DETAILED ACTION
This Final Office Action is responsive to the claims filed on April 27, 2026. Claims 1-5, 10-15, and 19-23 are under examination.
Claim 19 is rejected under 35 USC 112(b).
Claims 1-5, 10-15, and 19-23 are rejected under 35 USC 101.
Claims 1-3, 4-5, 10-15, and 19-23 are rejected under 35 USC 102 over Bigdeli.
Claim 3 is rejected under 35 USC 103 over Bigdeli in view of Reddy.
Response to Amendments/Arguments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting Rejection: The Applicant’s terminal disclaimer is acknowledged. The rejection is withdrawn.
35 USC 112(b): The Applicant’s amendment to remove the “physically remote” language is acknowledged, and the corresponding rejection is withdrawn. The Applicant did not amend claim 19 to correct the mixed-type claim, so that rejection is maintained.
35 USC 101: With regard to the software per se rejection, the Applicant’s arguments and amendments have been considered and are persuasive. With regard to the subject matter eligibility rejection, the Applicant’s amendments and arguments has been considered but are not persuasive. The Applicant’s arguments will be treated in the order presented in the most recent response.
Mimicking A Real-World Environment: The Applicant asserts that the operations of the claims mimic the real-world activity of robots. However, the claim language itself only ties the method to the real-world in a superficial way. That is, there is no interaction between the real-world and the features of the claims. MPEP 2106.05(f)(1) states,
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
The claims, as recited, fail to interact with the real world using a mechanism that qualifies under 35 USC 101. The claim also does not represent any improvement to computing, because the computer is merely being used as a tool to carry out operations entirely contained within the abstract idea. This mechanism must include additional limitations that confer eligibility by either integrating the abstract idea into a practical application or by combining with other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept. With respect to the possibility of inventive concept, the Applicant will have to provide a distinction from the references of record to demonstrate that the steps are not conventional. As it stands, the steps of the claims, as shown by the art of record, are well-understood, routine, and conventional (WURC) activity. Accordingly, the rejections are maintained.
Art Rejections: The Applicant’s amendments and arguments have been considered and are persuasive. However, new art rejections have been presented, responsive to the Applicant’s substantive amendment.
Claim Rejections - 35 USC § 112(b)
(Statute Presented In The 35 USC 112(a) Rejection)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The Robot System of Claim 1
Claim 19 depends from claim 1, but it appears it was intended to depend from claim 12. Claim 1 recites a method, and claim 12 recites a robot system. Further, the elements of claim 19 are apparatus elements. As it stands, it appears that claim 19 recites both apparatus elements and process steps, which under MPEP 2173.05(p)(II), is properly rejected under 35 USC 112(b).
Dependent claims that depend from rejected claims are rejected based on their dependency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Subject Matter Eligibility
Claims 1-20 are rejected under 35 U.S.C. 101 as ineligible subject matter.
Independent Claims
Claim 12 (Statutory Category – Machine)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process.
Claim 12 recites:
updating the simulation of the external environment based on the simulation instructions, wherein the updating the simulation of the external environment based on the simulation instructions includes applying the modification to at least one object representation in the simulation of the external environment to cause the at least one object representation to more closely resemble a corresponding real-world counterpart object in the real-time external environment. (Evaluation/Mental Process – Updating a simulation (e.g., modifying an image) can practically be performed in the mind or with aid of pen, paper, and/or a calculator.)
Claim 12 recites a mental process, which is an abstract idea.
Claim 12 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
The additional limitations:
a robot system operable to update a simulation of an external environment of a robot body to cause the simulation of the external environment to more closely match a real-time reality of the robot body’s external environment; a robot body; at least one sensor carried by the robot body; at least one processor; and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, the at least one non-transitory processor-readable storage medium storing data and/or processor-executable instructions that, when executed by the at least one processor, cause the robot system to:
These are generic computing elements recited at a high level of generality, and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
load a simulation of an external environment of the robot body;
provide data collected by at least one sensor on-board the robot body to a tele-operation system;
receive simulation instructions from the tele-operation system, wherein the simulation instructions describe a modification to at least one object representation in the simulation of the external environment; and
These steps are mere data gathering similar to the MPEP 2106.05(g) examples: “e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent” “iv. Obtaining information about transactions using the Internet to verify credit card transactions,” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display,” “v. Consulting and updating an activity log, Ultramercial.” Mere data gather is insignificant extra-solution activity and, under MPEP 2106.05(g), fails to integrate the abstract idea into a practical application.
