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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatented over WO 2021108680 A1 to Cella et al. (herein after “Cella”) in view of WO 2020007016 A1 to Ding et al. (herein after “Ding”).
Regarding claim 1, Cella teaches A computer-implemented method for collaborative machine capability enhancement (See Cella title INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS), the computer-implemented method comprising:
determining, by an automated mobile machine, whether the automated mobile machine is capable of performing an activity based on analysis of information corresponding to the activity and capabilities of the automated mobile machine; (see Cella para[0578] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range)
responsive to the automated mobile machine determining that the automated mobile machine is incapable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine, performing, by the automated mobile machine, a digital twin simulation associated with performing the activity using the information corresponding to the activity and the capabilities of the automated mobile machine; (See Cella para[0281] In embodiments, the design specification is determined using a digital twin simulation system.)
performing, by the automated mobile machine, an analysis of a result of the digital twin simulation associated with performing the activity (See Cella para [1622] changing at least one of the sensor inputs analyzed and a frequency of the sampling. In implementations, the selection operation can further comprise identifying a level of activity of a target associated with the target signal to be sensed and, based on the identified level of activity, changing at least one of the sensor inputs analyzed and a frequency of the sampling.);
However, Cella does not expressly disclose or otherwise teach determining, by the automated mobile machine, a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation. Nevertheless, Ding same field of endeavor teaches determining, by the automated mobile machine, a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 2, Cella and Ding remain applied as claim 1. Cella teaches sending, by the automated mobile machine, a message to nearby automated mobile machines within range of a utilized wireless communication protocol requesting collaborative performance of the activity with the automated mobile machine (See Cella para[1536] A result of the analysis may be communicated wirelessly to one or more wearable haptic feedback stimulators 11404 worn by a user associated with the industrial environment; para[1392] Additionally or alternatively, a sensor may communicate wirelessly, through a wired connection) ; and
determining, by the automated mobile machine, a collaborative pattern of automated mobile machines comprised of the automated mobile machine (see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
However, Cella does not expressly disclose or otherwise teach the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines. Nevertheless, Ding same field of endeavor teaches the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 3, Cella and Ding remain applied as claim 1. Cella teaches verifying, by the automated mobile machine, that merged dimensions of the collaborative pattern (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment ) of automated mobile machines can travel along a determined navigation path within a warehouse environment based on specifications of the warehouse environment. (See Cella para[0016] and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.)
Regarding claim 4, Cella and Ding remain applied as claim 1. Cella teaches performing, by the automated mobile machine, the activity using the collaborative pattern of automated mobile machines (see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
in the collaborative pattern of automated mobile machines is engaged (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment )
However, Cella does not expressly disclose or otherwise teach verifying, by the automated mobile machine, that each of the number of additional automated mobile machines, utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines. Nevertheless, Ding same field of endeavor teaches verifying, by the automated mobile machine, that each of the number of additional automated mobile machines (See Ding objects (work in progress, auxiliary tools, etc.) in the workshop, so that each physical entity can be identified, tracked, Communicable and interactive; digital twin technology builds the closed-loop logic of "situation awareness-simulation calculation-collaborative decision-production execution" in the workshop, and realizes the transparency of the workshop production process through real-time interoperation between information space data / models and physical space entities )
utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines. (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 5, Cella and Ding remain applied as claim 1. Cella teaches providing, by the automated mobile machine, feedback regarding performance of the activity to a set of machine learning models of the automated mobile machine as additional training data to increase predictive accuracy of activity performance. (see Cella para[0678]Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results,para[0022] In embodiments, the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array).
