DETAILED ACTION
This action is in response to communications filed on 07/01/2026, wherein independent claims 1, 8, and 15 were amended, claims 2-4, 9-11, 16-18, and 23-25 remain cancelled, claims 26-28 are cancelled per the amended claims, and claims 29-31 are added. Claims 1, 5-8, 12-15, 19-22, and 26-28 are presented for examination.
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 .
Response to Amendment
Amendments have been made to the independent claims 1, 8 and 15 and claims 29-31 have been newly added. The applicant provided citations to the pertinent sections of the specification (¶44-56) holding support for such amendments and newly added claims.
The cited portions of the specification have been evaluated by the examiner. It is apparent by the cited portions of the specification that the inventor(s) had possession of the claimed invention at the time of filing the application. No new matter has been introduced by way of amendment.
Response to Arguments
Rejections under 35 U.S.C. § 101
Applicant argues that the claimed invention, per the amended claims, provides an improvement in computer-related technology as well as an improvement to at least the field of manufacturing by leveraging digital twin simulation based dynamic KPI identification for adapting manufacturing processes.
There does not appear to be an improvement to computer technology reflected in the claims. The computing components of the claims appear to be recited at high levels of generality and used as tools to assist or enable processes which may be construed as mental process. There are no particular claimed mechanisms, unique arrangements, or otherwise meaningful limitations that demonstrate the improvement to a computer technology. While the amended claim now recites “…wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements…”, this limitation is recited in a conclusory manner while failing to include the components and steps of the invention that provide the improvement. MPEP 210604(d)(1) states: “if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology”. It is not apparent how the manufacturing process is particularly adjusted to achieve the desired outcome. Furthermore, when read in light of the disclosure and other claims, adjusting the manufacturing process may entail, per claim 22 and at least specification ¶57, altering the manufacturing process based on feedback from a user, wherein the altering includes displaying implementation details in the user interface. The mere output of information to a display has been found by the courts to be a well understood, routine, and conventional computer function and is insignificant extra solution activity. Such a limitation is not enough to integrate the exception into a practical application nor amount to significantly more than the exception(s) in the claim.
Applicant points to the specification, noting that the invention improves the identification of potential bottlenecks or issues with respect to a manufacturing process by comparing KPIs in simulations and further notes that the invention enables a user to monitor only KPIs required for each physical asset utilized during the manufacturing process.
These references to the specification are admissions by the applicant that the invention is rooted in mental process. The improvement of identification is the improvement of the mental process itself. Identification is understood as the process by which a human can make a judgement using observations and evaluations. Using simulations and digital representations of equipment as assistive aids to enable this identification is the mere application of the judicial exception in a computing environment, where the computing component aspects are used as tools.
The applicant makes reference to the August 4th memo regarding SME per the Office, emphasizing that examiners should consult the specification to determine whether the disclosed invention improves technology or a technical field and to further evaluate the claim to ensure it reflects the disclosed improvement. Applicant further references the specification and argues that the invention improves “the manufacturing process of a manufacture and/or business by providing one or more recommendations based on at least the simulated KPIs of a digital twin representing the manufacturing process under a plurality of conditions. The one or more recommendations may include, but are not limited to including, installation or more IoT devices, adjustment to production volume, utilization or production downtime, reduction of production costs, recommendations to improve physical asset effectiveness, recommendations to meet functional and/or non-functional requirements, physical asset upkeep, amongst other recommendations which may improve the manufacturing process” which enables the manufacturing and/or other businesses “to proactively improve and/or maintain physical assets utilized in a manufacturing process by monitoring the physical ecosystem which may be comprised of all the physical assets utilized in one or more manufacturing processes”.
While the claim and the specification recite the idea of an improvement to the manufacturing process, the claim fails to recite any specific mechanism, additional element, or relationship between the additional elements and/or between the judicial exception that set forth and provide any improvements to the manufacturing process. The providing of the recommendations based on insights gained from simulations is the mere output of data, as described above as well as within the updated rejection of this action. Such an additional element is insufficient to integrate the judicial exceptions of the claim into a practical application, per MPEP 2106.04(d)(I), and does not provide an inventive concept, per MPEP 2016.05(I)(A). Additionally, though this element is not claimed but for the sake of addressing the applicant’s arguments, the applicant alleges proactively improving and maintaining physical assets of a system is done by monitoring the physical ecosystem. Monitoring an ecosystem is a further recitation of a mental process- that is monitoring is observing information and may entail making judgements about said information. The specification and claims set forth improvements to the manufacturing process in a conclusory manner as a result of outputting recommendations or monitoring the ecosystem while failing to describe how the output recommendations or insights gained from monitoring are meaningfully applied to the manufacturing process.
The arguments presented by the applicant are not persuasive for the reasons stated herein. The claims remain rejected under 35 U.S.C. § 101.
Rejections under 35 U.S.C. § 103
The applicant has amended the independent claims 1, 8, and 15, and argues that the amended claims are not taught by the prior art of record. Specifically, the applicant argues that Rolo does not disclose or suggest “simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin, wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and wherein the one or more simulation methods include at least a Monte Carlo simulation process;”.
Rolo is not relied upon to disclose this limitation. Rolo in view of the teachings of Basu and Ruppert are relied upon to disclose this portion of the claim. Rolo provides a digital twin by which simulations can be performed upon. Basu discloses a field model that is subject to what-if simulations that enable analyzing the effect of prescribed solutions on performance metrics ((Basu, Col 18 Lines 36-46) " Colloquially speaking, predictive analytics allows users (for example, associated with entity 2240) to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which can cause any upcoming problems, optimization can determine substantially optimum solution to preempt ( or benefit from) any determined upcoming problems and what- if simulation allows a user to determine the effect of prescribed solutions on performance metrics"). Basu further describes the prescribed solutions (recommendations) to include adjustments of the drilling process ((Basu, Col 28, Lines 17-27) " Based on the detected drill events, the system can prescribe an adjusted drill recipe, as illustrated at 5610. For example, the system can prescribe a drill recipe having a different number of segments or locating segments at different positions to adjust the recipe given the nature of the drill events. In another example, the system can adjust spin rates or weights applied to the drill bit or can adjust mud parameters to suit conditions detected while drilling. As
illustrated at 5612, drilling can proceed using the adjusted drill recipe."). What Basu lacks teaching here is the use of the digital twin and the use of the Monte Carlo simulation methodology. The digital twin is given by Rolo, wherein combining the Rolo and Basu references by altering the field model of Basu to instead use the Digital twin model of Rolo would result in the claimed invention, except for the use of the Monte Carlo simulation. Rolo nor Basu explicitly contemplate the use of a Monte Carlo simulation process as one of the leveraged simulation methods; however Ruppert leverages Monte Carlo simulation using digital twin for projecting differences in strategies for manufacturing processes. ((Ruppert, Page 9, Col 1, ¶3) " A Monte Carlo simulation is used to study the differences between demand-driven and high/low complexity sequencing strategies (where products with lower degrees of complexity are produced immediately after the highly complex products) as is depicted in Fig. 10. "). The combined teachings cover the claimed limitation.
The applicant further argues that no prior art of record discloses “adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods.”.
Basu discloses this limitation except for the use of the digital twin, in the same manner as described in the previous limitation. By applying the teachings of Basu onto the digital twin of Rolo instead of using the field model used in Basu, the claimed limitation is achieved. Basu teaches the prescription of drill recipes with corresponding adjustments for the drill recipe, wherein the adjusted drill recipe is applied to the system to improve performance according to key metrics ((Basu, Col 28, Lines 17-30) " Based on the detected drill events, the system can prescribe an adjusted drill recipe, as illustrated at 5610. For example, the system can prescribe a drill recipe having a different number of segments or locating segments at different positions to adjust the recipe given the nature of the drill events. In another example, the system can adjust spin rates or weights applied to the drill bit or can adjust mud parameters to suit conditions detected while drilling. As illustrated at 5612, drilling can proceed using the adjusted drill recipe. Such an approach can be used to limit lost drilling time, the use of resources, such as mud, equipment wear, and 30 ultimately cost associated with drilling a well."). The actions may be determined to maintain performance indicators at desired states ((Basu, Col 5, Lines 40-42) " The process can be iterated to determine a particularly advantageous set of future actions that maintain the performance indicators at desired states."). The prescribed solutions are prepared according to the effect on performance metrics determined using simulation ((Basu, Col. 18, Lines 33-46) " More specifically, in one embodiment, performance metric analysis system 2220 can implement a set of analytics comprising at least predictive analytics, root-cause analytics, optimization and what-if simulation. Colloquially speaking, predictive analytics allows users (for example, associated with entity 2240) to identify and quantify prob
Lems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which can cause any upcoming problems, optimization can determine substantially optimum solution to preempt ( or benefit from) any determined upcoming problems and what- if simulation allows a user to determine the effect of prescribed solutions on performance metrics"). The combined teachings cover the claimed limitation.
Applicant further argues that the combination of Rolo and Basu do not disclose "displaying, in a manufacturing optimization user interface, KPis which require monitoring for each of the one or more physical assets to a user based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, and wherein identifying the KPis which require monitoring is performed by a machine learning model utilizing one or more binary classification methods] based on an output of the root cause analysis.".
