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 .
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.
Claim Status
This action is in response to applicant’s filing on 3/28/2025. Claims 1-20 are pending and considered below.
Claim Objections
Claims 2-4 and 12-14 are objected to because of the following informalities:
The term “to be used” is repeated, in claim 2, line 12; claim 3, lines 10-11; claim 4, lines 10-11; claim 12, line 11; claim 13, line 10; and claim 14, line 10.
The term “terminal topology type” should be replaced with “termination topology type” to be consistent with the remainder of the claim, in claim 4, line 4; and claim 14, lines 3-4.
Appropriate correction is required.
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.
Regarding claims 1-10, step 1 analysis, the subject matter of claims 1-10 is included in the four patent-eligible subject matter categories (e.g., process, machine, manufacture or composition of matter). Claims 1-10 are directed to a method.
Claims 1-10 are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). The claim limitations recite a revised step 2A, prong one, abstract idea (a mental process involving observation and evaluation which could be performed in the human mind). Claims 1-10 are directed to a method for generating pathways for autonomously navigating through an environment. This limitation is a simple process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind. For example, the claims encompass a vehicle operator determining a pathway for maneuvering a vehicle through an environment. Thus, the claims recite a mental process.
Claims 1-10 include the revised step 2A, prong two, additional elements of identifying a tensor; applying at least a first portion of a plurality of encodings to a machine learning (ML) model; applying at least a second portion of a plurality of encodings and a first index value to the ML model; generating a token; and storing a graph. These additional elements recite a technological solution to the technological problem of executing path planning and avoiding collisions by efficiently understanding the surroundings of an autonomous vehicle. These additional elements provide an improvement in how the machine learning model operates. Therefore, claims 1-10 are NOT rejected under 35 U.S.C. 101.
Regarding claims 11-20, step 1 analysis, the subject matter of claims 11-20 is included in the four patent-eligible subject matter categories. Claims 11-20 are directed to a system (one or more processors coupled with memory).
Claims 11-20 are directed to a judicial exception. The claim limitations recite a revised step 2A, prong one, abstract idea (a mental process involving observation and evaluation which could be performed in the human mind). Claims 11-20 are directed to a system for generating pathways for autonomously navigating through an environment. This limitation is a simple process that, under the broadest reasonable interpretation, covers performance of the limitation in the mind. For example, the claims encompass a vehicle operator determining a pathway for maneuvering a vehicle through an environment. Thus, the claims recite a mental process.
Claims 11-20 include the revised step 2A, prong two, additional elements of identifying a tensor; applying at least a first portion of a plurality of encodings to a machine learning (ML) model; applying at least a second portion of a plurality of encodings and a first index value to the ML model; generating a token; and storing a graph. These additional elements recite a technological solution to the technological problem of executing path planning and avoiding collisions by efficiently understanding the surroundings of an autonomous vehicle. These additional elements provide an improvement in how the machine learning model operates. Therefore, claims 11-20 are NOT rejected under 35 U.S.C. 101.
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.
Claims 1-4, 6-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pronovost (US-2024/0101150-A1, hereinafter Pronovost).
