DETAILED ACTION
Claims 1-20 rejected under 35 USC § 101 as directed to an abstract idea.
Claims 2-6, 9, 12-16, and 19 rejected under 35 USC § 112 as indefinite.
Claims 1-3, 8, 11-13, and 18 rejected under 35 USC § 102 as anticipated.
Claims 4-7, 9-10, 14-17, and 19-20 rejected under 35 USC § 103 as obvious.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to without significantly more.
Claims 1 and 11
[Step 1] Claims 1 and 11 recite a method, system, and/or medium for performing a process comprising steps of (1) generating a scene graph using an image and a human instruction and (2) generating a machine instruction set for objects in the scene graph.
[Step 2A – Prong One] The process recited in claims 1 and 11 are directed to an abstract idea. The recited limitations (1) and (2) are a process that, under its broadest reasonable interpretation, covers mental processes including "observations, evaluations, judgments, and opinions" that "can be performed in the human mind, or by a human using a pen and paper" MPEP 2106.04(a)(III). The step of (1) is accomplished by a human manually labeling an image with objects relevant to a task— if the instruction is “get the red cup,” a human can label a red cup in an image. The step of (2) is accomplished by a human generating a task procedure— a human can mentally determine that a robot should move to the table, grab the red cup, and move back to the user, i.e. an instruction set.
[Step 2A – Prong Two] Claims 1 and 11 do not recite additional elements that integrate the judicial exception into a practical application. The additional elements recited include computer hardware (e.g. processor and memory); these additional elements are merely instructions to implement an abstract idea on a computer. The recitation of an AI agent is merely an intended use, because the AI agent is only the source of the image; and the claim doesn’t actually require that the generated machine instruction set be performed. MPEP 2106.04(d). Further, the claim limitations attempt to cover any solution for generating an instruction set using a labeled image, without providing a particular solution or way to achieve a desired outcome. See MPEP 2106.05(f)(1).
[Step 2B] Claims 1 and 11 do not recite a combination of elements that amount to significantly more than the judicial exception itself. The broadest reasonable interpretation of the process comprising limitations (1) and (2) is a mental process. The additional elements recited are merely instructions to implement the mental process on a computer. Accordingly, these limitations are not enough to qualify as "significantly more" when recited with a judicial exception (i.e. the mental process). MPEP 2106.05(A). Further, these claim limitations fail to improve the functioning of the computer itself, because "a claim whose entire scope can be performed mentally, cannot be said to improve computer technology." See MPEP 2106.05(a)(I).
Claims 2 and 12
The additional step of including instructions corresponding to a lower level does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because the step is drawn to an abstract idea as a mental process. The step of including instructions corresponding to a lower level is accomplished by a human mentally determining sub-tasks necessary to complete a task— such as moving to the table to get the cup.
Claims 3, 10, 13, and 20
The additional step of determining the relevance of an object to a task does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because the step is drawn to an abstract idea as a mental process. The step of determining the relevance of an object to a task is accomplished by a human mentally determining with objects in a given scene are pertinent to the task— such as deciding the blue cup is not relevant to the task of picking up the red cup.
Claims 4-7, 9, 14-17, and 19
The additional step of determining whether a task is complex enough to require robot collaboration does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because the step is drawn to an abstract idea as a mental process. The step of determining whether a task is complex enough to require robot collaboration is accomplished by a human mentally determining that an object is too heavy or too awkwardly shaped to be handled by a single robot.
Claims 8 and 18
The additional element of executing the task using and AI neural network is an additional element reciting computer hardware (e.g. processor and memory); these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d). None of the limitations recited can be considered a specific improvement of executing an AI neural network.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-6, 9, 12-16, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 and 12
Claims 2 and 12 recite “wherein the machine instruction set includes machine instructions corresponding to a lower level of the human instruction.” The term “lower level of the human instruction” is representative of a subjective term, because defining a “lower level” of an instruction depends solely on the unrestrained, subjective opinion of a particular individual practicing the invention. The specification does not provide any standard for measuring the scope of the term, nor any restrictions on the exercise of subjective judgment. See MPEP 2173.05(b)(IV). Accordingly, claims 2 and 12 are indefinite.
The specification offers absolutely no guidance on what a “lower level of the human instruction” is supposed to mean. For purposes of examination, Examiner interprets a “lower level of the human instruction” to mean a sub-task necessary to complete a task.