Should it be found otherwise with respect to the load step, loading is a generic computing operation recited in the claim at a high level, and, therefore, under MPEP 2106.05(f), fails to integrate the abstract idea into a practical application.
Any recited data merely limits the abstract idea to a particular field of technology and, under MPEP 2106.05(h), fails to integrate the abstract idea into a practical application.
None of the additional limitations of claim 12, whether in isolation or combination, integrate the abstract idea into a practical application.
Accordingly, claim 12 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The additional limitations:
a robot system operable to update a simulation of an external environment of a robot body to cause the simulation of the external environment to more closely match a real-time reality of the robot body’s external environment; a robot body; at least one sensor carried by the robot body; at least one processor; and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, the at least one non-transitory processor-readable storage medium storing data and/or processor-executable instructions that, when executed by the at least one processor, cause the robot system to:
These are generic computing elements recited at a high level of generality, and, under MPEP 2106.05(f), fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
load a simulation of an external environment of the robot body;
provide data collected by at least one sensor on-board the robot body to a tele-operation system;
receive simulation instructions from the tele-operation system, wherein the simulation instructions describe a modification to at least one object representation in the simulation of the external environment; and
These steps are well-understood, routine, conventional (WURC) activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “i. Determining the level of a biomarker in blood by any means” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Because these steps are WURC and, as previously demonstrated, insignificant extra-solution activity, under MPEP 2106.05(d) and 2106.05(g), the steps to fail to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Should it be found otherwise with respect to the load step, loading is a generic computing operation recited in the claim at a high level, and, therefore, under MPEP 2106.05(f), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Any recited data merely limits the abstract idea to a particular field of technology and, under MPEP 2106.05(h), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
None of the additional limitations of claim 12, whether in isolation or combination, combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
Claim 12 is ineligible.
Claim 1 (Statutory Category – Process)
Regarding claim 1, claim 1 recites the method executed by the configuration of the system of claim 12 and is rejected for the same reasons as claim 12.
Claim 12 is ineligible.
Claim 20 (Statutory Category – None, Software Per Se)
Regarding claim 20, claim 20 is software per se and does not belong to one of the four categories. However, in the interest of compact prosecution, these claims will be addressed for eligibility as if the Applicant has amended the claims to positively recite the non-transitory CRM as an element. Claim 20 is the software and likely intended to be the CRM of claim 12, so claim 20 is rejected for the same reasons as claim 12.
Claim 20 is ineligible.
Dependent Claims
The dependent claims are also ineligible for the following reasons. Note that elements recognized as generic computing elements in the independent claims fail to confer eligibility under MPEP 2106.05(f) for the same reasons in the dependent claims. Similarly, the data description specific to the technological environment merely restrict the abstract idea to a particular technological environment and, under MPEP 2106.05(h), fail to confer eligibility.
Claims 2 and 13
further comprising: training an artificial intelligence to autonomously update the simulation based at least in part on data collected by at least one sensor on-board the robot body.
This training is a generic computing process described at a high level of generality, especially since there is no recitation of how the training is conducted based on the steps of the independent claims. Therefore, under MPEP 2106.05(f), this fails to confer eligibility.
Claims 2 and 13 fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 2 and 13 are ineligible
Claim 3
storing the artificial intelligence in a non-transitory processor-readable storage memory on-board the robot body.
The storing is mere data gathering and WURC and fails to confer eligibility for at least the same reasons as the providing and receiving steps of the independent claims.
Also, the use of a generic CRM is the use of a generic computing element recited at a high level of generality and fails to confer eligibility under MPEP 2106.05(f).
Claim 3 fails to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claim 3 is ineligible
Claims 4 and 14
wherein training an artificial intelligence to autonomously update the simulation based at least in part on data collected by at least one sensor on-board the robot body includes defining an objective function that updates the simulation to minimize discrepancies between the simulation and the data collected by at least one sensor on-board the robot body and optimizing the objective function by the robot system.
Determining an objective function and optimizing the objective function are evaluations, which are mental processes practically performable in the mind or with aid of pen, paper, and calculator, and which are mathematical calculations, which are mathematical concepts. Mental processes and mathematical calculations are abstract ideas. These abstract ideas merge with the abstract idea of the claim(s) from which this claim depends, and there remain no additional limitations to confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 4 and 14 fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 4 and 14 are ineligible
Claims 5 and 15
further comprising:
training the robot system to autonomously update the simulation of the external environment based on multiple iterations of:
providing data collected by at least one sensor on-board the robot body to a tele-operation system;
receiving simulation instructions from the tele-operation system; and
updating the simulation of the external environment based on the simulation instructions.
