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 .
Drawings
Drawings have been reviewed and accepted.
Specification
The specification filed on 05/30/24 has been entered. Specification has been reviewed and accepted.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “determining one or more commands for adjusting one or more operational settings of the one or more industrial devices based on the set of prediction data”.
The limitations of “determining one or more commands for adjusting one or more operational settings of the one or more industrial devices based on the set of prediction data” are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “processing system”, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “processing system” language, “determining” in the context of this claim encompasses that the user mentally could make a decision, calculation, and observation. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements- “receiving a set of data associated with the one or more industrial devices; retrieving one or more pre-processing files and one or more training datasets files associated with the one or more industrial devices from a database,” and “ sending the one or more commands to the one or more industrial devices” which is simply insignificant extra solution activity of data gathering and transmission by acquiring data and information and “a processing system comprising a memory, the memory encoded with instructions” which is simply insignificant extra solution activity of storing and retrieving information in memory, the claim also recites elements- : “A system, comprising: one or more industrial devices of an industrial system”, “configured to be executed by the processing system to cause the processing system to perform operations comprising” , “wherein the one or more pre-processing files are configured to transform the data for generating a model representative of the one or more industrial devices, and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time; generating a set of prediction data representative of one or more expected operations of the one or more industrial devices based on the set of data and the model” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Accordingly these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of receiving data which is simply insignificant extra solution activity of data gathering and transmitting which is considered to be well-understood, routine, conventional activity- see MPEP 2106.05(d) buySAFE Inc. v. Google Inc. (computer receives and sends information over a network). The claim element of the memory storing instructions is simply insignificant extra solution activity of storing and retrieving information in memory, which is considered to be well-understood, routine, conventional activity- see MPEP 2106.05(d) Versata Dev. Group, Inc. v. SAP Am. Additionally, “A system, comprising: one or more industrial devices of an industrial system”, “configured to be executed by the processing system to cause the processing system to perform operations comprising” , “wherein the one or more pre-processing files are configured to transform the data for generating a model representative of the one or more industrial devices, and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time; generating a set of prediction data representative of one or more expected operations of the one or more industrial devices based on the set of data and the model” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore, these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites- “wherein the operations comprise generating the model based on one or more inputs received via a user interface” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites- “wherein the one or more inputs correspond to adjusting one or more model parameters, one or more pre-processing parameters, or both” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites- “wherein the operations comprise generating a visualization representative of the model based on the set of data, the set of prediction data, or both” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites - “wherein the visualization comprises an original distribution, a predicted distribution, a mean squared error value associated with the one or more expected operations, or a combination thereof” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites - “wherein the visualization comprises a plot, wherein the plot comprises a first line associated with a predicted value based on the set of prediction data and a second line associated with an expected value based on the set of data” which falls under field of use and technological environment- see MPEP 2106.05(h) Parker v. Flook ("Flook established that limiting an abstract idea to one field of use or adding token postsolution components did not make the concept patentable"). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. Additionally the claim recites- “ wherein the operations comprise receiving the set of data via a server device of the industrial system and an Ethernet/Industrial Protocol”, and the additional element of receiving data which is simply insignificant extra solution activity of data gathering and transmitting which is considered to be well-understood, routine, conventional activity- see MPEP 2106.05(d) buySAFE Inc. v. Google Inc. (computer receives and sends information over a network). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim inherits mental abstract ideas from claim 1. The claim recites- “and generating the set of prediction data representative of the one or more expected operations of the one or more industrial devices based on the set of data, the model, and the one or more models” which is simply using a computer as a tool to perform abstract ideas -Mere instructions to apply an exception – see MPEP 2106.05(f). Additionally the claim recites- “wherein the operations comprise: retrieving one or more models associated with one or more additional industrial devices that correspond to the one or more industrial devices via the database”, and the additional element of receiving data which is simply insignificant extra solution activity of data gathering and transmitting which is considered to be well-understood, routine, conventional activity- see MPEP 2106.05(d) buySAFE Inc. v. Google Inc. (computer receives and sends information over a network). Therefore these do not integrate a judicial exception into a practical application or provide significantly more. The claim is not patent eligible.
Claim 9 is rejected under 35 U.S.C. 101 for similar reasons as to claim 1.
