Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is responsive to Application No. 19/038,609 and the amendments filed on 7/6/2026.
3. Claims 1-20 are presented for examination.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 4/18/2025 has been fully considered by the Examiner.
Response to Arguments
5. Applicant’s arguments, see page 12, filed 7/6/2026, with respect to the objection to claim 13 for minor informalities have been fully considered and are persuasive. The objection of 4/9/2026 has been withdrawn.
6. Applicant's arguments filed 7/6/2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Regarding independent claim 1, the Applicant argues two main points against the Examiner’s previous rejection of claim 1 under 35 U.S.C. 103 of US 20230409046 A1 to Huynh in view of US 20250145178 A1 to Knittel. Specifically, the Applicant argues that Huynh fails to teach the limitation of “the historical navigation data comprising a robot pose, a local map and a navigation planned path,” recited in lines 5-6 of claim 1 and that Huynh fails to teach the limitation of “fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment … wherein each robot corresponds to one agent,” recited in lines 7-11 of claim 1. However, the Examiner respectfully disagrees, in which will be explained below.
First, regarding the Applicant’s argument that Huynh fails to teach the limitation of “the historical navigation data comprising a robot pose, a local map and a navigation planned path,” the Applicant argues that cited paragraphs [0040] and [0050] of Huynh do not teach this feature. Specifically, arguing that Huynh only teaches past trajectory information comprising a path that an agent follows with respect to time and the position of the agent. The Examiner submits that the past trajectory information of Huynh (interpreted to be the historical navigation data) comprises a robot pose, with the past trajectory information comprising position data of the agents. The Examiner also submits that Huynh is silent on the past trajectory information comprising a local map and a navigation planned path. However, the Examiner already stated this missing aspect of Huynh in the Non-Final rejection mailed 4/9/2026 (hereinafter referred to as ‘the non-final rejection’) on page 5 of the non-final rejection, stating “Huynh is silent on the historical navigation data comprising a local map and a navigation planned path.”
In view of the missing aspect of Huynh, wherein the historical navigation data comprises a local map and a navigation planned path, the Examiner turned to secondary reference Knittel on pages 5-6 of the non-final rejection. Here, Knittel teaches these aspects by stating “with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment,” in paragraph [0048] of Knittel. Therefore, the Examiner submits that the combination of Huynh and Knittel teaches the limitation “the historical navigation data comprising a robot pose, a local map and a navigation planned path.”
Second, regarding the Applicant’s argument that Huynh fails to teach the limitation of “fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment … wherein each robot corresponds to one agent,” the Applicant argues that cited paragraphs [0049], [0053], and [0061] of Huynh fails to teach this concept. Specifically, the Applicant argues that Huynh teaches predicting future directions of each of the agents in the environment and strengths of the agents based on past trajectory information that are all provided in a prediction map, and argues that this does not correspond to the claimed limitation. However, the Examiner respectfully disagrees.
Huynh teaches the fusing of the historical navigation data of the plurality of robots concept in cited paragraph [0049] where it states “At block 302, the method 300 includes the motion encoder 120 determining future directions of agents of a plurality of agents, N, and strengths of the agents based on past trajectory information 402.” Cited paragraph [0053] also states “The result of the motion encoder 120 is future directions of agents 202-218 of a plurality of agents, N, and strengths of the agents 202-218 based past trajectory information 402. The future directions and strengths of the agents 202-218 may be shown prediction map 230 … in FIG. 2B in dashed arrows extending from the respective agent.” See Figure 2B of Huynh wherein all of the data for the plurality of agents is combined into one map. Also, see block 304 in Figure 3 of Huynh, wherein this directional and strength data of each of the agents is compared to each other. For these reasons, the Examiner submits that Huynh teaches the fusing of the historical navigation data concept recited in claim 1.
Further, the Examiner also submits that Huynh teaches the concept of “by taking the first target robot as an ego perspective,” in at least cited paragraph [0061] of Huynh, which states “The motion prioritization scores 504 define a priority order including a first priority agent having a highest priority score … and an Nth priority agent having an Nth highest priority score.” The Examiner interprets the agent with the highest priority score of Huynh as the first target robot that is being taken from an ego perspective.
Further, the Examiner submits that Huynh teaches the concept of “the multi-agent environment comprising a global map of N frames and poses of a plurality of agents in the global map of N frames, wherein each robot corresponds to one agent.” The Examiner turns to cited paragraph [0053] of Huynh which states “the future directions and strengths of the agents 202-218 may be shown prediction map 230 are shown individually for each agent of the plurality of agents in FIG. 2B in dashed arrows extending from the respective agent.” See Figure 2B of Huynh as well, wherein the Examiner submits that the future directions, as indicated by the dashed arrows, show at least one frame of each of the plurality of agents, and that this data is provided in a single map. Therefore, the Examiner submits that Huynh teaches the limitations “fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment … wherein each robot corresponds to one agent,” in combination with Knittel, as described above.
The Examiner notes that the Applicant appears to attack Huynh alone with respect to claim 1, as the Applicant makes no detailed remarks concerning secondary reference Knittel in view of claim 1. The Examiner requests that the combination of Huynh and Knittel be appreciated, as the Examiner submits that the combination of Huynh and Knittel fully anticipates all of the limitations of claim 1.
Therefore, the Examiner does not find the Applicant’s arguments against the rejection of claim 1 under 35 U.S.C. 103 persuasive and maintains that the combination of Huynh and Knittel fully anticipates all of the limitations of claim 1, in which will also be described later.
Regarding independent claims 14 and 20, as these claims contain similar limitations as claim 1, are still rejected for similar reasons as claim 1 is, in which will be described later.
Regarding dependent claims 2-13 and 15-19, as all of these claims depend from either claims 1 or 14, are still rejected, in which will be described later.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. Claim(s) 1, 2, 7, 10, 14, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel).