The training is a generic computing operation recited at a high level of generality. This is especially true as there is no indication as to how the robot system is trained using the iteratively repeated steps. For these reasons, the training step fails to confer eligibility under MPEP 2106.05(f).
The repeated iterations of the steps of the independent claims fail to confer eligibility for the same reasons as the first iteration of the steps rejected with respect to the independent claims.
Claims 5 and 15 fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 5 and 15 are ineligible
Claims 10
wherein receiving simulation instructions from the tele-operation system includes receiving instructions that describe a new object representation for the simulation of the external environment, and
This merely qualifies the receiving step and fails to confer eligibility for the same reasons as the receiving steps of the independent claims.
Also, the type of data received merely limits the abstract idea to the particular technological environment and, under MPEP 2106.05(h), fails to confer eligibility at Step 2A, Prong 2 and Step 2B.
wherein updating the simulation of the external environment based on the simulation instructions includes applying the simulation instructions to add the new object representation to the simulation of the external environment, the new object representation corresponding to a real-world counterpart in the external environment characterized, at least in part, by the data collected by at least one sensor on- board the robot body.
This qualifies the updating step of the independent claims, which is an element of the abstract idea. Therefore, this is an element of the abstract idea for at least the same reasons as the updating step of the independent claims. Therefore, this claim provides no additional limitations.
Claims 10 and 18 fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 10
Claims 11 and 19
further comprising:
providing additional data collected by at least one sensor on-board the robot body to the tele-operation system;
receiving additional simulation instructions from the tele-operation system; and
re-updating the simulation of the external environment based on the additional simulation instructions.
This a repetition of the steps in the independent claims and this fails to confer eligibility for the same reasons as the corresponding steps of the independent claims in the first iteration.
Claims 11 and 19 fail to recite any additional limitations that confer eligibility at Step 2A, Prong 2 and Step 2B.
Claims 11 and 19 are ineligible
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 10-15, and 19-23 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2021/0349478 A1 to Bigdeli et al. (Bigdeli).
Claims 1, 12, and 20
Regarding claim 1, Bigdeli teaches:
A robot system operable to update a simulation of an external environment of a robot body to cause the simulation of the external environment to more closely match a real-time reality of the robot body's external environment, the robot system comprising: a robot body; at least one sensor carried by the robot body; (Bigdeli [0045] “Further, the architecture builds upon a 3D representation/model of the environment. While at the start of a mission, the 3D digital twin is initialized to its static state, observations are accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, areas of change and location of moving objects are identified. Furthermore, the uncertainty about the locations of change as well as moving objects will be quantified and tracked. […] Again this prediction comes with quantification of uncertainty. Further, the gateway may be associated with continual correction and learning via (an active) filter. The most recent observations collected and reported by the drones (onboard mobile sensors) as well as static sensors distributed in an environment are compared against the predicted state of the digital twin, or more precisely its projection onto observation space. Further, the observations are not only enabling the gateway intelligence to make its knowledge of the space more precise but also allow for further learning. More specifically, a residual model will be deployed.” – This is a robot system that cause the simulation of the external environment around the robots to more closely resemble a real-time state using robots with sensors.)
at least one processor; and at least one non-transitory processor-readable storage medium communicatively coupled to the at least one processor, the at least one non-transitory processor-readable storage medium storing data and/or processor-executable instructions that, when executed by the at least one processor, cause the robot system to: (Bigdeli [0055] “Further, the device 200 may include a storage device 206 communicatively coupled with the processing device 204.”)
load a simulation of an external environment of the robot body; (Bigdeli [0083] “The disclosed device builds upon a 3D representation/model of the environment. At the initial start of a mission for path planning, the 3D digital twin may be initialized to a static state” – A simulation of the environment of the drones is loaded.)
provide data collected by at least one sensor on-board the robot body to a tele- operation system, wherein the simulation instructions describe a modification to at least one object representation in the simulation of the external environment; (Bigdeli [0045] “More specifically, at any time t, given the collective and integrated observations across the platform, areas of change and location of moving objects are identified.” [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” – Sensors provide data about a change in an object represented in the environment prior to update)
receive simulation instructions from the tele-operation system, wherein the simulation instructions describe a modification to at least one object representation in the simulation of the external environment; and (Bigdeli [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” [0084] “The present invention comprises robots, drones, and sensors that are lateralized which are designed to specialize in specific information gathering and tasks. Note that the state of digital twin can be projected onto the space of observations and the lateralization of robots, drones, and sensors means that the observation spaces of the drones” – The system provides data representing changes to objects in the environment.)
