Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Regarding applicant arguments on pages 1-3 of the remarks, directed at the rejection of claim 1 under 35 U.S.C. 103:
Applicant argues that the amended limitations are not taught by the cited references. Examiner respectfully disagrees and points to Chand:
Chand teaches wherein the AMS policy configures data selection criteria (0047; model configures and correlates the data input with the analytics functions part of the business objective) so that each of the two or more AFs can be selected for a respective different analytic (0047; suitable analytics for achieving business objective) prepared as part of the service capabilities in the service(0103; services of the platform), ([0047] Model configuration component 406 can be configured to generate an analytic model that can be leveraged by an AI or machine learning analytic system in connection with applying suitable analytics for achieving a desired business objective. The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items.; 0105; Each model template can define, for a given business objective, a set of data items (e.g., sensor inputs, controller data tags, motor drive data tags, etc.) relevant to determining actionable insights into the associated business objective, as well as relationships (e.g., correlations, causalities, etc.) between the data items. The data items and the relationships therebetween are based on domain expertise or knowledge encoded into the model templates. Example business objectives for which model templates may be made available can include, but are not limited to, minimizing machine downtime, determining a cause of machine downtime, predicting machine downtime, increasing product output, optimizing energy efficiency, improving product quality, or other such objectives)
wherein the AMS policy configures (the model configures) a protocol or a data format (the normalized format conversion for input data) to be used by each of the two or more AFs, ([0074] In general, model 702 specifies the data that should be analyzed to yield insights into the desired business objective, and also informs data modeling component 408 how to organize and combine the specified data items into meaningful clusters that can drive the analytics. These clusters are based on the correlations and causalities between the data items as defined by the model 702. Any raw data 708 that is not already pre-modeled and contextualized at the device level is transformed by the data modeling component 408 into smart data before being fed to AI analytics as structured and contextualized data 704. This creates a common representation of all the disparate data collected by the smart gateway platform 402. In an example scenario, overall equipment effectiveness (OEE) data from machines of a production line may be in different formats, depending on the vendor, model, and/or age of the industrial devices from which the data is collected. Data modeling component 408 can normalize this disparate OEE data into common representations. Data modeling component 408 can also add metadata to the data collected from the machines to yield contextualized smart data. Data modeling component 408 determines what metadata is to be added to a given item of raw data based on data correlations or causalities defined by model 702. See also 0090; 0101; and 0107; This augmentation step can also include normalization of the data items to a common format in preparation for collective AI analysis.)
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-8, 10-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 11870873 B2 hereinafter P1 in view of Chand et al. (US 20200326684 A1). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding claim 1 P1 teaches an apparatus for operating common analytics management service (AMS) in a service supporting service capabilities through a set of application programming interfaces (APIs), the service being provided as middleware between application protocols and applications, the apparatus comprising: one or more processors; and one or more memory coupled with the one or more processors, the one or more memory comprising executable instructions that when executed by the processor cause the one or more processors to effectuate operations comprising: (Claim 1: An apparatus for operating common analytics management service (AMS) in a service supporting service capabilities through a set of application programming interfaces (APIs), the service being provided as middleware between application protocols and applications, the apparatus comprising: one or more processors; and one or more memory coupled with the one or more processors, the one or more memory comprising executable instructions that when executed by the processor cause the one or more processors to effectuate operations comprising)
receiving from an application a request to create an AMS policy for analyzing internet of things (IoT) data, wherein the AMS policy specifies an analysis to be performed on the IoT data by configuring two or more analytic functions (AFs) of a plurality of AFs in the service to perform an analysis on the IoT data, (Claim 1: : receiving from an application a request to create an AMS policy for analyzing internet of things (IoT) data, wherein the AMS policy specifies an analysis to be performed on the IoT data by configuring two or more analytic functions (AFs) of a plurality of AFs in the service to perform an analysis on the IoT data)
and the AMS policy defines how analytic results received from the two or more AFs are conditionally updated with annotations to be stored in the service and defines information for accessing the analytic results stored in the service; (Claim 1:, each of the two or more AFs being for a respective different analytic prepared as part of the service capabilities in the service, and the AMS policy defines how analytic results received from the two or more AFs are conditionally updated with annotations and defines a way the updated analytic results are stored in the service and how to access the analytic results stored in the service)(
based on the received request, creating the AMS policy; configuring the two or more AFs according to the AMS policy; and executing the analysis based on the configured two or more AFs. (Claim 1: based on the received request, creating the AMS policy; configuring the two or more AFs according to the AMS policy; and executing the analysis based on the configured two or more AFs.)