Rolo and Basu in combination by themselves are not relied upon to teach the entirety of this limitation. Rolo, Basu, Weidl, and Hournbuckle in combination are relied upon to disclose the limitation. Basu teaches the display of KPIs which require monitoring, wherein projected future KPIs are established based on root cause analysis ((Basu, Col 24, Lines 66-67 and Col 25, Lines 1-6) "In addition, the system can display the projected production forecasts based on the present parameters and the improved values, as well as financial projections based on the current values and the improved values. As such, the system can provide both projected production values based on the prescribed values and current values, as well as the financial projections such as the present value, rate of return, and payback period."); ((Basu, Col 18, Lines 14-23) "Accordingly, entity 2240 can desire to utilize and monitor these performance metrics related to these processes for a variety of reasons, including improving the performance of such processes, reducing the cost of implementing such processes, controlling the quality of such processes, preempting issues which can occur in the future with respect to these processes, substantially optimizing solutions to future problems and predicatively determine the effect of certain solutions to anticipated future problems, etc."); ((Basu, Col 8, Lines 31-38) " As a result, root cause analysis can be performed specifically for the selected future time or generally across time periods. In addition, the system can automatically or iteratively determine a set of actionable tasks including changes to influencer values over time to provide future KPI values 3602 that do not violate business rules, subject to constraints 3608. "). Basu does not describe the root cause analysis implementation details, nor disclose how the KPIs required for monitoring are established- Basu merely states that a root cause analysis is performed and that KPIs may be monitored for a variety of reasons without indicating specific mechanisms for determining. However, Weidl describes a root cause analysis approach that includes fault domain isolation and impacted component analysis wherein the identification of pertinent KPIs is performed using a Bayesian network model built upon the root cause analysis ((Weidl, ¶85) "As mentioned above, data about the subject of an analysis may be organised in a structured manner such as in a hierarchical data file structure or model. In a hierarchically arranged data structure a failure object forms the parent object of a hierarchically structured data model generated for a failure. Since there are typically a plurality of possible causes for a failure, the parent object has a plurality of child objects presenting the possibilities. The possibilities are referred to in the following as hypotheses. Each of the hypotheses in turn may parent a plurality of child objects. These are referred to herein as symptoms. The symptoms represent abnormal changes in the process operation conditions, which lead to a failure in the problem domain (e.g. process and/or its operation and/or equipment and/or component) and/or other deviations from optimal conditions."); ((Weidl, ¶87) "If hierarchically structured data is used, the analysis is made so that the operator examines a hierarchically organised data structure displayed to him/her by a display device. The data examination of the possible root cause is then made in the direction: failure->hypothesis->symptoms");((Weidl, ¶106) "The symptom nodes of the BN graph can be of different character. For example, discrete nodes with mutually exclusive states may be provided. The exclusive states may be binary ( =Boolean) states such as "yes" ( ="true") when a symptom is observed and "no" ( ="false") when a symptom is not observed". The states may also indicate other features such as the intervals of the symptoms, relative symptoms levels (e.g. the ratio between measured value at an observation time point and value of the last set point) and so on. The symptoms nodes in a BN model may represent parameters that associate with the problem domain such as the performance measurement results, physical variables and so on.); ((Weidl, ¶111) "Multiple root causes of a failure can also be represented by binary nodes with states "yes" and "no" for each hypothesis, see FIG. 7. More than two states may also be used. For example, intervals or trends of the possible cause development can be used as classification criteria."); ((Weidl, ¶55) "The inference engine 21 is arranged to perform a simultaneous verification of a number of root cause hypotheses. The simultaneous processing of the hypotheses can be facilitated by use of causally oriented graphical models. A causally oriented graphical model can be described as being a combination of probability theory and graph theory. The causally oriented models can be seen as models that are oriented based on causal associations the various nodes of the model may have with each other."). These combined references further fail to disclose displaying particularly KPIs for each of the physical assets of the manufacturing system; however, Hournbuckle cures this deficiency by providing an interface that enables filtering of displayed information per specific objects of the system ((Hournbuckle, ¶111) " FIG. 23 illustrates an interface 2300 for forecasting and root cause analysis of complex networks and flows that includes an example interactive graphical object. The interface includes a Network/Process Flow diagram 2305, an interactive graphical object including a timeline of events 2310, an events or alerts list 2315 that pertain to the objects shown in the timeline, a recommendation panel 2320, line/bar/dot graph(s) 2325 showing key performance indicators (e.g., critical key performance indicators [KPIs]) with filters and search allowing the users to narrow down the display to the specific objects of interest. "). The combined teachings cover the claimed limitation.
The arguments with respect to the rejections under 35 U.S.C. § 103 have been considered and are not persuasive, according to the rationale provided above. The applicant largely singles out references with respect to the rejections of limitations and fails to view the rejections in light of the combined teachings of the prior art of record. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The prior art of record sufficiently covers each claimed limitation, as combined in the rejection of this office action, as well as presented above. The independent claims remain rejected under 35 U.S.C. § 103 with respect to the established prior art of record.
Claim Objections
Claim 31 is objected to because of the following informalities:
A minor typographical error is made in the claim. The claim recites “wherein the plurality of conditions are determined utilizing at least real time data received from one or more Internet of Things (IoT) devices, smart wearable data associated with one or more individuals operating the one or more physical assets, and seasonal and environmental conditions association with a real world location accessed from one or more publicly available resources” and should instead recite “wherein the plurality of conditions are determined utilizing at least real time data received from one or more Internet of Things (IoT) devices, smart wearable data associated with one or more individuals operating the one or more physical assets, and seasonal and environmental conditions associated with a real world location accessed from one or more publicly available resources”
Appropriate correction is required.
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, 5-8, 12-15, 19-22, and 29-31 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong 1 - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2a Prong 2 - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are further evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 5, 6, 7, 21, 22, 29, 30, and 31 are directed to a method which falls within one of the four statutory categories of a process.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
analyzing a performance of the digital twin under each of the plurality of conditions by comparing key performance indicators (KPIs) from simulations which the digital twin failed to meet requirements of the manufacturing process with simulations which the digital twin met the requirements of the manufacturing process; and The claim limitation can be reasonably read to entail observing a digital twin under a plurality of conditions and evaluating KPIs from the simulations in order to make a judgement as to the performance of the digital twin with regard to the requirements. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis This claim limitation can reasonably be read to entail performing a root cause analysis which can be practically performed in the human mind by using observations to create judgments. A human being is also capable of performing a fault domain analysis and an impacted component analysis by making judgements Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
and wherein identifying the KPis which require monitoring is performed …based on an output of the root cause analysis; This limitation can be reasonably read to entail making a judgment of KPIs that require monitoring based on the output of the root cause analysis, which can be done by a human being mentally or using pen and paper as assistive physical aids because this task is a judgement based on observation and evaluation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a plurality of recommendations based on the KPis which require monitoring; This claim can reasonably be read to entail creating a judgement as to recommended actions based on observed KPIs. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example, a human being can derive the recommendations mentally and present them physically for use by writing the recommendations down using a pen and paper. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
generating a digital twin, This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely invoking the use of a computer to implement an abstract idea
wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process;- This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to a particular technological environment and field of use of physical assets in the manufacturing process
performing a plurality of simulations using the digital twin; This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer to perform an existing task
wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to the technological environment of a manufacturing process with a specified set of conditions
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting
by a machine learning model utilizing one or more binary classification methods. -This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components recited at a high level of generality to enable the performance of the judicial exception
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for reciting the words “apply it” with regard to the judicial exception using generic computing components
wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of generic simulation as a tool to enable KPI projections
wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and -this limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the use of the judicial exception to a particular technological field
wherein the one or more simulation methods include at least a Monte Carlo simulation process; and - This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of Monte Carlo simulation as a simulation method by which to use as an assistive aid in applying the judicial exception.
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods. – This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for amounting to “apply it” or a generic equivalent with regard to the judicial exception. The limitation covers the idea of an outcome or solution while failing to recite details about how to achieve the intended outcome.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering, as stated previously. The claim under broadest reasonable interpretation encompasses receiving data over a network. Receiving data over a network has been identified by the courts as a well understood, routine, and conventional computer functionality when claimed in a merely generic manner.
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses transmitting data over a network to a display device. Transmitting data over a network has been identified by the courts as a well understood, routine, and conventional activity when claimed in a merely generic manner.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of a judicial exception to a particular technological environment does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The claim is generally linked to a particular technological environment and field of use to include a manufacturing process with corresponding physical assets. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 8:
Step 1: Claim 8 and its dependent claims 12, 13, and 14 are directed to a computer system which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 8 recites a judicial exception, noted in bold:
analyzing a performance of the digital twin under each of the plurality of conditions by comparing key performance indicators (KPIs) from simulations which the digital twin failed to meet requirements of the manufacturing process with simulations which the digital twin met the requirements of the manufacturing process; and The claim limitation can be reasonably read to entail observing a digital twin under a plurality of conditions and evaluating KPIs from the simulations in order to make a judgement as to the performance of the digital twin with regard to the requirements. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis This claim limitation can reasonably be read to entail performing a root cause analysis which can be practically performed in the human mind by using observations to create judgments. A human being is also capable of performing a fault domain analysis and an impacted component analysis by making judgements Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
and wherein identifying the KPis which require monitoring is performed …based on an output of the root cause analysis; This limitation can be reasonably read to entail making a judgment of KPIs that require monitoring based on the output of the root cause analysis, which can be done by a human being mentally or using pen and paper as assistive physical aids because this task is a judgement based on observation and evaluation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a plurality of recommendations based on the KPis which require monitoring; This claim can reasonably be read to entail creating a judgement as to recommended actions based on observed KPIs. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example, a human being can derive the recommendations mentally and present them physically for use by writing the recommendations down using a pen and paper. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is configured to performing a method comprising: This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of computers to implement an abstract idea
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
generating a digital twin, This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely invoking the use of a computer to implement an abstract idea
wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process;- This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to a particular technological environment and field of use of physical assets in the manufacturing process
performing a plurality of simulations using the digital twin; This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer to perform an existing task
wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to the technological environment of a manufacturing process with a specified set of conditions
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting
by a machine learning model utilizing one or more binary classification methods. -This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components recited at a high level of generality to enable the performance of the judicial exception
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for reciting the words “apply it” with regard to the judicial exception using generic computing components
wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of generic simulation as a tool to enable KPI projections
wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and -this limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the use of the judicial exception to a particular technological field
wherein the one or more simulation methods include at least a Monte Carlo simulation process; and - This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of Monte Carlo simulation as a simulation method by which to use as an assistive aid in applying the judicial exception.
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods. – This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for amounting to “apply it” or a generic equivalent with regard to the judicial exception. The limitation covers the idea of an outcome or solution while failing to recite details about how to achieve the intended outcome.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering, as stated previously. The claim under broadest reasonable interpretation encompasses receiving data over a network. Receiving data over a network has been identified by the courts as a well understood, routine, and conventional computer functionality when claimed in a merely generic manner.
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses transmitting data over a network to a display device. Transmitting data over a network has been identified by the courts as a well understood, routine, and conventional activity when claimed in a merely generic manner.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of a judicial exception to a particular technological environment does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The claim is generally linked to a particular technological environment and field of use to include a manufacturing process with corresponding physical assets. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject
Claim 15:
Step 1: Claim 15 and its dependent claims 19 and 20 are directed to a computer program product which falls within one of the four statutory categories of a manufacture.
Step 2A Prong 1: Claim 15 recites a judicial exception, noted in bold:
analyzing a performance of the digital twin under each of the plurality of conditions by comparing key performance indicators (KPIs) from simulations which the digital twin failed to meet requirements of the manufacturing process with simulations which the digital twin met the requirements of the manufacturing process; and The claim limitation can be reasonably read to entail observing a digital twin under a plurality of conditions and evaluating KPIs from the simulations in order to make a judgement as to the performance of the digital twin with regard to the requirements. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis This claim limitation can reasonably be read to entail performing a root cause analysis which can be practically performed in the human mind by using observations to create judgments. A human being is also capable of performing a fault domain analysis and an impacted component analysis by making judgements Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
and wherein identifying the KPis which require monitoring is performed …based on an output of the root cause analysis; This limitation can be reasonably read to entail making a judgment of KPIs that require monitoring based on the output of the root cause analysis, which can be done by a human being mentally or using pen and paper as assistive physical aids because this task is a judgement based on observation and evaluation. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a plurality of recommendations based on the KPis which require monitoring; and This claim can reasonably be read to entail creating a judgement as to recommended actions based on observed KPIs. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example, a human being can derive the recommendations mentally and present them physically for use by writing the recommendations down using a pen and paper. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising: This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of computers to implement an abstract idea
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
generating a digital twin, This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely invoking the use of a computer to implement an abstract idea
wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process;- This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to a particular technological environment and field of use of physical assets in the manufacturing process
performing a plurality of simulations using the digital twin; This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer to perform an existing task
wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the use of the judicial exception to the technological environment of a manufacturing process with a specified set of conditions
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting
by a machine learning model utilizing one or more binary classification methods. -This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components recited at a high level of generality to enable the performance of the judicial exception
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for reciting the words “apply it” with regard to the judicial exception using generic computing components
wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of generic simulation as a tool to enable KPI projections
wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and -this limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally linking the use of the judicial exception to a particular technological field
wherein the one or more simulation methods include at least a Monte Carlo simulation process; and - This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for invoking the use of Monte Carlo simulation as a simulation method by which to use as an assistive aid in applying the judicial exception.
