DETAILED ACTION
This communication is a Final Rejection Office Action in response to the 11/3/2025 filling of Application 18/666,234. Claims 1-8, 10-21 are now presented.
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 Arguments
Applicant's arguments filed 11/3/2025 have been fully considered but they are not persuasive.
Regarding the rejections under 101, the Applicant argues “At least these aspects, by tying independent claim 1 (and similarly independent claims 11 and 19) to the technical act of configuring one or more industrial devices to implement a modification to a production schedule by generating and sending configuration data to the one or more industrial devices, add meaningful limits on the use of the alleged abstract idea, and thus elevate the claims to significantly more than an abstract idea. By generating and sending the configuration data based on the other functions recited in the claims, and thereby configuring one or more industrial devices to implement a modified production schedule, the claims limitations, read as a whole, yield an improvement to the technical field of industrial automation control. Such improvements to a technical area render the claims eligible for patentability pursuant to 35 U.S.C. § 101.”
Further, the use of configuration data to configure the one or more industrial devices is indicative of adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f) states:
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem")).
In the instant case, the additional elements of the broadly recited configuring the one or more industrial devices to attempts to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. For example, the claims do not state how the configures the one or more industrial devices to modify a production schedule. As such, the broadly recited configuring the one or more industrial devices does not integrate a judicial exception into a practical application or provide significantly more.
Regarding the rejections under 103, the Applicant argues “Chao et al. does not indicate that a modification to a production schedule is determined by grouping a subset of multi-tenant data corresponding to manufacturing entities that operate within a same industrial vertical as the industrial customer entity; determining, based on a second analysis of the subset of the multi-tenant data, a business strategy that correlates with satisfaction of an optimization criterion by a business metric; and formulating, based on the current context and the business strategy, a modification to a production schedule of the industrial customer entity that causes the business metric to satisfy the optimization criterion given constraints of the current context, as set forth in independent claim 1 as amended. Instead, as indicted in the passages of Chao et al. quoted above, Chao et al.'s system uses predefined model data that defines preferred performance metrics as a function of contextual factors to determine whether and how to modify a schedule.”
The Examiner respectfully disagrees. Chao para. 113-114 teaches In some embodiments, the analysis component 304 can also track product output relative to a defined annual target and anticipate whether the target will be achieved given the current year-to-date product quantity, historical production rate trends for the year thus far, and other factors. If the system 302 anticipates that there is a risk that the target will not be achieved by a defined year end date, the system 302 will identify possible causes of the failure to meet the target, or possible modifications to the production process that can increase the likelihood of achieving the production target by the year end date. In some embodiments, the manner of analysis performed on the orchestrated data for a given type of assessment can be industry- or vertical-specific. For example, the type of industry within which the machine or process is being used (e.g., automotive, food and gas, mining, power generation, textiles, pharmaceutical, plastics, etc.) can determine the type of analysis performed by the analysis component 304 to generate a process model of preferred operation of the machine or process (as described above) and to compare real-time performance metrics relative to the model. Such industry-specific analysis can capture common practices or standards that are unique to each particular industry. Further, Chao para. 96 teaches once this model data 1202 is developed, the analysis component 304 can monitor relevant subsets of the normalized data 920 (e.g., subsets of the data corresponding to the machine or assets to which the model applies) for deviations from the model. If the performance model data 1202 defines preferred performance metrics as a function of contextual factors—such as work shift, the product being produced, an operating mode of a machine or automation system, etc. —analysis component 304 will compare current performance (represented by normalized data 920 and associated relationship metadata 922) against the subset of performance model data corresponding to a current context of the monitored system (the current work shift, the current product being manufactured, the current machine operating mode, etc.)
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 non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, in step 1 it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, in step 2A prong 1 it must then be determined whether the claim is recite a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). If the claim recites a judicial exception, under step 2A prong 2 it must additionally be determined whether the recites additional elements that integrate the judicial exception into a practical application. If a claim does not integrate the Abstract idea into a practical application, under step 2B it must then be determined if the claim provides an inventive concept.
In the Instant case Claims 1-8, 10, 21 are directed toward a system to formulate a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context. Claims 11-18 are directed toward a method to formulate a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context. Claims 19-20 are directed toward a computer program product formulate, based on a second analysis of the multi-tenant data, a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context. As such, each of the Claims is directed to one of the four statutory categories of invention.
