A method and system for intelligent generation and compliance detection of enterprise naming and business scope based on AI.

Abstract

This invention discloses an AI-based intelligent generation method for enterprise naming and business scope, with the following specific steps: S1, receiving enterprise establishment parameter elements, which include at least core keywords and natural language business descriptions. The core keywords include at least one of industry keywords, product or service keywords, regional keywords, and concept keywords, and allow input of existing preferred names or existing business scopes as constraints; S2, organizing the name features of the core keywords and the existing preferred names, and generating a candidate name set based on a pre-trained language model. This invention incorporates core keywords, natural language business descriptions, trademark databases, cultural semantic databases, industry high-frequency word data, regional feature data, and regulatory knowledge graphs into a unified processing flow, realizing integrated linkage processing of enterprise name generation, business scope generation, and compliance confirmation, thereby improving the intelligence level and processing efficiency of enterprise application.

Description

Technical Field

[0001] This invention relates to the fields of intelligent assistance in enterprise registration and artificial intelligence application technology, and in particular to an AI-based method and system for intelligent generation and compliance detection of enterprise naming and business scope. Background Technology

[0002] During the business establishment process, the determination of the company name, the completion of the business scope, and subsequent compliance confirmation are typically completed in stages by the applicant, agent, or registration assistant. The existing methods have the following main problems: First, company name generation relies on manual experience or simple deduplication, making it difficult to simultaneously consider name usability, cultural suitability, and business compatibility; second, the completion of the business scope relies on a static entry library or manual selection, making it difficult to automatically complete standardized mapping and entry combination based on natural language business descriptions; third, the matching between the business scope and regulations usually relies on manual verification, making it difficult to promptly identify permitted, filed, and prohibited items.

[0003] The searchable public information and public services related to this application include at least the following: 1. On August 31, 2023, the State Administration for Market Regulation issued the "Implementation Measures of the Regulations on Enterprise Name Registration Management". The measures detailed the self-declaration service for enterprise names and the standardized requirements for the constituent elements of enterprise names, but its focus was on the rules for name declaration and the standardization of registration management.

[0004] 2. On March 16, 2021, the State Administration for Market Regulation issued the "Notice on Publicly Soliciting Opinions on the 'Catalogue of Standardized Expressions of Business Scope'", proposing to unify the registration expression standards through the catalogue of standardized expressions of business scope and solve the problem of inconsistent business scope registration standards in various regions. However, the catalogue itself is mainly used to support standardized expression.

[0005] 3. On January 3, 2025, the State Administration for Market Regulation reported that the Shanghai Municipal Administration for Market Regulation had developed and launched an intelligent name assistance system. This system uses artificial intelligence models, big data algorithms, and high-frequency words in the industry to intelligently recommend enterprise names, focusing on solving the problems of name selection and duplication in name application.

[0006] 4. In 2025, the State Administration for Market Regulation reported that Hangzhou launched the AI-powered intelligent registration and approval service 1.0. This service can provide intelligent name recommendations during the name self-declaration process and intelligently match the business scope during the registration process, which will significantly improve the convenience of registration.

[0007] 5. In 2023, the State Administration for Market Regulation reported that Sichuan launched a one-click intelligent declaration service for "name-business scope", which enables convenient declaration by indexing the business entity name with frequently associated business scope descriptions.

[0008] 6. The National Industrial Classification of Economic Activities (GB / T 4754-2017) and the Negative List for Market Access (2025 Edition) provide a standardized classification basis and access rule basis for business scope mapping and compliance judgment.

[0009] As can be seen from the publicly available information above, although existing technologies or services cover some aspects such as self-declaration of names, standardized description of business scope, intelligent name recommendation, and intelligent matching of business scope, they still have the following shortcomings: 1. The processing of candidate names is mostly focused on plagiarism detection or rule recommendation, lacking a multi-dimensional evaluation mechanism that links trademark databases, cultural semantic databases, industry high-frequency word data, and regional characteristic data.

[0010] 2. The processing of natural language business descriptions is mostly concentrated on static item selection or quick matching, lacking a complete technical chain around business element extraction, standardized mapping, item combination, and logical verification.