Regarding Claim 1, Huynh teaches a navigation method in a multi-agent environment, comprising: acquiring navigation log data of a plurality of robots within a time period ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information 402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
parsing the navigation log data to obtain historical navigation data of N frames of each robot, the historical navigation data comprising a robot pose ([0040] via “The past trajectories may include a path that an agent follows through space as a function of time, position data, and time step information, among others.”), ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information 402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
determining one first robot from the plurality of robots, and fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment by taking the first robot as an ego perspective, the multi-agent environment comprising a global map of N frames and poses of a plurality of agents in the global map of N frames, wherein each robot corresponds to one agent ([0049] via “At block 302, the method 300 includes the motion encoder 120 determining future directions of agents of a plurality of agents, N, and strengths of the agents based on past trajectory information 402. Turning to FIG. 2A, the plurality of agents includes a first agent 202, a second agent 204, … in an environment, such as the half court 200.”), ([0053] via “The result of the motion encoder 120 is future directions of agents
202-218 of a plurality of agents, N, and strengths of the agents 202-218 based past trajectory information 402. The future directions and strengths of the agents 202-218 may be shown prediction map 230 are shown individually for each agent of the plurality of agents in FIG. 2B
in dashed arrows extending from the respective agent.”), ([0061] via “At block 306, the method 300 includes a motion prioritization module 124 calculating a motion prioritization score 504, shown in FIG. 5, for each agent of the plurality of agents 202-218 based on the relations 404 between the agents. The motion prioritization scores 504 define a priority order including a first priority agent having a highest priority score, a second priority agent having a second highest priority score, and an Nth priority agent having an Nth highest priority score.”), (Note: See Figures 2B and 4A of Huynh as well.); and
performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task ([0072] via “At block 310, the method 300 includes the execution module 128
causing at least one agent of the plurality of agents to navigate based on the future trajectories 406 of the agents.”).
Huynh is silent on the historical navigation data comprising a local map and a navigation planned path.
However, Knittel teaches the historical navigation data comprising a local map and a navigation planned path ([0048] via “FIG. 2 shows an example architecture for a trajectory generation system to generate trajectories for a set of agents at multiple levels, with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment, and with each subsequent level taking as a further input the results of a collision assessment result determined for the trajectories output from the previous level. This may be implemented by the prediction system 104 or the planner 106.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Knittel wherein the historical navigation data comprises a local map and a navigation planned path. Doing so improves the understanding of the multi-agent environment by providing additional information of how each agent interacts with one another, as stated by Knittel ([0009] via “A first aspect disclosed herein provides a computer-implemented method of generating a trajectory for a first agent of a plurality of agents navigating a mapped area, the method comprising: receiving an observed state of each of the plurality of agents, and map data of the mapped area; generating an initial estimated trajectory for each of the plurality of agents based on the observed state of each agent and the map data; performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent; and generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and the results of the first collision assessment.”).
Regarding Claim 2, modified reference Huynh teaches the method according to claim 1, but is silent on wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent, and the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data.
However, Knittel teaches using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent ([0008] via “The methods described herein apply a hierarchical approach in order to determine, at a first level (referred to as level 0), an initial set of candidate trajectories for agents of a given scene.”), ([0059] via “The output of the collision assessment 210 for the level-0 trajectories is then provided as input to the level-1 generator 228b, along with the level-0 trajectories 212a, and spatial uncertainties 310 and mode probabilities 312. The level-1 generator 228b also takes as input each of the observed states 202 and map data 220
defining the static scene. The level-1 trajectory generator 228b comprises a neural network which is trained to take as input the map data 220 and observed states 202, as well as the level-0 trajectories and collision assessment results in the form of a per-trajectory evaluation of collision, and output a new set of trajectories for each agent of the scene. … At this level, the behaviour of different agents in response to each other is considered. For example, if two level-0 trajectories associated with two agents of the scene are found to overlap, or to have a high collision probability, the level-1 trajectory generation network is likely to output level-1 trajectories 212b for one or both of those agents that take this collision probability into account.”), and
the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data ([0048] via “FIG. 2 shows an example architecture for a trajectory generation system to generate trajectories for a set of agents at multiple levels, with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment, and with each subsequent level taking as a further input the results of a collision assessment result determined for the trajectories output from the previous level.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Knittel wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent, and the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data. Doing so compares the behavior of each of the agents along the historical navigation data and adjusts the historical navigation policy of at least one robot to a new navigation policy when issues, such as a potential collision, arise, as stated above by Knittel in paragraph [0059].
Regarding Claim 7, modified reference Huynh teaches the method according to claim 1, wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: training a navigation policy to be trained in the multi-agent environment by using a reinforcement learning method or an imitation learning method ([0042] via “The motion encoder 120, the inter-agent encoder 122, the motion prioritization module 124, and/or the motion decoder 126 may be artificial neural networks that act as a framework for machine learning, including deep reinforcement learning.”).
Regarding Claim 10, modified reference Huynh teaches the method according to claim 2, wherein the new navigation policy is a policy obtained by a reinforcement learning method or an imitation learning method ([0042] via “The motion encoder 120, the inter-agent encoder
122, the motion prioritization module 124, and/or the motion decoder 126 may be artificial neural networks that act as a framework for machine learning, including deep reinforcement learning.”).
Regarding Claim 14, Huynh teaches an electronic device, comprising: at least one processor and a memory ([0041] via “The computing device 104 includes a processor 112, a memory 114, ….”), wherein the memory is configured to store a computer program, and the at least one processor is configured to invoke and run the computer program stored in the memory to implement a navigation method in a multi-agent environment ([0004] via “According to a further aspect, a non-transitory computer readable storage medium storing instructions that when executed by a computer having a processor to perform a method for trajectory prediction with agent prioritization is provided.”), and the method comprises:
acquiring navigation log data of a plurality of robots within a time period ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information
402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
parsing the navigation log data to obtain historical navigation data of N frames of each robot, the historical navigation data comprising a robot pose ([0040] via “The past trajectories may include a path that an agent follows through space as a function of time, position data, and time step information, among others.”), ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information 402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
determining one first robot from the plurality of robots, and fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment by taking the first robot as an ego perspective, the multi-agent environment comprising a global map of N frames and poses of a plurality of agents in the global map of N frames, wherein each robot corresponds to one agent ([0049] via “At block 302, the method 300 includes the motion encoder 120 determining future directions of agents of a plurality of agents, N, and strengths of the agents based on past trajectory information 402. Turning to FIG. 2A, the plurality of agents includes a first agent 202, a second agent 204, … in an environment, such as the half court 200.”), ([0053] via “The result of the motion encoder 120 is future directions of agents
202-218 of a plurality of agents, N, and strengths of the agents 202-218 based past trajectory information 402. The future directions and strengths of the agents 202-218 may be shown prediction map 230 are shown individually for each agent of the plurality of agents in FIG. 2B
in dashed arrows extending from the respective agent.”), ([0061] via “At block 306, the method 300 includes a motion prioritization module 124 calculating a motion prioritization score 504, shown in FIG. 5, for each agent of the plurality of agents 202-218 based on the relations 404 between the agents. The motion prioritization scores 504 define a priority order including a first priority agent having a highest priority score, a second priority agent having a second highest priority score, and an Nth priority agent having an Nth highest priority score.”), (Note: See Figures 2B and 4A of Huynh as well.); and
performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task ([0072] via “At block 310, the method 300 includes the execution module 128
causing at least one agent of the plurality of agents to navigate based on the future trajectories 406 of the agents.”).
