DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 8/25/2023 and the Remarks and Amendments filed on 8/6/2026.
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 4-6, 8 and 10-19 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 4
Step 1: The claim recites a processor; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move an object from a first pose to a second pose within a predetermined period of time: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that learning is unsuccessful, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “one or more circuits to:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “perform at least a first segment of a task and a second segment of the task corresponding to a robot” and “a set of instructions not derived from imitation learning is executed to perform the second segment” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic ML models are used to perform a first segment of a task or how the broad second set of instructions are used to perform a second segment of a task. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional elements of “wherein one or more machine learning models were trained to perform the first segment using imitation learning” and “wherein the one or more machine learning models are trained to perform the first segment using the imitation learning based at least on a determination that other than the imitation learning was unsuccessful for learning to perform the first segment” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move a first object from a first pose to a second pose without contacting a second object: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that learning is unsuccessful, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move an object from a first pose to a second pose with a predetermined level of precision: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that learning is unsuccessful, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein a transition pose is determined between the first segment and the second segment, the transition pose corresponding to an end or a beginning of imitation learning: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a transition pose between segments of a task, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 10
Step 1: The claim recites a system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
segment a task to be performed by a robot into segments: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of segmenting a task, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally divide or segment a paragraph into sentences for the task of reading a paragraph.
determine a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment of the segments, the plurality of sets of instructions including imitation-learning based sets of instructions and non-imitation-learning based sets of instructions, the first set of instructions being an imitation-learning based sets of instructions to execute a first machine-learning model trained using imitation learning: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining instructions for performing a first objective, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally read a sentence of a paragraph, the objective being the reading of the sentence.
determine that the plurality of sets of instructions is inadequate to perform a second objective of a second segment of the segments: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that instructions are inadequate to perform an objective, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can determine that one cannot read a second sentence of a paragraph because it is in a different language.
adding a set of instructions based on the trained second machine learning model to the plurality of sets of instructions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of adding instructions to a set of instructions, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can add more instructions or commands to a set of instructions or commands.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “one or more processors to:”, “send to a user device of an operator a request for human demonstration of the second segment”, “receive from the user device a second set of instructions for operating the robot for the second segment following an end of the first segment”, and “update one or more parameters of a machine learning model for controlling the robot using the second set of instructions for the second segment”.
The additional elements of “one or more processors to:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “update one or more parameters of a machine learning model for controlling the robot using the second set of instructions for the second segment” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic ML model is broadly updated using second instructions. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “send to a user device of an operator a request for human demonstration of the second segment” and “receive from the user device a second set of instructions for operating the robot for the second segment following an end of the first segment” are insignificant extra-solution activities (see MPEP § 2106.05(g)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “one or more processors to:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “update one or more parameters of a machine learning model for controlling the robot using the second set of instructions for the second segment” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic ML model is broadly updated using second instructions. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “send to a user device of an operator a request for human demonstration of the second segment” and “receive from the user device a second set of instructions for operating the robot for the second segment following an end of the first segment” are insignificant extra-solution activities (see MPEP § 2106.05(g), that are well-understood, routine, conventional activities (see MPEP §2106.05(d); “Receiving or transmitting data over a network”).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the plurality of sets of instructions comprises coordinates and vectors for controlling the robot” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 12
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the second objective comprises moving an object from a first pose to a second pose” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 13
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional elements of “wherein the plurality of sets of instructions is inadequate to perform the second objective based at least on the plurality of sets of instructions being inadequate to at least one of: control the robot to move the object from the first pose to the second pose within a predetermined period of time; control the robot to move the object from the first pose to the second pose without contacting a second object; or control the robot to move the object from the first pose to the second pose with a predetermined level of precision” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 14
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the one or more processing units are to send the request for human demonstration of the second segment by adding the second segment to a queue of segments for which demonstration is requested: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of adding a segment to a queue, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 15
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the one or more processing units are to add the second segment to the queue of segments based at least on an expected throughput of the human operator: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of adding a segment to a queue, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “wherein the queue of segments is associated with a human operator” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 16
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the first objective of the first segment includes reaching a transition pose for transitioning between the first segment and the second segment” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 17
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
mapping the segments to a human demonstration of the task: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of mapping task segments to a demonstration, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
identifying one or more poses of the robot at a portion of the human demonstration corresponding to a transition from the first segment to the second segment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying a robot pose, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 18
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the segments include a plurality of transition poses for transitioning to segments associated with human demonstrations” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 19
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional elements of “wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; … or a system implemented at least partially using cloud computing resources” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
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.
