DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/01/2026 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 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:
identifying 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.
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;
For example, a human can determine if a rule has been satisfied based on comparing data (set of values) 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.
wherein at least one of 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
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 based on the output of a computer program (machine learning model)
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 that includes one or more deterministic elements and a machine learning element
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;
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;
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)).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 11 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over HUSAIN (US 2022/0227231) in view of MEHDIPOUR et al (US 2023/0399014).
As to claim 11, HUSAIN et al teaches a computer-implemented method for automating vehicle routines (paragraph [0097]… applying artificial intelligence at an electric or hybrid electric vehicle for automated actuation of mechanical or electromechanical components, improved battery safety, or both), the method comprising:
receiving a rule that includes one or more deterministic elements and a machine learning element; (paragraph [0025]… vehicle operation data (136) may, in addition to or instead of sensor data (124), include control inputs (130). In some examples, the control inputs (130) may be, or are included as part of, vehicle operation data (136), where the control inputs (130) may be generated via vehicle operation sensors (not depicted) coupled to the one or more operator controls (128) and indicative of operation of the vehicle (102) ; paragraph [0028]… the one or more processors (120) may include, or are coupled to, a vehicle operation data interface (122) (“VOD I/F”) that is configured to receive vehicle operation data (136)) (Examiner’s Note: “a vehicle operation data interface (122) that is configured to receive vehicle operation data (136)” reads on “receiving a rule” ; “vehicle operation data (136) may, in addition to sensor data (124) include control inputs (130)” reads on “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, 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; (paragraph [0027]… the memory (112) may be configured to store one or more trained models (114) that are executable by the one or more processors (120) to determine operating characteristics related to the vehicle (102) based on various sensor and control inputs ; paragraph [0034]… the one or more trained models (114) are described as including the motor control model (116), in other implementations, the one or more trained models (114) also include other trained models that can provide inputs to, or operate in parallel with, the motor control model (116). Other trained models that may be included in the one or more trained models (114) include a travel type model, a fleet operation model, an operator type model, or any combination thereof, as described further with reference to FIGS. 2-4B. ; paragraph [0101]… determining (1004), using a trained model, whether the one or more components (1062) of the electric vehicle (102) are operating in an acceptable manner at least partially based on the operation data (1052) may be carried out to identify components that are not operating in an optimal, near-optimal, or other manner that satisfies a target threshold. For example, the operation data (1052) may reveal that the electric motor is operating at 60% of its load whereas the optimal or target load for the electric motor is achieved when the electric motor is operating at 75% of its load. Likewise, the operation data (1052) may reveal that while battery usage and the current operating characteristics of the battery do not create a dangerous situation, battery usage and the current operating characteristics of the battery may indicate that the battery is being utilized in a manner that causes battery life to be reduced at a rate that exceeds or satisfies a predetermined threshold. For example, while any operation of the vehicle (102) (including very efficient operation of the vehicle) may cause battery life to be reduced, reducing battery life at a rate that exceeds or satisfies a predetermined threshold may be flagged such that vehicle operation can be adjusted, as explained in greater detail above and below. Readers will appreciate that determining (1004), using a trained model, whether the one or more components (1062) of the electric vehicle (102) are operating in an acceptable manner at least partially based on the operation data (1052) can therefore include determining whether the one or more components are operating in a potentially dangerous manner, determining whether the one or more components are operating in an inefficient manner, determining whether the one or more components are operating in a manner that can reduce the expected life of one or more components in the vehicle, and so on)(Examiner’s Note: the memory (112) may be configured to store one or more trained models (114) that are executable by the one or more processors (120) to determine operating characteristics related to the vehicle (102) based on various sensor and control inputs” reads on “identifying a machine learning model corresponding to the machine learning element” ; “the one or more trained models (114) also include other trained models that can provide inputs to, or operate in parallel with, the motor control model (116)” reads on “wherein the machine learning model is trained by determining a context of the machine learning element based on the one or more deterministic elements” ; “the operation data (1052) may reveal that while battery usage and the current operating characteristics of the battery do not create a dangerous situation, battery usage and the current operating characteristics of the battery may indicate that the battery is being utilized in a manner that causes battery life to be reduced at a rate that exceeds or satisfies a predetermined threshold” reads on “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”);