Huynh is silent on the historical navigation data comprising a local map and a navigation planned path.
However, Knittel teaches the historical navigation data comprising a local map and a navigation planned path ([0048] via “FIG. 2 shows an example architecture for a trajectory generation system to generate trajectories for a set of agents at multiple levels, with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment, and with each subsequent level taking as a further input the results of a collision assessment result determined for the trajectories output from the previous level. This may be implemented by the prediction system 104 or the planner 106.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Knittel wherein the historical navigation data comprises a local map and a navigation planned path. Doing so improves the understanding of the multi-agent environment by providing additional information of how each agent interacts with one another, as stated by Knittel ([0009] via “A first aspect disclosed herein provides a computer-implemented method of generating a trajectory for a first agent of a plurality of agents navigating a mapped area, the method comprising: receiving an observed state of each of the plurality of agents, and map data of the mapped area; generating an initial estimated trajectory for each of the plurality of agents based on the observed state of each agent and the map data; performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent; and generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and the results of the first collision assessment.”).
Regarding Claim 15, modified reference Huynh teaches the electronic device according to claim 14, but is silent on wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent, and the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data.
However, Knittel teaches using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent ([0008] via “The methods described herein apply a hierarchical approach in order to determine, at a first level (referred to as level 0), an initial set of candidate trajectories for agents of a given scene.”), ([0059] via “The output of the collision assessment 210 for the level-0 trajectories is then provided as input to the level-1 generator 228b, along with the level-0 trajectories 212a, and spatial uncertainties 310 and mode probabilities 312. The level-1 generator 228b also takes as input each of the observed states 202 and map data 220
defining the static scene. The level-1 trajectory generator 228b comprises a neural network which is trained to take as input the map data 220 and observed states 202, as well as the level-0 trajectories and collision assessment results in the form of a per-trajectory evaluation of collision, and output a new set of trajectories for each agent of the scene. … At this level, the behaviour of different agents in response to each other is considered. For example, if two level-0 trajectories associated with two agents of the scene are found to overlap, or to have a high collision probability, the level-1 trajectory generation network is likely to output level-1 trajectories 212b for one or both of those agents that take this collision probability into account.”), and
the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data ([0048] via “FIG. 2 shows an example architecture for a trajectory generation system to generate trajectories for a set of agents at multiple levels, with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment, and with each subsequent level taking as a further input the results of a collision assessment result determined for the trajectories output from the previous level.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Knittel wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: using a new navigation policy to replace a historical navigation policy of a first agent corresponding to a second robot in the plurality of robots, and using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, the inference result comprising a new navigation trajectory of the first agent, and the historical navigation policy being a navigation policy used by the second robot to generate the historical navigation data. Doing so compares the behavior of each of the agents along the historical navigation data and adjusts the historical navigation policy of at least one robot to a new navigation policy when issues, such as a potential collision, arise, as stated above by Knittel in paragraph [0059].
Regarding Claim 20, Huynh teaches a non-transitory computer-readable storage medium, configured to store a computer program, the computer program causing a computer to execute a navigation method in a multi-agent environment ([0004] via “According to a further aspect, a non-transitory computer readable storage medium storing instructions that when executed by a computer having a processor to perform a method for trajectory prediction with agent prioritization is provided.”), and the method comprises:
acquiring navigation log data of a plurality of robots within a time period ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information
402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
parsing the navigation log data to obtain historical navigation data of N frames of each robot, the historical navigation data comprising a robot pose ([0040] via “The past trajectories may include a path that an agent follows through space as a function of time, position data, and time step information, among others.”), ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information 402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”);
determining one first robot from the plurality of robots, and fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment by taking the first robot as an ego perspective, the multi-agent environment comprising a global map of N frames and poses of a plurality of agents in the global map of N frames, wherein each robot corresponds to one agent ([0049] via “At block 302, the method 300 includes the motion encoder 120 determining future directions of agents of a plurality of agents, N, and strengths of the agents based on past trajectory information 402. Turning to FIG. 2A, the plurality of agents includes a first agent 202, a second agent 204, … in an environment, such as the half court 200.”), ([0053] via “The result of the motion encoder 120 is future directions of agents
202-218 of a plurality of agents, N, and strengths of the agents 202-218 based past trajectory information 402. The future directions and strengths of the agents 202-218 may be shown prediction map 230 are shown individually for each agent of the plurality of agents in FIG. 2B in dashed arrows extending from the respective agent.”), ([0061] via “At block 306, the method 300 includes a motion prioritization module 124 calculating a motion prioritization score 504, shown in FIG. 5, for each agent of the plurality of agents 202-218 based on the relations 404 between the agents. The motion prioritization scores 504 define a priority order including a first priority agent having a highest priority score, a second priority agent having a second highest priority score, and an Nth priority agent having an Nth highest priority score.”), (Note: See Figures 2B and 4A of Huynh as well.); and
performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task ([0072] via “At block 310, the method 300 includes the execution module 128
causing at least one agent of the plurality of agents to navigate based on the future trajectories 406 of the agents.”).
Huynh is silent on the historical navigation data comprising a local map and a navigation planned path.
However, Knittel teaches the historical navigation data comprising a local map and a navigation planned path ([0048] via “FIG. 2 shows an example architecture for a trajectory generation system to generate trajectories for a set of agents at multiple levels, with the first level generating an initial set of trajectories for the agents based on the current and/or past states of the agents and the map data describing the environment, and with each subsequent level taking as a further input the results of a collision assessment result determined for the trajectories output from the previous level. This may be implemented by the prediction system 104 or the planner 106.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Knittel wherein the historical navigation data comprises a local map and a navigation planned path. Doing so improves the understanding of the multi-agent environment by providing additional information of how each agent interacts with one another, as stated by Knittel ([0009] via “A first aspect disclosed herein provides a computer-implemented method of generating a trajectory for a first agent of a plurality of agents navigating a mapped area, the method comprising: receiving an observed state of each of the plurality of agents, and map data of the mapped area; generating an initial estimated trajectory for each of the plurality of agents based on the observed state of each agent and the map data; performing a first collision assessment to determine a likelihood of collision between the first agent and each other agent, based on the initial estimated trajectory for the first agent and the initial estimated trajectory for each other agent; and generating a second estimated trajectory for the first agent based on the observed states of each of the plurality of agents, the map data, and the results of the first collision assessment.”).