Regarding claim 1, Pronovost discloses:
A method of generating pathways for autonomously navigating through an environment, comprising: (paragraphs [0096-0099] and [0128]; and FIG. 8, vehicle-802, vehicle computing device-804, sensor system(s) - 806, processor(s) - 816, memory-818, perception component-822, planning component-824, map(s) - 828, model component-830, computing device(s) - 834, processor(s) - 836, memory-838, remote sensor system(s) - 842, and map component-844);
identifying, by one or more processors, a tensor comprising a plurality of encodings derived from sensor data from an ego and map data defining a topology of an environment surrounding the ego (paragraphs [0137] and [0144-0145]; FIG. 9, receive, by a transformer model, a request to generate a simulated environment that includes a vehicle and an object-902; and FIG. 10, receive, by a training component, state data representing a previous state of an object in an environment-1002, and receive, by the training component, feature vectors representing a vehicle and the object in the environment-1004);
determining, by the one or more processors, by applying at least a first portion of the plurality of encodings to a machine learning (ML) model, a first index value defining a point within a first plurality of points of a first grid defined over the environment (paragraphs [0053-0055] and [0140-0141]; and FIG. 9, input the sequence of tokens into a machine learned model-908, and generate, by the machine learned model, the simulated environment that includes an object trajectory for the object-910);
determining, by the one or more processors, by applying at least a second portion of the plurality of encodings and the first index value to the ML model, a second index value defining the point within a second plurality of points of a second grid within a subset of the first plurality of points of the first grid (paragraphs [0053-0055] and [0140-0141]);
generating, by the one or more processors, a token for at least one of a plurality of pathways through the environment based on the first index value and the second index value for the point (paragraphs [0138-0139] and [0146-0149]; FIG. 9, access, by the transformer model and based at least in part on the request, tokens from a codebook, at least one token in the codebook representing a behavior of the object-904, and arrange, by the transformer model, the tokens into a sequence of tokens-906; and FIG. 10, train a codebook as a trained codebook, the training comprising: - 1006, assign, based at least in part on the state data, a first token to represent the previous state of the object-1008, map the feature vectors to respective tokens-1010, and output the trained codebook for use by a machine learned model configured to access tokens from the trained codebook and to arrange the tokens to represent potential interactions between the vehicle and the object-1012); and
storing, by the one or more processors, a graph to include the token to be used to autonomously navigate the ego through the environment via one or more of the plurality of pathways (paragraphs [0044], [0046], [0053-0055] and [0142]; and FIG. 9, cause the vehicle to be controlled in a real-world environment based at least in part on the object trajectory-912).
Regarding claim 2, Pronovost further discloses:
further comprising classifying, by the one or more processors, by applying the at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a continuation topology type dependent on the first point (paragraphs [0044] and [0046]);
determining, by the one or more processors, responsive to the classification of the second point as the continuation topology type, a plurality of spline coefficients defining a path of the plurality of pathways between the first point and the second point through the environment (paragraphs [0053-0055]);
generating, by the one or more processors, a second token based on the third index point for the second point and the continuation topology type (paragraphs [0138-0139] and [0146-0149]); and
updating, by the one or more processors, the graph to include the second token and the plurality of spline coefficients to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 3, Pronovost further discloses:
further comprising: classifying, by the one or more processors, by applying at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a forking topology type from the point relative to a third point (paragraphs [0044] and [0046]);
determining, by the one or more processors, responsive to classifying the second point as the forking topology type, a fourth index value referencing the token for the point (paragraphs [0053-0055]);
generating, by the one or more processors, a second token for a first pathway different from a second pathway associated with the third point, based on the third index value, the fourth index value, and the forking topology type (paragraphs [0138-0139] and [0146-0149]); and
updating, by the one or more processors, the graph to include the second token to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 4, Pronovost further discloses:
further comprising: classifying, by the one or more processors, by applying at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a terminal topology type dependent on the first point (paragraphs [0044] and [0046]);
determining, by the one or more processors, responsive to the classification of the second point as the termination topology type, a pathway defined by the first point and the second point through the environment (paragraphs [0053-0055]);
generating, by the one or more processors, a second token based on the third index point for the second point and the termination topology type (paragraphs [0138-0139] and [0146-0149]); and
updating, by the one or more processors, the graph to include the second token to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 6, Pronovost further discloses:
further comprising: identifying, by the one or more processors, using the sensor data from the ego, a presence of a second ego stationary in the environment (paragraphs [0036], [0038], [0082] and [0145]); and
determining, by the one or more processors, using the graph, that at least one first pathway of the plurality of pathways for the ego intersects with the stationary second ego (paragraph [0148]).
