Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP 2106 (III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Claims 1-10 are directed to method (processes). Claims 11-15 are directed to a computer system (machine). Claims 16-20 are directed to a computer program product (article of manufacture). Therefore, claims 1-20 fall into one of four statutory categories (i.e., process, machine, article of manufacture).
As to claim 1,
Step 2A Prong 1: this claim recites the following abstract ideas:
an analysis of a ... simulation of an environment in accordance with a machine activity workflow corresponding to a plurality of machines located in the environment; (the limitation describes analyzing/evaluating a simulated environment according to a workflow, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generating ... a machine layout for the environment that includes at least one of a particular set of machines having a determined amount of computational capability needed to analyze a type, volume, and frequency of data generated in each logical group of machines within the environment based on the analysis of ... simulation in accordance with the machine activity workflow corresponding to the plurality of machines; (the limitation describes determining a needed amount of capability by evaluating the type, volume, and frequency of data and devising an arrangement/layout of machines based on that evaluation, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
performing, by a computer, using a machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
digital twin; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
implementing, by the computer, the machine layout automatically in the environment by positioning the at least one of the particular set of machines having the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in each logical group of machines within the environment using mobility systems corresponding to the plurality of machines. (This limitation describes carrying out the layout determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 11,
Step 2A Prong 1: this claim recites the following abstract ideas:
an analysis of a ... simulation of an environment in accordance with a machine activity workflow corresponding to a plurality of machines located in the environment; (the limitation describes analyzing/evaluating a simulated environment according to a workflow, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generate ... a machine layout for the environment that includes at least one of a particular set of machines having a determined amount of computational capability needed to analyze a type, volume, and frequency of data generated in each logical group of machines within the environment based on the analysis of ... simulation in accordance with the machine activity workflow corresponding to the plurality of machines; (the limitation describes determining a needed amount of capability by evaluating the type, volume, and frequency of data and devising an arrangement/layout of machines based on that evaluation, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
A computer system comprising: a communication fabric; a storage device connected to the communication fabric, wherein the storage device stores program instructions; and a processor connected to the communication fabric, wherein the processor executes the program instructions to: (This limitation is directed to mere instruction to store the abstract idea on a generic memory and apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using a machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
digital twin; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
implement the machine layout automatically in the environment by positioning the at least one of the particular set of machines having the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in each logical group of machines within the environment using mobility systems corresponding to the plurality of machines. (This limitation describes carrying out the layout determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 16,
Step 2A Prong 1: this claim recites the following abstract ideas:
an analysis of a ... simulation of an environment in accordance with a machine activity workflow corresponding to a plurality of machines located in the environment; (the limitation describes analyzing/evaluating a simulated environment according to a workflow, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
generating ... a machine layout for the environment that includes at least one of a particular set of machines having a determined amount of computational capability needed to analyze a type, volume, and frequency of data generated in each logical group of machines within the environment based on the analysis of ... simulation in accordance with the machine activity workflow corresponding to the plurality of machines; (the limitation describes determining a needed amount of capability by evaluating the type, volume, and frequency of data and devising an arrangement/layout of machines based on that evaluation, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method of: (This limitation is directed to mere instruction to store the abstract idea on a generic memory and apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
performing, by the computer, using a machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
digital twin; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
implementing, by the computer, the machine layout automatically in the environment by positioning the at least one of the particular set of machines having the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in each logical group of machines within the environment using mobility systems corresponding to the plurality of machines. (This limitation describes carrying out the layout determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 2, 12, and 17,
Step 2A Prong 1: those claims recite the following abstract ideas:
identifying or identify ... the plurality of machines located in the environment based on a real time feed ...; (the limitation describes identifying machines based on observed information, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
The additional limitation of claim 12 "wherein the processor further executes the program instructions to" (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
receiving or receive an input to generate the machine layout for the environment corresponding to an entity from a client device via a network; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
a real time feed received from a set of sensors within the environment via the network in response to receiving the input; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
retrieving or retrieve specification information for each particular machine of the plurality of machines located in the environment. (Describes a generic computer function of retrieving data, see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory.)
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 3, 13, and 18,
Step 2A Prong 1: those claims recite the following abstract ideas:
wherein the specification information for each particular machine includes amount of space needed by that particular machine, activity performed by that particular machine, and current computational capability of that particular machine. (the limitation describes the content of the information being considered, which merely specifies the type of data evaluated and is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) 1.), failing step 2A prong 2. The claims are ineligible.