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 (paragraph [0101]… determining (1004), using a trained model, whether the one or more components (1062) of the electric vehicle (102) are operating in an acceptable manner at least partially based on the operation data (1052) may be carried out to identify components that are not operating in an optimal, near-optimal, or other manner that satisfies a target threshold. For example, the operation data (1052) may reveal that the electric motor is operating at 60% of its load whereas the optimal or target load for the electric motor is achieved when the electric motor is operating at 75% of its load)(Examiner’s Note: “determining (1004), using a trained model, whether the one or more components (1062) of the electric vehicle (102) are operating in an acceptable manner at least partially based on the operation data (1052) may be carried out to identify components that are not operating in an optimal, near-optimal, or other manner that satisfies a target threshold.” reads on “”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 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 (paragraph [0104]… an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification. In such an example, generating (1006) a control signal (1056) to adjust operation of the one or more components (1062) of the electric vehicle (102) can include generating a control signal that causes an in-dash component, portion of an entertainment system, portion of a notification system, or some other component of the vehicle to deliver a notification via natural language presentation ; paragraph [0105]… The adjusted profile may, for example, cause the usage of the entertainment system to be limited in order to conserve battery power, cause the usage of a climate control system to be limited in order to conserve battery power, may limit the rate at which the vehicle can accelerate in order to conserve battery power, may change the extent to which autonomous driving features are be utilized to conserve battery power, or other alterations to the operation of the vehicle (102) may be made to conserve battery power)(Examiner’s Note: “an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification…from a portion of an entertainment system” reads on “in response to determining that 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 an entertainment subsystem); and
causing the one or more actions to be performed (paragraph [0104]… an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification. In such an example, generating (1006) a control signal (1056) to adjust operation of the one or more components (1062) of the electric vehicle (102) can include generating a control signal that causes an in-dash component, portion of an entertainment system, portion of a notification system, or some other component of the vehicle to deliver a notification via natural language presentation)(Examiner’s Note: “an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification…from a portion of an entertainment system” reads on “causing the one or more actions to be performed”);
wherein at least one of 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 (paragraph [0106]… In such an example, AI techniques can be used to correlate temperature data to profiles, and, when certain profiles are detected, automatic safety actions can be performed, such as disconnecting a particular battery or cell thereof)(Examiner’s Note: “when certain profiles are detected, automatic safety actions can be performed, such as disconnecting a particular battery or cell thereof” reads on “wherein at least one of 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”).
HUSAIN et al fails to explicitly show/teach that the vehicle operation data is a rule.
MEHDIPOUR et al teaches the vehicle operation data is a rule (paragraph [0090]…a rule indicative of a target expressive operation of the autonomous vehicle can be seen as a rule that evaluates and optionally enforces an expressive operation of the AV e.g. by selecting a trajectory based on a violation metric indicative of a non-violation of the rule. For example, a rule indicative of a target expressive operation of the autonomous vehicle can be seen as a rule configured to promote an operation of the AV, such as a particular behavior of the AV near a road user. A rule indicative of a target expressive operation of the autonomous vehicle can be seen as an expressive rule, such as a rule for expressive yielding. The rule indicative of the target expressive operation can include a rule scope and/or a rule statement. The rule scope can include the first criterion for initiating application of the rule. The first criterion may be seen as an entry criterion for entering the rule. The rule statement can include a second criterion for detecting the satisfaction of the rule. A rule may include one or more criterion related to a set of operations, such as actions, boundaries, and/or constraints, that the system 500 can be configured to operate the vehicle within. The rule may be based on evaluating an operation of the autonomous vehicle by the system 500 with respect to one or more criteria. A rule can detect whether an operation has been an expressive operation, such as an expressive maneuver, of the autonomous vehicle by the system 500. The rule can be configured to detect whether an operation of the autonomous vehicle by the system 500 is a violation. The rule may be obtained from a plurality of rules, such as a list of rules and/or a database of rules. The rule may be obtained from a single rule).