MPEP 2106.04 II. A. explains that in step 2A prong 1 Examiners are to determine whether a claim recites a judicial exception. MPEP 2106.04(a) explains that:
To facilitate examination, the Office has set forth an approach to identifying abstract ideas that distills the relevant case law into enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent, as is explained in MPEP § 2106.04(a)(2). This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
As per step 2A prong 1 of the eligibility analysis, claim 1 is directed to the abstract idea of determine, based on a first analysis of the multi-tenant data, a current context of a manufacturing or business operation of an industrial customer entity, of the industrial customer entities, and formulate, based on a second analysis of the multi-tenant data, a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context; and a scheduling component configured to implement the modification to the production schedule which falls into the abstract idea categories of certain methods of organizing human activity and mental processes.
The elements of Claim 1 that represent the Abstract idea include:
A system, comprising:
an analytics component configured to determine, based on a first analysis of the multi-tenant data, a current context of a manufacturing or business operation of an industrial customer entity, of the industrial customer entities,
group a subset of the multi-tenant data corresponding to manufacturing entities that operate within a same industrial vertical as the industrial customer entity, determine, based on a second analysis of the subset of the multi-tenant data, a business strategy that correlates with satisfaction of an optimization criterion by a business metric, and
formulate, based on a second analysis of the multi-tenant data, a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context; and a scheduling component configured to implement the modification to the production schedule.
MPEP 2106.04(a)(2) II. states:
The phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
In the instant case, the limitations of determine, based on a first analysis of the multi-tenant data, a current context of a manufacturing or business operation of an industrial customer entity, of the industrial customer entities, group a subset of the multi-tenant data corresponding to manufacturing entities that operate within a same industrial vertical as the industrial customer entity, determine, based on a second analysis of the subset of the multi-tenant data, a business strategy that correlates with satisfaction of an optimization criterion by a business metric, and formulate, based on a second analysis of the multi-tenant data, a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context; and a scheduling component configured to implement the modification to the production schedule are directed to commercial interactions including sales activities or behaviors, and business relations and fundamental economic principles such as determining modification to a production schedule to improve business operations.
MPEP 2106.04(a)(2) states:
The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions
In the instant case, the limitations of determine, based on a first analysis of the multi-tenant data, a current context of a manufacturing or business operation of an industrial customer entity, of the industrial customer entities, and formulate, based on a second analysis of the multi-tenant data, a modification to a production schedule of the industrial customer entity that causes a business metric of the industrial customer entity to satisfy an optimization criterion given constraints of the current context cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a processor” nothing in the claims precludes the steps from being performed in the human mind.
Under step 2A prong 2 the examiner must then determine if the recited abstract idea is integrated into a practical application. MPEP 2106.04 states:
Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
• An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
• Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
• Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e)
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In the instant case, this judicial exception is not integrated into a practical application. In particular, Claim 1 recites the additional elements of:
a memory that stores executable components; and a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising:
internal services that implement a manufacturing cloud system, wherein the manufacturing cloud system is a multi-tenant Software-as-a-Service (SaaS) system that executes a data collection and analysis service that collects multi-tenant data from industrial customer entities;
wherein the analytics component is further configured to generate and send configuration data to one or more industrial devices operating at a plant facility of the industrial customer entity, and the configuration data configures the one or more industrial devices to implement the modification to the production schedule.
However, the computer elements (processor, operatively coupled to the memory, that executes the executable components) are recited at a high level of generality and given the broadest reasonable interpretation are simply generic computers performing generic computer functions. Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea and mere instructions to implement an abstract idea on a computer.
Further, the collecting and sending data is recited broadly. Under the broadest reasonable interpretation, the limitations amounts to data gathering which the MPEP says is insignificant extra solution activity (see MPEP 2106.05(g).
Further, the use of configuration data to configure the one or more industrial devices is indicative of adding the words “apply it” (or an equivalent) with the judicial exception. MPEP 2106.05(f) states:
When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the claims described "the creation of a dynamic document based upon ‘management record types’ and ‘primary record types.’" 850 F.3d at 1339-40; 121 USPQ2d at 1945-46. The claims were found to be directed to the abstract idea of "collecting, displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In addition to the abstract idea, the claims also recited the additional element of modifying the underlying XML document in response to modifications made in the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the claims purported to modify the underlying XML document in response to modifications made in the dynamic document, nothing in the claims indicated what specific steps were undertaken other than merely using the abstract idea in the context of XML documents. The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem")).
In the instant case, the additional elements of the broadly recited configuring the one or more industrial devices to attempts to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. For example, the claims do not state how the configures the one or more industrial devices to modify a production schedule. As such, the broadly recited configuring the one or more industrial devices does not integrate a judicial exception into a practical application or provide significantly more.