Claims 3 and 13
Claims 3 and 13 recite “wherein generating the machine instruction set comprises generating the machine instruction set for objects, the relevance information of which is greater than a threshold.” The phrase “the relevance information of which is greater than a threshold " renders the claim indefinite because it is unclear whether the limitation(s) is part of the claimed invention. See MPEP § 2173.05(d). It is confusing as to whether the intended scope of generating the machine instruction set for objects is limited or modified by the relevance information being greater than a threshold. This is because (1) the specification offers absolutely no guidance on what this threshold represents, (2) the claim recites no limitation on what this threshold represents, and (3) the claim is silent as to what conditional action is performed when the relevance exceeds a threshold. Accordingly, claims 3 and 13 are indefinite.
Claims 4-6, 9, 14-16, and 19
Claims 4 and 14 recite “generating the machine instruction set based on information about an additional AI agent in a vicinity of the AI agent.” The term “in a vicinity of” is representative of a subjective term, because defining the “vicinity” depends solely on the unrestrained, subjective opinion of a particular individual practicing the invention. The specification does not provide any standard for measuring the scope of the term, nor any restrictions on the exercise of subjective judgment. See MPEP 2173.05(b)(IV). Accordingly, claims 4 and 14 are indefinite; claim(s) 5-6, 9, 15-16, and 19 are rejected under 35 USC 112(b) as dependent on an indefinite claim.
Claims 9 and 19
Claims 9 and 19 recite “generating the machine instruction set when a number of nearby Al agents capable of collaboration is greater than the number of Al agents required for performing the human instruction.” The term “nearby AI agents” is representative of a subjective term, because defining “nearby” depends solely on the unrestrained, subjective opinion of a particular individual practicing the invention. The specification does not provide any standard for measuring the scope of the term, nor any restrictions on the exercise of subjective judgment. See MPEP 2173.05(b)(IV).
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.
Claims 1-3, 8, 11-13, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu et al., U.S. PG-Publication No. 2022/0126445 A1.
Claim 1
Zhu discloses a method for task planning for collaboration of artificial intelligence (AI) agents. Zhu discloses a system that “controls an autonomous device such as a robot to solve a task and motion planning (“TAMP”) problem,” wherein the system “solves a task by obtaining sensory information which is used to generate both a geometric scene graph and a symbolic scene graph.” The symbolic scene graph “may be generated from a geometric scene graph, and described each object’s position and state at a task level.” The system “uses a symbolic scene graph to plan backwards from a desired end goal to a current state indicated in a symbolic scene graph,” including using the “symbolic scene graph to plan a number of intermediate goals … to accomplish an end goal.” When an intermediate goal is identified the system uses “a geometric scene graph … to plan specific motions that achieve a next intermediate goal,” and the system “performs task planning using a symbolic scene graph.” Zhu, ¶¶ 56-58; See Also ¶ 1 (disclosed system “pertains to processing resources used to perform and facilitate artificial intelligence”).
Zhu discloses generating a scene graph using an image acquired by an AI agent and a human instruction. A control computer system 102 “obtains perception and pose estimation information 104 that includes information about objects” that “is collected by obtaining images of objects within an environment.” The perception and pose estimation information 104 is used “to generate a geometric scene graph 106” comprising “information … that described position and pose of each object in an observed scene.” The control computer system 102 “uses information in geometric scene graph 106 to generate a symbolic scene graph 108” comprising “information … that described one or more relationships between objects in geometric scene graph 106” (i.e., relevance information between each of the objects). Id. at ¶¶ 65-66. In one embodiment, “nodes in both types of scene graphs represent objects in a task” (task → human instruction). Id. at ¶ 69.
Zhu discloses wherein the scene graph includes relevance information between each of the objects in the scene graph and the human instruction. In one embodiment, “a perception module is used to process raw perception and obtain features of an environment to generate scene graphs.” Once “both a scene graph of [a] current state and a scene graph of goal state is generated, a system uses a “task planning model” comprising a machine “learning based approach for backward planning” to predict subgoals, wherein “a new candidate scene graph containing one subgoal is generated.” Id. at ¶¶ 73-78; See Also ¶ 72 (scene graphs are constructed using “3D information about each object,” “object type,” “attributes of edges,” and “symbolic relations … between objects using edges”). The scene graphs depicting a goal state and subgoal state comprise information relevant to a human instructed task. See ¶ 140 (“storing task-specific state information into a symbolic graph”).
