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 June 9th, 2026. Claims 1-21 are presently pending and are presented for examination.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 9th, 2026 has been entered.
Response to Amendment
In response to the Applicant’s response filed June 9th, 2026, Examiner maintains the previous 35 U.S.C. 102 prior art rejections.
Response to Arguments
Applicant’s arguments filed June 9th, 2026, have been fully considered.
Regarding the arguments provided for the rejections of claim 1, as put forth on page 7 of the Applicant’s arguments, applicants’ arguments have been fully considered. Applicant argues “PG does not teach or suggest as least “receive one or more representations of one or more generalized behaviors of one or more users operating an autonomous device” and “train one or more transformer neural networks to control the autonomous device to perform the one or more tasks based, at least in part, on one or more other images of the autonomous device in the simulated environment performing the one or more tasks using the one or more actions and the generalized behaviors,” as recited in amended claim 1…In accordance with the teachings of PG, tasks are limited to being based on a natural language text sequence or instruction…the tasks are not “one or more representations of one or more generalized behaviors of one or more users operating an autonomous device,” as recited by claim 1. Likewise, the teaching of the interaction data in PG characterizes interactions of agents with an environment. Furthermore, the intention characterization of PG does not generalize behaviors of users operating an autonomous device”.
As to point (a), examiner respectfully disagrees. Examiner asserts that the newly added limitations are taught by PG. Specifically PG discloses at paragraph 136 of the specification that behavior of an expert interacting with a device may be observed and used to train the neural network. The expert interaction information obtained corresponds to generalized behavior of one or more users during a task operating an autonomous device. Examiner is interpreting generalized behavior as a behavior a user may generally make in that environment. The information obtained appears to be general real-world data of how the user interacts in the environment and this therefore corresponds to generalized behavior of one or more users, the agent being the user and the interaction being the generalized behavior.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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) 1-20 are rejected under 35 U.S.C. 102(a)(2) as anticipated by US-20240189994 (hereinafter, “PG”).
Regarding claim 1 PG discloses one or more processors (see at least [0141]; “more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions”), comprising: circuitry to;
receive one or more representations of one or more generalized behaviors of one or more users operating an autonomous device (see at least [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors, and [0120]; “the training dataset includes expert interaction data characterizing interactions of one or more expert agents with a corresponding environment…the expert agent may be…a person who is skilled at the task to be performed by the agent”);
use one or more images of a simulated environment to generate one or more actions to be performed by the autonomous device (see at least [0049]; “the agent may be an autonomous or semiautonomous land, air, or sea vehicle navigating through the environment to a specified destination in the environment”) to perform one or more tasks (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the first observation image used corresponds to applicant’s image); and
train one or more transformer neural networks to control the autonomous device to perform the one or more tasks based, at least in part, on one or more other images of the autonomous device in the simulated environment performing the one or more tasks using the one or more actions and the generalized behaviors (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,” and [0056-0057]; “the policy system 100 can be used to control the interactions of the agent with a simulated environment, and the policy system 100 (or another training system) can train the set of neural networks used to control the agent 102 based on the interactions of the agent 102 (or another agent) with the simulated environment to determine trained values of the parameters of the set of neural networks. Training the set of neural networks will be described in more detail below with reference to FIGS. 5-6. After the set of neural networks are trained based on the interactions of the agent 102 (or another agent) with a simulated environment, the trained neural networks can be used by the policy system 100 to control the interactions of a real-world agent with the real-world environment, i.e., to control the agent that was being simulated in the simulated environment,” the interactions to complete the task correspond to applicant’s actions, and [0064]; “a new observation image is obtained by the policy system 200 after each action that the agent performs,” the new observation image corresponds to Applicant’s other image, each new image would be used by the neural network to generate a policy output based on said image, and [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors).
Regarding claim 2 PG discloses all of the limitations of claim 1. Additionally, PG discloses wherein the one or more actions are generated by a task and motion planning module (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the policy system corresponds to the task and motion planning module).
