DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Examiner Notes
(1) In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121 (b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131 (b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as "Applicants believe no new matter has been introduced" may be deemed insufficient.
(2) Examiner cites particular columns, paragraphs, figures 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 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 or disclosed by the Examiner.
Remarks
Receipt of Applicant’s Amendment file on 03/24/2026 is acknowledged.
Response to Arguments
Applicant’s amendments to the claims have overcome 101 rejections previously set forth in the Non-Final Office Action mailed 12/31/2025.
Applicant's arguments filed 03/24/2026 have been fully considered but they are not persuasive.
Regarding claim 1, applicant argues that cited references do not disclose “obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information” (page 12). Applicant further argues “As for Baughman, he describes adding sensors to an ontology (see Baughman paragraph [0037]), not adding analytics algorithms or ML models as required in the amended claims” (page 12).
Respectfully, it is noted that Baughman paragraph [0037], teaches modifying the sensor network of an organization and/or an organization ontology hierarchy that represents the higher level processes and inter-relationships of concepts of the functioning of the organization so as to align and synchronize the sensor network with the organization ontology hierarchy, which may involve automatically generating, deploying, and configuring additional sensors in the sensor network, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like; also see paragraph [0058], the organization ontology engine comprises logic for receiving ontology data structure representing the operational concepts and processes for operating the organization and analyze the ontology data structure; the ontology data structure may comprise nodes that represent the various operational concepts, processes, etc.; also see paragraph [0063]; noted, the received information of operational concepts and processes, which read on “obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements”; and modifying the organization ontology hierarchy that represent the higher level processes and inter-relationship of concepts of the functioning of the organization with the received information of operational concepts and processes, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like, which read on “being added as a further element to the one or more elements; and updating the formal description based on the obtained information”; further noted, “operational concepts and processes; concepts of the functioning of the organization” is interpreted as “an analytics algorithm”; also see paragraph [0058], The organization ontology engine 220 comprises logic for receiving the organization ontology data structure 222 representing the operational concepts and processes for operating the organization and analyze the ontology data structure 222 to identify areas where the ontology data structure 222 may be extended based on an alignment with the sensor network. The ontology data structure may comprise nodes that represent the various operational concepts, processes, departments, personnel, and the like, that represent aspects of the operation of the organization, which are collectively referred to herein as operational concepts. Edges between these nodes represent relationships or dependencies between these operational concepts; noted, extending the ontology data structure with operation concepts and processes nodes to align with sensor network, is not simply added sensors to an ontology data structure, but extending the ontology data structure with operational concepts and processes nodes; thus, it reads on “obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information” as claimed. Therefore cited reference discloses the limitation.
Claims 9, and 11 are rejected for similar reason.
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 of this title, 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 1-5, 8-10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Marykowskiy et al. (“Knowledge Engineering for Wind Energy”; Oct 2023) in view of Baughman et al. (U.S. Pub. No. 2025/0165518 A1).
Regarding claim 1, Marykowskiy et al. (“Knowledge Engineering for Wind Energy”; Oct 2023) teaches a computer-implemented method for facilitation of findable, accessible, interoperable, and reusable (FAIR) data in a domain of a drive system, drive product and/or drive application ((‘for’ indicates intended use; Minton v. Nat ’l Ass ’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003) “whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.” Examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) “adapted to” or “adapted for” clauses; (B) “wherein” clauses; and (C) “whereby” clauses. Therefore intended use limitations are not required to be taught, see MPEP 2111.04 [R-3])), the method comprising:
providing a formal description of one or more elements and of one or more interrelations of the one or more elements in the domain of the drive system, drive product and/or drive application (page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harnonisation of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated; noted, ontology representation is interpreted as “formal description”; also see page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way; also see page 7, last paragraph, each concept, attribute, relationship, and rule in the ontology is precisely articulated, often through formal semantics; also see page 8, Fig. 2, and pages 10-12).
Marykowskiy does not explicitly disclose: obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information.
