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 .
Status of Claims
This is in response to applicant’s filing date of May 30, 2024. Claims 1-20 are currently pending.
Priority
Regarding U.S. Provisional Patent Application Nos. 63/469684, filed May 30, 2023, Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120 is acknowledged.
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 1-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. As to claim 1, the claim recites a system for modelling information about an industrial process and constructing/presenting a visualization of the modelled industrial process.
The limitation of modelling an industrial process and constructing/presenting a visualization of the modelled industrial process, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation as a manual modelling and writing the outputs of the industrial process but for the recitation of generic computer components. That is, other than reciting “a processor” and “a memory” (as recited in the system claim 1), nothing in the claim element precludes the step from practically being performed by a user manually modelling the industrial process using mathematical equations, determining the outputs of the model and writing the outputs of the model. For example, but for the “processor” and “memory” language, the modelling and “constructing/presenting” in the context of this claim encompasses the user manually modelling the industrial process using mathematical equations. Similarly, the step of constructing/presenting a visualization, is a process that, under its broadest reasonable interpretation, covers performance of the user manually determining the outputs of the model using the mathematical equations and writing the outputs down. 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 particularly, the claim only recites two additional elements – using a processor and memory to perform the modelling, constructing and presenting steps. The processors and memories in these steps is recited at a high-level of generality (i.e., as a generic processors and memories performing a generic computer function of modelling an industrial process and constructing/presenting its outputs) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an 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 elements of using processors and memories to perform the steps of modelling, constructing and presenting amounts to no more than mere instruction to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As per claims 2-8 and 10-12 depends from claim 1, and thus recites a similar limitation of modelling and visualizing an industrial process. For the reasons discussed for claim 1, this limitation recites an abstract idea. The steps of visualizing, storing, labeling the variables as dependent or independent, applying statistical techniques, and updating with additional inputs merely represents instructions to apply the judicial exceptions on a computer and constraining both the inputs and outputs of the mathematical model of the industrial process. Thus, the additional elements does not integrate the recited judicial exception into a practical application and these claims are directed to an abstract idea.
As per claim 9, which recites “adjusting one or more operations of the industrial automation equipment based on the one or more anomalies,” recites additional elements that amount to significantly more than the judicial exception.
Claim 13 recites the same limitations as claim 1 except as a readable medium, these claims are likewise rejected as being directed to an abstract idea. While each of these limitations, as drafted, are a simple process/acts that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of by a “processing circuitry” and by a “database” or memory/storage unit. That is, other than reciting “by a unit” nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the by a “processing circuitry” language, the claim encompasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of by a processor/controller or the like does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Claim 18 recites the same limitations as claim 1 except as a method, these claims are likewise rejected as being directed to an abstract idea. While each of these limitations, as drafted, are a simple process/acts that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of by a “processing circuitry” and by a “database” or memory/storage unit. That is, other than reciting “by a unit” nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the by a “processing circuitry” language, the claim encompasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of by a processor/controller or the like does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Claims 14-17 and 19-20 recite the same limitations as claims 2-8 and 10-12, these claims are likewise rejected as being directed to an abstract idea.
Thus, since independent claims 1, 13, and 18 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that the independent claims are directed towards non-statutory subject matter. Therefore, the invention of claims 1-8 and 10-20 as a whole, considering all claim elements both individually and in combination, are not patent eligible under 35 USC §101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bell et al (US-20170102696-A1)(“Bell”), Chand et al (US-20200326684 A1)(“Chand”), and Fabrice Ravignon (US-20240202617-A1)(“Ravignon”).