As to claims 4, 14, and 19,
Step 2A Prong 1: those claims recite the following abstract ideas:
monitoring or monitor ... an activity performed by each particular machine of the plurality of machines located in the environment ...; (the limitation describes observing activities performed by machines, which is an observation and evaluation activity that can be performed as a mental process in the human mind.)
determining or determine ... the machine activity workflow corresponding to the plurality of machines that includes identified machine relationships between the plurality of machines, identified relative machine positions of the plurality of machines, and identified different machine activities among the plurality of machines based on the monitoring of the activity performed by each particular machine of the plurality of machines located in the environment ...; (the limitation describes identifying relationships, positions, and activities among machines based on observation, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
The additional limitation of claim 14 "wherein the processor further executes the program instructions to" (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the real time feed received from the set of sensors within the environment; (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claims 5, 15, and 20,
Step 2A Prong 1: those claims recite the following abstract ideas:
identifying or identify ... a set of contextual scenarios predicted to occur in the environment based on the ... simulation of the environment. (the limitation describes predicting scenarios that may occur based on an evaluation of the simulated environment, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B: those claims recited the following additional elements:
The additional limitation of claim 15 "wherein the processor further executes the program instructions to" (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
generating or generate, using a digital twin component, the digital twin simulation of the environment based on the machine activity workflow corresponding to the plurality of machines that includes the identified machine relationships between the plurality of machines, the identified relative machine positions of the plurality of machines, and the identified different machine activities among the plurality of machines; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
using the machine learning model of the digital twin component; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 6,
Step 2A Prong 1: this claim recites the following abstract ideas:
wherein the set of contextual scenarios includes at least one of accident, machine damage, product damage, material handling problem, adverse event, or safety issue. (the limitation describes the content of the scenarios being predicted, which merely specifies the type of scenarios evaluated and is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
Step 2A Prong 2 and 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B. Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) 1.), failing step 2A prong 2. The claim is ineligible.
As to claim 7,
Step 2A Prong 1: this claim recites the following abstract ideas:
determining ... the type, volume, and frequency of the data generated by the particular set of machines of the plurality of machines located in the environment for each of the set of contextual scenarios predicted to occur; and (the limitation describes evaluating characteristics of data generated for each scenario, which is an evaluation and judgment activity that can be performed as a mental process in the human mind.)
determining ... an amount of computational capability needed by each of the particular set of machines to analyze the type, volume, and frequency of the data generated for each of the set of contextual scenarios predicted to occur. (the limitation describes calculating a needed amount of capability based on data characteristics, which is a mental process implemented using a pen and paper.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
using the machine learning model; (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 8,
Step 2A Prong 1: this claim recites the following abstract ideas:
determining ... whether a current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated; and (the limitation describes comparing a current capability value with a needed capability value, which is a mental process implemented in the human mind.)
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
upgrading, by the computer, the one or more of the particular set of machines automatically with the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in response to the computer determining that the current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated. (This limitation describes carrying out the result of the comparison determined by the abstract idea, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 9,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 8.
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
wherein the computer automatically upgrades the one or more of the particular set of machines with the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated by at least one of the computer downloading a software upgrade to the one or more of the particular set of machines or the computer instructing a mobile maintenance machine located in the environment to perform a hardware upgrade on the one or more of the particular set of machines. (This limitation describes carrying out the upgrade determined by the abstract idea through downloading software or instructing a maintenance machine, which amounts to insignificant post-solution activity appended to the judicial exception, and mere instruction to apply the abstract idea using generic machinery, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
As to claim 10,
Step 2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1.
Step 2A Prong 2 and 2B: the claim recited the following additional elements:
receiving, by the computer, feedback regarding the machine layout from an entity corresponding to the environment via a client device; and (this limitation describes data collection/receiving, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
utilizing, by the computer, the feedback regarding the machine layout as training data for the machine learning model to increase predictive accuracy of the machine learning model. (This limitation is directed to mere instruction to apply the abstract idea on a generic computer to process, which is a well-understood, routine, conventional activity, see MPEP 2106.05(d)(II)(i))
The additional element does not integrate the judicial exception into practical application and does not amount to significantly more than the Judicial exception.
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.
Claim(s) 1-4, 10-14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20220108262 A1) in view of Gottin et al. (US 20190324822 A1).