Therefore, HUSAIN et al discloses the claimed invention except for explicitly show/teach that the vehicle operation data is a rule. It would have been obvious to one having ordinary skill in the art at the time the invention was made that the vehicle operation data is a rule since it was known in the art that autonomous vehicles operate on a large amount of rules in order for the vehicle to operate. The rule(s) indicative of a target expressive operation.
As to claim 12, HUSAIN et al teaches the computer-implemented method, 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 (paragraph [0105]… AI model may receive the operating conditions, a previous predicted range, and/or other data as input, and may output a predicted remaining range of the vehicle)(Examiner’s Note: “AI model may receive the operating conditions, a previous predicted range, and/or other data as input, and may output a predicted remaining range of the vehicle” reads on “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”).
As to claim 13, HUSAIN et al teaches the computer-implemented method, 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(paragraph [0104]… an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification. In such an example, generating (1006) a control signal (1056) to adjust operation of the one or more components (1062) of the electric vehicle (102) can include generating a control signal that causes an in-dash component, portion of an entertainment system, portion of a notification system, or some other component of the vehicle to deliver a notification via natural language presentation)(Examiner’s Note: “an action to be taken in response to detecting that one or more components of the vehicle (102) are operating in a particular way can include generating a notification…from a portion of an entertainment system” reads on “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”).
As to claim 14, HUSAIN et al teaches the computer-implemented method, 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 (paragraph [0121]… generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future. For example, predicted traffic information for afternoon traffic on a set of routes may be generated in the morning before the actual state of traffic on each route is actually known. In the example method depicted in FIG. 12, a trained model may be responsible for generating (1208) the predicted traffic information for a plurality of routes. Machine learning techniques may be utilized to generate and train the model, for example, by providing traffic data that was collected over time to the model as training data. In such a way, a model may be generated that can accurately predict traffic data for the plurality of routes)(Examiner’s Note: “generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future.” reads on “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”).
As to claim 15, HUSAIN et al teaches the computer-implemented method, 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 (paragraph [0121]… generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future. For example, predicted traffic information for afternoon traffic on a set of routes may be generated in the morning before the actual state of traffic on each route is actually known. In the example method depicted in FIG. 12, a trained model may be responsible for generating (1208) the predicted traffic information for a plurality of routes. Machine learning techniques may be utilized to generate and train the model, for example, by providing traffic data that was collected over time to the model as training data. In such a way, a model may be generated that can accurately predict traffic data for the plurality of routes)(Examiner’s Note: “generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future.” reads on “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”).
As to claim 16, HUSAIN et al teaches the computer-implemented method, 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 (paragraph [0121]… generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future. For example, predicted traffic information for afternoon traffic on a set of routes may be generated in the morning before the actual state of traffic on each route is actually known. In the example method depicted in FIG. 12, a trained model may be responsible for generating (1208) the predicted traffic information for a plurality of routes. Machine learning techniques may be utilized to generate and train the model, for example, by providing traffic data that was collected over time to the model as training data. In such a way, a model may be generated that can accurately predict traffic data for the plurality of routes)(Examiner’s Note: “generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future.” reads on “the output generated by the machine learning model indicates whether at least one condition included in the one or more conditions has been satisfied”).
