Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-8 and 10-19 are amended. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 05/01/2026 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
The Claims Are Directed To An Abstract Idea
Applicant attempts to equate their claims and invention to Desjardins, but Desjardins is not analogous or applicable to applicant’s claimed invention.
The claimed invention in Desjardins is directed to the field of machine learning techniques, and the claims are aimed at specific and technically detailed ways of improving the operation of a machine learning system, or improving machine learning technology, such as by enhancing its training efficiency or preserving prior learning across sequential training. This is far from merely leveraging existing machine leaning technology to “apply it” (via computer) to optimize the supervision and maintenance in the monitoring of industrial equipment as is applicant’s claimed invention. Instead of Desjardins, the claimed in invention and finding in Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) is appropriate for applicant’s claims and invention. Applicant’s invention and claims are not directed to improving machine learning technology, but instead merely using machine learning in a specific filed of use, i.e., the supervision, monitoring, and maintenance of industrial equipment. This is not only evidenc3d by the claims, but also the specification. As stated in MPEP §2106.04(d)(1), the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, 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. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Here in the applicant’s invention, neither the specification nor claims describe invention such that the improvement would be apparent to one of ordinary skill in the art of machine learning. The claims also fail to reflect the alleged improvement in machine learning technology. In fact, the specification recites in [0159] that “…the invention foresees the usage of computer technology involving artificial intelligence, especially of the field of machine learning, which is based on mathematical and statistical approaches to give computers the ability to learn from data, that is to say to improve their performance in solving tasks without being explicitly programmed for each of them.” This gives a generic definition and use of machine learning in the applicant’s invention. Further, in [0160], the specification states “In particular, the supervision system 1 comprises a model 16 foreseen as virtual, resulting from such learning. Further, the training of the model 16 may involve machine learning of any type, that is to say, in a non-limiting manner, supervised, semi-supervised, unsupervised, reinforcement, or even by transfer. Further, machine learning can implement training methods of any category, said methods being able to be combined, that is to say, in a non- limiting manner: neural networks (including deep learning methods), the k- nearest neighbors method ("KNN"), genetic algorithms, genetic programming, or other methods such as, especially Bayesian networks, support vector machines (SVM), Q-learning, decision trees, statistical methods, logistic regression, linear discriminant analysis.” Again, applicant’s specification nor claims describe an improvement to machine learning technology as indicated in Desjardins, but merely using machine leaning via computer as a tool to implement the judicial exception. As started in Recentive, which is more applicable to the applicant’s invention, "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). Additionally, "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15. Lastly, but also very important, is that the court in Recentive recognized that "The requirements that the machine learning model be 'iteratively trained' or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 12.
For the reasons set forth above, the applicant’s claims are in no way similar to that of Desjardins, and the result in Desjardins does not fit for the applicant’s claimed invention.
The Claims Recite A Judicial Exception Under Step 2A Prong One
Applicant argues that the claims are not directed to a judicial exception because the claims “cannot be performed in the human mind” (mental processes), and that the claims do not recite certain methods of organizing human activity. This argument is unpersuasive. The Federal Circuit has explained that "the' directed to' inquiry applies a stage-one filter to claims, considered in light of the specification, based on whether 'their character as a whole is directed to excluded subject matter."' Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016) (quoting Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1346 (Fed. Cir. 2015)). It asks whether the focus of the claims is on a specific improvement in relevant technology or on a process that itself qualifies as an "abstract idea" for which computers are invoked merely as a tool. See id. at 1335-36. Here, it is clear from the Specification (including the claim language) that claim 1 focuses on an abstract idea, and not on an improvement to technology and/or a technical field. Applicant’s claim 1 limitations are drawn to supervising industrial equipment for reliability, failure, etc. and directly correspond to mental processes (observation, evaluation, judgment, opinion) as evidenced by the numerous limitations detailing the observation of various equipment data, and making a judgment or opinion based on the observed and evaluated data. The claim limitations also correspond to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as evidenced by limitations relating to the supervision of industrial equipment and performing actions based on the data supervised. The claim filtered in in light of the specification further supports that the claims are directed to an abstract idea. For instance, applicant’s specification provides in [0065] that the steps are performed by a person interacting with said computing terminal, especially via a virtual interface, or else specifically when an operator interacts with said equipment. Further, the specification recites in [0163] “The invention envisages taking into account the new manufacturing, maintenance, usage and status data generated since the first training of the model 16, in the context of the machine-learning project 142, and updating said virtual model 16. To do this, according to one embodiment, the preceding operations are repeated periodically: - the recovery of new data from at least one manufacturer, maintenance technician and/or operator is repeated. Preferably, the new data relating to all the equipment 2 of the series are recovered from the set of operators implementing the equipment of the series; - the extraction and the conversion 141 of said new data are then performed in order to obtain a completed dataset 15, followed by updating the training of said model 16 on the basis of said completed dataset 15.