Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS’s) submitted on 2/7/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 – 3, 5 – 13, & 15 – 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of generating driving instructions for a vehicle (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories. Similarly, Claims 11 & 20 are respectively directed towards non-transitory computer-readable media and a system for generating driving instructions for a vehicle, respectively, and are also within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 20 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 20 recites:
A system, comprising:
one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to:
receive first text that includes a description of a scene and a first plan for driving a vehicle,
extract at least one portion of a set of traffic rules based on the description of the scene and the first plan, [mental process/step]
generate a first prompt that requests driving instructions and includes the description of the scene, the first plan, and the at least one portion of the set of traffic rules, [mental process/step]
process the first prompt via a first trained language model to generate a second plan for driving the vehicle, and [mental process/step]
generate driving instructions based on the second plan. [mental process/step]
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “extract…” in the context of this claim encompasses a person looking at data collected regarding a scene and first plan, and forming a simple judgement as to the traffic rules. Similarly, “generate a first prompt…” in the context of the claim encompasses a person generating a prompt to request driving instructions based on data collected, which is a mental process under its broadest reasonable interpretation. Further, “process…” in the context of the claim encompasses utilizing a trained language model to generate a plan for driving a vehicle based on an input prompt determined in the previous step, which is a mental process of evaluating data and forming a simple judgment. Finally, “generate driving instructions…” in the context of the claim encompasses a person evaluating data collected regarding the second plan, and determining driving instructions based on such, which is a process of evaluating data collected and forming a simple judgment as to the driving instructions required to execute the plan, which is a mental process under its broadest reasonable interpretation. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A system, comprising: [Apply it, 2106.05(f)]
one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
receive first text that includes a description of a scene and a first plan for driving a vehicle, pre-solution activity (data gathering), 2106.05(g)]
extract at least one portion of a set of traffic rules based on the description of the scene and the first plan,
generate a first prompt that requests driving instructions and includes the description of the scene, the first plan, and the at least one portion of the set of traffic rules,
process the first prompt via a first trained language model to generate a second plan for driving the vehicle, and
generate driving instructions based on the second plan.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “one or more memories…,” “one or more processors…,” and “receive first text…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the “receive first text…” step is recited at a high level of generality (i.e. as a general means of gathering a description of a scene and a first plan for driving a vehicle), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Further, the “one or more memories…” and “one or more processors…” are recited at a high-level of generality (i.e., as generic processor(s) and memory for performing a computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using one or more memories and processors to perform the steps of the mental process amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitation of “receive first text…,” the examiner submits that this limitation is insignificant extra-solution activity.
Dependent claim(s) 2 – 3, 5 – 10, 12 – 13, & 15 – 19 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically:
Claim 2 recites wherein a second text is received indicating a situation which the prompt includes, and the set of traffic rules being based on said situation, which is the receipt of data and the evaluation of which under its broadest reasonable interpretation.
Claim 3 recites wherein the extracting of traffic rules comprises generating a prompt to extract keywords, which is a mental process of evaluating data under its broadest reasonable interpretation.
Claim 5 recites wherein the driving instructions are output using a speaker device, which is the insignificant extra-solution activity of outputting the results of the mental process using generic components that are well known in the art under the broadest reasonable interpretation of the claim.
Claim 6 recites wherein the driving instructions are output using a display device, which is the insignificant extra-solution activity of outputting the results of the mental process using generic components that are well known in the art under the broadest reasonable interpretation of the claim.
Claim 7 recites wherein a vision language model is utilized to process image data to generate a description of the scene, which is the receipt of data and the evaluation of such under its broadest reasonable interpretation.
Claim 8 recites wherein a first plan is generated based on a second trained language model and second text indicating sensor data/status information associated with the vehicle, which, similarly to as set forth in the independent claim(s), is the receipt of data and the evaluation of which under its broadest reasonable interpretation.