10. Claim(s) 3, 5, 16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), and further in view of Dupuis et al. (US 11179850 B2 hereinafter Dupuis).
Regarding Claim 3, modified reference Huynh teaches the method according to claim 2, but is silent on wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot.
However, Dupuis teaches taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, where “In some implementations, the method further includes dividing each of the candidate paths for the multiple robots into multiple path segments, wherein, for each of the candidate paths, each of the multiple path segments corresponds to movement over a different time period in a series of time periods. … The method further includes determining adjusted paths for the multiple robots by sequentially evaluating swept volumes of path segments corresponding to a same time period in the series of time periods that includes adjusting the path segments that correspond to the first time period based on swept volumes for the path segments that correspond to the first time period; and after adjusting the path segments that correspond to the first time period, adjusting the path segments that correspond to the second time period based on swept volumes for the path segments that correspond to the second time period.”), (Col. 14 lines 56-63, where “The server system 104 can also compare the generated score corresponding to the adjusted path to a threshold score. The threshold score can be determined based on a set of desired characteristics for a path. When the score for a particular adjusted path reaches the threshold level, the server system 104 can determine that the adjusted path sufficiently meets the applicable criteria, and the server system 104 can end the adjustment process.”), (Col. 19 lines 1-7, where “Each robot has a corresponding candidate path of movement. These can be considered candidate paths because each may be subject to further adjustment, including based on adjustments to the paths of the other robots. The path of movement for each robot may include moving from a starting location to an ending location.”); and
controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot (Col. 21 lines 20-26, where “Based on the assigned force vectors for each of the costs, the server system can generate a re-planned path for the robot 408. For example, as shown in illustration 407, the server system can generate a new path 413 for robot 408 that is in focus. The new path 413 for robot 408 can be generated to avoid the forces vectors corresponding to the swept regions of the other robots.”), (Col. 21 lines 51-61, where “In some implementations, this process performed for robot 408 may be iteratively performed for each of the other robots 402, 404, 406, and 410. For example, robot 402, robot 404, robot 406, or robot 410 may be the focus of processing, and the system can analyze the other robots swept motion volumes without analyzing the swept motion volume of the robot in focus. … In another example, this process may occur iteratively until no overlap regions exist.”), (Note: See Figure 4 picture 407 of Dupuis wherein the candidate paths of robots 402, 404, and 410, unaffected by the adjusted paths of robots 406 and 408, remain as the historical navigation trajectories.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dupuis wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot. Doing so adjusts the trajectories of the appropriate robots when the historical trajectories are determined to interfere between robots, and otherwise, keeping the default trajectories for robots not determined to interfere with each other, as stated by Dupuis (Col. 18 lines 55-63, where “Generally, the system 400 illustrates path planning in the context of movements of multiple robots. For example, the system 400 seeks to generate motions for a particular robot in view of the multiple other robots to avoid a highly congested area. For example, if many robots occupy a certain volume of space, than a particular robot can move along a path that avoids the highly congested volume of space in order to avoid collision with the other robots.”) and depicted by Dupuis in Figure 4.
Regarding Claim 5, modified reference Huynh teaches the method according to claim 2, but is silent on wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination.
However, Dupuis teaches taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, where “In some implementations, the method further includes dividing each of the candidate paths for the multiple robots into multiple path segments, wherein, for each of the candidate paths, each of the multiple path segments corresponds to movement over a different time period in a series of time periods. … The method further includes determining adjusted paths for the multiple robots by sequentially evaluating swept volumes of path segments corresponding to a same time period in the series of time periods that includes adjusting the path segments that correspond to the first time period based on swept volumes for the path segments that correspond to the first time period; and after adjusting the path segments that correspond to the first time period, adjusting the path segments that correspond to the second time period based on swept volumes for the path segments that correspond to the second time period.”), (Col. 14 lines 56-63, where “The server system 104 can also compare the generated score corresponding to the adjusted path to a threshold score. The threshold score can be determined based on a set of desired characteristics for a path. When the score for a particular adjusted path reaches the threshold level, the server system 104 can determine that the adjusted path sufficiently meets the applicable criteria, and the server system 104 can end the adjustment process.”), (Col. 19 lines 1-7, where “Each robot has a corresponding candidate path of movement. These can be considered candidate paths because each may be subject to further adjustment, including based on adjustments to the paths of the other robots. The path of movement for each robot may include moving from a starting location to an ending location.”); and
using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, Col. 14 lines 56-63, and Col. 19 lines 1-7 of Dupuis above.), (Col. 21 lines 51-61, where “In some implementations, this process performed for robot 408 may be iteratively performed for each of the other robots 402, 404, 406, and 410. For example, robot 402, robot 404, robot
406, or robot 410 may be the focus of processing, and the system can analyze the other robots swept motion volumes without analyzing the swept motion volume of the robot in focus. … In another example, this process may occur iteratively until no overlap regions exist.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dupuis wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination. Doing so adjusts the trajectories of the appropriate robots when the historical trajectories are determined to interfere between robots, and otherwise, keeping the default trajectories for robots not determined to interfere with each other, as stated by Dupuis (Col. 18 lines 55-63, where “Generally, the system 400 illustrates path planning in the context of movements of multiple robots. For example, the system 400 seeks to generate motions for a particular robot in view of the multiple other robots to avoid a highly congested area. For example, if many robots occupy a certain volume of space, than a particular robot can move along a path that avoids the highly congested volume of space in order to avoid collision with the other robots.”) and depicted by Dupuis in Figure 4.
Regarding Claim 16, modified reference Huynh teaches the electronic device according to claim 15, but is silent on wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot.
