DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on 04/11/2025. Claims 1 - 20 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/11/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0129], specification says “…router 1002 to transmit thet sensor data 504…”. Should say “transmit the sensor data 504”. This happens twice in this paragraph.
Paragraph [0129], specification says “…in response to such determinaiton,…”. Word should be spelled “determination”.
Paragraph [0130], specification says “…processor 1004 may compirse a route planner system 506 or algoritm…”. The word “comprise” and “algorithm” need to be corrected.
Appropriate correction is required.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Examiner has identified the method of Claim 1 as the claim that represents the claimed invention for analysis. Claim 1 recites the limitations of (additional elements emphasized in bold and are considered to be parsed from the remaining abstract idea):
A method comprising:
obtaining, by at least one processor, sensor data associated with an environment in which a vehicle operates;
determining, by the at least one processor, a set of candidate trajectories based on the sensor data;
determining, by the at least one processor, a human-driven trajectory based on the sensor data;
generating, by the at least one processor, a trajectory score for one or more candidate trajectories of the set of candidate trajectories, based on the human-driven trajectory; and
causing, by the at least one processor, an output to be provided to a device based on the trajectory score generated for the one or more candidate trajectories of the set of candidate trajectories, wherein the output comprises one or more of: the human-driven trajectory, the set of candidate trajectories, and the trajectory score.
which is a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) as a Mental process (concept performed in the human mind) but for the recitation of generic computer elements. For example, a person could mentally create multiple trajectories and determine a score of their own on which trajectory is the best or worst to take.
With respect to Step 2A, Prong II, this judicial exception is not practically integrated. The claim recites the additional elements of “processor” multiple times. These elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
With respect to Step 2B, the aforementioned additional elements are all generic computer elements have been held to be not significantly more than the abstract idea by Alice. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of using the processors to receive information, make decisions, and supply instructions amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Furthermore, the limitation step of “causing, by the at least one processor, an output to be provided to a device based on the trajectory score generated for the one or more candidate trajectories of the set of candidate trajectories, wherein the output comprises one or more of: the human-driven trajectory, the set of candidate trajectories, and the trajectory score.”, is not more than the judicial exception, because as detailed in Electric Power Group, additional elements that are used to simply output results do not amount to significantly more than the abstract idea itself.
Claims 8 & 15 cite the same limitations as that in claim 1, with the exception of adding more generic computer components, and are therefore also rejected under 35 USC § 101.
Claims 2, 4, 11, 9, 16, & 18 further define characteristics of the system. However, these characteristics do not add limitations that would integrate the abstract idea into a practical application and are therefore also rejected under 35 USC § 101.
Claims 3, 5 - 7, 10, 12 - 14, 16, 19 - 20 recite limitations that include generating & selecting which can also be performed in the human mind and do not integrate the abstract idea into a practical application. Therefore, these claims are also rejected under 35 USC § 101.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 8, & 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US20200298863A1 (hereinafter, “Lin”).
12. Regarding claims 1, 8, & 15, Lin teaches a method comprising: obtaining, by at least one processor, sensor data associated with an environment in which a vehicle operates [0027] – [0028]; Lin teaches a vehicle sensor subsystem (144) which is configured to sense information about the environment of the vehicle (105). A LiDAR may also be present to detect the local environment of the vehicle (105). Cameras may also capture images of the environment of the vehicle (105) as well. All these together constitute as sensor data associated with the environment.
determining, by the at least one processor, a set of candidate trajectories based on the sensor data ([0049] – [0051] Fig. 6 – 7); Lin teaches generating a first suggested trajectory (605) and a second suggested trajectory (610) which is a set of candidate trajectories. These suggested trajectories are based on passing an obstacle (602) which is detected by sensors on the vehicle (601). Due to this obstacle (602) being detected by vehicle sensor subsystem (144), these generated trajectories are based on acquired sensor data.
determining, by the at least one processor, a human-driven trajectory based on the sensor data [0049]; Human-driven trajectory will be interpreted as human made systems that generate trajectories for a vehicle. Lin generates a suggested trajectory (605) to pass a detected obstacle (602). Therefore, the suggested trajectory (605) is based on sensor data due to the vehicle sensor subsystem (144) capturing this obstacle (602).
