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 .
DETAILED ACTION
This is a non-final Office Action on the merits in response to communications filed by Applicant on March 14, 2025. Claims 1-20 are currently pending and examined below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on is/are being considered by the examiner.
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 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.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and/or 102(a)(2) as being anticipated by Gillett US2024/0181637 (“Gillett”).
Regarding claim(s) 1, 11, 19. Gillett discloses a method comprising:
determining a plurality of masks associated with one or more control modes for an articulated object ([0014] The 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.);
applying the plurality of masks to a plurality of goal state attributes for the articulated object to produce one or more masked goal state attributes ([0015] Accordingly by combinations thereof, the 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.);
generating, via execution of a trained machine learning model, one or more actions based at least on the one or more masked goal state attributes; and performing a task using the articulated object based at least on the one or more actions ([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.).
Regarding claim(s) 2, 12. Gillett discloses generating, via execution of a machine learning model, one or more additional actions based at least on one or more additional goal state attributes; and updating one or more parameters of the machine learning model based at least on the one or more additional actions and one or more reference actions to produce the trained machine learning model ([0196] As the processor 1001 makes use of various information, such as optical flow, entropy pattern of pixels as a result of motion, feature extractors, RGB, depth information, etc., the processor 1001 may resolve the uncertainty of association between the coordinate frame of reference of the sensor 119 and the frame of reference of the environment. In some embodiments, the processor 1001 uses a neural network to resolve the incoming information into distances or adjudicates possible sets of distances based on probabilities of the different possibilities. Concurrently, as the neural network processes data at a higher level, data is classified into more human understandable information, such as an object 116 or an obstacle name (e.g., human name or object 116 type such as remote), feelings and emotions, gestures, commands, words, etc. However, all the information may not be required at once for decision making.).
Regarding claim(s) 3,13 . Gillett discloses 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 ([0163] The computing system utilizing one or more control signals 1021 being associated with a 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(s) 4, 16. Gillett discloses determining the plurality of masks based at least on a selection of the one or more control modes; and determining the plurality of goal state attributes based at least on a control input received via a control interface ([0088] a calculation model provides a dynamic equation composed by an acceleration sensor and the orientation sensor, each are distributed and connected on a series of control points and are sequentially summed by the calculation model; the acceleration sensor, angular velocity sensor, orientation sensor, an acceleration sensor and dynamic equations control the orientation of the head 102 at the body's upper body 101(U).).
Regarding claim(s) 5. Gillett discloses wherein the plurality of masks is used to filter a subset of the plurality of goal state attributes that is not associated with the one or more control modes ([0053] The computing system, based on an application establishes a switching sequence to initiate a series of operating mode functions 1050 which allows the autonomous humanoid robot to perform various physical motion states, such that the autonomous humanoid robot can transport a payload or an object 116, and achieve one or more of the following acts to perform a vehicle-like mobility service to carry a payload 901, exampled in FIG. 9A; a ride-on vehicle 902, exampled in FIG. 9B; to perform a delivery service 903, exampled in FIG. 9C; and to perform a mule or a towing-vehicle 904, exampled in FIG. 9D.).
Regarding claim(s) 6. Gillett discloses wherein the one or more control modes comprise a first control mode for a first portion of the articulated object and a second control mode for a second portion of the articulated object ([0089] In various elements, 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] In various elements, 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] In various elements, 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(s) 7. Gillett discloses wherein the task comprises at least one of bimanual manipulation, bipedal locomotion, or navigation ([0089] In various elements, 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] In various elements, 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] In various elements, 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(s) 8, 17. Gillett discloses 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 the processor processes for estimating a first set of joint angular velocities of all joints of the autonomous humanoid robot according to an exemplary embodiment of this disclosure.[0036] FIG. 3 is a flow chart of a process of sub steps for calibrating joint velocity, motion and position of the arms and legs of the autonomous humanoid robot according to an exemplary embodiment of this disclosure.).
Regarding claim(s) 9, 15. Gillett discloses 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] In some embodiments, the processor 1001 of the autonomous humanoid robot 100 tracks object 116 that are moving within the scene while the autonomous humanoid robot 100 itself is moving. Moving object 116 may be SLAM capable (e.g., other autonomous humanoid robots 100 or service robots and the like), or SLAM incapable (e.g., humans and pets). the processor of the autonomous humanoid robot generates architectural plans based on SLAM data, for instance, in addition to the map the processor to locate doors and windows and other architectural elements; the processor uses the SLAM data to add accurate measurement to a generated architectural plan, in which a portion of this process can execute automatically using, for example, a software that may receive main dimensions of object 116 and/or architectural icons (e.g., rooms, stairs, paths, streets, etc.) corresponding to the space as input.).
Regarding claim(s) 10. Gillett discloses wherein the articulated object comprises a humanoid robot ([0188] In some embodiments, dynamic obstacles, such as people or pets, or obstacles may be added to the map by the processor 1001 of the autonomous humanoid robot 100 or a user using the application of the communication device paired with the autonomous humanoid robot 100.).
Regarding claim(s) 14. Gillett discloses 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] In some embodiments, the processor 1001 chooses to classify an object 116 or an obstacle or chooses to wait and keep the object 116 unclassified based on the consequences defined for a wrong classification. For instance, the processor 1001 of the autonomous humanoid robot 100 may be more conservative in classifying object 116 when a wrong classification results in an assigned punishment, such as a negative reward).
Regarding claim(s) 18, 20. Gillett discloses 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] In some embodiments, the processor 1001 may be interested in more than just the presence of the object 116. For example, the processor 1001 of the autonomous humanoid robot 100 may be interested in understanding a hand gesture, such as an instruction to stop or navigate to a certain place given by a hand gesture such as finger pointing. Or the processor 1001 may be interested in understanding sign language for the purpose of translating to audio in a particular language or to another signed language.).
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRUC M DO whose telephone number is (571)270-5962. The examiner can normally be reached on 9AM-6PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramón Mercado, Ph.D. can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRUC M DO/Primary Examiner, Art Unit 3658
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