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 .
This Non-Final Office Action is in response to the application 19/081,143 filed on 03/17/2025.
Status of claims:
Claims 1-21 are pending in this Office Action.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 08/07/2025, 10/01/2025, and 10/20/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 13 is objected to because of the following informalities:
Regarding claim 13, where “wherein the predictive data set is g3enerated based on…” should read “wherein the predictive data set is generated based on…”.
Appropriate correction is required.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1, 8, and 15:
Step 1:
Claim 1 recites “A method…”, the claim recites a series of steps and therefore is process.
Claim 8 recites “A non-transitory computer readable medium…”, therefore the claim is a manufacture.
Claim 15 recites “A system...” therefore the claim is a machine.
Step 2A Prong One:
Claims 1, 8, 15 recite limitations “generating a schema for each of a set of data sources configured at a data management system…, where each schema is distinct and comprises a set of data items associated with a corresponding data source, and each of the data items of that schema is associated with a corresponding extraction function and is mapped to one or more concepts of a semantic model; determining raw data for a data set to provide to a system based on a definition of the data set, wherein the definition of the data set comprises a concept of the semantic model and the raw data comprises data across multiple data sources associated with the concept of the semantic model; semantically contextualizing the determined raw data for data set by processing the determined raw data associated with each of the multiple data sources to represent the determined raw data from each data source according to the schema generated for that data source such that the determined raw data associated with that data source is mapped to the one or more concepts of the semantic model mapped to the data items of the schema for that data source”. The limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a “non-transitory computer readable medium”, “processor”, “system” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “generating a schema for each of a set of data sources configured at a data management system…, where each schema is distinct and comprises a set of data items associated with a corresponding data source, and each of the data items of that schema is associated with a corresponding extraction function and is mapped to one or more concepts of a semantic model” in the context of this claim encompass a user mentally, and with the aid of pen and paper generating schemas for data to be stored in. Under BRI, a schema is a representation of how data is organized and one of ordinary skills in the art can create such schema for particular data, for example, data of particular types can be presented in particular sections of a presentation. Also, one can ensure that each schema is unique and contain particular items of one source. Data items can also be labeled with features such as an extraction function and a concept.
Limitation “determining raw data for a data set to provide to a system based on a definition of the data set, wherein the definition of the data set comprises a concept of the semantic model and the raw data comprises data across multiple data sources associated with the concept of the semantic model” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing an evaluation or judgement process. One of ordinary skills in the art can determine data for a data set within a pool of raw data based on the definition of the data set.
Limitation “semantically contextualizing the determined raw data for data set by processing the determined raw data associated with each of the multiple data sources to represent the determined raw data from each data source according to the schema generated for that data source such that the determined raw data associated with that data source is mapped to the one or more concepts of the semantic model mapped to the data items of the schema for that data source” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing an evaluation or judgement process where the determined raw data can be further analyzed based on the data source and schema. One of ordinary skills in the art can analyze data in according with its schema and data source to contextualize data with new understandings or findings.
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional element “…using a predictive model and a semantic model” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as discussed in MPEP § 2106.05(f).
The claim recites the additional elements “receiving raw data from the set of data sources at the data management system”. The limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “…using a predictive model and a semantic model; and receiving raw data from the set of data sources at the data management system” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g) and MPEP 2106.05(f)).
Dependent claims 2, 9, and 16:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “wherein the definition of the data set comprises a time period and the determined raw data corresponds to the time period”. The limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “wherein the definition of the data set comprises a time period and the determined raw data corresponds to the time period” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)).
Dependent claims 3, 10, and 17:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “data set is a predictive data set”. The limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “data set is a predictive data set” are recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)).
Dependent claims 4, 11, and 18:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “the predictive data set is generated based on the predictive model”. The limitation merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea as discussed in MPEP § 2106.05(f).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “the predictive data set is generated based on the predictive model” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (f)).
Dependent claims 5, 12, and 19:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “the system comprises a search system”. The limitation amounts to simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP 2106.05(d))
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations “the system comprises a search system” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (d)).
Dependent claims 6, 13, and 20:
Step 2A Prong Two:
The judicial exception is not integrated into a practical application. The claim recites the additional elements “predictive data set is generated based on search data obtained from users' interactions with the search system”. The limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g)).
The computer system in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation “predictive data set is generated based on search data obtained from users' interactions with the search system” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05 (g)).