[0164] It will be noted that this update is performed periodically, the periodicity being determined by the designer, manufacturer, maintenance technician and operators, as a function of criteria deemed most relevant by the latter or from the ‘data science’ point of view. Additionally, [0233] recites “In view of the assumptions and the intention of the operator, the maintenance decision in respect of the maintenance 6 to be performed, is in this case no longer the decision D2 or the decision D4, but the decision D3, with an average deviation of 10%. In particular, the decisions D4 to D7 also appear to allow an average deviation greater than 10% but, with regard to the intention of the operator, appear to involve unnecessary over maintenance, with higher maintenance constraints. For the operator, the optimal maintenance decision in respect of the maintenance 6 to be performed is therefore in this case the decision D3 (i.e., the one that comprises replacing the injectors and the diesel filter)”. Specification [0259] discloses “Among the arguments to be submitted as input to the model 16, instead of submitting the usage of the equipment 2 foreseen in the context of the scenario 8, from said instant and for the rest of the projected period of operation 70 up to the day before the projected maintenance 7, the usage of the equipment 2 in the context of the unforeseen scenario is indeed submitted to the model 16, from said instant and for the rest of the projected period of operation 70 up to the day before the projected maintenance 7. Similarly, the invention refreshes, at a given instant in the course of the projected period 70, and with a view to executing a scenario different from the initially foreseen usage scenario 8, between said instant and the end of the projected period 70, the maximal limits 18 of the limit usage compatible with zero failures up to the next maintenance 7, the limit polygon 181, the margin of usage before failure (MUBF) as well as the chart of all the limit scenarios: which allows the user to identify the limits to which they can push the usage of the equipment without compromising the zero failures or in which they must restrict the usage initially envisaged in order to benefit from zero failures, and to do so with a view to executing of a scenario different from the initially foreseen usage scenario 8. Thus, the invention allows the operator to decide, in the course of the projected period 70, the usage that they can or should make of the equipment 2, to keep this usage compatible with the requirement of zero failures up to the date of the next maintenance, in the context of an initially foreseen scenario 8 as well as in the context of an unforeseen scenario, replacing the initially foreseen scenario 8.” Further, [0260] recites “Advantageously, the simulation by the model 16 additionally makes it possible to determine, step by step, the sufficient maintenance decisions for successive projected maintenance operations 7 in the life of the equipment 2, taking into account the target usage (namely the successive scenarios 8) intended by the operator over the short term, as well as in the long term. The system 1 thus makes it possible to determine, at all times, the various possible sufficient maintenance schedules, throughout the entire service life of the equipment 2, for an intended usage profile. To do this, according to one embodiment, at least the following steps are performed.” Other relevant portions of the specification that the examiner highlights include [0304], [0307], and [0311], all of which support that the claimed invention is merely leveraging computers (equipped with machine learning program) to analyze and observe data (which is a mental process) in order for technicians or operators to make a judgment or decision (based on the observed or evaluated data) on maintenance decisions for the equipment, and to supervise the operability of the equipment. The highlighted portions of the specification in this response in not exclusive, there are many other portions of the specification that reflects this position as well. The inventors Affidavit also supports the position that the claimed invention is directed to an abstract idea. The Affidavit highlights much the analysis and observation that is the crux of the invention. For instance, #8 recites how the technical analysis is structured technical methodology requiring access to engineering documentation, historical operational data, and the physical architecture of the equipment series, and it produces a specified structured output, the theoretical aging law model. Applicant and inventor further asserts that these things cannot be done via human, but they absolutely can. Applicant is merely automating this analysis via computer and machine learning for efficiency. The mental-steps inquiry under Step 2A Prong One is not “could a human do this at the same speed/precision.” It’s whether the claim limitation, under broadest reasonable interpretation, covers performance in the mind (observation, evaluation, judgment, opinion) but for the recitation of generic computer components. MPEP §2106.04(a)(2) is explicit that a claim can recite a mental process even though it’s performed with the assistance of a computer. The question is the underlying nature of the step, not whether a human could literally replicate the computer’s exact arithmetic. Further, MPEP §2106.04(a)(2) provides that the sub-groupings encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping. The specification, claims, and Affidavit makes it clear that the claims describe certain activity between a person and computer as well.