Viewing the generic computer elements in combination with the data collection does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea.
In step 2B, the Examiner must determine whether the claim adds a specific limitation other than what is well-understood, routine, conventional activity in the field - see MPEP 2106.05(d).
As discussed with respect to Step 2A Prong Two, the additional element of a server that is connected to a plan explanation datastore amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Further, similar to the analysis with respect to step 2A prong 2 recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished cannot provide an inventive concept under step 2B of the eligibility analysis.
Further, nothing in the claim indicates that the retrieval of information is anything other than conventional. See MPEP 2106.05(d) that states “Receiving or transmitting data over a network, e.g., using the Internet to gather data is conventional when claimed in a merely generic manner (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Also see MPEP 2106.05(d) that states storing and retrieving information in memory is conventional when claimed in a merely generic manner (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93).
Further Claims 2-8, 10, 21 further limit the abstract idea of an analysis that can be performed mentally or certain methods of human activity that were already rejected in claim 1, but fail to remedy the deficiencies of the parent claim as they do not impose any limitations that amount to significantly more than the abstract idea itself.
Further, Claim 8 further recites the additional elements of a generative artificial intelligence (AI) component configured to infer the business metric and the optimization criterion based on analysis of the natural language input. However, this attempt to cover any solution to the identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it”. For example, the claims do not state how the generative artificial intelligence (AI) component works. As such, the broadly recited generative artificial intelligence (AI) component does not integrate a judicial exception into a practical application or provide significantly more.
Accordingly, the Examiner concludes that there are no meaningful limitations in claims 2-8, 10, 21 that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
The analysis above applies to all statutory categories of invention. As such, the presentment of claim 1 otherwise styled as a method or computer program product, for example, would be subject to the same analysis. Therefore, Claims 11-20 are rejected for the same rational that applied to claims 1-8, 9-10, 21.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chao US 20180357334 A1 in view of Chan US 2020/0387818 A1.
As per Claim 1 Chao teaches a system, comprising:
a memory that stores executable components; and (see Chao para. 51)
a processor, operatively coupled to the memory, that executes the executable components, the executable components comprising: (see Chao para. 51)
internal services that implement a manufacturing cloud system, wherein the manufacturing cloud system is a multi-tenant system that executes a data collection and analysis service that collects multi-tenant data from industrial customer entities; (see Chao para. 47-48 that teaches the numerous industrial devices, machines, higher-level systems, and peripheral systems that make up an industrial enterprise can generate large amounts of data from many sources. Some sets of this data may be related despite originating from different sources; e.g., by virtue of their relevance to a common industrial process or a common aspect of plant operations. Still other sets of data may represent the same quantities duplicated at different sources, as when a telemetry device and a reporting application record values of the same performance parameter. Within a given industrial environment, there may be many correlations or causal relationships—both known and unknown to the owners of the industrial systems—between events, activities, and outcomes on the plant floor. However, since these related systems and data sources may record operational, statistic, and report data in different non-compatible formats, opportunities to collectively analyze these diverse data sets to gain greater insight into a plant's operations are lost. To address these and other issues, one or more embodiments described herein provide a cloud-based data ingestion and analysis architecture that integrates and collects data from multiple diverse sources at one or more industrial facilities.)
an analytics component configured to determine, based on a first analysis of the multi-tenant data, a current context of a manufacturing or business operation of an industrial customer entity, of the industrial customer entities, (Chao para. 96 teaches once this model data 1202 is developed, the analysis component 304 can monitor relevant subsets of the normalized data 920 (e.g., subsets of the data corresponding to the machine or assets to which the model applies) for deviations from the model. If the performance model data 1202 defines preferred performance metrics as a function of contextual factors—such as work shift, the product being produced, an operating mode of a machine or automation system, etc. —analysis component 304 will compare current performance (represented by normalized data 920 and associated relationship metadata 922) against the subset of performance model data corresponding to a current context of the monitored system (the current work shift, the current product being manufactured, the current machine operating mode, etc.)
group a subset of the multi-tenant data corresponding to manufacturing entities that operate within a same industrial vertical as the industrial customer entity, Chao para 114 teaches [0114] In some embodiments, the manner of analysis performed on the orchestrated data for a given type of assessment can be industry- or vertical-specific. For example, the type of industry within which the machine or process is being used (e.g., automotive, food and gas, mining, power generation, textiles, pharmaceutical, plastics, etc.) can determine the type of analysis performed by the analysis component 304 to generate a process model of preferred operation of the machine or process (as described above) and to compare real-time performance metrics relative to the model. Such industry-specific analysis can capture common practices or standards that are unique to each particular industry.
