DETAILED ACTION
This office action is in response to the communication filed on January 16, 2026 and February 16, 2026. Claims 1, 5, 8-10, 12, 15, and 16 are currently pending.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/16/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/16/26 has been considered by the examiner.
Response to Arguments
Applicant's arguments filed on January 16, 2026 have been fully considered but they are not persuasive for the following reasons:
Applicant in Page 12 of the Remarks argues that amended independent claims 1, 9, and 10 further tie the claims to real world application and thus obviate the 101 rejection of claims 1, 5, 8-10, and 12-17.
Examiner respectfully disagrees.
Amended independent claims 1, 9, and 10 cover several steps, such as the dynamically generating a representation..., determining a representation..., generating the representation...comprising determining mappings..., and generating results... steps, that recite an abstract idea within the “Mental Processes” grouping of abstract ideas, because a person can mentally or using a pen and paper perform the limitations recited in said steps, which was discussed in detail in the 101 rejection in the last office action.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The remaining limitations in the claims, such as the receiving, retrieving, storing, providing, and displaying steps and the wherein limitations, are identified as reciting additional elements, only adding insignificant extra-solution activity to the judicial exception, and/or recognized as a well understood, routine, and conventional activity within the field of computer functions, which is not sufficient to amount to significantly more than the judicial exception and are not directed to any specific improvement in computer technology.
Therefore, the claims are still directed to an abstract idea and are not patent eligible.
Applicant in Pages 12-15 of the Remarks argues that Brown and Goradia do not teach or even suggest the features "dynamically generating the representation of the industrial data including generating the representation of the industrial data based on the configuration setting value, the generating of the representation of the industrial data based on the configuration setting value including determining mappings between data sets of the disjoint data sets of the industrial data from the plurality of data sources using the configuration setting value”, as recited in amended independent claim 1 and similarly recited in amended independent claims 9 and 10.
Examiner respectfully disagrees. The cited prior art alone and/or in combination still discloses the argued features.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Brown in [0004], [0196], and [0243] discloses mapping query to the semantic model, mapping intent of a user query with enterprise specific information using the semantic model, semantic model organizes a semantic understanding of terminology user by the enterprise, responding to the query based on semantic relationships of the enterprise specific information.
Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device.
Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface.
Brown in [0080], [0161], and [0243] discloses bring data into semantic model as it appears in real time, providing data in real time, dynamically answering to query provided by the user.
Brown in [0083], [0097], and [0099] discloses semantic models mapping data in different forms into a shared knowledge domain, utilizing semantic webs as part of the semantic model for mapping knowledge, allowing natural language query of semantic models.
Brown in [0138], [0145], [0265] discloses configuring or adjusting user settings or preferences for information delivery.
Brown in [0165], [0166], and [0171] discloses ingest unstructured data, build a semantic representation, such as a model, from the ingested unstructured and structured data, ingest industrial information from enterprise data, enterprise system comprises cloud computing servers, enterprise data is interrelated and take on different forms and formats, storing enterprise data in data lakes
Therefore, Brown discloses "dynamically generating the representation of the industrial data...the generating of the representation of the industrial data...including determining mappings...”.
Brown discloses dynamically generating the representation of the industrial data comprising determining mappings, however, Brown does not explicitly disclose:
"dynamically generating the representation of the…data including generating the representation of the…data based on the configuration setting value, the generating of the representation of the…data based on the configuration setting value including determining mappings between data sets of the disjoint data sets of the…data from the plurality of data sources using the configuration setting value”.
Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data.
Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data.
Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, models are based on semantic concept of ontology and questions are built upon semantic queries, databases provided to store data for individual users.
Goradia in [0038], [0066], and [0152] discloses modifying data maps to facilitate mapping between semantic ontology and various data sources, which includes a variety of different data types and data structures.
Goradia in [0056] discloses accessing data sources, bringing together all data generated by enterprise systems in a private or public cloud of the enterprise, such as ERP, CRM, PLP etc..
Goradia in [0159], [0211], and [0236] discloses data being accessed is defined by configuration of a question, questions containing, among other configuration, a list of properties contained in the question output, the list of properties is used to generate a list of all the unique classifications attached to all the properties, assets belonging to several asset types or classes, in a configuration templates for different asset types are provided, each having a list of parameter types that define that asset.
Therefore, Goradia discloses "dynamically generating the representation of the…data including generating the representation of the…data based on the configuration setting value, the generating of the representation of the…data based on the configuration setting value including determining mappings between data sets of the disjoint data sets of the…data from the plurality of data sources using the configuration setting value”.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Brown and Goradia, to have combined Brown and Goradia. The motivation to combine Brown and Goradia would be to generate views of business data using a business knowledge model defined by an expert user.
