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 .
DETAILED ACTION
Status of Claim
1. Applicant's amendment dated 06/17/2026/2026 responding to the Office Action 03/23/2026 provided in the rejection of claims 1-20.
2. Claims 1, 4, 5 and 17 have been amended; claims 8-9 have been canceled.
3. Claims 1-7 and 10-20 are pending in the application, of which claims 1 and 15 in independent form and which have been fully considered by the examiner.
Response to Amendments
4. (A) Regarding specification: The specification raised in previous office action has been withdrawn in view of Applicant's amendment.
(B). Regarding Claim objections: Claim objections raised in previous office action has been withdrawn in view of Applicant's amendment.
(C). Regarding 112(b) rejection: 112(b) rejections raised in previous office action has been withdrawn in view of Applicant's amendment.
(D) Regarding art rejection: The art rejection or 102/103 rejection is maintained for the reasons set forth below:
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below 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 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 or disclosed by the examiner.
Response to Applicant’s Remarks
6. With respect to 35 U.S.C. §101 rejections:
Applicant's remarks filed on 06/17/2026 have been fully considered but are not persuasive. Applicant argues that the claims recite additional elements that integrate any alleged abstract idea into a concrete, industrial process. In particular, the validated component specification is used to configure and operate components in an actual industrial automation system. Thus, the claim goes beyond mere data analysis and triggers a real-world effect in a physical system (see Specification paragraph [0059], "configuring the industrial automation system to operate using the component as defined by the validated component specification") -- See Remarks, page 12. Ultimately, clustering contributes to a more robust and interoperable automation system, as only modules that meet all semantic and structural compatibility criteria are moved forward to real-world deployment. These technical advantages further integrate any exception into a practical application – See Remarks, page 13.
Examiner respectfully disagrees. The limitations " validating the component specification using the one or more invariants specified in the invariant specification, wherein at least one of the invariants relates to semantic correctness of the component specification" and the amended limitations “using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification; wherein the validated specification is used to configure an industrial automation system component” do qualify as additional element under step 2A, Prong 2 analysis. However, the limitation “validating the component specification using the one or more invariants specified in the invariant specification, wherein at least one of the invariants relates to semantic correctness of the component specification” is merely insignificant extra solution activity of evaluating data and does not reflect the way of achieving the improvement to technology. The limitation “using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification” is mere instructions to apply an exception to the field of use. The limitation “wherein the validated specification is used to configure an industrial automation system component” is merely to link/apply the result of the judicial exception to the field of use (the validated specification is used to configure an industrial automation system component). For these reasons, Examiner will maintain the 101 rejections as set forth below.
With respect to 35 U.S.C. §102/103 rejections:
Applicant argues that Abele does not disclose using a clustering algorithm to derive compatibility data for elements of the component specification. In the Office Action, the Examiner's mapping for original claims 8 and 9 relied on Cella et al. for a teaching of clustering algorithms, confirming that Abele itself lacks this feature.
Cella is focused on machine learning techniques (e.g., training neural networks using clustering algorithms). Cella does not address validation of industrial component specifications or the unique use of clustering to derive compatibility knowledge about system components. In fact, the only mention of "clustering" in Cella is in the context of training a Radial Basis Function (RBF) neural network. Specifically, Cella describes that machine learning training of an RBF network "may use clustering algorithms (such as k-means clustering)" to adjust model parameters. In other words, Cella's clustering is not used for compatibility checking or any design validation, and instead is an internal step in an AI training process for a classification problem – See Remarks, page 14
Examiner respectfully disagrees. Cella discloses at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine. In embodiments, at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine – See paragraph [0022]. The unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering– See paragraph [0396]. The collected data may be checked against a set of criteria that define an acceptable range of the condition. Upon validation that the collected data is either approaching one end of the acceptable limit or is beyond the acceptable range of the condition, data collection may commence from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a data collection template. In embodiments, an acceptable range of the condition is based on a history of applied analytics of the condition. In embodiments, upon validation of the acceptable range being exceeded, data storage resources of a module in which the sensed condition is detected may be configured to facilitate capturing data from the smart band group of sensors – See paragraph [0604]. Data collection in an industrial environment may include a method of establishing an acceptable range of sensor values for a plurality of industrial machine condition sensors by validating an operational deflection shape visualization of structural elements of the machine as exhibiting deflection within an acceptable range. In embodiments, data from the plurality of sensors used in the validated ODSV define the acceptable range of sensor values – See paragraph [0617].