For the reasons set forth above, it is clear that under Step 2A Prong One, a judicial exception, i.e. an abstract idea, is set forth or described in the claim.
The Claims are NOT Integrated Into A Practical Application
Applicant asserts that a technical analysis methodology is an improvement to machine learning technology. Examiner disagrees. Examiner has already provided how Desjardins does not apply to the applicant’s invention (see part I). A technical analysis is not an improvement to computers or technology, but instead further correspond to the judicial exception itself. Applicant claims that the analysis compensates for deficiencies of conventional machine learning in model equipment ageing and defines a theoretical model of the aging law of the equipment series, both of which further correspond to observation and evaluation. At best, the alleged improvement is an improvement to the judicial exception itself, and again, applicant provides no details such that the improvement would be apparent to one of ordinary skill in the art. Technical improvement focuses on enhancing the tools, software, or machinery, while business process improvement focuses on streamlining the steps, workflows, and methodologies people use to do their work. Applicant’s claimed invention falls under business process improvements. For instance, the technical analysis has no bearing on computer functionality or operations, and applicant has stated that any machine learning type may be used; that “…the invention foresees the usage of computer technology involving artificial intelligence, especially the field of machine learning…”. Applicant basically admits that the claimed invention simply uses machine learning technology (programmed on computers) as a tool to implement the judicial exception. It is important to note, the judicial exception alone cannot provide the improvement. See MPEP §2106.05(a). ). It is important to keep in mind that an improvement in the judicial exception itself (e.g., a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG LLC, the court determined that the claim simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Similarly, the Applicant’s claim recitations are an improvement in the judicial exception, not an improvement in technology.
Applicant further argues that the individualized long-term simulation is a specific improvement over the prior art. Applicant appears to be conflating novelty (35 U.S.C. 102) and obviousness (35 U.S.C. 103) with patent eligibility (35 U.S.C. 101). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973. As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. Applicant’s argument is severely misplaced.
Applicant further argues that the operation outputs govern equipment operations. This argument is unpersuasive. The operational outputs, as applicant states, determines the specific maintenances steps that must be performed, the physical operations constraints of the equipment, and maintenance schedule and spare parts. This further corresponds to the judicial exception itself. This is data used to make a decision (judgment, opinion) and also correspond to “following rules or instructions” based on the outputs.
Regarding the assertion of “high level of generality”
Applicant argues that sensor modalities were not examined. This is incorrect. The Office Action specifically states under the Step 2A Prong two section that “the industrial equipment, and sensors associated therewith, amounts to generally linking the judicial exception to a particular field of use (the supervision of industrial equipment in facilities)”. The sensors are recited at a high level of generality along with computer components. The sensors are merely collecting data. Applicant should not claim that the sensors were not addressed as additional elements when they were specifically mentioned along with the decision on whether the sensors integrate the judicial exception into a practical application (they did not), just because the applicant is not getting the outcome they desire. Applicant’s argument lacks merit.
The Additional Elements Are NOT Sufficient To Amount To Significantly More Than The Judicial Exception
Applicant provides the same argument presented in the previous sections regarding novelty and obviousness and 101, so examiner will simply restate the Office’s position here for the Step 2B analysis.
Applicant appears to be conflating novelty (35 U.S.C. 102) and obviousness (35 U.S.C. 103) with patent eligibility (35 U.S.C. 101). Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973. As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. Applicant’s argument is severely misplaced.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements (industrial equipment, digital system, sensors associated with the equipment, a system in which the equipment is embedded, and a virtual model) amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Regarding Inventor’s Affidavit
Inventor’s affidavit is not persuasive, and further solidified examiner’s position in the Office Action. Much of what the inventors argues is further directed to the judicial exception, and indicates no improvement in computers or machine learning technology. The Affidavit highlights much the analysis and observation that is the crux of the invention. For instance, #8 recites how the technical analysis is structured technical methodology requiring access to engineering documentation, historical operational data, and the physical architecture of the equipment series, and it produces a specified structured output, the theoretical aging law model. Applicant and inventor further asserts that these things cannot be done via human, but they absolutely can. Applicant is merely automating this analysis via computer and machine learning for efficiency. Examiner notes that this highlighted section of the affidavit is not exclusive, and there are multiple sections of the Affidavit that further indicates, at best, an alleged improvement to the judicial exception itself and not an improvement to computers or technology. No argument the inventor presented in the Affidavit was found to be persuasive. The Examiner also notes that the information that appears in the respective Affidavit appear to be statements of opinion regarding eligibility and technical improvements, respectively, and are not evidentiary facts, per se. Additionally, Mr. Fabrice Ravignon is a named inventor and is Founder and CEO of the assignee, SeADvance SAS, making their statements appear to be self-serving, and their conclusions do not appear to be made an objective review of the 101 analysis.
