DETAILED ACTION
This office action is in response to Applicant’s submission filed on 11/20/2024. Claims 1-20 are pending in the application. As such, claims 1-20 have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-2, 13, 15, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, the claim recites “(a) identifying… a database of an enterprise containing desired data”, “(b) selecting… a schema analysis tool that is believed to be suited to a type of the database”, “(c) establishing… a connection to the database”, “(d) extracting… schema information from the database”, “(e) comparing… based on the schema information, a structure of a schema of the database to a structure of a schema of a similar database”, “(f) determining… a change to make to the schema of the database”, “(g) sending… an instruction to a network element of the enterprise to make the change to the schema of the database”, and “(h) validating… the change to the schema.”. Limitations (a) – (h) recite mental processes that may be practically performed in the mind using pen and paper or a generic computer. For example, limitation (a) can be done by a person using a generic computer to open a database. Limitation (b) can be done by a person determining a tool to use to analyze a database. Limitation (c) can be done by a person using a generic computer to access a database. Limitation (d) can be done by determining schema information from a database. Limitation (e) can be done by a person comparing the schema of two different databases. Limitation (f) can be done by a person determining a change to make to a database. Limitation (g) can be done by someone using a generic computer to delete data from a database. Limitation (h) can be done by a person validating data in a database. Under its broadest reasonable interpretation when read in light of the specification, the actions of “identifying,” “selecting,” “establishing,” “extracting,” “comparing,” “determining,” “sending,” and “validating” encompass mental processes practically performed in the human mind by evaluation and judgement using pen and paper or a generic computer. Accordingly, the claim recites an abstract idea (Step 2A, Prong One).
The judicial exception is not integrated into a practical application. In particular, the claim recites additional elements of “(i) the processing system using a… natural language processing agent” and “(j) schema analysis tool.” Limitations (a) - (h) are recited as being performed by a computer. In limitations (a) - (h), the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. Further, the limitations (i) and (j) provide nothing more than mere instructions to implement an abstract idea on a generic computer. The agent recited in limitation (i) and the tool recited in limitations (j) are used to perform limitations (a) – (h) and (e), respectively, without placing any limits on how these elements function. Rather, these elements only recite the outcomes and do not include any details on how the outcomes are accomplished. Additionally, limitation (i) merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (natural language agents/models) and thus fails to add an inventive concept to the claims. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to an abstract idea (Step 2A: YES).
The claim does not include additional elements that are sufficient to amount to more than the judicial exception. As discussed above, the recitation of a computer to perform limitations (a) – (h) amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept (Step 2B).
Regarding claims 15 and 20, the claims are rejected with similar analysis to claim 1.
Similarly, dependent claims 2 and 13 include additional steps that are considered abstract ideas because they fail to provide meaningful significance that goes beyond generally linking the use of an abstract idea to a particular technological environment and using the computer to perform an abstract idea.
Claim 2 reads on a user having access to different databases containing different information.
Claim 13 reads on a person repeating steps of database analysis.
Dependent claims 3-12, 16-17, and 19 do not recite abstract ideas because they recite natural processing language agents being programmed to perform different functions related to database management based on a natural language prompt. Further, dependent claims 14 and 18 do not recite abstract ideas because they recite training the natural language agents to understand domain-specific information of an enterprise.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tong et al. (US 20240419950 A1; hereinafter referred to as Tong) in view of Smith et al. (US 12505089 B1; hereinafter referred to as Smith).
Regarding claim 1, Tong discloses: a method comprising: identifying, by a processing system including at least one processor using a first natural language processing agent, a database of an enterprise containing desired data… ([0055] Users may interact with an exemplary system to request information associated with different enterprise systems, and thus the respective domains associated with those enterprise systems, and one or more ML agents may process the request to generate outputs responsive to those requests. Data may be retrieved from a respective system using specialized DSL scripts executed by software included in a respective ML agents to call APIs and/or query databases and ML agents may dynamically configure user interfaces based on the retrieved data to display relevant information to the user);
establishing, by the processing system using a third natural language processing agent, a connection to the database… ([0083] one or more ML agents 120 are connected to a particular resource 150 distinct from one or more other ML agents via a respective DSL interpreter 120f based on the respective capabilities of that respective ML agent 120 (e.g., dependent on the domain for which the LLM 120e of the agent 120 is configured to generate DSL scripts for). Additionally, the LLM 120e of one or more of ML agents 120 may be connected to various resources 140 and 142 such that the LLM can access data sources directly without using the DSL interpreter 120f. In some examples, resource 140 includes an embedded vector database).