Regarding claim 7, Pronovost further discloses:
further comprising classifying, by the one or more processors, by applying at least a third portion of the plurality of encodings and the second index value to the ML model, the point as a topology type indicating a start of at least one of the plurality of pathways (paragraphs [0044], [0046] and [0053-0055]; and FIG. 2, vehicle-102, object-112, object trajectory-120, vehicle trajectory-122, and scene-212); and
wherein generating the token further comprises generating the token for at least one of the plurality of pathways through the environment based on the topology type (paragraphs [0138-0139] and [0146-0149]).
Regarding claim 8, Pronovost further discloses:
further comprising determining, by the one or more processors, using a plurality of tokens of the graph, a trajectory defining navigation of the ego via a pathway of the plurality of pathways through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 9, Pronovost further discloses:
further comprising presenting, by the one or more processors, via a graphical user interface (GUI), the graph defining the plurality of pathways relative to the topology of the environment surrounding the ego (paragraph [0120]; and FIG. 8, vehicle-802, and emitter(s) - 808).
Regarding claim 10, Pronovost further discloses:
wherein generating the token further comprises generating the token using (i) a first embedding generated from the first index value, (ii) a second embedding generated from the second index value, and (iii) one or more embeddings associated with the point (paragraphs [0053-0055], [0138-0139] and [0146-0149]).
Regarding claim 11, Pronovost further discloses:
A system for generating pathways for autonomously navigating through an environment, comprising: one or more processors coupled with memory, configured to: (paragraphs [0096-0099] and [0128]; and FIG. 8, vehicle-802, vehicle computing device-804, sensor system(s) - 806, processor(s) - 816, memory-818, perception component-822, planning component-824, map(s) - 828, model component-830, computing device(s) - 834, processor(s) - 836, memory-838, remote sensor system(s) - 842, and map component-844);
identify a tensor comprising a plurality of encodings derived from sensor data from an ego and map data defining a topology of an environment surrounding the ego (paragraphs [0137] and [0144-0145]; FIG. 9, receive, by a transformer model, a request to generate a simulated environment that includes a vehicle and an object-902; and FIG. 10, receive, by a training component, state data representing a previous state of an object in an environment-1002, and receive, by the training component, feature vectors representing a vehicle and the object in the environment-1004);
determine, by applying at least a first portion of the plurality of encodings to a machine learning (ML) model, a first index value defining a point within a first plurality of points of a first grid defined over the environment (paragraphs [0053-0055] and [0140-0141]; and FIG. 9, input the sequence of tokens into a machine learned model-908, and generate, by the machine learned model, the simulated environment that includes an object trajectory for the object-910);
determine, by applying at least a second portion of the plurality of encodings and the first index value to the ML model, a second index value defining the point within a second plurality of points of a second grid within a subset of the first plurality of points of the first grid (paragraphs [0053-0055] and [0140-0141]);
generate a token for at least one of a plurality of pathways through the environment based on the first index value and the second index value for the point (paragraphs [0138-0139] and [0146-0149]; FIG. 9, access, by the transformer model and based at least in part on the request, tokens from a codebook, at least one token in the codebook representing a behavior of the object-904, and arrange, by the transformer model, the tokens into a sequence of tokens-906; and FIG. 10, train a codebook as a trained codebook, the training comprising: - 1006, assign, based at least in part on the state data, a first token to represent the previous state of the object-1008, map the feature vectors to respective tokens-1010, and output the trained codebook for use by a machine learned model configured to access tokens from the trained codebook and to arrange the tokens to represent potential interactions between the vehicle and the object-1012); and
store a graph to include the token to be used to autonomously navigate the ego through the environment via one or more of the plurality of pathways (paragraphs [0044], [0046], [0053-0055] and [0142]; and FIG. 9, cause the vehicle to be controlled in a real-world environment based at least in part on the object trajectory-912).