Claim 9 recites wherein the set of traffic rules are included in a driving handbook, which merely recites the insignificant extra-solution activity of receiving data under the broadest reasonable interpretation of the claim.
Claim 10 recites wherein the first trained language model comprises a trained large language model, which merely recites a specific mathematical evaluation framework with which data is evaluated, which is an abstract idea under the broadest reasonable interpretation of the claim.
Claim 19 recites wherein the first plan and text are received from either a user or trained machine learning model, which merely recites the insignificant extra-solution activity of receiving data under the broadest reasonable interpretation of the claim.
Claims 12, 13, & 15 – 18 recite substantially similar limitations as those found in Claims 2, 3, & 5 – 8, and are rejected under similar rationale as set forth above.
Therefore, dependent claims 2 – 3, 5 – 10, 12 – 13, & 15 – 19 are not patent eligible under the same rationale as provided for in the rejection of Independent Claims 1, 11, & 20.
Therefore, claim(s) 1 – 3, 5 – 13, & 15 – 20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4, 9 - 11, 14, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandraker (US 2025/0145176 A1) in view of Wang (US 2022/0289238 A1).
Regarding Claim 1:
Chandraker discloses: A computer-implemented method for controlling a vehicle, the method comprising: (Chandraker discloses in at least Paragraphs 0004 & 0005 a method for operating a vehicle executable by a hardware processor and memory, the method including the prompting of a large language model to generate parameters for a rule-based planner, and the generation of a vehicle trajectory using the planner)
receiving first text that includes a description of a scene and… (Chandraker discloses in at least Paragraphs 0027, 0032, & 0036 wherein text-based scene descriptions, including vehicular positioning, traffic light status, and lane information, as well as a goal that states the target destination of the vehicle may be provided as an input to the LLM to generate a navigational plan for the vehicle [i.e. receiving first text that includes a description of a scene])
generating a first prompt that requests driving instructions and includes the description of the scene, the first plan, and the at least one portion of the set of traffic rules; (Chandraker discloses in at least Paragraph 0036 wherein a prompt may be supplied to the LLM from an LLM reasoning module, the prompt instructing the LLM to generate achievable sub-goals to reach a destination [i.e. generating a first prompt that requests driving instructions]. At least Paragraphs 0035 & 0036 of Chandraker further teach wherein the prompt may describe the positions and velocities of other vehicles [i.e. the prompt includes the description of the scene], include parameters, such as traffic rules [i.e. the prompt includes at least one portion of the set of traffic rules], and provide the target destination of the vehicle [i.e. a first plan])
processing the first prompt via a first trained language model to generate a second plan for driving the vehicle; and (Chandraker discloses in at least Paragraphs 0041 – 0043 wherein at each time step a prompt is issued from the LLM interface to the LLM, the prompt containing up-to-date scene information and position history for surrounding objects, as well as the prompt information set forth above. The LLM then proposes a trajectory for the vehicle based on the issued prompt, which may be scored and selected if the scoring meets a predefined criteria [i.e. a second plan for driving the vehicle is generated based on processing the first prompt via a first trained language model]. At least Paragraphs 0060 – 0062 of Chandraker further disclose the generation of trajectories by the rule-based planner based on the parameters output from the LLM when said LLM is prompted)
generating driving instructions based on the second plan. (Chandraker discloses in at least Paragraphs 0006 & 0060 – 0062 wherein based on the selected trajectory [i.e. based on the second plan], an action may be taken to execute the selected trajectory, including triggering the ECU to perform a driving action to implement the trajectory, including through braking, steering, and/or acceleration actions [i.e. generating driving instructions based on the second plan])
Chandraker however appears to be silent regarding:
receiving… a first plan for driving a vehicle;
extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan;
However Wang teaches wherein traffic rules may be extracted based on associating keywords tagged to rules stored in a database with vehicle state and environmental information to retrieve relevant traffic rules to the current context.