Tong does not explicitly, but Smith teaches: selecting, by the processing system using a second natural language processing agent, a schema analysis tool that is believed to be suited to a type of the database… ([col 15, lines 30-43] inferring a new schema of the unstructured data at block 220 can include utilizing statistical analysis techniques or implementations (e.g., identifying common patterns, distributions, or correlations within the data that suggest a particular schema), by executing AI/ML functions and/or algorithms, by comparing the data against known schemas/reference schemas (e.g., to identify similarities or deviations between the new schema of the unstructured input data and the known/predetermined schemas), and/or otherwise. In some embodiments, NLP functions or other semantic analysis tools can be utilized at block 220 by the one or more processing circuits to determine keywords, identify the presence of specific fields, and/or gather metadata);
extracting, by the processing system using a fourth natural language processing agent, schema information from the database ([col 13, lines 18-25] the schema comparison system 114 can employ computational techniques or algorithms (e.g., natural language processing (NLP) techniques, etc.) to analyze and extract key information (e.g., data fields such as event types, product identifiers, activity data, etc.) and to structure one or more of the extracted data fields into predefined categories and/or formats that align with a previously known schema (e.g., a schema of database 120));
comparing, by the processing system using a fifth natural language processing agent ([col 16-17, lines 62-2] in response to receiving unstructured data as input data via one or more components of the computing environment 100, the schema comparison system 114 can employ computational techniques or algorithms (e.g., natural language processing (NLP) techniques, etc.) to analyze and extract key information (e.g., data fields such as event types, product identifiers, activity data, etc.)) and the schema analysis tool and based on the schema information ([col 12, lines 29-38] the schema comparison system 114 can analyze version control history or data lineage records to predict and adapt to schema modifications preemptively. In another example, the schema comparison system 114 perform differential analysis (e.g., by comparing snapshots of database schemas at different times to identify changes), which can include querying metadata tables containing metadata (e.g., database 120, etc.) or using schema versioning tools that track changes across the database 120 and data sources 150), a structure of a schema of the database to a structure of a schema of a similar database ([col 12, lines 51-56] The schema comparison system 114 can utilize the ML models to analyze the structure of datasets in the legacy system and the new platform. The schema comparison system 114 can identify discrepancies such as new columns added or changes in data formats to align with the new platform's requirements);
determining, by the processing system based on the comparing, a change to make to the schema of the database ([col 12, lines 32-37] the schema comparison system 114 perform differential analysis (e.g., by comparing snapshots of database schemas at different times to identify changes), which can include querying metadata tables containing metadata (e.g., database 120, etc.) or using schema versioning tools that track changes across the database 120 and data sources 150);
sending, by the processing system using a sixth natural language processing agent, an instruction to a network element ([col 8, lines 2-7] the computing environment 100 includes a database 120, a network 130, one or more user computing systems 140, and one or more data sources 150. The hydration system 110 can be communicatively coupled, via the network 130, to the database 120, the user computing system 140, and the data sources 150) of the enterprise to make the change to the schema of the database ([col 13, lines 48-56] the in-flight transformation system 116 can map incoming data fields to the target schema fields and adjust input data structures in real-time (or near real-time, such as 50 milliseconds) to match destination schemas (e.g., a schema structured data stored in a target data source, such as data source 150). This can include adding, removing, or transforming data fields based on the schema comparison system 114's detection of divergences in schemas);
and validating, by the processing system using a seventh natural language processing agent ([col 17, lines 26-29] the processing circuits as natural language processing (NLP) to parse and interpret the semantic content of unstructured data. This can allow the processing circuits to autonomously identify and extract data points), the change to the schema ([col 16, lines 18-27] The processing circuits can perform schema mapping, aligning elements from both schemas to highlight additions, deletions, or alterations in the data structure. The processing circuits can use the schemas' metadata to determine changes in context (e.g., not just syntactical but also semantical). The divergence determination can include evaluating compatibility issues that might occur due to these schema changes. The output can be a categorized report of divergences, including matches, non-matches, and partial matches).
Tong and Smith are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong to combine the teachings of Smith because doing so would allow for database schema changes based comparisons between different databases using a machine learning model, leading to improved database flexibility and efficient management (Smith [col 5, lines 49-56] present disclosure provides improvements over current technology by improving interoperability with a diverse spectrum of data sources and formats. By determining new schemas for unstructured data using an inferring function, the system handles data in natural language formats and other unstructured forms. It identifies divergences between these new schemas and any previously established schemas, allowing for the integration of diverse data types).