determine, based on a second analysis of the subset of the multi-tenant data, a business strategy that correlates with satisfaction of an optimization criterion by a business metric, and Cao teaches
[0113] In some embodiments, the analysis component 304 can also track product output relative to a defined annual target and anticipate whether the target will be achieved given the current year-to-date product quantity, historical production rate trends for the year thus far, and other factors. If the system 302 anticipates that there is a risk that the target will not be achieved by a defined year end date, the system 302 will identify possible causes of the failure to meet the target, or possible modifications to the production process that can increase the likelihood of achieving the production target by the year end date. [0114] In some embodiments, the manner of analysis performed on the orchestrated data for a given type of assessment can be industry- or vertical-specific. For example, the type of industry within which the machine or process is being used (e.g., automotive, food and gas, mining, power generation, textiles, pharmaceutical, plastics, etc.) can determine the type of analysis performed by the analysis component 304 to generate a process model of preferred operation of the machine or process (as described above) and to compare real-time performance metrics relative to the model. Such industry-specific analysis can capture common practices or standards that are unique to each particular industry.
formulate, based on the current context and the business strategy, a modification to a production schedule of the industrial customer entity that causes the business metric to satisfy the optimization criterion given constraints of the current context; and (Chao para. 120 teaches in addition to directing instruction data 1306 to control devices on the plant floor to effectuate modifications to controlled industrial systems, some embodiments of system 302 can also perform modifications to related systems or applications that are not directly involved in control of the industrial machines or processes. For example, some embodiments of system 302 can modify maintenance schedules, work schedules, production schedules, or other related schedules based on results of the analysis performed by analysis component 304. In an example scenario, analysis component 304 may determine that a performance metric of a controlled process is beginning to drift and is expected to fall outside the preferred range for that metric defined by model data 1202. Based in part on the relationship metadata 922, analysis component 304 may also identify the relevant industrial devices, machines, or machine components that are likely causes of the performance drift. In response to these determinations, analysis component 304 can modify a maintenance schedule stored on a plant server to expedite maintenance for the identified device, machine, or component (e.g., by moving forward a scheduled maintenance date for the identified equipment). Further, para. 98 teaches Once an administrator has specified the desired outcome for which preferred operator behavior is to be modeled, system 1016 can begin monitoring appropriate subsets of normalized data 920 and associated relationship metadata 922 over multiple production cycles to learn a correlation between specific operator actions or sequences of actions and instance of the desired outcome. Based on these learned correlations, system 1016 will generate operator model data recording these learned operator behaviors that are determined to correlate with the desired outcome. Example operator sequences modeled by this operator model data can include, but is not limited to, timing and order of manual control panel and/or HMI interactions, locations of the operator relative to the machine or system during particular stages of the production cycle, or other such operator behaviors. As in previous examples, system 1016 can generate different operator behavior models for the same desired outcome as a function of contextual conditions, such as machine operating mode, a particular part being produced, or other such contextual factors.
a scheduling component configured to implement the modification to the production schedule.
(para. 119-120 teaches If system 302 is executing locally on the relevant industrial device (e.g., a device-level analytics system), analysis component 304 may only need to generate the instruction data 1306 for local execution on the industrial device (e.g., to change a setpoint or other control parameter on the device). If system 302 is executing on a gateway device 119 (e.g., an edge-level analytics system), instruction data 1306 can be sent to the relevant control device via the wired or wireless network(s) over which the gateway device 119 collects data from its assigned set of industrial devices. If system 302 is executing on a cloud platform (e.g., a cloud-level analytics system), instruction data 1306 can be delivered from the cloud platform to the target control device via any intermediate networks (e.g., the internet, the plant network, public or private subnets, etc.). In some embodiments, instruction data 1306 can be sent to the relevant control device via industrial data orchestration system 202 via the same data channels over which the structured and unstructured data 902a, 902b is collected. In addition to directing instruction data 1306 to control devices on the plant floor to effectuate modifications to controlled industrial systems, some embodiments of system 302 can also perform modifications to related systems or applications that are not directly involved in control of the industrial machines or processes. For example, some embodiments of system 302 can modify maintenance schedules, work schedules, production schedules, or other related schedules based on results of the analysis performed by analysis component 304. )
Chao does not explicitly disclose using a Software-as-a-Service (SaaS) system. However, Chan para. 164 teaches a known technique of the program product 92 may be implemented as a so-called Software as a Service (SaaS), or other installation or communication supporting end-users. This known technique is applicable to the system of Chao as they are both directed to optimizing production. One of ordinary skill in the art before the effective filing date of the Applicant’s invention would have recognized that applying the known technique of Chan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Chan to the teachings of Chao would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate a SaaS system into similar systems. Further, incorporating the Saas system as taught by Chan to the system taught by Chao would result in an improved system that provides a flexible means for an industry to use a third party provider to assist in production improvements.