As per claim 1, Bell discloses a system (Figure 1), comprising:
industrial automation equipment of an industrial system (Bell at Para. [0087] discloses an industrial system:” FIG. 1 is a detailed block diagram of an example process plant or process control environment 5 that includes or supports any or all of the distributed industrial process monitoring and analytics techniques described herein. The process control system 5 includes multiple distributed data engines of a distributed industrial process monitoring and analytics system that is included in, integrated with, or supported by the process control plant or environment 5.”);
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 (Bell at Figure 5A, distributed data engine 150 and analytic service 500, and Para. [0285] disclosing processor and memory for performing certain functions: Analytics Service 500 includes a web client process 502, a web server process 504, an execution service process 506, and one or more job processes 508. The various processes 502, 504, 506, 508 may execute on one or more processors, in one or more workstations or servers, in one or more physical and/or geographical locations. That is to say, while an instance of the web client process 502 may be executing on a workstation remote from a server on which the web server process 504 is executing, another instance of the web client process 502 may be executing on the same server on which the web server process 504 is executing and, in fact, may be running on the same processor. As another example, the execution service process 506 may be running on a workstation in the confines of the process control environment, while the one or more job processes 508 may be executing on one or more processors of a distributed process environment (e.g., a server farm) located remotely from the process control environment.”) comprising:
receiving a request to generate a model representative of one or more expected operations of the industrial automation equipment (Bell at Figure 5A, web client process 502, and Para. [0286] disclosing a web client process for requesting and rendering a model of an industrial process:” DDE User Interface Application described above. For instance, the web client process 502 includes a variety of software entities including, for instance: a viewing entity 510 which presents the user with the DDE User Interface Application (e.g., the canvas 245 and user controls 248a-n of the Data Analytics Studio 240, the Data Analytics Dashboard, etc.); a view model entity 512 which manages the transfer and translation of application data such as block state to a form that a user interface can utilized, and which generates a view that provides feedback from and/or that is otherwise indicative of the operation of an on-line data module; an application entity 514, which is the software entity (i.e., web-based application) downloaded to the web client process 502 and resident on the client workstation or device that the user uses to interact with the DDE User Interface Application; and a data services entity 516 that passes data to and from the DDE User Interface Application.”);
receiving data associated with the industrial automation equipment (Bell at Para. [0287] receiving data from the modeled process:” data services entity 516 receives, for example, data returned from various jobs executed in response to the user inputs and requests. As described above, and in additional detail below, the DDE User Interface Application may request various analytics be run on data from the process control environment (and in some cases, being currently generated by the process control environment).”);
retrieving one or more pre-processing files and one or more training datasets files associated with the model from a database (Bell at Figure 5C, blocks 553-559, and Para. [0295] discloses retrieving a file associated with the model:” When the block is placed on the canvas 245, the application entity 514 retrieves the corresponding block definition 255 from the data services entity 520 or, in embodiments, from the database 529 (block 553). Thereafter, application entity 514 may receive a command to display the properties dialog for the block that was placed on the canvas 245 (block 555), for example, when the user double-clicks on the block.”),;
receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display (Bell at Para. [0315] discloses receiving inputs to modify the parameters of the blocks describing the industrial process:” configuration file includes only an identification of the block and the required configuration parameters, and the block definition is retrieved from memory (e.g., from the block definition library 252). Regardless, the configuration parameters may vary according to the block definition. Some blocks may have zero configuration parameters, while others may have one, two, or many configuration parameters. Additionally, the configuration parameters may be required or optional. For example, a data load block (a block that loads a set of data) or a query block (a block that searches for specific data in a data set) may require a data path that specifies the location of the data to be loaded or queried.”); and
generating the model based on the training dataset files and the one or more inputs (Bell at Para. [0351] discloses generating a model that represents the process and uses live data to show how the parameters of the devices forming the process change:” DDE User Interface Application includes functionality that allows it to convert an offline diagram (such as the offline diagram 602) to an online diagram (i.e., one using at least one real-time value to predict an aspect of plant operation). As described above, an online diagram differs from the offline diagrams in that it is bound to at least one real-time data source (rather than purely historized data), and provides a real-time, continuous predictive output, which can be viewed, stored, and/or used in a control algorithm to trigger alarms, alerts, and/or effect changes in the operation of the process plant.”).
Bell does not explicitly disclose the inclusion of a file wherein the one or more pre-processing files are configured to transform the data.
Chand teaches to wherein the one or more pre-processing files are configured to transform the data (Chand at Para. [0047] discloses a pre-processing file that can transform the data to be normalized to the model of the industrial process:” “The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items. Model configuration component 406 can transform this model template to a customized model based on user input that maps the generic data items defined by the model template to actual sources of the data discovered by the device interface component 404.”).
Bell and Chand are analogous art because they are from the same field of endeavor in data modeling and integration. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Bell and Chand to incorporate Chand’s UI mapping and model library to facilitate data mapping and integration with internal and external applications. “the executable components comprising: user interface component configured to receive selection data selecting a model template, of the model templates, associated with a business objective of the business objectives, wherein the model template defines data inputs and relationships between the data inputs relevant to the business objective” (Chand, at Para. [0003])
Bell and Chand do not explicitly disclose the inclusion of a file wherein the one or more training dataset files are representative of one or more operational characteristics of the industrial automation equipment over time .
Ravignon teaches wherein the one or more training dataset files (Ravignon at Para. [0273] discloses a training dataset:” presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14.”) are representative of one or more operational characteristics of the industrial automation equipment over time (Ravignon at Para. [0273] discloses that the dataset is form from a collection of observed data of the machines at different locations and times:” [0274] recovering with transmission, with a view to merging them, raw datasets 150 extracted from several facilities 3 operating the equipment of the same series as the item of equipment 2, followed by conversions 141 applied to these data, to constitute a dataset 15; [0275] presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14. By thus modeling the behavior of the equipment 2 (or the behavior of the equipment of the series), the invention correlates, on the scale of the series, the behavior observed (namely the status 13) of each item of equipment 2 with the manufacturing and maintenance log 9 and with the usage log 11 of said equipment 2.”).
Bell, Chand, and Ravignon are analogous art because they are from the same field of endeavor in data modeling and integration. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bell and Chand further in view of Ravignon to allow for generating a model of industrial devices that includes training dataset from data collected over a period of time . Motivation to do so would allow for a manufacturing or industrial maintenance entity “to deduce the future behavior of the equipment from the past behavior observed, thus endeavoring to deduce the future result of aging, independently of the past and future causes of aging (Ravignon at Paras. [0045]-[0047]).