receiving… a first plan for driving a vehicle; (However Wang teaches in at least Paragraphs 0033 & 0057 wherein an external device may receive information, including information on environmental conditions and vehicle states, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory [i.e. receiving a first plan for driving a vehicle] as taught in at least Paragraph 0038 of Wang)
extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan; (However Wang teaches in at least Paragraphs 0046 – 0050 wherein a database of traffic laws may be pre-processed, including by adding tags or labels to the information corresponding to category-based keywords to traffic rules. Wang further teaches in at least Paragraph 0057 wherein based on the vehicle state, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory as taught in at least Paragraph 0038 [i.e. the first plan] and environmental information [i.e. the description of the scene], an information category is obtained and utilized to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database [i.e. extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the extraction of traffic rules based on the determined vehicle state and environmental information as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraph 0050, by tagging the traffic rules in the database with keywords associated with the situation, identifying the situation based on the vehicle state, and evaluating the present state & environment of the vehicle to compare to the identified keywords, the efficiency of retrieving relevant traffic rules may be improved.
Regarding Claim 4:
The computer-implemented method of claim 1, wherein the driving instructions are transmitted to a trained planning model, and further comprising: generating, by the trained planning model and based on the driving instructions, one or more trajectories for the vehicle; and causing one or more operations to control the vehicle to be performed based on the one or more trajectories.
Chandraker discloses in at least Paragraphs 0006 & 0060 – 0062 wherein the rule based planner [i.e. a trained planning model] may generate a set of trajectories [i.e. generating, by the trained planning model and based on the driving instructions, one or more trajectories for the vehicle], select a trajectory, and an action may be taken to execute the selected trajectory, including triggering the ECU to perform a driving action to implement the trajectory [i.e. causing one or more operations to control the vehicle to be performed based on the one or more trajectories].
Regarding Claim 9:
The computer-implemented method of claim 1, wherein the set of traffic rules are included in a driving handbook.
Chandraker appears to be silent regarding wherein the set of traffic rules are included in a driving handbook.
However Wang teaches in at least Paragraphs 0041 & 0042 wherein the traffic rule information obtained from the external device may include traffic rules documented in a traffic rule manual or guide [i.e. the set of traffic rules are included in a driving handbook]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the acquisition of traffic rules from a manual or handbook as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraphs 0041 & 0042, the system may acquire traffic rules that are not expected to change over time through official documentation, improving the acquisition of relevant traffic rules.
Regarding Claim 10:
The computer-implemented method of claim 1, wherein the first trained language model comprises a trained large language model.
Chandraker discloses in at least Paragraphs 0018, 0029, & 0030 wherein a large language model [LLM] may be used to determine behavioral sub-goals [i.e. wherein the first trained language model comprises a trained large language model].