Regarding claim 2, the combination of Tong and Smith teaches: the method of claim 1. Tong further teaches: wherein the database is one of a plurality of databases ([0128] expert 1108 may include a first plurality of ML agents (agents 1-4), each respectively configured to communicate with a particular input source (e.g., files, databases, code, APIs, multimedia)) of the enterprise ([0067] a plurality of ML agents 120 and/or a plurality of experts 122, which as described below, may include multiple ML agents 120 and/or nested experts 122 each including multiple ML agents 120, for integrating enterprise systems), and at least two databases of the plurality of databases store different types of information related to the enterprise ([0077] Data storage 130 may include any variety of data, for instance, emails, images, videos, sensor data, financial data, healthcare data, inventory data, etc. Data storage 130 may include embedded vector interfaces (e.g., vector databases)).
Regarding claim 3, the combination of Tong and Smith teaches: the method of claim 1. Tong further teaches: wherein each of the first natural language processing agent, the second natural language processing agent, the third natural language processing agent, the fourth natural language processing agent, the fifth natural language processing agent, the sixth natural language processing agent, and the seventh natural language processing agent is programmed to perform a different function in response to a prompt issued by the processing system ([0078] a user may provide a request/prompt via user interface 118. The request may include, but is not limited to, a natural language prompt and/or other media data (e.g., image, audio, text). The request may be initially received by an ML agent 120 including an LLM trained to receive and process user inputs to determine a context of the input. The context may include a particular domain associated with the request and/or a semantic description of the request. The ML agent may then transmit the request and context to at least one of the orchestrator 124, another ML agent 120, or an expert 122).
Regarding claim 4, the combination of Tong and Smith teaches: the method of claim 3. Tong further teaches: wherein the prompt is expressed in a natural language ([0027] the request is based on a natural language prompt received via a user interface).
Regarding claim 11, the combination of Tong and Smith teaches: the method of claim 3. Smith further teaches: wherein the sixth natural language processing agent is programmed to automate schema changes ([col 11-12, lines 60-3] a plurality of database triggers (e.g., update triggers, load triggers, modifications triggers, etc.) can be established on tables within the database 120 and/or the data sources 150, and the database triggers can act upon specific data manipulation events (e.g., insert, update, or delete operations) by logging the changes into a designated shadow table stored in a database (e.g., database 120 and/or the data sources 150, etc.). The change detection system 112 can periodically and/or automatically review entries in the shadow table to detect and process recent data modifications without requiring a full load of the input database).
Regarding claim 13, the combination of Tong and Smith teaches: the method of claim 1. Smith further teaches: further comprising repeating the determining and the sending ([col 22, lines 30-35, 45-49] the update detection application 528 can periodically (or repeatedly, or according to a prespecified time, etc.) determine whether there has been an update, deletion, or insertion of data (e.g., in a target data store such as glue database) without being prompted by a user request/query from user devices 534… in response to the update detection application 528 determining an update, insertion, or deletion of data (e.g., unstructured data) in a database, the update detection application 528 can communicate with the various elements of the computing environment) when the validating cannot be performed successfully ([col 16, lines 31-43] a non-match can be identified when a new field, such as “social media handles,” is introduced in the new schema without any corresponding field in the previous schema, indicating a clear addition. In yet another example, a partial match can be identified when a field such as “address” in the new schema is split into “street address” and “zip code” in the previous schema, suggesting a refinement or reorganization of data structure rather than a complete change. In some embodiments, the outputted divergence result can indicate or highlight the instances of matches, non-matches, and partial matches, providing an indication of compatibility of the modified data structure with existing systems).
Regarding claim 14, the combination of Tong and Smith teaches: the method of claim 1. Tong further teaches: wherein language models utilized by the first natural language processing agent, the second natural language processing agent, the third natural language processing agent, the fourth natural language processing agent, the fifth natural language processing agent, the sixth natural language processing agent, and the seventh natural language processing agent are trained to recognize and understand information that is specific to a domain of the enterprise ([0059] ML agents may include an LLM or other machine learning model that has been configured and trained to communicate with a specific enterprise system in a particular DSL and/or perform other specific tasks, such as user-interface configuration. Furthermore, any of the ML agents may be periodically and/or continuously retrained and/or fine tuned for its respective task based on new data ingested into the system, interactions with users, and/or interactions with other ML agents).
Regarding claim 15, Tong discloses: a non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor, cause the processing system to perform operations, the operations comprising… ([0026] an exemplary non-transitory computer readable storage medium stores instructions for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts, the instructions configured to be executed by one or more processors of a computing system). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly.