Zhu discloses generating a machine instruction set for objects in the scene graph. A “symbolic scene graph is used for backward task planning to select a motion primitive along with information about an object to manipulate.” A motion primitive command “is passed into a Motion Planner along with 3D information of an object from a geometric scene graph,” and the motion planner “sends a sequence of low-level motion commands to a robot until a robot finishes execution” (low-level robot motion commands to manipulate objects → machine instruction set for objects). Id. at ¶ 64. A candidate scene graph containing a subgoal is input to a reachability network that determines if the subgoal “is reachable,” then “a motion primitive will be selected based on a target subgoal and a motion planner will execute that plan.” Id. at ¶ 78. In one embodiment, if a “configuration for objects in a goal state is given, a system can pass goal info into motion primitives directly.” Id. at ¶ 90.
Claim 2
Zhu discloses wherein the machine instruction set includes machine instructions corresponding to a lower level of the human instruction. A “symbolic scene graph is used for backward task planning to select a motion primitive along with information about an object to manipulate.” A motion primitive command “is passed into a Motion Planner along with 3D information of an object from a geometric scene graph,” and the motion planner “sends a sequence of low-level motion commands to a robot until a robot finishes execution” (low-level robot motion commands to manipulate objects → machine instructions corresponding to a lower level of the human instruction). Zhu, ¶ 64.
Claim 3
Zhu discloses wherein generating the machine instruction set comprises generating the machine instruction set for objects, the relevance information of which is greater than a threshold. Zhu discloses an embodiment wherein the cycle of task planning, motion planning, and motion command execution “repeats until a state of objects represented in symbolic scene graph 108 matches a goal state of a TAMP task” (task relevant objects match goal state → relevance information threshold). Id. at ¶ 67. A motion planner “uses information in said geometric scene graph to modify a state of objects such that a state of objects matches that of said identified state,” and is repeated “until said position of objects matches said objective of said [TAMP} task.” Id. at ¶ 98. The system determines “if a current state of objects does not match that of a TAMP task, then a task is not complete” and the “process is repeated” (i.e., if objects do not match goal state, then threshold relevance for further machine manipulation is met). Id. at ¶ 102.
Claim 8
Zhu discloses wherein generating the machine instruction set is performed using an AI neural network trained using training data configured with images, human instructions, and machine instruction sets. Zhu discloses generating a scene graph of a current state and goal state and using “a RPN-based approach to do task planning, and decide a motion primitive for a down-streaming motion planning module to execute,” wherein the “task planning model is built on top of RPN, a learning based approach for backward planning.” The task planning module comprises a “precondition network, subgoal serialization, and reachability network,” that are implemented as neural networks. Id. at ¶¶ 76-88.
Claims 11-13 and 18
Claims 11-13 and 18 are rejected utilizing the aforementioned rationale for Claims 1-3 and 8; the claims are directed to a system performing the method.
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.
Claims 4-6, 9, 14-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., U.S. PG-Publication No. 2022/0126445 A1, in view of Brazeau, U.S. Patent No. 10,192,195 B1.
Claim 4
Brazeau discloses wherein generating the machine instruction set comprises generating the machine instruction set based on information about an additional AI agent in a vicinity of the AI agent. Brazeau discloses a “method for coordinating motion between robotic components of an inventory system” using “a management module … for determining tasks and assigning individual tasks to individual robotic devices.” The management module “may determine two particular robotic device should be utilized to ready an item for shipment” such that a “first robotic arm may retrieve the item … and may place the item … in an area … within reach of a second robotic arm.” Brazeau, 2:18-3:8. The management device “may determine that particular robotic devices (e.g., two or more robotic devices) should coordinate together to achieve a common task,” such as being “simultaneously utilized to move a heavy and/or awkwardly shaped item.” Id. at 4:8-32.