However, Dupuis teaches taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, where “In some implementations, the method further includes dividing each of the candidate paths for the multiple robots into multiple path segments, wherein, for each of the candidate paths, each of the multiple path segments corresponds to movement over a different time period in a series of time periods. … The method further includes determining adjusted paths for the multiple robots by sequentially evaluating swept volumes of path segments corresponding to a same time period in the series of time periods that includes adjusting the path segments that correspond to the first time period based on swept volumes for the path segments that correspond to the first time period; and after adjusting the path segments that correspond to the first time period, adjusting the path segments that correspond to the second time period based on swept volumes for the path segments that correspond to the second time period.”), (Col. 14 lines 56-63, where “The server system 104 can also compare the generated score corresponding to the adjusted path to a threshold score. The threshold score can be determined based on a set of desired characteristics for a path. When the score for a particular adjusted path reaches the threshold level, the server system 104 can determine that the adjusted path sufficiently meets the applicable criteria, and the server system 104 can end the adjustment process.”), (Col. 19 lines 1-7, where “Each robot has a corresponding candidate path of movement. These can be considered candidate paths because each may be subject to further adjustment, including based on adjustments to the paths of the other robots. The path of movement for each robot may include moving from a starting location to an ending location.”); and
controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot (Col. 21 lines 20-26, where “Based on the assigned force vectors for each of the costs, the server system can generate a re-planned path for the robot 408. For example, as shown in illustration 407, the server system can generate a new path 413 for robot 408 that is in focus. The new path 413 for robot 408 can be generated to avoid the forces vectors corresponding to the swept regions of the other robots.”), (Col. 21 lines 51-61, where “In some implementations, this process performed for robot 408 may be iteratively performed for each of the other robots 402, 404, 406, and 410. For example, robot 402, robot 404, robot 406, or robot 410 may be the focus of processing, and the system can analyze the other robots swept motion volumes without analyzing the swept motion volume of the robot in focus. … In another example, this process may occur iteratively until no overlap regions exist.”), (Note: See Figure 4 picture 407 of Dupuis wherein the candidate paths of robots 402, 404, and 410, unaffected by the adjusted paths of robots 406 and 408, remain as the historical navigation trajectories.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dupuis wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and controlling other agents to operate in the multi-agent environment according to historical navigation trajectories of corresponding robots, wherein the other agents are agents corresponding to remaining robots in the plurality of robots except the second robot. Doing so adjusts the trajectories of the appropriate robots when the historical trajectories are determined to interfere between robots, and otherwise, keeping the default trajectories for robots not determined to interfere with each other, as stated by Dupuis (Col. 18 lines 55-63, where “Generally, the system 400 illustrates path planning in the context of movements of multiple robots. For example, the system 400 seeks to generate motions for a particular robot in view of the multiple other robots to avoid a highly congested area. For example, if many robots occupy a certain volume of space, than a particular robot can move along a path that avoids the highly congested volume of space in order to avoid collision with the other robots.”) and depicted by Dupuis in Figure 4.
Regarding Claim 18, modified reference Huynh teaches the electronic device according to claim 15, but is silent on wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination.
However, Dupuis teaches taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, where “In some implementations, the method further includes dividing each of the candidate paths for the multiple robots into multiple path segments, wherein, for each of the candidate paths, each of the multiple path segments corresponds to movement over a different time period in a series of time periods. … The method further includes determining adjusted paths for the multiple robots by sequentially evaluating swept volumes of path segments corresponding to a same time period in the series of time periods that includes adjusting the path segments that correspond to the first time period based on swept volumes for the path segments that correspond to the first time period; and after adjusting the path segments that correspond to the first time period, adjusting the path segments that correspond to the second time period based on swept volumes for the path segments that correspond to the second time period.”), (Col. 14 lines 56-63, where “The server system 104 can also compare the generated score corresponding to the adjusted path to a threshold score. The threshold score can be determined based on a set of desired characteristics for a path. When the score for a particular adjusted path reaches the threshold level, the server system 104 can determine that the adjusted path sufficiently meets the applicable criteria, and the server system 104 can end the adjustment process.”), (Col. 19 lines 1-7, where “Each robot has a corresponding candidate path of movement. These can be considered candidate paths because each may be subject to further adjustment, including based on adjustments to the paths of the other robots. The path of movement for each robot may include moving from a starting location to an ending location.”); and
using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination (Col. 5 lines 42-59, Col. 14 lines 56-63, and Col. 19 lines 1-7 of Dupuis above.), (Col. 21 lines 51-61, where “In some implementations, this process performed for robot 408 may be iteratively performed for each of the other robots 402, 404, 406, and 410. For example, robot 402, robot 404, robot
406, or robot 410 may be the focus of processing, and the system can analyze the other robots swept motion volumes without analyzing the swept motion volume of the robot in focus. … In another example, this process may occur iteratively until no overlap regions exist.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Dupuis wherein the using agents corresponding to the plurality of robots to infer in the multi-agent environment to obtain an inference result, comprises: taking an initial value of the first agent as an input of the new navigation policy, using the new navigation policy to perform an inference of M1 steps in the multi-agent environment, to obtain the new navigation trajectory of the first agent, the initial value of the first agent comprising inference start time, an inference start position and a destination; and using the new navigation policy to replace historical navigation policies of other agents, taking initial values of the other agents as input of the new navigation policy, using the new navigation policy to perform an inference of M2 steps in the multi-agent environment, to obtain new navigation trajectories of the other agents, the initial values of the other agents comprising inference start time, an inference start position and a destination. Doing so adjusts the trajectories of the appropriate robots when the historical trajectories are determined to interfere between robots, and otherwise, keeping the default trajectories for robots not determined to interfere with each other, as stated by Dupuis (Col. 18 lines 55-63, where “Generally, the system 400 illustrates path planning in the context of movements of multiple robots. For example, the system 400 seeks to generate motions for a particular robot in view of the multiple other robots to avoid a highly congested area. For example, if many robots occupy a certain volume of space, than a particular robot can move along a path that avoids the highly congested volume of space in order to avoid collision with the other robots.”) and depicted by Dupuis in Figure 4.
11. Claim(s) 4, 6, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), further in view of Dupuis et al. (US 11179850 B2 hereinafter Dupuis), and further in view of Otsuki et al. (US 20220105962 A1 hereinafter Otsuki) and Fukunaga (US 20240302174 A1 hereinafter Fukunaga).
Regarding Claim 4, modified reference Huynh teaches the method according to claim 3, but is silent on the method further comprising: displaying following content in real time in the multi-agent environment during an inference process: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy.
However, Otsuki teaches displaying following content in real time in the multi-agent environment during an inference process ([0059] via “The information, processing device 130
displays the position of each autonomous robot 10 in real time based on the service robot information 300. More specifically, the information processing device 130 superposes and displays the map of the service area A and the position of each autonomous robot 10 on the display device 112.”).