Claims 1, 2, 7, and 20 are rejected under 35 U.S.C. 103 as being obvious over Gutzeit et al. (Gutzeit et al., “The BesMan learning platform for Automated Robot Skill learning”, May 31, 2018, Front. Robot. AI 5:43, pp. 1-14, hereinafter “Gutzeit”) in view of Angelov et al. (Angelov et al., “Composing Diverse Policies for Temporally Extended Tasks”, Feb. 17, 2020, arXiv:1907.08199v3, pp. 1-9, hereinafter “Angelov”).
Regarding claim 1, Gutzeit discloses [a] processor, comprising: one or more circuits to (§2; and §3; the sections implement the experiments of Gutzeit, and these experiments are inherently performed using a processor with circuits)
perform at least a first segment of a task and a second segment of the task corresponding to a robot, (Page 2, §2; “The ‘Behavior Segmentation’ will decompose demonstrations into simple behavioral building blocks. Segments that belong to the same type of movement are grouped together to obtain multiple demonstrations for the same motion plan”, which discloses segmenting a robotic task into “behavior building blocks” which are discrete sub-tasks or segments of a task corresponding to the robot’s manipulation behavior; and §2.2; and §2.3; the section discloses performing a first segment of a task using imitation learning that is implemented by a ML model; and §2.4; the section discloses performing a second segment of a task using reinforcement learning; and §4; the task is a ball throwing scenario)
wherein one or more machine learning models were trained to perform the first segment using imitation learning (Page 2, §2; “For each relevant segment, IL methods are used to represent the recorded trajectory segments as motion plans”, which discloses that the imitation learning module learns a motion plan or policy for each segment from human demonstration data; and §2.3; and §2.4; and §3.2.1).
Gutzeit fails to explicitly disclose but Angelov discloses a set of instructions not derived from imitation learning is executed to perform the second segment (§IV.B; “In this task the PR2 robot needs to assemble the first part of the Siemens Challenge2, which involves grasping a compound gear from a table, and placing it on a peg module held in the other hand of the robot. … a set of controllers that (1) picks up the gear from the table; (2) moves the left PR2 arm in proximity to the other arm; (3) inserts the gear on the peg module. Policy (1, 2) rely exclusively on scripted path planning techniques and work using discrete time steps, while (3) is learned entirely with a neural network … Additionally, we compare the performance of the scripted Motion Planning method (using RRT Connect [41]), Dynamic Motion Primitives (learned from the MPs) and the Visual Neural Policy on each subtask, as well as using the full sequence under the different controllers as a baseline”, which discloses a set of instructions not derived from imitation learning that is executed to perform a second segment of a task (picking up a gear and moving a robot arm). Note that policies 1 and 2 rely on scripted path planning techniques and operate in discrete time steps. Further, the claim requires only that the instructions performing the second segment of the task are not imitation learning, as these instructions are scripted and not “derived from imitation learning”; and Figure 2 and its caption; “In the Gear Assembly Task, our controller library includes motion planning (MP) primitives (operating on joint angles) for picking up or moving and a convolutional neural network (CNN) for inserting the gear (operating on images). The MP primitives produce a trajectory for executing a task.”).
Gutzeit and Angelov are analogous art because both are concerned with machine learning and robotic manipulation. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and robotics to combine the second set of instructions not derived from imitation learning of Angelov with the imitation learning and task segmentation of Gutzeit to yield to the predictable result of a set of instructions not derived from imitation learning is executed to perform the second segment. The motivation for doing so would be to automatically compose diverse policies comprising motion planning trajectories, dynamic motion primitives and neural network controller (Angelov; Abstract).
Regarding claim 20, it is a method claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 2, the rejection of claim 1 is incorporated and Gutzeit further discloses wherein the first segment of the task and the second segment of the task are performed based at least on coordinates and vectors associated with control of the robot (§2.3; the section describes learning motion plans as trajectory functions over robot joint coordinates; and §2.4; describes RL optimization over the same coordinate-space motion plans; and Figure 1).