update the simulation of the external environment based on the simulation instructions, wherein updating the simulation of the external environment based on the simulation instructions includes applying the modification to at least one object representation in the simulation of the external environment to cause the at least one object representation to more closely resemble a corresponding real-world counterpart object in the real-time external environment. (Bigdeli [0086] “Further, continual correction and learning through an active filter maybe a fourth architecture of the four architectures used in the processing/path planning of the disclosed device, as shown in FIG. 5. The continual correction and learning through an active filter uses the most recent observation and information collected and reported by the robots, drones, and sensors distributed in an environment is compared against the predicted state of the digital twin, or more specifically its projection onto observation space.” – The model is updated to provide a simulation that better represents the environment at the current time.)
Regarding claim 1, claim 1 recites the method the system of claim 12 is configured to execute and is rejected for at least the same reasons as claim 12.
Regarding claim 20, claim 20 recites the storage medium of the system of claim 12 and is rejected for at least the same reasons as claim 12.
Claims 2 and 13
Regarding claim 13, Bigdeli teaches the feature of claim 2 and further teaches:
further comprising: data and/or processor-executable instructions stored in the at least one non- transitory processor-readable storage medium that, when executed by the at least one processor, cause the robot system to train an artificial intelligence to autonomously update the simulation based at least in part on data collected by at least one sensor of the robot body. ([0039] “Further, the disclosed system is closely modeled after the lateralization of brain functions in humans, i.e. the tendency for some neural functions or cognitive processes to be specialized to one of the two cerebral hemispheres. In particular, left cerebral hemisphere representations have been proposed to be more focal, whereas right-lateralized mechanisms are believed to be more high-level representing broader contexts. Similarly, at the onset of a mission, different drones and sensors are tasked with complimentary functionalities acquiring information at a different resolution, scale, and subsequently different noise/reliability. The information is then aggregated at the gateway powered by appropriate machine learning models that can integrate this inherently heterogeneous, multi-resolution, and potentially multi-modal acquired information/dataset.” – The system uses the drone sensor data to train artificial intelligence to autonomously update the simulation.)
Regarding claim 2, claim 2 recites the method the system of claim 13 is configured to execute and is rejected for at least the same reasons.
Claims 4 and 14
Regarding claim 14, Bigdeli teaches the features of claim 12 and further teaches:
wherein the data and/or processor- executable instructions that, when executed by the at least one processor, cause the robot system to train an artificial intelligence to autonomously update the simulation based at least in part on data collected by at least one sensor of the robot body, cause the robot system to define an objective function that updates the simulation to minimize discrepancies between the simulation and the data collected by at least one sensor of the robot body and optimize the objective function. (Bigdeli [0048] “Due to environmental disturbance or fluctuation in drone's dynamics, it would deviate from the reference path X.sub.t ∈ :={X.sub.1, X.sub.2, . . . } set by the open-loop actions g.sub.t ∈ G :={g.sub.1, g.sub.2, . . . }. Further, the device may include a feedback controller that corrects the deviation from the reference trajectory without explicitly knowing the drone's position and the map. At each time step t, SOTERIA and SOTER drones/cameras' outputs are combined to estimate and correct SOTER's position. The edge gateway/controller receives a set of observations o.sub.t, corrects the location of the SOTER relative to a reference position X.sub.t, takes a move action to end at next position X.sub.t+1. Further, the disclosed system may be based on a reinforcement-learning-based imitation learning method for correcting the trajectory of the drone. The network architecture includes two parts, as shown in FIG. 10. The first part aims for extracting the relative pose information from the image pair and the second part aims to predict the control correction.” – The robot collects data to optimize an objective function that corrects discrepancies in the simulation.)
