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
2. This Office Action is responsive to Applicant’s Remarks/Amendment received on 7/14/2025, in which, claims 1-24 are pending. Claims 1, 5, 7-10, 15-18, 24 are amended. Claims 1, 18, 24 are independent claims. Claims 1-24 are rejected.
Summary of claims
3. Claims 1-24 are pending,
Claims 1, 5, 7-10, 15-18, 24 are amended,
Claims 1, 18, 24 are independent claims,
Claims 1-24 are rejected.
Remarks
4. Applicant’s arguments, see Remarks, filed on 7/14/2025, since Applicant amended claims 18-24, 35 USC 101 rejection on claims 18-24 is withdrawn; with respect to the rejection(s) of claim(s) 1-24 under 103 have been fully considered and are persuasive in view of new rejection ground(s).
Claim Rejections - 35 USC § 103
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claims 1-3, 5-10, 16, 18, 24 are rejected under 35 U.S.C. 103 as being unpatentable over Kristina Choo et al (US Publication 20180253088 A1, hereinafter Choo), and in view of Gaurav Ghare et al (US Publication 20200156243 A1, hereinafter Ghare), and Harold Artes et al (US Publication 20210001480 A1, hereinafter Artes).
As for independent claim 1, Choo discloses: A safety system, comprising: a robot comprising, a function module, configured to perform a robot function (Choo: Abstract, In an approach to non-functional requirement stimulus testing of a robot, one or more computer processors receive one or more stimulus parameters to test. The one or more computer processors trigger the one or more stimulus parameters in the robot. The one or more computer processors determine at least one response time to the one or more stimulus parameters); and a safety module, configured to communicate with the robot, the safety module comprising: a stimulus-response tester (Choo: [0011], Distributed data processing environment 100 includes non-functional requirements (NFR) stimulus tester 104 and robot 120 interconnected over network 102), configured to: send a first stimulus of a stimulus-response pair, comprising the first stimulus and an expected response to the first stimulus, to the robot for processing by the function module (Choo: [0013], controller program 106 identifies a list of agents associated with one or more physical robotic components, tests stimulus response parameters to external stimuli by executing commands, via the agents, using the physical robotic components, compares results to required criteria, tolerances, and acceptable threshold values for each of the defined stimuli, and provides a report to the user), wherein the function module is configured to process the first stimulus, without controlling an actuator to act upon the first stimulus (Choo: Abstract, In an approach to non-functional requirement stimulus testing of a robot, one or more computer processors receive one or more stimulus parameters to test)… and receive from the function module a first response representing the state-action response to the first stimulus (Choo: [0029], When robot 120 responds to the transmitted stimulus parameters, controller program 106 determines the type of response as well as any metadata included with the response. Required responses to various stimuli are pre-defined by the user, via user interface 108, and stored in database 118); wherein if a difference between the first response and the expected response is within a predetermined range (Choo: [0014], User interface 108 enables the user to define standards-based stimulus response parameters for physical components of robot 120. For example, stimulus response parameters can include, but are not limited to, action types such as sequential, parallel, singular, complex, and varying speeds, i.e., low, medium, and high. In another example, stimulus response parameters can include, but are not limited to, audio responses, which may vary in loudness or frequency. Additionally, each response parameter is associated with an acceptable response time or acceptable range of response times. User interface 108 may also enable the user to define criteria, tolerances, and acceptable threshold values for each of the defined stimulus response parameters to be tested during non-functional requirements testing. For example, a user can define a tolerance for response time for a particular physical component as moving within 0-20 milliseconds after a stimulus is triggered. In another example, a user can define a tolerance for response time of an audio response as “speaking” within 0-20 milliseconds after a stimulus is triggered), the safety module is configured to operate according to a first operational mode; and if the difference between the response and the expected response is outside of the predetermined range, the safety module is configured to operate according to a second operational mode (Choo: [0030], Controller program 106 compares the results tracked by agent(s) 124 to the criteria, tolerances, and acceptable thresholds defined by the user for each stimulus parameter and stored in database 118 (the expected response) to determine whether the response of robot 120 to the transmitted stimulus parameters meets the non-functional test requirements. For example, controller program 106 may compare the response time of a response by robot 120 to a visual stimulus to a pre-defined response time criteria. In another example, controller program 106 may compare the recorded angular velocity, final angle and final position of component 122.sub.1 to the pre-defined values for those characteristics in response to an aural stimulus, such as a command to “Wave to the audience.” In a further example, controller program 106 may compare a variation in slack time of two simultaneous, parallel actions, such as speaking a verbose response and moving a component; [0031], If controller program 106 determines that the results do not meet a requirement (“no” branch, decision block 214), then controller program 106 marks the results as a fail (step 216); [0032], If controller program 106 determines that the results meet a requirement (“yes” branch, decision block 214), or responsive to marking the results as a fail, controller program 106 generates test results (step 218); please note controller program compares the tracked response to the expected response, if the results are within the pre-defined range, then operates in “yes” mode, if the results are outside the pre-defined range, then operates in “no” mode).
