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 .
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 12-15 and 19-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by
Malakuti (US 2021/0405629).
As per claim 12, Malakuti discloses a method for establishing communication between a first digital twin and a second digital twin, a first REST-based API being assigned to the first digital twin and a second REST- based API being assigned to the second digital twin, comprising:
transmitting a request to at least one endpoint of the first API and receiving at least one response of the first API; (0024] Each digital representation service has an interface to receive a request for device life cycle information from an information consumer connected to the network. For example, the request may be received from an application via a respective API (digital twin API) and ask for life cycle data stored by an external information source.)
transmitting a request to at least one endpoint of the second API and receiving at least one response of the second API; (0024] Each digital representation service has an interface to receive a request for device life cycle information from an information consumer connected to the network. For example, the request may be received from an application via a respective API (digital twin API) and ask for life cycle data stored by an external information source.)
comparing and analyzing the at least one response of the first API with the at least one response of the second API; creating at least one mapping model on the basis of the comparing and analyzing, the mapping model containing semantic and technical requirements of the first API and of the second API; and([0031] In general, a mapping between model elements of data models associated with at least two different information systems is generated via the semantic relationships as defined in the semantic relations library. In the embodiment where additional data models are dynamically obtained via corresponding model providers to enhance the digital twin over time, a mapping may be generated between model elements of data models associated with model kind descriptions of the discovered model providers and corresponding model elements of data models stored in the digital representation.)
using the mapping model for mutual communication between the first digital twin and the second digital twin, the mapping model translating a data transfer from one of the two digital twins in a manner conforming to the technical and semantic requirements of the API of the other of the two digital twins. ([0032]; At runtime (i.e., the time at which the request is processed to provide a corresponding response), the mapping is generated at the instance level of the model elements. That is, once the mapping is generated, the digital representation service knows which model element of a first data model in a first data format corresponds to which model element of a second data model in a second data format. This describes a situation where the digital twin of the device is to be enhanced with data from two different information systems with different formats.; [0052] In response to the request, the management module finally executes 1600 the data exchange between at least two information systems in accordance with the mapping.)
As per claim 13, Malakuti discloses the method according to claim 12, with at least one list of at least some of all the endpoints of the first API and the second API being created by reading out the corresponding first API or second API. (0024] Each digital representation service has an interface to receive a request for device life cycle information from an information consumer connected to the network. For example, the request may be received from an application via a respective API (digital twin API) and ask for life cycle data stored by an external information source.)
As per claim 14, Malakuti discloses the method according to claim 12, the response of the first API and/or the second API including an additional piece of information which is also used for the steps of comparing and analyzing. ([0018]; The data provided by information systems is also augmented with semantic descriptions including descriptions of the kinds of data models provided by the information systems. The information systems are then dynamically discovered by using a discovery module of a digital representation service, and, based on semantic descriptions, data obtained from information sources is automatically matched against the data required by respective information consumers resulting in corresponding mappings of data model elements which allow the respective information systems (consumer and source systems) interoperable data exchange regarding device related data via said mappings.)
As per claim 15, Malakuti discloses the method according to claim 12, the steps of comparing, analyzing, and creating the mapping model being performed by a cloud application. ( [0055]; It is to be noted that there can be multiple digital twins in multiple DTS services for a particular device, each one comprising specific kinds of data models and having its own lifecycle semantics. In the example, external cloud computers C1 and internal cloud computers C2 are connected to the network via corresponding application programming interfaces API.)
As per claim 19, Malakuti discloses the method according claim 12, wherein the first API and/or second API are being used for transmitting the request and a web service being used for receiving the corresponding response. (0024] Each digital representation service has an interface to receive a request for device life cycle information from an information consumer connected to the network. For example, the request may be received from an application via a respective API (digital twin API) and ask for life cycle data stored by an external information source.)
As per claim 20, Malakuti discloses the method according to claim 12, the first digital twin relating to a first automation component. ([0049]; For this purpose, machine-readable semantics of information and discovery mechanisms in distributed, inter-networked automation systems may be used.)
As per claim 21, Malakuti discloses the method according to claim 20, the second digital twin relating to a second automation component, or the second digital twin relating to an application. ([0073]; Using AutomationML, the model elements can be represented as internal elements within an instance hierarchy. Internal Element Attributes with accompanying fields of “Value”, “Unit”, “Data type” can be used to store model element attributes and their values. The models with their model elements can originate from different information systems IS1, IS2 being used as origin indicators in the corresponding model/model element names. The models/model elements of Layer 1 are instances of the corresponding types in Layer 2. For example, “Engineering Model IS1” is an instance of model type “Engineering Model 1 Type”, “Engineering Model IS2” is an instance of model type “Engineering Model 2 Type”, and so on.)