Regarding claim 3 PG discloses all of the limitations of claim 2. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of an environment (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0032]; “the policy system 100 obtains an observation image 106 characterizing a state of the environment 104 at the time step.”).
Regarding claim 4 PG discloses all of the limitations of claim 2. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of the autonomous device (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0035]; “While this specification generally describes that the observations are images, in some cases the observations can include additional data in addition to image, e.g., proprioceptive data characterizing the agent or other data captured by other sensor of the agent. In these cases, the other data can be encoded jointly with the observation image 106 by the image encoder neural network 120.”).
Regarding claim 5 PG discloses all of the limitations of claim 1. Additionally, PG discloses wherein the one or more other images of the autonomous device in the simulated environment performing the one or more tasks are determined using an image sensor (see at least [0027]; “the observation images 106 can be images captured by a camera sensor of the agent 102 or by a camera sensor located in the environment 104. The camera sensor can for example be a still camera or a video camera).
Regarding claim 6 PG discloses all of the limitations of claim 1. Additionally, PG discloses wherein the one or more neural networks are trained using the one or more images (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,”) and one or more simulations of the performance of the one or more tasks (see at least [0054]; “Generally, when the environment 104 is a simulated environment, the actions 144 may include simulated versions of one or more of the previously described actions or types of actions.”).
Regarding claim 7 PG discloses all of the limitations of claim 1. Additionally, PG discloses wherein the circuitry is to use the one or more neural networks to identify one or more control inputs of the autonomous device to control the autonomous device to perform the one or more tasks (see at least [0113-0114]; “The system selects an action to be performed by the agent using the policy output (step 410). This selection can be made by selecting a respective value for one or more of the plurality of action dimensions using the respective categorical distributions that are defined by the policy output of the Transformer neural network. The system causes the agent to perform the selected action (step 412), e.g., by directly submitting the control input to the agent or by transmitting instructions or other data, e.g., over a data communication network, to a control system for the agent that will cause the agent to perform the selected action.”).
Regarding claim 8 PG discloses a system (see at least Fig. 1) comprising: one or more processors (see at least [0141]; “more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions”) to:
receive one or more representations of one or more generalized behaviors of one or more users operating an autonomous device (see at least [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors, and [0120]; “the training dataset includes expert interaction data characterizing interactions of one or more expert agents with a corresponding environment…the expert agent may be…a person who is skilled at the task to be performed by the agent”);
use one or more images of a simulated environment to generate one or more actions to be performed by the autonomous device (see at least [0049]; “the agent may be an autonomous or semiautonomous land, air, or sea vehicle navigating through the environment to a specified destination in the environment”) to perform one or more tasks (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the first observation image used corresponds to applicant’s image); and
train one or more transformer neural networks to control the autonomous device (see at least [0023]; “The physical process may include an industrial robot, three-dimensional (3D) printer, machine tool, self-driving car, and/or another type of automated technology”) to perform the one or more tasks based, at least in part, on one or more other images of the autonomous device in the simulated environment performing the one or more tasks using the one or more actions and the generalized behaviors (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,” and [0056-0057]; “the policy system 100 can be used to control the interactions of the agent with a simulated environment, and the policy system 100 (or another training system) can train the set of neural networks used to control the agent 102 based on the interactions of the agent 102 (or another agent) with the simulated environment to determine trained values of the parameters of the set of neural networks. Training the set of neural networks will be described in more detail below with reference to FIGS. 5-6. After the set of neural networks are trained based on the interactions of the agent 102 (or another agent) with a simulated environment, the trained neural networks can be used by the policy system 100 to control the interactions of a real-world agent with the real-world environment, i.e., to control the agent that was being simulated in the simulated environment,” the interactions to complete the task correspond to applicant’s actions, and [0064]; “a new observation image is obtained by the policy system 200 after each action that the agent performs,” the new observation image corresponds to Applicant’s other image, each new image would be used by the neural network to generate a policy output based on said image, and [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors).
Regarding claim 9 PG discloses all of the limitations of claim 8. Additionally, PG discloses wherein the one or more actions are generated by a task and motion planning module (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the policy system corresponds to the task and motion planning module).