For the above reasons, Examiner states that rejection of the current Office action is proper.
Claim Objections
Claims 1, 9, and 10 are objected to because of the following informalities:
In claim 1 the phrase “determining a representation of the industrial data is not found…” should be “determining that a representation of the industrial data is not found…”.
In claim 9 the phrase “determination a representation of the industrial data is not found…” should be “determination of a representation of the industrial data not being found…”.
In claim 10 the phrase “determining a representation of the industrial data is not found…” should be “determining that a representation of the industrial data is not found…”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In dependent claims 8 and 15 the phrase “generating a semantic model for accessing the industrial data from the data lake using the semantic query” is indefinite because it is not clear if the claim is referring to the semantic model of independent claims 1 and 10 or a different semantic model.
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.
Claims 1, 5, 8-10, 12, 15, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
At step 1:
Independent claims 1, 9, and 10 respectively recite a method, a cloud computing system, and a non-transitory computer-readable storage medium, which are directed to a statutory category such as a process, machine, or an article of manufacture.
At step 2A, prong one:
Independent claim 1 and similarly independent claims 9 and 10 recite the limitations:
“dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with the semantic query and a configuration setting value provided by the user”,
A person can mentally or using a pen and paper dynamically generate a representation of industrial data by mentally or using a pen and paper using data sets of industrial data in a data lake using a semantic model associated with a semantic query and a configuration setting provided by a user.
“dynamically generating the representation of the industrial data comprising:
determining a representation of the industrial data is not found in a database based on the configuration setting value”;
A person can mentally or using a pen and paper dynamically generate a representation of industrial data by mentally or using a pen and paper determining that a representation of the industrial data us not found in a database based on a configuration setting value.
“dynamically generating the representation of the industrial data comprising:…
generating the representation of the industrial data based on the configuration setting value, the generating of the representation of the industrial data based on the configuration setting value comprising determining mappings between data sets of the disjoint data sets of the industrial data from the plurality of data sources using the configuration setting value”;
A person can mentally or using a pen and paper dynamically generate a representation of industrial data based on configuration setting value by mentally or using a pen and paper determining mappings between data sets of disjoint data sets of industrial data from a plurality of data sources using the configuration setting value.
“generating results of the semantic query based on the representation of the industrial data, wherein the results comprise the requested industrial data from the data lake”;
A person mentally or using a pen and paper generate results comprising requested industrial data in response to a semantic query and based on a representation of industrial data.
“wherein the configuration setting value indicates mapping between different data sets in the data lake,
wherein the configuration setting value is at a class level,
wherein the configuration setting value is variable from the request to another request, such that a first combination of the industrial data is minable from the data lake using the configuration setting value and a second combination of the industrial data is minable from the data lake using a varied configuration setting value, the second combination of the industrial data being different than the first combination of the industrial data, and
wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof”.
A person can mentally or using a pen and paper dynamically generate a representation of industrial data by mentally or using a pen and paper determining mappings between data sets of disjoint data sets of industrial data from a plurality of data sources using the configuration setting value, wherein the configuration setting value indicates mapping between different data sets in the data lake, wherein the configuration setting value is at a class level, wherein the configuration setting value is variable from the request to another request, such that a first combination of the industrial data is minable from the data lake using the configuration setting value and a second combination of the industrial data is minable from the data lake using a varied configuration setting value, the second combination of the industrial data being different than the first combination of the industrial data, and wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof.
The limitations, as recited above in claims 1, 9, and 10, are processes that, under their broadest reasonable interpretation, cover steps that can be performed in the human mind or by a human using a pen and paper, but for recitation of generic computer components.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
At step 2A, prong two:
This judicial exception is not integrated into a practical application.
Independent claim 1 and similarly independent claims 9 and 10 recite the limitations:
“receiving/receipt of, by a processing unit, a request to access to the industrial data in the data lake from a user device, wherein the request comprises a semantic query for the industrial data, and wherein the semantic query is based on a semantic model”, which is a step of receiving a request for data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“retrieving the mapped data sets from the data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of the semantic model”, which is a step of retrieving data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“storing/storage of the representation of the industrial data and the configuration setting value in the database”, which is a step of storing data. The step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“providing/provision of the generated results of the semantic query to the user device”, which is a step of providing data. The step is recited at a high level of generality, and amounts to mere data gathering and outputting, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
“displaying/display the generated results of the semantic query on a graphical user interface of the user device”, which is a step of displaying data. The step is recited at a high level of generality, and amounts to mere data gathering and outputting, which is a form of insignificant extra-solution activity (MPEP 2106.05(g)).
