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 .
Claims 1-21 are pending.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3 Dec 2024, 27 June 2025, 6 Jan 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 12, 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 lines 2-3 “adequate and/or sufficient”, “preferably” seem merely subjective. It is not clear what is considered adequate or sufficient or preferably.
Claims 5, 12, 15 the language “in particular” renders the claims indefinite. It is not clear what the claimed limitations encompass. The specification merely repeats the claim language without further explanation.
Art rejection is applied to claims 5, 12, 15 as best understood in light of the rejection under 35 U.S.C. 112 discussed above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUBRAMANIYAN et al (US 20180121183 A1), further in view of Challenger et al (US 9396031 B2) both provided by the applicant.
Regarding claim 1, Subramaniyan substantially discloses, teaches or suggests a method for making assessments on a plurality of industrial systems at the same time comprising the preliminary steps of:
[0023] …to make assessment and/or predictions regarding the operation of a real world physical system, such as an electro- mechanical system.
[0024]… industrial-grade systems may be complex and may involve hundreds of building blocks to work together seamlessly in production environments.
Providing a plurality of computational analysis computer programs: [0024] computational models are used to analyze data and generate results that may be used to make assessments and/or predictions of the physical system.
Providing a systems database storing information regarding applicability relationship of computational analysis computer programs to data from industrial systems;
Wherein a data input application collects data from the industrial systems repeatedly and stores them in a data database:
[0026] …The digital twin may be a computer model that virtually represents the state of an installed product. The digital twin may include a code object with parameters and dimensions of its physical twin's parameters and dimensions that provide measured values, and keeps the values of those parameters and dimensions current by receiving and updating values via outputs from sensors embedded in the physical twin.
[0036]… A communication channel may be included in the system to supply data from at least one of the installed product and the data store to the modeling module.
[0065] …the updating API may provide the capability to update the model with new data. In some embodiments, the updating API may be combined with the security layer and the subscription layer to define what updates may be enabled, similar to the prediction API.
Wherein an orchestrator application repeatedly and independently from the data input application:
a) scans the systems database, b) for each industrial system in the systems database determines any computational analysis computer programs to be applied to data, c) for the determined computational analysis computer programs checks whether there are corresponding data in the data database,
d) for each determined and positively-checked computational analysis computer program creates an analysis task to be executed:
[0044]… the Task Module may be a task manager, and may start/ stop/get the status of any model-related task the Task Module may: prepare an input file for analysis; trigger the execution module to run the model; update the task status; parse the model output and register the input and output artifacts with a database.
[0052]… when the analytic model is executed (e.g., runtime analytic deployment), a containerized model may be registered and stored in the modeling technique registry.
e) progressively stores the created analysis tasks in at least one execution queue:
[0034] The modeling module may access the data store and utilize a model creation unit or task module to create an analytic model that may be used to create a prediction and/or result that may be transmitted to at least one of various user platforms, back to the installed product or to other systems, as appropriate.
The difference is Subramaniyan does not specifically show the limitations in italic above of:
A. providing a systems database storing information regarding applicability relationship of computational analysis computer programs to data from industrial systems;
B. wherein an orchestrator application repeatedly and independently from the data input application: a) scans the systems database, b) for each industrial system in the systems database determines any computational analysis computer programs to be applied to data, c) for the determined computational analysis computer programs checks whether there are corresponding data in the data database.
However the claimed limitations merely read on the fact that different analysis are performed on corresponding available data as shown by Challenger (col.2 lines 23-26: The method may also manage a plurality of constrained computing resources by allocating the plurality of constrained resources in the cluster for processing of the first job and a plurality of additional jobs.). Note the operations recited in a), b) and c) are mere computational tasks indispensable for performing any type of data analysis.
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include such a database and the claimed operations for the orchestrator application while implementing the method of Subra in order to manage resources for a plurality of additional jobs as taught by Challenger.
Regarding claim 2, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein at step b) the orchestrator application determines any computational analysis computer programs to be applied to data based on information stored in the systems database (Challenger col.1 line 66-col.2 line 3).
Regarding claim 3, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein at step b) the orchestrator application determines any computational analysis computer programs to be applied to data through an Application Programming Interface associated to the systems database (Challenger col.9 lines 41-52).
Regarding claim 4, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein at step c) the orchestrator application checks whether there are corresponding data in the data database based on information stored in in configuration files associated to the computational analysis computer programs (Challenger col.20 lines 22-41).
Regarding claim 5, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein at step c) the orchestrator application checks whether there are corresponding data, in particular adequate and/or sufficient data, in the data database based on information stored in the data database and preferably also on information stored in an orchestrator database (Challenger col.26 line 58- col.27 line 4).
Regarding claim 6, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein at step c) the orchestrator application checks whether there are corresponding data in the data database through an Application Programming Interface associated to the data database (Challenger col.9 lines 41-52).
Regarding claim 7, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein data collected by data input application are measured data and/or computed data (Subramaniyan [0025]… The digital twin may include a code object with parameters and dimensions of its physical twin's parameters and dimensions that provide measured values, and keeps the values of those parameters and dimensions current by receiving and updating values via outputs from sensors embedded in the physical twin.).
Regarding claim 8, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein the orchestrator application progressively stores the created analysis tasks in a plurality of execution queues (Challenger col.29 lines 27-34).