P1 does not explicitly teach wherein the AMS policy configures data selection criteria so that each of the two or more AFs can be selected for a respective different analytic prepared as part of the service capabilities in the service,
wherein the AMS policy configures a protocol or a data format to be used by each of the two or more AFs, wherein the AMS provides authorization allowing entities in the service without access to an original location of IoT data access to selected portions of the IoT data in the service based on the analytic results;
In an analogous art Chand teaches wherein the AMS policy configures data selection criteria (0047; model configures and correlates the data input with the analytics functions part of the business objective) so that each of the two or more AFs can be selected for a respective different analytic (0047; suitable analytics for achieving business objective) prepared as part of the service capabilities in the service(0103; services of the platform), ([0047] Model configuration component 406 can be configured to generate an analytic model that can be leveraged by an AI or machine learning analytic system in connection with applying suitable analytics for achieving a desired business objective. The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items.; 0105; Each model template can define, for a given business objective, a set of data items (e.g., sensor inputs, controller data tags, motor drive data tags, etc.) relevant to determining actionable insights into the associated business objective, as well as relationships (e.g., correlations, causalities, etc.) between the data items. The data items and the relationships therebetween are based on domain expertise or knowledge encoded into the model templates. Example business objectives for which model templates may be made available can include, but are not limited to, minimizing machine downtime, determining a cause of machine downtime, predicting machine downtime, increasing product output, optimizing energy efficiency, improving product quality, or other such objectives)
wherein the AMS policy configures (the model configures) a protocol or a data format (the normalized format conversion for input data) to be used by each of the two or more AFs, ([0074] In general, model 702 specifies the data that should be analyzed to yield insights into the desired business objective, and also informs data modeling component 408 how to organize and combine the specified data items into meaningful clusters that can drive the analytics. These clusters are based on the correlations and causalities between the data items as defined by the model 702. Any raw data 708 that is not already pre-modeled and contextualized at the device level is transformed by the data modeling component 408 into smart data before being fed to AI analytics as structured and contextualized data 704. This creates a common representation of all the disparate data collected by the smart gateway platform 402. In an example scenario, overall equipment effectiveness (OEE) data from machines of a production line may be in different formats, depending on the vendor, model, and/or age of the industrial devices from which the data is collected. Data modeling component 408 can normalize this disparate OEE data into common representations. Data modeling component 408 can also add metadata to the data collected from the machines to yield contextualized smart data. Data modeling component 408 determines what metadata is to be added to a given item of raw data based on data correlations or causalities defined by model 702. See also 0090; 0101; and 0107; This augmentation step can also include normalization of the data items to a common format in preparation for collective AI analysis.)
wherein the AMS (smart gateway platform) provides authorization allowing entities (authorized users having permission) in the service without access to an original location of IoT data (the system does not give access to the storage location but provides a visualization of data) access to selected portions of the IoT data (subset of the collected iot data) in the service based on the analytic results; (subset of data related to the analytics and the analytics results as well) ([0087] Subsets of the data items collected by the smart gateway platform 402, as well as results of AI or machine learning analytics applied by analytic system 1102 on the data, can be delivered as visualization presentations 1104 to one or more client devices 1106 having permission to access the analytic results; In this regard, reduction of the relevant data set by the smart gateway platform 402 to those data items known to be relevant to the business objective, as well as the known correlations and causalities between the data items encoded into the smart data based on model 702, can lead the AI analytic system 1102 more quickly to actionable insights regarding the defined business objective. In an example scenario, AI analytic system 1102 may determine, based on application of AI or machine learning to the structured and contextualized data 704, that downtime of a particular production line (e.g., Line 5) that is part of the overall process is a significant cause of productivity loss; [0093] This approach of locating data processing and analytics functions in a scalable manner is well-suited to industrial Internet-of-Things (IoT) applications with varying time domains of processing (e.g., milliseconds, seconds, minutes, hours, etc.), asset locations (centralized and remote), and system relationships (e.g., autonomous, in-line, buffered, batched, etc.). Analytics and AI/machine learning at each of these three defined levels can optimize processes and operations in industrial plants to achieve desired business objectives.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of P1 to include providing visualization of a subset of the data and the analytics results to users having permission in the platform as is taught by Chand;
The suggestion/motivation for doing so is to better integrate industrial automation systems with business objectives [0001].