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods. – This limitation has been identified as Mere Instructions To Apply An Exception (MPEP 2106.05(f)) for amounting to “apply it” or a generic equivalent with regard to the judicial exception. The limitation covers the idea of an outcome or solution while failing to recite details about how to achieve the intended outcome.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data for one or more physical assets utilized in a manufacturing process;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering, as stated previously. The claim under broadest reasonable interpretation encompasses receiving data over a network. Receiving data over a network has been identified by the courts as a well understood, routine, and conventional computer functionality when claimed in a merely generic manner.
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses transmitting data over a network to a display device. Transmitting data over a network has been identified by the courts as a well understood, routine, and conventional activity when claimed in a merely generic manner.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of a judicial exception to a particular technological environment does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. The claim is generally linked to a particular technological environment and field of use to include a manufacturing process with corresponding physical assets. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject
Dependent Claims:
Examiner notes limitations identified as judicial exceptions are indicated in italicized bold and limitations identified as additional elements are indicated using italics.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 5 additionally recites the limitation wherein a first portion of the plurality of conditions are determined based on data stored in a knowledge corpus which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. The claim also recites the limitation and varying the data based on a real world location of the one or more physical assets which has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). Lastly, the claim recites the limitation and wherein a second portion of the plurality of conditions are manually selected by the user within the manufacturing optimization user interface which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. The courts have ruled appending insignificant extra solution activity to a judicial exception and generally linking a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Because limitations were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)), they require further evaluation to determine if they are beyond well understood routine and conventional activity. Obtaining conditions from stored data amounts to retrieving data from memory and obtaining conditions from user selection encompasses receiving data over a network. Retrieving data from memory and receiving data over a network are both computer functionalities that have been recognized by the courts as well understood, routine, and conventional activity when claimed in a merely generic manner. The courts have found that limitations that amount to well understood, routine and conventional activity appended to the judicial exception, and limitations that amount to generally linking the use of the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 does not recite any additional judicial exceptions.
Step 2A Prong 2: The claim further recites and providing, in the manufacturing optimization user interface, one or more recommendations to the user based on the analysis of the digital twin under each of the plurality of conditions including the plurality of recommendations, which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)). Lastly, the claim recites wherein the manufacturing optimization user interface is displayed on a device of an individual operating the one or more physical assets which has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled invoking the use of computers to perform an existing process, appending insignificant extra solution activity to the judicial exception, and generally linking the use of the judicial exception to a particular technological environment and field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Because an element was identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)), it requires further analysis to determine if it is beyond well understood, routine and conventional activity. The limitation providing, in the manufacturing optimization user interface, one or more recommendations to the user based on the analysis of the digital twin under each of the plurality of conditions including the plurality of recommendations encompasses transmitting data over a network, which has been recognized by the courts as well understood routine and conventional activity when claimed in a merely generic manner. The courts have found that limitations that amount to appending well understood routine and conventional activity, using a computer as a tool to perform an existing process, and generally linking the use of the judicial exception to a particular technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 7 additionally recites the limitations receiving real time data from one or more IoT devices associated with the manufacturing process; updating the digital twin and the plurality of conditions; and displaying, in the manufacturing optimization user interface, one or more recommendations to a user based on the simulation of the updated digital twin which have all been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)). The claim additionally recites the limitation simulating an updated digital twin in an updated plurality of conditions; and which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of computers to perform an existing task. The courts have ruled that appending insignificant extra solution activity to a judicial exception and invoking the use of computers as a tool to perform an existing process does not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Limitations were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) as stated previously. Receiving real time data encompasses receiving data over a network. Updating the digital twin and the plurality of conditions encompasses transmitting data over a network. Displaying recommendations to a user interface encompasses transmitting data over a network to a display. The courts have recognized transmitting and receiving data over a network, when claimed in a merely generic manner, have been established as computer functions that are well understood routine and conventional. The courts have found that limitations that amount to appending well understood routine and conventional activity to a judicial exception and invoking the use of computers to perform existing processes are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
Step 1: Regarding dependent claim 12, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
The claim recites substantially similar limitations as that recited in claim 5 but with respect for an alternate independent claim For brevity, the analysis is not restated and the limitations are rejected under the same rationale provided for claim 5.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13
Step 1: Regarding dependent claim 13, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
The claim recites substantially similar limitations as that recited in claim 6 but with respect for an alternate independent claim For brevity, the analysis is not restated and the limitations are rejected under the same rationale provided for claim 6.
This claim is not eligible subject matter under 35 U.S.C. 101
Claim 14
Step 1: Regarding dependent claim 14, the judicial exception of independent claim 8 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
The claim recites substantially similar limitations as that recited in claim 7 but with respect for an alternate independent claim For brevity, the analysis is not restated and the limitations are rejected under the same rationale provided for claim 7.
This claim is not eligible subject matter under 35 U.S.C. 101
Claim 19
Step 1: Regarding dependent claim 19, the judicial exception of independent claim 15 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
The claim recites substantially similar limitations as that recited in claim 5 but with respect for an alternate independent claim For brevity, the analysis is not restated and the limitations are rejected under the same rationale provided for claim 5.
This claim is not eligible subject matter under 35 U.S.C. 101
Claim 20
Step 1: Regarding dependent claim 20, the judicial exception of independent claim 15 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
The claim recites substantially similar limitations as that recited in claim 6 but with respect for an alternate independent claim For brevity, the analysis is not restated and the limitations are rejected under the same rationale provided for claim 6.
This claim is not eligible subject matter under 35 U.S.C. 101
Claim 21
Step 1: Regarding dependent claim 21, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 21 does not recite any additional judicial exceptions
Step 2A Prong 2: Claim 21 additionally recites the limitation wherein at least one or more of the KPIs which require monitoring include visual indicators within the manufacturing user interface, wherein the visual indicators represent bottlenecks for either functional requirements or non-functional requirements of the manufacturing process. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of a judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 22
Step 1: Regarding dependent claim 22, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 22 additionally recites the judicial exception feedback from the user with respect to the functional requirements or the non-functional requirements of the manufacturing process; and wherein a human can mentally make a judgment based on functional and non-functional requirements of a manufacturing process. Therefore, this limitation entails a mental process.
Step 2A Prong 2: Claim 22 additionally recites the limitation receiving, in the manufacturing optimization user interface, which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. The claim further recites the limitation altering the manufacturing process based on the feedback received from the user, which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for merely reciting the words “apply it” with regard to the judgement of feedback as part of the recited mental process. The claim lastly recites wherein the altering of the manufacturing process includes implementation details displayed within the manufacturing optimization user interface which has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. The courts have ruled appending insignificant extra solution activity to a judicial exception and reciting the words “apply it” with regard to the judicial exception does not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) as stated previously. Receiving data to the user interface and displaying data to the user interface encompass receiving and transmitting data over a network. These tasks have been recognized by the courts as well understood routine and conventional computer functions. The courts have found that limitations that amount to appending well understood routine and conventional activity and reciting the words “apply it” with the judicial exception are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 29
Step 1: Regarding dependent claim 29, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 29 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 29 additionally recites the limitation wherein the updated manufacturing process includes a greater production volume and a shorter utilization of production downtime than the manufacturing process which has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) for linking the exception to a particular technological environment or field of use. Note that the specification describes the updating of the manufacturing process entailing displaying recommendations, whereby this particular technological information is what is considered for the display. The use of the phrase “includes” does not impose meaningful limits on the claim such that any physical manifestations of the recommendations are imparted in the manufacturing process. The courts have found that generally linking the use of the judicial exception to a particular technological environment and field of use do not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 30
Step 1: Regarding dependent claim 30, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 30 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 30 additionally recites the limitation wherein the one or more simulation methods are executed utilizing a hybrid machine learning model, wherein the hybrid machine learning model utilizes at least two or more of a Convolutional Neural Networks (CNNs), an Artificial Neural Networks (ANNs), a Support Vector Machine (SVM), K-Nearest Neighbors (KNN), or Naive Bayes which has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computers/computing algorithms as a tool by which to apply the judicial exception. The courts have found that limitations merely invoking the use of generic computing components do not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 31
Step 1: Regarding dependent claim 31, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 31 further recites wherein the plurality of conditions are determined utilizing at least real time data which can reasonably be read to entail making a judgement of the plurality of conditions to be used. This task can be practically performed in the human mind or using assistive aids. This claimed limitation includes the recitation of abstract ideas of mental process.
Step 2A Prong 2: Claim 31 additionally recites the limitation received from one or more Internet of Things (IoT) devices, smart wearable data associated with one or more individuals operating the one or more physical assets, and seasonal and environmental conditions association with a real world location accessed from one or more publicly available resources. which has been identified as Insignificant Extra-Solution Activity (MPEP 2106.05(g)) of mere data gathering. The courts have ruled that appending insignificant extra solution activity to a judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: Under broadest reasonable interpretation and when read in light of the specification, receiving data from devices or other resources encompasses transmitting and receiving data over a network. This computer function has been found by the courts to be well understood, routine, and conventional activity. The courts have found that limitations that amount to adding insignificant extra solution activity that is well understood, routine, and conventional to the judicial exception is not enough to amount to significantly more than the recited judicial exception. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
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.
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, 5-8, 12-15, 19-22, and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Rolo et al (Rolo, G., Rocha, A., Tripa, J., and Barata, J., “Application of a Simulation-Based Digital Twin for Predicting Distributed Manufacturing Control System Performance”, March 2021, Applied Sciences 11, No. 5, 2202, https://doi.org/10.3390/app11052202), hereinafter referred to as Rolo, in view of Basu et al (US Patent 10364662 B1), hereinafter referred to as Basu, further in view of Weidl (US 20050015217 A1), hereinafter referred to as Weidl, in view of Hournbuckle et al (US Patent Publication US 20190087990 A1), hereinafter referred to as Hournbuckle, and further in view of Ruppert et al (Ruppert, T. Abonyi, J., “Integration of real-time locating systems into digital twins”, Journal of Industrial Information Integration, 2020, Volume 20), hereinafter referred to as Ruppert.
