DETAILED ACTION
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 8, and 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SUBRAMANIYAN et al (US 20180121183 A1).
As to claims 1 and 16, SUBRAMANIYAN teaches A method for processing an analysis task regarding a computational analysis on data received from an industrial system and stored in a data database for making an assessment on the industrial system (SUBRAMANIYAN [0023] discloses to make assessment and/or predictions regarding the operation of a real world physical system, such as an electro-mechanical system. SUBRAMANIYAN [0024] discloses industrial-grade systems may be complex and may involve hundreds of building blocks to work together seamlessly in production environments.), wherein an execution engine:
a) collects execution parameters from a computational analysis computer program corresponding to the analysis task, the execution parameters comprising information on industrial system data to be processed by the computational analysis computer program (SUBRAMANIYAN [0026] discloses 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.),
b) extracts from the data database data coming from the industrial system to be processed by the computational analysis computer program based on the collected execution parameters (SUBRAMANIYAN [0026] discloses 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.),
c) creates an execution environment containing the extracted data, and
d) executes the computational analysis computer program using the created execution environment (SUBRAMANIYAN [0051] discloses the container module may wrap each analytic model in a complete filesystem that may contain everything needed to run the model (e.g., code, runtime, system tools, system libraries, and anything that may be installed on a server).).
As to claim 8, SUBRAMANIYAN teaches wherein at step c) the execution environment contains also execution parameters extracted from analysis task information (SUBRAMANIYAN [0051] discloses the container module may wrap each analytic model in a complete filesystem that may contain everything needed to run the model (e.g., code, runtime, system tools, system libraries, and anything that may be installed on a server).).
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) 2-7, 9-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over SUBRAMANIYAN et al (US 20180121183 A1) in view of Challenger et al (US 9396031 B2).
As to claim 2, SUBRAMANIYAN fails to teach wherein the execution engine identifies the analysis task in an execution queue, in particular a predetermined execution queue, the identified task being at the top of the execution queue.
However, Challenger teaches wherein the execution engine identifies the analysis task in an execution queue, in particular a predetermined execution queue, the identified task being at the top of the execution queue (Challenger column 11, lines 46-66 discloses that data analysis tasks are put in a queue for execution.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of SUBRAMANIYAN to incorporate the Distributed UIMA Cluster Computing (DUCC) Facility as taught by Challenger for the purpose of handle errors and carry out retry policies based upon parameters specified in the user submission or as configured in the DUCC System.
As to claim 3, SUBRAMANIYAN fails to teach wherein the execution engine identifies the analysis task in a plurality of execution queues, the identified task being at the top of any execution queues.
However, Challenger teaches wherein the execution engine identifies the analysis task in a plurality of execution queues, the identified task being at the top of any execution queues (Challenger column 11, lines 46-66 discloses that data analysis tasks are put in a queue for execution.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of SUBRAMANIYAN to incorporate the Distributed UIMA Cluster Computing (DUCC) Facility as taught by Challenger for the purpose of handle errors and carry out retry policies based upon parameters specified in the user submission or as configured in the DUCC System.
As to claim 4, SUBRAMANIYAN teaches wherein the execution engine identifies the analysis task database through an Application Programming Interface associated to the queue or queues (SUBRAMANIYAN throughout discloses the use of APIs).
As to claim 5, SUBRAMANIYAN teaches collects execution parameters from a computational analysis computer program through an Application Programming Interface associated to computational analysis computer programs (SUBRAMANIYAN throughout discloses the use of APIs).
As to claim 6, SUBRAMANIYAN teaches extracts data from the data database through an Application Programming Interface associated to the data database (SUBRAMANIYAN throughout discloses the use of APIs).
As to claim 7, SUBRAMANIYAN teaches wherein at step c) the execution environment contains also a reference to a computational analysis computer program or a portion of a computational analysis computer program or the whole computational analysis computer program, wherein the computational analysis computer program may be downloaded from a computational analysis database based on analysis task information (It is well-known to download a computer program in a distributed environment.).
As to claim 9, SUBRAMANIYAN teaches wherein an execution parameter extracted from analysis task information includes information regarding computational analyses already performed, in particular results of computational analyses already performed, that may be stored in an orchestrator database (It is obvious to use data stores, regardless if their physical representation and names, to store data, whether resulting from previous data processing or for future processing. Moreover, in a distributed system it is obvious to transmit data among its constituting entities.).
As to claim 10, SUBRAMANIYAN teaches wherein at step c) the execution engine arrange data in the execution environment according to a predetermined structure adapted to execution (It is obvious to use data stores, regardless if their physical representation and names, to store data, whether resulting from previous data processing or for future processing. Moreover, in a distributed system it is obvious to transmit data among its constituting entities.).