As per claim 2, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the operations further comprise generating a visualization for display via the electronic display, wherein the visualization comprises the one or more inputs to modify the one or more parameters (Bell at Paras. [0175]-[0180] discloses visualizing blocks to modify parameters:” a user control 248g via which a user may view and/or define properties of the data module that is currently open on the canvas 245; [0176] a user control 248h via which a user may save the currently open data module; [0177] a user control 248i via which a user may evaluate at least a portion of the currently open data module; [0178] a user control 248j via which a user may deploy the currently open data module; [0179] an indicator 248k that is indicative of an operational status of the currently open module; and/or [0180] one or more other user controls and/or indicators (not shown).”).
As per claim 3, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the operations further comprise storing the one or more training dataset files, and an association between the one or more training dataset files, the one or more inputs, and the model in the database (Bell at Figure 4J, storage 312, and Para. [0211] discloses a data storage for the datasets:” Block1-specific results 310 are generated and stored into a local or remote storage area 312 that is managed by the DDE User Application Interface. At a Data Studio instance 315 (e.g., a browser window), upon user selection of the “view block results” user control 212 displayed on the Block1 graphic, the computed statistics 308 (e.g., the standard set and/or any custom visualizations) for Block1 are loaded 318 to the Data Studio instance 315, and the user is able to select desired columns, tags, or portions of interest.”).
As per claim 4, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the one or more inputs comprise one or more independent variables, one or more dependent variables, a threshold for a target variable associated with the model, or any combination thereof (Bell at Para. [0006] disclosing that the analytic identifies variable and their dependency on upstream process variables :” the DeltaV™ batch analytics product or continuous data analytics tool to attempt to determine the contributions of various process variables and/or measurements to an abnormal or fault condition. Typically, a user decides which historical data logs and/or other time-series data to feed into the analytics tool and identifies candidate upstream factors (e.g., measurements, process variables, etc.) based on his or her knowledge of the process.”).
As per claim 5, Bell, Chand, and Ravignon disclose a system of claim 4, wherein the one or more independent variables and the one or more dependent variables correspond to a set of process variables associated with the model (Bell at Para. [0041] discloses working with process variables:” the frequency analysis analytics technique may create a new set of process variables corresponding to identified leading indicators of faults, abnormalities, decreases in performance, target performance levels, undesired conditions, and/or desired conditions, and may determine time-series data of the new process variables by performing a rolling FFT on streamed process data. The rolling FFT may convert the streamed process data from the time domain into the frequency domain, in which values of the new set of process variables may be determined. The determined values of the new process variables may be transformed back into the time domain for monitoring.”).
As per claim 6, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the one or more inputs comprise a selection of one or more visualizations for presenting one or more outputs of the model (Bell at Figure 4D, analytics studio 240, and Para. [0171] disclosing that a user is able to make changes and view operations of a module:” the Data Analytics Studio 240, which includes a navigation bar 242 and a workspace or drawing canvas 245. The navigation bar 242 provides controls and indicators via which a user is able to manage off-line and on-line data modules, e.g., by allowing a user to perform actions such as create a new off-line data module, identify an off-line or on-line data module that is currently open and shown in the canvas 245, readily view the status (e.g., off-line or on-line) of a data module that is currently open and shown in the canvas 245, save/store an off-line data module that is currently open and shown in the canvas 245, transform an off-line module into an on-line data module, toggle between viewing the off-line and the on-line data diagram of a data module, evaluate an off-line data module, deploy an on-line data module, browse to other data modules, and other such module management functions.”).
As per claim 7, Bell, Chand, and Ravignon disclose a system of claim 6, wherein the one or more visualizations comprise a Q-statistics plot, a T2-statistics plot, a variance ratio plot, a squared prediction error batch identification, a Raman spectrum, or any combination thereof (Bell at Para. [0210] discloses visualization of statistical analysis in the form of charts (Fig. 4N-2) and the like:” when a block is executed, a standard set of various statistics may be collected about the state of the data at the end of the block's execution, so that for each column, tag, or portion of the dataset, the mean, standard deviation and other such statistics may be computed and stored along with the resultant dataset. When the visualization dialog of a particular block instance is presented (e.g., via activation of the respective user control 212), the computed set of standard statistics for each column, tag, or portion is retrieved from the data store and presented to the user. The user may then select the columns/tags/portions of interest and request the Data Studio to generate respective charts or other visual formats that represent the statistics of said columns/tags/portions (e.g., line chart, scatter chart, histogram, data grid, data summary grid, computed statistics and histogram showing distribution of data, etc.).”).
As per claim 8, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the operations comprise receiving one or more additional inputs for modifying one or more pre-processing parameters associated with the one or more pre-processing files (Bell at Par. [0236] updating with live data and the like:” summary information for each live data stream may include an identifier of the data stream, an indication of the data source from which the data stream is being received, an indication of a corresponding process control system tag or other traditional process control system identifier of the live data source, information about subscription(s) to and/or the publication of the data stream, an indication of the one or more on-line data modules that are currently executing on the live data stream, a continuously updated visualization of the live data stream (e.g., line graph, bar chart, scatterplot, etc. and/or basic statistics thereof), and/or other information.”).