Regarding claim 1, Rolo discloses except the limitations surrounded by brackets ([[…]]) A method for manufacturing optimization, the method comprising: A method describing a way to predict and understand execution of manufacturing systems is described ((Rolo, Page 1, Abstract) "Hence, the proposed research aims to explore the utilisation of Digital Twins (DTs) to predict and understand the execution of these systems in runtime."). The methodology described is motivated by the improvement of efficiency and profitability of manufacturing systems, indicating the prediction and simulation method being used for optimization purposes ((Rolo, Page 1, Introduction, ¶3) "In order to improve the efficiency and profitability of manufacturing systems [4] by integrating activities involving human beings, machines and data [2], I4.0 relies on enabling technologies such as big data, Internet of Things (IoT) and simulations. Therefore, factories are progressively becoming smart factories, the main features of which are the capacity to accept on-the-fly changes to the ongoing manufacturing processes, provide remote access to every single resource and autonomously organise production tasks [5].").
receiving data for one or more physical assets utilized in a manufacturing process; A database file describes a manufacturing system’s current state and remaining production plan ((Rolo, Page 10, ¶4) "According to the model’s implementation, the database file must effectively describe the system’s current state and the remaining production plan. Thus, deciding which information was needed in order to depict the system’s state and the next products to be inserted was fundamental."). The system includes multiple physical assets utilized in a manufacturing process to include conveyors A-F ((Rolo, Page 11, ¶3) "This JSON sample, which maps each conveyor’s initial state to its name and the product type to their order in the production plan, would generate the csv file from Listing 1."); (See also Rolo, Page 11, Figure 15). The database file can be interacted with through GET and POST operations such that the information in the database file can be received ((Rolo, Page 4, ¶3) "The Integration Layer (IL) is responsible for making the physical system’s relevant information available to external applications. It contains a file, that plays the role of a database, where the demonstrator’s current state and the remaining production plan are stored, and an Application Programming Interface (API), whose purpose is to interact with the database. For that, the latter provides two services which endure concurrent access. By sending a GET request with no specific message, the API is instructed to return the production plan, after reading the file. On the other hand, through a POST request, whose message body must contain the system’s current state and the remaining production plan, one may ask the API to update the file.")
generating a digital twin, wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process; An architecture for a digital twin of a distributed control system for a manufacturing application is described ((Rolo, Page 3, ¶4) "With the aim of covering the aforementioned gaps, a three-layered architecture for a DT of a previously developed distributed control system was proposed, as portrayed by Figure 1."). A digital twin is created based on the architecture ((Rolo, Page 12, ¶3) "Consequently, in order to ensure the synchronisation between the manufacturing unit and the newly-created DT, a new Java class and a GUI were added to the initial project."). A digital twin is defined as a virtual entity that mirrors its physical counterparts ((Rolo, Page 2, ¶3) "On this regard, an identical concept to what is now referred to as DT was originally suggested by Michael Grieves on the occasion of an industry lecture on Product Lifecycle Management (PLM). According to him, a DT was no more than a virtual entity capable of mirroring its physical counterpart [15].").
performing a plurality of simulations using the digital twin, wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions; Multiple simulations are carried out, as indicated by the use of “each” simulation ((Rolo, Page 13, ¶5) "For each simulation, the ensuing steps must be followed."). The simulation model incorporates the digital twin of a manufacturing control system during a manufacturing process. ((Rolo, Page 17, ¶5) "The first one is that the utilisation of simulation-based DTs to predict the behaviour and, more specifically, a distributed manufacturing control system’s performance can be useful"); ((Rolo, Page 16, ¶4) "Despite the limitations found during the design and developments, it is possible to verify the proposed three-layered architecture’s potential to interface a simulation-based DT on top of an existing or new distributed manufacturing control system."). The simulation mirrors the physical system’s behavior ((Rolo, Page 14, ¶2) "Before using the model to make predictions, each skill’s processing time and the model’s conveyors speed had to be tuned so that the simulation closely matched the physical system’s behaviour"). The simulation operates in accordance with a production plan as a set of conditions that define the manufacturing process ((Rolo, Page 2, ¶2) "The authors believe that a simulation-based DT can be used to predict the system’s behaviour and performance. To do that, it must use the production line’s current status and the production plan in order to simulate the system to different time horizons").
analyzing a performance of the digital twin under each of the plurality of conditions The behavior and performance of the digital twin is analyzed with regard for the production plan that defines the operating conditions of the manufacturing process ((Rolo, Page 2, ¶2) "The authors believe that a simulation-based DT can be used to predict the system’s behaviour and performance. To do that, it must use the production line’s current status and the production plan in order to simulate the system to different time horizons"). by comparing key performance indicators (KPIs) from simulations which the digital twin [[failed to meet requirements]] of the manufacturing process with simulations which the digital twin [[met the requirements]] of the manufacturing process; Key performance indicator error percentages for cycle time and throughput are compared for simulations with production plans of different size, wherein the plan size defines a singular simulation ((Rolo, Page 16, ¶2) "Finally, since they represented the same practical situation, the KPIs for the twenty intermediate products out of sixty were calculated and compared with the values obtained for the last twenty products out of forty in the corresponding test cases"); (See also Rolo Pages 15-16, Figures 20 and 21).
[[displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, and wherein identifying the KPIs which require monitoring is performed by a machine learning model utilizing one or more binary classification methods based on an output of the root cause analysis;]]
[[generating a plurality of recommendations based on the KPIs which require monitoring; and]]
[[simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and wherein the one or more simulation methods include at least a Monte Carlo simulation process; and]]
[[adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods.]]
Rolo alone does not disclose; however, Rolo in view of Basu discloses (except the limitations surrounded by brackets ([[..]])) evaluating KPIs that failed to meet requirements and … met the requirements and of business criteria. A prediction subsystem is leveraged to identify performance indicators that do not conform to business criteria or business rules ((Basu, Col 13, Lines 36-44) "The configuration script 4110 can further include business criteria, thresholds, rules, functors, and other elements to configure one or more subsystems, such as the predictions subsystem 4106 or the prescription subsystem 4108. For example, upon developing a prediction, the prediction sub-system 4106 can identify performance indicators that do not conform to business criteria or business rules. For example, the prediction subsystem 4106 can identify those performance indicators that cross a threshold "). The KPIs may be evaluated using simulations ((Basu, Col 18, Lines 10-14) "No matter the type of processes implemented by the entity 2240 however, it can be useful to measure or otherwise analyze (including predicting, simulating, optimizing, etc.) the performance of such a process utilizing a performance metric, such as a KPI as discussed above. "). Business processes include manufacturing processes ((Basu, Col 3, Lines 37-40 ¶) "In additional examples, the business process can include transactions services, finance and accounting, manufacturing, logistics, sales, or any combination thereof.")
displaying, in a manufacturing optimization user interface, KPIs which require monitoring [[for each of the one or more physical assets]] to a user based on a root cause analysis, [[wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, and wherein identifying the KPIs which require monitoring is performed by a machine learning model utilizing one or more binary classification methods based on an output of the root cause analysis.]] KPIs such as production values and financial projections based on present and improved parameter values of the system can be displayed ((Basu, Col 24, Lines 66-67 and Col 25, Lines 1-6) "In addition, the system can display the projected production forecasts based on the present parameters and the improved values, as well as financial projections based on the current values and the improved values. As such, the system can provide both projected production values based on the prescribed values and current values, as well as the financial projections such as the present value, rate of return, and payback period."). An interface is depicted in Figure 16 that depicts KPIs corresponding to current system conditions and prescribed system conditions. (See Basu Figure 16). The interface is used as part of an optimization process for improving well production performance, and the methodology is suggested to similarly be applied to other manufacturing processes, thereby indicating that the user interface is utilized for manufacturing optimization ((Basu, Col 3, Lines 37-40) "In additional examples, the business process can include transactions services, finance and accounting, manufacturing, logistics, sales, or any combination thereof."). KPIs may need to be monitored for various objectives ((Basu, Col 18, Lines 14-23) "Accordingly, entity 2240 can desire to utilize and monitor these performance metrics related to these processes for a variety of reasons, including improving the performance of such processes, reducing the cost of implementing such processes, controlling the quality of such processes, preempting issues which can occur in the future with respect to these processes, substantially optimizing solutions to future problems and predicatively determine the effect of certain solutions to anticipated future problems, etc."). Warnings may be provided when KPIs indicate performance problems, thereby indicating that such KPIs require monitoring ((Basu, Col 5, Lines 21-26) " In an example, one or more of the performance indicators are projected to violate one or more business criteria at future times. For example, the value of a performance indicator can cross a threshold at a future time step. In this way, the business process is provided with warning about a potential problem that may arise in the future. "). Projected KPIs, which are displayed as stated previously, may be subject to root cause analysis ((Basu, Col 8, Lines 31-38) " As a result, root cause analysis can be performed specifically for the selected future time or generally across time periods. In addition, the system can automatically or iteratively determine a set of actionable tasks including changes to influencer values over time to provide future KPI values 3602 that do not violate business rules, subject to constraints 3608. ")
generating a plurality of recommendations based on the KPIs which require monitoring; and KPIs may need to be monitored for various objectives ((Basu, Col 18, Lines 14-23) "Accordingly, entity 2240 can desire to utilize and monitor these performance metrics related to these processes for a variety of reasons, including improving the performance of such processes, reducing the cost of implementing such processes, controlling the quality of such processes, preempting issues which can occur in the future with respect to these processes, substantially optimizing solutions to future problems and predicatively determine the effect of certain solutions to anticipated future problems, etc."). A set of recommended actionable tasks can be determined by the system with consideration to the KPIs ((Basu, Col 8, Lines 31-38) "In addition, the system can automatically or iteratively determine a set of actionable tasks including changes to influencer values over time to provide future KPI values 3602 that do not violate business rules, subject to constraints 3608. "); ((Basu, Col 21, Lines 55-66) " In particular, the proposed system integrates the data and 55 continually analyzes it together to generate actionable recommendations for changes that produce better results. Further, the proposed system predicts, prescribes, and adapts. The system generates predictions and prescriptions using a number of analytic techniques and scientific disciplines in combination, including adaptive machine learning, meta-heuristics, operations research, and applied statistics, operating within the context of client-defined business rules. As a result, the system is able to predict future outcomes and generate actionable recommendations to benefit from the predictions.")