The additional elements “a method of providing seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources, the method comprising”, “by a processing unit”, “a user device”, “in a database”, “on a graphical user interface of the user device”, and “wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof” in the steps in claim 1 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a cloud computing system comprising: at least one processing unit; a memory communicatively coupled to the at least one processing unit, wherein the memory comprises a data access module configured to provide seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources, the provision of the seamless access comprising:”, “by the at least one processing unit”, “a user device”, “in a database”, “on a graphical user interface of the user device”, and “wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof” in the steps in claim 9 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
The additional elements “a non-transitory computer-readable storage medium that stores machine-readable instructions executable by a processing unit to provide seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources, the machine-readable instructions comprising”, “by the processing unit”, “a user device”, “in a database”, “on a graphical user interface of the user device”, and “wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof” in the steps in claim 10 are recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using generic computer components.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
At step 2B:
Independent claims 1, 9, and 10 recite the same additional elements as identified in step 2A prong two above. These additional elements are not sufficient to amount to significantly more than the judicial exception.
Independent claim 1 and similarly independent claims 9 and 10 recite the limitations:
“receiving/receipt of, by a processing unit, a request to access to the industrial data in the data lake from a user device, wherein the request comprises a semantic query for the industrial data, and wherein the semantic query is based on a semantic model”, which is a step of receiving a request for data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“retrieving the mapped data sets from the data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of the semantic model”, which is a step of retrieving data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“storing/storage of the representation of the industrial data and the configuration setting value in the database”, which is a step of storing data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“providing/provision of the generated results of the semantic query to the user device”, which is a step of providing data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i)).
“displaying/display the generated results of the semantic query on a graphical user interface of the user device”, which is a step of displaying data, and is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of presenting offers and gathering statistics (MPEP 2106.05(d)(II)(iv)).
Accordingly, the additional limitations are not sufficient to amount to significantly more than the judicial exception. Therefore, the claims are directed to an abstract idea and are not patent eligible.
Dependent claim 5 and similarly dependent claim 12 recite additional limitations, such as:
wherein generating the representation of the industrial data based on the configuration setting value and the semantic model comprises:
“determining mapping between the data sets of the industrial data from the plurality of data sources using the configuration setting value”;
This limitation is directed to the same abstract idea under the mental processes grouping as independent claims 1 and 10, because a person can mentally or using a pen and paper determine mapping between data sets of industrial data from a plurality of sources using a configuration setting value, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
“retrieving the mapped data sets from the data lake”, which is a step of retrieving data.
At step 2A prong two, the step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
At step 2B, the receiving step is recognized as a well understood, routine, and conventional activity within the field of computer functions as an element of storing and retrieving information in memory (MPEP 2106.05(d)(II)(iv)).
“mapping the data sets retrieved from the data lake to one or more class properties associated with at least one class of the semantic model”;
This limitation is directed to the same abstract idea under the mental processes grouping as independent claim 1 and 10, because a person can mentally or using a pen and paper map data sets retrieved from a data lake to one or more class properties associated with at least one class of a semantic model, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
“generating the representation of the industrial data based on the data sets retrieved from the data lake mapped to the one or more class properties of the at least one class of the semantic model”.
This limitation is directed to the same abstract idea under the mental processes grouping as independent claim 1 and 10, because a person can mentally or using a pen and paper generate a representation of industrial data based on data sets retrieved from a data lake mapped to the one or more class properties of at least one class of a semantic model, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 8 and similarly dependent claim 15 recite additional limitations, such as:
“generating a semantic model for accessing the industrial data from the data lake using the semantic query”.
This limitation is directed to the same abstract idea under the mental processes grouping as independent claim 1 and 10, because a person can mentally or using a pen and paper generate a semantic model for accessing industrial data from a data lake using a semantic query, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claim 16 recites additional limitation, such as:
“wherein the configuration setting value is at a semantic model level”.
This limitation is directed to the same abstract idea under the mental processes grouping as independent claim 1, because a person can mentally or using a pen and paper use a configuration setting value at a semantic model level, and because the limitation does not recite any additional elements that are sufficient to amount to significantly more.
Accordingly, the additional elements, individually or in combination, do not integrate the abstract idea into a practical application, even viewing the claims a whole, because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, dependent claims 5, 8, 12, 15, and 16 are also directed to abstract idea without significantly more and are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5, 8-10, 12, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brown (US Pub 2019/0042988) in view of Goradia (US Pub 2020/0210857).