Regarding claim 2, P1 in view of Chand teach the apparatus of claim 1, wherein the AMS policy comprises an analytics function configuration for the two or more Afs (Claim 2: wherein the AMS policy comprises an analytics function configuration for the two or more Afs)
Regarding claim 3, P1 in view of Chand teach the apparatus of claim 1, wherein the AMS policy comprises an indication of how the IoT data is received from the two or more AFs stored. (Claim 3: wherein the AMS policy comprises an indication of how the IoT data is received from the two or more AFs stored)
Regarding claim 4, P1 in view of Chand teach the apparatus of claim 1, wherein the data selection criteria comprises an identity of a user equipment (Claim 4: wherein the AMS policy comprises IoT data selection criteria that comprises an identity of a user equipment)
Regarding claim 5, P1 in view of Chand teach the apparatus of claim 1, the operations further comprising: determining a data mode for the AMS policy when created, wherein the data mode is for a continuous data mode; and based on the data mode being a continuous data mode, checking the AMS policy each time an IoT data is created (Claim 5: the operations further comprising: determining a data mode for the AMS policy when created, wherein the data mode is for a continuous data mode; and based on the data mode being a continuous data mode, checking the AMS policy each time an IoT data is created)
Regarding claim 6, P1 teaches the apparatus of claim 1, the operations further comprising: determining a data mode for the AMS policy when created, wherein the data mode is for a periodic schedule data mode; and based on the data mode being a periodic schedule data mode, checking the AMS policy based on a schedule provided in the request (Claim 6: determining a data mode for the AMS policy when created, wherein the data mode is for a periodic schedule data mode; and based on the data mode being a periodic schedule data mode, checking the AMS policy based on a schedule provided in the request)
Regarding claim 7, P1 in view of Chand teach the apparatus of claim 1, the operations further comprising: discovering existing IoT data that matches IoT data selection criteria; and sending, based on data representation information, the discovered IoT data to an AF of the two or more AFs. (Claim 7: discovering existing IoT data that matches IoT data selection criteria; and sending, based on data representation information, the discovered IoT data to an AF of the two or more AFs.)
Regarding claim 8, P1 in view of Chand teach the apparatus of claim 1, the operations further comprising: receiving IoT data; responsive to receiving the IoT data, determining which AMS policies to apply; determining that the AMS policy applies to processing the IoT data; based on the determining that the AMS policy applies, sending the IoT data to an analytics function for analytics, wherein the analytics function produces analytics results; and storing the analytics results according to the AMS policy (Claim 8: receiving IoT data; responsive to receiving the IoT data, determining which AMS policies to apply; determining that the AMS policy applies to processing the IoT data; based on the determining that the AMS policy applies, sending the IoT data to an analytics function for analytics, wherein the analytics function produces analytics results; and storing the analytics results according to the AMS policy)
Claims 10-15 and 17-20 inherit the same rejections as claims 1-8 above for reciting similar limitations.