Regarding Claim 11:
Chandraker discloses: One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the steps of: (Chandraker discloses in at least Paragraphs 0005 & 0006 a system that may include a hardware processor and memory that stores a computer program configured to be executed by the hardware processor [i.e. one or more non-transitory computer-readable media storing instructions executable by the processor] to prompt the LLM disclosed for operating a vehicle, including prompting the LLM to generate parameters for a rule-based planner based on historical data for vehicles in a road scene, generating a trajectory using the parameters, and performing a driving action to implement the trajectory)
receiving first text that includes a description of a scene and… (Chandraker discloses in at least Paragraphs 0027, 0032, & 0036 wherein text-based scene descriptions, including vehicular positioning, traffic light status, and lane information, as well as a goal that states the target destination of the vehicle may be provided as an input to the LLM to generate a navigational plan for the vehicle [i.e. receiving first text that includes a description of a scene])
generating a first prompt that requests driving instructions and includes the description of the scene, the first plan, and the at least one portion of the set of traffic rules; (Chandraker discloses in at least Paragraph 0036 wherein a prompt may be supplied to the LLM from an LLM reasoning module, the prompt instructing the LLM to generate achievable sub-goals to reach a destination [i.e. generating a first prompt that requests driving instructions]. At least Paragraphs 0035 & 0036 of Chandraker further teach wherein the prompt may describe the positions and velocities of other vehicles [i.e. the prompt includes the description of the scene], include parameters, such as traffic rules [i.e. the prompt includes at least one portion of the set of traffic rules], and provide the target destination of the vehicle [i.e. the first plan])
processing the first prompt via a first trained language model to generate a second plan for driving the vehicle; and (Chandraker discloses in at least Paragraphs 0041 – 0043 wherein at each time step a prompt is issued from the LLM interface to the LLM, the prompt containing up-to-date scene information and position history for surrounding objects, as well as the prompt information set forth above. The LLM then proposes a trajectory for the vehicle based on the issued prompt, which may be scored and selected if the scoring meets a predefined criteria [i.e. a second plan for driving the vehicle is generated based on processing the first prompt via a first trained language model]. At least Paragraphs 0060 – 0062 of Chandraker further disclose the generation of trajectories by the rule based planner based on the parameters output from the LLM when said LLM is prompted)
generating driving instructions based on the second plan. (Chandraker discloses in at least Paragraphs 0006 & 0060 – 0062 wherein based on the selected trajectory [i.e. based on the second plan], an action may be taken to execute the selected trajectory, including triggering the ECU to perform a driving action to implement the trajectory, including through braking, steering, and/or acceleration actions [i.e. generating driving instructions based on the second plan])
Chandraker however appears to be silent regarding:
receiving… a first plan for driving a vehicle;
extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan;
However Wang teaches wherein traffic rules may be extracted based on associating keywords tagged to rules stored in a database with vehicle state and environmental information to retrieve relevant traffic rules to the current context.
receiving… a first plan for driving a vehicle; (However Wang teaches in at least Paragraphs 0033 & 0057 wherein an external device may receive information, including information on environmental conditions and vehicle states, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory as taught in at least Paragraph 0038 of Wang [i.e. receiving a first plan for driving a vehicle])
extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan; (However Wang teaches in at least Paragraphs 0046 – 0050 wherein a database of traffic laws may be pre-processed, including by adding tags or labels to the information corresponding to category-based keywords to traffic rules. Wang further teaches in at least Paragraph 0057 wherein based on the vehicle state, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory as taught in at least Paragraph 0038 [i.e. the first plan] and environmental information [i.e. the description of the scene], an information category is obtained and utilized to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database [i.e. extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the extraction of traffic rules based on the determined vehicle state and environmental information as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraph 0050, by tagging the traffic rules in the database with keywords associated with the situation, identifying the situation based on the vehicle state, and evaluating the present state & environment of the vehicle to compare to the identified keywords, the efficiency of retrieving relevant traffic rules may be improved.
Regarding Claim 14:
Claim 14 recites substantially similar limitations as those found in Claim 4, above, and is rejected under similar rationale.