Dependent claims 7, 14, and 21:
Step 2A Prong One:
Claims 7, 14, and 21 recite limitations “the raw data for the data set is determined dynamically as the raw data is received from the data sources at the data management system”. The limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a “non-transitory computer readable medium”, “processor”, “system” nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
For example, limitation “the raw data for the data set is determined dynamically as the raw data is received from the data sources at the data management system” in the context of this claim encompass a user mentally, and with the aid of pen and paper performing an evaluation or judgement process. One of ordinary skills in the art can determine data for a data set within a pool of raw data based on the definition of the data set.
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 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.
Claims 1, 3-8, 10-15, and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Jamkhedkar et al. (US PGPUB 20230205741) “Jamkhedkar” in view of Wan et al. (US PGPUB 20230062655) “Wan”.
Regarding claim 1, Jamkhedkar teaches a method ([0016]) for managing data, comprising: generating a schema for each of a set of data sources configured at a data management system using a predictive model and a semantic model, where each schema is distinct and comprises a set of data items associated with a corresponding data source, and each of the data items of that schema is associated with a corresponding extraction function and is mapped to one or more concepts of a semantic model ([0022] For each entity (e.g., the organization or an adjacency), the data management system may create a corresponding enterprise data model instance based on the enterprise data model schema for storing data received from that entity. An enterprise data model instance may include distinct data structures that follow the enterprise data model schema…As data is received from a particular data storage corresponding to a particular entity (e.g., the organization or an adjacency), the server may be configured to map the data from the corresponding data model schema associated with the entity to the enterprise data model schema… Examiner’s note: Distinct enterprise data model instance can be created for each entity and this can be equivalent to generating a schema for each of a set of data sources configured at a data management system. Also, data can be extracted and received from an entity and can be mapped to corresponding schema thus is equivalent to data items of a schema is associated with a corresponding extraction function and is mapped to one or more concepts of a semantic model); semantically contextualizing the determined raw data for data set by processing the determined raw data associated with each of the multiple data sources to represent the determined raw data from each data source according to the schema generated for that data source such that the determined raw data associated with that data source is mapped to the one or more concepts of the semantic model mapped to the data items of the schema for that data source ([0020] As such, according to various embodiments of the disclosure, the data management system may be configured to automatically integrate the data associated with different entities (e.g., the organization and the adjacencies) and provide consolidated data views and collective analyses of the data associated with the different entities… [0032] In some embodiments, the common enterprise data model schema used for storing data associated with different entities in the enterprise data model instances enables the data management systems to generate consolidated data views that combine data from multiple enterprise data model instances. Similar to the data view discussed herein, each consolidated data view may have a particular subset of data types and a particular organization of the particular subset of data types, that is different from the way that the data is organized when it is stored in the enterprise data model instances. Furthermore, each consolidated data view may have a particular focus (e.g., accounting, finance, risk, etc.). Instead of viewing only the data from a single enterprise data model instance according to the data organization, a consolidated data view may generate the virtual and temporary data structure for presenting data that is merged from multiple enterprise data model instances… [0041] In some embodiments, to enhance the performance of data analyses, the data management system may generate a consolidated data view that combines related data across different enterprise data model instances based on the index values stored in the enterprise data model instances… Examiner’s note: The system creates consolidated data views and collective analyses of the data associated with the different entities wherein each consolidated data view have a particular subset of data types and a particular organization of the particular subset of data types, that is different from the way that the data is organized when it is stored in the enterprise data model instances. Thus, the consolidated data view can be equivalent to data for data set and the data can be further analyzed or contextualized based on a particular focus or index values stored in the enterprise data model instances ); and making the semantically contextualized data available to the system ([0106]: The process 600 then provides (at step 625) a device (or the requesting application) access to the consolidated data view. For example, the data view module 206 may provide the applications 232, 234, and 236 access to the one or more consolidated data views.).
Jamkhedkar does not explicitly teach receiving raw data from the set of data sources at the data management system; determining raw data for a data set to provide to a system based on a definition of the data set, wherein the definition of the data set comprises a concept of the semantic model and the raw data comprises data across multiple data sources associated with the concept of the semantic model.