A specification module 99 facilitates “storage and management of specifications associated with one or more robotic devices,” and is “configured to receive specification requests,” wherein a request “indicates … a location.” The specification module 99 is configured to “identify any suitable robotic device (or specific types of robotic devices) within a threshold distance of the location” (devices within a threshold distance of a location → AI agents in the same vicinity). Id. at 23:21-52. In one embodiment, a “projection module 804” is “configured to identify one or more robotic devices (e.g., a number of robotic devices within a threshold distance with respect to a task location, a number of robotic devices to be utilized for a particular coordinate task (e.g., packaging, simultaneously moving an object, etc.) and the like).” Id. at 25:67-26:20.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling an autonomous robot to solve a task of Zhu to incorporate the method of coordinating robots to complete a task taught by Brazeau. One of ordinary skill in the art would be motivated to integrate the method of coordinating robots to complete a task into Zhu, with a reasonable expectation of success, in order to improve operating efficiently: the “system may operate more efficiently with respect to the various components (e.g., robotic devices) operating … as task completion delays may be reduced.” Brazeau, 5:28-35.
Claim 5
Brazeau discloses wherein the information about the additional AI agent includes information about a location thereof and information about whether collaboration is possible. A specification module 99 facilitates “storage and management of specifications associated with one or more robotic devices,” and is “configured to receive specification requests,” wherein a request “indicates … a location.” The specification module 99 is configured to “identify any suitable robotic device (or specific types of robotic devices) within a threshold distance of the location” (devices within a threshold distance of a location → AI agents in the same vicinity). Id. at 23:21-52. In one embodiment, a “projection module 804” is “configured to identify one or more robotic devices (e.g., a number of robotic devices within a threshold distance with respect to a task location, a number of robotic devices to be utilized for a particular coordinate task (e.g., packaging, simultaneously moving an object, etc.) and the like).” Id. at 25:67-26:20.
Claim 6
Brazeau discloses wherein generating the machine instruction set comprises generating the machine instruction set based on a number of AI agents that are located within a preset distance from the Al agent and capable of collaborating with the Al agent. A specification module 99 facilitates “storage and management of specifications associated with one or more robotic devices,” and is “configured to receive specification requests,” wherein a request “indicates … a location.” The specification module 99 is configured to “identify any suitable robotic device (or specific types of robotic devices) within a threshold distance of the location” (devices within a threshold distance of a location → AI agents in the same vicinity). Id. at 23:21-52. In one embodiment, a “projection module 804” is “configured to identify one or more robotic devices (e.g., a number of robotic devices within a threshold distance with respect to a task location, a number of robotic devices to be utilized for a particular coordinate task (e.g., packaging, simultaneously moving an object, etc.) and the like).” Id. at 25:67-26:20.
Claim 9
Brazeau discloses wherein generating the machine instruction set comprises estimating a number of AI agents required for performing the human instruction based on calculation of complexity of the human instruction and generating the machine instruction set when a number of nearby Al agents capable of collaboration is greater than the number of Al agents required for performing the human instruction. A specification module 99 facilitates “storage and management of specifications associated with one or more robotic devices,” and is “configured to receive specification requests,” wherein a request “indicates … a location.” The specification module 99 is configured to “identify any suitable robotic device (or specific types of robotic devices) within a threshold distance of the location” (devices within a threshold distance of a location → identify number of AI agents capable of collaboration). Id. at 23:21-52. In one embodiment, a “projection module 804” is “configured to identify one or more robotic devices (e.g., a number of robotic devices within a threshold distance with respect to a task location, a number of robotic devices to be utilized for a particular coordinate task (e.g., packaging, simultaneously moving an object, etc.) and the like).” Id. at 25:67-26:20. Thus, the human instructed task comprises a required number of robotic devices (i.e., number of AI agents required) and a number of available nearby robotic devices (i.e., number of nearby AI agents capable of collaboration).
A “coordination module 806” is configured “to coordinate a common task between multiple robots.” One example task indicates “that a first robotic arm and a second robotic arm are to move an item in a particular manner (e.g., simultaneously lift the item and move it to a second location while both robotic devices maintain physical contact with the item for at least a portion of the task execution).” Id. at 29:6-59. In this example, the number of robots (i.e., AI agents) required is two, and the machine instruction set if performed when two robots are nearby enough to both maintain physical contact with an item.
Claims 14-16 and 19
Claims 14-16 and 19 are rejected utilizing the aforementioned rationale for Claims 4-6 and 9; the claims are directed to a system performing the method.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., U.S. PG-Publication No. 2022/0126445 A1, in view of Sun et al., U.S. PG-Publication No. 2023/0134924 A1.