[0011] 3. Compliance assessments of business scope are mostly focused on licensing, filing, and identification of prohibited items, lacking a mechanism to verify consistency between the industry descriptions in the selected name and the draft business scope.

[0012] Therefore, a technical solution is still needed that can complete name evaluation, standardized generation of business scope, and compliance confirmation of name and business scope linkage based on multi-source heterogeneous data. Summary of the Invention

[0013] Purpose of the invention The purpose of this invention is to provide an AI-based intelligent generation and compliance detection method and system for enterprise naming and business scope, in order to solve the problems of insufficient name evaluation dimensions, incomplete business scope standardization processing links, and lack of linkage compliance verification between name and business scope in the existing technology.

[0014] Technical solution To achieve the above objectives, the present invention adopts the following technical solution.

[0015] An AI-based method for intelligent generation and compliance verification of enterprise naming and business scope includes the following steps: S1. Receive enterprise establishment parameters. The system receives core keywords and natural language business descriptions. The core keywords include at least one of industry keywords, product or service keywords, regional keywords, and concept keywords, and allows input of existing preferred names or existing business scopes as constraints.

[0016] S2. Name Generation and Evaluation. The system organizes the name features of core keywords and existing preferred names, and generates a candidate name set based on a pre-trained language model. Then, it calls the trademark database to screen the candidate name set for similarity, calls the cultural semantic database to perform cultural semantic matching of the candidate name set, and combines industry high-frequency word data and regional feature data to evaluate the market suitability of the candidate name set, generating a recommended name list and a corresponding evaluation report.

[0017] S3. Execute business scope generation and logic verification. The system extracts business elements from the natural language business description to obtain core business activities and business keywords; then, based on industry classification standards and the standard business item library, it performs standardized mapping of core business activities and business keywords, and combines items to generate a standardized business scope draft; subsequently, it performs duplicate item verification and conflict item verification on the standardized business scope draft, outputting verification prompts and returning to the standardized mapping or item combination stage for regeneration when verification fails.

[0018] S4. Perform joint compliance review. The system selects candidate names from the recommended name list and inputs the industry description information in the selected candidate names and the verified draft business scope into the compliance review module. The compliance review module uses a dynamically updated regulatory knowledge graph to identify the licensing, filing, and prohibited items corresponding to the draft business scope, and verifies the consistency between the industry description information in the selected candidate names and the draft business scope, generating a compliance confirmation report.

[0019] S5. Integration of Execution Results. The system integrates the recommended name list, assessment report, approved draft business scope, and compliance confirmation report to generate standardized application materials.

[0020] Preferably, the evaluation report in step S2 includes at least the distinctiveness information, cultural suitability information, trademark risk information, and market suitability information of the candidate name, and may further include domain name availability information.

[0021] Preferably, the duplicate entry check in step S3 is used to identify business entries with the same semantics or overlapping coverage, and the conflict entry check is used to identify contradictory business entries.

[0022] Preferably, the consistency check in step S4 is used to check whether the industry description information in the selected candidate name is consistent with the standard business items in the draft business scope; when the industry description information of the name is found to be inconsistent with the draft business scope, the compliance confirmation report outputs the corresponding consistency prompt information.

[0023] A system for implementing the above method includes an input processing module, a naming intelligent generation module, a business scope intelligent recommendation module, a compliance intelligent review module, and an integrated decision support module.

[0024] The input processing module is used to receive and structure the enterprise establishment parameter elements; The intelligent naming generation module is connected to the input processing module and is used to generate a set of candidate names based on the structured name-related parameters, and output a list of recommended names and an evaluation report. The intelligent business scope recommendation module is connected to the input processing module and is used to generate a standardized business scope draft based on the natural language business description, perform logical verification, and output a business scope draft that passes the verification. The compliance intelligent review module is connected to the naming intelligent generation module and the business scope intelligent recommendation module, respectively. It is used to identify the licensing items, filing items and prohibited items corresponding to the draft business scope, and to perform consistency verification between the name industry description information and the draft business scope, and generate a compliance confirmation report. The integrated decision support module is connected to the intelligent naming generation module, the intelligent business scope recommendation module, and the intelligent compliance review module, respectively, and is used to integrate the recommended name list, evaluation report, business scope draft, and compliance confirmation report to output standardized application materials.