With respect to claim 1, Brown discloses a method of providing seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources (Brown in [0165], [0166], and [0171] discloses ingest unstructured data, build a semantic representation, such as a model, from the ingested unstructured and structured data, ingest industrial information from enterprise data, enterprise system comprises cloud computing servers, enterprise data is interrelated and take on different forms and formats, storing enterprise data in data lakes), the method comprising:
receiving, by a processing unit, a request to access to the industrial data in the data lake from a user device, wherein the request comprises a semantic query for the industrial data, and wherein the semantic query is based on a semantic model (Brown in [0003], [0045], and [0166] discloses semantic model facilitates responding to query based on semantic relationships of enterprise specific information representing portion of enterprise knowledge, ingesting unstructured data, ingesting and scanning enterprise data including industrial information and incorporating the ingested data into the semantic model, semantic model providing deeper understanding of industrial information from each ingestion of enterprise data);
dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with the semantic query…provided by the user device (Brown in [0003], [0004], and [0171] discloses storing enterprise knowledge in a server, storing knowledge in a model, storing enterprise data in data lakes; Brown in [0083], [0097], and [0099] discloses semantic models mapping data in different forms into a shared knowledge domain, utilizing semantic webs as part of the semantic model for mapping knowledge, allowing natural language query of semantic models; Brown in [0004], [0196], and [0243] discloses mapping query to the semantic model, mapping intent of a user query with enterprise specific information using the semantic model, semantic model organizes a semantic understanding of terminology user by the enterprise, responding to the query based on semantic relationships of the enterprise specific information; Brown in [0080], [0161], and [0243] discloses bring data into semantic model as it appears in real time, providing data in real time, dynamically answering to query provided by the user; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface; Brown in [0138], [0145], [0265] discloses configuring or adjusting user settings or preferences for information delivery; Brown in [0166], [0180], and [0184] discloses storing ingested industrial data in a model of an enterprise or a database external to the model; here Brown does not explicitly generating data using a configuration setting value provided by the user, but the Goradia reference discloses the feature, as discussed below),…
generating results of the semantic query based on the representation of the industrial data, wherein the results comprise the requested industrial data from the data lake (Brown in [0097]-[0099] discloses mapping query to the semantic model, processing queries and producing semantically consistent results, allowing natural language query of semantic models, responding to the query based on semantic relationships of the enterprise specific information, Brown in [0003] and [0196] discloses mapping an intent of a query with enterprise specific information using a semantic model; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface); and
providing the generated results of the semantic query to the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface); and
displaying the generated results of the semantic query on a graphical user interface of the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface),…
wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof (Brown in [0043] and [0078] discloses extraction of enterprise data from data sources, enterprise data services include resource planning (ERP); Brown in [0068] and [0081] discloses interacting with various data sources of the enterprise system on one or more enterprise servers, obtain information and data from enterprise system for updating world model, world model has knowledge to deal with various types of enterprise data in different enterprise data sources; Brown in [0192] and [0206] discloses querying a data source such as an enterprise database or external data source, processing data from field devices to identify information).