Therefore, Cella discloses using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification (the unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering – See paragraph [0604]; provided herein for a computer vision system configured to identify operating characteristics, such as vibration or other suitable characteristics, of one or more industrial IoT devices using input from one or more data capture devices. The one or more data capture devices may include image data capture devices that capture visible and non-visible light, sensors that measure various characteristics of the one or more industrial IoT devices, or other suitable data capture devices – See paragraph [0049]. Examine respectfully notes that classification algorithms using advance analysis techniques such as clustering is as clustering algorithms and machine learning and analysis techniques to identify or obtain data relating to suitable characteristics of one or more device is as data relating to compatibility of elements in the component specification.
Cella also discloses obtaining data relating to compatibility of elements in the component specification (this configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data – See paragraphs [0281-0282 and 0069-0073]).
For this reason, Examiner will maintain the 103 rejections as set forth below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
7. Claims 1-7 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1 and 15 is being presented below.
Claims 1 and 15:
Step 1 Analysis:
Claims 1-7 and 10-14 of the instant application is direct to process/method.
Claims 15-20 of instant application is direct to system/apparatus.
Step 2 Analysis:
Claim 1 recites:
(a) obtaining by one or more hardware processors an invariant specification specifying one or more invariants that must be satisfied for the component specification to be deemed fit for use in conjunction with the industrial automation system;
(b) validating the component specification using the one or more invariants specified in the invariant specification, wherein at least one of the invariants relates to semantic correctness of the component specification;
(c) obtaining data relating to compatibility of elements in the component specification, wherein validating the component specification comprises determining compatibility of elements in the component specification using the obtained data;
(d) using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification;
(e) wherein the validated specification is used to configure an industrial automation system component
Step 2A -- Prong 1:
The claim 1 recites the limitations of:
(a) obtaining by one or more hardware processors an invariant specification specifying one or more invariants that must be satisfied for the specification to be deemed fit for use in conjunction with the industrial automation system;
(c) obtaining data relating to compatibility of elements in the component specification, wherein validating the component specification comprises determining compatibility of elements in the component specification using the obtained data;
Limitations (a) and (c) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitation of “A computer-implemented method…” and “an industrial automation system”. The limitations of “A computer-implemented method…” and “an industrial automation system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitation (b) is merely insignificant extra solution activity of evaluating data. The limitation (d) is mere instructions to apply an exception to the field of use. The limitation (e) is merely to link/apply the result of the judicial exception to the field of use (the validated specification is used to configure an industrial automation system component). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 15 recites:
(a) obtaining an invariant specification specifying one or more invariants that must be satisfied for the component specification to be deemed fit for use in conjunction with the industrial automation system;
(b) validating the component specification using the one or more invariants specified in the invariant specification, wherein at least one of the invariants relates to semantic correctness of the component specification;
(c) obtaining data relating to compatibility of elements in the component specification, wherein validating the component specification comprises determining compatibility of elements in the component specification using the obtained data;
(d) using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification;
(e) wherein the validated specification is used to configure an industrial automation system component.
Step 2A -- Prong 1:
The claim 15 recites the limitations of:
(a) obtaining an invariant specification specifying one or more invariants that must be satisfied for the component specification to be deemed fit for use in conjunction with the industrial automation system;
(c) obtaining data relating to compatibility of elements in the component specification, wherein validating the component specification comprises determining compatibility of elements in the component specification using the obtained data;
Limitations (a) and (c) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 15 recites the additional limitation of “A validation system”, “at least one hardware processor”, “a memory” and “an industrial automation system”. The limitations of “A validation system”, “at least one hardware processor”, “a memory” and “an industrial automation system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitation (b) is merely insignificant extra solution activity of evaluating data. The limitation (d) is mere instructions to apply an exception to the field of use. The limitation (e) is merely to link/apply the result of the judicial exception to the field of use (the validated specification is used to configure an industrial automation system component). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B (claims 1 and 15):
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing/uploading data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g). Therefore, claims are ineligible.
Dependent claims
Additionally, claim 2 recites “wherein semantic correctness is validated with reference to a model of the IEC 63280 specification” is merely insignificant extra solution activity of evaluating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 2 is ineligible.
Additionally, claims 3 and 16 recite “wherein at least one of the invariants relates to structural correctness of the component specification, and wherein validating the component specification comprises using the at least one invariant to validate structural correctness of the component specification” is merely insignificant extra solution activity of evaluating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 3 and 16 are ineligible.