Further, Fukunaga teaches displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy ([0117] via “FIG. 10 and FIG. 11 illustrate examples in which routes of the robots 1 and 2 serving as the plurality of robots are displayed.”), ([0118] via “One of the features of the route planning method according to the technology of the present disclosure is to predict positions of the plurality of robots at the time of replanning, issue stop instructions at (or near) the predicted positions, and search for new routes while their start points are set to the stop instruction positions. Therefore, at the time of replanning, the user interface screen may display “Old route plan (before stop instruction position)”, “Old route plan (after stop instruction position)”, “Stop instruction position”, or “New route plan (after stop instruction position)”. The “Old route plan (before stop instruction position)” means routes that the robots definitely pass through in future, whereas the “Old route plan (after stop instruction position)” means routes that may change in future. Such a difference between the routes is displayed comprehensibly on the user interface screen. This provides an advantage that a user can distinguish the routes that the respective robots definitely pass through from the routes that may change in future.”), (Note: See Figures 10 and 11 of Fukunaga as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Otsuki wherein the method further comprises: displaying following content in real time in the multi-agent environment during an inference process. Doing so allows an operator observing the multi-agent environment to understand the current operating status of the environment, as stated by Otsuki ([0057] via “In particular, the information processing device 130 communicates with the autonomous robot 10 in the service area A to acquire the service robot information 300 in real time. Then, based on the service robot information 300, the information processing device 130 displays the operating status of the service in the service area A on the display device 112. Thus, the operator is able to understand and monitor the operating status of the service in the service area A.”).
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fukunaga wherein the method further comprises: displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy. Doing so improves the experience of the user predicting the future movements of the plurality of robots by being able to distinguish between historical and new paths of the robots, as stated above by Fukunaga in paragraph [0118].
Regarding Claim 6, modified reference Huynh teaches the method according to claim 5, but is silent on the method further comprising: displaying following content in real time in the multi-agent environment during an inference process: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy.
However, Otsuki teaches displaying following content in real time in the multi-agent environment during an inference process ([0059] via “The information, processing device 130
displays the position of each autonomous robot 10 in real time based on the service robot information 300. More specifically, the information processing device 130 superposes and displays the map of the service area A and the position of each autonomous robot 10 on the display device 112.”).
Further, Fukunaga teaches displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy ([0117] via “FIG. 10 and FIG. 11 illustrate examples in which routes of the robots 1 and 2 serving as the plurality of robots are displayed.”), ([0118] via “One of the features of the route planning method according to the technology of the present disclosure is to predict positions of the plurality of robots at the time of replanning, issue stop instructions at (or near) the predicted positions, and search for new routes while their start points are set to the stop instruction positions. Therefore, at the time of replanning, the user interface screen may display “Old route plan (before stop instruction position)”, “Old route plan (after stop instruction position)”, “Stop instruction position”, or “New route plan (after stop instruction position)”. The “Old route plan (before stop instruction position)” means routes that the robots definitely pass through in future, whereas the “Old route plan (after stop instruction position)” means routes that may change in future. Such a difference between the routes is displayed comprehensibly on the user interface screen. This provides an advantage that a user can distinguish the routes that the respective robots definitely pass through from the routes that may change in future.”), (Note: See Figures 10 and 11 of Fukunaga as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Otsuki wherein the method further comprises: displaying following content in real time in the multi-agent environment during an inference process. Doing so allows an operator observing the multi-agent environment to understand the current operating status of the environment, as stated by Otsuki ([0057] via “In particular, the information processing device 130 communicates with the autonomous robot 10 in the service area A to acquire the service robot information 300 in real time. Then, based on the service robot information 300, the information processing device 130 displays the operating status of the service in the service area A on the display device 112. Thus, the operator is able to understand and monitor the operating status of the service in the service area A.”).
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fukunaga wherein the method further comprises: displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy. Doing so improves the experience of the user predicting the future movements of the plurality of robots by being able to distinguish between historical and new paths of the robots, as stated above by Fukunaga in paragraph [0118].
Regarding Claim 17, modified reference Huynh teaches the electronic device according to claim 16, but is silent on the electronic device further comprising: displaying following content in real time in the multi-agent environment during an inference process: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy.
However, Otsuki teaches displaying following content in real time in the multi-agent environment during an inference process ([0059] via “The information, processing device 130
displays the position of each autonomous robot 10 in real time based on the service robot information 300. More specifically, the information processing device 130 superposes and displays the map of the service area A and the position of each autonomous robot 10 on the display device 112.”).
Further, Fukunaga teaches displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy ([0117] via “FIG. 10 and FIG. 11 illustrate examples in which routes of the robots 1 and 2 serving as the plurality of robots are displayed.”), ([0118] via “One of the features of the route planning method according to the technology of the present disclosure is to predict positions of the plurality of robots at the time of replanning, issue stop instructions at (or near) the predicted positions, and search for new routes while their start points are set to the stop instruction positions. Therefore, at the time of replanning, the user interface screen may display “Old route plan (before stop instruction position)”, “Old route plan (after stop instruction position)”, “Stop instruction position”, or “New route plan (after stop instruction position)”. The “Old route plan (before stop instruction position)” means routes that the robots definitely pass through in future, whereas the “Old route plan (after stop instruction position)” means routes that may change in future. Such a difference between the routes is displayed comprehensibly on the user interface screen. This provides an advantage that a user can distinguish the routes that the respective robots definitely pass through from the routes that may change in future.”), (Note: See Figures 10 and 11 of Fukunaga as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Otsuki wherein the electronic device further comprises: displaying following content in real time in the multi-agent environment during an inference process. Doing so allows an operator observing the multi-agent environment to understand the current operating status of the environment, as stated by Otsuki ([0057] via “In particular, the information processing device 130 communicates with the autonomous robot 10 in the service area A to acquire the service robot information 300 in real time. Then, based on the service robot information 300, the information processing device 130 displays the operating status of the service in the service area A on the display device 112. Thus, the operator is able to understand and monitor the operating status of the service in the service area A.”).
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fukunaga wherein the electronic device further comprises: displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, and historical positions of the other agents, wherein the new position is a navigation position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy. Doing so improves the experience of the user predicting the future movements of the plurality of robots by being able to distinguish between historical and new paths of the robots, as stated above by Fukunaga in paragraph [0118].
Regarding Claim 19, modified reference Huynh teaches the electronic device according to claim 18, but is silent on the electronic device further comprising: displaying following content in real time in the multi-agent environment during an inference process: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy.
However, Otsuki teaches displaying following content in real time in the multi-agent environment during an inference process ([0059] via “The information, processing device 130
displays the position of each autonomous robot 10 in real time based on the service robot information 300. More specifically, the information processing device 130 superposes and displays the map of the service area A and the position of each autonomous robot 10 on the display device 112.”).