Regarding claim 6, Cella and Ding remain applied as claim 1. Cella teaches receiving, by the automated mobile machine, an assignment of the activity from a central warehouse automation system (See Cella para[0579] Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. ) , wherein the automated mobile machine is one of a plurality of automated mobile machines operating in a warehouse environment (See Cella para[4074] In embodiments, the CMMS subsystem 28622 may execute algorithms that gather information about a plurality of industrial machines, including a plurality of industrial machines of different types of machine (e.g., stationary machines, mobile machines, machines on vehicles, machines deployed at job sites, and the like) along with service provider information, parts and parts provider information); and performing, by the automated mobile machine, the analysis of the information corresponding to the activity that includes object weight (See Cella para[2139] the sensor data collected by the edge device 28704 may include a weight or mass measurements indicating a weight or mass of an object (e.g., a pot or tray containing one or more plants) that is resting upon a weight sensor 30104.) , object dimensions (See Cella Para[4322] the digital representation may include a set of data structures (e.g., classes) that collectively define a set of properties of a represented physical object 40022, device 40024, sensor 40026, or environment 40020 and/or possible behaviors thereof. For example, the set of properties of a physical object 40022 may include a type of the physical object, the dimensions of the object, the mass of the object, the density of the object, the material(s) of the object,), object fragility level, object warehouse position (See Cella para[4442] For example, temperature sensors in a warehouse may each take a temperature measurement at specific geospatial coordinates, but these limited measurements do not give values for the other locations in the warehouse, such as where there is no sensor coverage. In this example, the dynamic models can be configured to model temperatures throughout the warehouse using the limited number of sensor measurements to provide a more enriched representation of the warehouse digital twin), object destination range, and customer service level agreement in response to receiving the assignment of the activity (see Cella para[4255] indications of correct or incorrect labeling or classification, and success metrics such as those relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and other measures of success). ).
Regarding claim 7, Cella and Ding remain applied as claim 1. Cella teaches responsive to the automated mobile machine determining that the automated mobile machine is capable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine, performing, by the automated mobile machine, the activity itself. (see Cella para[0578] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range).
Regarding claim 8, Cella teaches An automated mobile machine for collaborative machine capability enhancement, the automated mobile machine comprising: a communication fabric (See Cella para[4244] As briefly mentioned above, the set of protocol adaptors facilitate adaptive protocol transformations of data within the IIoT system. For example only, the set of protocol adaptors can facilitate adaptive in-flight data protocol transformations, communication network protocol transformations, and linking (gateways, routers, switches, etc.); a storage device connected to the communication fabric, wherein the storage device stores program instructions (See Cella para[0905] a plurality of analog-to-digital converters (ADCs), a processor, local storage, and an external interface) ; and
a processor (See Cella para[0014] one or more processors configured to: maintain, via the digital twin datastore, an industrial-environment digital twin for the industrial environment; receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real-world element from the plurality of elements) connected to the communication fabric, wherein the processor executes the program instructions to:
determine whether the automated mobile machine is capable of performing an activity based on analysis of information corresponding to the activity and capabilities of the automated mobile machine (see Cella para[0578] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range);
perform a digital twin simulation associated with performing the activity using the information corresponding to the activity and the capabilities of the automated mobile machine in response to determining that the automated mobile machine is incapable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine(See Cella para[0281] In embodiments, the design specification is determined using a digital twin simulation system.);
perform an analysis of a result of the digital twin simulation associated with performing the activity; (See Cella para [1622] changing at least one of the sensor inputs analyzed and a frequency of the sampling. In implementations, the selection operation can further comprise identifying a level of activity of a target associated with the target signal to be sensed and, based on the identified level of activity, changing at least one of the sensor inputs analyzed and a frequency of the sampling.)
However, Cella does not expressly disclose or otherwise teach determine a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation. Nevertheless, Ding same field of endeavor teaches determine a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation(See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 9, Cella and Ding remain applied as claim 8. Cella teaches wherein the processor further executes the program instructions to:
send a message to nearby automated mobile machines within range of a utilized wireless communication protocol requesting collaborative performance of the activity with the automated mobile machine(See cella para[1536] A result of the analysis may be communicated wirelessly to one or more wearable haptic feedback stimulators 11404 worn by a user associated with the industrial environment; para[1392] Additionally or alternatively, a sensor may communicate wirelessly, through a wired connection); and
determine a collaborative pattern of automated mobile machines comprised of the automated mobile machine (see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
However, Cella does not expressly disclose or otherwise teach the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines. Nevertheless, Ding same field of endeavor teaches the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines(See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 10, Cella and Ding remain applied as claim 8. Cella teaches wherein the processor further executes the program instructions to: verify that merged dimensions of the collaborative pattern (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment ) of automated mobile machines can travel along a determined navigation path within a warehouse environment based on specifications of the warehouse environment(See Cella para[0016] and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.).