As per claim 9, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the operations comprise:
performing a simulation of the one or more expected operations of the industrial automation equipment (Ravignon at Para. [0071] discloses the use of simulation:” behavior model generated is powerful: it makes it possible to simulate the behavior of an item of equipment of the series, for a given maintenance decision and a projected usage of the given item of equipment, especially taking into account the manufacturing, maintenance and usage log of the equipment.”) to determine one or more anomalies based on the model (Ravignon at Para. [0429] discloses analyzing data to determine an abnormal condition:” during operation, due to an actual usage of the equipment 2 (more restrictive for the equipment 2 than the initially foreseen scenario 8), during the elapsed part of the projected period 70, and due to any abnormal operating transients.”); and
adjusting one or more operations of the industrial automation equipment based on the one or more anomalies (Ravignon at Para. [0430] discloses adjusting the operation so as mitigate for potential failure:” during operation, the limit usage compatible with zero failures up to the next projected maintenance 7, the invention foresees allowing the user to thus identify the maximal limits 18 up to which they can push the usage of the equipment without compromising zero failures or in which it must restrict the initially envisaged usage in order to benefit from zero failures.”).
As per claim 10, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the user interface comprises a pre-procession code editor configured to manage an organization of the data (Bell at Para. [0170) discloses an editor or studio virtual interface for selecting and manipulating modules which under broadest interpretation is a coding editor:” FIG. 4B depicts an example user interface presented by the Data Analytics Studio 240, which includes a navigation bar 242 and a workspace or drawing canvas 245. The navigation bar 242 provides controls and indicators via which a user is able to manage off-line and on-line data modules, e.g., by allowing a user to perform actions such as create a new off-line data module, identify an off-line or on-line data module that is currently open and shown in the canvas 245, readily view the status (e.g., off-line or on-line) of a data module that is currently open and shown in the canvas 245, save/store an off-line data module that is currently open and shown in the canvas 245, transform an off-line module into an on-line data module, toggle between viewing the off-line and the on-line data diagram of a data module, evaluate an off-line data module, deploy an on-line data module, browse to other data modules, and other such module management functions. As such, the Data Analytics Studio 240 includes numerous user controls and indicators 248a-248n thereon”).
As per claim 11, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the user interface comprises a predictor data and target data selection to transform the data (Bell at Para. [0121] discloses the use of a prediction module for analyzing the stored dataset: performance monitoring/analytics applications operating on or in conjunction with the platform may include, e.g., inferred measurements, equipment monitoring, fault detection, process predictions, causality, other monitoring applications, and/or other analytics applications. Techniques that may be utilized by the applications include data mining, optimization, predictive modeling, machine learning, simulation, distributed state estimation, and the like. As such, performance monitoring/analytics applications may be used to monitor, predict, and diagnose performance degradation and faults of any number of any portions of the process control system 5, including in areas such as instrumentation, equipment, control, user interactions, and process.”).
As per claim 12, Bell, Chand, and Ravignon disclose a system of claim 1, wherein the user interface comprises a range selection to select a number of process variables associated with the model for display via the electronic display (Bell at Para. [0122] discloses selecting a range of variable (critical process) for monitoring and analyzing:” distributed industrial performance monitoring and analytics techniques described herein may include inferential mechanisms that provide continuous on-line estimations of critical process variables from readily available process measurements. To sustain data models over an extended period of time, the system further may support the ability to monitor, tune and enhance the data models.”).
As per claim 13, Bell discloses a non-transitory, tangible, computer-readable medium storing instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations(Figures 1 & 2B) comprising:
receiving data associated with one or more industrial devices of an industrial system (Bell at Para. [0287] receiving data from the modeled process:” data services entity 516 receives, for example, data returned from various jobs executed in response to the user inputs and requests. As described above, and in additional detail below, the DDE User Interface Application may request various analytics be run on data from the process control environment (and in some cases, being currently generated by the process control environment).”);
retrieving one or more pre-processing files and one or more training datasets files associated with the model from a database (Bell at Figure 5C, blocks 553-559, and Para. [0295] discloses retrieving a file associated with the model:” When the block is placed on the canvas 245, the application entity 514 retrieves the corresponding block definition 255 from the data services entity 520 or, in embodiments, from the database 529 (block 553). Thereafter, application entity 514 may receive a command to display the properties dialog for the block that was placed on the canvas 245 (block 555), for example, when the user double-clicks on the block.”),;
receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display (Bell at Para. [0315] discloses receiving inputs to modify the parameters of the blocks describing the industrial process:” configuration file includes only an identification of the block and the required configuration parameters, and the block definition is retrieved from memory (e.g., from the block definition library 252). Regardless, the configuration parameters may vary according to the block definition. Some blocks may have zero configuration parameters, while others may have one, two, or many configuration parameters. Additionally, the configuration parameters may be required or optional. For example, a data load block (a block that loads a set of data) or a query block (a block that searches for specific data in a data set) may require a data path that specifies the location of the data to be loaded or queried.”); and
generating the model based on the training dataset files and the one or more inputs (Bell at Para. [0351] discloses generating a model that represents the process and uses live data to show how the parameters of the devices forming the process change:” DDE User Interface Application includes functionality that allows it to convert an offline diagram (such as the offline diagram 602) to an online diagram (i.e., one using at least one real-time value to predict an aspect of plant operation). As described above, an online diagram differs from the offline diagrams in that it is bound to at least one real-time data source (rather than purely historized data), and provides a real-time, continuous predictive output, which can be viewed, stored, and/or used in a control algorithm to trigger alarms, alerts, and/or effect changes in the operation of the process plant.”).
