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 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/09/2025 has been entered.
Introduction
Claims 1-10, 12-20, and 22 are pending and have been examined in this Office Action.
Examiner’s Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references 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 their 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. Furthermore, the Examiner is not limited to Applicants' definition which is not specifically set forth in the disclosure.
Claim Objections
Claims 6 and 18 are objected to because of the following informalities:
In claim 6 lines 4-5, the term “second” is repeated. One instance should be deleted.
In claim 6 line 11, the term “within” appears to have been unintentionally struck-through. “within” should be re-inserted.
In claim 18 line 7, the term “an” was deleted, but “the” was not inserted in its place. A “the” should be inserted before “real-world” for clarity and proper antecedent basis.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the pose data" in line 15. There is insufficient antecedent basis for this limitation in the claim and it is unclear what this limitation is referring to or how it fits into the scope of the claim.
Claim(s) 2-8 is/are rejected because it/they depend(s) from claim 1 and fail(s) to cure the deficiencies above.
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 1-10, 12-20, and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is taken as the representative claim. Claim 1 recites receiving map data indicating a position of an object, generating goals data for the object, generating trajectory data for the object, determining a simulated trajectory, and causing a performance of a simulation. The claim is directed to receiving data, generating data, and determining a trajectory, which is a mental process that could be performed within the human mind or with pen and paper. The additional elements of causing a performance of a simulation and using neural networks do not integrate the abstract idea into a practical application or amount to significantly more. Causing a performance of a simulation is insignificant extra-solution activity. As discussed in the background section of the specification, simulations are well-known and are an integral part of developing systems. Therefore, this additional element does not provide a practical application or significantly more. The additional elements of using neural networks are known computer components recited at a high level upon which the abstract idea is implemented on. The generating of the goals data and actions data is not performed by the neural network and is only based on neural networks. Therefore, the neural networks do not amount to more than a generic computer component being used as a tool, and do not add a practical application or significantly more. The claim as a whole is direct to a mental process without a practical application or significantly more.
Claim(s) 2-8 and 22 is/are rejected because it/they depend(s) from claim 1 and fail(s) to cure the deficiencies above. Claims 2-8 and 22 merely expand the mental process, such as generating additional elements, or add generic computer components.
Similar to claims 1-8 and 22, claims 9, 10, and 12-20 are directed to a mental process comprising receiving data, generating data, and making determinations. The additional element of a processor is a generic computer component and does not add a practical application or significantly more.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-10 and 12-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication 2019/0025841 to Haynes et al.
As per claim 1, Haynes discloses a method (Haynes; At least the abstract) comprising:
receiving map data representative of one or more maps indicating at least a location of an object within an environment at a current time instance and one or more representations of one or more prior locations of the object within the environment at one or more prior time instances (Haynes; At least paragraph(s) 19, 22, and 27);
generating, based at least on one or more first neural networks of a simulation system processing at least a first portion of the map data, goals data representative of one or more navigational goals for the object within the environment at one or more future time instances, one or more first parameters of the one or more first neural networks updated during training in order for the one or more first neural networks to determine the one or more navigational goals associated with the object (Haynes; At least paragraph(s) 29-31, 34, 35, 49, and 50);
generating, based at least on one or more second neural networks of the simulation system processing at least a second portion of the map data and the pose data, actions data representative of one or more actions for the object within the environment in order to accomplish the one or more navigational goals over at least the one or more future time instances, one or more second parameters of the one or more second neural networks updated during training in order for the one or more second neural networks to determine the one or more actions in order to accomplish the one or more navigational goals of the one or more first neural networks (Haynes; At least paragraph(s) 35 and 52-54; based on the goal and the map data, actions are determined, such as make a left turn);
determining, using the simulation system and based at least on the actions data, a simulated trajectory for the object within the environment over at least the one or more future time instances (Haynes; At least paragraph(s) 56, 57, and 62); and
causing, using the simulation system, a performance of a simulation that includes at least the object performing the simulated trajectory within the environment over the one or more future time instances (Haynes; At least paragraph(s) 73-75; a simulation of the different trajectories for the different objects is performed in order to determine the best motion plan for the subject vehicle).