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the model (digital twin) Simulations may be performed to evaluate the effect of prescribed solutions ((Basu, Col 18, Lines 36-46, ¶) " Colloquially speaking, predictive analytics allows users (for example, associated with entity 2240) to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which can cause any upcoming problems, optimization can determine substantially optimum solution to preempt ( or benefit from) any determined upcoming problems and what- if simulation allows a user to determine the effect of prescribed solutions on performance metrics") wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, KPIs are measured or analyzed ((Basu, Col 18, Lines 10-14) " No matter the type of processes implemented by the entity 2240 however, it can be useful to measure or otherwise analyze (including predicting, simulating, optimizing, etc.) the performance of such a process utilizing a performance 15 metric, such as a KPI as discussed above. "). The performance metrics may be analyzed using simulations ((Basu, Col 18, Lines 33-36) "More specifically, in one embodiment, performance metric analysis system 2220 can implement a set of analytics comprising at least predictive analytics, root-cause analytics, optimization and what-if simulation.") wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, Actionable recommendations are generated for changes ((Basu, Col 21, Lines 64-66) " As a result, the system is able to predict future outcomes and generate actionable recommendations to benefit from the predictions."). The actionable recipes provided are for drilling, completing, and producing wells that are responsible for manufacturing oil ((Basu, Col 20, Lines 28-35) "The proposed system helps operators anticipate and improve wells. The proposed system incorporates data, regardless of source, structure, size, or format, to prescribe actionable recipes for drilling, completing, and producing wells that maximize their economic value at every point over the course of their serviceable lifetimes. The bottom line for operators is improved returns on deployed capital: more oil, more predictably, for less cost."); ((Basu, Col 24, Lines 58-60) " In particular, the output can display design, completion, and operational 60 parameters as a list of actionable variables.") [[and wherein the one or more simulation methods include at least a Monte Carlo simulation process; and]]
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, Recipes/recommendations for actionable tasks are suggested to be used/implemented to as to improve the process ((Basu, Col 28, Lines 1-9) "Exemplary drill recipes include a number of segments, location of such segments, drilling spin rates, weights, mudflow parameters, and other factors relating to the drilling of the well. As illustrated at 5606, drilling can be initiated in accordance with the drill recipe. For example, drilling can proceed using the established segments and segment locations, as well as spin rates, weights, mudflows, other factors, or a combination thereof."); ((Basu, Col 21, Lines 55-57) "In particular, the proposed system integrates the data and 55 continually analyzes it together to generate actionable recommendations for changes that produce better results.") wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the model (digital twin) and the one or more simulation methods. The manufacturing process may be adjusted according to recipes created per projected improvements (in such an example given- for production improvement as the KPI) ((Basu, Col 31, Lines 21-41) "The system can also project a completion recipe, as illustrated at 6110. The completion recipe can, for example, include a number of stages, number and type and direction perforations, treatment options, such as fracking parameters, or any combination thereof. The well can be drilled and completed at the proposed location, as illustrated at 6112, in accordance with the drilling recipe and completion recipe. Optionally, the system can provide feedback and adjust the drilling recipe and completion recipe in response to drilling events or other factors experienced while preparing the well. Such methods can lead to improved overall field management, including improved production from the field, as well as reducing costs associated with maintaining the field. For example, as illustrated in FIG. 32, a system 6200 includes projecting production improvements of existing wells, as illustrated at 6202. In an example, existing wells producing with given depletion curves can be influenced by reworking, additional treatments, installation of artificial lift, or other factors. Such methods can also propose enhanced 40 recovery for a field or set of wells.”) (See examiner note in the motivation to combine references which address the discrepancy between the utilization of a model by Basu vs DT by Rolo to perform the above tasks )
Rolo is analogous art to the claimed invention because it is related to the same field of endeavor of leveraging digital twin technology for performance monitoring of a manufacturing system. Basu is analogous art to the claimed invention because it is related to the same field of endeavor of KPI predictions and utilizing such information to improve processes in a manufacturing environment. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have combined the teachings of Rolo and Basu together in order to arrive at the claimed invention because simple substitution of a known element for another would yield predictable results. Rolo discloses the utilization of a digital twin for predicting manufacturing control system performance, wherein the digital twin provides a basis of information that enables prediction to leverage the current state of the system and the production plan to make the predictions. Basu discloses the projection of KPIs using a field specific model that is generated from field information from an array of data sources and including synthetic variables ((Basu, Col 27, Lines 20-26) "As illustrated at 5406, the field specific model can be built based on the field information and the synthetic variables. Such a field model can be used by analysis engines to generate prescriptions, such as drilling recipes, completion recipes, postproduction maintenance, treatment, and configuration recipes, as well as predict production from the wells."). By replacing the field model as disclosed by Basu with the digital twin model disclosed by Rolo, one would arrive at the claimed invention. One having skill in the art would be compelled to make the substitution because, while the field model of Basu incorporates real data from the system in order to make predictions, the field model also relies on synthetic data; whereas the digital twin of Rolo provides a source of information that accurately reflects the current state of the system. Such a substitution would yield predictions that reflect the physical system in real-time, thereby enabling more accurate predictions and recommendations. Accordingly, the combination would have been obvious. Examiner note: Examiner further notes that the second-to-last limitation and last limitation of the claim both refer to utilization of the digital twin for performing the simulations. Examiner acknowledges that the digital twin is not disclosed by Basu, but the overarching concepts are disclosed by Basu instead utilizing a field model. The combination of references as described herein teach the limitations given above when applying the digital twin as the field model.
Rolo in view of Basu does not explicitly disclose, however, the proposed combination in view of Weidl discloses wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, A scheme that simultaneously considers the hypothesized hierarchical structure for a failure object (as domain isolations forming a problem domain) and the symptoms of a failure, which may be at the component level (as the impact to the component) is leveraged so as to identify root causes of the failures ((Weidl, ¶85) "As mentioned above, data about the subject of an analysis may be organised in a structured manner such as in a hierarchical data file structure or model. In a hierarchically arranged data structure a failure object forms the parent object of a hierarchically structured data model generated for a failure. Since there are typically a plurality of possible causes for a failure, the parent object has a plurality of child objects presenting the possibilities. The possibilities are referred to in the following as hypotheses. Each of the hypotheses in turn may parent a plurality of child objects. These are referred to herein as symptoms. The symptoms represent abnormal changes in the process operation conditions, which lead to a failure in the problem domain (e.g. process and/or its operation and/or equipment and/or component) and/or other deviations from optimal conditions."); ((Weidl, ¶87) "If hierarchically structured data is used, the analysis is made so that the operator examines a hierarchically organised data structure displayed to him/her by a display device. The data examination of the possible root cause is then made in the direction: failure->hypothesis->symptoms") and wherein identifying the KPIs which require monitoring is performed by a machine learning model utilizing one or more binary classification methods based on an output of the root cause analysis Symptoms are identified by a Bayesian network model as nodes determined via binary classification, wherein the symptom nodes may represent parameters that associate with the problem domain ((Weidl, ¶106) "The symptom nodes of the BN graph can be of different character. For example, discrete nodes with mutually exclusive states may be provided. The exclusive states may be binary ( =Boolean) states such as "yes" ( ="true") when a symptom is observed and "no" ( ="false") when a symptom is not observed". The states may also indicate other features such as the intervals of the symptoms, relative symptoms levels (e.g. the ratio between measured value at an observation time point and value of the last set point) and so on. The symptoms nodes in a BN model may represent parameters that associate with the problem domain such as the performance measurement results, physical variables and so on.); ((Weidl, ¶111) "Multiple root causes of a failure can also be represented by binary nodes with states "yes" and "no" for each hypothesis, see FIG. 7. More than two states may also be used. For example, intervals or trends of the possible cause development can be used as classification criteria."). Symptoms are identified for given fault domains (wherein symptoms would be equivalent to KPIs and the presentation of them to the operator would indicate a requirement for investigation/monitoring) ((Weidl, ¶88) "As mentioned above, use of the structured data may not always be the most desirable. For example, if hierarchically organised data models are used the operator has to select a hypothesis before being able to get a display of the symptoms of that hypothesis, the displayed symptoms forming a checklist for the operator. The operator may need to check each of the symptoms to find the actual root cause for the failure or other deviation from normal operating conditions.");((Weidl, ¶142) "FIG. 8 shows a Graphical User Interface (GUI) that may be presented to the operator for selecting the observed symptoms after the operator has selected root cause analysis. The user interface may present a list of representative symptoms for a fault domain. The operator may then choose from the presented list the observed/ measured symptoms of the fault."). Root cause hypotheses are evaluated (as outputs of the RCA) and fed into the inference engine which leverages the graphical models including the Bayesian Network model, thereby indicating that the identification of the symptoms is based on such information ((Weidl, ¶55) "The inference engine 21 is arranged to perform a simultaneous verification of a number of root cause hypotheses. The simultaneous processing of the hypotheses can be facilitated by use of causally oriented graphical models. A causally oriented graphical model can be described as being a combination of probability theory and graph theory. The causally oriented models can be seen as models that are oriented based on causal associations the various nodes of the model may have with each other."). The BN model may also be based on structure data in a hierarchical RCA model, understood to be an output of a root cause analysis. ((Weidl, ¶60) "An example of data structure that can be more readily processed by the Bayesian network (BN) inference engine 21 is a graphical BN model that is referred to as a directed acyclic graph (DAG). The directed acyclic graph (DAG) creator 22 is a translation engine that is arranged to generate a directed acyclic graph (DAG) based on structure data such as a hierarchical RCA model"); ((Weidl, ¶118) "Returning now to FIG. 4, BN models are first created based on the RCA models stored at the data storage 33, step 100.")
Weidl is analogous to the claimed invention because it pertains to the same field of endeavor of industrial facility optimizations by using modeling and simulation techniques for guiding the maintenance, control and operation of such facilities. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination with the teachings of Weidl because some teaching, suggestion, or motivation would have led one having ordinary skill to do so in order to arrive at the claimed invention. The proposed combination in light of the teachings of Basu discloses performing root cause analyses as part of a performance metric analysis system that enables the identification, quantification, and ranking of influencers of performance metrics but does not particularly disclose the details of the innerworkings of what the root cause analysis may entail ((Basu, Col 18, Lines 33-46) " More specifically, in one embodiment, performance metric analysis system 2220 can implement a set of analytics comprising at least predictive analytics, root-cause analytics, optimization and what-if simulation. Colloquially speaking, predictive analytics allows users (for example, associated with entity 2240) to identify and quantify problems (including opportunities) related to one or more performance metrics, root-cause analysis allows users to identify, quantify and rank influencers of performance metrics which can cause any upcoming problems, optimization can determine substantially optimum solution to preempt ( or benefit from) any determined upcoming problems and what- if simulation allows a user to determine the effect of pre 45 scribe solutions on performance metrics"). However, Weidl discloses a root cause analysis scheme that leverages a Bayesian network approach to be able to identify and rank most probably root causes which may be used to subsequently determine an optimal sequent of control, operation, or maintenance actions ((Weidl, ¶77) " In accordance with a further embodiment the inference engine 21 may also access evidences automatically from a control system such as a distributed control system (DCS). The operator may also input evidences. The evidences may be propagated through the BN model 32 to produce a guidance list with ranking of most probable root causes and a list providing an optimal sequence of control, operation and/or maintenance actions."). Accordingly, because Basu suggests the utilization of a root cause analysis for predictive analytics and Weidl demonstrates an effective approach for root cause analysis in complex facility applications (see Weidl ¶6) that enables predictive diagnostic capabilities (See Weidl ¶28), the combination would have been obvious to one having skill in the art.