[0025] Preferably, the naming intelligent generation module includes a semantic generation unit, a trademark screening unit, a cultural assessment unit, and a market adaptability assessment unit; The semantic generation unit is used to generate a set of candidate names based on a pre-trained language model; The trademark screening unit is used to call the trademark database to perform similarity screening on the candidate name set; The cultural assessment unit is used to call the cultural semantics library to perform cultural semantic matching on the candidate name set. The market fit assessment unit is used to combine industry high-frequency word data and regional characteristic data to output the market fit assessment results of the candidate name set.

[0026] Preferably, the business scope intelligent recommendation module includes an NLP parsing unit, an industry mapping unit, an item combination unit, and a logic verification unit; The natural language parsing unit is used to extract business activity information and business keywords; The industry mapping unit is used to generate standard business entries based on industry classification standards and the standard business entry library; The item combination unit is used to generate a standardized business scope draft; The logical verification unit is used to perform duplicate entry verification and conflict entry verification, and returns a verification prompt result when the verification fails.

[0027] Preferably, the compliance intelligent review module includes a regulatory knowledge graph unit, an item identification unit, a consistency verification unit, and a report generation unit; The aforementioned regulatory knowledge graph unit is used to dynamically maintain licensing rules, filing rules, and prohibition rules; The item identification unit is used to identify the licensing items, filing items, and prohibited items corresponding to the draft business scope; The consistency verification unit is used to verify the consistency between the industry description information in the selected candidate name and the draft business scope. The report generation unit is used to output a compliance confirmation report.

[0028] Beneficial effects Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention incorporates core keywords, natural language business descriptions, trademark databases, cultural semantic databases, industry high-frequency word data, regional feature data, and regulatory knowledge graphs into a unified processing flow, enabling integrated processing of enterprise name generation, business scope generation, and compliance confirmation, thereby improving the intelligence level and processing efficiency of enterprise declarations.

[0029] 2. In the process of generating enterprise names, this invention conducts trademark similarity screening, cultural semantic matching, and market suitability assessment by combining industry high-frequency word data and regional characteristic data for candidate names. It can output a comprehensive assessment report that includes recognizability, cultural suitability, trademark risk, and market suitability, thereby reducing human judgment bias and the number of name application modifications and improving the success rate of name selection.

[0030] 3. In the process of generating the business scope, this invention extracts business elements from the natural language business description, standardizes and maps them, and combines them into entries. It also performs duplicate entry checks and conflict entry checks. This allows it to automatically generate a standardized draft business scope that is non-overlapping and non-contradictory, thereby improving the standardization and accuracy of the business scope.

[0031] 4. During the compliance review stage, this invention identifies the licensing, filing, and prohibited items corresponding to the business scope based on a dynamically updated regulatory knowledge graph. At the same time, it verifies the consistency between the name industry description information and the business scope to ensure that the name and business scope match each other and comply with regulatory requirements, thereby improving the compliance of application materials and the first-time pass rate. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall system architecture in an embodiment of the present invention.

[0033] Figure 2 This is a schematic diagram of the method processing flow in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1: Method Flow Example This embodiment provides an AI-based method for intelligent generation and compliance verification of enterprise naming and business scope. (See also...) Figure 2 Specifically, it includes the following steps.

[0036] Step S1: Parameter reception and preprocessing The system receives enterprise setup parameters via a web page, mobile device, or application programming interface. These parameters include at least the following: 1. Core keywords, which include at least one of the following: industry keywords, product or service keywords, regional keywords, and concept keywords; 2. Natural Language Business Description; 3. Optional existing preferred names; 4. Optional existing business scope.

[0037] The system preprocesses the above parameters, organizing the parameters applicable to name generation into name-related parameters, and organizing the natural language business description and existing business scope into business scope-related parameters.

[0038] Step S2: Name Generation and Evaluation Step S2 can be further broken down into the following sub-steps: S21. Name Feature Organization. The system organizes core keywords and existing preferred names to form the input data required for name generation.

[0039] S22. Candidate Name Generation. The system generates a set of candidate names based on a pre-trained language model.

[0040] S23. Trademark Similarity Screening. The system calls the trademark database to perform similarity screening on the candidate name set and obtains the trademark risk results.