Further, Choo does not clearly disclose a state-action response, in an analogous art of robotic device simulation testing system, Ghare discloses: wherein the first stimulus comprises sequence metadata associated with a state-action response (Ghare: [0087], the simulation application container 408 may utilize a value function to select, from a set of pairings of states and actions, a pairing comprising an initial state and a corresponding action that is performable in response to the state. This may be used as input to the simulation application to cause the simulation application to perform the action);
Choo and Ghare are analogous arts because they are in the same field of endeavor, robotic device simulation testing system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Ghare to include utilizing a set of pairings of states and actions in the simulation. It would provide Choo’s robot safety system with enhanced capabilities of using the reinforcement function to determine the corresponding reward value for the state and the corresponding action pair.
Furthermore, Choo discloses comparing the tracked response to the expected response in order to determine if the robot meets the requirement, but Choo does not disclose the safety module operates according to different modes, in an analogous art of controlling robot, Artes discloses: the safety module is configured to operate according to a first operational mode; and if the difference between the response and the expected response is outside of the predetermined range, the safety module is configured to operate according to a second operational mode (Artes: [0019], If the behavior of the robot in a detected dangerous situation is assessed to be wrong, dangerous or inadequate, the safety module 150 can initiate counter measures (safety measures). Counter measures may consist, for example, in stopping the robot 100 or in altering the robot's 100 direction of movement. Here advantage is taken of the fact that, as a rule, it is easier to determine what movement may not be carried out, because it is unsafe, than it is to determine, what movement is the correct one; please note the safety module may determine if the behavior of the robot is wrong, dangerous or inadequate, and operate accordingly);
Choo and Artes are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Artes to include the safety module operates in different modes according to if the behavior of the robot is wrong, dangerous or inadequate. It would provide Choo’s robot safety system with enhanced capabilities of making correction to robot operations in order to assure safety.
As for claim 2, Choo-Ghare-Artes discloses: wherein sending the stimulus to the robot comprises sending an instruction comprising one or more instruction bits representing the stimulus, and one or more stimulus identification bits, the stimulus identification bits indicating that the instruction bits are a stimulus for stimulus-response testing (Choo: [0012], NFR stimulus tester 104 can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data; [0013], controller program 106 identifies a list of agents associated with one or more physical robotic components, tests stimulus response parameters to external stimuli by executing commands, via the agents, using the physical robotic components, compares results to required criteria, tolerances, and acceptable threshold values for each of the defined stimuli, and provides a report to the user).
As for claim 3, Choo-Ghare-Artes discloses: wherein the robot is configured to recognize the one or more stimulus identification bits, and in response to the one or more stimulus identification bits, disable one or more actuators such that the stimulus is not physically performed (Choo: [0013], controller program 106 identifies a list of agents associated with one or more physical robotic components, tests stimulus response parameters to external stimuli by executing commands, via the agents, using the physical robotic components).