Bell does not explicitly disclose the inclusion of a file wherein the one or more pre-processing files are configured to transform the data.
Chand teaches to wherein the one or more pre-processing files are configured to transform the data (Chand at Para. [0047] discloses a pre-processing file that can transform the data to be normalized to the model of the industrial process:” “The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items. Model configuration component 406 can transform this model template to a customized model based on user input that maps the generic data items defined by the model template to actual sources of the data discovered by the device interface component 404.”).
Bell and Chand are analogous art because they are from the same field of endeavor in data modeling and integration. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Bell and Chand to incorporate Chand’s UI mapping and model library to facilitate data mapping and integration with internal and external applications. “the executable components comprising: user interface component configured to receive selection data selecting a model template, of the model templates, associated with a business objective of the business objectives, wherein the model template defines data inputs and relationships between the data inputs relevant to the business objective” (Chand, at Para. [0003])
Bell and Chand do not explicitly disclose the inclusion of a file wherein the one or more training dataset files are representative of one or more operational characteristics of the industrial automation equipment over time .
Ravignon teaches wherein the one or more training dataset files (Ravignon at Para. [0273] discloses a training dataset:” presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14.”) are representative of one or more operational characteristics of the industrial automation equipment over time (Ravignon at Para. [0273] discloses that the dataset is form from a collection of observed data of the machines at different locations and times:” [0274] recovering with transmission, with a view to merging them, raw datasets 150 extracted from several facilities 3 operating the equipment of the same series as the item of equipment 2, followed by conversions 141 applied to these data, to constitute a dataset 15; [0275] presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14. By thus modeling the behavior of the equipment 2 (or the behavior of the equipment of the series), the invention correlates, on the scale of the series, the behavior observed (namely the status 13) of each item of equipment 2 with the manufacturing and maintenance log 9 and with the usage log 11 of said equipment 2.”).
Bell, Chand, and Ravignon are analogous art because they are from the same field of endeavor in data modeling and integration. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bell and Chand further in view of Ravignon to allow for generating a model of industrial devices that includes training dataset from data collected over a period of time . Motivation to do so would allow for a manufacturing or industrial maintenance entity “to deduce the future behavior of the equipment from the past behavior observed, thus endeavoring to deduce the future result of aging, independently of the past and future causes of aging (Ravignon at Paras. [0045]-[0047]).
As per claim 14, Bell, Chand, and Ravignon disclose a non-transitory, tangible, computer-readable medium of claim 13, wherein the instructions cause the processing circuitry to perform operations comprising storing the one or more training dataset files, and an association between the one or more training dataset files, the one or more inputs, and the model in the database (Bell at Figure 4J, storage 312, and Para. [0211] discloses a data storage for the datasets:” Block1-specific results 310 are generated and stored into a local or remote storage area 312 that is managed by the DDE User Application Interface. At a Data Studio instance 315 (e.g., a browser window), upon user selection of the “view block results” user control 212 displayed on the Block1 graphic, the computed statistics 308 (e.g., the standard set and/or any custom visualizations) for Block1 are loaded 318 to the Data Studio instance 315, and the user is able to select desired columns, tags, or portions of interest.”).
As per claim 15, Bell, Chand, and Ravignon disclose a non-transitory, tangible, computer-readable medium of claim 13, wherein the data comprises operational data associated with the one or more industrial devices, environmental data associated with the one or more industrial devices (Bell at Para. [0087] discloses an industrial system:” FIG. 1 is a detailed block diagram of an example process plant or process control environment 5 that includes or supports any or all of the distributed industrial process monitoring and analytics techniques described herein. The process control system 5 includes multiple distributed data engines of a distributed industrial process monitoring and analytics system that is included in, integrated with, or supported by the process control plant or environment 5.”) (Bell at Para. [0087] discloses an industrial system:” FIG. 1 is a detailed block diagram of an example process plant or process control environment 5 that includes or supports any or all of the distributed industrial process monitoring and analytics techniques described herein. The process control system 5 includes multiple distributed data engines of a distributed industrial process monitoring and analytics system that is included in, integrated with, or supported by the process control plant or environment 5.”), energy consumption data associated with the one or more industrial devices, diagnostic data associated with the one or more industrial devices (Bell at Para. [0125] discloses monitoring energy and other parameters of the process:” operates under physical constraints such as limited energy and the need for adequate heat dissipation, as it may be embedded into a process control device such as a temperature or other type of sensor.”), historical data associated with the one or more industrial devices, or any combination thereof (Bell at Para. [0006] disclosing that the analytic identifies variable and their dependency on upstream process variables :” the DeltaV™ batch analytics product or continuous data analytics tool to attempt to determine the contributions of various process variables and/or measurements to an abnormal or fault condition. Typically, a user decides which historical data logs and/or other time-series data to feed into the analytics tool and identifies candidate upstream factors (e.g., measurements, process variables, etc.) based on his or her knowledge of the process.”).