Claims 5 and 15
Regarding claim 15, Bigdeli teaches the features of claim 12 and further teaches:
data and/or processor-executable instructions stored in the at least one non- transitory processor-readable storage medium that, when executed by the at least one processor, cause the robot system to: train the robot system to autonomously update the simulation of the external environment based on multiple iterations of: (Bigdeli [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” [0086] “Further, continual correction and learning through an active filter maybe a fourth architecture of the four architectures used in the processing/path planning of the disclosed device, as shown in FIG. 5. The continual correction and learning through an active filter uses the most recent observation and information collected and reported by the robots, drones, and sensors distributed in an environment is compared against the predicted state of the digital twin, or more specifically its projection onto observation space.” – The model continually updates iteratively over time for each time step.)
providing data collected by at least one sensor of the robot body to a tele- operation system that is physically remote from the robot body; receiving simulation instructions from the tele-operation system; and updating the simulation of the external environment based on the simulation instructions. (Bigdeli – These are repetitions of the steps of claim 1 and are rejected for the same reasons as the steps of claim 1).
Claim 10
Regarding claim 18, Bigdeli teaches the features of claim 12,
wherein receiving simulation instructions from the tele-operation system includes receiving instructions that describe a new object representation for the simulation of the external environment, and wherein updating the simulation of the external environment based on the simulation instructions includes applying the simulation instructions to add the new object representation to the simulation of the external environment, the new object representation corresponding to a real-world counterpart in the external environment characterized, at least in part, by the data collected by at least one sensor on- board the robot body. (Bigdeli [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” [0086] “Further, continual correction and learning through an active filter maybe a fourth architecture of the four architectures used in the processing/path planning of the disclosed device, as shown in FIG. 5. The continual correction and learning through an active filter uses the most recent observation and information collected and reported by the robots, drones, and sensors distributed in an environment is compared against the predicted state of the digital twin, or more specifically its projection onto observation space.” – The model continually updates iteratively over time for each time step, including representing changes in the environment and to the objects. The new representation can include a change to the area, a change to the object, and/or a change in position of the object within the environment.)
Claims 11 and 19
Regarding claim 19, Reddy teaches the features of claim 1 or claim 12. Reddy further teaches:
data and/or processor-executable instructions stored in the at least one non- transitory processor-readable storage medium that, when executed by the at least one processor, cause the robot system to: (Bigdeli [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” [0086] “Further, continual correction and learning through an active filter maybe a fourth architecture of the four architectures used in the processing/path planning of the disclosed device, as shown in FIG. 5. The continual correction and learning through an active filter uses the most recent observation and information collected and reported by the robots, drones, and sensors distributed in an environment is compared against the predicted state of the digital twin, or more specifically its projection onto observation space.” – The model continually updates iteratively over time for each time step.)
provide additional data collected by at least one sensor of the robot body to the tele-operation system; receive additional simulation instructions from the tele-operation system; and re-updating the simulation of the external environment based on the additional simulation instructions. (Bigdeli – These are repetitions of the steps of claim 1 and are rejected for the same reasons as the steps of claim 1 but with the new information from the next iteration/time step).
Regarding claim 11, claim 11 recites the operations of claim and is rejected for at least the same reasons as claim 19.
Claims 21-23
Regarding claim 22, Bigdeli teaches the features of claim 12, and further teaches:
wherein the modification to at least one object representation corrects a discrepancy between the simulation and the actual external environment of the robot body. (Bigdeli [0083] “observations may be accumulated throughout the mission to provide a temporally updated model. More specifically, at any time t, given the collective and integrated observations across the platform, area of changes, and location of moving objects are identified.” [0086] “Further, continual correction and learning through an active filter maybe a fourth architecture of the four architectures used in the processing/path planning of the disclosed device, as shown in FIG. 5. The continual correction and learning through an active filter uses the most recent observation and information collected and reported by the robots, drones, and sensors distributed in an environment is compared against the predicted state of the digital twin, or more specifically its projection onto observation space.” – The model continually updates iteratively over time for each time step.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 3: Bigdeli and Reddy
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0349478 A1 to Bigdeli et al. (Bigdeli) in view of NPL: “Shared Autonomy via Deep Reinforcement Learning” by Reddy et al. (Reddy).
Claim 3
Regarding claim 3, Bigedli teaches the features of claim 2.
further comprising: storing the artificial intelligence in a non-transitory processor-readable storage memory . ([0055] “Further, the device 200 may include a storage device 206 communicatively coupled with the processing device 204.” [0100] “As stated above, a number of program modules and data files may be stored in system memory 1104, including operating system 1105. While executing on processing unit 1102, programming modules 1106 (e.g., application 1120) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above.”)
