(f)).
The presentation of the content being at least one or more of the following: (i) text; (ii) images; (iii) audio; (iv) video; (v) augmented reality and/or (vi) virtual reality merely provides a general link to a particular technological environment in which to practice the abstract concepts (i.e. digital environment vs written or verbal). Nothing in the claims improves any of these types of content, technology, or a technical field and merely acts as the medium to communicate the feedback (See MPEP 2106.05(h)).
Accordingly, when considered both individually and as an ordered combination, the additional elements do not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Similarly, as above with regard to practical application, the additional elements when considered both individually and as an ordered combination, do not provide an inventive concept as they merely provide generic computing components used as a tool to implement the abstract idea or provide a general link to a particular technological environment or field of use.
As a result, the claims are not patent eligible.
With regard to prior art:
The examiner was unable to find a suitable combination of references to teach each and every limitation in the context of the claimed invention. Specifically, the examiner was unable to find a suitable combination of references to teach “updating, by the one or more processors, the first response to reduce a distance between the first response and a second response, wherein the updating is based on a first value indicating a preference for the first response, a second value indicating a preference for the second response, and a cost function.”
Chintakindi et al (US2020/0104876) is considered the closest prior art. Chintakindi generally teaches receiving various data regarding a user driving a vehicle. A machine learning model is used to analyze the data and provide a user with suggestions for improving the users driving. The user can be provided with a reward for improved driving behavior including discounts or preferred rates on insurance.
Douglas et al (US 2024/0256780) teaches using a large language model to provide feedback and updating the model based on responses to the feedback.
Isackson et al (US 2023/0267512) teaches wherein the model is an AI chatbot for providing feedback to a user. The AI Chatbot can be configured to selectively generate messages conveying insights automatically, or in response to a user input.
Volos et al (US 2023/0343149) generally teaches identifying a driving improvement goal according to a determined driving score, monitoring multiple driving scores relative to the identified driving improvement goal, the multiple driving scores calculated based on multiple reconstruction error scores generated by the machine learning model over multiple time periods, and providing a status notification to a driver via the user interface, based on the monitored multiple driving scores and the identified driving improvement goal. An accurate assessment of a user's driving behavior may facilitate applying relevant goals and motivation (e.g., incentives, rewards, etc.) that are specific to a driver. The user is provided with rewards based on the assessment results.
Nordh (US 2022/0176971) generally teach using artificial intelligence to monitor driving and providing suggestions for how to improve driving.
Yu (US 2019/0122121) generally teaches using AI to monitor driving to improve driving safety.
Park (US 2018/0334176) generally teaches using AI to provide feedback to drivers to improve traffic culture and promote safe driving.
Response to Arguments
The examiner has considered but does not find persuasive applicant’s arguments regarding rejections under 35 USC 101. With respect to example 39, the examiner respectfully disagrees. The claims of example 39 are for training a neural network to perform facial recognition in a way that a human analog would not perform in the same way. The present claims merely train and use the LLM to analyze driving data. Applicant should specifically point out what part of training the model would not be able to be performed mentally or with pen and paper and then claim those aspects. The claims recite no meaningful details regarding the LLM such that a human would do any of the steps in a manner different than whether it was performed on a computer or mentally with paper and pencil.
The present claims further have nothing in common with the Desjardins findings. Desjardins was held to improve machine learning itself. Nothing in the present claims improves machine learning in any way. It’s use is merely recited at a high level of generality for the particular data of the invention. There is no process claimed that improves LLM’s themselves, technology, or a technical field.
The claims recite at a high level a generic updating step that reduces the distance between responses based on a first response, second response, and a cost function. The claims provide no details as to how this process is done and further such process is a known technique not invented by the applicant (See attached “8 Simple Techniques to Prevent Overfitting” by David Chuan-En Lin - 2020 – specifically 5. L1/L2 regularization.) As a result, such rejections have been maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER STROUD whose telephone number is (571)272-7930. The examiner can normally be reached Mon. - Fri. 9AM-5PM.
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CHRISTOPHER STROUD
Primary Examiner
Art Unit 3621B
/CHRISTOPHER STROUD/Primary Examiner, Art Unit 3621