Claim 9 and 16 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. US 11870873 B2 hereinafter P1, in view of Chand et al. (US 20200326684 A1) in view of Tabet et al. (US 10193753 B1)
Regarding claim 9, P1 in view of Chand teach the apparatus of claim 1, and is disclosed above, P1 does not explicitly recite wherein the information for accessing the analytic results defined by the AMS policy comprise an identification or a link to the analytic results stored in the service
In an analogous art Tabet teaches wherein the information for accessing the analytic results defined by the AMS policy comprise an identification or a link to the analytic results stored in the service (Examiner notes the data center and vGateways, and vCPE include analysis and storage, and the IoT platform configuration includes processing, collection/ communication and control of flow and storage configurations. See Col 9-10 + Mapping below in claim 1 103 rejection; Col 4; Lines 54-59 Data center resources include storage; Col 6 Lines 25-30; Cloud compute and storage nodes; Col 9 Line 49- Col 10 Line 23; The gateways also perform analytics storage and forwarding; Storing and archiving data; As well as the Data Center which stores and archives data; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of P1 in view of Chand to include a workflow that stores and analyzes data based on user configurations as is taught by Tabet
The suggestion/motivation for doing so is to improve IoT systems.
Claim 16 inherits the same rejections as claims 9 above for reciting similar limitations.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 7-13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tabet et al. (US 10,193,753 B1) in view of Wang et al. (US 20160112487 A1) in view of Chand et al. (US 20200326684 A1).
Regarding claim 1, Tabet teaches an apparatus for operating common analytics management service (AMS) in a service supporting service capabilities through a set of application programming interfaces (APIs), the service being provided as middleware between application protocols and applications, the apparatus comprising: (Fig 1; Fig 5; Col 3 Lines 27-45; Information processing system; Examiner respectfully points to Fig 5 that the deployed Iot platform works as a middleware between the Iot devices and the user (equivalent to provided as a middleware); Col 12 Lines 54-61; teaches that the Iot platform is used for integrating across protocols (equivalent to middleware between protocols))
one or more processors; and one or more memory coupled with the one or more processors, the one or more memory comprising executable instructions that when executed by the processor cause the one or more processors to effectuate operations comprising: (Col 18 Lines 1-6; memory stored in memory and executed by a processor)
receiving from an application a request (user request) to create an AMS policy (configuration for deployment of IoT platform) for analyzing (analytics) internet of things (IoT) data, (Col 3 Lines 27-45; Information processing system for configuring and deploying an Iot Platform; Col 4 Lines 4-27; Receiving from a user device requirements for deploying an Iot platform; Col 15 Lines 5-16; IoT blueprint for creating Iot platform includes analytics; Col 13 Line 5 [Wingdings font/0xE0] Col 14 Line 3; the Gateways are configured to perform the analytics or the Data Center. Examiner points to Fig 2; Figs 3 and 5 showing platform 310 which is configured by the user (see Col 4); Col 3 Lines 27; The information processing system 100 is assumed to be built on at least one processing platform and provides functionality for automated configuration and deployment of IoT platforms; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
wherein the AMS policy (configuration) specifies an analysis (user selected configuration) to be performed on the IoT data by configuring two or more analytic functions (AFs) of a plurality of Afs (deep analytics functions) in the service to perform an analysis on the IoT data, (Examiner notes the configuration is for configuring the Iot Platform, the IoT platform includes vGateways, vCPEs, and Data centers that are configured to perform data analysis using a plurality of deep analytic functions, at each level; (All mapping above) Col 49 Line 50 [Wingdings font/0xE0] Col 50 Line 24; vGateways perform Iot data stream analytics, the vCPEs perform another part of the analytics, and the data center performs data transformation, and deep data analytics; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms..)
wherein the AMS policy (configuration) configures data selection criteria (Col 9-10 Lines 50-3; The system filters out data based on context, analytics, and store and forward operations; Col 13 Lines11-24; configuration of ingestion, processing and filtering of data among other things; therefore the system must have a data selection method so that it forwards data and processes data through the big data analytics functionality )
each of the two or more AFs being for a respective different analytic prepared as part of the service capabilities in the service, (Examiner notes the mapping above, Tabet teaches a plurality of functions and where they are implemented. Examiner also notes that though configurations are customized they are provided to the user through an interface, and therefore is equivalent to a service see Col 4; However, the term “requirements input” as used herein is intended to be broadly construed so as to encompass, for example, indications received from a given one or the user devices 102 responsive to lists of available features presented to a user in one or more user interface screens; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms..)
