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 .
Pursuant to communications filed on 11/01/2024, this is a First Action Non-Final Rejection on the Merits wherein claims 1-20 are currently pending in the instant application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/01/2024 and 03/28/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Examiner's Note
Examiner has cited particular paragraphs and/or columns / lines numbers or figures in the reference(s) as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Examiner has also cited references in PTO-892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 8-13, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Manas et al. (NPL “Robust Traffic Rules and Knowledge Representation for Conflict Resolution in Autonomous Driving” – 2022 – From IDS), hereinafter “Manas”.
Regarding claims 1, 11 and 20, Manas discloses a computer-implemented method for operating a vehicle / the associated system and the associated vehicle (e.g., via a Robust Traffic Rules and Knowledge Representation for Conflict Resolution in Autonomous Driving), comprising:
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prompting a large language model (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 (e.g., see figures 1-2 above - see abstract - page 1 disclosing finding optimal traffic rules representation for the hybrid learning tasks and leverage large language models for the automated representation of traffic rules and regulation needed for downstream AD task. The hybrid learning approach will reduce the data dependency, generate the vehicle’s traffic rule complaint trajectory, and make the model more generalizable even for corner cases or less representative cases in the datasets due to reasoning capability. See also page 6 disclosing a large language model (LLM) like T5 [14] and GPT-3 [15] which allow us to learn the meaning behind the traffic rules and can help generate the formal representation of traffic rules. Due to the safety requirements of autonomous driving, human verification of the formal representation of rules will be required for testing purposes. See page 7 disclosing Another module shown in Fig. 1 is the trajectory prediction module, which generates the trajectory of vehicles based on their past behavior, velocity, acceleration, etc. These two modules output will be combined with formalized knowledge mentioned in Sec. 5.1 to be processed by the reasoning module.).
Regarding claims 2 and 12, Manas discloses further comprising: generating a set of trajectories without LLM-generated parameters; and simulating the set of trajectories to determine one or more respective scores, before prompting the LLM (e.g., see figure 2 and page 8 disclosing using scenario-based testing, a safe distance from another vehicle, and the occurrence of rule violation as a starting point of testing coupled with typical trajectory prediction evaluation criteria such as the final displacement error (FDE), the mean absolute error (MAE). These criteria generally measure the deviation of ground truth trajectory values with the predicted trajectory provided by models.).
Regarding claims 3 and 13, Manas discloses further comprising comparing the one or more respective scores to respective thresholds, wherein prompting is performed responsive to a determination that at least one of the one or more respective scores, for each of the set of trajectories, falls below the respective threshold (see figures 1-2; see abstract disclosing the vehicle’s long-term trajectory (3 to 5 seconds horizon) can be predicted using hybrid learning, which incorporates both rules and data into the ML models. Only rule-based systems confront difficulties in depicting complicated interactions among multiple traffic scene participants….. The hybrid learning approach will reduce the data dependency, generate the vehicle’s traffic rule complaint trajectory).
Regarding claims 8 and 18, Manas discloses wherein prompting the LLM includes a history of positions, headings, and speeds for vehicles, pedestrians, and objects, along with a current lane identifier for vehicle (e.g., see figures 1-2; see page 7 disclosing the trajectory prediction module generates the trajectory of the vehicles based on the past behavior, the velocity, the acceleration, etc.).).
Regarding claims 9 and 19, Manas discloses wherein the parameters include a lateral offset of a vehicle relative to a lane center, a fraction of a speed limit in free traffic, a fallback speed in free traffic, a minimum distance to a lead car, a minimum time to the lead car, a maximum acceleration, and a maximum deceleration (e.g., see figure 1; see page 6 disclosing Reasoning in our case is to first find the presence of conflicts among trajectories and then to resolve the conflicts among traffic participants. Among multiple interacting participants, their priority can be resolved at a specific instant based on rules and commonsense knowledge.).
Regarding claim 10, Manas discloses wherein the driving action is selected from the group consisting of a braking action, a steering action, and an acceleration action (e.g., see figure 1; see page 7 disclosing the Trajectory has a notion of time and speed, and it is a vehicle state with respect to time.).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-7, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Manas in view of Cui et al. (NPL “DriveLLM: Charting the Path Toward Full Autonomous Driving With Large Language Models” – 2023 – From IDS), hereinafter “Cui”.
Regarding claims 4 and 14, Manas discloses as discussed above in claims 2 and 12. Manas is silent to disclose wherein simulating the set of trajectories is performed with a constant-velocity real-time simulator.
However, in the same field of endeavour or analogous art, Cui teaches the claimed features of wherein simulating the set of trajectories is performed with a constant-velocity real-time simulator (see pages 1451-1452 and figures 1-3: a DriveLLM system for operating an autonomous vehicle comprises a rule-based simulation check system developed to screen illegal requests from an LLM in real-time).
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Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Manas to include the idea of implementing the rule-based simulation, as taught by Cui, for the benefit of having a novel two-tier evaluation and feedback mechanism for LLM-based decision-making systems. This mechanism combines evaluation from both simulated and physical environments for iterative online learning while ensuring driving safety.
Regarding claims 5-6, and 15-16, Manas discloses as discussed above in claims 1 and 11. Manas is silent to disclose (claims 5 and 15) further comprising decomposing a goal into a plurality of sub-goals, with the trajectory being generated to accomplish one of the plurality of sub-goals. (claims 6 and 16) further comprising generating new code for the rule-based planner to implement the sub-goals using the LLM.
However, in the same field of endeavour or analogous art, Cui teaches the claimed features of decomposing a goal into a plurality of sub-goals, with the trajectory being generated to accomplish one of the plurality of sub-goals…and generating new code for the rule-based planner to implement the sub-goals using the LLM (see pages 1452, 1459-1460 and figures 1-3, 7-8: high-level decision-making focuses on strategic choices that guide overall behavior of the autonomous vehicle considering factors like vehicle status, traffic environment, and ethical considerations, and the DriveLLM system can deny the passenger's illegal requests to achieve prudent and safe driving).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Manas to include the idea of implementing the rule-based simulation, as taught by Cui, for the benefit of having a simulation check system, we enable a safer online iterative learning procedure for theDriveLLM to learn from past mistakes and continuously improve its performance.
Regarding claims 7 and 17, Manas discloses as discussed above in claims 5 and 15. Manas is silent to disclose further comprising generating a semantic representation of the road scene in natural language, wherein decomposing the goal includes prompting the LLM with the semantic representation.
However, in the same field of endeavour or analogous art, Cui teaches the claimed features of generating a semantic representation of the road scene in natural language, wherein decomposing the goal includes prompting the LLM with the semantic representation (see page 1461: input for the LLM can be given in a natural language format such as "the vehicle is driving on an university campus road"). See motivation to combine as set forth above in claims 5 and 15).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached form PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jaime Figueroa whose telephone number is (571)270-7620. The examiner can normally be reached on Monday-Friday 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wade Miles can be reached on 571-270-7777. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAIME FIGUEROA/Primary Patent Examiner, Art Unit 3656