DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Pending
1-20
35 U.S.C. 102
1-20
Priority
Applicant’s indication of Domestic Benefit and/or National Stage information based on provisional application 63/704,120 filed 10/07/2024 is acknowledged.
Information Disclosure Statement
The information disclosure statement(s) (IDS(s)) submitted on 04/25/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Examiner Note - Prior Art
Examiner has cited particular paragraphs, columns, lines, or figures in the references as applied to the claims for the convenience of Applicant. Although the specified citations are representative of the teachings in the art and are applied to limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from Applicant, in preparing the 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-01.2024] VI. and MPEP §2123.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gillett (US 2024/0181637 A1, “Gillett”).
Regarding claim 1: Gillett teaches: A method comprising: ([0007] method for controlling humanoid autonomous humanoid robot)
applying a plurality of masks to a plurality of goal state attributes for an articulated object to produce one or more masked goal state attributes; ([0014] computing system, based on an application establishes a switching sequence to initiate an operating mode function involving; a step mode, a walking mode, a roll/skate mode, a leap/jump mode, a battery charging mode which causes a sleep state. [0168] determining understanding 1066 of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame)
generating, via execution of a machine learning model, one or more actions based at least on the one or more masked goal state attributes; and ([0050] using artificial intelligence, neural networks. [0015] autonomous humanoid robot can perform various physical motion states involving at least one of the following acts; a sports activity, a series of dance movements, perform a vehicle-like mobility service, or when operating as a mule or a towing-vehicle, accordingly the autonomous humanoid robot can transport a payload or an object. [0168] determining understanding 1066 of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame)
updating one or more parameters of the machine learning model based at least on the one or more actions and one or more reference actions for the articulated object to produce a trained machine learning model ([0057] The computing system configured to provide instruction and programming for estimating and controlling pivotal movement of body components involving arms, legs and a waist module which are configured to support the body and reposition the body such that the autonomous humanoid robot can step, walk, roll or skate or perform various handling maneuvers to complete task 1060. [0180] adjusts weight given to classification based on collection of past experiences of autonomous humanoid robot 100 and classification based on experiences of respective autonomous humanoid robot 100 itself. processor 1001 may be trained in object 116 classification using reinforcement training).
Regarding claim 2: Gillett further teaches: The method of claim 1, further comprising: generating, via execution of the trained machine learning model, one or more additional actions based at least on one or more additional goal state attributes; and performing a task using the articulated object based at least on the one or more additional actions ([0196] As processor makes use of various info, such as optical flow, entropy pattern of pixels as result of motion, feature extractors, RGB, depth info, processor may resolve uncertainty of association between coordinate frame of reference of sensor and frame of reference of environment. processor uses neural network to resolve incoming info into distances or adjudicates possible sets of distances based on probabilities of different possibilities. Concurrently, as neural network processes data at higher level, data is classified into more human understandable info, such as object or obstacle name (human name or object type such as remote), feelings and emotions, gestures, commands, words. However, all info may not be required at once for decision making).
Regarding claim 3: Gillett further teaches: The method of claim 2, wherein the task comprises at least one of bimanual manipulation, bipedal locomotion, or navigation ([0089] waist module further securable relative to main body, waist module having joint assembly adapted to pivot with at least two degrees of freedom relative to bending or twisting at center portion of body. [0090] drive assembly secured to hip portion 106a of the body, swivel assembly adapted to cooperate with swivel shafts to pivot leg 105 with at least two degrees of freedom relative to body. [0091] plurality of perception sensors and cameras configured for detecting object surrounding autonomous humanoid robot, sensors and cameras providing object and image data to computing system comprising processors).