wherein the AMS policy configures a protocol (protocol normalization) or a data format (formats of incoming data) to be used by each of the two or more AFs (the functions received normalized and formatted data part of the processing step prior to the analytics) (Col 2 Lines 28 [Wingdings font/0xE0] 35; configuring requirements including data formats; Col 9-10 Lines 50-3; The vGateways 314 are software-defined gateways in the present embodiment in order to provide dynamically reconfigurable functionality such as initial filtering, stream analytics and network protocol normalization for potentially massive amounts of IoT data in multiple distinct data formats received over various types of networks.)
and defines information for accessing (storage) the analytic results stored in the service; (Examiner notes the data center and vGateways, and vCPE include analysis and storage, and the IoT platform configuration includes processing, collection/ communication and control of flow and storage configurations. See Col 9-10 + Mapping above; Col 4; Lines 54-59 Data center resources include storage; Col 6 Lines 25-30; Cloud compute and storage nodes; Col 9 Line 49- Col 10 Line 23; The gateways also perform analytics storage and forwarding; Storing and archiving data; As well as the Data Center which stores and archives data; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
based on the received request, creating the AMS policy; (Mapping above; Col 7 Lines 4-64; deploying the IoT platform based on the request)
configuring the two or more AFs according to the AMS policy; (Mapping above; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms..)
and executing the analysis based on the configured two or more AFs. (Mapping above; Col 7 Lines 4-64; deploying the IoT platform based on the request)
Tabet does not fully and explicitly teach wherein the AMS policy configures data selection criteria so that each of the two or more AFs can be selected for a respective different analytic prepared as part of the service capabilities in the service, and the AMS policy defines how analytic results received from the two or more AFs are conditionally updated with annotations to be stored in the service, wherein the AMS provides authorization allowing entities in the service without access to an original location of IoT data access to selected portions of the IoT data in the service based on the analytic results;
In an analogous art Wang teaches the AMS policy (configuration) defines how analytic results received from the two or more AFs (contextual and semantic information) are conditionally updated with annotations (annotations) to be stored in the service (0027-0028; 0035-0036; Performing data stream analysis on the stream of IoT data and performing annotations on the data based on the analysis which includes semantic analysis and context analysis (equivalent to two functions) 0028; deployable on a gateway; Fig 6-7 [0049-0057] The annotation configuration includes parameters for which data to annotate, if the data is streamed or where the data is stored, and Fig 6 shows that 673-676 that the data is analyzed annotated and stored. )
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of Tabet to include defining the annotation and storage of data as is taught by Wang.
The suggestion/motivation for doing so is to increase communication performance and efficiency [0002-0003].
Tabet in view of Wang do not explicitly teach wherein the AMS policy configures data selection criteria so that each of the two or more AFs can be selected for a respective different analytic prepared as part of the service capabilities in the service, wherein the AMS policy configures a protocol or a data format to be used by each of the two or more AFs, wherein the AMS provides authorization allowing entities in the service without access to an original location of IoT data access to selected portions of the IoT data in the service based on the analytic results;
In an analogous art Chand teaches wherein the AMS policy configures data selection criteria (0047; model configures and correlates the data input with the analytics functions part of the business objective) so that each of the two or more AFs can be selected for a respective different analytic (0047; suitable analytics for achieving business objective) prepared as part of the service capabilities in the service(0103; services of the platform), ([0047] Model configuration component 406 can be configured to generate an analytic model that can be leveraged by an AI or machine learning analytic system in connection with applying suitable analytics for achieving a desired business objective. The analytic model can be generated based on a model template—selected from a library of model templates 420 stored on memory 418—that encodes domain expertise relevant to the business objective. The model template can define data items (e.g., sensor inputs, measured process variables, key performance indicators, machine operating modes, environmental factors, etc.) that