generating, by the at least one processor, a trajectory score for one or more candidate trajectories of the set of candidate trajectories, based on the human-driven trajectory; and [0051] – [0052] Lin teaches a path planning module (200) that can score both the first suggested trajectory (605) and the second suggested trajectory (610) based on a variety of factors and predetermined factors.
causing, by the at least one processor, an output to be provided to a device based on the trajectory score generated for the one or more candidate trajectories of the set of candidate trajectories, wherein the output comprises one or more of: the human-driven trajectory, the set of candidate trajectories, and the trajectory score ([0051, [0053] – [0054]] Fig. 9). Lin teaches generating trajectory scores for candidate trajectories and outputting the second suggested trajectory (610) to the vehicle control subsystem (146) (device) which implements control commands causing the autonomous vehicle (601) to follow the selected trajectory.
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.
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.
Claim(s) 2, 9, & 16 are rejected under 35 U.S.C. 103 as being unpatentable over US20200298863A1 (hereinafter, “Lin”), and further in view of US20220234618A1 (hereinafter, “Kabzan”).
15. Regarding claims 2, 9, & 16, Lin does not explicitly teach the method of claim 1, wherein determining the set of candidate trajectories based on the sensor data comprises:
generating, based on the sensor data, homotopy data indicative of one or more candidate homotopies from a first location to a second location associated with route data; and
generating, by the at least one processor, the set of candidate trajectories based on the homotopy data, wherein the set of candidate trajectories is constrained by the one or more candidate homotopies.
However, Kabzan in the same field of endeavor, teaches the method of claim 1, wherein determining the set of candidate trajectories based on the sensor data comprises:
generating, based on the sensor data, homotopy data indicative of one or more candidate homotopies from a first location to a second location associated with route data; and ([0094], [0135], [0137] Fig. 13) The homotopy extractor (453) finds all feasible maneuvers the AV can perform. This finding of feasible maneuvers requires the use of sensors to map out a region for the AV to use as a map representation. This constitutes as homotopy data being based on sensor data. Kabzan teaches using homotopy data from a starting position (first location) to an ending (second location) the AV takes (route data). Kabzan further evaluates the plurality of homotopies and determines a subset of feasible homotopies from which vehicle trajectories are generated. Accordingly, the determined plurality of homotopies constitutes as homotopy data indicative of one or more homotopies associated with traversal between locations along a planned route.
generating, by the at least one processor, the set of candidate trajectories based on the homotopy data, wherein the set of candidate trajectories is constrained by the one or more candidate homotopies ([0135], [0137] – [0138] Fig. 13). Kabzan teaches generating a set of candidate trajectories based on homotopy data. Specifically, Kabzan determines a plurality of homotopies, identifies a subset of feasible homotopies, and generates one or more trajectories for each feasible homotopy. Kabzan further teaches that the generated trajectories enable traversal of the route while adhering to the candidate constraints associated with the corresponding homotopy. Due to each homotopy being defined by a respective combination of candidate constraints, the generated trajectories are based on the homotopy data and are constrained by the corresponding candidate homotopy.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of Lin with the teachings of Kabzan, to create faster real-time planning for the unlimited number of possibilities that may occur.
Claim(s) 3, 7, 10, 14, & 17 are rejected under 35 U.S.C. 103 as being unpatentable over US20200298863A1 (hereinafter, “Lin”), and further in view of US20220234618A1 (hereinafter, “Kabzan”), and further in view of US20220234575A1 (hereinafter, “Frazzoli”).
17. Regarding claims 3, 10, & 17, Lin teaches …in the output ([0051, [0053] – [0054]] Fig. 9). Lin teaches generating trajectory scores for candidate trajectories and outputting the second suggested trajectory (610) to the vehicle control subsystem (146) (device) which implements control commands causing the autonomous vehicle (601) to follow the selected trajectory.
Lin does not explicitly teach the method of claim 2, further comprising:
generating, by the at least one processor, based on the human-driven trajectory and the trajectory score, a homotopy score for the one or more candidate homotopies; and
including, by the at least one processor, the homotopy score…
However, Frazzoli teaches the method of claim 2, further comprising:
generating, by the at least one processor, based on the human-driven trajectory and the trajectory score, a homotopy score for the one or more candidate homotopies; and
including, by the at least one processor, the homotopy score… [0169] - [0171] Frazzoli assigns a quality metric to each homotopy of the multiple homotopies generated by the homotopy extractor (453). Frazzoli further teaches generating trajectory scores using a trajectory score generator (455) for trajectories associated with the homotopies. Due to the quality metric being assigned to each homotopy based on evaluation of trajectories associated with that homotopy, the quality metric constitutes a homotopy score generated based on trajectory scoring results.