As per claim 16, Bell, Chand, and Ravignon disclose a non-transitory, tangible, computer-readable medium of claim 13, wherein the instructions cause the processing circuitry to perform operations comprising generating a visualization for display via the electronic display, wherein the visualization comprises the one or more inputs to modify the one or more parameters (Bell at Paras. [0175]-[0180] discloses visualizing blocks to modify parameters:” a user control 248g via which a user may view and/or define properties of the data module that is currently open on the canvas 245; [0176] a user control 248h via which a user may save the currently open data module; [0177] a user control 248i via which a user may evaluate at least a portion of the currently open data module; [0178] a user control 248j via which a user may deploy the currently open data module; [0179] an indicator 248k that is indicative of an operational status of the currently open module; and/or [0180] one or more other user controls and/or indicators (not shown).”).
As per claim 17, Bell, Chand, and Ravignon disclose a non-transitory, tangible computer-readable medium of claim 13, wherein the one or more inputs comprise one or more independent variables corresponding to a set of process variables associated with the model, one or more dependent variables corresponding to the set of process variables associated with the model, a threshold for a target variable associated with the model, or any combination thereof (Bell at Para. [0006] disclosing that the analytic identifies variable and their dependency on upstream process variables :” the DeltaV™ batch analytics product or continuous data analytics tool to attempt to determine the contributions of various process variables and/or measurements to an abnormal or fault condition. Typically, a user decides which historical data logs and/or other time-series data to feed into the analytics tool and identifies candidate upstream factors (e.g., measurements, process variables, etc.) based on his or her knowledge of the process.”).
As per claim 18, Bell discloses a method (Figures 4P-5A.) comprising:
receiving , via processing circuitry, data associated with one or more industrial devices of an industrial system (Bell at Para. [0287] receiving data from the modeled process:” data services entity 516 receives, for example, data returned from various jobs executed in response to the user inputs and requests. As described above, and in additional detail below, the DDE User Interface Application may request various analytics be run on data from the process control environment (and in some cases, being currently generated by the process control environment).”);
retrieving via the processing circuitry one or more pre-processing files and one or more training datasets files associated with the model from a database (Bell at Figure 5C, blocks 553-559, and Para. [0295] discloses retrieving a file associated with the model:” When the block is placed on the canvas 245, the application entity 514 retrieves the corresponding block definition 255 from the data services entity 520 or, in embodiments, from the database 529 (block 553). Thereafter, application entity 514 may receive a command to display the properties dialog for the block that was placed on the canvas 245 (block 555), for example, when the user double-clicks on the block.”),;
receiving, via the processing circuitry. one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display (Bell at Para. [0315] discloses receiving inputs to modify the parameters of the blocks describing the industrial process:” configuration file includes only an identification of the block and the required configuration parameters, and the block definition is retrieved from memory (e.g., from the block definition library 252). Regardless, the configuration parameters may vary according to the block definition. Some blocks may have zero configuration parameters, while others may have one, two, or many configuration parameters. Additionally, the configuration parameters may be required or optional. For example, a data load block (a block that loads a set of data) or a query block (a block that searches for specific data in a data set) may require a data path that specifies the location of the data to be loaded or queried.”); and
generating, via the processing circuitry, the model based on the training dataset files and the one or more inputs (Bell at Para. [0351] discloses generating a model that represents the process and uses live data to show how the parameters of the devices forming the process change:” DDE User Interface Application includes functionality that allows it to convert an offline diagram (such as the offline diagram 602) to an online diagram (i.e., one using at least one real-time value to predict an aspect of plant operation). As described above, an online diagram differs from the offline diagrams in that it is bound to at least one real-time data source (rather than purely historized data), and provides a real-time, continuous predictive output, which can be viewed, stored, and/or used in a control algorithm to trigger alarms, alerts, and/or effect changes in the operation of the process plant.”).
Bell does not explicitly disclose the inclusion of a file wherein the one or more pre-processing files are configured to transform the data.
Chand teaches to wherein the one or more pre-processing files are configured to transform the data (Chand at Para. [0047] discloses a pre-processing file that can transform the data to be normalized to the model of the industrial process:” “The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items. Model configuration component 406 can transform this model template to a customized model based on user input that maps the generic data items defined by the model template to actual sources of the data discovered by the device interface component 404.”).