are relevant to the business objective, as well as correlations between these data items.; 0105; Each model template can define, for a given business objective, a set of data items (e.g., sensor inputs, controller data tags, motor drive data tags, etc.) relevant to determining actionable insights into the associated business objective, as well as relationships (e.g., correlations, causalities, etc.) between the data items. The data items and the relationships therebetween are based on domain expertise or knowledge encoded into the model templates. Example business objectives for which model templates may be made available can include, but are not limited to, minimizing machine downtime, determining a cause of machine downtime, predicting machine downtime, increasing product output, optimizing energy efficiency, improving product quality, or other such objectives)
wherein the AMS policy configures (the model configures) a protocol or a data format (the normalized format conversion for input data) to be used by each of the two or more AFs, ([0074] In general, model 702 specifies the data that should be analyzed to yield insights into the desired business objective, and also informs data modeling component 408 how to organize and combine the specified data items into meaningful clusters that can drive the analytics. These clusters are based on the correlations and causalities between the data items as defined by the model 702. Any raw data 708 that is not already pre-modeled and contextualized at the device level is transformed by the data modeling component 408 into smart data before being fed to AI analytics as structured and contextualized data 704. This creates a common representation of all the disparate data collected by the smart gateway platform 402. In an example scenario, overall equipment effectiveness (OEE) data from machines of a production line may be in different formats, depending on the vendor, model, and/or age of the industrial devices from which the data is collected. Data modeling component 408 can normalize this disparate OEE data into common representations. Data modeling component 408 can also add metadata to the data collected from the machines to yield contextualized smart data. Data modeling component 408 determines what metadata is to be added to a given item of raw data based on data correlations or causalities defined by model 702. See also 0090; 0101; and 0107; This augmentation step can also include normalization of the data items to a common format in preparation for collective AI analysis.)
wherein the AMS (smart gateway platform) provides authorization allowing entities (authorized users having permission) in the service without access to an original location of IoT data (the system does not give access to the storage location but provides a visualization of data) access to selected portions of the IoT data (subset of the collected iot data) in the service based on the analytic results; (subset of data related to the analytics and the analytics results as well) ([0087] Subsets of the data items collected by the smart gateway platform 402, as well as results of AI or machine learning analytics applied by analytic system 1102 on the data, can be delivered as visualization presentations 1104 to one or more client devices 1106 having permission to access the analytic results; In this regard, reduction of the relevant data set by the smart gateway platform 402 to those data items known to be relevant to the business objective, as well as the known correlations and causalities between the data items encoded into the smart data based on model 702, can lead the AI analytic system 1102 more quickly to actionable insights regarding the defined business objective. In an example scenario, AI analytic system 1102 may determine, based on application of AI or machine learning to the structured and contextualized data 704, that downtime of a particular production line (e.g., Line 5) that is part of the overall process is a significant cause of productivity loss; [0093] This approach of locating data processing and analytics functions in a scalable manner is well-suited to industrial Internet-of-Things (IoT) applications with varying time domains of processing (e.g., milliseconds, seconds, minutes, hours, etc.), asset locations (centralized and remote), and system relationships (e.g., autonomous, in-line, buffered, batched, etc.). Analytics and AI/machine learning at each of these three defined levels can optimize processes and operations in industrial plants to achieve desired business objectives.)
It would have been obvious to one of ordinary skill in the art prior to the effective filing of the application to modify the teachings of Tabet in view of Wang to include providing visualization of a subset of the data and the analytics results to users having permission in the platform as is taught by Chand;
The suggestion/motivation for doing so is to better integrate industrial automation systems with business objectives [0001].