Rolo in view of Basu and Weidl does not explicitly disclose; however, the proposed combination in view of Hournbuckle discloses displaying KPIs for each of the one or more physical assets ((Hournbuckle, ¶66) "Exemplary interface 300 can provide insight, management, and control over a large number of assets including over assets from different industrial facilities. Interface 300 can be a cloud based service. "); ((Hournbuckle, ¶67) "The vertical taskbar 310 implements a faceted search for searching through available assets and filtering on facets of the asset. "). KPIs may be displayed as part of the interface, wherein narrowing down the display to specific objects of interest is a functionality ((Hournbuckle, ¶111) " FIG. 23 illustrates an interface 2300 for forecasting and root cause analysis of complex networks and flows that includes an example interactive graphical object. The interface includes a Network/Process Flow diagram 2305, an interactive graphical object including a timeline of events 2310, an events or alerts list 2315 that pertain to the objects shown in the timeline, a recommendation panel 2320, line/bar/dot graph(s) 2325 showing key performance indicators (e.g., critical key performance indicators [KPIs]) with filters and search allowing the users to narrow down the display to the specific objects of interest. ")
Hournbuckle is analogous to the claimed invention because it is pertinent to the problem faced by the inventor- that is creating a display interface by which to inform users of a production facility about information regarding the assets of the facility for predictive management purposes. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented incorporated the filtered viewing of KPIs per asset into the methodology of the proposed combination because combining prior art elements according to known methods would yield predictable results. Rolo discloses the display of KPIs (such as cycle time and throughput time) in a graphical user interface as shown in Figure 14 but the KPIs are given for the combined elements of the system’s physical assets. Basu presents an exemplary image of a user interface by which KPIs can be visualized but does not appear to contemplate associating the KPIs for each physical asset, as depicted in Figure 16. Hournbuckle discloses filtering displays by assets, wherein the displays include KPI information. It would have been obvious to a person having skill in the art to incorporate this functionality into the user interface because the ability to filter relevant data per asset enables granular views of the system and enables fine-tuned monitoring and control over a large number of physical assets in a system. Accordingly, the combination would have been obvious.
The proposed combination does not teach; however the combination in view of Ruppert teaches and wherein the one or more simulation methods include at least a Monte Carlo simulation process; and A Monte Carlo simulation is used in conjunction with digital twin functionality to model operator activity in a manufacturing environment ((Ruppert, Page 9 Col 2, ¶3) "A framework that allows the automated generation of real-time Monte Carlo simulation models of uncertain operator activities has been developed. "); ((Ruppert, Page 09, Col 1, ¶3) "A Monte Carlo simulation is used to study the differences between demand-driven and high/low complexity sequencing strategies (where products with lower degrees of complexity are produced immediately after the highly complex products) as is depicted in Fig. 10")
Ruppert is analogous art in that it is related to the same field of endeavor of leveraging digital twin simulations for improvements in manufacturing environment by providing monitoring capabilities for production performance. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have utilized particularly the Monte Carlo simulation process in the methodology of the proposed combination because some teaching, suggestion, or motivation would have led one having ordinary skill to modify the prior art references to incorporate the functionality in order to arrive at the claimed invention. Rolo discloses the utilization of a digital-twin based simulation for evaluating the performance of a system and specifically notes the utilization of AnyLogic software to create the digital model but does not particularly disclose the utilization of a Monte Carlo simulation ((Rolo, Page 5, ¶3) "The digital model itself was developed using AnyLogic, a powerful and well-documented agent-based simulation tool"). Basu discloses the utilization of what-if simulations for analyzing operational metrics but also does not particularly point out utilizing a Monte Carlo simulation ((Basu, Col 18, Lines 33-36) "More specifically, in one embodiment, performance metric analysis system 2220 can implement a set of analytics comprising at least predictive analytics, root-cause analytics, optimization and what-if simulation."). Ruppert discloses the utilization of a Monte Carlo simulation with a digital twin to predict the production status and provide monitoring of production performance, particularly for human resource effectiveness in manufacturing ((Ruppert, Page 7, Col 2, ¶2) "The distribution of the activity-times (illustrated in Fig. 7) is also analysed to calculate the key performance indicators, activity time models and stochastic models that can be used in Monte Carlo simulations. The resultant information is forwarded to an MS Excel file that serves as an input/configuration file of the simulator"); ((Ruppert, Page 9, Col 1, ¶3-4) "The applicability of the proposed RTLS-based digital twin in the analysis of human resource effectiveness is tested in the case of the comparison of two scheduling strategies. The scheduling of modular production is a complicated task as the products with high and low degrees of complexity have significantly different station times that make line balancing challenging. A Monte Carlo simulation is used to study the differences between demand-driven and high/low complexity sequencing strategies (where products with lower degrees of complexity are produced immediately after the highly complex products) as is depicted in Fig. 10."). Ruppert further states that Monte Carlo methodology is of interest because it effectively reflects the stochastic nature of operator behavior in the system ((Ruppert, Page 6, Col 2, ¶2) "Layout, resource and production sequence optimisation methods were used to improve the manufacturing process. In the case study that will be described in Section 4, the details of the production sequence analysis are presented. For such an analysis, Monte Carlo simulations can be elaborated on with the developed MS Excel interface that continuously changes the operating times to mimic the stochastic nature of operators. "). Accordingly, to accurately model the human behaviors within the system which cannot be directly measured, as in other aspects of a manufacturing system that could be directly measured (i.e. sensors, physical systems, items which can be modeled by physical models), one having skill in the art would be motivated to simulate using a Monte Carlo method within the digital twin environment to predict measurement data for KPIs. Therefore, the combination would have been obvious.
Regarding claim 5, the proposed combination discloses The method of claim 1, as stated previously.
The proposed combination in further view of Rolo discloses wherein a first portion of the plurality of conditions are determined based on data stored in a knowledge corpus A database is utilized to store the current state of a real system which defines the starting point of a simulation environment ((Rolo, Page 16, ¶4) "To summarise, the utilisation of an API to regularly store in a database the current state of the real system can be used as the starting point for a simulation environment."). and varying the data based on a real world location of the one or more physical assets, The database file contains a station flag that indicates whether a conveyor has a station attached to it or not (such that the data is varied to include a 1 or 0 and the station is exemplary of a physical asset) ((Rolo, Page 10, ¶5) "According to the model’s implementation, the database file must effectively describe the system’s current state and the remaining production plan. Thus, deciding which information was needed in order to depict the system’s state and the next products to be inserted was fundamental. After considering all of the system’s relevant aspects, the following structure emerged. The first six lines are mandatory and contain a maximum of five fields that describe each conveyors’ initial state, sorted as follows Timestamp—UNIX timestamp, in milliseconds, related to the corresponding conveyor’s state last update. Station Flag—indicates whether a conveyor has a station attached to it (1) or not (0). "). The station’s location is varied for different experiments such that the station is placed at different conveyors of the system, thereby indicating that the flag is set according to the location of the station ((Rolo, Page 14, ¶2) "For this first calibration, single-product production plans were considered with a station at conveyor D. "); ((Rolo, Page 15,, ¶1) "However, the change in the station’s location from conveyor D to E is a turning point on this regard"); ((Rolo, Page 15, ¶2) “These discrepancies led to a second calibration. For the simplicity’s sake, only two possible locations for the station (conveyors B and D) and the two scenarios below were tested: ").
and wherein a second portion of the plurality of conditions are manually selected by the user within the manufacturing optimization user interface. The conditions that define the manufacturing process are part of a production plan ((Rolo, Page 2, ¶2) "The authors believe that a simulation-based DT can be used to predict the system’s behaviour and performance. To do that, it must use the production line’s current status and the production plan in order to simulate the system to different time horizons"). A GUI, where users can manually input information, is used to create and define the production plan. ((Rolo, Page 12, ¶5) "In order to provide an intuitive way of describing the production plan to the MAS, a GUI with two sections was created. The first section, dedicated to the production pattern, is where the products’ types are inserted and sorted into the desired order, whereas the second one is where the plan size is defined and the instruction to create the plan is given. This graphical interface is represented in Figure 18."); (See also Rolo Page 13, Figure 18). The GUI is part of an architecture and system that targets improving the efficiency and profitability of manufacturing systems ((Rolo, Page 1, ¶3) "In order to improve the efficiency and profitability of manufacturing systems [4] by integrating activities involving human beings, machines and data [2], I4.0 relies on enabling technologies such as big data, Internet of Things (IoT) and simulations. Therefore, factories are progressively becoming smart factories, the main features of which are the capacity to accept on-the-fly changes to the ongoing manufacturing processes, provide remote access to every single resource and autonomously organise production tasks [5]."); ((Rolo, Page 3, ¶5) "With the aim of covering the aforementioned gaps, a three-layered architecture for a DT of a previously developed distributed control system was proposed, as portrayed by Figure 1."); (See also Rolo Page 3, Figure 1).
Regarding claim 6, the proposed combination discloses The method of claim 1, further comprising: as stated previously.
The proposed combination in further view of Rolo discloses (except the limitations surrounded by brackets ([[..]])) [[providing, in the manufacturing optimization user interface, one or more recommendations to the user based on the analysis]] of the digital twin under each of the plurality of conditions [[including the plurality of recommendations, wherein the manufacturing optimization user interface is displayed on a device of an individual operating the one or more physical assets. ]] The digital twin is used to simulate a system’s behavior and performance using the current status and a production plan ((Rolo, Page 2, ¶2) "The authors believe that a simulation-based DT can be used to predict the system’s behaviour and performance. To do that, it must use the production line’s current status and the production plan in order to simulate the system to different time horizons. ")
The proposed combination in further view of Rolo does not disclose; however the proposed combination in further view of Basu discloses (except the limitations surrounded by brackets ([[..]])) providing, in the manufacturing optimization user interface, one or more recommendations to the user Prescribed actions are output to a user interface ((Basu, Col 24, Lines 57-60) "The output of the prescription can be displayed to the user, for example, as illustrated in FIG. 16. In particular, the output can display design, completion, and operational parameters as a list of actionable variables. "); ((Basu, Col13, Lines52-57) "In particular example, such an analytics system 4100 can perform analysis of data and utilizing the configuration script, can process the data through each subsystem to provide a desirable set of user actions as indicated by the prescribed influencer future values. The user actions can be displayed on a display device."). based on the analysis … including the plurality of recommendations, The prescribed actions can incorporate previously prescribed actions ((Basu, Col 21, Lines 64-67 and Col 22, Lines 1-3) "As a result, the system is able to predict future outcomes and generate actionable recommendations to benefit from the predictions. Because real-world conditions are anything but static-especially true in shale plays-the system can continually incorporate new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options"). wherein the manufacturing optimization user interface is displayed on a device of an individual operating the one or more physical assets. Actions are prescribed to operators ((Basu, Col 24, Lines 33-35) "The prescription analytics engine can utilize the engines to prescribed actions taken by an operator or driller, or field management."). User actions are displayed to an interface ((Basu, Col 15, Lines 29-32) "Once a set of prescribed future influencer values is determined, the prescribed influencer values may be translated into prescribed actions and displayed, as illustrated at 4212."). The methodology is described as being implemented by a number of computing devices, including a personal data assistant device, which is understood to be a device of an individual ((Basu, Col 17, Lines 55-62) "Performance metric analysis system 2220 (e.g. one or more of computing devices 2230) can be coupled through network 2270 to computing devices 2210 (e.g. computer systems, personal data assistants, kiosks, dedicated terminals, etc.), one or more locations of an entity 2240 and one or more third party data sources 2250 operable to provide, for example, market data, benchmarking data, etc.")
Regarding claim 7, the proposed combination discloses The method of claim 1, further comprising: as stated previously.