[0041] S24. Cultural semantic matching. The system calls the cultural semantic database to perform cultural semantic matching on the candidate name set and obtain the cultural suitability result.

[0042] S25. Market Suitability Assessment. The system combines industry high-frequency word data and regional characteristic data to perform a market suitability assessment on the candidate name set, and obtain the market suitability results.

[0043] S26. Report Generation. Based on the results of steps S23 to S25, the system generates a list of recommended names and an evaluation report. The evaluation report includes at least the distinctiveness information, cultural suitability information, trademark risk information, and market suitability information of the candidate names.

[0044] Through the above processing, the system achieves the coordinated processing of candidate name generation, trademark risk screening, cultural semantic matching, and market suitability assessment, rather than simply performing name duplication checks.

[0045] Step S3: Business Scope Generation and Logic Verification Step S3 can be further broken down into the following sub-steps: S31. Business Element Extraction. The system performs natural language processing on the natural language business description to extract core business activities and business keywords.

[0046] S32. Standardized Mapping. Based on industry classification standards and a standard business item library, the system performs standardized mapping on core business activities and business keywords to obtain a set of standard business items.

[0047] S33. Item Combination. The system combines the set of standard business items according to business logic to generate a draft of standardized business scope.

[0048] S34. Logical Verification. The system performs duplicate entry verification and conflict entry verification on the draft standardized business scope.

[0049] S35. Failure rollback. If step S34 passes the verification, the draft business scope that has passed the verification is output; if step S34 fails the verification, a verification prompt is output, and the process returns to step S32 or step S33 to regenerate the draft business scope.

[0050] Through the above processing, the system achieves automatic generation and logical verification of natural language business descriptions into a standardized business scope draft.

[0051] Step S4: Joint Compliance Review Step S4 can be further broken down into the following sub-steps: S41. Name Selection. The system selects candidate names from the recommended name list and extracts industry description information from the selected candidate names.

[0052] S42. Item Identification. The system inputs industry description information and the verified draft business scope into the compliance review module, and uses a regulatory knowledge graph to identify the licensing items, filing items, and prohibited items corresponding to the draft business scope.

[0053] S43. Consistency Verification. The system verifies the consistency between the industry description information in the selected candidate names and the standard business items in the draft business scope.

[0054] S44. Report Generation. The system generates a compliance confirmation report based on the event identification results and consistency verification results.

[0055] Through the above processing, the system can not only identify licensing items, filing items, and prohibited items, but also verify the compatibility between the selected candidate name and the draft business scope.

[0056] Step S5: Results Integration The system integrates the following results in step S5: 1. List of recommended names; 2. Assessment report; 3. The draft business scope that has passed verification; 4. Compliance Confirmation Report.

[0057] Based on this, the system outputs standardized application materials.

[0058] Example 2: System Structure Example See Figure 1 This embodiment provides a system for implementing the above method, preferably adopting a layered structure, including a user interface layer, an AI processing layer, a data service layer, and an output decision layer.

[0059] 1. User interface layer, used to receive enterprise establishment parameters and display a list of recommended names, a draft business scope, and a compliance confirmation report; 2. AI processing layer, used to perform name generation and evaluation, business scope generation and logical verification, and linked compliance review; 3. Data service layer, used to provide trademark database, cultural semantic database, industry high-frequency word data, regional characteristic data, industry classification standards, standard business item database and legal knowledge graph; 4. Output to the decision-making level, used to generate standardized application materials.

[0060] The AI ​​processing layer includes the following functional modules.

[0061] 1. Input Processing Module This is used to structure the parameters of enterprise establishment and send the structured results to the naming intelligent generation module and the business scope intelligent recommendation module respectively.

[0062] 2. Intelligent Naming Generation Module The intelligent naming generation module includes: 1. Semantic generation unit, used to generate a set of candidate names based on a pre-trained language model; 2. Trademark screening unit, used to access the trademark database to perform similarity screening; 3. Cultural assessment unit, used to call the cultural semantic library to perform cultural semantic matching; 4. Market fit assessment unit, used to combine industry high-frequency word data and regional characteristic data to output market fit assessment results.