Brown discloses dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with a semantic query, adjusting user settings for data delivery, and storing data, and data sources such as enterprise resource planning (ERP) and field devices, however, Brown does not explicitly disclose:
dynamically generating the representation of the…data comprising:
determining a representation of the…data is not found in a database based on the configuration setting value;
generating the representation of the…data based on the configuration setting value, the generating of the representation of the…data based on the configuration setting value comprising determining mappings between data sets of the disjoint data sets of the…data from the plurality of data sources using the configuration setting value;
retrieving the mapped data sets from the data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of the semantic model;
storing the representation of the…data and the configuration setting value in the database; and
wherein the configuration setting value indicates mapping between different data sets in the data lake,
wherein the configuration value is at a class level,
wherein the configuration setting value is variable from the request to another request, such that a first combination of the…data is minable from the data lake using the configuration setting value and a second combination of the…data is minable from the data lake using a varied configuration setting value, the second combination of the…data being different than the first combination of the industrial data,
The Goradia reference discloses dynamically generating a representation of data comprising: determining a representation of the data is not found in a database based on a configuration setting value, generating the representation of the data based on the configuration setting value, the generating of the representation of the data based on the configuration setting value comprising determining mappings between data sets of disjoint data sets of the data from a plurality of data sources using the configuration setting value, retrieving the mapped data sets from a data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of a semantic model, storing the representation of the data and the configuration setting value in the database, wherein the configuration setting value indicates mapping between different data sets in the data lake, wherein the configuration value is at a class level, and wherein the configuration setting value is variable from a request to another request, such that a first combination of the data is minable from the data lake using the configuration setting value and a second combination of the data is minable from the data lake using a varied configuration setting value, the second combination of the data being different than the first combination of the data (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, models are based on semantic concept of ontology and questions are built upon semantic queries, databases provided to store data for individual users; Goradia in [0038], [0066], and [0152] discloses modifying data maps to facilitate mapping between semantic ontology and various data sources, which includes a variety of different data types and data structures; Goradia in [0051], [0099], and [0142] discloses ingestion to multi-model data stores used for various data types, mapping stored in a mapping file, ingested data and mapping configuration stored in a data store; Goradia in [0056] discloses accessing data sources, bringing together all data generated by enterprise systems in a private or public cloud of the enterprise, such as ERP, CRM, PLP etc.; Goradia in [0159], [0211], and [0236] discloses data being accessed is defined by configuration of a question, questions containing, among other configuration, a list of properties contained in the question output, the list of properties is used to generate a list of all the unique classifications attached to all the properties, assets belonging to several asset types or classes, in a configuration templates for different asset types are provided, each having a list of parameter types that define that asset; Goradia in [0294], [0306], and [0307] discloses data stores such as databases provided to store data for users, stored data can be organized and queried).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Brown and Goradia, to have combined Brown and Goradia. The motivation to combine Brown and Goradia would be to generate views of business data using a business knowledge model defined by an expert user (Goradia: [0006]).
With respect to claim 5, Brown in view of Goradia discloses the method of claim 1, wherein generating the representation of the industrial data based on the configuration setting value and the semantic model comprises:
determining mapping between the data sets of the industrial data from the plurality of data sources using the configuration setting value (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data; );
retrieving the mapped data sets from the data lake (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data);
mapping the data sets retrieved from the data lake to one or more class properties associated with at least one class of the semantic model (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data); and
generating the representation of the industrial data based on the data sets retrieved from the data lake mapped to the one or more class properties of the at least one class of the semantic model (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data).
With respect to claim 8, Brown in view of Goradia discloses the method of claim 1, further comprising:
generating a semantic model for accessing the industrial data from the data lake using the semantic query (Brown in [0050], [0166], and [0171] discloses generating a semantic model using enterprise data including industrial information stored in data lakes; Goradia in [0079] and [0239] discloses mapping data sources to semantic models, allow users to answer questions with the data using the model).
With respect to claim 9, Brown discloses a cloud computing system (Brown in [0063] and [0303] discloses cloud computing system) comprising:
at least one processing unit (Brown in [0063] and [0303] discloses cloud computing system including one or more processors); and
a memory communicatively coupled to the at least one processing unit, wherein the memory comprises a data access module configured to provide seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources (Brown in [0063] and [0303] discloses cloud computing system including one or more processors, non-transitory storage medium such as memory storing instructions executed by processing device; Brown in [0165], [0166], and [0171] discloses ingest unstructured data, build a semantic representation, such as a model, from the ingested unstructured and structured data, ingest industrial information from enterprise data, enterprise system comprises cloud computing servers, enterprise data is interrelated and take on different forms and formats, storing enterprise data in data lakes), the provision of the seamless access comprising:
receipt, by the at least one processing unit, of a request to access to the industrial data in the data lake from a user device, wherein the request comprises a semantic query for the industrial data, and wherein the semantic query is based on a semantic model (Brown in [0003], [0045], and [0166] discloses semantic model facilitates responding to query based on semantic relationships of enterprise specific information representing portion of enterprise knowledge, ingesting unstructured data, ingesting and scanning enterprise data including industrial information and incorporating the ingested data into the semantic model, semantic model providing deeper understanding of industrial information from each ingestion of enterprise data);