Regarding claim 7, the rejection of claim 1 is incorporated and Gutzeit further discloses wherein, for the second segment, human demonstration is not requested (§2; “the “Motion Plan Refinement” module can use RL (see Section 2.4) to adapt the motion plan. This requires interaction with the real or simulated target system and the specification of a reward function which tells the learning algorithm how well a motion plan solves the task”, without requesting additional human demonstration for the segment).
Claims 3-6 are rejected under 35 U.S.C. § 103 as being obvious over Gutzeit in view of Angelov and further in view of Zadeh et al. (US 20220297303 A1, hereinafter “Zadeh”).
Regarding claim 3, the rejection of claim 1 is incorporated and Gutzeit further discloses wherein the one or more machine learning models are trained to perform the first segment using the imitation learning (§2, and §2.3; the sections disclose that RL is applied when IL does not produce the same result on the robot and the determination of RL inadequacy triggers the use of different methods for the segment).
Gutzeit fails to explicitly disclose but Zadeh discloses a determination that other than the [[imitation]] learning was unsuccessful for learning to perform the first segment ([0004]; “he given robotic control policy can be further trained to determine whether the given robot will fail in performance of the robotic task”).
Gutzeit, Angelov, and Zadeh are analogous art because both are concerned with imitation learning for refining robot control policies. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in imitation learning and robotic control to combine the failure-triggered human demonstration request of Zadeh with the IL/RL framework of Gutzeit and Angelov to yield to the predictable result of wherein the one or more machine learning models are trained to perform the first segment using the imitation learning based at least on a determination that other than the imitation learning was unsuccessful for learning to perform the first segment. The motivation for doing so would be to enable robots to perform various real-world tasks (Zadeh; [0001]).
Regarding claim 4, the rejection of claims 1 and 3 are incorporated and Gutzeit fails to explicitly disclose but Zadeh discloses wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move an object from a first pose to a second pose within a predetermined period of time ([0004]; and Figures 3A-3C and Figure 5).
The motivation to combine Gutzeit, Angelov, and Zadeh is the same as discussed above with respect to claim 3.
Regarding claim 5, the rejection of claims 1 and 3 are incorporated and Gutzeit fails to explicitly disclose but Zadeh discloses wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move a first object from a first pose to a second pose without contacting a second object ([0004]; and [0045-0046]; and Figures 3A-3C and Figure 5).
The motivation to combine Gutzeit, Angelov, and Zadeh is the same as discussed above with respect to claim 3.
Regarding claim 6, the rejection of claims 1 and 3 are incorporated and Gutzeit fails to explicitly disclose but Zadeh discloses wherein the determination that other than imitation learning was unsuccessful is based at least on the robot being unable to move an object from a first pose to a second pose with a predetermined level of precision ([0004]; and [0045-0046]; and [0080]; and Figures 3A-3C and Figure 5).
The motivation to combine Gutzeit, Angelov, and Zadeh is the same as discussed above with respect to claim 3.
Claims 9-15, and 19 are rejected under 35 U.S.C. § 103 as being obvious over Gutzeit in view of Angelov and Zadeh and further in view of Kolluri et al. (US 20210362332 A1, hereinafter “Kolluri1”).
Regarding claim 9, the rejection of claim 1 is incorporated and Gutzeit fails to explicitly disclose but Kolluri1 further discloses wherein an indication is sent to a computing device to indicate human demonstration is required for the imitation learning based at least on a determination that other than imitation learning was unsuccessful in training the one or more machine learning models to perform the first segment ([0198]; “The system generates a user interface presentation that presents a suggested demonstration based on the respective progress value for each demonstration subtask (940). As discussed above, the user who is configuring a robot to execute a skill template is not expected to be an expert at machine learning or skill templates. Thus, the user interface presentation is designed to guide the user in collecting enough local demonstration data so that there is a high probability of success in getting the robot to perform the task defined by the skill template”, which discloses sending an indication or suggested demonstration to a computing device through a GUI to user to indicate that human demonstration is required based on progress values that would indicate success or not in training the robot; and [0288-0296]).
Gutzeit, Angelov, Zadeh, and Kolluri1 are analogous art because all are concerned with imitation learning for refining robot control policies. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in imitation learning and robotic control to combine the indication of Kolluri1 with the IL/RL framework of Gutzeit, Angelov, and Zadeh to yield to the predictable result of wherein an indication is sent to a computing device to indicate human demonstration is required for the imitation learning based at least on a determination that other than imitation learning was unsuccessful in training the one or more machine learning models to perform the first segment. The motivation for doing so would be to present a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask (Kolluri1; [0288]).