Claim 10 is rejected under 35 U.S.C. 101 for similar reasons as to claim 2.
Claim 11 is rejected under 35 U.S.C. 101 for similar reasons as to claim 3.
Claim 12 is rejected under 35 U.S.C. 101 for similar reasons as to claim 4.
Claim 13 is rejected under 35 U.S.C. 101 for similar reasons as to claim 7.
Claim 14 is rejected under 35 U.S.C. 101 for similar reasons as to claim 8.
Claim 15 is rejected under 35 U.S.C. 101 for similar reasons as to claim 1.
Claim 16 is rejected under 35 U.S.C. 101 for similar reasons as to claim 2.
Claim 17 is rejected under 35 U.S.C. 101 for similar reasons as to claim 3.
Claim 18 is rejected under 35 U.S.C. 101 for similar reasons as to claim 4.
Claim 19 is rejected under 35 U.S.C. 101 for similar reasons as to claim 5.
Claim 20 is rejected under 35 U.S.C. 101 for similar reasons as to claim 6.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, and 7-18 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US20200050178, herein Gao), in view of Reynolds et al. (US20230096837, herein Reynolds).
Regarding claim 1, Gao teaches A system, comprising: one or more industrial devices of an industrial system ([0011] an industrial facility setting slate that the machine learning system predicts will optimize an efficiency of an industrial facility, [0013] The industrial facility controls may comprise control parameters which modify the physical operation of the physical units and/or control parameters for modifying the operation of additional equipment (e.g., cooling equipment) which is used to maintain the operating state of the physical units); a processing system comprising a memory, the memory encoded with instructions configured to be executed by the processing system to cause the processing system to perform operations comprising ([0108] a central processing unit will receive instructions and data from a read-only memory or a random access memory or both) : receiving a set of data associated with the one or more industrial devices ([0058] The efficiency management system 100 receives state data 140 characterizing the current state of a data center (or other industrial facility) 104 and provides updated settings 120 to a control system 102 that manages the settings of the data center 104) ; …one or more pre-processing files and one or more training datasets files associated with the one or more industrial devices …(Fig. 1, [0105] the apparatus can optionally include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes …a database management system, [0072] A setting slate management subsystem 110 within the efficiency management system 100 preprocesses the state data 140 and constructs a set of setting slates that represent one or more (typically a plurality of) data center setting values that can be set for various parts of the data center given the known operating conditions and the current state of the data center 104, [0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0079] Since each machine learning model in the ensemble of models 132A-132N is trained on a different dataset than the other models, each model has the potential to provide a different predicted PUE output when all the machine learning models in the ensemble are run with the same data center setting values as input), wherein the one or more pre-processing files are configured to transform the data for generating a model representative of the one or more industrial devices ([0076] During preprocessing, the setting slate management subsystem 110 can modify the state data 140. For example, it may remove data within invalid power usage efficiency, replace missing data for a given data setting with a mean value for that data setting, and/or remove a percentage of data settings. The setting slate management system 110 discretizes all of the action dimensions and generates an exhaustive set of possible action combinations), and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time ([0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting); generating a set of prediction data representative of one or more expected operations of the one or more industrial devices based on the set of data and the model ([0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting. For example, the model training subsystem 160 can train one constraint model to predict the future water temperature of the data center over the next hour given input state data 140 and potential settings. The model training subsystem 120 can train another constraint model to predict the water pressure over the next hour given the state data 140 and potential settings) ; determining one or more commands for adjusting one or more operational settings of the one or more industrial devices based on the set of prediction data; and sending the one or more commands to the one or more industrial devices ([0055] A machine learning system receives state data characterizing the current state of an industrial facility and provides updated industrial facility settings to a control system that manages the settings of the industrial facility, [0005] Neural networks can be trained using reinforcement learning to generate predicted outputs, [0075] slate settings that impact efficiency of the data center 104 include: potential power usage across various parts of the data center; certain temperature settings across the data center; a given water pressure; specific fan or pump speeds; and a number and type of the running data center equipment such as cooling towers and water pumps).