Bigdeli teaches the use of distributed computing and swarm technology (Bigdeli [0101] “Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.” [0082] “While there is a multiplicity of work on multi-drone cooperation and swarming, very little has been done in terms of hierarchical representation and acquisition of information multi-drone platforms. Specifically, this is true when the type of devices or sensors being used for data acquisition are diverse, and in many cases, not necessarily drones at all.”), but does not appear to explicitly teach, but Bigdeli in view of Reddy teaches:
storing the artificial intelligence in a non-transitory processor-readable storage memory on-board the robot body; (Reddy Page 3, B. Method Overview “Our method takes observations of the environment and the user’s controls or inferred goal (when available) as input, and produces a high value action or control output that is as close as possible to the user’s control. We learn state-action values via Q-learning with neural network function approximation. In this section, we will describe how the agent combines user input with environmental observations, motivate and describe our choice of deep Q-learning for training the agent, and describe how the agent shares control with the user. -The agent is the robot, and the robot, as an independent agent, is responsible for providing its own training to use in interpreting how to use the user’s cooperative input. Page 5, Left Column, Second Paragraph “The agent uses a multi-layer perceptron with two hidden layers of 64 units each to approximate the Q function […]. The action-similarity function […] in the agent’s behavior policy counts the number of dimensions in which actions a and ah agree […].” – The agent/robot itself uses the machine learning model, so it must have memory that stores elements of the artificial intelligence.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claims to modify the simulation modeling of Bigdeli by the machine learning functionality of Reddy because the person of ordinary skill in the art would be motivated by the aim of Bigdeli to provide for live automated control of robots in a real-time environment, to look to Reddy, which provides a shared autonomy model for drone pilots that is better than completely automated or completely manual control alone. (Bigdeli [0048] “Further, the disclosed system may be based on a reinforcement-learning-based imitation learning method for correcting the trajectory of the drone. The network architecture includes two parts, as shown in FIG. 10. The first part aims for extracting the relative pose information from the image pair and the second part aims to predict the control correction.” [0091] “After the processing/path planning with different architecture, the path planning adjustments are next as the integrated information is used to give a real-time command to the drones as they traverse the surrounding space. The path planning adjustments are done in a two-step operation, macro-scale drone path planning with lateralized functionalities, and dynamic path adjustments and contextual optimization.”; Reddy Abstract “This approach poses the challenge of following user commands closely enough to provide the user with real-time action feedback and thereby ensure high-quality user input, but also deviating from the user’s actions when they are suboptimal. We balance these two needs by discarding actions whose values fall below some threshold, then selecting the remaining action closest to the user’s input. Controlled studies with users (n = 12) and synthetic pilots playing a video game, and a pilot study with users (n = 4) flying a real quadrotor, demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user’s private information through observations, but receives a reward signal and user input that both depend on the user’s intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user’s input. This enables the assisted user to complete the task more effectively than the user or an autonomous agent could on their own. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems.”)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(From This Action)
US 2021/0157312 A1 to Cella et al. (Teaches essentially all of the features of claims, similarly to Bigdeli)
NPL: “Drone-based AI and 3D Reconstruction for Digital Twin Augmentation” by To et al. (Teaches using drone and AI fusion technology to map buildings)
NPL: “Distributed Situational Awareness in Robot Swarms” by Jones et al. (Teaches AI-based drone collective situational awareness in an environment)
NPL: “Distributed Data Storage and Fusion for Collective Perception in Resource-Limited Mobile Robot Swarms” by Majcherczyk et al. (Teaches collective distributed computing amongst drones for object classification in an environment)
(From Previous Actions)
NPL: “You Can Give A Robot A Paintbrush, But Does It Create Art” by Caren. (Teaches using a robot-human tele-operative system that produces paintings)
NPL: “The Telegarden” by Goldberg et al. (Teaches human-robot collaborative planting methods)
NPL: “Active exploration and parameterized reinforcement learning applied to a simulated human-robot interaction task” by Khamassi et al. (Teaches using reinforcement learning to train a robot to collaborate with human in manufacturing)
NPL: “Reinforcement learning-based dynamic field exploration and reconstruction using multi-robot systems for environmental monitoring” by Lu et al. (Teaches reinforcement learning for robot exploration)
NPL: “The PumaPaint Project” by Stein (Teaches an early example of human-robot collaborative painting)
NPL: “Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments” by Surmann et al. (Teaches training robots to navigate using reinforcement learning)
NPL: “Remote Robot Control with Human-in-the-Loop Over Long Distances using Digital Twins” by Tsokalo et al. (Teaches robot control in a digital twin environment with a human-in-the-loop)
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/J.M.W./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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