Regarding Claim 20:
Chandraker discloses: A system, comprising: (Chandraker discloses in at least Paragraph 0005 a system for operating a vehicle, including prompting an LLM to generate parameters for a rule-based planner based on historical data for vehicles in a road scene, generating a trajectory using the parameters, and performing a driving action to implement the trajectory)
one or more memories storing instructions; and one or more processors that are coupled to the one or more memories and, when executing the instructions, are configured to: (Chandraker discloses in at least Paragraphs 0005 & 0006 wherein the system for operating the vehicle may include a hardware processor and memory that stores a computer program configured to be executed by the hardware processor to prompt the LLM disclosed)
receive first text that includes a description of a scene and… (Chandraker discloses in at least Paragraphs 0027, 0032, & 0036 wherein text-based scene descriptions, including vehicular positioning, traffic light status, and lane information, as well as a goal that states the target destination of the vehicle may be provided as an input to the LLM to generate a navigational plan for the vehicle [i.e. receiving first text that includes a description of a scene])
generate a first prompt that requests driving instructions and includes the description of the scene, the first plan, and the at least one portion of the set of traffic rules, (Chandraker discloses in at least Paragraph 0036 wherein a prompt may be supplied to the LLM from an LLM reasoning module, the prompt instructing the LLM to generate achievable sub-goals to reach a destination [i.e. generating a first prompt that requests driving instructions]. At least Paragraphs 0035 & 0036 of Chandraker further teach wherein the prompt may describe the positions and velocities of other vehicles [i.e. the prompt includes the description of the scene], include parameters, such as traffic rules [i.e. the prompt includes at least one portion of the set of traffic rules], and provide the target destination of the vehicle [i.e. the first plan])
process the first prompt via a first trained language model to generate a second plan for driving the vehicle, and (Chandraker discloses in at least Paragraphs 0041 – 0043 wherein at each time step a prompt is issued from the LLM interface to the LLM, the prompt containing up-to-date scene information and position history for surrounding objects, as well as the prompt information set forth above. The LLM then proposes a trajectory for the vehicle based on the issued prompt, which may be scored and selected if the scoring meets a predefined criteria [i.e. a second plan for driving the vehicle is generated based on processing the first prompt via a first trained language model]. At least Paragraphs 0060 – 0062 of Chandraker further disclose the generation of trajectories by the rule based planner based on the parameters output from the LLM when said LLM is prompted)
generate driving instructions based on the second plan. (Chandraker discloses in at least Paragraphs 0006 & 0060 – 0062 wherein based on the selected trajectory [i.e. based on the second plan], an action may be taken to execute the selected trajectory, including triggering the ECU to perform a driving action to implement the trajectory, including through braking, steering, and/or acceleration actions [i.e. generating driving instructions based on the second plan])
Chandraker however appears to be silent regarding:
receive… a first plan for driving a vehicle,
extract at least one portion of a set of traffic rules based on the description of the scene and the first plan,
However Wang teaches wherein traffic rules may be extracted based on associating keywords tagged to rules stored in a database with vehicle state and environmental information to retrieve relevant traffic rules to the current context.
receive… a first plan for driving a vehicle, (However Wang teaches in at least Paragraphs 0033 & 0057 wherein an external device may receive information, including information on environmental conditions and vehicle states, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory as taught in at least Paragraph 0038 of Wang [i.e. receiving a first plan for driving a vehicle])
extract at least one portion of a set of traffic rules based on the description of the scene and the first plan, (However Wang teaches in at least Paragraphs 0046 – 0050 wherein a database of traffic laws may be pre-processed, including by adding tags or labels to the information corresponding to category-based keywords to traffic rules. Wang further teaches in at least Paragraph 0057 wherein based on the vehicle state, which may include traveling direction, speed, acceleration, currently travelling lane and motion trajectory as taught in at least Paragraph 0038 [i.e. the first plan] and environmental information [i.e. the description of the scene], an information category is obtained and utilized to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database [i.e. extracting at least one portion of a set of traffic rules based on the description of the scene and the first plan)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the extraction of traffic rules based on the determined vehicle state and environmental information as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraph 0050, by tagging the traffic rules in the database with keywords associated with the situation, identifying the situation based on the vehicle state, and evaluating the present state & environment of the vehicle to compare to the identified keywords, the efficiency of retrieving relevant traffic rules may be improved.
Claim(s) 2, 3, 8, 12, 13, & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandraker (US 2025/0145176 A1) in view of Wang (US 2022/0289238 A1) as applied to claims 1 & 11 above, and further in view of Jang (US 2025/0145174 A1).
Regarding Claim 2:
The computer-implemented method of claim 1, further comprising receiving second text that indicates a situation, wherein extracting the at least one portion of the set of traffic rules is further based on the situation, and wherein the prompt further includes the situation.