The proposed combination in further view of Rolo discloses receiving real time data from one or more IoT devices associated with the manufacturing process; An API regularly retrieves the current state of the real system as it operates in real time ((Rolo, Page 16, ¶4) "To summarise, the utilisation of an API to regularly store in a database the current state of the real system can be used as the starting point for a simulation environment."). The components of the system are suggested to include smart factory elements, which are otherwise understood in the art as IoT devices used in manufacturing processes ((Rolo, Page 1, Introduction, ¶3) "In order to improve the efficiency and profitability of manufacturing systems [4] by integrating activities involving human beings, machines and data [2], I4.0 relies on enabling technologies such as big data, Internet of Things (IoT) and simulations. Therefore, factories are progressively becoming smart factories, the main features of which are the capacity to accept on-the-fly changes to the ongoing manufacturing processes, provide remote access to every single resource and autonomously organise production tasks [5].")
updating the digital twin and the plurality of conditions; The system’s initial state and remaining production plan are known ((Rolo, Page 4, ¶4) "Finally, the Simulation Layer (SL) contains the digital model. So as to serve its purpose, a correct description of the system’s initial state and the remaining production plan are required. This way, the model knows where to start simulating from and the type of products to be inserted afterwards"). The digital model (digital twin) of the system is updated with the state of the production plan as the production plan is executed ((Rolo, Page 4, ¶4) "Similarly to its physical twin, the digital model updates the KPIs whenever a product leaves the system, even though the simulation only ends upon the production plan’s completion"). See also Rolo Figure 3 that depicts the cross-functional flowchart showing how the digital twin is updated based on the current state and the production plan that is defined by the database file produced by the physical system.
simulating an updated digital twin in an updated plurality of conditions; and A simulation is performed based on the updated digital model defined by an updated production plan ((Rolo, Page 4, ¶4) "Finally, the Simulation Layer (SL) contains the digital model. So as to serve its purpose, a correct description of the system’s initial state and the remaining production plan are required. This way, the model knows where to start simulating from and the type of products to be inserted afterwards. Additionally, the user is expected to configure the time interval between the insertion of two consecutive products and the delay associated with the consumption of each type of skill, according to the data model. Similarly to its physical twin, the digital model updates the KPIs whenever a product leaves the system, even though the simulation only ends upon the production plan’s completion.") See also Rolo Figure 3 that depicts the cross-functional flowchart showing how the digital twin is updated based on the current state and the production plan that is defined by the database file produced by the physical system. The simulation model utilizes the updated data to execute a simulation.
[[displaying, in the manufacturing optimization user interface, one or more recommendations to a user]] based on the simulation of the updated digital twin. The simulation of the updated twin results in a set of updated KPIs ((Rolo, Page 4, ¶4) "Similarly to its physical twin, the digital model updates the KPIs whenever a product leaves the system, even though the simulation only ends upon the production plan’s completion.")
The proposed combination in further view of Rolo does not disclose; however the proposed combination in further view of Basu discloses displaying, in the manufacturing optimization user interface, one or more recommendations to a user Prescribed operations parameters (recommendations) can be generated to improve processes ((Basu, Col 24, Lines 41-46) “Further, the prescription analytics engine can include financial projections and optimization can be performed based on a financial projection constraint. For example, design, completion, or operations parameters can be prescribed to provide a desirably high net present value of the between stages of nearby wells, a time difference between well. "). The prescribed output can be displayed to the user ((Basu, Col 24, Lines 57- 65) "The output of the prescription can be displayed to the user, for example, as illustrated in FIG. 16. In particular, the output can display design, completion, and operational parameters as a list of actionable variables. The list can be an ordered list. For example, the list can include an order or higher influence variable to lower influence variables. The system can provide both a current value of the actual variable and a proposed improved value. Optionally, the display can further display reservoir parameters")
Regarding claim 8, Rolo discloses A computer system for manufacturing optimization, comprising: The development of a disclosed system is driven by improving efficiency and profitability of manufacturing systems ((Rolo, Page 1, Introduction, ¶3) "In order to improve the efficiency and profitability of manufacturing systems [4] by integrating activities involving human beings, machines and data [2], I4.0 relies on enabling technologies such as big data, Internet of Things (IoT) and simulations. Therefore, factories are progressively becoming smart factories, the main features of which are the capacity to accept on-the-fly changes to the ongoing manufacturing processes, provide remote access to every single resource and autonomously organise production tasks [5"). An architecture is described to enable improved efficiency and profitability wherein the architecture includes a manufacturing unit simulator as a multi-agent system ((Rolo, Page 3, ¶4) " With the aim of covering the aforementioned gaps, a three-layered architecture for a DT of a previously developed distributed control system was proposed, as portrayed by Figure 1."). One having ordinary skill in the art would recognize a multi-agent system as a computerized system composed of multiple interacting computing elements.
one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is configured to perform a method comprising: The simulation tool AnyLogic is described as being used for the creation of a digital model, wherein it is understood that the software that runs on a computing platform and requires disk space and memory, as well as a processor to execute the instructions defining the software ((Rolo, Page 5, ¶4) "The digital model itself was developed using AnyLogic, a powerful and well-documented agent-based simulation tool."). The Multi-Agent System utilizes Java Agent Development Framework (JADE) and includes an additional java class and a GUI and the Java project is executed ((Rolo, Page 12, ¶3) "The original version of the MAS was developed using Java Agent Development Framework (JADE) but no communications with external applications were allowed. Furthermore, products used to be manually added, which did not match the desired working mode for this project, where they ought to be inserted periodically. Consequently, in order to ensure the synchronisation between the manufacturing unit and the newly-created DT, a new Java class and a GUI were added to the initial project."); ((Rolo, Page 13, ¶5) " After executing the Java project, a product has to be put on conveyor A."). The software utilities are utilized as part of an architecture that executes the method ((Rolo, Page 3, ¶4) "With the aim of covering the aforementioned gaps, a three-layered architecture for a DT of a previously developed distributed control system was proposed, as portrayed by Figure 1."); (See Rolo Page 3, Figure 1). One having ordinary skill in the art would recognize these tools as software utilities and understand that the claimed computing components including processors, computer-readable memories, computer-readable tangible storage medium, and program instructions stored on a storage medium for execution by a processor are necessary for their usage.
The remaining limitations: receiving data for one or more physical assets utilized in a manufacturing process;
generating a digital twin, wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process;
performing a plurality of simulations using the digital twin, wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions;
analyzing a performance of the digital twin under each of the plurality of conditions by comparing key performance indicators (KPIs) from simulations which the digital twin failed to meet requirements of the manufacturing process with simulations which the digital twin met the requirements of the manufacturing process; and
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, and wherein identifying the KPis which require monitoring is performed by a machine learning model utilizing one or more binary classification methods based on an output of the root cause analysis;
generating a plurality of recommendations based on the KPIs which require monitoring; and
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and wherein the one or more simulation methods include at least a Monte Carlo simulation process; and
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods.. are substantially similar to that recited in claim 1 and are likewise rejected under the same rationale.
Regarding claim 12, the proposed combination discloses The computer system of claim 8, as stated previously.
The remaining limitation: wherein a first portion of the plurality of conditions are determined based on data stored in a knowledge corpus and varying the data based on a real world location of the one or more physical assets, and wherein a second portion of the plurality of conditions are manually selected by the user within the manufacturing optimization user interface. is substantially similar to that recited in claim 5 and is therefore rejected under the same rationale.
Regarding claim 13, the proposed combination discloses The computer system of claim 8, further comprising: as stated previously.
The remaining limitations: providing, in the manufacturing optimization user interface, one or more recommendations to the user based on the analysis of the digital twin under each of the plurality of conditions including the plurality of recommendations, wherein the manufacturing optimization user interface is displayed on a device of an individual operating the one or more physical assets. are substantially similar to that recited in claim 6 and are therefore rejected under the same rationale.
Regarding claim 14, the proposed combination discloses The computer system of claim 8, further comprising: as stated previously.
The remaining limitations: receiving real time data from one or more IoT devices associated with the manufacturing process;
updating the digital twin and the plurality of conditions;
simulating an updated digital twin in an updated plurality of conditions; and
displaying, in the manufacturing optimization user interface, one or more recommendations to a user based on the simulation of the updated digital twin. Are substantially similar to that recited in claim 7 and therefore the claim is rejected under the same rationale.
Regarding claim 15, Rolo discloses A computer program product for manufacturing optimization, comprising: Software with algorithms and a user interface is developed that utilizes multiple software tools to enable a method for improving efficiency and profitability of manufacturing systems. ((Rolo, Page 17, footnote) " Author Contributions: Conceptualization, A.D.R. and J.B.; methodology, G.R.R. and A.D.R.; software,"); ((Rolo, Page 17, ¶4) " Moreover, none of the used technologies narrows down the range of target-problems, given that widely-compatible programming languages and file formats were opted for.")
one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more non-transitory computer-readable storage media, the program instructions executable by a processor to cause the processor to perform a method comprising: AnyLogic is described as being used for the creation of a digital model, wherein it is understood that the software that runs on a computing platform and requires disk space and memory, as well as a processor to execute the instructions defining the software ((Rolo, Page 5, ¶4) "The digital model itself was developed using AnyLogic, a powerful and well-documented agent-based simulation tool."). The Multi-Agent System utilizes Java Agent Development Framework (JADE) and includes an additional java class and a GUI and the Java project is executed ((Rolo, Page 12, ¶3) "The original version of the MAS was developed using Java Agent Development Framework (JADE) but no communications with external applications were allowed. Furthermore, products used to be manually added, which did not match the desired working mode for this project, where they ought to be inserted periodically. Consequently, in order to ensure the synchronisation between the manufacturing unit and the newly-created DT, a new Java class and a GUI were added to the initial project."); ((Rolo, Page 13, ¶5) "After executing the Java project, a product has to be put on conveyor A."). The software utilities are utilized as part of an architecture that executes the method ((Rolo, Page 3, ¶4) "With the aim of covering the aforementioned gaps, a three-layered architecture for a DT of a previously developed distributed control system was proposed, as portrayed by Figure 1."); (See Rolo Page 3, Figure 1). One having ordinary skill in the art would recognize these tools as software utilities and understand that the claimed computing components including non-transitory computer-readable storage medium and program instructions stored on a tangible storage medium for execution by a processor are necessary for their usage.