Additionally, claims 4 and 17 recite “wherein the component specification comprises at least one value relating to at least one component of the industrial automation system, wherein validating the component specification comprises validating the at least one value with reference to an external agency” is merely insignificant extra solution activity of evaluating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 4 and 17 are ineligible.
Additionally, claims 5 and 18 recite “wherein at least one of the invariants is defined with reference to a model or metamodel representing a relevant standard specification, and wherein validating the component specification comprises performing the validation with reference to the model or metamodel” is merely insignificant extra solution activity of evaluating data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 5 and 18 ineligible.
Additionally, claims 6 and 19 recite “wherein at least one of the invariants is expressed using one or more first-order logic-based constraints” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 6 and 19 are ineligible.
Additionally, claims 7 and 20 recite “further comprising compiling the invariant specification to generate at least one executable rule for validation, wherein validating the component specification comprises executing the at least one executable rule” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “generating” can be performed in the human mind with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 7 and 20 are ineligible.
Additionally, claim 10 recites “further comprising, in response to detecting a validation error when validating the component specification, recommending one or more remedial actions to remedy the validation error” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 10 is ineligible.
Additionally, claim 11 recites “further comprising sending at least one of the recommended remedial actions to a user and receiving user feedback relating to the at least one recommended remedial action” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 11 is ineligible.
Additionally, claim 12 recites “further comprising using the received feedback to determine subsequent recommendations of remedial action for the same validation error” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “determine” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 12 is ineligible.
Additionally, claim 13 recites “further comprising using a reinforcement learning approach to improve subsequent recommendations” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 13 is ineligible.
Additionally, claim 14 recites “further comprising configuring an industrial automation system to operate using the component as defined by the validated component specification” is merely insignificant extra solution activity of processing/validating data. These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 14 is ineligible.
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.
8. Claim(s) 1, 3-4, 6 and 10-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abele et al. (EP 2772877 A1 – IDS filed 01/19/2024 – herein after Abele) in view of Cella et al. (US Pub. No. 2020/0133257 A1 – art of record -- herein after Cella).
Regarding claim 1.
Abele discloses
A computer-implemented method for validating a component specification defining at least one component of an industrial automation system (suitable candidates for supplying functional units within the plant may be identified from catalogues of devices and their suitability may be validated by considering formally and explicitly stated requirements in the ontology – See paragraphs [0016-0017]. Allows formally verifiable conditions for requirements of plant components – See paragraph [0024]), wherein the component specification is arranged in an object-oriented data format (CAEX supports object-oriented modeling for all of these aspects. EIs depicting SUC instances are connected by ILs via EIs which in turn are instances of ICs – See paragraphs [0019 and 0034]), the method comprising:
obtaining by one or more hardware processors an invariant specification specifying one or more invariants that must be satisfied for the component specification to be deemed fit for use in conjunction with the industrial automation system (representing requirements of the plant components and/or the plant devices in the ontology by means of the logic-based constraints, logic-based constraints are checked against likewise represented properties of the plant components and the plant devices using the inference mechanism – See paragraphs [0014-0015]. In this way, suitable candidates for supplying functional units within the plant may be identified from catalogues of devices and their suitability may be validated by considering formally – See paragraph [0018]. Support plant designers during their work, semantically rich queries based on the semantic structure of the ontology underlying the plant model may be formulated and answered using inference mechanisms. Hereby, automated identification of suitable devices is supported, reducing the effort required for manual selection of fitting functional units – See paragraph [0027]); and
validating the component specification using the one or more invariants specified in the invariant specification (logic-based constraints are checked against likewise represented properties of the plant components and the plant devices using the inference mechanism – See paragraphs [0014-0015]), wherein at least one of the invariants (standard/constraints – See paragraphs [0031-0032]) relates to semantic correctness of the component specification (transforming the plant model from the predetermined data format into an ontology as a means of knowledge representation having a formal semantics; and performing an automated reasoning and/or querying for validation based on the ontology as a transformation result by evaluating logic-based constraints using an inference mechanism – See paragraph [0021]. An ontology of the application scenario as defined above serves as example. First, the operator is supported in identifying identical IEs. Further, the Role requirements of all Internal Elements and then the correctness of internal links is validated – See paragraph [0051]);
obtaining data relating to compatibility of elements in the component specification (the selection of specific components and devices that meet all requirements of the planned functional units can require significant manual effort when requirements have to be matched and validated separately and manually for all components involved –see paragraph [0005]), wherein validating the component specification comprises determining compatibility of elements in the component specification using the obtained data (the selection of specific components and devices that meet all requirements of the planned functional units can require significant manual effort when requirements have to be matched and validated separately and manually for all components involved – See paragraph [0005]. Suitable candidates for supplying functional units within the plant may be identified from catalogues of devices and their suitability may be validated by considering formally and explicitly stated requirements in the ontology – See paragraph [0018]);
wherein the validated specification is used to configure an industrial automation system component (performing an automated reasoning and/or querying (S5) for validation (S6) based on the ontology as a transformation result by evaluating logic-based constraints using an inference mechanism – See Abstract. By automating the validation of plant models for inconsistencies, incorrect assembly of production plants can be avoided – See paragraph [0024]).