wherein the analytics component is further configured to generate and send configuration data to one or more industrial devices operating at a plant facility of the industrial customer entity, and the configuration data configures the one or more industrial devices to implement the modification to the production schedule. (see Chao para. 67 example analytical applications can learn or predict manufacturing floor outcomes, operational outcomes, device and equipment outcomes (e.g., predictive maintenance, life cycle alerts, optimal device configurations, etc.), production outcomes (e.g., whether a current production rate will meet demand, which facility is best suited to carry out a production order, etc.), quality outcomes, performance outcomes, etc. The analytics platform 506 can generate and deliver dashboards or other graphical interfaces to authorized client devices to visualize results of the analyses. Para. 60 teaches In the example architecture depicted in FIG. 6, data management functions such as data orchestration, analytics, and storage can be carried out on a private subnet managed by an owner of the analytics system, while analytic results can be sent to client devices via a public subnet 610. Such results can include alerts, real-time or historical data visualization, recommended modifications to a control process (e.g., setpoint or process variable recommendations, production schedule recommendations, recommendations to replace an identified line operator at a specified time, etc.), maintenance recommendations (e.g., recommendations to replace or reconfigure a specified industrial device, recommended maintenance schedules for a specified machine, etc.). (see Chao para. 115 that teaches in some embodiments, rather than or in addition to delivering notifications via dashboards 1018, the analytic system 1016 may also be configured to deliver control instructions to one or more industrial assets to alter a controlled process based on the detected deviation or another result generated by analysis component 304. FIG. 13 is a diagram illustrating an example architecture that implements control modifications to industrial assets based on analysis of the normalized data 920 and associated relationship metadata 922. As described in previous examples, industrial data and orchestration system 202 collects or receives structured and unstructured data 902a, 902b from devices, systems, and/or applications distributed throughout an industrial environment. This can include data received from sets of industrial assets 1302 that operate on the plant floor. These industrial assets 1302 can include automation or process control systems that each include one or more industrial controllers and associated input devices, output devices, and peripheral devices (e.g., motor drives, vision systems, lot control systems, quality check systems, etc.) that perform automated control of a machine or process. Orchestration system 202 normalizes the structured and unstructured data 902a, 902b, records discovered relationships between the data items and/or files as relationship metadata 922, and provides this information to predictive maintenance and process supervision system 302. Para. 126 teaches 126 in the case of device-level analytic systems 402 residing on industrial controllers 118 or gateway devices 119, risks associated with operation of more complex industrial automation systems—rather than a single device—can be monitored and identified, and appropriate countermeasures that are scoped to the entire automation system can be implemented. In an example scenario, if a device-level analytic system 402 executing on a gateway device 119 predicts—based on analysis of normalized data 920 and associated relationship metadata 922 in view of model data 1202 or a digital twin 1204—that a production line is at risk of producing an excessive amount of product waste (e.g., due to an increase in the number of part rejections, a discovered inefficiency in a process carried out by the production line, an indication of excessive wear on one or more devices that carry out the process, etc.), the device-level analytic system 402 can implement a multi-device countermeasure intended to mitigate the risk of product waste. This can involve generating, by the gateway device 119 under the instruction of the device-level analytic system 402, multiple sets of instruction data directed to devices that make up the automation system, where the instructions are configured to alter operation of the target devices in a coordinated manner to effect the countermeasure on the automation system as a whole, thereby reducing the risk of product waste. Such system-wide countermeasures can include, for example, reducing the rate of production on a production line, switching production of the product to an alternative production line (which may involve sending instructions to control devices of the current production line to cease production, sending further instructions to the new production line to begin production, and sending still further instructions to appropriate upstream systems to begin providing material or parts to the new production line), sourcing a production line with parts from an alternate production line or source, or other such countermeasures.