dynamic generation of a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with the semantic query…provided by the user device (Brown in [0003], [0004], and [0171] discloses storing enterprise knowledge in a server, storing knowledge in a model, storing enterprise data in data lakes; Brown in [0083], [0097], and [0099] discloses semantic models mapping data in different forms into a shared knowledge domain, utilizing semantic webs as part of the semantic model for mapping knowledge, allowing natural language query of semantic models; Brown in [0004], [0196], and [0243] discloses mapping query to the semantic model, mapping intent of a user query with enterprise specific information using the semantic model, semantic model organizes a semantic understanding of terminology user by the enterprise, responding to the query based on semantic relationships of the enterprise specific information; Brown in [0080], [0161], and [0243] discloses bring data into semantic model as it appears in real time, providing data in real time, dynamically answering to query provided by the user; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface; Brown in [0138], [0145], [0265] discloses configuring or adjusting user settings or preferences for information delivery; Brown in [0166], [0180], and [0184] discloses storing ingested industrial data in a model of an enterprise or a database external to the model; here Brown does not explicitly generating data using a configuration setting value provided by the user, but the Goradia reference discloses the feature, as discussed below),…
generation of results of the semantic query based on the representation of the industrial data, wherein the results comprise the requested industrial data from the data lake (Brown in [0097]-[0099] discloses mapping query to the semantic model, processing queries and producing semantically consistent results, allowing natural language query of semantic models, responding to the query based on semantic relationships of the enterprise specific information, Brown in [0003] and [0196] discloses mapping an intent of a query with enterprise specific information using a semantic model; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface);
provision of the generated results of the semantic query to the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface);
display of the generated results of the semantic query on a graphical user interface of the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface),…
wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof (Brown in [0043] and [0078] discloses extraction of enterprise data from data sources, enterprise data services include resource planning (ERP); Brown in [0068] and [0081] discloses interacting with various data sources of the enterprise system on one or more enterprise servers, obtain information and data from enterprise system for updating world model, world model has knowledge to deal with various types of enterprise data in different enterprise data sources; Brown in [0192] and [0206] discloses querying a data source such as an enterprise database or external data source, processing data from field devices to identify information).
Brown discloses dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with a semantic query, adjusting user settings for data delivery, and storing data, and data sources such as enterprise resource planning (ERP) and field devices, however, Brown does not explicitly disclose:
the dynamic generation of the representation of the…data comprising:
determination of a representation of the…data is not found in a database based on the configuration setting value;
generation of the representation of the…data based on the configuration setting value, the generation of the representation of the…data based on the configuration setting value comprising determination of mappings between data sets of the disjoint data sets of the…data from the plurality of data sources using the configuration setting value;
retrieval of the mapped data sets from the data lake, the mapped data set retrieved from the data lake being mapped to one or more class properties associated with at least one class of the semantic model; and
storage of the representation of the…data and the configuration setting value in the database; and
wherein the configuration setting value indicates mapping between different data sets in the data lake,
wherein the configuration value is at a class level, and
wherein the configuration setting value is variable from the request to another request, such that a first combination of the…data is minable from the data lake using the configuration setting value and a second combination of the…data is minable from the data lake using the varied configuration setting value, the second combination of the industrial data being different than the first combination of the industrial data,
The Goradia reference discloses dynamically generating a representation of data comprising: determining a representation of the data is not found in a database based on a configuration setting value, generating the representation of the data based on the configuration setting value, the generating of the representation of the data based on the configuration setting value comprising determining mappings between data sets of disjoint data sets of the data from a plurality of data sources using the configuration setting value, retrieving the mapped data sets from a data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of a semantic model, storing the representation of the data and the configuration setting value in the database, wherein the configuration setting value indicates mapping between different data sets in the data lake, wherein the configuration value is at a class level, and wherein the configuration setting value is variable from a request to another request, such that a first combination of the data is minable from the data lake using the configuration setting value and a second combination of the data is minable from the data lake using a varied configuration setting value, the second combination of the data being different than the first combination of the data (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, models are based on semantic concept of ontology and questions are built upon semantic queries, databases provided to store data for individual users; Goradia in [0038], [0066], and [0152] discloses modifying data maps to facilitate mapping between semantic ontology and various data sources, which includes a variety of different data types and data structures; Goradia in [0051], [0099], and [0142] discloses ingestion to multi-model data stores used for various data types, mapping stored in a mapping file, ingested data and mapping configuration stored in a data store; Goradia in [0056] discloses accessing data sources, bringing together all data generated by enterprise systems in a private or public cloud of the enterprise, such as ERP, CRM, PLP etc.; Goradia in [0159], [0211], and [0236] discloses data being accessed is defined by configuration of a question, questions containing, among other configuration, a list of properties contained in the question output, the list of properties is used to generate a list of all the unique classifications attached to all the properties, assets belonging to several asset types or classes, in a configuration templates for different asset types are provided, each having a list of parameter types that define that asset; Goradia in [0294], [0306], and [0307] discloses data stores such as databases provided to store data for users, stored data can be organized and queried).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Brown and Goradia, to have combined Brown and Goradia. The motivation to combine Brown and Goradia would be to generate views of business data using a business knowledge model defined by an expert user (Goradia: [0006]).