As to claim 17, MEHDIPOUR et al teaches 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 (paragraph [0090]…a rule indicative of a target expressive operation of the autonomous vehicle can be seen as a rule that evaluates and optionally enforces an expressive operation of the AV e.g. by selecting a trajectory based on a violation metric indicative of a non-violation of the rule. For example, a rule indicative of a target expressive operation of the autonomous vehicle can be seen as a rule configured to promote an operation of the AV, such as a particular behavior of the AV near a road user. A rule indicative of a target expressive operation of the autonomous vehicle can be seen as an expressive rule, such as a rule for expressive yielding. The rule indicative of the target expressive operation can include a rule scope and/or a rule statement. The rule scope can include the first criterion for initiating application of the rule. The first criterion may be seen as an entry criterion for entering the rule. The rule statement can include a second criterion for detecting the satisfaction of the rule. A rule may include one or more criterion related to a set of operations, such as actions, boundaries, and/or constraints, that the system 500 can be configured to operate the vehicle within. The rule may be based on evaluating an operation of the autonomous vehicle by the system 500 with respect to one or more criteria. A rule can detect whether an operation has been an expressive operation, such as an expressive maneuver, of the autonomous vehicle by the system 500. The rule can be configured to detect whether an operation of the autonomous vehicle by the system 500 is a violation. The rule may be obtained from a plurality of rules, such as a list of rules and/or a database of rules. The rule may be obtained from a single rule)(Examiner’s Note: “a rule indicative of a target expressive operation of the autonomous vehicle can be seen as a rule that evaluates and optionally enforces an expressive operation of the AV e.g. by selecting a trajectory based on a violation metric indicative of a non-violation of the rule” reads on “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”).
It would have been obvious for 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 the same reasons as above.
As to claim 18, HUSAIN et al teaches the computer-implemented method, wherein the output generated by the machine learning model indicates a predicted action, and wherein the one or more actions include the predicted action value (paragraph [0121]… generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future. For example, predicted traffic information for afternoon traffic on a set of routes may be generated in the morning before the actual state of traffic on each route is actually known. In the example method depicted in FIG. 12, a trained model may be responsible for generating (1208) the predicted traffic information for a plurality of routes. Machine learning techniques may be utilized to generate and train the model, for example, by providing traffic data that was collected over time to the model as training data. In such a way, a model may be generated that can accurately predict traffic data for the plurality of routes)(Examiner’s Note: “generating (1208), in dependence upon the traffic data, predicted traffic information for a plurality of routes. The predicted traffic information for a plurality of routes may be embodied, for example, as information that is describing the anticipated state of traffic on a particular route at a particular time in the future.” reads on “the output generated by the machine learning model indicates a predicted action, and wherein the one or more actions include the predicted action value”).
As to claim 19, HUSAIN et al teaches the computer-implemented method, wherein causing the one or more actions to be performed comprises: identifying one or more vehicle components associated with the one or more actions; and generating one or more commands for the one or more vehicle components (paragraph [0026]…Examples of sensor data (124) include various measurements corresponding to temperatures, pressures, motor speed, battery condition, air intake and exhaust flows, exhaust oxygen levels, one or more other measurements, or any combination thereof. Examples of the control inputs (130) includes data representing position and movement of one or more operator controls (128), such as from one or more sensor coupled to a throttle (e.g., an accelerator pedal), a brake pedal, a steering wheel, a traction control button, a ride height control, a cruise control, one or more other controls, or any combination thereof)(Examiner’s Note: “sensor data (124) include various measurements corresponding to temperatures, pressures, motor speed, battery condition, air intake and exhaust flows, exhaust oxygen levels, one or more other measurements, or any combination thereof” reads on “identifying one or more vehicle components associated with the one or more actions” ; “the control inputs (130) includes data representing position and movement of one or more operator controls (128), such as from one or more sensor coupled to a throttle (e.g., an accelerator pedal), a brake pedal, a steering wheel, a traction control button, a ride height control, a cruise control, one or more other controls, or any combination thereof” reads on “generating one or more commands for the one or more vehicle components”).
Claim 20 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Response to Arguments
Applicant's arguments filed 5/01/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 102
Applicant’s arguments with respect to claim(s) 11 - 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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:
First, according to the 2019 Guidance, for a claim to constitute an abstract idea, the claim must fall within the subject matter grouping of at least one of: mathematical concepts, certain methods of organizing human activity, or mental processes. See MPEP
In that regard, the amended claims do not recite any mathematical relations, formulas, or calculations. See MPEP § 2106.04(a)(2)(I). See Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. § 101 at p. 3 ("[e]xaminers should be careful to distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis)"); see also MPEP 2106.04(a)(2).