As to claim 11, SUBRAMANIYAN teaches wherein after step d) the execution engine: e) stores results of execution in a results database (It is obvious to use data stores, regardless if their physical representation and names, to store data, whether resulting from previous data processing or for future processing. Moreover, in a distributed system it is obvious to transmit data among its constituting entities.).
As to claim 12, SUBRAMANIYAN teaches wherein at step e) the execution engine stores results of execution in a results database through an Application Programming Interface associated to the results database (It is obvious to use data stores, regardless if their physical representation and names, to store data, whether resulting from previous data processing or for future processing. Moreover, in a distributed system it is obvious to transmit data among its constituting entities.).
As to claim 13, SUBRAMANIYAN teaches wherein the results database is configured to be accessed locally or remotely (SUBRAMANIYAN discloses the system is distributed. Thus, it anticipates its various components, regardless of their names, being “local” or “remote” with respect to each other.).
As to claim 14, SUBRAMANIYAN teaches wherein a data output application: f) sends results in the results database to a user application (SUBRAMANIYAN [0034] discloses The modeling module, according to some embodiments, 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 (e.g., for display to a user, operation of the installed product, operation of another system, or input to another system).).
As to claim 15, SUBRAMANIYAN teaches wherein after step d), in particular after step e), the execution engine: g) sends to an orchestrator application information regarding an execution outcome (It is obvious to use data stores, regardless if their physical representation and names, to store data, whether resulting from previous data processing or for future processing. Moreover, in a distributed system it is obvious to transmit data among its constituting entities.).
As to claim 17, SUBRAMANIYAN teaches wherein the system comprises further the data database, wherein the data database is local or remote (SUBRAMANIYAN discloses the system is distributed. Thus, it anticipates its various components, regardless of their names, being “local” or “remote” with respect to each other.).
As to claim 18, SUBRAMANIYAN teaches wherein the system comprises further one or more computational analysis computer programs and/or a computational analysis database, wherein the computational analysis database is local or remote (SUBRAMANIYAN discloses the system is distributed. Thus, it anticipates its various components, regardless of their names, being “local” or “remote” with respect to each other.).
As to claim 19, SUBRAMANIYAN fails to teach one or more queues configured to store analysis tasks and/or a queue database, wherein the queues database is local or remote.
However, Challenger teaches one or more queues configured to store analysis tasks and/or a queue database, wherein the queues database is local or remote (Challenger column 11, lines 46-66 discloses that data analysis tasks are put in a queue for execution.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of SUBRAMANIYAN to incorporate the Distributed UIMA Cluster Computing (DUCC) Facility as taught by Challenger for the purpose of handle errors and carry out retry policies based upon parameters specified in the user submission or as configured in the DUCC System.
As to claim 20, SUBRAMANIYAN teaches wherein the system comprises further the results database, wherein the results database is local or remote (SUBRAMANIYAN discloses the system is distributed. Thus, it anticipates its various components, regardless of their names, being “local” or “remote” with respect to each other.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al (US 20220108262 A1)
Cella et al (US 20210157312 A1)
CELLA et al (US 20200348662 A1)
CELLA et al (US 20200225655 A1)
PRABATH et al (US 20200133814 A1) - Various embodiments are 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.
Bliss et al (US 20170351226 A1) - A cloud-based diagnosis and maintenance system facilitates discovery and indexing of plant-wide data residing on various data platforms across multiple industrial facilities. The system includes an indexing system that automatically inventories industrial devices and other data sources located throughout the facilities, and identifies available data items on each data source. The indexing system indexes the discovered data items in a federated data model that can subsequently be searched to locate data items or tags of interest. The diagnosis and maintenance system also includes analysis tools that facilitate cross-facility analysis of the data model, allowing performance metrics to be compared across similar automation systems at different facilities. The system can also generate recommendations for improving performance metrics based on results of the comparative analysis.
WANG et al (US 20120310898 A1) - A method executed by a processor of a server sets device parameters and system parameters in relation to the server and one or more monitoring devices, and collects data from each of the one or more monitoring devices according to the set device parameters and system parameters. The collected data is stored into a first queue, and read at a specified time interval, and then stored into a database. Any abnormality in the operation of the monitoring devices is stored into a second queue and processed in real-time.
Luo (US 20030210269 A1) - 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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED M BIBBEE whose telephone number is (571)270-1054. The examiner can normally be reached Monday-Thursday 8AM-6PM.
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/JARED M BIBBEE/Primary Examiner, Art Unit 2161
