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 .
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 and 5 - 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lyu et al (US 2024/0176989, hereafter Lyu).
As per claim 1, Lyu discloses a method comprising:
obtaining map data of a geographic region and a plurality of historical trajectories of a plurality of agents located in the geographic region (¶ 44 and 49), the map data comprising a plurality of map elements, wherein the plurality of agents and the plurality of map elements have a corresponding plurality of physical locations in the geographic region (¶ 88 -100);
building, from the plurality of historical trajectories and the map data, a heterogeneous graph for the plurality of agents and the plurality of map elements (¶ 45),
wherein the heterogeneous graph defines the corresponding plurality of physical locations of the plurality of agents and the plurality of map elements relative to each other of the plurality of agents and the plurality of map elements (¶ 46);
modelling, by a first graph neural network, a plurality of agent actions of an agent of the plurality of agents using the heterogeneous graph to generate an agent goal location (¶ 48); and
operating an autonomous system based on the agent goal location (¶ 48).
As per claim 5, Lyu discloses the method of claim 1, wherein building the heterogeneous graph comprises:
calculating, from the plurality of historical trajectories, a first plurality of relative positions of the agent with respect a subset of the plurality of agents, the subset excluding the agent, calculating, from a historical trajectory of the agent, a second plurality of relative positions of the agent with respect to a current position of the agent, wherein the historical trajectory is in the plurality of historical trajectories, and generating an agent position encoding for the agent that encodes the first plurality of relative positions and the second plurality of relative positions (¶ 50 - 58).
As per claim 6, Lyu discloses the method of claim 5, wherein building the heterogeneous graph further comprises:
generating an agent layer of the heterogeneous graph using a plurality of agent position encodings generated for the plurality of agents, wherein the plurality of agent position encodings comprises the agent position encoding, wherein the agent layer comprises a plurality of agent nodes for the plurality of agents, the plurality of agent nodes connected by a plurality of edges based on distances between the plurality of agents, the plurality of edges having a corresponding agent position encoding of the plurality of agent position encodings for the corresponding pair of agent nodes of the plurality of agent nodes (¶ 94 - 116 ).
As per claim 7, Lyu discloses the method of claim 1, wherein building the heterogeneous graph further comprises:
obtaining the plurality of physical locations of the plurality of map elements, calculating, from the plurality of physical locations of the plurality of map elements, a plurality of relative positions of the plurality of map elements with respect to other of the plurality of map elements, generating a plurality of map element encodings of the plurality of relative positions, and generating a map layer of the heterogeneous graph using the plurality of map element encodings (¶ 94 - 116).
As per claim 8, Lyu discloses the method of claim 1, wherein building the heterogeneous graph further comprises:
generating an agent layer of the heterogeneous graph using a plurality of agent position encodings generated for the plurality of agents, wherein the agent layer comprises a plurality of agent nodes for the plurality of agents, the plurality of agent nodes connected by a first plurality of edges based on distances between the plurality of agent, the first plurality of edges comprising a corresponding agent position encoding of the plurality of agent position encodings for the corresponding agents, and wherein the plurality of agent position encodings encode relative positions of the plurality of agents with respect to each other, generating a map layer of the heterogeneous graph using a plurality of map element encodings generated for the plurality of map elements, wherein the map layer comprises a plurality of map element nodes for the plurality of map elements, the plurality of map element nodes connected by a second plurality of edges based on distances between the plurality of map elements, the second plurality of edges comprising a corresponding map element encoding of the plurality of map element encodings for the corresponding map elements, and wherein the plurality of map element encodings encode relative positions of the plurality of map elements with respect to each other, and connecting the agent layer to the map layer using a third plurality of edges based on relative positions of the plurality of agents to the plurality of map elements to generate the heterogenous graph (¶ 40, 42, 58, 87, 97 - 100).
As per claim 9, Lyu discloses the method of claim 8, further comprising:
executing a scene encoder on the heterogeneous graph to generate a plurality of agent embeddings of the plurality of agents and a plurality of graph embeddings of the plurality of map elements (¶ 88 and 97 - 116).
As per claim 10, Lyu discloses the method of claim 9, wherein the scene encoder comprise a second graph neural network (¶ 88).
As per claim 11, Lyu discloses the method of claim 10, wherein the second graph neural network comprises a first linear layer specific to the first plurality of edges, a second linear layer specific to the second plurality of edges, and a third linear layer specific to the third plurality of edges (¶ 40 and 88).