Regarding claim 11, Cella and Ding remain applied as claim 8. Cella teaches wherein the processor further executes the program instructions to: perform the activity using the collaborative pattern of automated mobile machines(see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
in the collaborative pattern of automated mobile machines is engaged (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment ).
However, Cella does not expressly disclose or otherwise teach verify that each of the number of additional automated mobile machines, utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines. Nevertheless, Ding same field of endeavor teaches verify that each of the number of additional automated mobile machines (See Ding objects (work in progress, auxiliary tools, etc.) in the workshop, so that each physical entity can be identified, tracked, Communicable and interactive; digital twin technology builds the closed-loop logic of "situation awareness-simulation calculation-collaborative decision-production execution" in the workshop, and realizes the transparency of the workshop production process through real-time interoperation between information space data / models and physical space entities )
utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines(See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 12, Cella and Ding remain applied as claim 8. Cella teaches wherein the processor further executes the program instructions to: provide feedback regarding performance of the activity to a set of machine learning models of the automated mobile machine as additional training data to increase predictive accuracy of activity performance(see Cella para[0678]Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results, para[0022] In embodiments, the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array).
Regarding claim 13, Cella and Ding remain applied as claim 8. Cella teaches wherein the processor further executes the program instructions to: receive an assignment of the activity from a central warehouse automation system(See Cella para[0579] Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. ), wherein the automated mobile machine is one of a plurality of automated mobile machines operating in a warehouse environment(See Cella para[4074] In embodiments, the CMMS subsystem 28622 may execute algorithms that gather information about a plurality of industrial machines, including a plurality of industrial machines of different types of machine (e.g., stationary machines, mobile machines, machines on vehicles, machines deployed at job sites, and the like) along with service provider information, parts and parts provider information);; and perform the analysis of the information corresponding to the activity that includes object weight(See Cella para[2139] the sensor data collected by the edge device 28704 may include a weight or mass measurements indicating a weight or mass of an object (e.g., a pot or tray containing one or more plants) that is resting upon a weight sensor 30104.) , object dimensions (See Cella Para[4322] the digital representation may include a set of data structures (e.g., classes) that collectively define a set of properties of a represented physical object 40022, device 40024, sensor 40026, or environment 40020 and/or possible behaviors thereof. For example, the set of properties of a physical object 40022 may include a type of the physical object, the dimensions of the object, the mass of the object, the density of the object, the material(s) of the object,), object dimensions, object fragility level, object warehouse position(See Cella para[4442] For example, temperature sensors in a warehouse may each take a temperature measurement at specific geospatial coordinates, but these limited measurements do not give values for the other locations in the warehouse, such as where there is no sensor coverage. In this example, the dynamic models can be configured to model temperatures throughout the warehouse using the limited number of sensor measurements to provide a more enriched representation of the warehouse digital twin),, object destination range, and customer service level agreement in response to receiving the assignment of the activity(see Cella para[4255] indications of correct or incorrect labeling or classification, and success metrics such as those relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and other measures of success). ).