Baughman teaches: obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information (paragraph [0037], modifying the sensor network of an organization and/or an organization ontology hierarchy that represents the higher level processes and inter-relationships of concepts of the functioning of the organization so as to align and synchronize the sensor network with the organization ontology hierarchy, which may involve automatically generating, deploying, and configuring additional sensors in the sensor network, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like; also see paragraph [0058], the organization ontology engine comprises logic for receiving ontology data structure representing the operational concepts and processes for operating the organization and analyze the ontology data structure; the ontology data structure may comprise nodes that represent the various operational concepts, processes, etc.; also see paragraph [0063]; noted, the received information of operational concepts and processes, which read on “obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements”; and modifying the organization ontology hierarchy that represent the higher level processes and inter-relationship of concepts of the functioning of the organization with the received information of operational concepts and processes, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like, which read on “being added as a further element to the one or more elements; and updating the formal description based on the obtained information”; further noted, “operational concepts and processes; concepts of the functioning of the organization” is interpreted as “an analytics algorithm”; also see paragraph [0058], The organization ontology engine 220 comprises logic for receiving the organization ontology data structure 222 representing the operational concepts and processes for operating the organization and analyze the ontology data structure 222 to identify areas where the ontology data structure 222 may be extended based on an alignment with the sensor network. The ontology data structure may comprise nodes that represent the various operational concepts, processes, departments, personnel, and the like, that represent aspects of the operation of the organization, which are collectively referred to herein as operational concepts. Edges between these nodes represent relationships or dependencies between these operational concepts; noted, extending the ontology data structure with operation concepts and processes nodes to align with sensor network, is indicating extending the ontology data structures with operation concepts and processes nodes, wherein operation concepts and processes nodes for operating the organization and analyze the ontology data structure; which reads on “obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information” as claimed).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information into ontology representation of Marykowskiy.
Motivation to do so would be to include obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information for synchronizing a sensor network with an organization ontology hierarchy (Baughman, paragraph [0006], line 1-3).
Marykowskiy as modified by Baughman further teach:
wherein the providing the formal description comprises providing the updated formal description (Marykowskiy, page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; in combination with the updated ontology data structure taught by Baughman, it reads on as claimed);
wherein the formal description and the interrelations are used to perform an action associated with the drive system, drive product, or drive application (Marykowskiy, page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; noted, “drawing more meaningful conclusions while performing data analysis” which reads on “wherein the formal description and the interrelations are used to perform an action associated with the drive system, drive product, or drive application”).
Regarding Claim 2, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach wherein the one or more elements are indicative of at least one of: a physical quantity obtained through measurement and associated with the drive system, drive product and/or drive application, a parameter associated with the measurement, a tool usable in the domain, a repository of tools comprising the tool, a machine learning (ML) model usable in the domain, a repository of machine learning, ML, models comprising the ML model, a use case that has run on the drive system, drive product and/or drive application and historic data associated with the use case, and a key performance indicator, KPI, associated with the drive system, drive product and/or drive application; and wherein the providing comprises providing the formal description of at least one of: the physical quantity, an interrelation of the physical quantity, the parameter, an interrelation of the parameter, the tool, an interrelation of the tool, the repository of tools, an interrelation of the repository of tools, the ML model, an interrelation of the ML model, the repository of ML models, an interrelation of the repository of ML models, the use case and the historic data, an interrelation of the use case and the historical data, the KPI, and an interrelation of the KPI (Marykowskiy, page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way).
Regarding Claim 3, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach wherein the providing the formal description comprises providing a formal description of a meaning of the one or more elements and/or of a meaning of the one or more interrelations (Marykowskiy, page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way).
Regarding Claim 4, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach wherein the providing the formal description comprises providing a representation of the one or more elements and/or the one or more interrelations as at least one of: ontological concepts, interrelations, instantiations, as a collection of triples, and as an ontological representation (Marykowskiy, page 6, fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way).
Regarding Claim 5, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach: combining at least part of the provided formal description with usage of semantic technologies (Marykowskiy, page 4, Section 3.1, last two paragraphs, the process of understanding and interpretating a particular representation involves semantics, pragmatics, and context; comprehensive semantics ensure that the terms used to describe data and information are unambiguous and clearly defined; also see page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way; also see page 7, last paragraph, each concept, attribute, relationship, and rule in the ontology is precisely articulated, often through formal semantics).
Regarding Claim 8, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach: combining an information representation setup associated with at least part of the formal description with a predetermined communication setup or protocol; and/or making an information representation setup associated with at least part of the formal description consistent with a predetermined communication setup or protocol (Marykowskiy, page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way; also see page 7, last paragraph, each concept, attribute, relationship, and rule in the ontology is precisely articulated, often through formal semantics).