Regarding claim 10 PG discloses all of the limitations of claim 9. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of an environment (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0032]; “the policy system 100 obtains an observation image 106 characterizing a state of the environment 104 at the time step.”).
Regarding claim 11 PG discloses all of the limitations of claim 9. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of the autonomous device (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0035]; “While this specification generally describes that the observations are images, in some cases the observations can include additional data in addition to image, e.g., proprioceptive data characterizing the agent or other data captured by other sensor of the agent. In these cases, the other data can be encoded jointly with the observation image 106 by the image encoder neural network 120.”).
Regarding claim 12 PG discloses all of the limitations of claim 8. Additionally, PG discloses wherein the one or more images of the autonomous device in the simulated environment performing the one or more tasks are determined using an image sensor (see at least [0027]; “the observation images 106 can be images captured by a camera sensor of the agent 102 or by a camera sensor located in the environment 104. The camera sensor can for example be a still camera or a video camera).
Regarding claim 13 PG discloses all of the limitations of claim 8. Additionally, PG discloses wherein the one or more neural networks are trained using the one or more images (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,”) and the one or more simulations of the performance of the one or more tasks (see at least [0054]; “Generally, when the environment 104 is a simulated environment, the actions 144 may include simulated versions of one or more of the previously described actions or types of actions.”).
Regarding claim 14 PG discloses all of the limitations of claim 8. Additionally, PG discloses wherein the one or more processors are to use the one or more neural networks to identify one or more control inputs of the autonomous device to control the autonomous device to perform the one or more tasks (see at least [0113-0114]; “The system selects an action to be performed by the agent using the policy output (step 410). This selection can be made by selecting a respective value for one or more of the plurality of action dimensions using the respective categorical distributions that are defined by the policy output of the Transformer neural network. The system causes the agent to perform the selected action (step 412), e.g., by directly submitting the control input to the agent or by transmitting instructions or other data, e.g., over a data communication network, to a control system for the agent that will cause the agent to perform the selected action.”).
Regarding claim 15 PG discloses a method (see at least Fig. 4) comprising:
receiving one or more representations of one or more generalized behaviors of one or more users operating an autonomous device (see at least [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors, and [0120]; “the training dataset includes expert interaction data characterizing interactions of one or more expert agents with a corresponding environment…the expert agent may be…a person who is skilled at the task to be performed by the agent”);
using one or more images of a simulated environment to generate one or more actions to be performed by the autonomous device (see at least [0049]; “the agent may be an autonomous or semiautonomous land, air, or sea vehicle navigating through the environment to a specified destination in the environment”) to perform one or more tasks (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the first observation image used corresponds to applicant’s image); and
training one or more transformer neural networks to control the autonomous device to perform the one or more tasks based, at least in part, on one or more other images of the autonomous device in the simulated environment performing the one or more tasks using the one or more actions and the generalized behaviors (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,” and [0056-0057]; “the policy system 100 can be used to control the interactions of the agent with a simulated environment, and the policy system 100 (or another training system) can train the set of neural networks used to control the agent 102 based on the interactions of the agent 102 (or another agent) with the simulated environment to determine trained values of the parameters of the set of neural networks. Training the set of neural networks will be described in more detail below with reference to FIGS. 5-6. After the set of neural networks are trained based on the interactions of the agent 102 (or another agent) with a simulated environment, the trained neural networks can be used by the policy system 100 to control the interactions of a real-world agent with the real-world environment, i.e., to control the agent that was being simulated in the simulated environment,” the interactions to complete the task correspond to applicant’s actions, and [0064]; “a new observation image is obtained by the policy system 200 after each action that the agent performs,” the new observation image corresponds to Applicant’s other image, each new image would be used by the neural network to generate a policy output based on said image, and [0136]; “As another example, the joint training of the set of neural networks can include imitation learning. For example, when the training dataset includes expert interaction data, the system can train the set of neural networks through behavior cloning on the expert interaction data to generate training policy outputs from which actions that closely mimic those performed by the expert agents can be selected,” the expert interaction behavior is data performed by the expert agents and corresponds to Applicant’s representations of one or more generalized behaviors).