As per claim 2, Haynes discloses further comprising: generating, using one or more third neural networks and based at least on at least a third portion of the map data, trajectory data representative of one or more predicted trajectories for one or more second objects within the environment, wherein the determining the simulated trajectory is further based at least on the trajectory data (Haynes; At least paragraph(s) 73 and 76; the predicted trajectory is determined for each object based on the object data and map (e.g., lane) data associated with that object).
As per claim 3, Haynes discloses wherein: the one or more second neural networks are trained separately from the one or more first neural networks (Haynes; At least paragraph(s) 48 and 54; the neural networks are separate networks that are trained separately on different types of data).
As per claim 4, Haynes discloses further comprising: generating fourth simulation data representative of the simulation associated with the environment, the simulation including at least the object performing the simulated trajectory for the object within the environment over the one or more future time instances, wherein the causing the performance of the simulation is based at least on the fourth simulation data (Haynes; At least paragraph(s) 73-75).
As per claim 5, Haynes discloses further comprising: generating, using the one or more first neural networks and based at least on the map data, second goals data representative of one or more second navigational goals for a second object within the environment; generating, using the one or more second neural networks and based at least on the second goals data, second actions data representative of one or more possible trajectories second actions for the second object within the environment to accomplish the one or more second navigational goals; and determining, based at least on the second actions data, a second simulated trajectory for the second object within the environment, wherein the simulation further includes the second object performing the second simulated trajectory within the environment (Haynes; At least paragraph(s) 29 and as discussed above; a predicted goal and trajectory is determined for each object and then the self vehicle path is determined based on simulating trajectories for all of the vehicles).
As per claim 6, Haynes discloses further comprising: generating, using the one or more first neural networks and based at least on one or more second locations of the object at the one or more future time instances, second goals data representative of one or more second second navigational goals for the object within the environment; generating, using the one or more second neural networks and based at least on the second goals data, second actions data representative of one or more second possible trajectories actions for the object within the environment in order to perform the one or more second navigational goals; and determining, based at least on the second actions data, a second simulated trajectory for the object the environment, wherein at least a portion of the second simulated trajectory includes an extension of the simulated trajectory, wherein the simulation further includes the object performing the second simulated trajectory as the extension of the simulated trajectory (Haynes; At least paragraph(s) 19 and 29; one of skill in the art would understand that the steps would be continuously repeated generating new goal and trajectories for each object in order to drive the autonomous vehicle, as discussed in the background section. This can be further seen in at least paragraph(s) 103, where information is continuously evaluated in order to define objects).
As per claim 7, Haynes discloses wherein: the one or more actions for the object within the environment include at least a first possible trajectory for the object within the environment and a second possible trajectory for the object within the environment (Haynes; At least paragraph(s) 62 and 63); and
the determining the simulated trajectory for the object within the environment comprises:
determining, based at least on the actions data, a first score associated with the first possible trajectory and a second score associated with the second possible trajectory (Haynes; At least paragraph(s) 62 and 63); and
determining, based at least on the first score and the second score, that the simulated trajectory includes the first possible trajectory (Haynes; At least paragraph(s) 63 and 73).
As per claim 8, Haynes discloses wherein the determining the first score associated with the first possible trajectory and the second score associated with the second possible trajectory is further based at least on one or more of: one or more locations of one or more roads within the environment; and one or more distances between objects that include at least the object (Haynes; At least paragraph(s) 58 and 62).
