DETAILED ACTION
Claims 1-20 are presented for examination.
Claims 1, 10, and 19 have been amended.
This office action is in response to the RCE submitted on 30-July-2026.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
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 – 35 USC 103
The applicant argues on pg. 12-13 that Florissi doesn’t teach configuration workflow for predefined models. On pg. 14 the applicant continues to argue that Florissi does not teach the tools for mapping to external applications.
However, as indicated in the office action below, Chand is relied upon for those limitations including the UI configuration workflow and the interactive data mapping. Please see Chang para ([0047,0059])
The applicant is attacking the reference in isolation ignoring the combination. Additionally, Florissi discloses the data catalog administrators and their administration of the data catalog. See Fig. 24-28 and respective explanation in the specification.
The applicant on pg. 15-16 argues that Chand does not teach mapping to external applications. However, the applicant is attacking the reference in isolation without considering the combination of Florissi which is relied upon for the disclosure of integrating to the external application. Please see Col 13, ln 15-33 as well as Fig. 35.
Additionally, Chand’s [0085] explicitly discusses mapping the raw device data and providing it to various possible analytics, statistic, machine learning or AI systems as example systems that are external to the gateway. Mapping the raw data into customized format that is readable by these various possible systems is precisely what the invention is claiming. Additionally, Florrissi explicitly covers the integration and export with external services. It would have been obvious to a PHOSITA to incorporate Chand’s user enabled UI mapping capabilities and contextualization of pre-defined templates into Florrissi’s integration and export methodologies for more formal integration and export capabilities.
Examiner’s Note
The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Florissi et al. (US10791063B1) in view of Chand et al. (US20200326684A1)
Regarding Claim 1, Florissi teaches a memory that stores executable components; and
a processor, operatively coupled to the memory, that executes the executable components (“an apparatus includes at least one processing device comprising a processor coupled to a memory”).
an edge gateway component configured to execute on a cloud platform (Col. 31, 41-43, "The WWH platform 1002 is suitable for use in a wide variety of information processing contexts, including numerous cloud, hybrid cloud and multi-cloud")
and to discover, based on a device profile selected from a library of device profiles corresponding to respective types of industrial devices or assets, data items on one or more industrial devices deployed at an industrial facility, wherein the data items are identified by the device profile (Col 35, 54-57, "The catalog in some embodiments is configured to track “things” based on metadata representative of their properties, using this metadata to discover the availability of “things” and to gain reachability to these “things.”” The metadata indicates the profile. Additionally, Col 32, 17-22, "The catalog is illustratively configured to be flexible, extensible and applicable to tracking any type of “thing.” Logical components of the catalog can be mapped to physical entities in numerous real-world scenarios").
a user interface component configured to display the data items on a model configuration interface rendered on a client device via the cloud platform (Col 39, 2-6, "a user may be utilizing a command line interface to interact with the catalog, an app may be interacting with the catalog to provide a graphical user interface to browse the content of the catalog data model and the catalog data")
(Col 13, Ln 15-33, “A given meta-resource of the WWH catalog may additionally or alternatively comprise one or more other types of information, such as, for example, information regarding transformation of the data resource into one or more designated formats, access control information, policy rules, etc. The WWH catalog therefore illustratively provides a catalog of entries, each comprising a meta-resource. Each meta-resource describes the respective resource and may contain the code or an API required to transform the resource to the format required by the application. End users or other types of clients may browse the WWH catalog via a browsing API or other type of browsing interface in order to obtain information about meta-resources, and WWH applications may query it for information about how to access the data. As noted above, the WWH catalog is assumed to be distributed across multiple data zones and their respective YARN clusters. Such a distributed arrangement helps to provide security and privacy for the underlying data resources.” EN: Florissi is explicitly disclosing mapping data fields as required by the external applications. Also see Fig. 35)
an identifier of the external application (Col 26, 1-24, “It is assumed that the local cluster that receives the WWH-MapReduce-GlobalReduce application from the submitting client is denoted as cluster C0, and that there are two additional participating clusters denoted as clusters C1 and C2, respectively. It is further assumed that these clusters are in respective separate data zones and that each of the clusters has access to the local data resources of its corresponding data zone. The clusters C0, C1 and C2 in this example are implemented as respective Docker-based clusters, each running YARN and HDFS. Each cluster runs an instance of a distributed WWH catalog as a YARN application. The different WWH catalog instances are differentiated by their respective configuration files. More particularly, each WWH catalog instance has a unique configuration file that describes the local and remote meta-resources relative to the corresponding cluster. The local meta-resources are assumed to be described by information identifying their location in the local file system (e.g., file name or file path), and the remote meta-resources are assumed to be described by information identifying their respective remote clusters. Other types of information indicative of location and accessibility of local or remote data resources can be used in other embodiments.” EN: The clusters with ids C0, C1, C2 are the external application. Also see Fig. 14)
a modeling component configured to generate the information model based on the selection of the one or more of the data items to be mapped to the predefined model (Col 33, 49-57, "a graph is used to represent a model-driven dataset formed as a set of class instances and relationships among these instances. Such a graph illustratively comprises a plurality of vertices or nodes, which represent respective instances of the classes, and one or more edges, which represent respective relationships among the instances of the classes. Once a model-driven dataset is represented as a graph, then properties of the dataset can be easily conceptualized as properties of a graph." and Col 34, 29-30, " Catalog Data Model: providing a class hierarchy and associated properties defined for the model.")