As per Claim 2 Chao teaches the system of claim 1, wherein the optimization criterion is at least one of maximization of overall profit, maximization of profit for a specified product, maximization of overall product throughput, maximization of throughput of a specified product, overall demand fulfillment, fulfillment of demand for a specified product, minimization of energy consumption, minimization of emissions, or a product quality target. (see Chao para. 95 that teaches 95 in one or more embodiments, analysis component 304 can identify normal or acceptable behavior of an automation system based on repeated observations of production cycles by identifying correlations between positive outcomes and values of performance or behavior metrics observed during production runs that yielded the positive outcomes. In some embodiments, the desired positive outcomes on which the model data 1202 is to be based can be selected by an administrator or other user prior to generation of the model data 1202, so that the resulting model data 1202 will be representative of acceptable automation system behavior or statuses that are expected to yield the indicated desired outcome (e.g., highest product throughput, least energy consumption, least amount of machine downtime, highest product quality, etc.). Positive outcomes that can be selected as the basis for generation of the model data 1202 can include, but are not limited to, production of parts that satisfy all quality checks, production of at least a specified minimum number of parts, acceptably small machine downtime durations or abnormal conditions, acceptably small amounts of energy consumption, or other such outcomes. The performance or behavior metrics correlated to these positive outcomes by the analysis component 304 can include, but are not limited to, machine or speed setpoints, control loop tuning parameters, machine mode settings, optimal sequences of manual control panel actuations (e.g., an order in which selector switches, push buttons, or other manual controls are actuated by the operator, which can be determined by monitoring the states of the control panel devices during various steps of the production cycle), or other such metrics.
As per Claim 3 Chao teaches The system of claim 1, wherein the current context is at least one of an inventory level of a product manufactured by the industrial customer entity, an inventory level of a component part or material used to manufacture the product, a current or predicted demand for the product, or a current or predicted production capacity of a production line operated by the industrial customer entity. (see Chao para. 79 that teaches Other data collected by the gateway device 119 can include, but is not limited to, log files 1012 from a data historian device 110 residing on the plant network 116, spreadsheet files 1011 stored on a maintenance schedule server 902 (residing on office network 108) and containing information regarding maintenance schedules for various machines throughout the industrial facility, database files 1008 stored on a work order database 1004 and containing work order information relating to products produced at the industrial facility, database files 1010 stored on an inventory database 1006 and containing information regarding current and/or expected product availability, or other such data files.
As per Claim 4 Chao teaches the system of claim 1, wherein the multi-tenant data comprises at least one of production data from the industrial customer entity, the production schedule, an inventory level of a product manufactured by the industrial customer entity or a component part used to manufacture the product, customer demand data for the product, purchase order data for the industrial customer entity, transportation scheduling data from a transportation entity, shipping route information for the transportation entity, or a production schedule of a supplier entity that manufactures a component part or material used by the industrial customer entity to manufacture the product. (see Chao para. 66 that teaches 66 Sources of data 502 can include plant-level industrial devices (e.g., industrial controllers, motor drives, sensors, telemetry devices, power monitor devices, human-machine interface terminals, vision systems, quality check systems, lot traceability systems, etc.), higher level business systems (e.g., accounting applications, ERP or MES systems, auditing applications, etc.) or other on-premise data sources (e.g., maintenance schedules, operator work schedules, product inventory databases, data historians, etc.).
As per Claim 5 Chao teaches the system of claim 1, wherein the modification to the production schedule at least one of changes a type of product scheduled to be manufactured on a production line for a specified time period, changes a time period during which a product is scheduled to be produced, [Chao para. 120 teaches In addition to directing instruction data 1306 to control devices on the plant floor to effectuate modifications to controlled industrial systems, some embodiments of system 302 can also perform modifications to related systems or applications that are not directly involved in control of the industrial machines or processes. For example, some embodiments of system 302 can modify maintenance schedules, work schedules, production schedules, or other related schedules based on results of the analysis performed by analysis component 304. In an example scenario, analysis component 304 may determine that a performance metric of a controlled process is beginning to drift and is expected to fall outside the preferred range for that metric defined by model data 1202. Based in part on the relationship metadata 922, analysis component 304 may also identify the relevant industrial devices, machines, or machine components that are likely causes of the performance drift. In response to these determinations, analysis component 304 can modify a maintenance schedule stored on a plant server to expedite maintenance for the identified device, machine, or component (e.g., by moving forward a scheduled maintenance date for the identified equipment).