Regarding claim 4: Gillett further teaches: The method of claim 1, further comprising: determining a first subset of the plurality of masks based at least on a selection of one or more command spaces associated with the one or more actions; and determining a second subset of the plurality of masks based at least on a subset of the plurality of goal state attributes that is associated with the one or more command spaces ([0088] calculation model provides dynamic equation composed by acceleration sensor and orientation sensor, each are distributed and connected on series of control points and are sequentially summed by calculation model; acceleration sensor, angular velocity sensor, orientation sensor, acceleration sensor and dynamic equations control orientation of head 102 at body's upper body 101(U)). [0014] computing system, based on an application establishes a switching sequence to initiate an operating mode function involving; a step mode, a walking mode, a roll/skate mode, a leap/jump mode, a battery charging mode which causes a sleep state. [0015] autonomous humanoid robot can perform various physical motion states involving at least one of the following acts; a sports activity, a series of dance movements, perform a vehicle-like mobility service, or when operating as a mule or a towing-vehicle, accordingly the autonomous humanoid robot can transport a payload or an object. [0168] determining understanding of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame).
Regarding claim 5: Gillett further teaches: The method of claim 4, wherein the first subset of the plurality of masks is used to filter a second subset of the plurality of goal state attributes that is not associated with the one or more command spaces ([0053] computing system, based on application establishes switching sequence to initiate series of operating mode functions 1050 which allows autonomous humanoid robot to perform various physical motion states, such that autonomous humanoid robot can transport payload or object 116, and achieve one or more of following acts to perform vehicle-like mobility service to carry payload 901, exampled in FIG. 9A; ride-on vehicle 902, exampled in FIG. 9B; to perform delivery service 903, exampled in FIG. 9C; and to perform mule or towing-vehicle 904, exampled in FIG. 9D).
Regarding claim 6: Gillett further teaches: The method of claim 4, wherein the second subset of the plurality of masks is determined by sampling from a distribution associated with each state included in the subset of the plurality of goal state attributes ([0014] computing system, based on application establishes switching sequence to initiate operating mode function involving; step mode, walking mode, roll/skate mode, leap/jump mode, battery charging mode which causes sleep state. [0053] computing system, based on application establishes switching sequence to initiate series of operating mode functions 1050 which allows autonomous humanoid robot to perform various physical motion states. [0065] computing system establishes switching sequence to initiate operating mode function involving; step mode, walking mode, roll or skating mode, leap mode or jumping mode, battery charging mode and fall recovery mode, autonomous humanoid robot can perform various physical motion states involving; sports activity, series of dance movements, perform vehicle-like mobility service, or when operating as mule or towing-vehicle, accordingly autonomous humanoid robot can transport payload or object. [0150] execute command process to maneuver autonomous humanoid robot into one or more unique pose maneuvers, such that autonomous humanoid robot can reposition body parts to achieve series of steps; [0163] computing system utilizing one or more control signals 1021 being associated with plurality of training sets 1021(TS) configured to execute degree axis of rotation of one or more joint mechanisms, servos, actuator; plurality of training sets configured to execute controls. See also [0168]).
Regarding claim 7: Gillett further teaches: The method of claim 1, further comprising generating, via execution of a second trained machine learning model, the one or more reference actions based at least on the plurality of goal state attributes ([0050] using artificial intelligence, neural networks. [0163] computing system utilizing one or more control signals 1021 being associated with plurality of training sets 1021(TS) configured to execute a degree axis of rotation of one or more joint mechanisms, servos, actuator; to execute a degree axis of rotation of one or more joint mechanisms, servos, actuators; to accomplish maneuvering actions of a steering controller 1013, a propulsion controller, a brake controller; one or more control signals 1021 being associated with a plurality of training sets configured to execute a steering controller, a propulsion controller, a brake controller all associated with the computing system processors 1014 and microprocessors 1014(MP); one or more control signals 1017 being associated with a plurality of training sets 1021(TS) configured to execute a degree axis of rotation 1022 of one or more joint mechanisms, servos, actuators).
Regarding claim 8: Gillett further teaches: The method of claim 1, wherein the plurality of goal state attributes comprises at least one of a set of joint positions, a set of joint angles, or a set of root attributes ([0035] FIG. 2A-2B illustrate flow charts of processor processes for estimating first set of joint angular velocities of all joints of autonomous humanoid robot. [0036] FIG. 3 is flow chart of process of sub steps for calibrating joint velocity, motion and position of arms and legs of autonomous humanoid robot).