The 35 U.S.C. 101 rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-20 recite a system (i.e. machine). Therefore claims 1-20 fall within one of the four statutory categories of invention.
Independent claim 1 recites the limitations of: installing within a facility (3) at least one [industrial equipment] (2) resulting from a process of manufacturing (4) and being representative of a series, then at least operating [said equipment] (2) in a context of a period (5) up to a step of a maintenance (6) to be performed; defining at least one projected maintenance (7) subsequent to said maintenance (6) to be performed, after at least one scenario (8) with projected usage conditions (110) of [said equipment] (2) over a projected period (70) of operation, characterized in that the manufacturing (4), installation, operation and maintenance of [said equipment] (2) induce at least: a manufacturing and maintenance log (9) comprising: tasks (90) for manufacturing said at least one equipment (2) until said installation, and/or tasks (90) of at least one prior maintenance (10) of [said equipment] (2); a usage log (11) of [said equipment] (2) over said period (5) between the installation and said maintenance (6) to be performed, said usage log (11) comprising usage conditions (110) of [said equipment] (2) during said period (5); wherein the usage conditions (110) comprise measured physical quantities obtained from [sensors associated with the equipment] and/or with [a system in which the equipment (2) is embedded] and/or with an environment in which [the equipment (2) ]operates, selected from the group consisting of temperature, pressure, vibration, flow rate, electrical current, mileage, load, speed, and combinations thereof, the measured values being time-stamped and recorded automatically; a log (120) of status (13) of [said equipment] (2), said log (12) of status (13) comprising sensor-derived material indicators (120) representative of performance or degradation of the equipment (2), including flow rate, delivery pressure, compression pressure, vibration, electrical current, speed, state or performance measurement on a test bench or other non-destructive test measurements; wherein by means of a technical analysis of [said equipment] (2) from the series, and of its environment during operation or shutdown, said technical analysis being configured to:(i) account for substantially all internal components of said equipment (2) and the principal tasks (90) that affect the status (13) of [said equipment] (2) during operation;(ii) account for substantially all principal degradation mechanisms to which said equipment (2) is subjected in its environment during operation or when stopped;(iii) represent said degradation mechanisms through usage conditions (110) and their effect on the status (13) of [said equipment] (2) via material indicators (120); and(iv) identify a complete set of data to be taken into account, thereby compensating for deficiencies of conventional machine learning in modeling equipment aging; at least one correlation (14) is determined between at least one of said tasks (90) and/or at least one of said usage conditions (110), and at least one of the material indicators (120) of said status (13), said correlation (14) establishing at least one link between causes of aging and consequences of aging of the equipment (2); said correlation (14) further being configured to define a theoretical model of the aging law of said equipment of the series, prior to training the related virtual model (16); in the correlation (14): the tasks (90) are characterized by those identified as critical, and/or the usage conditions (110) characterized by those to which the equipment (2) is sensitive and exposed during operation or when stopped, the tasks (90) and the conditions (110) considered impacting the status (13) of [said equipment] (2); the material status (13) of [the equipment] (2) is characterized by the indicators (120) identified as being representative of this status (13) of [said equipment] (2); then, in the correlation (14), the following are determined: measured physical quantities or functions of the measured physical quantities that directly characterize the usage conditions (110) to which said equipment (2) is sensitive and exposed during operation or when stopped and that drive the degradation mechanisms of said equipment (2), or that indirectly reconstruct the usage conditions (110) when not directly available; measured physical quantities or functions of the measured physical quantities characterizing the material status (13) of the equipment (2) at a given instant; and then for a subset of equipment of said series, recovering and extracting data associated with these tasks (90), and data associated with the physical quantities or functions of physical quantities relating to these usage conditions (110) and to these material indicators (120), as identified in said correlation (14), so as to obtain a dataset (15); training at least one [virtual model] (16), on the basis of the dataset (15); and wherein during the maintenance (6) of said equipment (2) to be performed, values are submitted to said [model] (16): of at least one of the tasks (90) of the manufacturing and maintenance log (9) and of at least one of the usage conditions (110) of the usage log (11), and of at least one of the tasks (90) of the maintenance (6) to be performed and of said projected usage conditions (110) of the scenario (8); said model (16) being configured to simulate the projected status (130) of said equipment (2) at the end of the operating period of the scenario (8), prior to the next scheduled maintenance (7), based on:(i) the manufacturing and maintenance log (9) and the usage log (11) of said equipment (2),(ii) at least one of the tasks (90) associated with the maintenance (6) to be performed, and (iii) the projected usage conditions (110) of the equipment (2) over the operating period of the scenario (8), said simulation corresponding to a long-term operating period having a typical duration of 24 months