Regarding claim 10, Gutzeit discloses [a] system comprising: one or more processors to: (§2; and §3; the sections implement the experiments of Gutzeit, and these experiments are inherently performed using a system with processing units)
segment a task to be performed by a robot into segments; (Page 2, §2; “The “Behavior Segmentation” will decompose demonstrations into simple behavioral building blocks. Segments that belong to the same type of movement are grouped together to obtain multiple demonstrations for the same motion plan.”)
determine a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment of the segments; (§2.3; the section discloses, for each segment, generating motion plans by the IL module, the plans being a set of instructions, that describe trajectories that the robot executes).
Gutzeit fails to explicitly disclose but Angelov discloses the plurality of sets of instructions including imitation-learning based sets of instructions and non-imitation-learning based sets of instructions, the first set of instructions being an imitation-learning based sets of instructions to execute a first machine-learning model trained using imitation learning (§IV.B; “In this task the PR2 robot needs to assemble the first part of the Siemens Challenge2, which involves grasping a compound gear from a table, and placing it on a peg module held in the other hand of the robot. … a set of controllers that (1) picks up the gear from the table; (2) moves the left PR2 arm in proximity to the other arm; (3) inserts the gear on the peg module. Policy (1, 2) rely exclusively on scripted path planning techniques and work using discrete time steps, while (3) is learned entirely with a neural network … Additionally, we compare the performance of the scripted Motion Planning method (using RRT Connect [41]), Dynamic Motion Primitives (learned from the MPs) and the Visual Neural Policy on each subtask, as well as using the full sequence under the different controllers as a baseline”, which discloses the plurality of sets of instructions including imitation-learning based sets of instructions (policy 3 that uses a neural network) and non-imitation-learning based sets of instructions (policies 1 and 2 that are scripted), the first set of instructions being an imitation-learning based sets of instructions to execute a first machine-learning model trained using imitation learning; and Figure 2 and its caption; “In the Gear Assembly Task, our controller library includes motion planning (MP) primitives (operating on joint angles) for picking up or moving and a convolutional neural network (CNN) for inserting the gear (operating on images). The MP primitives produce a trajectory for executing a task.”).
Gutzeit and Angelov are analogous art because both are concerned with machine learning and robotic manipulation. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and robotics to combine the second set of instructions not derived from imitation learning of Angelov with the imitation learning and task segmentation of Gutzeit to yield to the predictable result of the plurality of sets of instructions including imitation-learning based sets of instructions and non-imitation-learning based sets of instructions, the first set of instructions being an imitation-learning based sets of instructions to execute a first machine-learning model trained using imitation learning. The motivation for doing so would be to automatically compose diverse policies comprising motion planning trajectories, dynamic motion primitives and neural network controller (Angelov; Abstract).
Gutzeit fails to explicitly disclose but Zadeh discloses determine that the plurality of sets of instructions is inadequate to perform a second objective of a second segment of the segments;… ([0004]; “determine whether the given robot will fail in performance of the robotic task”); and Claim 1).
receive from the user device a second set of instructions for operating the robot for the second segment following an end of the first segment; and ([0056]; “In additional or alternative implementations, the user may proactively intervene in performance of the robotic task based on the representation of the sequence of actions visually rendered for presentation to the user”; and Claim 1)
update one or more parameters of a second machine learning model for controlling the robot using the second set of instructions for the second segment ([0059]; “In some implementations, and subsequent to performance of the semi-autonomous robotic task 330, the robotic control policy can be automatically updated based on one or more losses generated based on the interventions”; and Claim 1)
adding a set of instructions based on the trained second machine learning model to the plurality of sets of instructions: ([0059]; “In some implementations, and subsequent to performance of the semi-autonomous robotic task 330, the robotic control policy can be automatically updated based on one or more losses generated based on the interventions”; and Claim 1; and [0038]).
Gutzeit, Angelov, and Zadeh are analogous art because both are concerned with imitation learning for refining robot control policies. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in imitation learning and robotic control to combine the failure-triggered human demonstration request of Zadeh with the IL/RL framework of Gutzeit and Angelov to yield to the predictable result of determine that the plurality of sets of instructions is inadequate to perform a second objective of a second segment of the segments; … receive from the user device a second set of instructions for operating the robot for the second segment following an end of the first segment; and update one or more parameters of a machine learning model for controlling the robot using the second set of instructions for the second segment … adding a set of instructions based on the trained second machine learning model to the plurality of sets of instructions. The motivation for doing so would be to enable robots to perform various real-world tasks (Zadeh; [0001]).