Further, Fukunaga teaches displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy ([0117] via “FIG. 10 and FIG. 11 illustrate examples in which routes of the robots 1 and 2 serving as the plurality of robots are displayed.”), ([0118] via “One of the features of the route planning method according to the technology of the present disclosure is to predict positions of the plurality of robots at the time of replanning, issue stop instructions at (or near) the predicted positions, and search for new routes while their start points are set to the stop instruction positions. Therefore, at the time of replanning, the user interface screen may display “Old route plan (before stop instruction position)”, “Old route plan (after stop instruction position)”, “Stop instruction position”, or “New route plan (after stop instruction position)”. The “Old route plan (before stop instruction position)” means routes that the robots definitely pass through in future, whereas the “Old route plan (after stop instruction position)” means routes that may change in future. Such a difference between the routes is displayed comprehensibly on the user interface screen. This provides an advantage that a user can distinguish the routes that the respective robots definitely pass through from the routes that may change in future.”), (Note: See Figures 10 and 11 of Fukunaga as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Otsuki wherein the electronic device further comprises: displaying following content in real time in the multi-agent environment during an inference process. Doing so allows an operator observing the multi-agent environment to understand the current operating status of the environment, as stated by Otsuki ([0057] via “In particular, the information processing device 130 communicates with the autonomous robot 10 in the service area A to acquire the service robot information 300 in real time. Then, based on the service robot information 300, the information processing device 130 displays the operating status of the service in the service area A on the display device 112. Thus, the operator is able to understand and monitor the operating status of the service in the service area A.”).
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Fukunaga wherein the electronic device further comprises: displaying following content: a new position of the first agent, a historical position of the first agent, a new planned path of the first agent, new positions of the other agents, and historical positions of the other agents, wherein the new position is a position inferred according to the new navigation policy, the historical position is a position indicated by the historical navigation data at a same time, and the new planned path is a path inferred according to the new navigation policy. Doing so improves the experience of the user predicting the future movements of the plurality of robots by being able to distinguish between historical and new paths of the robots, as stated above by Fukunaga in paragraph [0118].
12. Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), and further in view of Asato et al. (US 20250026004 A1 hereinafter Asato).
Regarding Claim 8, modified reference Huynh teaches the method according to claim 1, but is silent on wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: performing navigation playback by the agents corresponding to the plurality of robots according to historical navigation trajectories of the plurality of robots in the multi-agent environment.
However, Asato teaches performing navigation playback by the agents corresponding to the plurality of robots according to historical navigation trajectories of the plurality of robots in the multi-agent environment ([0144] via “The image generator 4 of the teaching system 100
also includes a playback mode of displaying the VR image 8 in which the virtual robot 81
moves in accordance with teaching points. … The playback mode is performed in the case of checking teaching data, for example. In the playback mode, neither generation of teaching points nor generation of teaching data is performed.”), ([0148] via “In the playback mode, the VR image 8 in which the entire virtual robot 81 is included in the angle of view tends to be generated. The user can check teaching data by observing a motion of the virtual robot 81 displayed on the display 5. In the playback mode, the display 5 is also tracked and the VR image 8 in accordance with the position and posture of the display 5 is also generated, and thus, the user can observe a motion of the virtual robot 81 from a desired position and a desired angle.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Asato wherein the performing a multi-agent navigation in the multi-agent environment to execute a multi-agent task, comprises: performing navigation playback by the agents corresponding to the plurality of robots according to historical navigation trajectories of the plurality of robots in the multi-agent environment. Doing so allows an operator to check for validating the trajectories of the plurality of robots by replaying the entire trajectories, as stated above by Asato in both citations.
13. Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), and further in view of Pajovic et al. (US 20210103290 A1 hereinafter Pajovic).
Regarding Claim 9, modified reference Huynh teaches the method according to claim 1, wherein the fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment by taking the first robot as an ego perspective, comprises: aligning historical navigation data of each frame of the plurality of robots ([0050] via “Turning to FIG. 4A, the agents 202-218 are associated with past trajectory information 402, shown individually for each agent of the plurality of agents in FIG. 2A in solid arrows terminating at the respective agent. The past trajectory information includes the past trajectory information 402 of the agents at a number of time steps over a given time horizon for the agents.”).
Huynh is silent on fusing local maps of N frames of the plurality of robots to obtain a global map of each frame; and determining poses of other robots in the global map according to historical navigation data of the other robots by taking a pose of the first robot in the global map of each frame as a reference.
However, Pajovic teaches fusing local maps of N frames of the plurality of robots to obtain a global map of each frame; and determining poses of other robots in the global map according to historical navigation data of the other robots by taking a pose of the first robot in the global map of each frame as a reference ([0041] via “More specifically, each robot traveling in an area performs a single-robot SLAM on its own using measurements from environment and motion sensing. The map and path/location estimates are obtained using a conventional single-robot particle filter (PF)-based SLAM algorithm and represented with a certain number of particles. Upon the encounter, the two robots exchange their particles. … In addition to particles, the robot measuring the relative pose between robots sends that measurement to the other robot. Each robot then fuses particles representing its map and pose prior to the encounter with the other robot, with the particles received from the other robot and relative pose measurement between the two robots. The result of the information fusion, done locally on each robot, is a set of updated particles.”), ([0092] via “FIG. 5C shows a principle block diagram of the cooperative multi-robot SLAM according to some embodiments. The set of SLAM particles of robot A 532, the set of SLAM particles of robot B and received from robot B 534 are fused together with relative pose measurement(s) 535
according to our algorithm implemented in 530. The output of this block 537 is the resulting set of updated particles each representing one hypothesis of the map, pose of robot A in that map, together with its importance weight.”), (Note: See Figures 5A-C of Pajovic as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Pajovic wherein the fusing the historical navigation data of the plurality of robots to obtain the multi-agent environment by taking the first robot as an ego perspective, comprises: fusing local maps of N frames of the plurality of robots to obtain a global map of each frame; and determining poses of other robots in the global map according to historical navigation data of the other robots by taking a pose of the first robot in the global map of each frame as a reference. Fusing the local maps of each of the plurality of robots results in a more accurate global map and more accurate historical navigation data, as stated by Pajovic ([0037] via “However, robots may cooperate while performing SLAM with the potential benefit of obtaining more accurate estimates of the environment's map and their location in the map. This invention discloses methods for multi-robot cooperative and distributed SLAM.”).
14. Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), and further in view of Otsuki et al. (US 20220105962 A1 hereinafter Otsuki).