As per claim 12, Lyu discloses the method of claim 1, wherein modeling the plurality of agent actions comprises:
generating, by executing the first graph neural network for the agent, an agent location probability for each of a plurality of locations, the agent location probability indicating a probability that the agent is moving to be located at a location defined by a map element and an offset from the map element, and wherein the map element is in the plurality of map elements and the location is in the plurality of locations, and sampling the plurality of locations using the agent location probability to obtain a plurality of agent goal locations, wherein the agent goal location is in the plurality of agent goal locations (¶ 88).
As per claim 13, Lyu discloses the method of claim 12, wherein sampling comprises:
repetitively performing: sampling, according to the agent location probability of the plurality of locations, the plurality of locations, removing, from the plurality of locations, a first location having a distance to a current location of the agent less than a first threshold, and reducing the agent location probability of a second location having a distance to the current location of the agent less than a second threshold, wherein the first location and the second location are in the plurality of locations (¶ 114).
As per claim 14, Lyu discloses the method of claim 1, further comprising:
generate a trajectory of the agent to the agent goal location, wherein operating the autonomous system is further based on the trajectory (¶ 97).
As per claim 15, Lyu discloses the method of claim 13, wherein a multilayer perceptron model is used to perform trajectory completion of the trajectory of the agent (¶ 97 - 116).
As per claim 16, Lyu discloses the method of claim 1, wherein, in the heterogenous graph, a relative position of a first agent to a second agent is defined by:
a distance between the first agent to the second agent, a first difference between angles of heading of the first agent compared to the second agent, and a second difference between an angle of heading of the second agent relative to a straight line between the first agent and the second agent (¶ 112 - 116).
Regarding claim 17, arguments analogous to those presented for claim 1 are applicable for claim 17.
Regarding claim 18, arguments analogous to those presented for claim 8 are applicable for claim 18.
Regarding claim 19, arguments analogous to those presented for claim 1 are applicable for claim 19.
Regarding claim 20, arguments analogous to those presented for claim 8 are applicable for claim 20.
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.
Claim(s) 2 – 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyu in view of Manivasagam et al (US 2021/0152997, hereafter Manivasagam).
As per claim 2, Lyu discloses the method of claim 1.
However, Lyu does not teach the method of claim 1, wherein operating the autonomous system comprises: outputting by an autonomous system controller of a virtual driver of the autonomous system, a control signal to an actuator of the autonomous system
In the field of endeavor, Manivasagam discloses the method of claim 1, wherein operating the autonomous system comprises:
outputting by an autonomous system controller of a virtual driver of the autonomous system, a control signal to an actuator of the autonomous system (¶ 86).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention as effectively filed to modify the invention Lyu in view of Manivasagam. The advantage is improving the technology to help improve the safety of passengers.
As per claim 3, Lyu discloses the method of claim 2.
However, Lyu does not explicitly teach wherein the control signal is intercepted by a simulator that simulates the autonomous system in a simulated environment to train the virtual driver.
In the same field of endeavor, Manivasagam discloses the method of claim 2, wherein the control signal is intercepted by a simulator that simulates the autonomous system in a simulated environment to train the virtual driver (¶ 57 - 59).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention as effectively filed to modify the invention Lyu in view of Manivasagam. The advantage is improving the technology to help improve the safety of passengers.
As per claim 4, Lyu discloses the method of claim 1.
However, Lyu does not explicitly teach wherein operating the autonomous system comprises: generating, by a simulator, a simulated environment simulating the agent being a virtual agent moving in the geographic region according to the agent goal location, outputting, by a virtual driver, a control signal based at least in part on simulated sensor input generated by the moving of the virtual agent in the geographic region, and simulating, by the simulator, the autonomous system moving in the simulated environment responsive to the control signal, wherein the plurality of agents and the autonomous system are virtually located in the geographic region as simulated by the simulator.
In the same field of endeavor, Manivasagam discloses wherein operating the autonomous system comprises: generating, by a simulator, a simulated environment simulating the agent being a virtual agent moving in the geographic region according to the agent goal location, outputting, by a virtual driver, a control signal based at least in part on simulated sensor input generated by the moving of the virtual agent in the geographic region, and simulating, by the simulator, the autonomous system moving in the simulated environment responsive to the control signal, wherein the plurality of agents and the autonomous system are virtually located in the geographic region as simulated by the simulator (¶ 55 - 60).
Therefore, it would have been obvious for one of ordinary skill in the art at the time the invention as effectively filed to modify the invention Lyu in view of Manivasagam. The advantage is improving the technology to help improve the safety of passengers.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHIKAODILI E ANYIKIRE whose telephone number is (571)270-1445. The examiner can normally be reached 8 am - 4:30 pm.
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, David Czekaj can be reached at 571-272-7327. 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.
/CHIKAODILI E ANYIKIRE/Primary Examiner, Art Unit 2487
Prosecution Insights