Chandraker does not appear to specifically disclose receiving second text that indicates a situation wherein extracting the at least one portion of the set of traffic rules is further based on the situation.
However Jang teaches in at least Paragraphs 0050, 0056, & 0059 wherein a driving scenario [i.e. situation] data may be obtained and used to determine traffic law(s) associated with the situation, such as receiving a scenario in which a vehicle turns right, and the relevant law(s) may be determined for said right turn scenario, such as stopping requirements [i.e. extracting the at least one portion of the set of traffic rules is further based on the situation]. Jang however also appears to be silent regarding wherein the situation is received via a second text, as well as wherein the prompt includes the situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the determination of traffic laws associated with a specific situation input as taught by Jang.
The motivation to do so is that, as acknowledged by Jang in at least Paragraph 0082, the vehicle may drive in compliance with road traffic laws for each region and determined situation, improving the operation and safety of the vehicle.
However Wang teaches in at least Paragraphs 0046 – 0050 wherein a database of traffic laws may be pre-processed, including by adding tags or labels to the information corresponding to category-based keywords to traffic rules. Wang further teaches in at least Paragraph 0057 wherein based on the vehicle state and environmental information, an information category is obtained and utilized to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database [i.e. receiving second text that indicates a situation… and wherein the prompt further includes the situation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the tagging of traffic rule data with keywords for retrieval as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraph 0050, by tagging the traffic rules in the database with keywords associated with the situation, the efficiency of retrieving relevant traffic rules may be improved.
Regarding Claim 3:
The computer-implemented method of claim 1, wherein extracting the at least one portion of the set of a traffic rules comprises: generating a second prompt that asks for traffic phrases and includes the description of the scene and the first plan; processing the second prompt via the first trained language model to extract one or more keywords from the description of the scene and the first plan; and extracting, from the set of traffic rules, one or more paragraphs that include at least a first keyword that is included in the one or more keywords.
Chandraker does not appear to specifically disclose wherein a portion of the set of traffic rules are extracted based on prompting or keywords.
However Jang teaches in at least Paragraphs 0050, 0056, & 0059 wherein a driving scenario [i.e. situation] data may be obtained and used to determine traffic law(s) associated with the situation, such as receiving a scenario in which a vehicle turns right, and the relevant law(s) may be determined for said right turn scenario, such as stopping requirements [i.e. extracting the at least one portion of the set of traffic rules is further based on the situation]. Jang further teaches in at least Paragraphs 0006, 0025, & 0048 – 0050 wherein traffic laws may be understood by inputting [i.e. prompting] a LLM with a dataset relating to driving from a database, from which inferences may be made regarding the driving rules [i.e. generating a second prompt that asks for traffic phrases and includes the description of the scene and the first plan; processing the second prompt via the first trained language model]. Jang however also appears to be silent regarding wherein the situation is received via a second text, as well as wherein the prompt includes the situation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the extraction of traffic rules through the use of an LLM as taught by Jang.
The motivation to do so is that, as acknowledged by Jang in at least Paragraphs 0051 & 0062, traffic laws may be dynamically updated through the use of a learning model, improving the updating and learning of traffic laws by the vehicle based on the current environment.
However Wang teaches in at least Paragraphs 0046 – 0050 wherein a database of traffic laws may be pre-processed, including by adding tags or labels to the information corresponding to category-based keywords to traffic rules. Wang further teaches in at least Paragraph 0057 wherein based on the vehicle state and environmental information, an information category is obtained [i.e. one or more keywords is extracted from the description of the scene and the first plan] and utilized to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database [i.e. extract one or more keywords from the description of the scene and the first plan; and extracting, from the set of traffic rules, one or more paragraphs that include at least a first keyword that is included in the one or more keywords].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the tagging of traffic rule data with keywords for retrieval as taught by Wang.