As to claim 1 Cella teaches a computer-implemented method for managing machine layouts to improve machine activity workflows, the computer-implemented method comprising: ( see Cella paragraph [1814] "a computer-implemented method for implementing a monitoring system for data collection in an industrial environmen", see Cella paragraph [3045] "represent the layout or arrangement of entities 13736 (such as, without limitation, the arrangement of components, assets, machines, workers, or other elements on a factory floor); augmented, virtual and/or mixed reality digital twins that provide a realistic experience for a user")
performing, by a computer, using a machine learning model, an analysis of a digital twin simulation of an environment in accordance with a machine activity workflow corresponding to a plurality of machines located in the environment; (see Cella paragraph [5371] "a digital twin may be a digital representation of a manufacturing process, a logistics workflow, an agricultural process, a mineral extraction process, or the like … the digital twin may include references to the industrial entities that are included in the workflow or process.", and see Cella paragraph [5391] "the cognitive processes system 40010 trains machine learned models using the output of simulations executed by the digital twin simulation system 40006 … leverages machine learned models to make predictions, identifications, classifications and provide decision support relating to the real-world environments and/or processes represented by respective digital twins.")
generating, by the computer, using the machine learning model, a machine layout for the environment that includes at least one of a particular set of machines having a determined amount of computational capability needed to analyze a type, volume, and frequency of data generated in each logical group of machines within the environment based on the analysis of the digital twin simulation in accordance with the machine activity workflow corresponding to the plurality of machines; and (see Cella paragraph [1177] "A second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data" and "A third collector in the swarm with robust storage capabilities might be assigned the task of collecting and storing a category of data, such as vibration sensor data, that consumes considerable bandwidth … iterating based on feedback to the machine learning facility regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, profit measures, and others).", and see Cella paragraph [3045] "arrangement digital twins that represent the layout or arrangement of entities 13736 (such as, without limitation, the arrangement of components, assets, machines, workers, or other elements on a factory floor).", and see Cella paragraph [5418] "the digital twin system 40000 may optimize features of the environment through use of one or more simulated elements.", and see Cella paragraph [5567] "the machine learning model 55052 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal.")
implementing, by the computer, the machine layout automatically in the environment by positioning the at least one of the particular set of machines having the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in each logical group of machines within the environment using mobility systems corresponding to the plurality of machines. (see Cella paragraph [1178] "one or more of the data collectors 102 may have mobility capabilities, such as in cases where a data collector is disposed on or in a mobile robot, drone, mobile submersible, or the like, so that organization may include the location and positioning of the data collectors 102 … Organization may be automated based on one or more rules, models, conditions, processes, or the like … routing movement of mobile data collectors 102 to locations, positioning and orienting collectors 102 and the like relative to points of data acquisition.", and see Cella paragraph [5396] "the environment control system 40012 may control one or more machines within an environment, robots within an environment, an HVAC system of the environment, an alarm system of the environment, an assembly line in an environment, or the like … the environment control system 40012 may leverage the digital twin simulation system 40006, the digital twin dynamic model system 40008, and/or the cognitive processes system 40010 to determine one or more control instructions … In response to determining a control instruction, the environment control system 40012 may output the control instruction to the intended device within a specific environment via the digital twin I/O system 40004")
Cella does not expressly teach "having a determined amount of computational capability needed to analyze a type, volume, and frequency of data"
However Gottin teaches
having a determined amount of computational capability needed to analyze a type, volume, and frequency of data (see Gottin paragraph [0025] "One or more embodiments of the disclosure dynamically determine one or more control variables that impact a cost associated with the execution of a given workflow. Such variables include, for example, a number of processing cores and/or an amount of memory allocated to a given workflow.", and see Gottin paragraph [0045] "a reinforcement learning solution is described for the problem of setting environmental control variables (e.g., amount of memory and number of CPU (central processing unit) cores) aiming at a substantially optimal workflow execution.", and see Gottin paragraph [0053] "the reinforcement learning agent performs, using the simulation model, (i) the evaluating, (ii) the obtaining the expected utility score, and/or (iii) a training of a model used by the reinforcement learning agent.")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the machine layout management method of Cella to include a determined amount of computational capability needed to analyze data, as taught by Gottin, in order to minimize resource allocation while ensuring workflow execution requirements are met (see Gottin paragraph [0035] "an important aspect for optimization is to minimize resource allocation while still ensuring that Service Level Agreement (SLA) conditions are met."), yielding predictable results.