[0063] 3. Intelligent business scope recommendation module The intelligent business scope recommendation module includes: 1. Natural Language Processing (NLP) unit, used to extract core business activities and business keywords; 2. Industry mapping unit, used to generate standard business entries based on industry classification standards and the standard business entry library; 3. Item combination unit, used to generate a standardized draft of the business scope; 4. Logical verification unit, used to perform duplicate entry verification and conflict entry verification.

[0064] 4. Compliance Intelligent Review Module The compliance intelligent review module includes: 1. A legal knowledge graph unit, used to maintain licensing rules, filing rules, and prohibition rules; 2. Item identification unit, used to identify licensing items, filing items, and prohibited items corresponding to the draft business scope; 3. Consistency verification unit, used to verify the consistency between the industry description information in the selected candidate name and the draft business scope; 4. Report generation unit, used to output compliance confirmation reports.

[0065] 5. Integrated Decision Support Module The integrated decision support module is used to integrate the recommended name list, assessment report, draft business scope and compliance confirmation report, and generate standardized application materials.

[0066] Example 3: Specific Application Case Taking a company planning to engage in the research and development and sales of smart home devices as an example, the system processing procedure is as follows: 1. Input Phase: Users input core keywords such as "smart home, technology, innovation, Beijing" and a natural language business description; 2. Name generation stage: The system generates multiple candidate names and forms a recommended name list through trademark similarity screening, cultural semantic matching, and market suitability assessment; 3. Business Scope Generation Stage: The system extracts business information such as R&D, sales, proprietary brands, and agency sales from the natural language business description to generate a standardized draft business scope; 4. Logical verification stage: The system checks whether there are duplicate or conflicting entries in the draft business scope. If any problems are found, it returns to the mapping or combination stage for reprocessing. 5. Compliance Review Stage: The system selects candidate names from the recommended name list, identifies the licensing, filing, and prohibited items corresponding to the draft business scope, and verifies the consistency between the industry description information in the selected candidate names and the draft business scope; 6. Output stage: The system integrates the recommended name list, draft business scope, and compliance confirmation report to generate standardized application materials.

[0067] Those skilled in the art will understand that any equivalent substitutions, improvements, or adjustments made to the above embodiments without departing from the core concept of the present invention should fall within the protection scope of the present invention.

Claims

1. An AI-based enterprise naming and scope of business intelligent generation and compliance detection method, characterized in that: The specific steps include the following: S1. Receive enterprise establishment parameter elements, which include at least core keywords and natural language business descriptions. The core keywords include at least one of industry keywords, product or service keywords, regional keywords, and concept keywords, and allow input of existing preferred names or existing business scopes as constraints. S2. Organize the name features of the core keywords and the existing preferred names, and generate a candidate name set based on the pre-trained language model; after the pre-trained language model generates the candidate name set based on the name features, it sequentially calls the trademark database for similarity screening, calls the cultural semantic database for cultural semantic matching, and combines industry high-frequency word data and regional feature data for market adaptability assessment, and finally generates a recommended name list and corresponding assessment report. S3. Extract business elements from the natural language business description to obtain core business activities and business keywords; standardize and map the core business activities and business keywords according to industry classification standards and standard business item library, and combine items to generate a standardized business scope draft; perform duplicate item verification and conflict item verification on the standardized business scope draft, output the verified business scope draft when the verification passes, and output verification prompts and return to the standardization mapping or item combination stage to regenerate when the verification fails; S4. Select candidate names from the recommended name list, input the industry description information in the selected candidate names and the verified draft business scope into the compliance review module. The compliance review module identifies the licensing items, filing items and prohibited items corresponding to the draft business scope through a dynamically updated regulatory knowledge graph, and performs consistency verification between the industry description information in the selected candidate names and the draft business scope to generate a compliance confirmation report. S5. Integrate the recommended name list, the evaluation report, the verified draft business scope, and the compliance confirmation report to generate standardized application materials.

2. The AI-based intelligent generation and compliance detection method for enterprise naming and business scope as described in claim 1, characterized in that: The core keywords in step S1 include at least one of industry keywords and regional keywords. The natural language business description is used to provide the business activity information required for generating the business scope. The existing preferred name or existing business scope is used as a constraint condition for generating the candidate name or business scope.