Gao does not teach retrieving… from a database
Reynolds teaches retrieving… from a database ([0075] processing system 1302 may comprise a micro-processor and other circuitry that retrieves and executes software 1305 from storage system 1303. Processing system 1302 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions, [0054] Database 421 may comprise a model database that is operatively coupled to the industrial line such that machine learning asset 423 can be swapped with other models stored in database 421).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gao’s teaching of preprocessing state data and training datasets using model training to predict operating property with Reynolds teaching of retrieving models stored in a database. The combined teaching provides an expected result of retrieving models from a database that preprocess state data and train datasets to predict operating property. Therefore, one of ordinary skill in the art would be motivated to retrieve models form a database in order to “utilize operational data in a useful manner to improve control logic programming and data scientists may utilize control logic and modeling data to improve their analytic capabilities” as shown by Reynolds [0025].
Regarding claim 2, the combination of Gao and Reynolds teach The system of claim 1, wherein the operations comprise generating the model based on one or more inputs received via a user interface (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0003] A machine learning model receives input and generates output based on its received input and on values of model parameters).
Regarding claim 3, the combination of Gao and Reynolds teach The system of claim 2, wherein the one or more inputs correspond to adjusting one or more model parameters, one or more pre-processing parameters, or both (Gao, [0067] Each layer of the neural network generates an output from a received input in accordance with current values of a respective set of parameters for the layer, [0061] The control system 102 uses the updated data center settings 120 to set one or more data center values (control values) for controlling the data center. For example, if the efficiency management system 100 determines that an additional cooling tower should be turned on in the data center 104, the efficiency management system 100 can either provide the updated data center settings 120 to a user who updates the settings or to the control system 102, which automatically adopts the settings without user interaction. The control system 102 can send the signal to the data center to increase the number of cooling towers that are powered on and functioning in the data center 104) .
Regarding claim 4, the combination of Gao and Reynolds teach The system of claim 1, wherein the operations comprise generating a visualization representative of the model based on the set of data, the set of prediction data, or both (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0112] a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device).
Regarding claim 7, the combination of Gao and Reynolds teach The system of claim 1, wherein the operations comprise receiving the set of data via a server device of the industrial system and an Ethernet/Industrial Protocol (Gao, [0031] The proxy may comprise physical components (e.g., a physical interface to a communications network) and/or a communication protocol, [0112] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device).
Regarding claim 8, the combination of Gao and Reynolds teach The system of claim 1, …and generating the set of prediction data representative of the one or more expected operations of the one or more industrial devices based on the set of data, the model, and the one or more models (Gao, [0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting. For example, the model training subsystem 160 can train one constraint model to predict the future water temperature of the data center over the next hour given input state data 140 and potential settings. The model training subsystem 120 can train another constraint model to predict the water pressure over the next hour given the state data 140 and potential settings).
Reynolds further teaches wherein the operations comprise: retrieving one or more models associated with one or more additional industrial devices that correspond to the one or more industrial devices via the database ([0075] processing system 1302 may comprise a micro-processor and other circuitry that retrieves and executes software 1305 from storage system 1303. Processing system 1302 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions, [0054] Database 421 may comprise a model database that is operatively coupled to the industrial line such that machine learning asset 423 can be swapped with other models stored in database 421).;
Regarding claim 9, Gao teaches A non-transitory, tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations comprising ([0108] a central processing unit will receive instructions and data from a read-only memory or a random access memory or both): receiving a set of data associated with one or more industrial devices of an industrial system ([0058] The efficiency management system 100 receives state data 140 characterizing the current state of a data center (or other industrial facility) 104 and provides updated settings 120 to a control system 102 that manages the settings of the data center 104); … one or more pre-processing files and one or more training datasets files associated with the one or more industrial devices … Fig. 1, [0105] the apparatus can optionally include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes …a database management system, [0072] A setting slate management subsystem 110 within the efficiency management system 100 preprocesses the state data 140 and constructs a set of setting slates that represent one or more (typically a plurality of) data center setting values that can be set for various parts of the data center given the known operating conditions and the current state of the data center 104, [0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0079] Since each machine learning model in the ensemble of models 132A-132N is trained on a different dataset than the other models, each model has the potential to provide a different predicted PUE output when all the machine learning models in the ensemble are run