With respect to claim 10, Brown discloses a non-transitory computer-readable storage medium that stores machine-readable instructions executable by a processing unit to provide seamless access to unstructured industrial data in a data lake in a cloud computing environment, wherein the data lake comprises disjoint data sets of the industrial data from a plurality of data sources (Brown in [0063] and [0303] discloses cloud computing system including one or more processors, non-transitory storage medium such as memory storing instructions executed by processing device; Brown in [0165], [0166], and [0171] discloses ingest unstructured data, build a semantic representation, such as a model, from the ingested unstructured and structured data, ingest industrial information from enterprise data, enterprise system comprises cloud computing servers, enterprise data is interrelated and take on different forms and formats, storing enterprise data in data lakes), the machine-readable instructions comprising:
receiving, by the processing unit, a request to access to the industrial data in the data lake from a user device, wherein the request comprises a semantic query for the industrial data, and wherein the semantic query is based on a semantic model (Brown in [0003], [0045], and [0166] discloses semantic model facilitates responding to query based on semantic relationships of enterprise specific information representing portion of enterprise knowledge, ingesting unstructured data, ingesting and scanning enterprise data including industrial information and incorporating the ingested data into the semantic model, semantic model providing deeper understanding of industrial information from each ingestion of enterprise data);
dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with the semantic query…provided by the user device (Brown in [0003], [0004], and [0171] discloses storing enterprise knowledge in a server, storing knowledge in a model, storing enterprise data in data lakes; Brown in [0083], [0097], and [0099] discloses semantic models mapping data in different forms into a shared knowledge domain, utilizing semantic webs as part of the semantic model for mapping knowledge, allowing natural language query of semantic models; Brown in [0004], [0196], and [0243] discloses mapping query to the semantic model, mapping intent of a user query with enterprise specific information using the semantic model, semantic model organizes a semantic understanding of terminology user by the enterprise, responding to the query based on semantic relationships of the enterprise specific information; Brown in [0080], [0161], and [0243] discloses bring data into semantic model as it appears in real time, providing data in real time, dynamically answering to query provided by the user; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface; Brown in [0138], [0145], [0265] discloses configuring or adjusting user settings or preferences for information delivery; Brown in [0166], [0180], and [0184] discloses storing ingested industrial data in a model of an enterprise or a database external to the model; here Brown does not explicitly generating data using a configuration setting value provided by the user, but the Goradia reference discloses the feature, as discussed below),…
generating results of the semantic query based on the representation of the industrial data, wherein the results comprise the requested industrial data from the data lake (Brown in [0097]-[0099] discloses mapping query to the semantic model, processing queries and producing semantically consistent results, allowing natural language query of semantic models, responding to the query based on semantic relationships of the enterprise specific information, Brown in [0003] and [0196] discloses mapping an intent of a query with enterprise specific information using a semantic model; Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface); and
providing the generated results of the semantic query to the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface); and
displaying the generated results of the semantic query on a graphical user interface of the user device (Brown in [0045] and [0078] discloses interfacing between user device and enterprise data stored in databases and data lakes, communicating with the user device; Brown in [0076], [0079], and [0160] discloses receiving query results from the enterprise data services, displaying information on a user interface),…
wherein the plurality of data sources comprise field devices, enterprise resource planning (ERP) systems, product lifecycle management (PLM) systems, design tools, or any combination thereof (Brown in [0043] and [0078] discloses extraction of enterprise data from data sources, enterprise data services include resource planning (ERP); Brown in [0068] and [0081] discloses interacting with various data sources of the enterprise system on one or more enterprise servers, obtain information and data from enterprise system for updating world model, world model has knowledge to deal with various types of enterprise data in different enterprise data sources; Brown in [0192] and [0206] discloses querying a data source such as an enterprise database or external data source, processing data from field devices to identify information).