Regarding claim 9: Gillett further teaches: The method of claim 1, wherein the plurality of goal state attributes is associated with at least one of a kinematic position tracking command space, a joint angle tracking command space, or a root tracking command space ([0189] processor of autonomous humanoid robot tracks object moving within the scene while autonomous humanoid robot itself is moving. Moving object may be SLAM capable (other autonomous humanoid robots or service robots), or SLAM incapable (humans and pets). processor of autonomous humanoid robot generates architectural plans based on SLAM data in addition to map, processor to locate doors and windows and other architectural elements; processor uses SLAM data to add accurate measurement to generated architectural plan, in which portion of this process can execute automatically using software that may receive main dimensions of object and/or architectural icons (rooms, stairs, paths, streets) corresponding to space as input).
Regarding claim 10: Gillett further teaches: The method of claim 1, wherein the articulated object comprises a humanoid robot ([0188] processor of autonomous humanoid robot or user using application of communication device paired with autonomous humanoid robot).
Regarding claim 11: Gillett teaches: At least one processor comprising: processing circuitry to perform operations comprising: ([0009] autonomous humanoid robot provides processor implemented methods and steps for controlling a humanoid autonomous humanoid robot's motion state and entertainment performance)
applying a plurality of masks to a plurality of goal state attributes associated with a plurality of command spaces for an articulated object to produce one or more masked goal state attributes; ([0014] computing system, based on an application establishes a switching sequence to initiate an operating mode function involving; a step mode, a walking mode, a roll/skate mode, a leap/jump mode, a battery charging mode which causes a sleep state. [0168] determining understanding 1066 of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame)
generating, via execution of a machine learning model, one or more actions based at least on the one or more masked goal state attributes and a proprioception associated with the articulated object; and ([0050] using artificial intelligence, neural networks. [0015] autonomous humanoid robot can perform various physical motion states involving at least one of the following acts; a sports activity, a series of dance movements, perform a vehicle-like mobility service, or when operating as a mule or a towing-vehicle, accordingly the autonomous humanoid robot can transport a payload or an object. [0168] determining understanding 1066 of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame)
updating one or more parameters of the machine learning model based at least on the one or more actions and one or more reference actions for the articulated object to produce a trained machine learning model ([0057] The computing system configured to provide instruction and programming for estimating and controlling pivotal movement of body components involving arms, legs and a waist module which are configured to support the body and reposition the body such that the autonomous humanoid robot can step, walk, roll or skate or perform various handling maneuvers to complete task 1060. [0180] adjusts weight given to classification based on collection of past experiences of autonomous humanoid robot 100 and classification based on experiences of respective autonomous humanoid robot 100 itself. processor 1001 may be trained in object 116 classification using reinforcement training).
Regarding claim 12: Gillett further teaches: The at least one processor of claim 11, wherein the operations further comprise: generating, via execution of the trained machine learning model, one or more additional actions based at least on one or more additional goal state attributes associated with the articulated object; and ([0196] As processor makes use of various info, such as optical flow, entropy pattern of pixels as result of motion, feature extractors, RGB, depth info, processor may resolve uncertainty of association between coordinate frame of reference of sensor and frame of reference of environment. processor uses neural network to resolve incoming info into distances or adjudicates possible sets of distances based on probabilities of different possibilities. Concurrently, as neural network processes data at higher level, data is classified into more human understandable info, such as object or obstacle name (human name or object type such as remote), feelings and emotions, gestures, commands, words. However, all info may not be required at once for decision making)
causing the articulated object to perform a motion based at least on the one or more additional actions, wherein the motion is associated with at least one of bimanual manipulation, bipedal locomotion, or navigation ([0089] waist module further securable relative to main body, waist module having joint assembly adapted to pivot with at least two degrees of freedom relative to bending or twisting at center portion of body. [0090] drive assembly secured to hip portion 106a of the body, swivel assembly adapted to cooperate with swivel shafts to pivot leg 105 with at least two degrees of freedom relative to body. [0091] plurality of perception sensors and cameras configured for detecting object surrounding autonomous humanoid robot, sensors and cameras providing object and image data to computing system comprising processors).