with the same data center setting values as input), wherein the one or more pre-processing files are configured to transform the data for generating a model representative of the one or more industrial devices ([0076] During preprocessing, the setting slate management subsystem 110 can modify the state data 140. For example, it may remove data within invalid power usage efficiency, replace missing data for a given data setting with a mean value for that data setting, and/or remove a percentage of data settings. The setting slate management system 110 discretizes all of the action dimensions and generates an exhaustive set of possible action combinations), and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time ([0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting); generating a set of prediction data representative of one or more expected operations of the one or more industrial devices based on the set of data and the model ([0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting. For example, the model training subsystem 160 can train one constraint model to predict the future water temperature of the data center over the next hour given input state data 140 and potential settings. The model training subsystem 120 can train another constraint model to predict the water pressure over the next hour given the state data 140 and potential settings); determining one or more commands for adjusting one or more operational settings of the one or more industrial devices based on the set of prediction data; and sending the one or more commands to the one or more industrial devices ([0055] A machine learning system receives state data characterizing the current state of an industrial facility and provides updated industrial facility settings to a control system that manages the settings of the industrial facility, [0005] Neural networks can be trained using reinforcement learning to generate predicted outputs, [0075] slate settings that impact efficiency of the data center 104 include: potential power usage across various parts of the data center; certain temperature settings across the data center; a given water pressure; specific fan or pump speeds; and a number and type of the running data center equipment such as cooling towers and water pumps).
Gao does not teach retrieving… from a database
Reynolds teaches retrieving… from a database ([0075] processing system 1302 may comprise a micro-processor and other circuitry that retrieves and executes software 1305 from storage system 1303. Processing system 1302 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions, [0054] Database 421 may comprise a model database that is operatively coupled to the industrial line such that machine learning asset 423 can be swapped with other models stored in database 421).
Regarding claim 10, the combination of Gao and Reynolds teach The non-transitory, tangible, computer-readable medium of claim 9, wherein the instructions cause the processing circuitry to perform operations comprising generating the model based on one or more inputs received via a user interface (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0003] A machine learning model receives input and generates output based on its received input and on values of model parameters).
Regarding claim 11, the combination of Gao and Reynolds teach The non-transitory, tangible, computer-readable medium of claim 10, wherein the one or more inputs correspond to adjusting one or more model parameters, one or more pre-processing parameters, or both (Gao, [0067] Each layer of the neural network generates an output from a received input in accordance with current values of a respective set of parameters for the layer, [0061] The control system 102 uses the updated data center settings 120 to set one or more data center values (control values) for controlling the data center. For example, if the efficiency management system 100 determines that an additional cooling tower should be turned on in the data center 104, the efficiency management system 100 can either provide the updated data center settings 120 to a user who updates the settings or to the control system 102, which automatically adopts the settings without user interaction. The control system 102 can send the signal to the data center to increase the number of cooling towers that are powered on and functioning in the data center 104) .
Regarding claim 12, the combination of Gao and Reynolds teach The non-transitory, tangible, computer-readable medium of claim 9, wherein the instructions cause the processing circuitry to perform operations comprising generating a visualization representative of the model based on the set of data, the set of prediction data, or both (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0112] a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device).
Regarding claim 13, the combination of Gao and Reynolds teach The non-transitory, tangible, computer-readable medium of claim 9, wherein the instructions cause the processing circuitry to perform operations comprising receiving the set of data via a server device of the industrial system and an Ethernet/Industrial Protocol (Gao, [0031] The proxy may comprise physical components (e.g., a physical interface to a communications network) and/or a communication protocol, [0112] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device).
Regarding claim 14, the combination of Gao and Reynolds teach The non-transitory, tangible, computer-readable medium of claim 9, wherein the instructions cause the processing circuitry to perform operations comprising: retrieving one or more models associated with one or more additional industrial devices that correspond to the one or more industrial devices via the database; and generating the set of prediction data representative of the one or more expected operations of the one or more industrial devices based on the set of data, the model, and the one or more models (Gao, [0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting. For example, the model training subsystem 160 can train one constraint model to predict the future water temperature of the data center over the next hour given input state data 140 and potential settings. The model training subsystem 120 can train another constraint model to predict the water pressure over the next hour given the state data 140 and potential settings).