As for claim 5, Choo-Ghare-Artes discloses: wherein the safety module further comprises an anomaly detector, comprising: an anomaly detector processor, configured to receive an anomaly detector input, representing an output of the function module, and to detect an anomaly in the anomaly detector input; wherein if the anomaly detector detects no anomaly, the safety module is configured to operate according to the first operational mode; and if the anomaly detector detects an anomaly, the safety module is configured to operate according to the second operational mode (Artes: [0040], If one or more faulty sensor signals are detected, the robot can be stopped and completely shut down until the user allows it to be further operated as).
As for claim 6, Choo-Ghare-Artes discloses: wherein the function module is a first function module, and wherein the robot further comprises a second function module; and wherein the anomaly detector processor is configured to receive anomaly detector input, representing an output of the first function module and the second function module, and to detect an anomaly in the anomaly detector input; wherein if the anomaly detector detects no anomaly, the safety module is configured to operate according to the first operational mode; and if the anomaly detector detects an anomaly, the safety module is configured to operate according to the second operational mode (Artes: [0040], If one or more faulty sensor signals are detected, the robot can be stopped and completely shut down until the user allows it to be further operated as).
As for claim 7, Choo-Ghare-Artes discloses: wherein the robot is a first robot and the function module of the first robot is a first function module, and wherein the safety system further comprises a second robot; wherein the second robot comprises a second function module; and wherein the anomaly detector processor is configured to receive an anomaly detector input, representing an output of the first function module and an output of the second function module, and to detect an anomaly in the anomaly detector input; wherein if the anomaly detector detects no anomaly, the safety module is configured to operate according to the first operational mode; and if the anomaly detector detects an anomaly, the safety module is configured to operate according to the second operational mode (Artes: [0040], If one or more faulty sensor signals are detected, the robot can be stopped and completely shut down until the user allows it to be further operated as).
As for claim 8, Choo-Ghare-Artes discloses: wherein the output of the function module comprises one or more control outputs of the function module, wherein the one or more control outputs of the function module comprise at least one of a processing delay of the robot, a temperature of a component of the robot, an image sensor output of the robot, an image processing output of the robot, a distance measured using a proximity sensor, a light intensity using a light sensor, a volume measured using a microphone, or a velocity or acceleration measured using a sensor of the robot (Choo: [0020], Robot 120 may also include a plurality of sensors, cameras, microphones, speakers, etc. that can receive and react to commands and sensory stimuli).
As for claim 9, Choo-Ghare-Artes discloses: wherein the output of the function module comprises one or more navigation outputs of the function module, wherein the one or more navigation outputs of the function module comprise at least one of a torque of an actuator of the robot, a velocity of the robot, an acceleration of the robot, an angle of movement of the robot compared to a reference point, or a position of the robot (Choo: [0029], controller program 106 may determine metadata such as the clarity of the audio response or the angular velocity, final angle and final position of the moving component; Artes: [0024], current sensors for determining the condition of an actuator, in particular that of a motor, wheel contact sensors for determining whether the robot is in firm contact with the floor surface, position sensors for determining an inclination of the robot 100 or odometers such as, for example, sensors that measure wheel rotation (wheel encoders), as well as inertial sensors such as, for example, acceleration sensors and rotation rate sensors (combined, for example, in an inertial measurement unit (IMU) for detecting the movement of the robot).
As for claim 10, Choo-Ghare-Artes discloses: wherein the safety system further comprises a server, configured to receive data from, and to send data to, the safety module; wherein the server comprises a stimulus-response library, the stimulus-response library comprising a plurality of stimulus-response pairs for the stimulus-response tester; wherein the server is configured to select one or more of the stimulus-response pairs for testing by the stimulus-response tester; and wherein the server is configured to send the selected one or more of the stimulus-response pairs to the safety module (Choo: [0012], NFR stimulus tester 104 can represent a server computing system utilizing multiple computers as a server system, such as in a cloud computing environment; Artes: [0020], by an external computer or server 300 that communicates with the robot via a data link).
As for claim 16, Choo-Artes discloses: wherein the robot is a first robot; further comprising a second robot; and wherein the first robot is configured to transmit a message to the second robot; wherein the message represents anomalous data detected by the first robot; and wherein a transmission of the message is a broadcast of the message (Choo: [0032], controller program 106 may transmit the results as an email or text message to the user).