As to claim 2, Cella as modified by Gottin teaches the computer-implemented method of claim 1, further comprising:
receiving, by the computer, an input to generate the machine layout for the environment corresponding to an entity from a client device via a network; (see Cella paragraph [5388] "the digital twin system 40000 may provide a portal for users to create and manage their digital twins … a user may upload one or more files (e.g., image files, LIDAR scans, blueprints, and the like) in connection with a new digital twin that is being created.")
identifying, by the computer, the plurality of machines located in the environment based on a real time feed received from a set of sensors within the environment via the network in response to receiving the input; and (see Cella paragraph [3205] "a set of simultaneous location and mapping systems that provide a set of scans of a set of industrial environments where the set of industrial entities are located … the system provides real time updating of the digital twins based on data collected about the industrial entities.")
retrieving, by the computer, specification information for each particular machine of the plurality of machines located in the environment. (see Cella paragraph [1296] "the DAQ instruments 5002 disclosed herein may interrogate the one or more RFID chips to learn of the machine, its componentry, its service history, and the hierarchical structure of how everything is connected … some of the information that may be retrieved from the RFID tags includes manufacturer, machinery type, model, serial number, model number, manufacturing date, installation date, lots numbers, and the like.")
As to claim 3, Cella as modified by Gottin teaches the computer-implemented method of claim 2,
wherein the specification information for each particular machine includes amount of space needed by that particular machine, activity performed by that particular machine, and current computational capability of that particular machine. (see Cella paragraph [1177] "a member of the swarm may track information about what data other members are handling, so that data collection activities, data storage, data processing, and data publishing can be allocated intelligently across the swarm, taking into account conditions of the environment, capabilities of the members of the swarm, operating parameters, rules (such as from a rules engine that governs the operation of the swarm), and current conditions of the members … a second collector with good power levels and robust processing capability might be assigned more complex functions, such as processing data", and see Cella paragraph [1296] "some of the information that may be retrieved from the RFID tags includes manufacturer, machinery type, model, serial number, model number, manufacturing date, installation date, lots numbers, and the like.", and see Cella paragraph [1465] "the monitoring application 8150 may have access to equipment specifications, equipment geometry, component specifications, component materials, anticipated state information for a plurality of sensors, operational history, historical detection values, sensor life models, and the like")
As to claim 4, Cella as modified by Gottin teaches the computer-implemented method of claim 2, further comprising:
monitoring, by the computer, an activity performed by each particular machine of the plurality of machines located in the environment using the real time feed received from the set of sensors within the environment; and (see Cella paragraph [5371] "the digital twin system 40000 receives real-time data from the industrial facility (e.g., from a sensor system 40030 of the environment 40020) in which the manufacturing process takes place and reflects a current (or substantially current) state of the process in real-time.", and see Cella paragraph [5512] "for monitoring various parameters and features of machines, devices, components, parts, operations, functions, conditions, states, events, workflows and other elements")
determining, by the computer, the machine activity workflow corresponding to the plurality of machines that includes identified machine relationships between the plurality of machines, identified relative machine positions of the plurality of machines, and identified different machine activities among the plurality of machines based on the monitoring of the activity performed by each particular machine of the plurality of machines located in the environment using the real time feed received from the set of sensors within the environment. (see Cella paragraph [1178] "the swarm 4202 may assign data collectors 102 to serially collect diagnostic, sensor, instrumentation and/or telematic data from each of a series of machines that execute an industrial process (such as a robotic manufacturing process), such as at the time and location of the input to and output from each of those machines", and see Cella paragraph [1855] "location of data collectors, location of workers, location of machines and equipment.")
As to claim 10, Cella as modified by Gottin teaches the computer-implemented method of claim 1, further comprising:
receiving, by the computer, feedback regarding the machine layout from an entity corresponding to the environment via a client device; and (see Cella paragraph [6056] "the learning feedback is derived from user feedback metrics … The user feedback metrics may be based on market usage of sensed collected data over time.")
utilizing, by the computer, the feedback regarding the machine layout as training data for the machine learning model to increase predictive accuracy of the machine learning model. (see Cella paragraph [1177] "iterating based on feedback to the machine learning facility regarding measures of success (such as utilization measures, efficiency measures, measures of success in prediction or anticipation of states, productivity measures, yield measures, profit measures, and others).")