or changes a source from which to obtain a component part or material used by the industrial customer entity to produce the product. Chao para. 126 teaches 126 in the case of device-level analytic systems 402 residing on industrial controllers 118 or gateway devices 119, risks associated with operation of more complex industrial automation systems—rather than a single device—can be monitored and identified, and appropriate countermeasures that are scoped to the entire automation system can be implemented. In an example scenario, if a device-level analytic system 402 executing on a gateway device 119 predicts—based on analysis of normalized data 920 and associated relationship metadata 922 in view of model data 1202 or a digital twin 1204—that a production line is at risk of producing an excessive amount of product waste (e.g., due to an increase in the number of part rejections, a discovered inefficiency in a process carried out by the production line, an indication of excessive wear on one or more devices that carry out the process, etc.), the device-level analytic system 402 can implement a multi-device countermeasure intended to mitigate the risk of product waste. This can involve generating, by the gateway device 119 under the instruction of the device-level analytic system 402, multiple sets of instruction data directed to devices that make up the automation system, where the instructions are configured to alter operation of the target devices in a coordinated manner to effect the countermeasure on the automation system as a whole, thereby reducing the risk of product waste. Such system-wide countermeasures can include, for example, reducing the rate of production on a production line, switching production of the product to an alternative production line (which may involve sending instructions to control devices of the current production line to cease production, sending further instructions to the new production line to begin production, and sending still further instructions to appropriate upstream systems to begin providing material or parts to the new production line), sourcing a production line with parts from an alternate production line or source, or other such countermeasures.
As per Claim 6 Chao teaches the system of claim 1, wherein the analytics component is further configured to formulate, based on the second analysis of the multi-tenant data, a modification to another schedule of the industrial customer entity that that causes the business metric to satisfy the optimization criterion given constraints of the current context, and the other schedule is at least one of a work schedule, a shipping schedule for a product manufactured by the industrial customer entity, a purchase order for component parts used to manufacture the product, an inventory schedule for the product or the component part, or a bill of materials. (see Chao para. 108 that teaches In addition to identifying maintenance or performance issues, some embodiments of predictive maintenance and process supervision system 302 can also generate recommendations for countermeasures to identified issues, including recommendations that consider multiple manufacturing sites that comprise an industrial enterprise. This can include, for example, recommending that an operation be dispatched to an alternate site, re-planning availability to ship, triggering a new scheduling run, etc. Some embodiments of system 302 can also learn to identify factors that impact production, taking into consideration energy cost/usage, labor cost, machine downtime, etc. Para. 120 teaches in addition to directing instruction data 1306 to control devices on the plant floor to effectuate modifications to controlled industrial systems, some embodiments of system 302 can also perform modifications to related systems or applications that are not directly involved in control of the industrial machines or processes. For example, some embodiments of system 302 can modify maintenance schedules, work schedules, production schedules, or other related schedules based on results of the analysis performed by analysis component 304. )
As per Claim 7 Chao teaches the system of claim 6, wherein the modification to the other schedule at least one of changes a scheduled inventory level of a product manufactured by the industrial customer entity, changes a scheduled inventory level of a component part or material used to manufacture the product, changes an amount of an ordered component or material, changes a supplier entity from which to source the ordered component or material, or changes an operator scheduled to operate a manufacturing process during a specified time period. (see Chao para. 69 that teaches In the example architecture depicted in FIG. 6, data management functions such as data orchestration, analytics, and storage can be carried out on a private subnet managed by an owner of the analytics system, while analytic results can be sent to client devices via a public subnet 610. Such results can include alerts, real-time or historical data visualization, recommended modifications to a control process (e.g., setpoint or process variable recommendations, production schedule recommendations, recommendations to replace an identified line operator at a specified time, etc.), maintenance recommendations (e.g., recommendations to replace or reconfigure a specified industrial device, recommended maintenance schedules for a specified machine, etc.).
As per Claim 10 Chao teaches the system of claim 9, wherein the configuration data at least one of changes a configuration setting of an industrial device or changes control code being executed by an industrial controller to monitor and control an automation system. . (see Chao para. 126 that teaches in the case of device-level analytic systems 402 residing on industrial controllers 118 or gateway devices 119, risks associated with operation of more complex industrial automation systems—rather than a single device—can be monitored and identified, and appropriate countermeasures that are scoped to the entire automation system can be implemented. In an example scenario, if a device-level analytic system 402 executing on a gateway device 119 predicts—based on analysis of normalized data 920 and associated relationship metadata 922 in view of model data 1202 or a digital twin 1204—that a production line is at risk of producing an excessive amount of product waste (e.g., due to an increase in the number of part rejections, a discovered inefficiency in a process carried out by the production line, an indication of excessive wear on one or more devices that carry out the process, etc.), the device-level analytic system 402 can implement a multi-device countermeasure intended to mitigate the risk of product waste. This can involve generating, by the gateway device 119 under the instruction of the device-level analytic system 402, multiple sets of instruction data directed to devices that make up the automation system, where the instructions are configured to alter operation of the target devices in a coordinated manner to effect the countermeasure on the automation system as a whole, thereby reducing the risk of product waste. Such system-wide countermeasures can include, for example, reducing the rate of production on a production line, switching production of the product to an alternative production line (which may involve sending instructions to control devices of the current production line to cease production, sending further instructions to the new production line to begin production, and sending still further instructions to appropriate upstream systems to begin providing material or parts to the new production line), sourcing a production line with parts from an alternate production line or source, or other such countermeasures.