Brown discloses dynamically generating a representation of the industrial data using data sets of the industrial data in the industrial data lake using the semantic model associated with a semantic query, adjusting user settings for data delivery, and storing data, and data sources such as enterprise resource planning (ERP) and field devices, however, Brown does not explicitly disclose:
dynamically generating the representation of the…data comprising:
determining a representation of the…data is not found in a database based on the configuration setting value;
generating the representation of the…data based on the configuration setting value, the generating of the representation of the…data based on the configuration setting value comprising determining mappings between data sets of the disjoint data sets of the…data from the plurality of data sources using the configuration setting value;
retrieving the mapped data sets from the data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of the semantic model;
storing the representation of the…data and the configuration setting value in the database; and
wherein the configuration setting value indicates mapping between different data sets in the data lake,
wherein the configuration value is at a class level,
wherein the configuration setting value is variable from the request to another request, such that a first combination of the…data is minable from the data lake using the configuration setting value and a second combination of the…data is minable from the data lake using a varied configuration setting value, the second combination of the…data being different than the first combination of the industrial data,
The Goradia reference discloses dynamically generating a representation of data comprising: determining a representation of the data is not found in a database based on a configuration setting value, generating the representation of the data based on the configuration setting value, the generating of the representation of the data based on the configuration setting value comprising determining mappings between data sets of disjoint data sets of the data from a plurality of data sources using the configuration setting value, retrieving the mapped data sets from a data lake, the mapped data sets retrieved from the data lake being mapped to one or more class properties associated with at least one class of a semantic model, storing the representation of the data and the configuration setting value in the database, wherein the configuration setting value indicates mapping between different data sets in the data lake, wherein the configuration value is at a class level, and wherein the configuration setting value is variable from a request to another request, such that a first combination of the data is minable from the data lake using the configuration setting value and a second combination of the data is minable from the data lake using a varied configuration setting value, the second combination of the data being different than the first combination of the data (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, models are based on semantic concept of ontology and questions are built upon semantic queries, databases provided to store data for individual users; Goradia in [0038], [0066], and [0152] discloses modifying data maps to facilitate mapping between semantic ontology and various data sources, which includes a variety of different data types and data structures; Goradia in [0051], [0099], and [0142] discloses ingestion to multi-model data stores used for various data types, mapping stored in a mapping file, ingested data and mapping configuration stored in a data store; Goradia in [0056] discloses accessing data sources, bringing together all data generated by enterprise systems in a private or public cloud of the enterprise, such as ERP, CRM, PLP etc.; Goradia in [0159], [0211], and [0236] discloses data being accessed is defined by configuration of a question, questions containing, among other configuration, a list of properties contained in the question output, the list of properties is used to generate a list of all the unique classifications attached to all the properties, assets belonging to several asset types or classes, in a configuration templates for different asset types are provided, each having a list of parameter types that define that asset; Goradia in [0294], [0306], and [0307] discloses data stores such as databases provided to store data for users, stored data can be organized and queried).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, having the teachings of Brown and Goradia, to have combined Brown and Goradia. The motivation to combine Brown and Goradia would be to generate views of business data using a business knowledge model defined by an expert user (Goradia: [0006]).
With respect to claim 12, Brown in view of Goradia discloses the non-transitory computer-readable storage medium of claim 10, wherein generating the representation of the industrial data based on the configuration setting value and the semantic model comprises:
determining mapping between the data sets of the industrial data from the plurality of data sources using the configuration setting value (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data);
retrieving the mapped data sets from the data lake (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data);
mapping the data sets retrieved from the data lake to one or more class properties associated with at least one class of the semantic model (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data); and
generating the representation of the industrial data based on the data sets retrieved from the data lake mapped to the one or more class properties of the at least one class of the semantic model (Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0036], [0040], and [0294] discloses a data expert user can configure business knowledge models using a user interface to enter questions and solutions and generate mapping data, this permits to configure the business knowledge models used to generate information, databases provided to store data for individual users; Goradia in [0033] and [0240] discloses answering business questions associated with data sources such as enterprise and industrial ecosystems, allowing users to specify list of classifications unique to each user’s industry, which are applied to data).
With respect to claim 15, Brown in view of Goradia discloses the non-transitory computer-readable storage medium of claim 10, wherein the machine-readable instructions further comprise:
generating a semantic model for accessing the industrial data from the data lake using the semantic query (Brown in [0050], [0166], and [0171] discloses generating a semantic model using enterprise data including industrial information stored in data lakes; Goradia in [0079] and [0239] discloses mapping data sources to semantic models, allow users to answer questions with the data using the model).
With respect to claim 16, Brown in view of Goradia discloses the method of claim 16, wherein the configuration setting value is at a semantic model level (Brown in [0050], [0166], and [0171] discloses generating a semantic model using enterprise data including industrial information stored in data lakes; Goradia in [0003], [0006], and [0035] discloses data expert inputs information to define business questions and a business knowledge model, a semantic ontology is generated from the model, the model is mapped to data sources associated with a data lake and is used to generate views of business data; Goradia in [0079] and [0239] discloses mapping data sources to semantic models, allow users to answer questions with the data using the model).
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/R.M/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159