Gutzeit fails to explicitly disclose but Kolluri1 discloses send to a user device of an operator a request for human demonstration of the second segment; ([0198]; “The system generates a user interface presentation that presents a suggested demonstration based on the respective progress value for each demonstration subtask (940). As discussed above, the user who is configuring a robot to execute a skill template is not expected to be an expert at machine learning or skill templates. Thus, the user interface presentation is designed to guide the user in collecting enough local demonstration data so that there is a high probability of success in getting the robot to perform the task defined by the skill template”, which discloses sending an indication or suggested demonstration to a computing device through a GUI to user to indicate that human demonstration is required based on progress values that would indicate success or not in training the robot; and [0288-0296]).
Gutzeit, Angelov, Zadeh, and Kolluri1 are analogous art because all are concerned with imitation learning for refining robot control policies. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in imitation learning and robotic control to combine the indication of Kolluri1 with the IL/RL framework of Gutzeit and Angelov and Zadeh to yield to the predictable result of send to a user device of an operator a request for human demonstration of the second segment. The motivation for doing so would be to present a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask (Kolluri1; [0288]).
Regarding claim 11, the rejection of claim 10 is incorporated and Gutzeit further discloses wherein the plurality of sets of instructions comprises coordinates and vectors for controlling the robot (§2.3; the section describes learning motion plans as trajectory functions over robot joint coordinates; and §2.4; describes RL optimization over the same coordinate-space motion plans; and Figure 1).
Regarding claim 12, the rejection of claim 10 is incorporated and Gutzeit further discloses wherein the second objective comprises moving an object from a first pose to a second pose (§3; the section describes throwing with a robotic arm that involves moving an object or ball from an initial position or pose to a second position or pose).
Regarding claim 13, the rejection of claims 10 and 12 are incorporated and Gutzeit fails to explicitly disclose but Zadeh further discloses wherein the plurality of sets of instructions is inadequate to perform the second objective based at least on the plurality of sets of instructions being inadequate to at least one of: control the robot to move the object from the first pose to the second pose within a predetermined period of time; control the robot to move the object from the first pose to the second pose without contacting a second object; or control the robot to move the object from the first pose to the second pose with a predetermined level of precision ([0004]; and [0045-0046]; and [0080]; and Figures 3A-3C and Figure 5).
The motivation to combine Gutzeit, Angelov, and Zadeh is the same as discussed above with respect to claim 10.
Regarding claim 14, the rejection of claims 10 and 12 are incorporated and Gutzeit fails to explicitly disclose but Kolluri1 further discloses wherein the one or more processors are to send the request for human demonstration of the second segment by adding the second segment to a queue of segments for which demonstration is requested ([0198]; “The system generates a user interface presentation that presents a suggested demonstration based on the respective progress value for each demonstration subtask (940). As discussed above, the user who is configuring a robot to execute a skill template is not expected to be an expert at machine learning or skill templates. Thus, the user interface presentation is designed to guide the user in collecting enough local demonstration data so that there is a high probability of success in getting the robot to perform the task defined by the skill template”, which discloses sending an indication or suggested demonstration to a computing device through a GUI to user to indicate that human demonstration is required based on progress values that would indicate success or not in training the robot; and [0288-0296]).
The motivation to combine Gutzeit, Angelov, Zadeh, and Kolluri1 is the same as discussed above with respect to claim 10.
Regarding claim 15, the rejection of claims 10 and 12 and 14 are incorporated and Gutzeit fails to explicitly disclose but Zadeh further discloses wherein the queue of segments is associated with a human operator, and wherein the one or more processing units are to add the second segment to the queue of segments based at least on an expected throughput of the human operator ([0004]; and [0045-0046]; and [0080]; and Figures 3A-3C and Figure 5).
The motivation to combine Gutzeit, Angelov, and Zadeh is the same as discussed above with respect to claim 10.