Regarding claim 9, Marykowskiy teaches a method for usage of findable, accessible, interoperable, and reusable (FAIR) data in a domain of a drive system, drive product and/or drive application ((‘for’ indicates intended use; Minton v. Nat ’l Ass ’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003) “whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.” Examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) “adapted to” or “adapted for” clauses; (B) “wherein” clauses; and (C) “whereby” clauses. Therefore intended use limitations are not required to be taught, see MPEP 2111.04 [R-3])), the method comprising: obtaining a formal description of one or more elements and/or of one or more interrelations of the one or more elements in the domain of the drive system, drive product and/or drive application (page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harnonisation of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated; noted, ontology representation is interpreted as “formal description”; also see page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way; also see page 7, last paragraph, each concept, attribute, relationship, and rule in the ontology is precisely articulated, often through formal semantics; also see page 8, Fig. 2, and pages 10-12);
and based on the obtained formal description, performing an action of enabling and/or performing semantic reasoning and/or analytics in relation to the drive system, drive product and/or drive application (page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harmonization of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated).
Marykowskiy does not explicitly disclose: wherein information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information.
Baughman teaches: wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information (paragraph [0037], modifying the sensor network of an organization and/or an organization ontology hierarchy that represents the higher level processes and inter-relationships of concepts of the functioning of the organization so as to align and synchronize the sensor network with the organization ontology hierarchy, which may involve automatically generating, deploying, and configuring additional sensors in the sensor network, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like; also see paragraph [0058], the organization ontology engine comprises logic for receiving ontology data structure representing the operational concepts and processes for operating the organization and analyze the ontology data structure; the ontology data structure may comprise nodes that represent the various operational concepts, processes, etc.; also see paragraph [0063]; noted, the received information of operational concepts and processes, which read on “obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements”; and modifying the organization ontology hierarchy that represent the higher level processes and inter-relationship of concepts of the functioning of the organization with the received information of operational concepts and processes, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like, which read on “being added as a further element to the one or more elements; and updating the formal description based on the obtained information”; further noted, “operational concepts and processes; concepts of the functioning of the organization” is interpreted as “an analytics algorithm”; also see paragraph [0058], The organization ontology engine 220 comprises logic for receiving the organization ontology data structure 222 representing the operational concepts and processes for operating the organization and analyze the ontology data structure 222 to identify areas where the ontology data structure 222 may be extended based on an alignment with the sensor network. The ontology data structure may comprise nodes that represent the various operational concepts, processes, departments, personnel, and the like, that represent aspects of the operation of the organization, which are collectively referred to herein as operational concepts. Edges between these nodes represent relationships or dependencies between these operational concepts; noted, extending the ontology data structure with operation concepts and processes nodes to align with sensor network, is indicating extending the ontology data structures with operation concepts and processes nodes, wherein operation concepts and processes nodes for operating the organization and analyze the ontology data structure; which reads on “obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information” as claimed).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information into ontology representation of Marykowskiy.
Motivation to do so would be to include wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information for synchronizing a sensor network with an organization ontology hierarchy (Baughman, paragraph [0006], line 1-3).
Regarding Claim 10, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 9, further teach wherein the enabling and/or performing semantic reasoning and/or analytics comprises at least one of: making use of a semantic reasoner for development of a ML algorithm for the drive system, drive product and/or drive application, preparing data associated with the drive system, drive product and/or drive application for analyzation purposes, interpreting of a feature associated with the drive system, drive product and/or drive application, enabling and/or identifying guidance, recommendations and/or improvement potential for analytics applications development for the drive system, drive product and/or drive application, providing a pre-selection of features and/or parameters for KPIs and/or uses cases of the drive system, drive product and/or drive application, based on querying historical setups of tools and use cases, integrating an analytics tool-box into the drive system, drive product and/or drive application, enabling and/or identifying support, simplification, speed-up and/or improvement of analytics algorithms and/or ML models, and enabling and/or identifying support, simplification, speed-up and/or improvement of development of analytics algorithms and/or ML models (Marykowskiy, page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harnonisation of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated).
Regarding Claim 13, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, further teach wherein the enabling and/or performing semantic reasoning and/or analytics comprises at least one of: making use of a semantic reasoner for development of a ML algorithm for the drive system, drive product and/or drive application, preparing data associated with the drive system, drive product and/or drive application for analyzation purposes, interpreting of a feature associated with the drive system, drive product and/or drive application, enabling and/or identifying guidance, recommendations and/or improvement potential for analytics applications development for the drive system, drive product and/or drive application, providing a pre-selection of features and/or parameters for KPIs and/or uses cases of the drive system, drive product and/or drive application, based on querying historical setups of tools and use cases, integrating an analytics tool-box into the drive system, drive product and/or drive application, enabling and/or identifying support, simplification, speed-up and/or improvement of analytics algorithms and/or ML models, and enabling and/or identifying support, simplification, speed-up and/or improvement of development of analytics algorithms and/or ML models (Marykowskiy, page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harnonisation of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Marykowskiy et al. (“Knowledge Engineering for Wind Energy”; Oct 2023) in view of Baughman et al. (U.S. Pub. No. 2025/0165518 A1), further in view of Mirhaji (U.S. Pub. No. 2020/0042523 A1).