Regarding claim 13: Gillett further teaches: The at least one processor of claim 12, wherein the operations further comprise determining the one or more additional goal state attributes based at least on a control input and one or more control modes associated with the articulated object ([0089] the waist module further securable relative to the main body, the waist module having a joint assembly adapted to pivot with at least two degrees of freedom relative to bending or twisting at a center portion of the body. [0090] the drive assembly secured to a hip portion 106a of the body, the swivel assembly adapted to cooperate with the swivel shafts to pivot the leg 105 with at least two degrees of freedom relative to the body. [0091] the plurality of perception sensors and cameras configured for detecting object 116 surrounding the autonomous humanoid robot, the sensors and cameras providing object data and image data to a computing system comprising a plurality of processors).
Regarding claim 14: Gillett further teaches: The at least one processor of claim 11, wherein the operations further comprise: updating one or more additional parameters of a second machine learning model based at least on a set of reference motions and a set of rewards to produce a second trained machine learning model; and generating, via execution of the second trained machine learning model based at least on an additional proprioception and the plurality of goal state attributes, the one or more reference actions ([0180] processor chooses to classify object or obstacle or chooses to wait and keep object unclassified based on consequences defined for wrong classification. processor of autonomous humanoid robot may be more conservative in classifying object when wrong classification results in assigned punishment, such as negative reward. [0050] using artificial intelligence, neural networks. [0163] computing system utilizing one or more control signals 1021 being associated with plurality of training sets 1021(TS) configured to execute a degree axis of rotation of one or more joint mechanisms, servos, actuator; to execute a degree axis of rotation of one or more joint mechanisms, servos, actuators; to accomplish maneuvering actions of a steering controller 1013, a propulsion controller, a brake controller; one or more control signals 1021 being associated with a plurality of training sets configured to execute a steering controller, a propulsion controller, a brake controller all associated with the computing system processors 1014 and microprocessors 1014(MP); one or more control signals 1017 being associated with a plurality of training sets 1021(TS) configured to execute a degree axis of rotation 1022 of one or more joint mechanisms, servos, actuators).
Regarding claim 15: Gillett further teaches: The at least one processor of claim 14, wherein the set of rewards is computed based at least on at least one of a penalty term, a regularization term, or a set of task rewards ([0180] processor chooses to classify object or obstacle or chooses to wait and keep object unclassified based on consequences defined for wrong classification. processor of autonomous humanoid robot may be more conservative in classifying object when wrong classification results in assigned punishment, such as negative reward).
Regarding claim 16: Gillett further teaches: The at least one processor of claim 11, wherein the proprioception comprises at least one of a joint position, a joint velocity, a base angular velocity, a gravity vector, or an action history ([0035] FIG. 2A-2B illustrate flow charts of processor processes for estimating first set of joint angular velocities of all joints of autonomous humanoid robot. [0036] FIG. 3 is flow chart of process of sub steps for calibrating joint velocity, motion and position of arms and legs of autonomous humanoid robot).
Regarding claim 17: Gillett further teaches: The at least one processor of claim 11, wherein the plurality of masks comprises: a first set of masks associated with one or more selected command spaces included in the plurality of command spaces; and a second set of masks applied to a subset of the plurality of goal state attributes that is associated with the one or more selected command spaces ([0088] calculation model provides dynamic equation composed by acceleration sensor and orientation sensor, each are distributed and connected on series of control points and are sequentially summed by calculation model; acceleration sensor, angular velocity sensor, orientation sensor, acceleration sensor and dynamic equations control orientation of head 102 at body's upper body 101(U)). [0014] computing system, based on an application establishes a switching sequence to initiate an operating mode function involving; a step mode, a walking mode, a roll/skate mode, a leap/jump mode, a battery charging mode which causes a sleep state. [0015] autonomous humanoid robot can perform various physical motion states involving at least one of the following acts; a sports activity, a series of dance movements, perform a vehicle-like mobility service, or when operating as a mule or a towing-vehicle, accordingly the autonomous humanoid robot can transport a payload or an object. [0168] determining understanding of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame).