or more; said model (16) being further configured to provide an individualized prediction for said equipment (2), identified by its serial number, said prediction being indexed to the manufacturing and maintenance log (9) and usage log (11) specific to said equipment (2); said projected status (130) being compared to a minimal status (17) identified as being required for the operation of said equipment (2); and wherein, in response to said comparison, and based on a plurality of simulations performed with different combinations of tasks (90) associated with the maintenance (6) to be carried out and different projected usage conditions (110) of the scenario (8), the system automatically generates at least one operational output selected from:(i) a maintenance decision identifying sufficient or optimal tasks (90) for execution, so as to avoid a failure deemed critical of said equipment (2) before the end of the operating period of the scenario (8); (ii) calculation of maximal usage limits (18) for at least one controllable projected usage condition (110), so as to avoid a failure deemed critical of said equipment (2) before the end of the operating period of the scenario (8); and (iii) a maintenance schedule during operation, and identification of maintenance spare parts for said equipment (2) to be ordered, so as to enable repair of said equipment (2) prior to a predicted failure not deemed critical during the operating period of the scenario (8). The claim limitations are drawn to supervising industrial equipment for reliability, failure, etc. and directly correspond to mental processes (observation, evaluation, judgment, opinion) as evidenced by limitations detailing the observation of various equipment data, and making a judgment or opinion based on the observed and evaluated data. The claim limitations also correspond to certain methods of organizing human activity (managing personal behavior, following rules or instructions) as evidenced by limitations relating to the supervision of industrial equipment and performing actions based on the data supervised. The claim recites an abstract idea.
Note: the features or elements in brackets in the above section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: industrial equipment, digital system, sensors associated with the equipment, a system in which the equipment is embedded, and a virtual model. The additional element of the virtual model and digital system are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional element is no more than mere instructions to apply the judicial exception using a generic computer. Further, the industrial equipment, and sensors associated therewith, amounts to generally linking the judicial exception to a particular field of use (the supervision of industrial equipment in facilities). Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claims 2-20 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above. The claims also recite additional elements that have been analyzed in the rejected claims above. Thus, claims 2-20 are also rejected under 35 U.S.C. 101.
Allowable Subject Matter
Claim 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The closest patent or patent application prior art reference found that is relevant to the applicant’s invention includes Smiley (2014/0358601) and Mishra (US 11,017,321). Smiley discloses one or more techniques and/or systems for developing a health profile of an industrial asset based upon data pertaining to such an industrial asset. At least some of the data is transformed into primary state indicators, respectively representative of the status or condition of an aspect of the industrial asset. Using the primary state indicators, one or more events that are likely to occur to the industrial asset are identified and a health profile is developed based upon such events. The health profile may describe maintenance actions that will reduce a likelihood of an event (s) occurring, may describe a business impact on an entity associated with the industrial asset if an event(s) occurs, and/or may describe a performance impact on the industrial asset if an event(s) occurs. Mishra discloses a system that leverages artificial intelligence and machine learning to analyze and categorize events associated with an equipment asset, such as industrial machinery, to determine a status associated with the equipment asset, and to determine maintenance actions to be performed with respect to the equipment asset to prevent, or reduce the likelihood or severity of, occurrence of a fault at the equipment asset. Machine learning models may be trained to categorize events that are detected based on operating characteristics data associated with the equipment asset, to determine a status of the equipment asset, and to recommend one or more maintenance actions. The prior art references do not appear to disclose the details of the applicant’s claims as amended. The claims appear to overcome the prior art.
The closest non-patent literature prior art reference found that is relevant to the applicant’s invention includes the publication “On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: an Analytical Literature Review of Maintenance Strategies (O. Merkt, 2019) which discloses a data driven perspective for improving the quality of maintenance for machines and processes in industrial environments. The publication reviews existing maintenance works by highlighting the main challenges and benefits and shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. The publication highlights that in any industrial setup, the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. The article also explores the entire data driven model development life cycle: data acquisition and analysis, model development and model evaluation. The prior art reference does not appear to disclose the details of the applicant’s claims as amended. The claims appear to overcome the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/
Primary Examiner, Art Unit 3629
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