Claim 7
Sun discloses requesting collaboration from a nearby AI agent when complexity of performing the machine instruction set is greater than a preset value. Sun discloses a robotic system “to control two or more robots … to work cooperatively to perform a task.” In one embodiment, “robots work independently to pick/place items the robots can grasp and move without using multiple robots,” and payloads “that are too large, heavy, awkwardly shaped, etc., to be moved by a single robot are moves by two or more robots working in cooperation.” A determination is made “to use two or more robots to move a payload cooperatively,” wherein “image data may be used to classify the payload by type, and a weight or other attribute of a payload of that type may be used to decide to use multiple robots to move the item” (weight exceeds value → complexity is greater than a preset value). Attributes and “grasp-strategies (e.g., single robot, multi-robot) are learned by the system over time, e.g., based on prior experience attempting to grasp and move a similar item and/or items of the same type.” Sun, ¶¶ 13-14.
Figure 4 illustrates “an embodiment of a robotic system configured to control a plurality of robots to perform a task cooperatively.” At 404, “an indication that an item that has been perceived and which needs to be picked and placed is too large to grasp and move with one robot” (i.e., complexity of performing the machine instruction is greater than a preset value), then “the robot and/or controller transitions to a state 406 in which cooperative performance of the task is initiated.” Id. at ¶¶ 29-30.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling an autonomous robot to solve a task of Zhu to incorporate the method of controlling robots to work cooperatively to perform a task of Sun. One of ordinary skill in the art would be motivated to integrate controlling robots to work cooperatively to perform a task into Zhu, with a reasonable expectation of success, in order to improve efficiency: method enables “a robotic system to perform a wider variety of tasks with respect to a wider range of objects, e.g., by using two or more robots to perform a task cooperatively.” See Sun, ¶ 40.
Claim 17
Claim 17 is rejected utilizing the aforementioned rationale for Claim 7; the claim is directed to a system performing the method.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al., U.S. PG-Publication No. 2022/0126445 A1, in view of Banerjee et al., U.S. PG-Publication No. 2023/0162494 A1.
Claim 10
Banerjee discloses wherein generating the scene graph comprises generating the scene graph using the image and generating a relevance map, including the relevance information between the object in the scene graph and the human instruction, based on the scene graph and the human instruction. Banerjee discloses a method “for ontology guided indoor scene understanding for cognitive robotic tasks.” The ontology is updated using an “external knowledge-base and observed information, and is “shared and used by other robots.” The knowledge of the environment is “grounded in an ontology that can be referenced for generation of semantic summary of scenes via scene graphs” (ontology → relevance map). Banerjee, ¶¶ 19-20.
The method provides “for technique linking of objects in [a] scene with image processing techniques, in order to use only suitable scene processing techniques based on declaration in ontology to a specific object, environment and task.” Specifically, “the task instruction may be processed to extract keywords that can be mapped to tasks possible in that environment and enabling runtime actuation execution calls” (ontology used to process scene and task instruction → relevance map used to determine relevance between scene object and human instruction). Id. at ¶ 36. For example when the instruction contains “red cup,” when “the object is detected as ‘cup’, from ontology lookup, the object properties relevant to the object ‘cup’ is fetched.” The fetching “is done by a path stored in the ontology, so that when ‘color’ property is activated, the corresponding technique mapped to it is called and the result is checked within the range of colors.” Specifically, “to detect ;red cup’ as per instruction … the technique to detect ‘red’ feature is called from technique mapping stored in the ontology.” Id. at ¶¶ 37-38.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the method of controlling an autonomous robot to solve a task of Zhu to incorporate the method of ontology guided indoor scene understanding for cognitive robotic tasks. One of ordinary skill in the art would be motivated to integrate the method of ontology guided indoor scene understanding for cognitive robotic tasks into Zhu, with a reasonable expectation of success, in order to improve accuracy and performance: “the scene graph generation is an ontology driven, hence change of semantic error at knowledge levels is very low and processing also happens fast as only selective image processing techniques are run.” See Banerjee, ¶ 20.
Claim 20
Claim 20 is rejected utilizing the aforementioned rationale for Claim 10; the claim is directed to a system performing the method.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Hallock et al., U.S. PG-Publication No. 2020/0171650 A1 (abstract discloses robotic manipulators working cooperatively to grasp an object).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANK D MILLS whose telephone number is (571)270-3172. The examiner can normally be reached M-F 10-6 ET.
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/FRANK D MILLS/Primary Examiner, Art Unit 2194 January 8, 2026