Lin and Frazzoli are analogous art because Lin teaches outputting trajectory scores to a vehicle control subsystem while Frazzoli teaches assigning a quality metric to each homotopy of the multiple homotopies generated by the homotopy extractor. A person of ordinary skill in the art would have had the motivation to combine Lin and Frazzoli because both references are directed to autonomous vehicle path planning and trajectory selection. Combining the references together would allow trajectory scores and homotopy quality metrics to be used together when selecting and outputting a trajectory, thereby improving the ability of the autonomous vehicle to choose a trajectory that is not only optimal at the trajectory level but also associated with a highly rated maneuver class.
18. Regarding claims 7 & 14, Lin teaches updating, by the at least one processor, a trajectory scoring model… [0051] – [0052] Lin teaches a path planning module (200) that can score both the first suggested trajectory (605) and the second suggested trajectory (610) based on a variety of factors and predetermined factors. This path planning module (200) constitutes as a trajectory scoring model.
Lin does not explicitly teach the method of claim 3, further comprising:
constructing, by the at least one processor, one or more trajectory scoring cost functions based on the homotopy score; and
…based on the one or more trajectory scoring cost functions.
However, Frazzoli teaches the method of claim 3, further comprising:
constructing, by the at least one processor, one or more trajectory scoring cost functions based on the homotopy score; and
…based on the one or more trajectory scoring cost functions [0109] – [0110], [0115], [0169]. Frazzoli teaches using homotopy derived information and homotopy quality metrics (homotopy score) as inputs to trajectory evaluation and scoring [0169], where the trajectory score generator formulates and applies cost functions to rank trajectories generated from homotopies [0109] – [0110], [0115]. Frazzoli teaches generating for the extracted homotopies, determining quality metrics associated with the trajectories and homotopies, and using the trajectory score generator (455) to score the trajectories [0109] – [0110]. Frazzoli further teaches that a cost function is formulated using evaluation metrics and that the resulting score cost function is used to generate trajectory scores. Due to the trajectories being scored by being generated from the extracted homotopies and the quality metrics are used to evaluate those trajectories, a person of ordinary skill in the art would understand the disclosed cost function formulation as being based on homotopy related scoring information when evaluating and ranking trajectories corresponding to the different homotopies.
Lin and Frazzoli are analogous art because Lin teaches a path planning module that can score a first suggested trajectory and a second suggested trajectory while Frazzoli teaches using cost functions to score trajectories that are based on the homotopy scores. A person of ordinary skill in the art would have had the motivation to combine Lin and Frazzoli in order to improve the accuracy and robustness of Lin’s trajectory scoring process. Doing so would allow Lin’s planner to account for higher level maneuver characteristics represented by the homotopies, thereby enabling selection of trajectories that better satisfy safety, comfort, efficiency, and rule compliance objectives.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Frazzoli, to modify the teachings of the combination of Lin to include the teachings of Frazzoli to improve trajectory selection using scoring techniques.
Claim(s) 4, 11, & 18 are rejected under 35 U.S.C. 103 as being unpatentable over US20200298863A1 (hereinafter, “Lin”), and further in view of US20220035375A1 (hereinafter, “Rezaee”).
20. Regarding claims 4, 11, & 18, Lin does not explicitly teach the method of claim 1, the method comprising updating, by the at least one processor, a selector model for selecting a future trajectory from a set of future candidate trajectories, based on the output.
However, Rezaee in the same field of endeavor, teaches the method of claim 1, the method comprising updating, by the at least one processor, a selector model for selecting a future trajectory from a set of future candidate trajectories, based on the output ([0057], [0082], [0093], [0095] Fig. 4). Rezaee teaches a trajectory selector (336) (selector model) configured to select a trajectory from a set of candidate trajectories generated by a trajectory generator (332) [0057]. During training, Rezaee modifies the training phase to account for future selected trajectories and incorporates TS(s.sub.t+1), representing the output of the trajectory selector (336) at a future time step, into the training equation [0095]. Due to how the training process utilizes the selector output to learn and refine trajectory selection behavior ([0082], [0093] Fig. 4), Rezaee teaches updating a selector model for selecting a future trajectory from a set of future candidate trajectories based on the output.