Wan teaches receiving raw data from the set of data sources at the data management system ([0133] IDP 2100 may receive raw data from a variety of data sources… [0140] Raw data may be received and stored into a staging area. The staging area may be part of a “data lake” foundation from which groups across the organization can draw needed data); determining raw data for a data set to provide to a system based on a definition of the data set, wherein the definition of the data set comprises a concept of the semantic model and the raw data comprises data across multiple data sources associated with the concept of the semantic model ([0184]: At step 5300, IDP 2100 may receive or otherwise determine a request to generate consumption data for a specific purpose. The specific purpose may be, for example, for data analytics, or for displaying certain information to a specific group of users (e.g. Retail Banking employee). At step 5400, IDP 2100 may, in response to the request, identify and select a group of data from the raw data based on a data map… Based on this data map, IDP 2100 may select the appropriate data columns from level 1 data at step 5400 in response to the request for data consumption. At step 5500, the selected set of data may be transformed (e.g., cleaned, rationalized or otherwise processed) into a curated set of data (e.g., level 2 or level 2.5) based an the data map. … Examiner’s note: a group of data from the raw data can be determined for a specific purpose such as data analytics, or for displaying certain information to a specific group of users. The specific purpose can correspond to a definition of the data set that comprise a concept of the semantic model). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Wan teachings in the Jamkhedkar system. Skilled artisan would have been motivated to incorporate determining data set from a raw data pool based on data set definition taught by Wan in the Jamkhedkar system to utilize the raw data in specific use cases. Also, extraction of data set from raw data categorizes data to a particular field thus, can improve data consolidation process, enhance data handing process, and generate deeper insights of the data. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 3, Jamkhedkar in view of Wan teaches all of the limitations of claim 1. Jamkhedkar further teaches wherein the data set is a predictive data set ([0031] As such, data views can be generated to provide different virtual views of the data for different data consumers. A data view, which may also be referred to as a data set or a data mart is a virtual and temporary data structure for visualization of at least a portion of the data stored in a data repository. A data view may have an organization of various data types that is different from the way that the data is actually organized when it is stored in the enterprise data model instances... For example, an account-focused data view may be generated that compiles data in an accounting-focused organization (e.g., having a focus on monetary amounts being transacted instead of other attributes, etc.) while a risk-focused data view may be generated that compiles data in a risk-focused organization (e.g., having a focus on risk attributes, such as transaction locations, transaction frequencies, etc. instead of other attributes, etc.). Thus, each data view may include a different subset of data types from the enterprise data model schema and/or a different organization of the data types than the actual organization in the enterprise data model instances... Examiner’s note: The system determines particular data types to create particular consolidated view such as using data in an accounting-focused organization for account-focused data view or compiles data in a risk-focused organization for a risk-focused data view. Thus, a data set can be a predictive data set such that a data set can be identified for a particular output.).
Regarding claim 4, Jamkhedkar in view of Wan teaches all of the limitations of claim 3. Jamkhedkar further teaches wherein the predictive data set is generated based on the predictive model ([0106] For a particular data consumer, a consolidated data view that combines portions of different enterprise data model instances. For example, the data view module 206 may receive a data request from any one of the applications 232, 234, and 236. The request may specify data types that the data consumer desire to view and/or analyze. Based on the request, the data view module 206 may generate one or more consolidated data views based on the enterprise data model schema and the enterprise data model instances… Examiner’s note: The consolidated data views can be generated by a data view module which can be equivalent to a predictive model ).
Regarding claim 5, Jamkhedkar in view of Wan teaches all of the limitations of claim 4. Jamkhedkar further teaches wherein the system comprises a search system ([0054]: As such, the service provider server 110 may be adapted to interact with the user devices 180, and 190, and/or merchant servers over the network 160 to facilitate the searching, selection, purchase, payment of items, and/or other services offered by the service provider server 110… [0106] The process 600 determines (at step 620), for a particular data consumer, a consolidated data view that combines portions of different enterprise data model instances. For example, the data view module 206 may receive a data request from any one of the applications 232, 234, and 236. The request may specify data types that the data consumer desire to view and/or analyze).
Regarding claim 6, Jamkhedkar in view of Wan teaches all of the limitations of claim 5. Jamkhedkar further teaches wherein the predictive data set is generated based on search data obtained from users' interactions with the search system ([0106] The process 600 determines (at step 620), for a particular data consumer, a consolidated data view that combines portions of different enterprise data model instances. For example, the data view module 206 may receive a data request from any one of the applications 232, 234, and 236. The request may specify data types that the data consumer desire to view and/or analyze. ).
Regarding claim 7, Jamkhedkar in view of Wan teaches all of the limitations of claim 1. Jamkhedkar does not explicitly teach raw data for the data set is determined dynamically as the raw data is received from the data sources at the data management system.