Regarding Claim 7, Marykowskiy as modified by Baughman teach all claimed limitations as set forth in rejection of claim 1, but do not explicitly disclose: populating the ontological representation by instance data; and updating the formal description based on the populating, wherein the providing the formal description comprises providing the updated formal description.
Mirhaji teaches: populating the ontological representation by instance data; and updating the formal description based on the populating, wherein the providing the formal description comprises providing the updated formal description (paragraph [0085], ontologies that represent collections of knowledge may be utilized; ontologies that represent knowledge associated with certain domain may be presented as a graph; concepts in the graph representing obtained data may be mapped to the concepts of one or more ontologies represent domain knowledge; obtained data may be placed in the context of a particular domain by unifying the graph representing obtained data and the graph representing the ontology for particular domain; also see paragraph [0182], creating a unified graph between the graph representing the clinical text, the domain ontology and the semantic ontology; the resulting unified graph may be search to obtain data about clinical text).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include populating the ontological representation by instance data; and updating the formal description based on the populating, wherein the providing the formal description comprises providing the updated formal description into ontology representation of Marykowskiy.
Motivation to do so would be to include populating the ontological representation by instance data; and updating the formal description based on the populating, wherein the providing the formal description comprises providing the updated formal description to utilize a unifying format to represent data obtained or utilized by the system to facilitate linking between data from different sources and commensurate ability to mine such data (Mirhaji, paragraph [0006]).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Marykowskiy et al. (“Knowledge Engineering for Wind Energy”; Oct 2023) in view of Baughman et al. (U.S. Pub. No. 2025/0165518 A1), further in view of Vlachidis et al. (“Semantic metadata enrichment and data augmentation of small museum collections following the FAIR principles”; 2022).
Regarding claim 11, Marykowskiy teaches a computer-implemented method for supporting a human in setting up analytics and/or a machine learning, ML, model related to a drive system, drive product and/or drive application, ((‘for’ indicates intended use; Minton v. Nat ’l Ass ’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003) “whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.” Examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are: (A) “adapted to” or “adapted for” clauses; (B) “wherein” clauses; and (C) “whereby” clauses. Therefore intended use limitations are not required to be taught, see MPEP 2111.04 [R-3])), the method comprising:
providing, to the human, a formal description of one or more elements and of one or more
interrelations of the one or more elements in a domain of a drive system, a drive product or a drive application (page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harmonization of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated; noted, ontology representation is interpreted as “formal description”; also see page 6, Fig. 2, page 9, a taxonomy can include subsumption relationships between concepts in the paragraph; for example, “LIDAR”, and “pressure measurement system” can be subsumed by “measurement system”; it is also possible to include other relationships, such as part-of relationships between wind turbine and its blades, or between the measurement system and the turbine; a schema can include additional information about the properties of the concepts and their relationship; for example, it can specify the expected attributes of the wind turbine, such as its manufacturer, capacity, and location; an ontology can further specify the meaning and relationships of the concepts in a formal and machine-interpretable way; also see page 7, last paragraph, each concept, attribute, relationship, and rule in the ontology is precisely articulated, often through formal semantics; also see page 8, Fig. 2, and pages 10-12).
Marykowskiy does not explicitly disclose: wherein information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information.
Baughman teaches: wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information (paragraph [0037], modifying the sensor network of an organization and/or an organization ontology hierarchy that represents the higher level processes and inter-relationships of concepts of the functioning of the organization so as to align and synchronize the sensor network with the organization ontology hierarchy, which may involve automatically generating, deploying, and configuring additional sensors in the sensor network, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like; also see paragraph [0058], the organization ontology engine comprises logic for receiving ontology data structure representing the operational concepts and processes for operating the organization and analyze the ontology data structure; the ontology data structure may comprise nodes that represent the various operational concepts, processes, etc.; also see paragraph [0063]; noted, the received information of operational concepts and processes, which read on “obtaining information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements”; and modifying the organization ontology hierarchy that represent the higher level processes and inter-relationship of concepts of the functioning of the organization with the received information of operational concepts and processes, automatically modifying ontology structure to include additional ontology nodes and corresponding processes, and the like, which read on “being added as a further element to the one or more elements; and updating the formal description based on the obtained information”; further noted, “operational concepts and processes; concepts of the functioning of the organization” is interpreted as “an analytics algorithm”; also see paragraph [0058], The organization ontology engine 220 comprises logic for receiving the organization ontology data structure 222 representing the operational concepts and processes for operating the organization and analyze the ontology data structure 222 to identify areas where the ontology data structure 222 may be extended based on an alignment with the sensor network. The ontology data structure may comprise nodes that represent the various operational concepts, processes, departments, personnel, and the like, that represent aspects of the operation of the organization, which are collectively referred to herein as operational concepts. Edges between these nodes represent relationships or dependencies between these operational concepts; noted, extending the ontology data structure with operation concepts and processes nodes to align with sensor network, is indicating extending the ontology data structures with operation concepts and processes nodes, wherein operation concepts and processes nodes for operating the organization and analyze the ontology data structure; which reads on “obtaining information indicative of at least one of an ML model, an analytics algorithm and a tool being added as a further element to the one or more elements; and updating the formal description based on the obtained information” as claimed).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information into ontology representation of Marykowskiy.