Abele does not disclose
using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification;
Cella discloses
using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification (the unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering – See paragraph [0396]. The collected data may be checked against a set of criteria that define an acceptable range of the condition. Upon validation that the collected data is either approaching one end of the acceptable limit or is beyond the acceptable range of the condition, data collection may commence from a smart-band group of sensors associated with the sensed condition based on a smart-band collection protocol configured as a data collection template. In embodiments, an acceptable range of the condition is based on a history of applied analytics of the condition. In embodiments, upon validation of the acceptable range being exceeded, data storage resources of a module in which the sensed condition is detected may be configured to facilitate capturing data from the smart band group of sensors – See paragraph [0604]. Data collection in an industrial environment may include a method of establishing an acceptable range of sensor values for a plurality of industrial machine condition sensors by validating an operational deflection shape visualization of structural elements of the machine as exhibiting deflection within an acceptable range. In embodiments, data from the plurality of sensors used in the validated ODSV define the acceptable range of sensor values – See paragraph [0617].
Therefore, Cella discloses using a clustering algorithm to obtain the data relating to compatibility of elements in the component specification (the unsupervised learning classification algorithms may operate by finding hidden structures in unlabeled data using advanced analysis techniques such as segmentation and clustering – See paragraph [0604]; provided herein for a computer vision system configured to identify operating characteristics, such as vibration or other suitable characteristics, of one or more industrial IoT devices using input from one or more data capture devices. The one or more data capture devices may include image data capture devices that capture visible and non-visible light, sensors that measure various characteristics of the one or more industrial IoT devices, or other suitable data capture devices – See paragraph [0049]. Examine respectfully notes that classification algorithms using advance analysis techniques such as clustering is as clustering algorithms and machine learning and analysis techniques to identify or obtain data relating to suitable characteristics of one or more device is as data relating to compatibility of elements in the component characteristic);
Cella also discloses
obtaining data relating to compatibility of elements in the component specification (this configured streamed data can be stored in a data structure that is compatible with existing sensed data structures so that existing processing systems and facilities can access and process the data substantially as if it were the existing data – See paragraphs [0281-0282 and 0069-0073]); and
wherein the validated specification is used to configure an industrial automation (upon validation of the acceptable range being exceeded, data storage resources of a module in which the sensed condition is detected may be configured to facilitate capturing data from the smart band group of sensors – See paragraph [0604]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to enable to use the analysis techniques in unsupervised learning may include K-means clustering as suggested by Cella (paragraph [0396]).
Regarding claim 3, the method of claim 1,
Abele discloses
wherein at least one of the invariants relates to structural correctness of the component specification (to support the operator, RC requirements are validated (VF22 in Fig. 2) by checking the attributes of all RCs and SUCs assigned to an IE. A SPARQL query is defined to identify all IEs where the attributes do not match. SPARQL queries under OWL entailement regime are used as described by expressed in OWL functional-style syntax with the following structure – See paragraphs [0054]), and wherein validating the component specification comprises using the at least one invariant to validate structural correctness of the component specification (for validation of attribute consistency an example is illustrated. The attributes 512, 513 of the IE 501 ("m1FK7") which is an instance of Motor1FK7 and supports the role requirements 502 of the RC 503 ("ConveyorDrive") are validated – See paragraph [0055]).