Regarding claim 19, the rejection of claim 10 is incorporated and Gutzeit further 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 for generating or presenting at least one of augmented reality content, virtual reality content, or mixed reality content;
a system for hosting one or more real-time streaming applications;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system implementing one or more language models;
a system implementing one or more large language models (LLMs);
a system for performing one or more generative 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 (§1; “Autonomous robotic systems can be deployed in unknown or unpredictable, dynamic environments, e.g., in space, search and rescue, and underwater scenarios”).
Claim 8 is rejected under 35 U.S.C. § 103 as being obvious over Gutzeit in view of Angelov and further in view of Payton et al. (US 20150336268 A1, hereinafter “Payton”).
Regarding claim 8, the rejection of claim 1 is incorporated and Gutzeit fails to explicitly disclose but Payton discloses wherein a transition pose is determined between the first segment and the second segment, the transition pose corresponding to an end or a beginning of imitation learning ([0007]; “Using the collected training data set, the controller next performs a time segmentation process in which a processor of the controller divides a time sequence of the task demonstrations into distinct task segments. That is, the time sequence is analyzed via logic of the controller to identify certain transition events between the segments”; and Claim 1; and [0024]).
Gutzeit, Angelov, and Payton are analogous art because both are concerned with robotic task segmentation and learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in robotic task segmentation and learning to combine the transition poses of Payton with the IL/RL framework of Gutzeit and Angelov to yield to the predictable result of wherein a transition pose is determined between the first segment and the second segment, the transition pose corresponding to an end or a beginning of imitation learning. The motivation for doing so would be to provide for rapid robotic imitation learning of force-torque tasks (Payton; [0001]).
Claims 16-18 are rejected under 35 U.S.C. § 103 as being obvious over Gutzeit in view of Angelov, Zadeh, Kolluri1, and Payton.
Regarding claim 16, the rejection of claim 10 is incorporated and Gutzeit fails to explicitly disclose but Payton discloses wherein the first objective of the first segment includes reaching a transition pose for transitioning between the first segment and the second segment ([0007]; “Using the collected training data set, the controller next performs a time segmentation process in which a processor of the controller divides a time sequence of the task demonstrations into distinct task segments. That is, the time sequence is analyzed via logic of the controller to identify certain transition events between the segments”; and Claim 1; and [0024]).
Gutzeit, Angelov, Zadeh, Kolluri, and Payton are analogous art because all are concerned with robotic task segmentation and learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in robotic task segmentation and learning to combine the transition poses of Payton with the IL/RL framework of Gutzeit, Angelov, Zadeh, and Kolluri to yield to the predictable result of wherein the first objective of the first segment includes reaching a transition pose for transitioning between the first segment and the second segment. The motivation for doing so would be to provide for rapid robotic imitation learning of force-torque tasks (Payton; [0001]).
Regarding claim 17, the rejection of claims 10 and 16 are incorporated and Gutzeit fails to explicitly disclose but Payton discloses mapping the segments to a human demonstration of the task; and identifying one or more poses of the robot at a portion of the human demonstration corresponding to a transition from the first segment to the second segment ([0007]; “Using the collected training data set, the controller next performs a time segmentation process in which a processor of the controller divides a time sequence of the task demonstrations into distinct task segments. That is, the time sequence is analyzed via logic of the controller to identify certain transition events between the segments”; and Claim 1; and [0024]).
The motivation to combine Gutzeit, Angelov, Zadeh, Kolluri1, and Payton is the same as discussed above with respect to claim 16.
Regarding claim 18, the rejection of claims 10 and 16 are incorporated and Gutzeit fails to explicitly disclose but Payton discloses wherein the segments include a plurality of transition poses for transitioning to segments associated with human demonstrations ([0007]; “Using the collected training data set, the controller next performs a time segmentation process in which a processor of the controller divides a time sequence of the task demonstrations into distinct task segments. That is, the time sequence is analyzed via logic of the controller to identify certain transition events between the segments”; and Claim 1; and [0024]; and [0008]).
The motivation to combine Gutzeit, Angelov, Zadeh, and Payton is the same as discussed above with respect to claim 16.
Response to Arguments
Applicant’s arguments and amendments, filed on 8/6/2026, with respect to the objection to claims 1-9 and 20 have been fully considered and are persuasive. The objection to claims 1-9 and 20 are withdrawn.
Applicant’s arguments and amendments, filed on 8/6/2026, with respect to the 35 USC § 101 rejection of claims 4-6, 8 and 10-19 have been fully considered and are not persuasive.