As to claim 11, this is directed to a system that corresponds to the method of claim 1, See the rejection for claim 1 above, which also applies to claim 11. in addition claim 11 recites the following elements
a communication fabric; (see Cella paragraph [1073] "this subsystem may communicate via a specialized hardware bus with the communication processing section.", and see Cella paragraph [2666] "The processor may access a non-transitory storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere.")
a storage device connected to the communication fabric, wherein the storage device stores program instructions; and (see Cella paragraph [2666] "The storage medium associated with the processor for storing methods, programs, codes, program instructions, or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and the like.")
a processor connected to the communication fabric, wherein the processor executes the program instructions to: (see Cella paragraph [1817] "one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions comprising", and see Cella paragraph [2666] "A processor may be any kind of computational or processing device capable of executing program instructions, codes, binary instructions, and the like.")
As to claim 12, this is directed to a system that corresponds to the method of claim 2, See the rejection for claim 2 above, which also applies to claim 12.
As to claim 13, this is directed to a system that corresponds to the method of claim 3, See the rejection for claim 3 above, which also applies to claim 13.
As to claim 14, this is directed to a system that corresponds to the method of claim 4, See the rejection for claim 4 above, which also applies to claim 14.
As to claim 16, this is directed to a computer program that corresponds to the method of claim 1, See the rejection for claim 1 above, which also applies to claim 16.
As to claim 17, this is directed to a computer program that corresponds to the method of claim 2, See the rejection for claim 2 above, which also applies to claim 17.
As to claim 18, this is directed to a computer program that corresponds to the method of claim 3, See the rejection for claim 3 above, which also applies to claim 18.
As to claim 19, this is directed to a computer program that corresponds to the method of claim 4, See the rejection for claim 4 above, which also applies to claim 19.
Claim(s) 5-7, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20220108262 A1) in view of Gottin et al. (US 20190324822 A1) and Nakagawa et al. (US 20170285584 A1).
As to claim 5, Cella as modified by Gottin teaches the computer-implemented method of claim 4, further comprising:
generating, by the computer, using a digital twin component of the computer, the digital twin simulation of the environment based on the machine activity workflow corresponding to the plurality of machines that includes the identified machine relationships between the plurality of machines, the identified relative machine positions of the plurality of machines, and the identified different machine activities among the plurality of machines; and see Cella paragraph [5371] "a digital twin may be a digital representation of a manufacturing process, a logistics workflow, an agricultural process, a mineral extraction process, or the like … the digital twin may include references to the industrial entities that are included in the workflow or process.")
Cella does not expressly teach "identifying, by the computer, using the machine learning model of the digital twin component, a set of contextual scenarios predicted to occur in the environment based on the digital twin simulation of the environment"
However, Nakagawa teaches
identifying, by the computer, using the machine learning model of the digital twin component, a set of contextual scenarios predicted to occur in the environment based on the digital twin simulation of the environment. (see Nakagawa paragraph [0030] "a machine learning unit 21 that performs machine learning and outputs a control command; a simulator 22 that executes simulation of the work of the robot 14 based on the control command; a first determination unit 23 that determines the control command based on an execution result of the simulation by the simulator 22", and see Nakagawa paragraph [0033] "the distance between the peripheral device and the robot 14 and the movement of the workpiece 12 are simulated beforehand, and the possibility of occurrence of interference or failure to take out the workpiece 12 is determined by the first determination unit 23.")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the digital twin simulation method of Cella as modified by Gottin to include identifying, using the machine learning model, a set of contextual scenarios predicted to occur based on the simulation, as taught by Nakagawa, in order to determine the possibility of harmful events before the machines are actually operated and thereby prevent damage to the machines and failure of the work (see Nakagawa paragraph [0033] "making it possible to prevent the damage of the actual machine, failure of the work, or the like."), and the combination is merely the use of a known technique to improve a similar simulation-based machine control method in the same way with predictable results.
As to claim 6, Cella-Gottin as modified by Nakagawa teaches the computer-implemented method of claim 5,
wherein the set of contextual scenarios includes at least one of accident, machine damage, product damage, material handling problem, adverse event, or safety issue. (see Nakagawa paragraph [0033] "making it possible to prevent the damage of the actual machine, failure of the work, or the like.")
As to claim 7, Cella-Gottin as modified by Nakagawa teaches the computer-implemented method of claim 5, further comprising:
determining, by the computer, using the machine learning model, the type, volume, and frequency of the data generated by the particular set of machines of the plurality of machines located in the environment for each of the set of contextual scenarios predicted to occur; and (see Cella paragraph [5567] "which types of sensor data are most relevant to achieving the modeling goal … the machine learning model 55052 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal … may use sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more manufacturing entities.")