Regarding claim 16 PG discloses all of the limitations of claim 15. Additionally, PG discloses wherein the one or more actions are generated by a task and motion planning module (see at least [0064]; “At each of the plurality of time steps, the policy system 200 obtains an observation image 206 characterizing a state of the environment at the time step. In the example of FIG. 2, the agent performs a single action in response to each observation image 206,” the policy system corresponds to the task and motion planning module).
Regarding claim 17 PG discloses all of the limitations of claim 16. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of an environment (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0032]; “the policy system 100 obtains an observation image 106 characterizing a state of the environment 104 at the time step.”).
Regarding claim 18 PG discloses all of the limitations of claim 16. Additionally, PG discloses wherein the task and motion planning module is to access an initial state of the autonomous device (see at least Fig. 4, step 402 – Obtain an observation image, the observation image corresponds to an initial state of an environment, and [0035]; “While this specification generally describes that the observations are images, in some cases the observations can include additional data in addition to image, e.g., proprioceptive data characterizing the agent or other data captured by other sensor of the agent. In these cases, the other data can be encoded jointly with the observation image 106 by the image encoder neural network 120.”).
Regarding claim 19 PG discloses all of the limitations of claim 15. Additionally, PG discloses wherein the one or more other images of the autonomous device in the simulated environment performing the one or more tasks are determined using an image sensor (see at least [0027]; “the observation images 106 can be images captured by a camera sensor of the agent 102 or by a camera sensor located in the environment 104. The camera sensor can for example be a still camera or a video camera).
Regarding claim 20 PG discloses all of the limitations of claim 15. Additionally, PG discloses wherein the one or more neural networks are trained using the one or more images (see at least [0044]; “After having generated the sequence of input tokens 132, the policy system 100 then processes the sequence of input tokens 132 using a Transformer neural network 140 to generate a policy output 142 that defines an action to be performed by the agent 102 in response to the observation image 106 received at the time step,”) and one or more simulations of the performance of the one or more tasks (see at least [0054]; “Generally, when the environment 104 is a simulated environment, the actions 144 may include simulated versions of one or more of the previously described actions or types of actions.”).
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 21 is rejected under 35 U.S.C. 103 as being unpatentable over PG, as applied to claim 15 above, in view of US-20180029226 (hereinafter, “Dani”).
Regarding claim 21 PG discloses all of the limitations of claim 15. PG does not disclose wherein one or more planning modules define the one or more tasks and one or more actions as one or more continuous joint trajectories, at least in part, on the generalized behaviors.
Dani, in the same field of endeavor, teaches wherein one or more planning modules define the one or more tasks and one or more actions as one or more continuous joint trajectories, at least in part, on the generalized behaviors (see at least [0016]; “the trajectory generation unit 140 may observe a state of the robot and generate a trajectory enabling the robot to perform the task, based on the BB and GMM; and the motion planning unit 150 may apply the trajectory to the robot 110, thus causing the robot 110 to perform the task,” [0023]; “the learning system 100 may observe one or more demonstrations of a user performing a task,” and [0067]; “At block 350, the learning system 100 may convert the trajectory generated above from the Cartesian space into a trajectory in the joint space of the robot 110. In some embodiments, this may be executed through the use of IKFast, a robot kinematics solver, or by some other solver. At block 360, the learning system 100 may implement the trajectory, such as by using a low-level joint controller to control the robot 110 according to the trajectory in joint space”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the neural network system of PG with the joint trajectory of Dani. One of ordinary skill in the art would have been motivated to make this modification for the benefit of enabling robots to perform in manufacturing contexts (see at least Dani; [0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20220126445 teaches a system and apparatus for solving task and motion planning problems using neural networks
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEIGH NICOLE TURNBAUGH whose telephone number is (703)756-1982. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hitesh Patel can be reached at (571) 270-5442. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASHLEIGH NICOLE TURNBAUGH/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
6/24/26
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