One of ordinary skill in the art, before the effective filing date of the instant application with a reasonable expectation of success, would have been motivated to modify the disclosure of Lin with the teachings of Rezaee, to always select the trajectory that is the most up to date with the current environment and scenario.
Claim(s) 5, 12, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over US20200298863A1 (hereinafter, “Lin”), and further in view of US20220035375A1 (hereinafter, “Rezaee”), and further in view of US20220234618A1 (hereinafter, “Kabzan”).
22. Regarding claims 5, 12, & 19, Lin does not explicitly teach the method of claim 4, wherein updating the selector model comprises updating, a homotopy model for generating and/or selecting one or more future homotopies based on the output.
However, Rezaee teaches the method of claim 4, wherein updating the selector model comprises updating,…based on the output [0082], [0095] Rezaee teaches a training phase (updating) for the trajectory selector (336). During training, Rezaee modifies the training phase to account for future selected trajectories and incorporates TS(s.sub.t+1), representing the output of the trajectory selector (336) at a future time step, into the training equation [0095].
Lin does not explicitly teach …a homotopy model for generating and/or selecting one or more future homotopies based on the output.
However, Kabzan teaches …a homotopy model for generating and/or selecting one or more future homotopies… ([0148], [0153] Fig. 13). Kabzan teaches an homotopy extractor (453) that can generate one or more homotopies.
Rezaee and Kabzan are analogous art to Lin because Rezaee teaches constantly training the trajectory selector and also incorporating a variable that represents an output of the trajectory selector of which the trajectory selector is based on while Kabzan teaches a homotopy extractor that generates one or more homotopies. A person of ordinary skill in the art would have had the motivation to combine Rezaee and Kabzan because both references are directed to autonomous vehicle motion planning and trajectory selection. Doing so would allow the vehicle to improve future trajectory and maneuver selection based on prior selected outputs rather than repeatedly evaluating homotopies and trajectories from scratch.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Rezaee, to modify the teachings of Lin to include the teachings of Rezaee to improve future maneuver selection.
Claim(s) 6, 13, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over US20200298863A1 (hereinafter, “Lin”), and further in view of US20220035375A1 (hereinafter, “Rezaee”), and further in view of US20220234618A1 (hereinafter, “Kabzan”), and further in view of US20220234575A1 (hereinafter, “Frazzoli”).
24. Regarding claims 6, 13, & 20, Lin does not explicitly teach the method of claim 5, further comprising selecting, by the at least one processor one or more future homotopies based on the homotopy model.
However, Frazzoli teaches the method of claim 5, further comprising selecting, by the at least one processor one or more future homotopies… [0106] Frazzoli teaches that when there are multiple homotopies, a subset of the multiple homotopies can be selected.
Lin does not explicitly teach …based on the homotopy model.
However, Kabzan teaches …based on the homotopy model ([0148], [0153] Fig. 13). Kabzan teaches an homotopy extractor (453) which is considered a homotopy model since it can generate one or more homotopies.
Frazzoli and Kabzan are analogous art to Lin because Frazzoli teaches selecting multiple homotopies while Kabzan teaches a homotopy extractor that can generate one or more homotopies which is considered a homotopy model. A person of ordinary skill in the art would have had the motivation to combine Frazzoli and Kabzan because both references are directed at determining which maneuver class (homotopy) and resulting trajectory should be executed by the autonomous vehicle. Thus, a person of ordinary skill in the art would have been motivated to evaluate the homotopies generated by Kabzan using the quality metrics taught by Frazzoli in order to improve selection among candidate homotopies and the trajectories derived therefrom, thereby achieving the benefit of improved planning quality and maneuver selection.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Frazzoli and Kabzan, to modify the teachings of Lin to include the teachings of Frazzoli and Kabzan to obtain better trajectory selection decisions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID MESQUITI OVALLE JR. whose telephone number is (571)272-6229. The examiner can normally be reached Monday - Friday 7:30am - 5pm EST.
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/DAVID MESQUITI OVALLE/Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669
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