Cella does not expressly teach "determining, by the computer, using the machine learning model, an amount of computational capability needed by each of the particular set of machines to analyze the type, volume, and frequency of the data generated for each of the set of contextual scenarios predicted to occur"
However, Gottin teaches
determining, by the computer, using the machine learning model, an amount of computational capability needed by each of the particular set of machines to analyze the type, volume, and frequency of the data generated for each of the set of contextual scenarios predicted to occur. (see Gottin paragraph [0025] "Such variables include, for example, a number of processing cores and/or an amount of memory allocated to a given workflow.", and see Gottin paragraph [0045] "a reinforcement learning solution is described for the problem of setting environmental control variables (e.g., amount of memory and number of CPU (central processing unit) cores) aiming at a substantially optimal workflow execution.", and see Gottin paragraph [0053] "the reinforcement learning agent performs, using the simulation model, (i) the evaluating, (ii) the obtaining the expected utility score, and/or (iii) a training of a model used by the reinforcement learning agent.")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the method of Cella as modified by Gottin and Nakagawa to include determining, using the machine learning model, an amount of computational capability needed by each of the particular set of machines to analyze the data generated, as taught by Gottin, in order to minimize resource allocation while still ensuring workflow execution requirements are met (see Gottin paragraph [0035] "an important aspect for optimization is to minimize resource allocation while still ensuring that Service Level Agreement (SLA) conditions are met."), and the combination is merely the use of a known technique to improve a similar method in the same way with predictable results.
As to claim 15, this is directed to a system that corresponds to the method of claim 5, See the rejection for claim 5 above, which also applies to claim 15.
As to claim 20, this is directed to a computer program that corresponds to the method of claim 5, See the rejection for claim 5 above, which also applies to claim 20.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20220108262 A1) in view of Gottin et al. (US 20190324822 A1) and Garrabrant et al. (US 20210141628 A1).
As to claim 8, Cella as modified by Gottin teaches the computer-implemented method of claim 1, further comprising:
Cella does not expressly teach "determining, by the computer, whether a current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated; and", and "upgrading, by the computer, the one or more of the particular set of machines automatically with the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in response to the computer determining that the current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated."
However, Garrabrant teaches
determining, by the computer, whether a current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated; and (see Garrabrant paragraph [0032] "instructions 210 to scan the communication network 120 and to identify at least one industrial device 130 that requires an update (also referred to as patch) of the firmware on the industrial device 130.", and see Garrabrant paragraph [0033] "Current firmware version information of all identified devices 130 is collected.", and see Garrabrant paragraph [0035] "the current firmware versions listed in the inventory list are checked or compared with available update files 260 of the database 250 to determine whether firmware update files 260 are available with respect to the identified devices 130.")
upgrading, by the computer, the one or more of the particular set of machines automatically with the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated in response to the computer determining that the current computational capability of one or more of the particular set of machines is less than the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated. (see Garrabrant paragraph [0045] "the updating is performed for all available patches/update files 260 automatically.")
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the method of Cella as modified by Gottin to include determining whether a machine's current capability is less than the needed capability and automatically upgrading the machine, including by downloading a software upgrade to the machine, as taught by Garrabrant, in order to provide a fully automated upgrade solution that updates multiple machines simultaneously while reducing user errors (see Garrabrant paragraph [0046] "The described system 100, computer program 200 and method 300 provide a single, easy to use and fully automated solution for updating firmware on devices 130. Specifically, multiple devices 130 can be updated simultaneously. Further, user errors are reduced and a unified firmware update process across multiple device families including an improved documentation of the update process is provided."), and the combination is merely the use of a known technique to improve a similar method of managing industrial machines in the same way with predictable results.
As to claim 9, Cella-Gottin as modified by Garrabrant teaches the computer-implemented method of claim 8,
wherein the computer automatically upgrades the one or more of the particular set of machines with the determined amount of computational capability needed to analyze the type, volume, and frequency of the data generated by at least one of the computer downloading a software upgrade to the one or more of the particular set of machines or the computer instructing a mobile maintenance machine located in the environment to perform a hardware upgrade on the one or more of the particular set of machines. (see Garrabrant paragraph [0043] "the corresponding update files 260 are acquired, for example downloaded to user interface device 110, from the database 250 and verified.", and see Garrabrant paragraph [0044] "the firmware on the industrial devices is updated via the network 120 and utilizing the update files 260.")
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAH K ABOUD whose telephone number is (571)272-0025. The examiner can normally be reached Mon-Fri 8am-5pm.
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/ABDULLAH KHALED ABOUD/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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