Regarding claim 14, Cella teaches A computer program product for collaborative machine capability enhancement (See Cella title INTELLIGENT VIBRATION DIGITAL TWIN SYSTEMS AND METHODS FOR INDUSTRIAL ENVIRONMENTS), the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by an automated mobile machine to cause the automated mobile machine to perform a method of (See Cella para [1983] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor. The present disclosure may be implemented as a method on the machine, as a system or apparatus as part of or in relation to the machine, or as a computer program product embodied in a computer readable medium executing on one or more of the machines):
determining, by the automated mobile machine, whether the automated mobile machine is capable of performing an activity based on analysis of information corresponding to the activity and capabilities of the automated mobile machine(see Cella para[0578] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range);
responsive to the automated mobile machine determining that the automated mobile machine is incapable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine, performing, by the automated mobile machine, a digital twin simulation associated with performing the activity using the information corresponding to the activity and the capabilities of the automated mobile machine(See Cella para[0281] In embodiments, the design specification is determined using a digital twin simulation system.);
performing, by the automated mobile machine, an analysis of a result of the digital twin simulation associated with performing the activity(See Cella para [1622] changing at least one of the sensor inputs analyzed and a frequency of the sampling. In implementations, the selection operation can further comprise identifying a level of activity of a target associated with the target signal to be sensed and, based on the identified level of activity, changing at least one of the sensor inputs analyzed and a frequency of the sampling.);
However, Cella does not expressly disclose or otherwise teach determining, by the automated mobile machine, a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation. Nevertheless, Ding same field of endeavor teaches determining, by the automated mobile machine, a number of additional automated mobile machines needed to collaboratively perform the activity based on the analysis of the result of the digital twin simulation (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 15, Cella and Ding remain applied as claim 14. Cella teaches sending, by the automated mobile machine, a message to nearby automated mobile machines within range of a utilized wireless communication protocol requesting collaborative performance of the activity with the automated mobile machine(See Cella para[1536] A result of the analysis may be communicated wirelessly to one or more wearable haptic feedback stimulators 11404 worn by a user associated with the industrial environment; para[1392] Additionally or alternatively, a sensor may communicate wirelessly, through a wired connection) ; and
determining, by the automated mobile machine, a collaborative pattern of automated mobile machines comprised of the automated mobile machine (see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
However, Cella does not expressly disclose or otherwise teach the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines. Nevertheless, Ding same field of endeavor teaches the number of additional automated mobile machines that will be collaboratively performing the activity in response to receiving an affirmative response from a sufficient number of automated mobile machines (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 16, Cella and Ding remain applied as claim 14. Cella teaches verifying, by the automated mobile machine, that merged dimensions of the collaborative pattern (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment ) of automated mobile machines can travel along a determined navigation path within a warehouse environment based on specifications of the warehouse environment(See Cella para[0016] and model, in response to obtaining the path information for each mobile element, traffic within the industrial environment via a digital twin simulation system.)
Regarding claim 17, Cella and Ding remain applied as claim 14. Cella teaches performing, by the automated mobile machine, the activity using the collaborative pattern of automated mobile machines(see Cella para [1432] An example system 11000 further includes a system collaboration circuit 11024 that interprets external data 11036, and where the pattern recognition circuit 11020 further determines the recognized pattern value 11028 further in response to the external data 11036.)
in the collaborative pattern of automated mobile machines is engaged (See Cella para[0013] In embodiments, generating the digital twin of the industrial environment includes one of generating a set of surfaces of the industrial environment and configuring a set of dimensions of the industrial environment )
However, Cella does not expressly disclose or otherwise teach verifying, by the automated mobile machine, that each of the number of additional automated mobile machines, utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines. Nevertheless, Ding same field of endeavor teaches verifying, by the automated mobile machine, that each of the number of additional automated mobile machines (See Ding objects (work in progress, auxiliary tools, etc.) in the workshop, so that each physical entity can be identified, tracked, Communicable and interactive; digital twin technology builds the closed-loop logic of "situation awareness-simulation calculation-collaborative decision-production execution" in the workshop, and realizes the transparency of the workshop production process through real-time interoperation between information space data / models and physical space entities )
utilized while performing the activity until completion using coupled connections between the automated mobile machine and the number of additional automated mobile machines forming the collaborative pattern of automated mobile machines. (See Ding (4) Perform real-time simulation of the intelligent workshop operation status in the digital twin model, calculate the progress deviation in the actual production and operation process, and call the machine tool scheduling algorithm to predict and determine the number of the processing machine to execute the next process of the workpiece).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Cella’s digital twin simulation with Ding’s performing digital twin simulation to determine the number of the machine in order to allow the production process control capabilities to meet individual customer needs.