With respect to Step 2A, Prong One, Applicant argues “the claims as amended do not recite an abstract idea under Step 2A Prong 1 … The Office Action alleges that limitations such as "determine that the plurality of sets of instructions is inadequate to perform a second objective of a second segment of the segments" (claim 10) and the determinations of unsuccessful learning in claims 4-6 are mental processes … A human cannot practically perform these operations mentally or with pen and paper”. Examiner respectfully disagrees.
Applicant has failed to provide any evidence from the originally filed specification or claim language that demonstrates why the identified abstract ideas of claims 4-6 or 10 do not recite an abstract idea. Rather, Applicant merely concludes that the limitations cannot be performed mentally or with pen and paper.
With respect to Step 2A, Prong Two, Applicant argues “the claims as amended integrate any alleged abstract idea into a practical application under Step 2A Prong 2 … Amended claim 10 recites specific technical features directed to a robot skill acquisition system that distinguishes between "imitation-learning based sets of instructions" and "non- imitation-learning based sets of instructions," trains "a second machine learning model" from an operator's human demonstration, and adds "a set of instructions based on the trained second machine learning model to the plurality of sets of instructions." These limitations recite a specific technical improvement to robot training systems … The amended claim language directly reflects this technical improvement by reciting the specific mechanism through which the system selectively applies imitation learning, trains a machine learning model from human demonstration, and integrates the resulting instructions into the system's instruction set … These limitations are not mental processes that can be performed with pen and paper; rather, they are specific technical criteria tied to physical robot manipulation that determine how the robot skill acquisition system operates. These limitations reflect the technical improvement of reducing the burden of human demonstration by identifying precisely when such demonstration is needed based on actual robot performance metrics … Thus, the trained model's output relates to operating the robot rather than merely being output or displayed, reflecting the throughput and technical improvement described in the specification” and cites to paragraphs [0003] and [0023] of the originally filed specification as alleged support for a technical improvement. Examiner respectfully disagrees.
Applicant has failed to identify specific additional elements beyond the identified abstract ideas that reflect a technical improvement or integrate the abstract ideas into a practical application. Applicant has also failed to provide any evidence that the additional elements of the claim language as claimed reflect any alleged technical improvements. Looking at claim 4, for example, the additional elements of “one or more circuits to:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “perform at least a first segment of a task and a second segment of the task corresponding to a robot” and “a set of instructions not derived from imitation learning is executed to perform the second segment” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic ML models are used to perform a first segment of a task or how the broad second set of instructions are used to perform a second segment of a task. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional elements of “wherein one or more machine learning models were trained to perform the first segment using imitation learning” and “wherein the one or more machine learning models are trained to perform the first segment using the imitation learning based at least on a determination that other than the imitation learning was unsuccessful for learning to perform the first segment” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). There are no positively claimed steps of training and Applicant has failed to provide evidence that these specifically claimed additional elements in the claim language reflect any alleged technical improvement or integrate the abstract ideas of the claims into a practical application.
With respect to Step 2B, Applicant argues “The claims as amended recite significantly more than any alleged abstract idea under Step 2B … This ordered combination provides a technical improvement to robot skill acquisition systems by selectively soliciting human demonstrations only when needed and integrating the resulting trained model into the system's instruction set, which is not a well- understood, routine, or conventional activity. Accordingly, the claims recite significantly more than any alleged abstract idea”. Examiner respectfully disagrees.
The “segment”, “determine”, and “adding” limitations of claim 10 recite abstract ideas, and there are no additional elements in claim 10 that reflect a technical improvement, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas. It appears that any alleged technical improvement is reflected in the abstract ideas of the claim. Abstract ideas alone cannot provide a technical improvement. See MPEP §2106.05(a).
Accordingly, Applicant’s arguments and amendments are not persuasive, and the 35 USC § 101 rejection of claims 4-6, 8 and 10-19 is maintained.
Applicant’s arguments and amendments, filed on 8/6/2026, with respect to the 35 USC § 102(a)(1) rejection of claims 1, 2, 7, and 20 and the 35 USC § 103 rejection of claims 3-6 and 8-19 have been fully considered but are moot because the arguments do not apply to the combination of references used to reject the amended independent claims. Gutzeit and Angelov are now being used to render independent claims 1 and 20 obvious under 35 USC § 103 and Gutzeit, Angelov, Zadeh, and Kolluri are now being used to render claim 10 obvious under 35 USC § 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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