Regarding claim 15, Gao teaches A method comprising: receiving, via processing circuitry, a set of data associated with one or more industrial devices of an industrial system ([0058] The efficiency management system 100 receives state data 140 characterizing the current state of a data center (or other industrial facility) 104 and provides updated settings 120 to a control system 102 that manages the settings of the data center 104); … via the processing circuitry, one or more pre-processing files and one or more training datasets files associated with the one or more industrial devices… (Fig. 1, [0105] the apparatus can optionally include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes …a database management system, [0072] A setting slate management subsystem 110 within the efficiency management system 100 preprocesses the state data 140 and constructs a set of setting slates that represent one or more (typically a plurality of) data center setting values that can be set for various parts of the data center given the known operating conditions and the current state of the data center 104, [0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0079] Since each machine learning model in the ensemble of models 132A-132N is trained on a different dataset than the other models, each model has the potential to provide a different predicted PUE output when all the machine learning models in the ensemble are run with the same data center setting values as input), wherein the one or more pre-processing files are configured to transform the data for generating a model representative of the one or more industrial devices ([0076] During preprocessing, the setting slate management subsystem 110 can modify the state data 140. For example, it may remove data within invalid power usage efficiency, replace missing data for a given data setting with a mean value for that data setting, and/or remove a percentage of data settings. The setting slate management system 110 discretizes all of the action dimensions and generates an exhaustive set of possible action combinations), and wherein the one or more training dataset files are representative of one or more operational characteristics of the one or more industrial devices over time ([0068] The model training subsystem 160 uses historical data from a data center 104 to create different datasets of sensor data from the data center. Each machine learning model 132A-132N in the ensemble of machine learning models can be trained on one dataset of historical sensor data, [0069] The efficiency management system 100 can train additional ensembles of constraint machine learning models 112A-112N using the model training subsystem 160 to predict an operating property of the data center that corresponds to an operating constraint if the data center 104 adopts certain data center settings 102, [0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting); generating, via the processing circuitry, a set of prediction data representative of one or more expected operations of the one or more industrial devices based on the set of data and the model ([0071] Each constraint model 112A-112N is a machine learning model, e.g., a deep neural network, that is trained to predict certain values of an operating property of the data center over a period of time if the data center adopts a given input setting. For example, the model training subsystem 160 can train one constraint model to predict the future water temperature of the data center over the next hour given input state data 140 and potential settings. The model training subsystem 120 can train another constraint model to predict the water pressure over the next hour given the state data 140 and potential settings); determining, via the processing circuitry, one or more commands for adjusting one or more operational settings of the one or more industrial devices based on the set of prediction data; and sending, via the processing circuitry, the one or more commands to the one or more industrial devices ([0055] A machine learning system receives state data characterizing the current state of an industrial facility and provides updated industrial facility settings to a control system that manages the settings of the industrial facility, [0005] Neural networks can be trained using reinforcement learning to generate predicted outputs, [0075] slate settings that impact efficiency of the data center 104 include: potential power usage across various parts of the data center; certain temperature settings across the data center; a given water pressure; specific fan or pump speeds; and a number and type of the running data center equipment such as cooling towers and water pumps).
Gao does not teach retrieving… from a database
Reynolds teaches retrieving… from a database ([0075] processing system 1302 may comprise a micro-processor and other circuitry that retrieves and executes software 1305 from storage system 1303. Processing system 1302 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions, [0054] Database 421 may comprise a model database that is operatively coupled to the industrial line such that machine learning asset 423 can be swapped with other models stored in database 421).
Regarding claim 16, the combination of Gao and Reynolds teach The method of claim 15, comprising generating, via the processing circuitry the model based on one or more inputs received via a user interface (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0003] A machine learning model receives input and generates output based on its received input and on values of model parameters).
Regarding claim 17, the combination of Gao and Reynolds teach The method of claim 16, wherein the one or more inputs correspond to adjusting one or more model parameters, one or more pre-processing parameters, or both (Gao, [0067] Each layer of the neural network generates an output from a received input in accordance with current values of a respective set of parameters for the layer, [0061] The control system 102 uses the updated data center settings 120 to set one or more data center values (control values) for controlling the data center. For example, if the efficiency management system 100 determines that an additional cooling tower should be turned on in the data center 104, the efficiency management system 100 can either provide the updated data center settings 120 to a user who updates the settings or to the control system 102, which automatically adopts the settings without user interaction. The control system 102 can send the signal to the data center to increase the number of cooling towers that are powered on and functioning in the data center 104).