As per claim 9, Haynes discloses a system (Haynes; At least the abstract) comprising:
one or more processors (Haynes; At least paragraph(s) 81) to:
receive input data representative of one or more maps indicating at least a first location of a first object within an environment at a first time and one or more representations of one or more second locations of the first object within the environment prior to the first time (Haynes; At least paragraph(s) 19, 22, and 27);
generate, based at least on one or more first neural networks processing at least a first portion of the input data, first data representative of one or more possible poses for the first object within the environment over one or more second times that are after the first time (Haynes; At least paragraph(s) 44-47 and 49);
generate, based at least on one or more second neural networks processing at least a second portion of the input data and the first data representative of the one or more possible poses of the first object, second data representative of one or more possible trajectories for the first object within the environment over the one or more second times (Haynes; At least paragraph(s) 32 and 52-54);
generate, based at least on one or more third neural networks processing at least a third portion of the input data, one or more predicted trajectories for one or more second objects within the environment over the one or more second times (Haynes; At least paragraph(s) 32, 52-54, and 73);
determine, based at least on the one or more possible trajectories for the first object and the one or more predicted trajectories for the one or more second objects, a simulated trajectory for the first object within the environment (Haynes; At least paragraph(s) 73 and 74); and
causing a performance of a simulation that includes at least the first object performing the simulated trajectory within the environment (Haynes; At least paragraph(s) 75).
As per claim 10, Haynes discloses wherein the one or more processors are further to: determine, based at least on the one or more possible trajectories for the first object and the one or more predicted trajectories for the one or more second object, at least a second simulated trajectory for a second object of the one or more second objects, wherein the simulation further includes the second object performing the second simulated trajectory within the environment (Haynes; At least paragraph(s) 38 and 62).
As per claim 12, Haynes discloses wherein the one or more processors are further to: generate simulation data representative of the simulation associated with the environment, the simulation including at least the first object performing the simulated trajectory within the environment, wherein the performance of the simulation is caused based at least on the simulation data (Haynes; At least paragraph(s) 73 and 74).
As per claim 13, Haynes discloses wherein the one or more processors are further to: generate, using the one or more first neural networks and based at least on the input data, one or more second possible poses for a third object within the environment; generate, using the one or more second neural networks and based at least on the input data and the one or more second possible poses, one or more second possible trajectories for the second third object within the environment; and determine, based at least on the one or more second possible trajectories for the second third object and the one or more possible trajectories for the first object, a second simulated trajectory for the second third object within the environment, wherein the simulation further includes the second third object performing the second simulated trajectory within the environment (Haynes; At least paragraph(s) 29 and as discussed above; a predicted pose and trajectory is determined for each object and then the self vehicle path is determined based on simulating trajectories for all of the vehicles).
As per claim 14, Haynes discloses wherein the one or more processors are further to generate, using the one or more first neural networks and based at least on the simulated trajectory, one or more second possible poses for the first object within the environment; generate, using the one or more second neural networks and based at least on the one or more second possible poses, one or more second possible trajectories for the first object within the environment; and determine, based at least on the one or more second possible trajectories for the first object, a second simulated trajectory for the first object within the environment, wherein at least a portion of the second simulated trajectory includes an extension of the simulated trajectory (Haynes; At least paragraph(s) 19 and 29; one of skill in the art would understand that the steps would be continuously repeated generating new poses and trajectories for each object in order to drive the autonomous vehicle, as discussed in the background section. This can be further seen in at least paragraph(s) 31, where the vehicle is continuously tracked/predicted to be following a lane and 103, where information is continuously evaluated in order to define objects).
As per claim 15, Haynes discloses the one or more possible trajectories for the first object within the environment include at least a first possible trajectory for the first object within the environment and a second possible trajectory for the first object within the environment (Haynes; At least paragraph(s) 62 and 63); and
the determination of the simulated trajectory for the first object within the environment comprises: determining, based at least on a predicted trajectory for a the one or more predicted trajectories for the one or more second object, a first score associated with the first possible trajectory and a second score associated with the second possible trajectory (Haynes; At least paragraph(s) 62 and 63); and
determining, based at least on the first score and the second score, that the simulated trajectory includes the first possible trajectory (Haynes; At least paragraph(s) 63 and 73).
As per claim 16, Haynes discloses wherein the determination of the first score associated with the first possible trajectory and the second score associated with the second possible trajectory is further based at least on one or more of: one or more locations of one or more roads within the environment; and one or more distances between the first object and the one or more second objects (Haynes; At least paragraph(s) 58 and 62).
As per claim 17, Haynes discloses wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Haynes; At least the abstract).