The motivation to do so is that, as acknowledged by Wang in at least Paragraph 0050, by tagging the traffic rules in the database with keywords associated with the situation, the efficiency of retrieving relevant traffic rules may be improved.
Regarding Claim 8:
The computer-implemented method of claim 1, further comprising generating, via a second trained language model, the first plan based on second text that indicates at least one of sensor data or status information associated with the vehicle.
Chandraker appears to be silent regarding wherein the first plan is generated via a second trained language model based on second text that indicates at least one of sensor data or status information associated with the vehicle.
However Jang teaches in at least Paragraphs 0052 & 0053 and Figure 2, below, wherein a first LLM may receive various natural language inputs, understand the meaning, intention, and context of input sentences, [i.e. second text that indicates at least one of sensor data or status information associated with the vehicle] and output a proper corresponding result to the behavior/trajectory planner as taught in at least Paragraph 0056 of Jang [i.e. the first plan is generated via a second trained language model], the tuning of the LLM to learn traffic laws being made based on the output of the behavior-and-trajectory planner from the first LLM as taught in at least Paragraph 0048 of Jang.
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the determination of the first plan via a LLM as taught by Jang.
The motivation to do so is that, as acknowledged by Jang in at least Paragraphs 0053 – 0055, the behavior and trajectory planner may be updated based on an initial plan output, as well as the scene information, improving the learning and implementation of traffic rules and optimal behaviors by the vehicle.
Regarding Claim 12:
Claim 12 recites substantially similar limitations as those found in Claim 2, above, and is rejected under similar rationale.
Regarding Claim 13:
Claim 13 recites substantially similar limitations as those found in Claim 3, above, and is rejected under similar rationale.
Regarding Claim 18:
Claim 18 recites substantially similar limitations as those found in Claim 8, above, and is rejected under similar rationale.
Claim(s) 5, 6, 15, & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandraker (US 2025/0145176 A1) in view of Wang (US 2022/0289238 A1) as applied to claims 1 & 11 above, and further in view of Palanisamy (US 2023/0375351 A1).
Regarding Claim 5:
The computer-implemented method of claim 1, further comprising transmitting the driving instructions to a driver via a speaker device.
Chandraker does not appear to specifically disclose wherein the driving instructions are transmitted to a driver via a speaker device.
However Palanisamy teaches in at least Paragraphs 0017 & 0038 wherein the vehicle may include a speaker that provides auditory turn-by-turn instructions for a user to follow, the instructions being determined by way of a machine learning model, which may include a language model, as taught in at least Paragraphs 0092 & 0095 of Palanisamy.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the provision of turn-by-turn driving instructions via a speaker as taught by Palanisamy.
The motivation to do so is that, as acknowledged by Palanisamy in at least Paragraph 0038, the driving instructions may be presented to a user for driving and verification, improving the communication of determined driving instructions to a driver of a vehicle for execution.
Regarding Claim 6:
The computer-implemented method of claim 1, further comprising displaying the driving instructions to a driver via a display device.
Chandraker does not appear to specifically disclose wherein the driving instructions are transmitted to a driver via a display device.
However Palanisamy teaches in at least Paragraphs 0017 & 0038 wherein the vehicle may include a digital display that presents visual information such as an overview map or turn-by-turn instructions for a user to follow, the instructions being determined by way of a machine learning model, which may include a language model, as taught in at least Paragraphs 0092 & 0095 of Palanisamy.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the provision of turn-by-turn driving instructions via a visual display as taught by Palanisamy.
The motivation to do so is that, as acknowledged by Palanisamy in at least Paragraph 0038, the driving instructions may be presented to a user for driving and verification, improving the communication of determined driving instructions to a driver of a vehicle for execution.
Regarding Claim 15:
Claim 15 recites substantially similar limitations as those found in Claim 5, above, and is rejected under similar rationale.
Regarding Claim 16:
Claim 16 recites substantially similar limitations as those found in Claim 6, above, and is rejected under similar rationale.