Regarding claim 18: Gillett further teaches: The at least one processor of claim 11, wherein the at least one processor is comprised in at least one of: a system for performing simulation operations; a system for performing digital twin operations; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations; a system implemented using one or more large language models (LLMs); a system implemented using one or more small language models (SLMs); a system implementing one or more vision language models (VLMs); a system implementing one or more multi modal language models; a system for generating synthetic data; a system for performing one or more generative AI operations; a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container); a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources ([0190] processor interested in more than just presence of object. processor of autonomous humanoid robot may be interested in understanding hand gesture, such as instruction to stop or navigate to certain place given by hand gesture such as finger pointing. Or processor may be interested in understanding sign language for purpose of translating to audio in particular language or to another signed language).
Regarding claim 19: Gillett teaches: A system comprising: one or more processors to generate a trained machine learning model by training a machine learning model using a plurality of masked goal state attributes, wherein the plurality of masked goal state attributes are generated by applying one or more masks to a plurality of goal state attributes associated with a plurality of command spaces for an articulated object ([0050] using artificial intelligence, neural networks. [0014] computing system, based on an application establishes a switching sequence to initiate an operating mode function involving; a step mode, a walking mode, a roll/skate mode, a leap/jump mode, a battery charging mode which causes a sleep state. [0015] autonomous humanoid robot can perform various physical motion states involving at least one of the following acts; a sports activity, a series of dance movements, perform a vehicle-like mobility service, or when operating as a mule or a towing-vehicle, accordingly the autonomous humanoid robot can transport a payload or an object. [0168] determining understanding of said physical space domain and actions of user, scheduling appropriate response based on understanding, updating discourse model based on retrieved inputs, communicating understanding to reactive processing; combining selected inputs into frame providing coherent understanding of physical space domain and actions of user, accessing static knowledge base about physical space domain with reference to selected inputs; accessing dynamic knowledge base and discourse model to infer understanding of current discourse between user and operating environment; accessing discourse model to infer understanding of current discourse; combining information from static knowledge base, dynamic knowledge base, and discourse model to produce frame. [0057] computing system configured to provide instruction and programming for estimating and controlling pivotal movement of body components involving arms, legs and a waist module which are configured to support the body and reposition the body such that the autonomous humanoid robot can step, walk, roll or skate or perform various handling maneuvers to complete task 1060. [0180] adjusts weight given to classification based on collection of past experiences of autonomous humanoid robot 100 and classification based on experiences of respective autonomous humanoid robot 100 itself. processor 1001 may be trained in object 116 classification using reinforcement training).
Regarding claim 20: Gillett further teaches: The system of claim 19, wherein the system is comprised in at least one of: a system for performing simulation operations; a system for performing digital twin operations; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations; a system implemented using one or more large language models (LLMs); a system implemented using one or more small language models (SLMs); a system implementing one or more vision language models (VLMs); a system implementing one or more multi modal language models; a system for generating synthetic data; a system for performing one or more generative AI operations; a system incorporating one or more virtual machines (VMs); a system using or deploying one or more inference microservices; a system that incorporates one or more machine learning models deployed in a service or microservice along with an OS-level virtualization package (e.g., a container); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources ([0190] processor interested in more than just presence of object. processor of autonomous humanoid robot may be interested in understanding hand gesture, such as instruction to stop or navigate to certain place given by hand gesture such as finger pointing. Or processor may be interested in understanding sign language for purpose of translating to audio in particular language or to another signed language).
Conclusion
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/MADISON B EMMETT/Examiner, Art Unit 3658
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