Regarding claim 18, Cella and Ding remain applied as claim 14. Cella teaches providing, by the automated mobile machine, feedback regarding performance of the activity to a set of machine learning models of the automated mobile machine as additional training data to increase predictive accuracy of activity performance(see Cella para[0678]Thus, an automatically adapting, multi-sensor data collection system is provided, where cognitive input selection is used (with feedback) to improve the effectiveness, efficiency, or other performance parameters of the data collection system within its particular environment. Performance parameters may relate to overall system metrics (such as financial yields, process optimization results,para[0022] In embodiments, the one or more processors are further configured to: determine existence of a conflict between the navigational route data and the industrial-environment digital twin; alter, in response to determining accuracy of the industrial-environment digital twin via the sensor array).
Regarding claim 19, Cella and Ding remain applied as claim 14. Cella teaches receiving, by the automated mobile machine, an assignment of the activity from a central warehouse automation system(See Cella para[0579] Figure 2 depicts a mobile ad hoc network (“MANET”) 20, which may form a secure, temporal network connection 22 (sometimes connected and sometimes isolated), with a cloud 30 or other remote networking system, so that network functions may occur over the MANET 20 within the environment, without the need for external networks, but at other times information can be sent to and from a central location. ), wherein the automated mobile machine is one of a plurality of automated mobile machines operating in a warehouse environment(See Cella para[4074] In embodiments, the CMMS subsystem 28622 may execute algorithms that gather information about a plurality of industrial machines, including a plurality of industrial machines of different types of machine (e.g., stationary machines, mobile machines, machines on vehicles, machines deployed at job sites, and the like) along with service provider information, parts and parts provider information);; and performing, by the automated mobile machine, the analysis of the information corresponding to the activity that includes object weight(See Cella para[2139] the sensor data collected by the edge device 28704 may include a weight or mass measurements indicating a weight or mass of an object (e.g., a pot or tray containing one or more plants) that is resting upon a weight sensor 30104.) , object dimensions (See Cella Para[4322] the digital representation may include a set of data structures (e.g., classes) that collectively define a set of properties of a represented physical object 40022, device 40024, sensor 40026, or environment 40020 and/or possible behaviors thereof. For example, the set of properties of a physical object 40022 may include a type of the physical object, the dimensions of the object, the mass of the object, the density of the object, the material(s) of the object,), object dimensions, object fragility level, object warehouse position(See Cella para[4442] For example, temperature sensors in a warehouse may each take a temperature measurement at specific geospatial coordinates, but these limited measurements do not give values for the other locations in the warehouse, such as where there is no sensor coverage. In this example, the dynamic models can be configured to model temperatures throughout the warehouse using the limited number of sensor measurements to provide a more enriched representation of the warehouse digital twin), object destination range, and customer service level agreement in response to receiving the assignment of the activity(see Cella para[4255] indications of correct or incorrect labeling or classification, and success metrics such as those relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and other measures of success). ).
Regarding claim 20, Cella and Ding remain applied as claim 14. Cella teaches responsive to the automated mobile machine determining that the automated mobile machine is capable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine, performing, by the automated mobile machine, the activity itself(see Cella para[0578] Methods used to process existing data may be associated with certain characteristics of sensed data, such as certain frequency ranges, sources of data, and the like. As an example, methods for processing bearing sensing information for a moving part of an industrial machine may be capable of processing data from bearing sensors that fall into a particular frequency range).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAZIA AFRIN whose telephone number is (703)756-1175. The examiner can normally be reached Monday-Friday 7:30-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A Browne can be reached at 5712700151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NAZIA AFRIN/Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3666