As per claim 22,. Malakuti discloses the method according to 12, the first digital twin relating to a first application, in particular to a cloud application, and the second digital twin relating to a second application. ([0061]; Another example of a particular configuration is about the deployment of digital twins, which can be fully or partially on the cloud, edge or device, or any combination of these. In other words, the DTMC may be adapted to select a deployment mode for the digital twin. The deployment mode can be selected for each data model in the collected data models for deployment to a cloud network, edge or device itself. The term edge is used herein in the meaning of edge computing which pushes applications, data and computing power (services) away from centralized points to locations closer to the user.)
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 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Thomsen et al. – hereinafter Thomsen (US 11,900,277)
As per claim 16, Malakuti discloses the method according to claim 15. Malakuti fails to disclose with the cloud application using an AI algorithm for the steps of comparing, analyzing, and creating the mapping model.
Thomsen discloses with the cloud application using an AI algorithm for the steps of comparing, analyzing, and creating the mapping model. (Col 53 lines 21-48; Since this analytic problem statement is embedded within the data structure of the digital twin 2306 itself, real-time analytics systems that interface with the digital twin 2306 can identify the prescribed type of analytics defined by the AI fields 4104 and carry out the defined analysis with minimal user intervention even if the digital twin 2306 is transferred to a different platform (e.g., an edge device, a cloud platform, a server, an embedded platform, etc.)
It would have been obvious before the earliest effective filing date for the teachings of Malakuti to be modified so that the cloud application uses an AI algorithm for comparing, analyzing and creating service mapping between the digital twins and identify the asset classification rules. This would have facilitated discovery of new relationships between digital twin variables, validation of the digital twin relative to its modeled industrial asset, and adaptation of the digital twin for use with other assets or in other operating conditions. (Thomsen, Col 13 lines 51-57)
As per claim 17, Malakuti / Thomsen disclose the method according to claim 16. Thomsen discloses with the AI algorithm accessing additional information of an instance assigned or assignable to the cloud application for the steps of comparing, analyzing, and creating the mapping model, the instance being a classification model. (Col 53 lines 21-48; Since this analytic problem statement is embedded within the data structure of the digital twin 2306 itself, real-time analytics systems that interface with the digital twin 2306 can identify the prescribed type of analytics defined by the AI fields 4104 and carry out the defined analysis with minimal user intervention even if the digital twin 2306 is transferred to a different platform (e.g., an edge device, a cloud platform, a server, an embedded platform, etc. ; Col 58 lines 36-59; Some embodiments of AI engine component 514 can employ classifiers in connection with learning, inferring, or validating relationships defined in the digital twin 2306. A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class; that is, f(x)=confidence(class). This classification can employ a probabilistic or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example classifier that can be employed in some embodiments.)
As per claim 18, Malakuti / Thomsen disclose the method according to claim 17. Thomsen discloses with the AI algorithm updating the created mapping model in the event that the instance has new additional information. (Col 35 lines 1-22; Parallel development of asset model 422 and mechanical model 2304 using model configuration application 2502 allows both models to share a common organization and defined property structure. The BIDT-based type system shared by the asset model 422 and the mechanical model 2304 creates a mapping of properties between the two models, allowing the mechanical formulas or transformations defined by the mechanical model 2304 to be applied to measured contextualized automation data to yield additional real-time or historical behavior or response data for the industrial asset (including but not limited to forces, positions, orientations, shapes, or temperatures of mechanical components).
Conclusion
The prior art made of record and not relied upon is considered pertinent toapplicant's disclosure. See PTO-892 form.
Any inquiry concerning this communication or earlier communications from theexaminer should be directed to Chirag R Patel whose telephone number is (571)272-7966. The examiner can normally be reached on Monday to Friday from 8:00AM to 4:30PM. If attempts to reach the examiner by telephone are unsuccessful, theexaminer's supervisor, Glenton Burgess, can be reached on 571-272-3949. The fax phone number for the organization where this application or proceedingis assigned is 571-273-8300.
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/Chirag R Patel/
Primary Examiner, Art Unit 2454