Regarding claim 2, Tabet in view of Wang in view of Chand teach the apparatus of claim 1, and is disclosed above, Tabet further teaches wherein the AMS policy comprises an analytics function configuration (configuration is for the user to design and deploy the analysis functionality of the Iot platform) for the two or more Afs (Mapping above; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
Regarding claim 3, Tabet in view of Wang in view of Chand teaches the apparatus of claim 1, and is disclosed above, Tabet further teaches wherein the AMS policy comprises an indication of how the IoT data (Data storage and archiving Fig 1 and Fig 5 318 Data center, vGateways, and vCPE) is received from the two or more AFs stored (Examiner notes the data center and vGateways, and vCPE include analysis and storage, and the IoT platform configuration includes processing, collection/ communication and control of flow. See Col 9-10 + Mapping above; Col 4; Lines 54-59 Data center resources include storage; Col 6 Lines 25-30; Cloud compute and storage nodes; Col 9 Line 49- Col 10 Line 23; The gateways also perform analytics storage and forwarding; Storing and archiving data; As well as the Data Center which stores and archives data; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
Regarding claim 4, Tabet in view of Wang in view of Chand teach the apparatus of claim 1, and is disclosed above, Tabet further teaches wherein the data selection comprises an identity of a user equipment. (Iot platform includes configuration for data collection; therefore it must know which device to collect data from and noted that that Col 4 Lines 4-41 teaches the IoT platform having a set of IoT devices, examiner notes IoT device is equivalent to UE (Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms)
Regarding claim 7, Tabet in view of Wang in view of Chand teach the apparatus of claim 1, and is disclosed above, Tabet further teaches the operations further comprising: discovering existing IoT data that matches the data selection criteria; (Col 13 Lines 11-24; Control of data Iot data collection; Col 8 lines 37-52; the gateways collect data from the IoT devices; examiner notes that if the data is collected it must match the selection)
and sending, based on data representation information, the discovered IoT data to an AF of the two or more AFs. (Mapping above in claim 1; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms..)
Regarding claim 8, Tabet in view of Wang in view of Chand teach the apparatus of claim 1, Tabet further teaches the operations further comprising: receiving IoT data; (Mapping above, and Figs 3, and 5 teaches IoT data collection Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms; Col 9 Lines 49-67; received IoT data)
responsive to receiving the IoT data, determining which AMS policies to apply; (Examiner notes Fig 3 310-1 is the IoT platform, equivalent to the AMS policy being deployed and therefore it always know which AMS policy applies, and therefore it receives the IoT data from Iot device, and analyzes the data and stores it using vGaeways and vCPE and forwards it; and then sent to the Datacenter where it is further transformed, analyzed, communication, collection and storage)
determining that the AMS policy applies to processing the IoT data; based on the determining that the AMS policy applies, (Examiner notes the definition of the policy above, and Col 14, after the policy is deployed, the communication, flow, processing, collection is all configured and therefore they system already knows what to do with the IoT data, because the configuration has been applied)
sending the IoT data to an analytics function for analytics, wherein the analytics function produces analytics results; (Examiner notes Fig 3 310-1 is the IoT platform, equivalent to the AMS policy being deployed and therefore it always know which AMS policy applies, and therefore it receives the IoT data from Iot device, and analyzes the data and stores it using vGaeways and vCPE and forwards it; and then sent to the Datacenter where it is further transformed, analyzed, communication, collection and storage) (Examiner notes the configuration is for configuring the Iot Platform, the IoT platform includes vGateways, vCPEs, and Data centers that are configured to perform data analysis using a plurality of deep analytic functions, at each level; (All mapping above) Col 49 Line 50 [Wingdings font/0xE0] Col 10 Line 24; vGateways perform Iot data stream analytics, the vCPEs perform another part of the analytics, and the data center performs data transformation, and deep data analytics; Col 14 Lines 10-23; allows a given customer or other user to design, develop and deploy a full end-to-end IoT solution comprising the IoT platform 310-1. This embodiment utilizes a multi-tiered adaptive service catalog comprising an IoT blueprint library to implement desired IoT functionality such as collection, ingestion, processing, filtering, tagging, analysis, feedback and output of data, as well as associated communication and action/control flows. Such an arrangement provides a new paradigm configured to meld OT and IT requirements in achieving the efficient and flexible deployment of IoT platforms..)
and storing the analytics results according to the AMS policy (Mapping claim 3) (Examiner notes Fig 3 310-1 is the IoT platform, equivalent to the AMS policy being deployed and therefore it always know which AMS policy applies, and therefore it receives the IoT data from Iot device, and analyzes the data and stores it using vGaeways and vCPE and forwards it; and then sent to the Datacenter where it is further transformed, analyzed, communication, collection and storage)
Regarding claim 9, Tabet in view of Wang in view of Chand teach the