Regarding claim 12, Pronovost further discloses:
wherein the one or more processors are further configured to classify, by applying at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a continuation topology type dependent on the first point (paragraphs [0044] and [0046]);
determine, responsive to the classification of the second point as the continuation topology type, a plurality of spline coefficients defining a path of the plurality of pathways between the first point and the second point through the environment (paragraphs [0053-0055]);
generate a second token based on the third index point for the second point and the continuation topology type (paragraphs [0138-0139] and [0146-0149]); and
update the graph to include the second token and the plurality of spline coefficients to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 13, Pronovost further discloses:
wherein the one or more processors are further configured to: classify, by applying at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a forking topology type from the point relative to a third point (paragraphs [0044] and [0046]);
determine, responsive to classifying the second point as the forking topology type, a fourth index value referencing the token for the point (paragraphs [0053-0055]);
generate a second token for a first pathway different from a second pathway associated with the third point, based on the third index value, the fourth index value, and the forking topology type (paragraphs [0138-0139] and [0146-0149]); and
update the graph to include the second token to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 14, Pronovost further discloses:
wherein the one or more processors are further configured to: classify, by applying at least a third portion of the plurality of encodings and a third index value for a second point to the ML model, the second point as a terminal topology type dependent on the first point (paragraphs [0044] and [0046]);
determine, responsive to the classification of the second point as the termination topology type, a pathway defined by the first point and the second point through the environment (paragraphs [0053-0055]);
generate a second token based on the third index point for the second point and the termination topology type (paragraphs [0138-0139] and [0146-0149]); and
update the graph to include the second token to be used to autonomously navigate the ego through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 16, Pronovost further discloses:
wherein the one or more processors are further configured to: identify, using the sensor data from the ego, a presence of a second ego stationary in the environment (paragraphs [0036], [0038], [0082] and [0145]); and
determine, using the graph, that at least one first pathway of the plurality of pathways for the ego intersects with the stationary second ego (paragraph [0148]).
Regarding claim 17, Pronovost further discloses:
wherein the one or more processors are further configured to: classify, by applying at least a third portion of the plurality of encodings and the second index value to the ML model, the point as a topology type indicating a start of at least one of the plurality of pathways (paragraphs [0044], [0046] and [0053-0055]; and FIG. 2, vehicle-102, object-112, object trajectory-120, vehicle trajectory-122, and scene-212); and
generate the token for at least one of the plurality of pathways through the environment based on the topology type (paragraphs [0138-0139] and [0146-0149]).
Regarding claim 18, Pronovost further discloses:
wherein the one or more processors are further configured to determine, using a plurality of tokens of the graph, a trajectory defining navigation of the ego via a pathway of the plurality of pathways through the environment (paragraphs [0044], [0046], [0053-0055] and [0142]).
Regarding claim 19, Pronovost further discloses:
wherein the one or more processors are further configured to present, via a graphical user interface (GUI), the graph defining the plurality of pathways relative to the topology of the environment surrounding the ego (paragraph [0120]; and FIG. 8, vehicle-802, and emitter(s) - 808).
Regarding claim 20, Pronovost further discloses:
wherein the one or more processors are further configured to generate the token using (i) a first embedding generated from the first index value, (ii) a second embedding generated from the second index value, and (iii) one or more embeddings associated with the point (paragraphs [0053-0055], [0138-0139] and [0146-0149]).
Allowable Subject Matter
Claims 5 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marchetti-Bowick et al. (US-2021/0004012-A1) discloses an autonomous vehicle which obtains state data associated with an object in an environment, obtains map data including information associated with spatial relationships between at least a subset of lanes of a road network, and determines a set of candidate paths that the object may follow in the environment based at least in part on the spatial relationships between at least two lanes of the road network (Abstract).
Zeng et al. (U.S. Patent Number 11,731,663) discloses a system for forecasting the motion of actors within a surrounding environment of an autonomous platform. A computing system predicts a motion trajectory for an actor based on an associated actor-specific graph, which captures actor-to-actor interactions and actor-to-map relations (Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAMARA L WEBER whose telephone number is (303)297-4249. The examiner can normally be reached 8:30-5:00 MTN.
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, Faris Almatrahi can be reached at 3134464821. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
TAMARA L. WEBER
Examiner
Art Unit 3667
/TAMARA L WEBER/ Examiner, Art Unit 3667
Prosecution Insights