an egress component configured to apply the information model to device data collected from the one or more of the data items to yield the modeled data contextualized in accordance with the data contextualization and to export the modeled data to the data fields of the external application in accordance with the data mappings (Please see Fig. 35 for the meta data contextualizing the data. Col 35, 1-3, "10. Outbound Integration Services (O-Integration Services): allowing the catalog to export catalog data to external components" and Col. 43, 17-23, "persistency services can include persisting the catalog data model by providing a programmatic way, leveraging the catalog core access services, to obtain, from the catalog data model, the list of classes, relationships, and all the other properties defined for these classes. Then, these microservices leverage the outbound integration services to store this information in an entity external to the catalog").
However, Florissi doesn’t seem to explicitly teach:
and to receive, via interaction with the model configuration interface, selection of one or more of the data items to be mapped to a predefined model to yield an information model;
selected from a library of predefined models corresponding to respective external applications
the predefined models define respective data mappings to data fields of the respective external applications
Wherein the predefined model corresponds to an external application, of the respective external applications and defines a data contextualization to be applied to the one or more of the data items to yield modeled data, an identifier of the external application, and data mappings between items of the modeled data and data fields of the external application
Chand teaches to receive, via interaction with the model configuration interface, selection of one or more of the data items to be mapped to a predefined model to yield an information model ([0003] “the user interface component is further configured to receive mapping data that defines data mappings between the data inputs and data tags of industrial devices; a model configuration component configured to customize the model template in accordance with the mapping data to yield a customized model”).
selected from a library of predefined models corresponding to respective external applications ([0047]) “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. Model configuration component 406 can transform this model template to a customized model based on user input that maps the generic data items defined by the model template to actual sources of the data discovered by the device interface component 404.” The sources of the data are external applications. Florissi additionally teaches external applications in detail as presented above).
the predefined models define respective data mappings to data fields of the respective external applications ([0085] The external application if provided by Florissi as per Col 13, ln 15-33)
Wherein the predefined model corresponds to an external application, of the respective target external applications and defines a data contextualization to be applied to the one or more of the data items to yield modeled data, an identifier of the external application, and data mappings between items of the modeled data and data fields of the external application ([0085] “As noted above, the structure and contextualized data 704 generated by the smart gateway platform 402 can be fed to an analytic system, ... FIG. 11 is a diagram illustrating an example architecture in which the smart gateway platform 402 collects, contextualizes, and structures data from industrial devices 504 and provides the resulting structured and contextualized data 704 to an AI analytic system 1102. Although FIG. 11 (and other examples described herein) depicts data 704 being fed to an AI analytic system 1102, ... After model 702 has been customized by mapping the model's defined data items to specific data items on the industrial devices (e.g., smart tags 506 and/or standard data tags 508), the smart gateway platform 402 begins collecting and transforming the data from the mapped data sources as described above to yield structured and contextualized data 704. Since the collected data is contextualized and structured based on relevant correlations and causalities defined by the model 702—that is, correlations and causalities defined as being relevant to the particular business objective being examined—these correlations and causalities are provided to the AI analytic system 1102 as metadata associated with the collected data items. The platform's analytics interface component 412 can be configured to exchange data with the AI analytic system 1102, and is capable of feeding the data 704 to the analytics system.” EN: The external application here is the AI service which can run as a cluster provided by Florissi)
Florissi and Chand are analogous art because they are from the same field of endeavor in data modeling and integration. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Florissi and Chand to incorporate Chand’s UI mapping and model library to facilitate data mapping and integration with internal and external applications. “the executable components comprising: user interface component configured to receive selection data selecting a model template, of the model templates, associated with a business objective of the business objectives, wherein the model template defines data inputs and relationships between the data inputs relevant to the business objective” (Chand, [0003])
Regarding Claim 2, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches wherein the information model configures the edge gateway component to collect the device data from the one or more of the data items (Col 36, 17-27, "The catalog services 1105 comprise sets of microservices that deliver functionality on top of the catalog data 1104, while remaining completely bound to all the definitions and constraints specified in the catalog data model 1102." and "The system 3400 utilizes data models of the type shown in FIG. 35 to facilitate near real-time discovery of data sources, in effect allowing the data sources to be discovered at or near the time of performance of the distributed analytics").