As for independent claim 18, Choo discloses: A safety system, comprising: a data augmentation module comprising a processor and a memory containing instructions configured to cause the processor to: receive operational data from one or more sources, the operational data representing operations of a robot and comprising sensor data from one or more sensors of the robot (Choo: Abstract, In an approach to non-functional requirement stimulus testing of a robot, one or more computer processors receive one or more stimulus parameters to test. The one or more computer processors trigger the one or more stimulus parameters in the robot. The one or more computer processors determine at least one response time to the one or more stimulus parameters); and augment the sensor data according to one or more data augmentation techniques (Choo: [0009], A CR is built with software components integrated with kinematic gesture components, resulting in a robot that responds and moves by perceiving signals through sensors, audio interfaces, visual interfaces, etc. For a CR to be considered a cognitive companion, serving as a supplement for performing many human functions, the CR is expected to deliver at least the same level of accuracy, quality, and throughput as may be delivered by a human; Embodiments of the present invention recognize that non-functional requirements stimulus testing of robots can be accelerated and improved by providing a test framework that enables defining an extensible, standards-based interface for testing reaction time of physical components of a robot to various sensory stimuli as the cognitive interface matures. Implementation of embodiments of the invention may take a variety of forms)…; and a virtual sensor, configured to determine a safety factor for the robot, based on at least the augmented data (Choo: [0015], Tactile simulator engine 110 provides inputs to a sensory skin component of robot 120. Inputs may include a range of touch pressure types of tactile stimuli); wherein if the safety factor is within a predetermined range, the safety system is configured to operate according to a first operational mode; and if the safety factor is outside of the predetermined range, the safety system is configured to operate according to a second operational mode (Choo: [0030], Controller program 106 compares the results tracked by agent(s) 124 to the criteria, tolerances, and acceptable thresholds defined by the user for each stimulus parameter and stored in database 118 (the expected response) to determine whether the response of robot 120 to the transmitted stimulus parameters meets the non-functional test requirements. For example, controller program 106 may compare the response time of a response by robot 120 to a visual stimulus to a pre-defined response time criteria. In another example, controller program 106 may compare the recorded angular velocity, final angle and final position of component 122.sub.1 to the pre-defined values for those characteristics in response to an aural stimulus, such as a command to “Wave to the audience.” In a further example, controller program 106 may compare a variation in slack time of two simultaneous, parallel actions, such as speaking a verbose response and moving a component; [0031], If controller program 106 determines that the results do not meet a requirement (“no” branch, decision block 214), then controller program 106 marks the results as a fail (step 216); [0032], If controller program 106 determines that the results meet a requirement (“yes” branch, decision block 214), or responsive to marking the results as a fail, controller program 106 generates test results (step 218); please note controller program compares the tracked response to the expected response, if the results are within the pre-defined range, then operates in “yes” mode, if the results are outside the pre-defined range, then operates in “no” mode); wherein operating according to the second operational mode comprises…without controlling an actuator to act upon the augmented stimuli (Choo: Abstract, In an approach to non-functional requirement stimulus testing of a robot, one or more computer processors receive one or more stimulus parameters to test).
Further, Choo does not clearly disclose a state-action response, in an analogous art of robotic device simulation testing system, Ghare discloses: wherein the data augmentation techniques comprise augmenting stimuli with sequence metadata associated with a state-action response (Ghare: [0087], the simulation application container 408 may utilize a value function to select, from a set of pairings of states and actions, a pairing comprising an initial state and a corresponding action that is performable in response to the state. This may be used as input to the simulation application to cause the simulation application to perform the action);
Choo and Ghare are analogous arts because they are in the same field of endeavor, robotic device simulation testing system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Ghare to include utilizing a set of pairings of states and actions in the simulation. It would provide Choo’s robot safety system with enhanced capabilities of using the reinforcement function to determine the corresponding reward value for the state and the corresponding action pair.