The remaining limitations: receiving data for one or more physical assets utilized in a manufacturing process;
generating a digital twin, wherein the digital twin includes a digital representation of the one or more physical assets utilized in the manufacturing process;
performing a plurality of simulations using the digital twin, wherein each simulation of the digital twin simulates the manufacturing process under one set of a plurality of conditions;
analyzing a performance of the digital twin under each of the plurality of conditions by comparing key performance indicators (KPIs) from simulations which the digital twin failed to meet requirements of the manufacturing process with simulations which the digital twin met the requirements of the manufacturing process; and
displaying, in a manufacturing optimization user interface, KPIs which require monitoring for each of the one or more physical assets to a user based on a root cause analysis, wherein the root cause analysis includes performing both a fault domain isolation analysis and an impacted component analysis, and wherein identifying the KPIs which require monitoring is performed by a machine learning model utilizing one or more binary classification methods based on an output of the root cause analysis;
generating a plurality of recommendations based on the KPIs which require monitoring; and
simulating, using one or more simulation methods, an implementation of each of the plurality of recommendations using the digital twin, wherein the one or more simulation methods are utilized in projecting KPI measurements for each of the plurality of recommendations, wherein each of the plurality of recommendations include one or more adjustments to the manufacturing process, and wherein the one or more simulation methods include at least a Monte Carlo simulation process; and
adjusting the manufacturing process such that an updated manufacturing process includes the one or more adjustments corresponding to improved KPI measurements of at least one of the plurality of recommendations, wherein the manufacturing process is adjusted such that the updated manufacturing process performs according to a projected improvement of the KPI measurements when simulated using the digital twin and the one or more simulation methods are substantially similar to that recited in claim 1 and are likewise rejected under the same rationale provided for claim 1.
Regarding claim 19, the proposed combination discloses The computer program product of claim 15, as stated previously.
The remaining limitations: wherein a first portion of the plurality of conditions are determined based on data stored in a knowledge corpus and
varying the data based on a real world location of the one or more physical assets, and wherein a second portion of the plurality of conditions are manually selected by the user within the manufacturing optimization user interface. are substantially similar to that recited in claim 5 and are therefore rejected under the same rationale.
Regarding claim 20, the proposed combination discloses The computer program product of claim 15, further comprising: as stated previously.
The remaining limitations: providing, in the manufacturing optimization user interface, one or more recommendations to the user based on the analysis of the digital twin under each of the plurality of conditions including the plurality of recommendations, wherein the manufacturing optimization user interface is displayed on a device of an individual operating the one or more physical assets. are substantially similar to that recited in claim 6 and are therefore rejected under the same rationale.
Regarding claim 21, the proposed combination discloses The method of claim 1, as stated previously. The proposed combination in further view of Basu discloses (except the limitations surrounded by brackets ([[..]])) wherein at least one or more of the KPIs which require monitoring include visual indicators within the manufacturing user interface, the KPIs may have associated indicators that may be displayed on an interface device when the KPIs are in violation of business rules ((Basu, Col 10, Lines 12-22) " Further, the system can provide a warning when suggested actions are not implemented. For example, when the system predicts that a future value of a key performance indicator will be in violation of a business rule and prescribes an action and when new data indicates that the action was not implemented and the key performance indicator will be in violation of the business rule, the system can provide an indication or send a message to a supervisor indicating that the actions were not taken. For example, an indication can be displayed on an interface device, sent via email, sent
as a text message, or provided as a voicemail.") [[wherein the visual indicators represent bottlenecks]] for either functional requirements or non-functional requirements of the manufacturing process. Functional constraints and otherwise may be imposed on a production system, which can dictate the determination of predicted KPI values and actionable tasks of the process ((Basu, Col 8, Lines 33-45) " In addition, the system can automatically or iteratively deter mine a set of actionable tasks including changes to influencer values over time to provide future KPI values 3602 that do not violate business rules, subject to constraints 3608. A business rule can be a constraint. Alternatively, a business rule can be different than a constraint. In a further example, a user can manipulate one or more future values of a selected influencer 3604 to determine the effect on the future value of a key performance indicator. The constraints 3608 can take a variety of forms including box constraints, functional constraints, quantized constraints, step constraints or any combination thereof. ")
The proposed combination in further view if Basu does not disclose; however the proposed combination in view of Ruppert discloses wherein the visual indicators represent bottlenecks Figure 6 depicts a heatmap of assembly time measurement for a manufacturing process at different areas of the system, wherein the zones closer to red indicate a higher time period spent at the station (See Ruppert Figure 6); ((Ruppert, Page 7 Col 1, ¶2 – Col 2, ¶1) "The extracted information can be visualised ith spaghetti diagram (shown in the top of Fig. 6) and on a heatmap (see the bottom of Fig. 6), which highlights how time is spent on the production line."). The distribution of the activity times is used to calculate KPIs ((Ruppert, Col 2, ¶2) "The distribution of the activity-times (illustrated in Fig. 7) is also analysed to calculate the key performance indicators, activity time models and stochastic models that can be used in Monte Carlo simulations. ")
Ruppert is analogous art in that it is related to the same field of endeavor of leveraging digital twin simulations for improvements in manufacturing environment by providing monitoring capabilities for production performance. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have implemented the visual indicators of bottlenecks for processes into the manufacturing interface of the proposed combination because some teaching, suggestion, or motivation in the prior art references would have led one having skill in the art to do so in order to arrive at the claimed invention. Basu discloses the utilization of visual indicators for KPI metrics and also discloses the utilization of constraints for KPIs ((Basu, Col 10, Lines 12-22) "Further, the system can provide a warning when suggested actions are not implemented. For example, when the system predicts that a future value of a key performance indicator will be in violation of a business rule and prescribes
an action and when new data indicates that the action was not implemented and the key performance indicator will be in violation of the business rule, the system can provide an indication or send a message to a supervisor indicating that the actions were not taken. For example, an indication can be displayed on an interface device, sent via email, sent
as a text message, or provided as a voicemail."). While Basu does contemplate that business rules that are crossed may indicate undesirable conditions, Basu does not particularly output indicators into the user interface that represent bottlenecks in the manufacturing process ((Basu, Col 16, Lines 17-21) "Further, the constraint section or a separate business rules section may identify thresholds or other boundaries that if crossed by a performance indicator indicate a problem, error, or undesirable condition"). Basu notes that the duration of a shut-in event may be a predicted KPI value ((Basu, Col 43, Lines 23-26) " In another example of the eighteenth aspect and the above examples, the maintenance includes well shut-ins. For example, projecting the improved production includes projecting a time and a duration of a shut-in."). Ruppert provides a heat map visualization of time spent at different areas in the manufacturing process, wherein events which last longer durations are indicated more intensely by red color value indicators, indicated areas of slowed or accumulated production such as would be indicative of a bottleneck (See Ruppert Figure 6); ((Ruppert, Page 7 Col 1, ¶2 – Col 2, ¶1) "The extracted information can be visualised ith spaghetti diagram (shown in the top of Fig. 6) and on a heatmap (see the bottom of Fig. 6), which highlights how time is spent on the production line."). Ruppert explicitly notes that such visual feedback is important to provide to operators ((Ruppert, Page 6, Col 2, ¶1) "As it is extremely important to give visual feedback to the operators about the OEE and the related downtimes [58], OEE and the related human resource effectiveness (HRE) [16] of the operators are analysed."). Accordingly, the combination would have been obvious.
Regarding claim 22, the proposed combination discloses The method of claim 21, further comprising as stated previously.
The proposed combination in further view of Basu discloses receiving, in the manufacturing optimization user interface, feedback from the user with respect to the functional requirements or the non-functional requirements of the manufacturing process; and A user can provide input to the system through the user interface regarding the relationship to the constraints ((Basu, Col 8, Lines 16-25) "In further explanation of a system 3600, key performance indicators 3602 are influenced by influencers 3604 as constrained by constraints 3608, as illustrated in FIG. 7. Further, a user 3612 can influence the relationships established between constraints (R) and influencers (I). For example, a user can select parameters, a type of model, or other factors that influence how a relationship (r) is established between the influencers 3604, the constraints 3608, and the KPI 3602. In an example, the user 3612 can influence the system through a user interface or by using a configuration script."). Constraints include functional constraints and otherwise ((Basu, Col 8, Lines 43-45) "The constraints 3608 can take a variety of forms including box constraints, functional constraints, quantized constraints step constraints or any combination thereof.")
altering the manufacturing process based on the feedback received from the user, wherein the altering of the manufacturing process includes implementation details displayed within the manufacturing optimization user interface. The relationship set by the user regarding constraints is leveraged to determine a set of actionable tasks or changes to influencer values of the system ((Basu, Col 8, Lines 26-38) "Such a relationship (r) permits the determination of the KPI 3602 at one or more future time periods based on present and future values of influencers 3604 subject to constraints 3608. In addition, such a relationship (r) is useful for determining the influence of changes in the influencers 3604 on the KPI 3602 at a selected future time. As a result, root cause analysis can be performed specifically for the selected future time or generally across time periods. In addition, the system can automatically or iteratively determine a set of actionable tasks including changes to influencer values over time to provide future KPI values 3602 that do not violate business rules, subject to constraints 3608."). Values of influencers can be manipulated ((Basu, Col 8, Lines 39-42) "In a further example, a user can manipulate one or more future values of a selected influencer 3604 to determine the effect on the future value of a key performance indicator."). Influencers characterize process parameters, thereby indicating that changes to influencers yield changes to the manufacturing processes ((Basu, Col 4, Lines 30-39) "To correct the deviation, associated influencers can be manipulated. For example, more staff can be added to reduce hold time. However, immediate manipulation of the influencers to solve a problem predicted in the future can provide less than desirable solutions to the problems in the business process. For example, hiring more staff long before the hold times are expected to increase leads to higher cost in the call center. The present system can assists with determining a desirable set of future actions to maintain a business process incompliance with business criteria."); ((Basu, Col 7, Lines 20-24) "As such, embodiments of the present system can assist with determining a set of future actions (changes to influencers) that maintain a business process, as quantified by performance indicators, in compliance with business criteria."). User actions are prescribed (implementation details) to alter influencer values and the user actions are displayed to a display device ((Basu, Col 13, Lines 52-57) "In particular example, such an analytics system 4100 can perform analysis of data and utilizing the configuration script, can process the data through each subsystem to provide a desirable set of user actions as indicated by the prescribed influencer future values. The user actions can be displayed on a display device.")
Regarding claim 29, the proposed combination teaches The method of claim 1, as stated previously and further teaches in view of Basu wherein the updated manufacturing process includes a greater production volume ((Basu, Col 20, Lines 28-35) " The proposed system helps operators anticipate and improve wells. The proposed system incorporates data, regardless of source, structure, size, or format, to prescribe actionable recipes for drilling, completing, and producing wells that maximize their economic value at every point over the course of their serviceable lifetimes. The bottom line for operators is improved returns on deployed capital: more oil, more predictably, for less cost.") and a shorter utilization of production downtime than the manufacturing process. ((Basu, Col 28, Lines 25-30) " As illustrated at 5612, drilling can proceed using the adjusted drill recipe. Such an approach can be used to limit lost drilling time, the use of resources, such as mud, equipment wear, and ultimately cost associated with drilling a well.").
Allowable Subject Matter
Claims 30 and 31 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, as well as rewritten to overcome any other outstanding rejections, including 35 U.S.C. § 101, as presented in this office action.
The following is a statement of reasons for the indication of allowable subject matter:
The prior art of record fails to disclose or reasonably suggest the limitations of claims 30 and 31. Because the addition of these claims modified the scope of the claimed invention, additional search for prior art was necessary. Though a comprehensive search was completed for the claimed limitations, no additional references were found to cure the deficiencies of the prior art of record for the limitations of claim 30 or claim 31.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/E.G.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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