Regarding Claim 3, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches wherein the external application is at least one of an analytic system that applies analytics to the modeled data, a predictive maintenance application that predicts industrial asset failures or performance issues based on analysis of the modeled data, a work order management system that generates maintenance work orders in response to detection of a performance issue based on analysis of the modeled data, or a visualization system that displays the modeled data (Col. 62, 55-60, "The system 3400 then obtains metadata characterizing the IoT data sources. The metadata is utilized to determine the particular IoT data sources that should participate in the given distributed analytics application, and to otherwise control other aspects of the performance of the distributed analytics," Col. 61, 6-25 "the information processing system 3400 as shown illustrates the flow of data between the distributed data processing platform 3405 and … management systems, including businesses that gain insight from IoT data, sustainability officers that drive social change, device manufacturers that improve performance, reliability and accuracy, and security officers that fight cybercrime. The distributed analytics performed in the distributed data processing platform 3405 can therefore generate insights that lead to new business revenue models," and Col 70, 33-37, “The catalog in some embodiments is extensible, flexible, strong typed, and at scale, and configured for implementation on top of existing persistency and visualization technologies, and can be readily integrated with a wide variety of different data sources and data targets”).
Regarding Claim 4, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches the selection of the one or more of the data items to be mapped to the predefined model maps at least one of the one or more of the data items at least one of the data fields of to the external application in accordance with the data mappings (Col. 43, 3-7 and 17-23, “2. Outbound Integration Services: providing a mechanism to export data from the catalog data model and the catalog data to entities external to the catalog. This set of microservices focuses on the specific details and intricacies of the integration with the external source.” "persistency services can include persisting the catalog data model by providing a programmatic way, leveraging the catalog core access services, to obtain, from the catalog data model, the list of classes, relationships, and all the other properties defined for these classes. Then, these microservices leverage the outbound integration services to store this information in an entity external to the catalog" EN: The properties are data fields. Also see Fig. 14)
Regarding Claim 5, Florissi in view of Chand teaches the system of claim 4. Florissi further teaches the predefined model defines as the data contextualization an organization of data, and the egress component is configured to model the device data in accordance with the organization to yield the modeled data ( Col. 43, 17-23, "persistency services can include persisting the catalog data model by providing a programmatic way, leveraging the catalog core access services, to obtain, from the catalog data model, the list of classes, relationships, and all the other properties defined for these classes. Then, these microservices leverage the outbound integration services to store this information in an entity external to the catalog" Fig. 35 illustrates the meta data for data contextualization).
Regarding Claim 6, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches the device profile defines a modeling of the device data collected from the one or more of the data items, and the modeling component is configured to generate the information model further based on the modeling defined by the device profile (Col. 62, 17-27, "FIG. 35 shows a portion of an example data model 3500 for meta-resources comprising IoT devices in an illustrative embodiment. The model in this example characterizes a given IoT device as a WWH-meta-resource that includes multiple device profiles as well as addressable characteristics of the IoT device. One of the device profiles of the data model 3500 includes entries such as name, manufacturer, model, labels and objects. The objects are illustratively specified as JavaScript Object Notation (JSON) objects, although other formats could be used").
Regarding Claim 7, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches the edge gateway component is further configured to, in response to discovery of a device-level information model defined on an industrial device of the one or more industrial devices, import the device-level information model, and the modeling component is further configured to generate the information model further based on the device-level information model (Fig. 21 illustrates the import process in detail. Additionally, Col. 62, 47-54, "The system 3400 may be triggered, responsive to initiation of a given distributed analytics application, to engage in an automated discovery process with the edge devices in order to discover their associated data sources. For example, this may involve interacting with edge device management systems, such as VMware Pulse or EdgeXFoundry, which manage the IoT gateways and/or their associated IoT data sources").
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Regarding Claim 8, Florissi in view of Chand teaches the system of claim 7. Florissi further teaches the device-level information model defines a hierarchical organization of the data items indicative of hierarchical relationships between industrial assets of one or more automation systems (Col. 34, 29-30, "2. Catalog Data Model: providing a class hierarchy and associated properties defined for the model” as well as fig. 29).
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Regarding Claim 9, Florissi in view of Chand teaches the system of claim 1. Florissi further teaches application of the information model to the device data by the egress component at least one of organizes the device data based on a hierarchical organization of industrial assets defined by the information model or adds contextual metadata to the device data that defines functional or mathematical relationships between the one or more of the data items (Please see Fig. 29 for the hierarchical organization and Fig. 35 for the meta data).
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Claims 10-18 are method claims reciting limitations similar to claims 1-9 respectively and are rejected under the same rationale.
Claims 19-20 are medium claims reciting limitations similar to claims 1 and 6 respectively and are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cooley et al (US20220147000A1): Discloses data integration pipeline with tags and covers most of the current claim set.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.D./Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199
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