As per Claim 21 Chao teaches The system of claim 1, wherein the industrial vertical is at least one of automotive, food and drug, textiles, marine, oil and gas, or mining. Chao para. 114 teaches In some embodiments, the manner of analysis performed on the orchestrated data for a given type of assessment can be industry- or vertical-specific. For example, the type of industry within which the machine or process is being used (e.g., automotive, food and gas, mining, power generation, textiles, pharmaceutical, plastics, etc.) can determine the type of analysis performed by the analysis component 304 to generate a process model of preferred operation of the machine or process (as described above) and to compare real-time performance metrics relative to the model. Such industry-specific analysis can capture common practices or standards that are unique to each particular industry.
Claims 11, 12, 13, 14, 15, 16, 17 recite similar limitations to those recited in claims 1, 2, 3, 4, 5, 6, 7 and are rejected for similar reasons, Further, Chao teaches a method, comprising performing the recited steps (see para. 4).
Claims 19, 20 recite similar limitation to those recited in claims 1, 2 and are rejected for similar reasons. Further, Chao teaches a non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a manufacturing cloud system comprising a processor to perform operations, the operations comprising the recited steps (see para. 6).
Claim(s) 8, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chao US 20180357334 A1 in view of Chan US 2020/0387818 A1 as applied to claim 1 and in further view of Rigterink US 2024/0403658 A1 (citations will be made in reference to provisional-application US 63/505,611.
As per Claim 8 Chao teaches the system of claim 1, wherein the executable components further comprise: a user interface configured to render a chat interface configured to receive, from the industrial customer entity, a natural language input describing the business metric and the optimization criterion; and a generative artificial intelligence (AI) component configured to infer the business metric and the optimization criterion based on analysis of the natural language input, and the analytics component is configured to perform the second analysis based on the business metric and the optimization criterion inferred by the generative AI component. However, Rigterink para. 102 teaches implementations leverage generative Al techniques to enable responses to requests that are provided to users in an intuitive, non-complex manner. An example Enterprise Generative Al framework can include a human computer interface for receiving natural language queries and presenting relevant information with predictive analysis from the enterprise information environment in response to the queries. An enterprise comprehension module is used to understand the language, intent, and context of a user natural language query. The enterprise comprehension module executes the user natural language query to discern relevant information from the enterprise information environment to present to the human computer interface. The generative AI models interact with one or more of the one or more retrieval models. One or more retrieval models are used for understanding underlying data, documents, applications of an enterprise information environment. Underlying data of the enterprise information environment can be embedded by an orchestration module, for example, by a model driven architecture for the conceptual representation of enterprise and external data sets for data virtualization and access control. Information presented with predictive analysis can include text summary, ranked results, smart cards, Al-powered chat interface, content generated on-the-fly, etc. The predictive analysis can be from one or more Al applications that include, for example, Supply Chain, CRM, ERP, Reliability, Defense, Sustainability, or Energy, etc. Both Chao and Rigterink are directed to industrial analysis. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the Applicant’s invention to modify the teachings of Chao to include a user interface configured to render a chat interface configured to receive, from the industrial customer entity, a natural language input describing the business metric and the optimization criterion; and a generative artificial intelligence (AI) component configured to infer the business metric and the optimization criterion based on analysis of the natural language input, and the analytics component is configured to perform the second analysis based on the business metric and the optimization criterion inferred by the generative AI component as taught by Rigterink to more rapidly locate, retrieve, and present relevant data across the entire corpus of an enterprise’s information systems (see para. 103).
Claims 18 recite similar limitations to those recited in claims 8 and are rejected for similar reasons, Further, Chao teaches a method, comprising performing the recited steps (see para. 4).
Conclusion
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/DEIRDRE D HATCHER/Primary Examiner, Art Unit 3625