DETAILED ACTION
This action is made FINAL in response to the amendments filed on 8/28/206.
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 11 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 11,
Step One
The claims are directed to a computer-implemented method (claims 11 - 19) and a vehicle with structural components (claim 20). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A, Prong One
The claim recites in part:
parsing the at least one tag associated with the rule to identify the one or more deterministic elements and the machine learning element;
For example, a human can determine the best tag or tags to identify the deterministic elements and the machine learning element
selecting a machine learning model corresponding to the machine learning element
For example, a human can select one of a plurality of machine learning models based on context.
evaluating the one or more deterministic elements based on a comparison with the set of values;
For example, a human can decide based on evaluations and comparisons.
evaluating the machine learning element based on the output generated by the selected machine learning model;
For example, a human can decide based on evaluations and comparisons.
determining whether the one or more conditions specified by the rule have been satisfied based on both the evaluation of the one or more deterministic elements and the evaluation of the machine learning element;
For example, a human can determine if a rule has been satisfied based on evaluations against the rule.
in response to determining that the one or more conditions have been satisfied, determining one or more actions specified by the rule, wherein the one or more actions modify at least one setting of a vehicle subsystem of the vehicle, wherein the vehicle subsystem comprises a climate control subsystem, an entertainment subsystem, or a seat adjustment subsystem
For example, a human can determine if a rule has been satisfied based on comparing data (set of values) against the rule. Based on the rule being satisfied a human can determine which action to take.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receiving a rule specifying one or more conditions that trigger an action, wherein the one or more conditions include one or more deterministic elements and a machine learning element, and wherein the rule includes at least one tag;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
wherein the machine learning model is trained by determining a
context of the machine learning element based on the one or more
deterministic elements, generating a set of data collection criteria based on the context, and collecting a set of vehicle data during operation of the vehicle when a current set of vehicle conditions satisfies the set of data
collection criteria;
executing the selected machine learning model by providing a set of values associated with operation of a vehicle as an input to the machine learning model to generate an output;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites:
causing the one or more actions to be performed
is recited at a high-level of generality and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The vehicle, vehicle subsystem, a climate control subsystem, an entertainment subsystem, or a seat adjustment subsystem are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of deterministic elements, machine learning element, and machine learning model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receiving a rule that includes one or more deterministic elements and a machine learning element
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
wherein the machine learning model is trained by determining a
context of the machine learning element based on the one or more
deterministic elements, generating a set of data collection criteria based on the context, and collecting a set of vehicle data during operation of the vehicle when a current set of vehicle conditions satisfies the set of data
collection criteria;
executing the selected machine learning model by providing a set of values associated with operation of a vehicle as an input to the machine learning model to generate an output;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The claim further recites:
causing the one or more actions to be performed
is recited at a high-level of generality and amounts to no more than 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. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The vehicle, vehicle subsystem, a climate control subsystem, an entertainment subsystem, or a seat adjustment subsystem are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of deterministic elements, machine learning element, and machine learning model amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claims 12,
Step 2A, Prong One
The claim recites in part:
wherein determining whether the one or more conditions have been satisfied comprises determining a condition corresponding to the machine learning element based on the output generated by the machine learning model
For example, a human can determine if an output is above a threshold (satisfies a condition).
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 13,
Step 2A, Prong One
The claim recites in part:
wherein determining the one or more actions comprises determining an action corresponding to the machine learning element based on the output generated by the machine learning model
For example, a human can determine an action based on an output of a computer program (machine learning model)
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 14,
Step 2A, Prong One
The claim recites in part:
wherein the output generated by the machine learning model indicates one or more predicted values associated with a vehicle, and wherein determining whether the one or more conditions have been satisfied comprises evaluating the one or more conditions based on the one or more predicted values
For example, a human can predict values, as humans have been making predictions before computers were even invented.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 15,
Step 2A, Prong One
The claim recites in part:
wherein the output generated by the machine learning model indicates a predicted condition, and wherein determining that the one or more conditions have been satisfied comprises determining whether the predicted condition is satisfied based on the set of values
For example, a human can predict conditions, as humans have been making predictions before computers were even invented.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 16,
Step 2A, Prong One
The claim recites in part:
wherein the output generated by the machine learning model indicates whether at least one condition included in the one or more conditions has been satisfied
For example, a human can determine if an output is above a threshold (satisfies a condition).
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 17,
Step 2A, Prong One
The claim recites in part:
wherein the output generated by the machine learning model indicates a target value associated with a vehicle, and wherein determining the one or more actions comprises identifying at least one action for operating the vehicle to obtain the target value
For example, a human can determine target data and compare one or more actions to the target data.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 18,
Step 2A, Prong One
The claim recites in part:
wherein the output generated by the machine learning model indicates a predicted action, and wherein the one or more actions include the predicted action
For example, a human can predict actions, as humans have been making predictions before computers were even invented.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claims 19,
Step 2A, Prong One
The claim recites in part:
wherein causing the one or more actions to be performed comprises: identifying one or more vehicle components associated with the one or more actions;
For example, a human can predict associated vehicle components with actions.