Regarding claim 16, the claim recites similar limitations as claim 3 and therefore is rejected similarly.
Regarding claim 17, the claim recites similar limitations as claim 4 and therefore is rejected similarly.
Regarding claim 18, the claim recites similar limitations as claim 14 and therefore is rejected similarly.
Regarding claim 19, the claim recites similar limitations as claim 13 and therefore is rejected similarly.
Regarding claim 20, Tong discloses: a device comprising: a processing system including at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the processing system, cause the processing system to perform operations, the operations comprising… ([0026] an exemplary non-transitory computer readable storage medium stores instructions for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts, the instructions configured to be executed by one or more processors of a computing system). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Smith, as applied to claims 1-4, 11, and 13-20 above, and further in view of Wu et al. (US 20250335402 A1; hereinafter referred to as Wu).
Regarding claim 5, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Wu teaches: wherein the first natural language processing agent is programmed to identify and classify the database based on information that is specific to a domain of the enterprise ([0106] the system can determine a database type for each database of the plurality of databases… the system can determine property value types for the properties of each database of the plurality of databases. The property value types can be, for example, text, binary, date, numeric, currency, Boolean, list (e.g., property values can be selected from a list of possible property values), etc. At operation 612, the system can, using the property names and/or the property value types, determine the similarity between one or more databases).
Tong, Smith, and Wu are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong and Smith to combine the teachings of Wu because doing so would allow for databases to be easily classified and modified based on their information type using an interface, leading to improved database flexibility involving schemas comparisons, aggregations, and adjustments (Wu [0027] Unlike conventional databases which tend to have rigidly-defined schemas (e.g., explicitly defined fields that rarely if ever change, specific data types that rarely or never change, etc.), databases as contemplated herein can be dynamic structures with which users routinely interact beyond merely adding, deleting, or modifying records. For example, users can easily add properties, remove properties, rename properties, rename databases, etc., from within a graphical user interface).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Smith, as applied to claims 1-4, 11, and 13-20 above, and further in view of Khan et al. (US 20250378053 A1; hereinafter referred to as Khan).
Regarding claim 6, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Khan teaches: wherein the second natural language processing agent is programmed to detect the type of the database ([0237] The data quality rules recommendation engine will first analyze datasets to detect various data issues, including whether a column or field can have null values or not. It will also calculate the percentage of data that can be null in each column, identify specific patterns in the data, such as email addresses, mail addresses, contact information such as phone numbers, or numeric field patterns. The data quality rules recommendation engine may also detect columns where data types do not match the expected types, for example, an integer is expected but a string of characters is found) and to recommend the schema analysis tool based on the type ([0238] Based on the results of the engine's analysis of the datasets, the data quality rules recommendation engine evaluates and recommends specific data quality rules. These data quality rules can be used as a type of tool to determine the structure of a database to ensure data quality.).
Tong, Smith, and Khan are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong and Smith to combine the teachings of Khan because doing so would allow for different recommendations for analyzing and processing a database based on the type of the database, leading to improved data consistency and accuracy for the database (Khan [0242] The data quality rule recommendation engine is important and innovative in many ways. The data quality rules recommendation engine automation helps to enforce data governance policies, ensuring compliance with regulations. This is crucial for industries with strict data regulations such as banking and financial services. The automation engine saves times and resources by automating the process of identifying data quality issues and recommending rules to address them).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Smith, as applied to claims 1-4, 11, and 13-20 above, and further in view of Stevens (US 20250156303 A1).
Regarding claim 7, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Stevens teaches: wherein the third natural language processing agent ([0040] system includes a natural language processing (NLP) module for processing written instructions in natural language into machine level code in order to execute operations associated with cyber security operations. The system also includes a service or agent program for executing the identified actions) is programmed to manage database credentials ([0393] the implementation of an AI agent is a pivotal aspect, encompassing sophisticated mechanisms for control, access permissions, credentials management, and software interaction) and automate connection processes ([0042] the system and method may be used to automate various cyber security and IT operations, such as scanning, remediation, report generation, auditing, requirement generation, and diagram generation. The system and method may be used to automate cyber security and IT operations for various types of systems, including but not limited to: networks, servers, workstations, and mobile devices).
Tong, Smith, and Stevens are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong and Smith to combine the teachings of Stevens because doing so would allow for the automation of credential management and database connections using an agent, leading to more efficient database access and user verification (Stevens [0041] the present disclosure relate to systems and methods for automating cyber security and information technology (IT) operations. In some embodiments, the system includes a natural language processing (NLP) module for processing written instructions in natural language into machine level code in order to execute operations associated with cyber security and IT operations. The system may also include a service or agent program for executing the identified actions).