Regarding claim 9, Subramaniyan/Challenger further teaches or suggests the method of claim 8, wherein an execution queue is associated to one or more specific execution engines or one or my types of execution engines (Challenger col.20 line 63- col.21 line 16).
Regarding claim 10, Subramaniyan/Challenger further teaches or suggests the method of claim 8, wherein an execution queue is associated to a debug execution engine (Challenger col.20 line 63- col.21 line 16).
Regarding claim 11, Subramaniyan/Challenger further teaches or suggests the method of claim 8, wherein the orchestrator application chooses an execution queue in a plurality of execution queues based on information in configuration files associated to the computational analysis computer programs (Challenger col.20 lines 42-45, col.25 lines 18-32).
Regarding claim 12, Subramaniyan/Challenger further teaches or suggests the method of claim 1, wherein the orchestrator application stores information regarding computational analyses already performed, in particular results of computational analyses already performed, in an orchestrator database (Subramaniyan [0032] The system 100 may include a platform 200 that may host a computer data store 106 (e.g., storage), a modeling module 108 and other elements 109. The computer data store 106 may provide information to the modeling module 108 and may store results from the modeling module 108).
Claims 13, 15 essentially recite limitations similar to claims 1, 12 in form of system thus is rejected for the same reasons discussed in claims 1, 12 above.
Regarding claim 14, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further the systems database and/or the data database (Challenger col.26 line 58- col.27 line 4).
Regarding claim 16, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system stores computational analysis computer programs to be applied to data from industrial systems and/or configuration files relating to the computational analysis computer programs (Challenger col.20 lines 42-45).
Regarding claim 17, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further a data server for managing the data database, wherein the data server is local to or remote from the orchestrator server (Challenger col.28 lines 25-40).
Regarding claim 18, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further a systems server for managing the systems database, wherein the systems server is local to or remote from the orchestrator server (Challenger col.28 lines 25-40).
Regarding claim 19, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further a queues server for managing one or more execution queues, wherein the queues server is local to or remote from the orchestrator server (Challenger col.25 lines 18-32, col.28 lines 25-40).
Regarding claim 20, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further a data server for managing data from industrial systems wherein, the data server is local to or remote from the orchestrator server (Challenger col.28 lines 25-40).
Regarding claim 21, Subramaniyan/Challenger further teaches or suggests the system of claim 13, wherein the system comprises further a computational analysis server for managing one or more computational analysis computer programs, wherein the computational analysis server is local to or remote from the orchestrator server (Challenger col.28 lines 25-40).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Luo (US 20030210269 A1) teaches Visual Flow Interface (VFI) is a generic management software tool for any user in different industries which is similar to implementing a Graphical User Interface (GUI) on Database Applications for the front end. VFI revolutionizes the principle of Management Information System (MIS) design and will launch a new generation of MIS. VFI's innovative contribution to MIS includes two key points: (1) A user's specific managerial and technological process is independent from the program of the MIS. Users can design their managerial and technologic process themselves or make any change freely without redesigning the program of the MIS; and (2) VFI focuses on the unique functional common character of different flows in all different industries and designs a visual image "Sandwich" to represent the functional common character. The Sandwich is a very simple idea but solves a very complicated problem for designing a generic MIS.
Prabath et al (US 10671507 B2) teach embodiments generally directed to techniques of collecting analyzing information on various types of applications in an environment by an application performance analytics platform and acting on the analyzed information. The application performance analytics platform may include at least a monitoring system, a time series database, and an APM tool collector, all of which may be programmatically interfaced with or connected to each other. The APM tool collector may access or pull APM metrics from one or more APM tools and input the metrics to the monitoring system. The time series database may access or pull log files and extract log data for analysis. Based on the analysis, the application performance analytics platform may generate one or more alerts.
Prabath et al (US 20200133814 A1) teach a system having processing circuitry that interfaces the monitoring system (104) with the series database. The processing circuitry interfaces an APM tool collector (108) with the monitoring system. The processing circuitry pulls or accesses APM metrics from APM tools (110) to aggregate and input the APM metrics to the monitoring system by the APM tool collector. The processing circuitry pulls or accesses log files (112) and input the log files to the time series database (106). The processing circuitry performs analysis on the APM metrics by the monitoring system. The processing circuitry extracts log data from the log files by the time series database. The processing circuitry performs analysis on extracted log data. The processing circuitry generates alerts based on the analysis performed on the APM metrics and/or the analysis performed on the extracted log data.
Bliss et al (US 20170351226 A1) teach a system (302) has a memory (322) that stores computer-executable components. An analysis component (314) is configured to identify sets of data items recorded in federated data model that correspond to respective automation systems in operation at multiple industrial facility and to perform comparative analysis across sets of data items. A device interface component (318) is configured to output recommendation data. The recommendation data identifies a recommended modification to one or more of the automation systems determined to improve a performance metric.
WANG et al (US 20120310898 A1) teach a method involves setting device parameters and system parameters in relation to a server and a monitoring device (2) connected to the server. The monitored data is collected from the monitoring device according to the set device parameters and system parameters. The collected monitored data is stored into an initial queue. The monitored data in the initial queue is read at a specified time interval. The monitored data is stored into a database (30).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UYEN T LE whose telephone number is (571)272-4021. The examiner can normally be reached M-F 9-5.
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/UYEN T LE/Primary Examiner, Art Unit 2156 24 April 2026
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