Claim(s) 7 & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandraker (US 2025/0145176 A1) in view of Wang (US 2022/0289238 A1) as applied to claims 1 & 11 above, and further in view of Hausman (US 2023/0311335 A1).
Regarding Claim 7:
The computer-implemented method of claim 1, further comprising processing image data associated with the vehicle using a vision language model to generate the description of the scene.
Chandraker appears to be silent regarding wherein the description of the scene is generated by processing image data using a vision language model.
However Hausman teaches in at least Paragraphs 0062 & 0095 wherein an image obtained by a robot may be processed by a trained model to generate an image embedding, including scene descriptors describing objects detected in the environment, such as “keys,” “human,” “table,” and the like, which are incorporated into a LLM prompt used to generate a desired output for a robot as taught in at least Paragraphs 0062 & 0063 of Hausman.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the use of a trained model to generate embeddings of objects describing a scene as taught by Hausman.
The motivation to do so is that, as acknowledged by Hausman in at least Paragraphs 0062 & 0063, objects may be determined and categorized in an environment, improving the explanation of the scene to the LLM prompt that said prompt uses as a basis to generate an output.
Regarding Claim 17:
Claim 17 recites substantially similar limitations as those found in Claim 7, above, and is rejected under similar rationale.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chandraker (US 2025/0145176 A1) in view of Wang (US 2022/0289238 A1) as applied to claim 11 above, and further in view of Jang (US 2025/0145174 A1) and Hausman (US 2023/0311335 A1).
Regarding Claim 19:
The one or more non-transitory computer-readable media of claim 11, wherein the first text and the first plan are received from at least one of a user or one or more trained machine learning models.
Chandraker does not appear to specifically disclose wherein the first text and plan are received from at least one of a user or one or more trained machine learning models.
However Jang teaches in at least Paragraphs 0052 & 0053 and Figure 2, below, wherein a first LLM may receive various natural language inputs, understand the meaning, intention, and context of input sentences, output a proper corresponding result to the behavior/trajectory planner as taught in at least Paragraph 0056 of Jang [i.e. the first plan is received from one or more trained machine learning models], the tuning of the LLM to learn traffic laws being made based on the output of the behavior-and-trajectory planner from the first LLM as taught in at least Paragraph 0048 of Jang.
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the determination of the first plan via a LLM as taught by Jang.
The motivation to do so is that, as acknowledged by Jang in at least Paragraphs 0053 – 0055, the behavior and trajectory planner may be updated based on an initial plan output, as well as the scene information, improving the learning and implementation of traffic rules and optimal behaviors by the vehicle.
However Hausman teaches in at least Paragraphs 0062 & 0095 wherein an image obtained by a robot may be processed by a trained model to generate an image embedding, including scene descriptors describing objects detected in the environment, such as “keys,” “human,” “table,” and the like, which are incorporated into a LLM prompt used to generate a desired output for a robot as taught in at least Paragraphs 0062 & 0063 of Hausman [i.e. the first text is received from one or more trained machine learning models].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present claimed invention to have modified the disclosure of Chandraker by incorporating the use of a trained model to generate embeddings of objects describing a scene as taught by Hausman.
The motivation to do so is that, as acknowledged by Hausman in at least Paragraphs 0062 & 0063, objects may be determined and categorized in an environment, improving the explanation of the scene to the LLM prompt that said prompt uses as a basis to generate an output.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Neindorf (US 2022/0048535 A1): Neindorf recites a path planning system for a vehicle including the receipt of environmental data associated with a detected environment, generating goal states, and generating candidate trajectories for the vehicle based on the goal state. Various machine learning models may be utilized to determine and select candidate trajectories.
Xin (CN117734728A): Xin recites a autonomous driving system for a vehicle, including the use of a LLM and prompting to determine driving instructions for the vehicle. The driving preferences of an operator may be taken into account, along with driving scene information, and the LLM may output driving decisions for the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
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/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661