Regarding Claim 11, modified reference Huynh teaches the method according to claim 2, but is silent on the method further comprising: displaying, in real time, positions of the agents corresponding to the plurality of robots in the multi-agent environment during an inference process.
However, Otsuki teaches displaying, in real time, positions of the agents corresponding to the plurality of robots in the multi-agent environment during an inference process ([0059] via “The information, processing device 130 displays the position of each autonomous robot 10 in real time based on the service robot information 300. More specifically, the information processing device 130 superposes and displays the map of the service area A and the position of each autonomous robot 10 on the display device 112.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Otsuki wherein the method further comprises: displaying, in real time, positions of the agents corresponding to the plurality of robots in the multi-agent environment during an inference process. Doing so allows an operator observing the multi-agent environment to understand the current operating status of the environment, as stated by Otsuki ([0057] via “In particular, the information processing device 130 communicates with the autonomous robot 10 in the service area A to acquire the service robot information 300 in real time. Then, based on the service robot information 300, the information processing device 130 displays the operating status of the service in the service area A on the display device 112. Thus, the operator is able to understand and monitor the operating status of the service in the service area A.”).
15. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), and further in view of Wang et al. (US 20250249574 A1 hereinafter Wang).
Regarding Claim 12, modified reference Huynh teaches the method according to claim 2, but is silent on the method further comprising: evaluating the new navigation policy by comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent.
However, Wang teaches evaluating the new navigation policy by comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent ([0092] via “As shown in FIG. 9, a method 900 begins with step 901, where robot control application 416 receives multi-modal user input(s) 601 and current motion scene 608. … Current motion scene 608 provides a snapshot of the operational environment for robot 460 and can include current positions, poses, and velocities of the joints of robot 460, and mapping the spatial arrangement of objects within the operation environment of robot
460.”), ([0095] via “At step 904, interactive denoiser 606 generates revised robot motion plans 611. Interactive denoiser 606 receives estimated noise 610, motion hints 607, and current motion scene 608 and generates revised robot motion plans 611. Interactive denoiser 606 iteratively denoises robot motion plan candidates and then generates revised robot motion plans 611 based on motion hints 607 in a reverse process, which is discussed in more detail with respect to FIG. 10.”), ([0096] via “At step 905, max likelihood estimator 604
selects the revised motion plan 611 with the maximum likelihood.”), (Note: See Figures 7A-C of Wang as well.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang wherein the method further comprises: evaluating the new navigation policy by comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent. Doing so evaluates the navigation policy to output the most likely navigation trajectories for the robots that will succeed, as stated by Wang ([0096] via “At step 905, max likelihood estimator 604
selects the revised motion plan 611 with the maximum likelihood. Given various revised motion plans 611 with various probabilities of prediction, max likelihood estimator 604
selects the revised robot motion plan which has the highest probability, which is the revised robot motion plan 611 that is considered to be the most likely to correspond to the desired motion plan indicated by motion hint(s) 607.”).
16. Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huynh et al. (US 20230409046 A1 hereinafter Huynh) in view of Knittel (US 20250145178 A1 hereinafter Knittel), further in view of Dupuis et al. (US 11179850 B2 hereinafter Dupuis), and further in view of Wang et al. (US 20250249574 A1 hereinafter Wang).
Regarding Claim 13, modified reference Huynh teaches the method according to claim 5, but is silent on the method further comprising: comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent to evaluate the new navigation policy, to obtain a first evaluation result; comparing new navigation trajectories of the other agents and historical navigation trajectories of the other agents to evaluate the new navigation policy, to obtain a second evaluation result; and evaluating the new navigation policy according to the first evaluation result and the second evaluation result.
However, Wang teaches comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent to evaluate the new navigation policy, to obtain a first evaluation result; comparing new navigation trajectories of the other agents and historical navigation trajectories of the other agents to evaluate the new navigation policy, to obtain a second evaluation result ([0092] via “As shown in FIG. 9, a method 900 begins with step 901, where robot control application 416 receives multi-modal user input(s) 601 and current motion scene 608. … Current motion scene 608 provides a snapshot of the operational environment for robot 460 and can include current positions, poses, and velocities of the joints of robot 460, and mapping the spatial arrangement of objects within the operation environment of robot 460.”), ([0095] via “At step 904, interactive denoiser 606
generates revised robot motion plans 611. Interactive denoiser 606 receives estimated noise
610, motion hints 607, and current motion scene 608 and generates revised robot motion plans 611. Interactive denoiser 606 iteratively denoises robot motion plan candidates and then generates revised robot motion plans 611 based on motion hints 607 in a reverse process, which is discussed in more detail with respect to FIG. 10.”), ([0096] via “At step 905, max likelihood estimator 604 selects the revised motion plan 611 with the maximum likelihood.”), (Note: The Examiner interprets the likelihoods of each of the revised robot motion plans of Wang as the first and second evaluation results. Also, see Figures 7A-C of Wang as well.”); and
evaluating the new navigation policy according to the first evaluation result and the second evaluation result ([0096] via “At step 905, max likelihood estimator 604 selects the revised motion plan 611 with the maximum likelihood. Given various revised motion plans
611 with various probabilities of prediction, max likelihood estimator 604 selects the revised robot motion plan which has the highest probability, which is the revised robot motion plan 611 that is considered to be the most likely to correspond to the desired motion plan indicated by motion hint(s) 607.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang wherein the method further comprising: comparing the new navigation trajectory of the first agent and a historical navigation trajectory of the first agent to evaluate the new navigation policy, to obtain a first evaluation result; comparing new navigation trajectories of the other agents and historical navigation trajectories of the other agents to evaluate the new navigation policy, to obtain a second evaluation result; and evaluating the new navigation policy according to the first evaluation result and the second evaluation result. Doing so evaluates the navigation policy to output the most likely navigation trajectories for the robots that will succeed, as stated above by Wang in paragraph [0096].
Examiner’s Note
17. The Examiner has cited particular paragraphs or columns and line numbers in the
references applied to the claims above for the convenience of the Applicant. Although the
specified citations are representative of the teachings of the art and are applied to specific
limitations within the individual claim, other passages and figures may apply as well. It is
respectfully requested of the Applicant in preparing responses, to fully consider the references
in their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP
2141.02 [R-07.2015] VI. A prior art reference must be considered in its entirety, i.e., as a whole,
including portions that would lead away from the claimed Invention. W.L. Gore & Associates,
Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851
(1984). See also MPEP §2123.
Conclusion
18. THIS ACTION IS MADE FINAL. 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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/BYRON XAVIER KASPER/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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