Bell and Chand are analogous art because they are from the same field of endeavor in data modeling and integration. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Bell and Chand to incorporate Chand’s UI mapping and model library to facilitate data mapping and integration with internal and external applications. “the executable components comprising: user interface component configured to receive selection data selecting a model template, of the model templates, associated with a business objective of the business objectives, wherein the model template defines data inputs and relationships between the data inputs relevant to the business objective” (Chand, at Para. [0003])
Bell and Chand do not explicitly disclose the inclusion of a file wherein the one or more training dataset files are representative of one or more operational characteristics of the industrial automation equipment over time .
Ravignon teaches wherein the one or more training dataset files (Ravignon at Para. [0273] discloses a training dataset:” presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14.”) are representative of one or more operational characteristics of the industrial automation equipment over time (Ravignon at Para. [0273] discloses that the dataset is form from a collection of observed data of the machines at different locations and times:” [0274] recovering with transmission, with a view to merging them, raw datasets 150 extracted from several facilities 3 operating the equipment of the same series as the item of equipment 2, followed by conversions 141 applied to these data, to constitute a dataset 15; [0275] presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14. By thus modeling the behavior of the equipment 2 (or the behavior of the equipment of the series), the invention correlates, on the scale of the series, the behavior observed (namely the status 13) of each item of equipment 2 with the manufacturing and maintenance log 9 and with the usage log 11 of said equipment 2.”).
Bell, Chand, and Ravignon are analogous art because they are from the same field of endeavor in data modeling and integration. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bell and Chand further in view of Ravignon to allow for generating a model of industrial devices that includes training dataset from data collected over a period of time . Motivation to do so would allow for a manufacturing or industrial maintenance entity “to deduce the future behavior of the equipment from the past behavior observed, thus endeavoring to deduce the future result of aging, independently of the past and future causes of aging (Ravignon at Paras. [0045]-[0047]). receiving data associated with one or more industrial devices of an industrial system (Bell at Para. [0287] receiving data from the modeled process:” data services entity 516 receives, for example, data returned from various jobs executed in response to the user inputs and requests. As described above, and in additional detail below, the DDE User Interface Application may request various analytics be run on data from the process control environment (and in some cases, being currently generated by the process control environment).”);
retrieving one or more pre-processing files and one or more training datasets files associated with the model from a database (Bell at Figure 5C, blocks 553-559, and Para. [0295] discloses retrieving a file associated with the model:” When the block is placed on the canvas 245, the application entity 514 retrieves the corresponding block definition 255 from the data services entity 520 or, in embodiments, from the database 529 (block 553). Thereafter, application entity 514 may receive a command to display the properties dialog for the block that was placed on the canvas 245 (block 555), for example, when the user double-clicks on the block.”),;
receiving one or more inputs to modify one or more parameters of the model via a user interface presented via an electronic display (Bell at Para. [0315] discloses receiving inputs to modify the parameters of the blocks describing the industrial process:” configuration file includes only an identification of the block and the required configuration parameters, and the block definition is retrieved from memory (e.g., from the block definition library 252). Regardless, the configuration parameters may vary according to the block definition. Some blocks may have zero configuration parameters, while others may have one, two, or many configuration parameters. Additionally, the configuration parameters may be required or optional. For example, a data load block (a block that loads a set of data) or a query block (a block that searches for specific data in a data set) may require a data path that specifies the location of the data to be loaded or queried.”); and
generating the model based on the training dataset files and the one or more inputs (Bell at Para. [0351] discloses generating a model that represents the process and uses live data to show how the parameters of the devices forming the process change:” DDE User Interface Application includes functionality that allows it to convert an offline diagram (such as the offline diagram 602) to an online diagram (i.e., one using at least one real-time value to predict an aspect of plant operation). As described above, an online diagram differs from the offline diagrams in that it is bound to at least one real-time data source (rather than purely historized data), and provides a real-time, continuous predictive output, which can be viewed, stored, and/or used in a control algorithm to trigger alarms, alerts, and/or effect changes in the operation of the process plant.”).
Bell does not explicitly disclose the inclusion of a file wherein the one or more pre-processing files are configured to transform the data.
Chand teaches to wherein the one or more pre-processing files are configured to transform the data (Chand at Para. [0047] discloses a pre-processing file that can transform the data to be normalized to the model of the industrial process:” “The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items. Model configuration component 406 can transform this model template to a customized model based on user input that maps the generic data items defined by the model template to actual sources of the data discovered by the device interface component 404.”).
Bell and Chand are analogous art because they are from the same field of endeavor in data modeling and integration. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Bell and Chand to incorporate Chand’s UI mapping and model library to facilitate data mapping and integration with internal and external applications. “the executable components comprising: user interface component configured to receive selection data selecting a model template, of the model templates, associated with a business objective of the business objectives, wherein the model template defines data inputs and relationships between the data inputs relevant to the business objective” (Chand, at Para. [0003])
Bell and Chand do not explicitly disclose the inclusion of a file wherein the one or more training dataset files are representative of one or more operational characteristics of the industrial automation equipment over time .