Furthermore, Choo discloses comparing the tracked response to the expected response in order to determine if the robot meets the requirement, but Choo does not disclose the safety module operates according to different modes, in an analogous art of controlling robot, Artes discloses: wherein if the safety factor is within a predetermined range, the safety system is configured to operate according to a first operational mode; and if the safety factor is outside of the predetermined range, the safety system is configured to operate according to a second operational mode; wherein operating according to the second operational mode comprises determining a corrective action for the robot and sending a signal representing an instruction to perform the corrective action to the robot (Artes: [0019], If the behavior of the robot in a detected dangerous situation is assessed to be wrong, dangerous or inadequate, the safety module 150 can initiate counter measures (safety measures). Counter measures may consist, for example, in stopping the robot 100 or in altering the robot's 100 direction of movement. Here advantage is taken of the fact that, as a rule, it is easier to determine what movement may not be carried out, because it is unsafe, than it is to determine, what movement is the correct one; please note the safety module may determine if the behavior of the robot is wrong, dangerous or inadequate, and operate accordingly);
Choo and Artes are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Artes to include the safety module operates in different modes according to if the behavior of the robot is wrong, dangerous or inadequate. It would provide Choo’s robot safety system with enhanced capabilities of making correction to robot operations in order to assure safety.
As per claim 24, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
6. Claims 4, 11, 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Choo, Ghare and Artes as applied on claims 1 and 18, and further in view of Barr Rosenberg (US Patent 11351680 B1, hereinafter Rosenberg).
As for claim 4, Choo-Ghare-Artes does not clearly disclose a testing schedule, Rosenberg discloses: wherein the stimulus-response tester is further configured to send a stimulus to the robot according to a stimulus-response testing schedule; wherein the stimulus-response testing schedule represents predicted periods of inactivity of the function module (Rosenberg: Column 94, Lines 5-6, The user may also be permitted to set parameters for monitoring fault such as schedules, conditions and the like; Column 96, Lines 7-10, Users may be allowed to review the fault history of the robot or set schedules for performing exercises, including self-evaluation exercises, pre-determined exercises or randomized exercises).
Choo and Ghare and Artes and Rosenberg are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Rosenberg to include the safety module schedule. It would provide Choo’s robot safety system with enhanced capabilities of allowing user to schedule operations so users may test robot with more flexibility.
As for claim 11, Choo-Ghare-Artes does not disclose sending an activity log, Rosenberg discloses: wherein the robot is configured to send an activity log to the safety module, the activity log representing past activities of the function module; wherein safety module is configured to send activity information representing the one or more activity logs to the server (Rosenberg: Column 96, Lines 7-10, Users may be allowed to review the fault history of the robot or set schedules for performing exercises, including self-evaluation exercises, pre-determined exercises or randomized exercises); and wherein the server comprises a predictive scheduler, the predictive schedule being configured to generate the stimulus-response testing schedule, wherein the stimulus-response testing schedule represents predicted periods of inactivity of the function module based on the activity information (Rosenberg: Column 94, Lines 5-6, The user may also be permitted to set parameters for monitoring fault such as schedules, conditions and the like; Column 96, Lines 7-10, Users may be allowed to review the fault history of the robot or set schedules for performing exercises, including self-evaluation exercises, pre-determined exercises or randomized exercises).
As for claim 19, Choo-Ghare-Artes does not disclose receiving an activity log, Rosenberg discloses: wherein the data augmentation module is further configured to receive operational log data, representing actions of one or more robots; and wherein the data augmentation module is further configured to augment the operational log data; wherein the operational data comprises the augmented operational log data (Rosenberg: Column 96, Lines 7-10, Users may be allowed to review the fault history of the robot or set schedules for performing exercises, including self-evaluation exercises, pre-determined exercises or randomized exercises).
Choo and Artes and Rosenberg are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Rosenberg to include the history log.
As for claim 20, Choo-Ghare-Artes-Rosenberg discloses: further comprising a data tuner, wherein the data tuner is configured to execute one or more recurrent learning procedures using: the signal representing the instruction to the robot to perform the corrective action; and data representing one or more outputs of the robot (Artes: [0018], Robots 100 are known whose control software employs non-deterministic Monte Carlo Methods or methods of machine learning (e.g. Deep Machine Learning); [0019], If the behavior of the robot in a detected dangerous situation is assessed to be wrong, dangerous or inadequate, the safety module 150 can initiate counter measures (safety measures). Counter measures may consist, for example, in stopping the robot 100 or in altering the robot's 100 direction of movement. Here advantage is taken of the fact that, as a rule, it is easier to determine what movement may not be carried out, because it is unsafe, than it is to determine, what movement is the correct one).