Regarding claim 4, the method of claim 1,
Abele discloses
wherein the component specification comprises at least one value relating to the at least one component of the industrial automation system (the property 311 ("isLinkedTo") is meant to express ILs between the EIs related to ICs. Thus, ICs are represented in terms of the class interface and EIs are represented in terms of instances thereof – See paragraphs [0043-0046]), wherein validating the component specification comprises validating the at least one value with reference to an external agency (the selection of specific components and devices that meet all requirements of the planned functional units can require significant manual effort when requirements have to be matched and validated separately – See paragraph [0005]).
Regarding claim 6, the method of claim 1,
Abele discloses
wherein at least one of the invariants is expressed using one or more first-order logic-based constraints (transforming the plant model from the predetermined data format into an ontology as a means of knowledge representation having a formal semantics; and performing an automated reasoning and/or querying for validation based on the ontology as a transformation result by evaluating logic-based constraints using an inference mechanism – See paragraphs [0011 and 0014-0015]).
Regarding claim 10, the method of claim 1,
Cella discloses
further comprising, in response to detecting a validation error when validating the component specification (validate a candidate failure condition, and/or diminish the likelihood of a potential failure – See paragraphs [4285-4286]), recommending one or more remedial actions to remedy the validation error (a knowledge base associated with the industrial environment. In embodiments, corrective actions may be identified and taken in response to the state-related measurements captured using the mobile devices – See paragraphs [0042-0044]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to enable to identify corrective actions as suggested by Cella (paragraph [0042]).
Regarding claim 11, the method of claim 10,
Cella discloses
further comprising sending at least one of the recommended remedial actions to a user (this correlation may be noted by an expert system or by a user observing the visualization and corrective action may be taken – See paragraphs [1327-1328]) and receiving user feedback relating to the at least one recommended remedial action (provide an operator feedback on conditions in the handling environment that the user – See paragraphs [1342-1344]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to enable to operate the device correctly and provide feedback as suggested by Cella (paragraph [1401]).
Regarding claim 12, the method of claim 11,
Cella discloses
further comprising using the received feedback to determine subsequent recommendations of remedial action for the same validation error (make recommendations for the replacement of certain sensors in the future with sensors having different response rates, sensitivity, ranges, and the like. The response circuit 8110 may recommend design alterations for future embodiments of the component, the piece of equipment, the operating conditions, the process, and the like– See paragraphs [0067-0068]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to enable to recommend design alterations for future embodiments of the component as suggested by Cella (paragraphs [0067-0068]).
Regarding claim 13, the method of claim 11,
Cella discloses
further comprising using a reinforcement learning approach to improve subsequent recommendations (the system in FIG. 157 is informed, based on a scheduled event, to evaluate the condition of various aspects of a factory floor. The system, configured with a learning algorithm, takes samples of various sensors in various positions. It is provided with positive reinforcement of a correctly operating factory floor on a regular basis – See paragraph [1396]. Pattern detection and/or feature learning. Reinforcement learning may include the machine learning systems performing in a dynamic environment and then providing feedback about correct and incorrect decisions – See paragraph [0397]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to enable to perform in a dynamic environment and then provide feedback about correct and incorrect decisions as suggested by Cella (paragraph [0397]).
Regarding claim 14, the method of claim 1,
Cella discloses
further comprising configuring an industrial automation system to operate using the component as defined by the validated component specification (The machine learning system may learn system alarm condition patterns, such as alarm conditions expected under normal operating conditions, under peak operating conditions, expected over time based on age of components (e.g., new, during operational life, during extended life, during a warrantee period), and the like – See paragraph [1075-1076]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cella’s teaching into Abele’s invention because incorporating Cella’s teaching would enhance Abele to permit utilization of many of the capabilities, linkages, compatibilities, and extensions that conventional raw data technologies as suggested by Cella (paragraphs [0631-0632]).
Regarding claim 15.
Abele discloses
A validation system (a system – see paragraph [0001]), the validation system comprising at least one hardware processor and a memory storing instructions (memory – See paragraph [0020]), which, when executed by the at least one hardware processor, configure the validation system to perform functions for validating a component specification defining at least one component of an industrial automation system (suitable candidates for supplying functional units within the plant may be identified from catalogues of devices and their suitability may be validated by considering formally and explicitly stated requirements in the ontology – See paragraphs [0016-0017]. Allows formally verifiable conditions for requirements of plant components – See paragraph [0024]), wherein the component specification is arranged in an object-oriented data format (CAEX supports object-oriented modeling for all of these aspects. EIs depicting SUC instances are connected by ILs via EIs which in turn are instances of ICs – See paragraphs [0019 and 0034]), the functions including
Regarding claim 15, recites the same limitations as rejected claim 1 above.