generating one or more commands for the one or more vehicle components
For example, a human can generate a command using a generic computer component.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Claim 20 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The vehicle, plurality of vehicle components, one or more memories, and one or more processors are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Response to Arguments
Applicant's arguments filed 8/28/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 103
The newly added limitations overcome the 103 Rejection, and the 103 Rejection has been withdrawn.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
Additionally, Applicant further submits that the claims recite limitations that integrate any purported abstract idea into a practical application. See MPEP § 2106.04(d). In Ex Parte Desjardins, the Appeals Review Panel of the Patent Trial and Appeal Board explained that "[o]n the one hand, claims '[g]enerally linking the use of a judicial exception to a particular technological environment or field of use' are not patent eligible [o]n the other, claims directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field are patent eligible." See Ex Parte Desjardins, Decision on Request for Rehearing at 7-8; see also August 4, 2025 Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 at 4-5 ("[i]n computer-related technologies, examiners can conclude that claims are eligible in Step 2A Prong Two by finding that a claim reflects an improvement to the functioning of a computer or to another technology or technical field, integrating a recited judicial exception into a practical application of the exception"); MPEP § 2106.04(d) (referencing Ex Parte Desjardins). Further, the Appeals Review Panel of the Patent Trial found in Ex Parte Desjardins that claims directed to a "computer-implemented method of training a machine learning model" constituted an improvement to how the machine learning model itself operates, which integrated a mathematical calculation into a practical application. See MPEP § 2106.04(d)(III). Applicant submits that the claims meet this standard.
Importantly, the claimed approach is directed towards an improvement to machine learning technology, including how a machine learning model can be utilized to modify settings of a vehicle subsystem of a vehicle. As the present Application makes clear, the claimed approach imparts the technological improvement of an automation system creates and executes vehicle automation routines that flexibly adapt to the behavior and preferences of individual users. Additionally, the automation system more accurately performs actions that conform to the expectations and preferences of the individual user compared to prior art approaches. Furthermore, because the automation system allows users to identify specific portions of an automation routine that should be learned based on user behavior, users do not have to fully define both the conditions and the actions for an automation routine when creating the automation routine. As a result, using the disclosed techniques, a user can create an automation routine even when the user does not know the exact scenario in which they would like a given action to be taken and/or the exact action(s) that they would like to have taken, which the user could not do using prior art approaches. See Application at paragraph [0009]. The approach recited in the claims provides a specific solution that solves the referenced problems that existed in prior art solutions. Thus, among other things, the claimed approach solves the technical problems that existed in the prior art.
The examiner disagrees: The applicant mentions ‘Ex Parte Desjardins’ as an example but the applicant does not explain how the cited example is relevant to the presently claimed invention. The example is not tied to the claimed features, nor is any comparison provided demonstrating how it supports patent eligibility. It is unclear why the applicant relies on this example. The applicant’s arguments improperly rely on the complexity of the ‘Ex Parte Desjardins’ example and the specification rather than the actual claims that were written. In Ex Parte Desjardins the claims reflected an improvement to the operation of the machine learning model itself. Merely employing a model to determine preferred vehicle setting does not provide a comparable improvement. The applicant’s discussion of paragraph [0009] does not adequately connect the asserted benefits to the claim limitations providing such an improvement.
The so-called benefits of adapting automation routines to user preferences, improving conformity with user expectations, and reducing manual input do not establish an improvement to machine learning or vehicle technology. The applicant has not identified specific claim limitations that provide a technical improvement in how the model learns or how the vehicle subsystem operates, rather than using machine learning to achieved personalized results.
The applicant argues:
Accordingly, similar to the claims at-issue in Ex Parte Desjardins, the claimed approach provides an improvement to machine learning technology, including to how a machine learning model can be utilized to modify settings of a vehicle subsystem of a vehicle. These improvements to technology are effectuated by the pending claims through the express steps of receiving a rule that includes one or more deterministic elements and a machine learning element; identifying a machine learning model corresponding to the machine learning element; determining whether one or more conditions specified by the rule have been satisfied based on a comparison with a set of values associated with operation of a vehicle; in response to determining that the one or more conditions have been satisfied, determining one or more actions specified by the rule, wherein the one or more actions modify at least one setting of a vehicle subsystem of the vehicle; and causing the one or more actions to be performed. The steps also include determining whether the one or more conditions have been satisfied or determining the one or more actions specified by the rule is based on output generated by the machine learning model in response to the machine learning model receiving the set of values as input. Accordingly, an improvement to machine learning technology is effectuated by at least these steps recited in the claims.
Additionally, the claims provide a strong nexus between the claims and the technical advantages set forth above that are described in Applicant's specification in a manner analogous to ex parte Desjardins. When read in view of Applicant's Specification, the claims recite a particular way to achieve a desired outcome, rather than reciting the idea of a solution. Accordingly, the claims are subject-matter eligible, as supported by the MPEP and the Board decision in ex parte Desjardins. As the foregoing illustrates, any purported abstract idea recited in claims is necessarily integrated into a practical application. Accordingly, the claims are subject-matter eligible.
Because the claims are directed towards an improvement to technology, the claims necessarily integrate any purported abstract idea into a practical application and, accordingly, are not abstract. See MPEP §§ 2106.04(d), 2106.05(a)(II). Therefore, the claims are subject-matter eligible under 35 U.S.C. § 101, and Applicants request the rejection be withdrawn.
The examiner disagrees. The identified limitations use machine learning output to evaluate rules and modify vehicle settings, but do no establish how the claimed combination improves machine learning or vehicle technology. Unlike Ex Parte Desjardins, the so-called improvement uses a model to obtain preferred results rather than improving the model’s operation. Applicant has not established integration of the abstract idea into a practical application.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