As for claim 21, Choo-Ghare-Artes-Rosenberg discloses: wherein the instruction to perform the corrective action is an instruction at a first time period, and wherein the data representing one or more outputs of the robot is from a second time period, after the first time period, wherein the virtual sensor is configured to determine based on the data of the first time period and the second time period whether the instruction resulted in an increased safety factor (Artes: [0030], The information may be directly processed upon reception in the safety module 150 and/or it may be stored there for a specifiable time period or a specifiable distance (the distance travelled by the robot 100) before being processed; Rosenberg: Column 96, Lines 7-10, Users may be allowed to review the fault history of the robot or set schedules for performing exercises, including self-evaluation exercises, pre-determined exercises or randomized exercises).
7. Claims 12-15, 22, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Choo and Ghare and Artes and Rosenberg as applied on claims 11 and 21, and further in view of Charles Howard Cella et al (US Publication 20220366494 A1, hereinafter Cella).
As for claim 12, Choo-Ghare-Artes-Rosenberg does not discloses a federated learning operation, Cella discloses: wherein the robot is a first robot and the function module of the first robot is a first function module, and wherein the safety system further comprises a second robot; wherein the second robot comprises a second function module; and wherein the server is configured to receive data representing a data output of the first function module and an output of the second function module, and wherein the sever is configured to perform a federated learning operation using the data representing the data output of the first function module and the output of the second function module (Cella: [0036], leverages machine learning and artificial intelligence trained to recognize the stage of a marketplace. In embodiments, a machine learning system trains a set of machine-learned models to output a determination related to the stage of a marketplace using training data comprising marketplace features and outcomes. In embodiments, an artificial intelligence system receives a request for a determination related to the stage of the marketplace and generates the determination related to the stage of the marketplace based on the set of machine-learned models and the request. In embodiments, the determination related to the stage of the marketplace is leveraged, at least in part, to automatically adjust parameters of the marketplace. In embodiments, the set of machine-learned models employ a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing; [0923], the distributed ledgers are federated distributed ledgers, as the distributed ledgers may be stored on pre-selected or pre-approved nodes that are associated with the parties to a management of digital knowledge 16804 via the knowledge distribution system; [0949], the ledger network 16970 is a federated network, such that the ledger management system 16910 of the knowledge distribution system 16802 may act as an arbiter to simplify the consensus mechanism).
Choo and Artes and Rosenberg and Cella are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Cella to include federated network. It would provide Choo’s robot safety system with enhanced capabilities of machine learning.
As for claim 13, Choo-Ghare-Artes-Rosenberg-Cella discloses: wherein the safety module is a first safety module; wherein the safety system further comprises a second safety module; and wherein the server is configured to receive data representing a data output of the first safety module and an output of the second safety module, and wherein the sever is configured to perform a federated learning operation using the data representing the data output of the first safety module and the output of the second safety module (Cella: [0036], leverages machine learning and artificial intelligence trained to recognize the stage of a marketplace. In embodiments, a machine learning system trains a set of machine-learned models to output a determination related to the stage of a marketplace using training data comprising marketplace features and outcomes. In embodiments, an artificial intelligence system receives a request for a determination related to the stage of the marketplace and generates the determination related to the stage of the marketplace based on the set of machine-learned models and the request. In embodiments, the determination related to the stage of the marketplace is leveraged, at least in part, to automatically adjust parameters of the marketplace. In embodiments, the set of machine-learned models employ a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LTSM) neural network, a self-organizing neural network, and hybrids and combinations of the foregoing; [0923], the distributed ledgers are federated distributed ledgers, as the distributed ledgers may be stored on pre-selected or pre-approved nodes that are associated with the parties to a management of digital knowledge 16804 via the knowledge distribution system; [0949], the ledger network 16970 is a federated network, such that the ledger management system 16910 of the knowledge distribution system 16802 may act as an arbiter to simplify the consensus mechanism).