Motivation to do so would be to include wherein information indicative of at least one of a ML model, an analytics algorithm and a tool being added as a further element to the one or more elements is obtained; and updating the formal description based on the obtained information for synchronizing a sensor network with an organization ontology hierarchy (Baughman, paragraph [0006], line 1-3).
Marykowskiy as modified by Baughman do not explicitly disclose: providing, to the human, findable, accessible, interoperable, and reusable (FAIR) data facilitated according to the formal description.
Vlachidis teaches: providing, to the human, findable, accessible, interoperable, and reusable (FAIR) data facilitated according to the formal description (page 109, web Ontology Language (OWL) and Linked data vocabularies; OWL, another W3C standard, is a family of knowledge representation languages for autoring ontology, namely “explicit formal specification” that aim at providing a shared conceptualization and understanding of common domains between different communities; the adoption of semantic technologies to implement the FAIR data principles; Linked data principle is that when someone look up a URI, some relevant information should be provided; this can seen as a way to implement the FAIR data principles, according to which data should be described with rich metadata, using a formal, accessible, shared and broadly application language for knowledge representation; formal models such as ontologies can guarantee the used of well-defined and interoperable knowledge representations that carry definitions and conceptual arrangements of entities and relationships to describe a domain or a resource).
It would have been obvious to one of ordinary skill in art before the effective filing date of the claim invention to include providing, to the human, findable, accessible, interoperable, and reusable (FAIR) data facilitated according to the formal description into ontology representation of Marykowskiy.
Motivation to do so would be to include providing, to the human, findable, accessible, interoperable, and reusable (FAIR) data facilitated according to the formal description, that adoption of semantic technologies to implement the FAIR data principles for implementing FAIR data principles.
Regarding Claim 12, Marykowskiy as modified by Baughman and Vlachidis teach all claimed limitations as set forth in rejection of claim 11, further teach wherein the enabling and/or performing semantic reasoning and/or analytics comprises at least one of: making use of a semantic reasoner for development of a ML algorithm for the drive system, drive product and/or drive application, preparing data associated with the drive system, drive product and/or drive application for analyzation purposes, interpreting of a feature associated with the drive system, drive product and/or drive application, enabling and/or identifying guidance, recommendations and/or improvement potential for analytics applications development for the drive system, drive product and/or drive application, providing a pre-selection of features and/or parameters for KPIs and/or uses cases of the drive system, drive product and/or drive application, based on querying historical setups of tools and use cases, integrating an analytics tool-box into the drive system, drive product and/or drive application, enabling and/or identifying support, simplification, speed-up and/or improvement of analytics algorithms and/or ML models, and enabling and/or identifying support, simplification, speed-up and/or improvement of development of analytics algorithms and/or ML models (Marykowskiy, page 4, last paragraph and page 5, first paragraph, metadata is data that provide this context a structured information about the data set; contextual information can be expressed in natural language, in a form of technical specification sheet provided by the producer, or, ultimately, as some kind of formal representation; such metadata would enable a data scientist to draw more meaningful conclusions while performing data analysis; also see page 7, last paragraph, in the context of knowledge engineering and KBS, ontology has been defined as “explicit specification of conceptualization”; also see page 8-9, Fig. 2, schema ontology illustrates formal description/expression of Wind Turbine system; also see page 14-15, through the use of ontologies, Ontology-based data integration enables the harnonisation of diverse data sources into coherent, query-able whole, promoting knowledge discovery and inference across systems that may otherwise isolated).
Conclusion
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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/KEN HOANG/Examiner, Art Unit 2168
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