Ravignon teaches wherein the one or more training dataset files (Ravignon at Para. [0273] discloses a training dataset:” presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14.”) are representative of one or more operational characteristics of the industrial automation equipment over time (Ravignon at Para. [0273] discloses that the dataset is form from a collection of observed data of the machines at different locations and times:” [0274] recovering with transmission, with a view to merging them, raw datasets 150 extracted from several facilities 3 operating the equipment of the same series as the item of equipment 2, followed by conversions 141 applied to these data, to constitute a dataset 15; [0275] presenting said dataset 15 as the input of a machine-learning project 142 and training, on the basis of said dataset 15, a virtual model 16 modeling the correlation 14. By thus modeling the behavior of the equipment 2 (or the behavior of the equipment of the series), the invention correlates, on the scale of the series, the behavior observed (namely the status 13) of each item of equipment 2 with the manufacturing and maintenance log 9 and with the usage log 11 of said equipment 2.”).
Bell, Chand, and Ravignon are analogous art because they are from the same field of endeavor in data modeling and integration. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bell and Chand further in view of Ravignon to allow for generating a model of industrial devices that includes training dataset from data collected over a period of time . Motivation to do so would allow for a manufacturing or industrial maintenance entity “to deduce the future behavior of the equipment from the past behavior observed, thus endeavoring to deduce the future result of aging, independently of the past and future causes of aging (Ravignon at Paras. [0045]-[0047]).
As per claim 19, Bell, Chand, and Ravignon disclose a method of claim 18, comprising receiving, via the processing circuitry, one or more additional inputs for modifying one or more pre-processing parameters associated with the one or more pre-processing files (Bell at Par. [0236] updating with live data and the like:” summary information for each live data stream may include an identifier of the data stream, an indication of the data source from which the data stream is being received, an indication of a corresponding process control system tag or other traditional process control system identifier of the live data source, information about subscription(s) to and/or the publication of the data stream, an indication of the one or more on-line data modules that are currently executing on the live data stream, a continuously updated visualization of the live data stream (e.g., line graph, bar chart, scatterplot, etc. and/or basic statistics thereof), and/or other information.”).
As per claim 20, Bell, Chand, and Ravignon disclose a method of claim 18, comprising storing, via the processing circuitry, the one or more training dataset files, and an association between the one or more training dataset files, the one or more inputs, and the model in the database (Bell at Figure 4J, storage 312, and Para. [0211] discloses a data storage for the datasets:” Block1-specific results 310 are generated and stored into a local or remote storage area 312 that is managed by the DDE User Application Interface. At a Data Studio instance 315 (e.g., a browser window), upon user selection of the “view block results” user control 212 displayed on the Block1 graphic, the computed statistics 308 (e.g., the standard set and/or any custom visualizations) for Block1 are loaded 318 to the Data Studio instance 315, and the user is able to select desired columns, tags, or portions of interest.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
HALLIHOLE; Sriram Poojary et al. (US-20240354684-A1) APPARATUSES, COMPUTER-IMPLEMENTED METHODS, AND COMPUTER PROGRAM PRODUCTS FOR IMPROVED ASSET PERFORMANCE MONITORING AND RANKING;
Ravignon; Fabrice (US-20240202617-A1) SYSTEM FOR SUPERVISION OF THE OPERATION AND MAINTENANCE OF INDUSTRIAL EQUIPMENT;
SANTOSO; Jati et al. (US-20230315072-A1) SYSTEMS, METHODS, AND DEVICES FOR ASSET MONITORING;
Bonner; Maria et al. (US-20230274134-A1) A NEURAL NETWORK MODEL, A METHOD AND MODELLING ENVIRONMENT FOR CONFIGURING NEURAL NETWORKS;
Paulitsch; Christoph et al. (US-20230267368-A1) SYSTEM, DEVICE AND METHOD OF DETECTING ABNORMAL DATAPOINTS;
XUE, FEI et al. (CN-114721345-A) An industrial control method based on reinforcement learning, device, system and electronic device;
Chand; Sujeet et al. (US-20200326684-A1) SMART GATEWAY PLATFORM FOR INDUSTRIAL INTERNET OF THINGS;
Florissi; Patricia Gomes Soares et al. (US-10791063-B1) Scalable edge computing using devices with limited resources;
Sirohi; Ajay et al. (US-20200174462-A1) METHOD AND SYSTEM FOR ELIMINATION OF FAULT CONDITIONS IN A TECHNICAL INSTALLATION;
Jin; Zuwei (US-20170177754-A1) METHODS AND APPARATUS FOR USING ANALYTICAL/STATISTICAL MODELING FOR CONTINUED PROCESS VERIFICATION (CPV);
Bell; Noel Howard et al. (US-20170102696-A1) DISTRIBUTED INDUSTRIAL PERFORMANCE MONITORING AND ANALYTICS.
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ELLIS B. RAMIREZ
Primary Examiner
Art Unit 3658
/ELLIS B. RAMIREZ/ Examiner, Art Unit 3658
Prosecution Insights