Claims 8-10 is rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Smith, as applied to claims 1-4, 11, and 13-20 above, and further in view of Dang et al. (US 20260050583 A1; hereinafter referred to as Dang).
Regarding claim 8, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Dang teaches: wherein the fourth natural language processing agent is programmed to parse the schema information and identify patterns and relationships in the schema of the database that are specific to a domain of the enterprise ([0099] the user 502 can provide additional task context 504, which can be inserted into the first and/or second prompt templates. Such task context 504 can include domain specific information that can assist the generative AI model 550 in better understanding the specific requirements of the schema matching task. This could include, for example, information about the specific industry or business sector the databases belong to, the types of data typically stored in such databases, or any known relationships or patterns between the tables or attributes).
Tong, Smith, and Dang are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong and Smith to combine the teachings of Dang because doing so would allow for particular patterns and metadata between two schemas of different databases to be compared in a specific domain, leading to more accurate and efficient analysis of the differences and relationship between the different schemas for further action (Dang [0044] target tables that fail to match the source table in the first stage are excluded from the further attribute match in the second stage. Generally, the attribute-level matching involves content comparison (comparison of data within tables) and often requires more resources for data collection and embedding generation. Thus, by prioritizing the most relevant matches (e.g., matching tables) first, the framework 200 reduces computational overhead associated with exhaustive matching attributes of all tables. As a result, the framework 200 can improve the schema matching by balancing accuracy with efficiency).
Regarding claim 9, the combination of Tong, Smith, and Dang teaches: the method of claim 8. Dang further teaches: wherein the fourth natural language processing agent relies on metadata associated with the database to identify the patterns and relationships ([0034] The schema extractor 132 can be used in the first stage (table-level) of schema matching, which involves automatic and runtime retrieval of metadata (e.g., 115, 125) of source tables and target tables such as schema information (e.g., 116, 126), text descriptions from dictionaries (e.g., 117, 127), and statistics (e.g., 118, 128)).
Regarding claim 10, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Dang teaches: wherein the fifth natural language processing agent ([0048] the similarity measurement 216 can be performed using a generative AI model (e.g., the generative AI model 150). or instance, the filtered metadata of the source and target tables can be forwarded to the generative AI model. The generative AI model can be prompted (using specific instructions from a prompt template) to measure similarities between the source and target tables) is programmed to perform schema comparisons ([0043] one or more matching tables 218 among a plurality of target tables in a target database can be identified based on comparison of metadata 212 (e.g., schema information) of the source and target tables) which account for knowledge of a domain of the enterprise ([0099] the user 502 can provide additional task context 504, which can be inserted into the first and/or second prompt templates. Such task context 504 can include domain specific information that can assist the generative AI model 550 in better understanding the specific requirements of the schema matching task. This could include, for example, information about the specific industry or business sector the databases belong to, the types of data typically stored in such databases, or any known relationships or patterns between the tables or attributes).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Tong in view of Smith, as applied to claims 1-4, 11, and 13-20 above, and further in view of Sharma et al. (US 20210224245 A1; hereinafter referred to as Sharma).
Regarding claim 12, the combination of Tong and Smith teaches: the method of claim 3. The combination of Tong and Smith does not explicitly, but Sharma teaches: wherein the seventh natural language processing agent ([0055] the data management platform 102 may determine that a natural language text corresponds to a characteristic of a job feature, based on data relating to other job features, data identifying characteristics of job features, and/or the like) is programmed to validate updated databases ([0015] the data management platform may perform metadata validation tests by automatically comparing metadata schemas between source and application, source and data structure, and data structure and application, and updating a log with failure entries).
Tong, Smith, and Sharma are considered analogous in the field of databases. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tong and Smith to combine the teachings of Sharma because doing so would allow for validation of data updates made to databases and responsive actions based on the validation, improving data accuracy and handling for databases (Sharma [0108] the data management platform (e.g., using processor 320, memory 330, storage component 340, input component 350, output component 360, communication interface 370, and/or the like) may perform, based on the metadata validation, at least one of: updating a metadata field in a metadata validation log, stopping the metadata validation, or updating the metadata validation log with a failure entry, as described above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Matthew et al. (US 20260072902 A1) – discloses using an agent system to interact with a large language model (LLM) for handling natural language queries to a database.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nathan Tengbumroong whose telephone number is (703)756-1725. The examiner can normally be reached Monday - Friday, 11:30 am - 8:00 pm EST.
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/NATHAN TENGBUMROONG/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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