As for claim 14, Choo-Ghare-Artes-Rosenberg-Cella discloses: wherein, operating according to the second operational mode further comprises sending the stimulus and/or the response to the server; wherein the server further comprises an artificial neural network, configured to perform a machine learning operation using the stimulus and/or the response; or wherein, operating according to the second operational mode further comprises the server generating a virtual stimulus that is sent to one or more robots; receiving a response to the virtual stimulus, and generating a confidence score based on the response (Cella: [1988], the given ranking is based on confidence intervals of the performance of a set of related or comparable assets and/or companies).
As for claim 15, Choo-Ghare-Artes-Rosenberg-Cella discloses: wherein at least one of the response representing the first stimulus received from the function module; the stimulus sent by the stimulus-response tester to the robot for processing by the function module; the anomaly detector input received by the anomaly detector processor from the function module (Artes: [0040], If one or more faulty sensor signals are detected, the robot can be stopped and completely shut down until the user allows it to be further operated as); the one or more of the stimulus-response pairs sent from the server to the safety module (Choo: [0012], NFR stimulus tester 104 can be a standalone computing device, a management server, a web server, a mobile computing device, or any other electronic device or computing system capable of receiving, sending, and processing data; [0013], controller program 106 identifies a list of agents associated with one or more physical robotic components, tests stimulus response parameters to external stimuli by executing commands, via the agents, using the physical robotic components, compares results to required criteria, tolerances, and acceptable threshold values for each of the defined stimuli, and provides a report to the user); or the activity log sent from the robot to the safety module are encoded as part of a distributed public ledger.
As for claim 22, Choo-Ghare-Artes-Rosenberg does not discloses a reward function, Cella discloses: wherein the executing the one or more recurrent learning procedures (Cella: [0036], a recurrent neural network) comprises executing a reward function (Cella: [0033], a reward based on the outcomes of the use of the artificial intelligence system. In embodiments, the benefit is a reward based on the productivity of the artificial intelligence system. In embodiments, the benefit is a reward based on a measure of the expertise of the artificial intelligence system).
Choo and Ghare and Artes and Rosenberg and Cella are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Cella to include recurrent neural network and reward function. It would provide Choo’s robot safety system with enhanced capabilities of machine learning.
As for claim 23, Choo-Ghare-Artes-Rosenberg-Cella discloses: wherein the data tuner is further configured to determine a subset of sensor data from the robot and wherein the data tuner executing the one or more recurrent learning procedures comprises executing the one or more recurrent learning procedures based on the subset of data (Cella: [0036], a recurrent neural network).
8. Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Choo and Ghare and Artes as applied on claim 1, and further in view of Charles Howard Cella et al (US Publication 20220366494 A1, hereinafter Cella).
As for claim 17, Choo-Ghare-Artes discloses: comprising a safety learning module, wherein the safety learning module (Choo: [0004], machine learning models; Artes: [0018], Robots 100 are known whose control software employs non-deterministic Monte Carlo Methods or methods of machine learning (e.g. Deep Machine Learning))) is configured to receive at least one of safety data representing sensor data of one or more robots, data information from a server, or information from a tuning module, and based on the safety data, generate and send a corrective action for implementation in one or more robots (Artes: [0040], If one or more faulty sensor signals are detected, the robot can be stopped and completely shut down until the user allows it to be further operated as);
Choo-Ghare-Artes does not clearly disclose using reinforcement learning, Cella discloses: wherein the safety learning module is configured to generate the corrective action using reinforcement learning (Cella: [0493], training can also be done based on feedback received by the system, which is also referred to as “reinforcement learning.” [0510], The machine learning model 13702 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning).
Choo and Ghare and Artes and Cella are analogous arts because they are in the same field of endeavor, controlling robots based on the monitored robot behavior. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Choo using the teachings of Cella to include using reinforcement learning. It would provide Choo’s robot safety system with enhanced capabilities of machine learning.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
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