Regarding claim 18, the combination of Gao and Reynolds teach The method of claim 15, comprising generating, via the processing circuitry, a visualization representative of the model based on the set of data, the set of prediction data, or both (Gao, [0110] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer, [0112] a server transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device).
Claim(s) 5-6, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reynolds et al. (US20200050178, herein Gao), in view of Reynolds et al. (US20230096837, herein Reynolds), and in further view of Kokotov et al. (US20080177408, herein Kokotov).
Regarding claim 5, the combination of Gao and Reynolds teach The system of claim 4,
The combination of Gao and Reynolds do not teach wherein the visualization comprises an original distribution, a predicted distribution, a mean squared error value associated with the one or more expected operations, or a combination thereof.
Kokotov teaches wherein the visualization comprises an original distribution, a predicted distribution, a mean squared error value associated with the one or more expected operations, or a combination thereof (Fig. 7, Fig. 8, [0020] FIGS. 9A-B are graphs illustrating curves depicting distributions before and after using embodiments of the present invention, [0058] FIGS. 9A-B show statistical distribution of predicted removal values estimated by the initial model and estimated by the embodiments of the present invention, respectively when they are compared with the actual removal values)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gao’s teaching of displaying information to the user with Kokotov’s teaching of visualization graphs displaying distributions. The combined teaching provides an expected result of displaying visualization graphs showing distributions to the user. Therefore, one of ordinary skill in the art would be motivated to improve accuracy of predictions, support shown by Kokotov [0005] “as is widely practiced today, the resulting predictions can become inaccurate”.
Regarding claim 6, the combination of Gao and Reynolds teach The system of claim 4,
The combination of Gao and Reynolds do not teach wherein the visualization comprises a plot, wherein the plot comprises a first line associated with a predicted value based on the set of prediction data and a second line associated with an expected value based on the set of data
Kokotov teaches wherein the visualization comprises a plot, wherein the plot comprises a first line associated with a predicted value based on the set of prediction data and a second line associated with an expected value based on the set of data (Fig. 7, Fig. 8, [0020] FIGS. 9A-B are graphs illustrating curves depicting distributions before and after using embodiments of the present invention, [0058] FIGS. 9A-B show statistical distribution of predicted removal values estimated by the initial model and estimated by the embodiments of the present invention, respectively when they are compared with the actual removal values)
Regarding claim 19, the combination of Gao and Reynolds teach The method of claim 18, The combination of Gao and Reynolds do not teach wherein the visualization comprises an original distribution, a predicted distribution, a mean squared error value associated with the one or more expected operations, or a combination thereof.
Kokotov teaches wherein the visualization comprises an original distribution, a predicted distribution, a mean squared error value associated with the one or more expected operations, or a combination thereof (Fig. 7, Fig. 8, [0020] FIGS. 9A-B are graphs illustrating curves depicting distributions before and after using embodiments of the present invention, [0058] FIGS. 9A-B show statistical distribution of predicted removal values estimated by the initial model and estimated by the embodiments of the present invention, respectively when they are compared with the actual removal values)
Regarding claim 20, the combination of Gao and Reynolds teach The method of claim 18,
The combination of Gao and Reynolds do not teach wherein the visualization comprises a plot, wherein the plot comprises a first line associated with a predicted value based on the set of prediction data and a second line associated with an expected value based on the set of data
Kokotov teaches wherein the visualization comprises a plot, wherein the plot comprises a first line associated with a predicted value based on the set of prediction data and a second line associated with an expected value based on the set of data (Fig. 7, Fig. 8, [0020] FIGS. 9A-B are graphs illustrating curves depicting distributions before and after using embodiments of the present invention, [0058] FIGS. 9A-B show statistical distribution of predicted removal values estimated by the initial model and estimated by the embodiments of the present invention, respectively when they are compared with the actual removal values)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
a) Zarur (US20220027529) discloses control system based digital twin for supervisory control.
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/YVONNE T FOLLANSBEE/
Examiner, Art Unit 2117
/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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