As per claim 18, Haynes discloses one or more processors comprising: processing circuitry (Haynes; At least paragraph(s) 81) to:
receive log data representative of information associated with a real-world object located within a real-world environment, the information including at least one or more locations of the real-world object within the real-world environment (Haynes; At least paragraph(s) 19);
generate, based at least on the log data, input data representative of at least the one or more locations of the real-world object within real-world environment (Haynes; At least 19 and 23; The processor receives past states of objects and generates the location of the object in the environment);
generate, based at least on one or more first neural networks processing at least a first portion of the input data, first data representative of one or more possible poses for a simulated object within a simulated environment at one or more future times, the simulated object corresponding to the real-world object and the simulated environment corresponding to the real-world environment (Haynes; At least paragraph(s) 27, 44-47, and 49);
generate, based at least on one or more second neural networks processing at least a second portion of the input data and the first data representative of the one or more poses, second data representative of one or more possible trajectories for the simulated object within the simulated environment over the one or more future times (Haynes; At least paragraph(s) 32 and 52-54);
determine, based at least on the one or more possible trajectories, a simulated trajectory for the simulated object within the simulated environment (Haynes; At least paragraph(s) 32, 52-54, and 73; and
cause a performance of a simulation that includes at least the simulated object performing the simulated trajectory within the simulated environment over the one or more future times (Haynes; At least paragraph(s) 73 and 74).
As per claim 19, Haynes discloses wherein the processing circuitry is further to: generate, using one or more third neural networks and based at least on at least a third portion of the input data, one or more predicted trajectories for one or more second simulated objects within the environment, wherein the determination of the simulated trajectory is further based at least on the one or more predicted trajectories (Haynes; At least paragraph(s) 73 and 76; the predicted trajectory is determined for each object based on the object data and map (e.g., lane) data associated with that object).
As per claim 20, Haynes discloses wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Haynes; At least the abstract).
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.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Haynes.
As per claim 22, Haynes does not explicitly disclose wherein the causing the performance of the simulation uses one or more hardware processors that generate the environment using one or more ray-tracing techniques.
However, the above feature(s) are admitted prior art per at least paragraph(s) 142 of the specification. At the time of filing, it would have been obvious to one of ordinary skill in the art to have incorporated using ray-tracing techniques into the invention of Haynes with a reasonable expectation of success with the motivation of using a known technique to improve a similar device in the same way with predictable results. As discussed in the specification, ray-tracing techniques can be used to quickly and efficiently determine the positions and extent of objects.
Response to Arguments
Applicant’s arguments, see page 14, filed 12/09/2025, with respect to objections and 35 U.S.C. 112(b) rejection of the claims have been fully considered and are persuasive. The objections and 35 U.S.C. 112(b) rejection of the claims have been withdrawn.
Applicant's arguments, see pages 14-20, filed 12/09/2025, have been fully considered but they are not persuasive. With respect to Applicant's arguments that the amendments overcome the 35 U.S.C. 101 rejection, the Examiner respectfully disagrees.
With respect to Applicant's arguments that the claims are similar to example 47, the Examiner respectfully disagrees. At a high level, the claims of example 47 took real-time remedial action in response to analyzing information, thus providing a practical application of performing actions in response to the analysis. The current claims do not contain a practical application. They make a determination, which is a mental process, and perform a simulation, which is extra-solution activity as discussed in the rejection above.
With respect to Applicant's arguments that the claims provide improvements, the Examiner respectfully disagrees. The claims do not adequately incorporate any improvement discussed in the specification. The generating steps are only based on first and second neural networks, which results in the neural network only being tools used in mental steps of generating information.
With respect to Applicant's arguments the claims encompass AI, the Examiner respectfully disagrees. As discussed above, the neural networks and other computer components are merely used as tools and not significantly integrated into the process.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892. The prior art shows the state of the art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID P MERLINO whose telephone number is (571)272-8362. The examiner can normally be reached M-Th 5:30am-3:00pm F 5:30-9:00 am ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/David P. Merlino/ Primary Examiner, Art Unit 3665