Regarding claim 16, recites the same limitations as rejected claim 3 above.
Regarding claim 17, recites the same limitations as rejected claim 4 above.
Regarding claim 18, recites the same limitations as rejected claim 5 above.
Regarding claim 19, recites the same limitations as rejected claim 6 above.
9. Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abele and Cella as applied to claim 1 above, and further in view of Jeske (Making process control more flexible, 2020 – art of record -- herein after Jeske).
Regarding claim 2, the method of claim 1,
Jeske discloses
wherein semantic correctness is validated with reference to a model of the IEC 63280 specification (the standard VDI 2658, which was developed in Germany, but is now being adopted as IEC 63280 for automation engineering of modular systems in the process industry – see page 3).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Jeske’s teaching into Abele’s and Cella’s inventions because incorporating Jeske’s teaching would enhance Abele and Cella to enable to adopt IEC 63280 for automation engineering of modular system in the process industry as suggested by Jeske (page 3).
10. Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abele and Cella as applied to claim 1 above, and further in view of Von der Fakultat (Capturing and Exploiting Plant Topology and Process Information as a Basis to Support Engineering and Operational Activities in Process Plants, 2017 – art of record-- herein after Von).
Regarding claim 5, the method of claim 1,
Von discloses
wherein at least one of the invariants is defined with reference to a model or metamodel representing a relevant standard specification (Based on the occurrence frequency of certain symbols and the analysis connectivity patterns in the analyzed schematic, it would be feasible to define areas in which, for example, valves are likely to be found given an a priori identified tank or vessel. Such information can be used for prioritizing search regions or as a means for statistical consistency check – See page 137), and wherein validating the component specification comprises performing the validation with reference to the model or metamodel (applied for locating regions in greyscale images that match a determined template of a reference pattern. These methods are capable of finding template matches regardless of lighting variation, blur, noise, occlusion, and geometric transformations such as shifting, rotation, or scaling – See page 19. Such a knowledge base can be defined for a set of standard plant assets, i.e., a vendor-independent catalogue of plant devices commonly found in process facilities (e.g., generic valve, generic tank, and generic centrifugal pump) – See page 120).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Von’s teaching into Abele’s and Cella’s inventions because incorporating Von’s teaching would enhance Abele and Cella to enable to find template matches as suggested by Von (page 19).
11. Claim(s) 7 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abele and Cella as applied to claims 1 and 15 respectively above, and further in view of Abele (DE 102013223833 A1 – art of record -- herein after Abele1).
Regarding claim 7, the method of claim 1,
Abele1 discloses
further comprising compiling the invariant specification to generate at least one executable rule for validation (conversion RUDE of the rules takes place in such a way that with a rule generation means RGM the ontology PSON is translated into so-called RIF rules RR (see arrow P6). For this, all rule-related information of the ontology PSON is exported into a rule ontology, which is then converted into the RIF rules RR. RIF is a well-known format that can be represented, for example, by the RIF XML Serialization syntax. This syntax is converted with the rule generation means RGM via methods known per se into executable rules EXR of a rule speech format– See pages 5-6), wherein validating the component specification comprises executing the at least one executable rule (corresponding monitoring or diagnostic states of the technical installation and their components can be derived in their operation based on sensor data or process data using the executable rules – See page 6).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Abele1’s teaching into Abele’s and Cella’s inventions because incorporating Abele1’s teaching would enhance Abele and Cella to enable to generate executable rules as suggested by Abele (pages 5-6).
Regarding claim 20, recites the same limitations as rejected claim 7 above.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krishnaswamy (US Pub. No. 2021/0192412 A1) discloses ML & context-based dispatch. Action Filtering: Triggers & requirements validation by external system – See paragraphs [0047-0048].
Cella et al. (US Pub. No. 2020/0348662 A1) discloses based on the detected protocol, the programmable logic component may configure routing resources to facilitate support and efficient processing of the protocol. In an example, a programmable logic component configured data collection module in an industrial environment may implement an intelligent sensor interface specification, such as IEEE 1451.2 intelligent sensor interface specification – See paragraphs [0819-0820].
Kaira et al. (US Pub. No. 2022/0004174 A1) discloses one potential solution is for the data scientist to find a middle ground that strikes an appropriate balance between performance and scalability, such as manually grouping machines with similar characteristics and then developing one model for each group of machines – See paragraph [0028].
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