Wan teaches wherein the raw data for the data set is determined dynamically as the raw data is received from the data sources at the data management system ([0184]: At step 5100, raw data may be extracted from various source systems (e.g. traditional sources such as BORTS or non-traditional sources such as cloud databases). At step 5200, IDP 2100 may load and store the raw data at a data store (e.g. HIVE or HBase); the data may be stored at level 0 or level 1 at this stage. At step 5300, IDP 2100 may receive or otherwise determine a request to generate consumption data for a specific purpose. The specific purpose may be, for example, for data analytics, or for displaying certain information to a specific group of users (e.g. Retail Banking employee). At step 5400, IDP 2100 may, in response to the request, identify and select a group of data from the raw data based on a data map). Please refer to claim 1 for the motivational statement.
Regarding claim 8 note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 10, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 11, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 12, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 13, note the rejections of claim 6. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 14, note the rejections of claim 7. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 15, note the rejections of claim 1. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 17, note the rejections of claim 3. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 18, note the rejections of claim 4. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 19, note the rejections of claim 5. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 20, note the rejections of claim 6. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 21, note the rejections of claim 7. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jamkhedkar et al. (US PGPUB 20230205741) “Jamkhedkar” in view of Wan et al. (US PGPUB 20230062655) “Wan” in view of Fan et al. (US PGPUB 20210303584) “Fan”.
Regarding claim 2, Jamkhedkar in view of Wan teaches all of the limitations of claim 1. Jamkhedkar in view of Wan does not explicitly teach wherein the definition of the data set comprises a time period and the determined raw data corresponds to the time period.
Fan teaches wherein the definition of the data set comprises a time period and the determined raw data corresponds to the time period ([0010]: A data pipeline controller of the present disclosure may create new schemas to handle new source data retrievals and/or to integrate new data pipeline component types, and may assemble and tear down data pipelines in real-time… [0075]: The processing system may obtain a request for a delivery of a data set to at least one destination. In one example, the request may be in accordance with a request template. In one example, the request may comprise a plurality of parameters such as the desired data set, a specific data source or data sources, one or more target(s), a relevant time period for obtaining the data of the data set (e.g., for streaming and/or real-time data) and/or a relevant time period for which stored data is being requested, a specification of geographic bounds of the requested data set, one or more network regions for which data is being requested, other keywords, and so forth…Examiner’s note: A data set can have particular parameters or definitions such as relevant time period for which stored data is being requested and this is equivalent to definition of the data set comprises a time period and the determined data corresponds to the time period ). It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the Fan teachings in the Jamkhedkar and Wan system. Skilled artisan would have been motivated to incorporate data set definition that corresponds to time period taught by Fan in the Jamkhedkar and Wan system to improve collection process where data can be collected based on time period, thus can improves entities to see patterns or understand data behavior over time, track metrics, and build historical models for future predictions. This close relation between both of the references highly suggests an expectation of success.
Regarding claim 9, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Regarding claim 16, note the rejections of claim 2. The instant claims recite substantially same limitations as the above-rejected claims and are therefore rejected under the same prior-art teachings.
Prior Art
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure.
Maurya et al. (US PGPUB 20180285389) is directed to system for translating data, extracted from disparate data sources, into a homogeneous dataset to provide meaningful information. The database schema definition module defines a database schema in order to extract meaningful information pertaining to a specific use-case. The data source determination module determines one or more disparate data sources pertinent to extract the meaningful information. The data extraction module extracts heterogeneous dataset from the one or more disparate data sources. The data extraction module further passes the heterogeneous dataset to a Data-Translate Markup Language (DTML) executer to translate the heterogeneous dataset into a homogeneous dataset. The data translation module translates the heterogeneous dataset into the homogeneous dataset by using at least one data adapter. In one aspect, the heterogeneous dataset may be translated to perform data analytics on the homogeneous dataset in order to provide the meaningful information pertaining to the specific use-case.
Morrison et al. (US PGPUB 20180018353) is directed to systems and methods generating schemas that represent multiple data sources are provided herein. According to some embodiments, methods may include determining a schema for each of the multiple data sources via a computing device communicatively couplable with each of the multiple data sources, each of the multiple data sources including one or more data structures that define how data is stored in the data source, generating a negotiated schema by comparing the schemas of the multiple data sources to one another and interrelating data points of the multiple data sources based upon the schemas, interrelating the negotiated schema with the schema for each of the multiple data sources based upon the interrelation of the data points, and storing the negotiated schema in a storage media by way of the computing device.
Conclusion
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/C.D.V./Examiner, Art Unit 2153 01/09/2026
/KRIS E MACKES/Primary Examiner, Art Unit 2153