3. The AI-based intelligent generation and compliance detection method for enterprise naming and business scope as described in claim 1, characterized in that: The evaluation report in step S2 includes at least the distinctiveness information, cultural suitability information, trademark risk information, and market adaptability information for each candidate name; the market adaptability information is generated based at least on industry high-frequency word data and regional characteristic data.

4. The AI-based intelligent generation and compliance detection method for enterprise naming and business scope as described in claim 1, characterized in that: The duplicate entry verification in step S3 is performed on business entries with the same semantics or overlapping coverage, and the conflict entry verification is performed on business entries that contradict each other. When duplicate or conflict entries are identified, the system outputs a verification prompt and returns to the standardization mapping or entry combination stage to regenerate the business scope draft.

5. The AI-based intelligent generation and compliance confirmation method for enterprise naming and business scope as described in claim 1, characterized in that: The compliance confirmation report in step S4 includes at least the following: reminders of licensing matters, filing matters, and prohibited matters corresponding to the draft business scope, as well as the consistency verification results between the industry description information in the selected candidate names and the draft business scope.

6. A system for implementing the AI-based intelligent generation and compliance detection method for enterprise naming and business scope as described in any one of claims 1 to 5, characterized in that, It includes: system initialization and user input, intelligent naming generation module, intelligent business scope recommendation module, intelligent compliance review module, and integrated decision support module. The system initialization and user input, intelligent naming generation module, intelligent business scope recommendation module, and intelligent compliance review module are respectively connected to the integrated decision support module. The input processing module is used to receive and structure the enterprise establishment parameter elements; The intelligent naming generation module is connected to the input processing module and is used to generate a set of candidate names based on the structured name-related parameters, and output a list of recommended names and an evaluation report. The intelligent business scope recommendation module is connected to the input processing module and is used to generate a standardized business scope draft based on the natural language business description, perform logical verification, and output a business scope draft that passes the verification. The compliance intelligent review module is connected to the naming intelligent generation module and the business scope intelligent recommendation module, respectively. It is used to identify the licensing items, filing items and prohibited items corresponding to the draft business scope, and to perform consistency verification between the name industry description information and the draft business scope, and generate a compliance confirmation report. The integrated decision support module is connected to the intelligent naming generation module, the intelligent business scope recommendation module, and the intelligent compliance review module, respectively, and is used to integrate the recommended name list, evaluation report, business scope draft, and compliance confirmation report to output standardized application materials.

7. The system for intelligent generation and compliance detection of enterprise naming and business scope based on AI according to claim 6, characterized in that: The intelligent naming generation module includes a semantic generation unit, a trademark screening unit, a cultural evaluation unit, and a market adaptability evaluation unit. The semantic generation unit is used to generate a set of candidate names based on a pre-trained language model; The trademark screening unit is used to call the trademark database to perform similarity screening on the candidate name set; The cultural assessment unit is used to call the cultural semantics library to perform cultural semantic matching on the candidate name set. The market fit assessment unit is used to combine industry high-frequency word data and regional characteristic data to output the market fit assessment results of the candidate name set.

8. The system for intelligent generation and compliance detection of enterprise naming and business scope based on AI according to claim 6, characterized in that: The intelligent recommendation module for business scope includes an NLP parsing unit, an industry mapping unit, an item combination unit, and a logic verification unit; The natural language parsing unit is used to extract business activity information and business keywords; The industry mapping unit is used to generate standard business entries based on industry classification standards and the standard business entry library; The item combination unit is used to generate a standardized business scope draft; The logical verification unit is used to perform duplicate entry verification and conflict entry verification, and returns a verification prompt result when the verification fails.

9. The system for intelligent generation and compliance detection of enterprise naming and business scope based on AI according to claim 6, characterized in that: The compliance intelligent review module includes a regulatory knowledge graph unit, an event identification unit, a consistency verification unit, and a report generation unit. The aforementioned regulatory knowledge graph unit is used to dynamically maintain licensing rules, filing rules, and prohibition rules; The item identification unit is used to identify the licensing items, filing items, and prohibited items corresponding to the draft business scope; The consistency verification unit is used to verify the consistency between the industry description information in the selected candidate name and the draft business scope. The report generation unit is used to output a compliance confirmation report.

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