Notably, though, the amended claims quite clearly do not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols. See Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 at p. 3 ("the limitation [must] set forth or describe mathematical relationships, calculations, formulas, or equations using words or mathematical symbols," or else the limitation merely involves or relies on a mathematical concept); see also MPEP 2106.04(a)(2).
In addition, the amended claims do not recite any limitations that can be properly interpreted as being directed towards organizing human activities that are categorized as abstract, such as fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people. See MPEP § 2106.04(a)(2)(II). Accordingly, the amended claims cannot be considered abstract under the category of organizing human activities.
The examiner disagrees because the applicant only focuses on whether the claims expressly recite mathematical concepts or or organizing human activity, but do not address the actual rejection. A claim may still recite an abstract idea as a mental process even without explicitly formulas or equations. The claimed evaluation, analysis, and determination steps reasonable can be performed mentally or with pen and paper under a broad interpretation. The claims recite an abstract idea without integrating it into a practical application.
The applicant argues:
Lastly, the amended claims are not directed towards mental processes because the claims recite limitations cannot be practically performed in the human mind or using pen and paper. See Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 at 2 ("[a] claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)"); see also MPEP § 2106.04(a)(2)(III). For example, the amended claims recite the specific limitations 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, 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; 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, wherein the vehicle subsystem comprises a climate control subsystem, an entertainment subsystem, or a seat adjustment subsystem; and causing the one or more actions to be performed; wherein at least one of 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.
These limitations quite clearly require the use of a computing device and are not operations that can be performed in someone's mind or using pen/paper. For at least these reasons, Applicant submits that the amended claims cannot be properly interpreted as being directed towards a mental process. See MPEP § 2106.04(III) ("[s]tep 2A determines whether: [t]he claim as a whole is directed to a judicial exception"); see also MPEP § 2106.04(II) ("[t]he claim as a whole is not directed to a judicial exception (Step 2A: NO) and thus is eligible at Pathway B, thereby concluding the eligibility analysis").
Because none of the limitations recited in the amended claims are directed towards any of the enumerated categories of abstract ideas, the amended claims cannot be properly interpreted as being abstract.
The examiner disagrees as the arguments not persuasive because merely reciting machine learning, vehicle data, or computer implementation does not remove the claims from the mental process category where the underlying steps are still directed to evaluation, comparison, determination, and decision-making. The claims broadly recite analyzing inputs, determining whether conditions are satisfied, and selecting responsive actions, which are act that can practically be performed mentally.
As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
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The applicant argues:
Lastly, the amended claims are not directed towards mental processes because the claims recite limitations cannot be practically performed in the human mind or using pen and paper. See Memorandum: Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101 at 2 ("[a] claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)"); see also MPEP § 2106.04(a)(2)(III). For example, the amended claims recite the specific limitations 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, 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; 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, wherein the vehicle subsystem comprises a climate control subsystem, an entertainment subsystem, or a seat adjustment subsystem; and causing the one or more actions to be performed; wherein at least one of 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.
These limitations quite clearly require the use of a computing device and are not operations that can be performed in someone's mind or using pen/paper. For at least these reasons, Applicant submits that the amended claims cannot be properly interpreted as being directed towards a mental process. See MPEP § 2106.04(III) ("[s]tep 2A determines whether: [t]he claim as a whole is directed to a judicial exception"); see also MPEP § 2106.04(II) ("[t]he claim as a whole is not directed to a judicial exception (Step 2A: NO) and thus is eligible at Pathway B, thereby concluding the eligibility analysis").
Because none of the limitations recited in the amended claims are directed towards any of the enumerated categories of abstract ideas, the amended claims cannot be properly interpreted as being abstract.
The examiner disagrees. The arguments are not persuasive because the recited use of machine learning, vehicle data, and subsystem control does not, by itself, remove the claims from the mental process category. The claims simply recite receiving information, evaluation conditions, comparing data, determining whether criteria are met, and selecting responsive actions, which are forms of observation, evaluation, and decision-making that can practically be performed mentally. Simply automating these abstract steps with generic computing components or machine learning does not integrate the exception into a practical application.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Conclusion
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).
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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