JP-7856989-B2 - Trademark risk management system and method thereof

JP7856989B2JP 7856989 B2JP7856989 B2JP 7856989B2JP-7856989-B2

Inventors

ウー、ペンチュン
ルー、エンピン
チェン、ヴィンセント

Assignees

エーアイプラックステクノロジーカンパニーリミテッド

Dates

Publication Date: 20260512
Application Date: 20240117
Priority Date: 20230117

Claims (17)

A trademark risk management method in which an electronic device is operated by a user, the processor of the electronic device is connected to a server via a network interface controller, and an application program is executed to perform category recommendation and risk management, (S100) The user inputs explanatory text or figures via the user interface of the electronic device, and the processor executes an input module to accept the explanatory text or figures . (S200) The processor performs the steps of executing a semantic analysis module in the application program to analyze the explanatory text , (S300) The semantic analysis module is further connected to a classification module, a search module, and a database module, classifies the explanatory text using industry technology, performs a matching search in the database module, and transmits the matching data to the intellectual property information disclosure module. (S400) The intellectual property information disclosure module analyzes the matching data and compiles it into intellectual property information , and presents the intellectual property information to the user via the user interface of the electronic device, (S500) The analyzed and compiled data is also classified and compiled by intellectual property type in the data, and the recommended intellectual property types are displayed in ranking order on the user interface and sent to a category recommendation module for application. (S600) The user selects a trademark type via the user interface, (S700) The user inputs a brand description via the user interface, and the processor executes an input module to input the brand description. (S800) The user completes the login process using the login module, the processor executes the semantic analysis module, the search module performs a matching search against the analysis results in the database module, and sends the search results to the recommendation module to create recommended application trademark categories. The (S900) processor executes the search module to perform a second matching search in the database module based on the recommended application trademark category, and the second search results are sent to the search document creation module to create a risk assessment report. Trademark risk management methods including those mentioned above.
After step (S900), further, (S901) The trademark risk management method according to claim 1, comprising the step of recreating trademark text or figures based on similar trademark text or figures in a risk assessment report and conceptual information from a user's brainstorming session received by an input module.
A trademark risk management system for receiving clients that accept users by operating electronic devices, The processor of the aforementioned electronic device is connected to a server via a network interface controller and executes application programs for category recommendation and risk management , and at least , An input module that receives explanatory text or figures entered by the user, converts the explanatory text into a string, performs marking processing, transmits the string information, and records the input language of the string information in temporary memory. A semantic analysis module that receives the aforementioned string information, analyzes and segments the string information using a natural language database, creates and transmits semantic analysis results, A classification module that analyzes the industry category classification codes used in semantic analysis results, connects to a database module, and determines and creates at least one set of industry classification codes, A search module that performs a matching search in the database module based on the aforementioned at least one set of industry classification codes and creates data of the matching search results, An intellectual property information disclosure module that receives the aforementioned data and further statistically analyzes the aforementioned data to create basic intellectual property information, A recommendation module that further receives the aforementioned data, classifies the intellectual property types in the aforementioned data, and creates a set of intellectual property types for which applications are recommended, The system includes a login module in which the user operates the electronic device to authenticate their ID, A trademark risk management system in which the input module receives a brand description re-entered by the user when the user selects a trademark from the intellectual property types for which they recommend filing an application, and the login module completes user authentication; the semantic analysis module receives string information related to the brand description, analyzes and splits it to create and transmit the analysis results of the technical description; the search module performs a matching search against the analysis results in the database module and sends the search results to the recommendation module to create trademark categories for which applications are recommended; and the search module performs a second matching search in the database module based on the recommended application trademark categories and sends the second search results to the search document creation module to create a risk assessment report.
The trademark risk management system according to claim 3 , further comprising a risk management module that recreates trademark text or figures based on similar trademark text or figures in the risk assessment report and conceptual information from the user's brainstorming received by the input module.
A trademark risk management system for receiving clients that accept users by operating electronic devices, The processor of the aforementioned electronic device is connected to a server via a network interface controller and executes application programs for category recommendation and risk management , and at least , A login module in which the user operates the electronic device to authenticate the ID, After the user selects the first target country, an order processing module creates a new case order and periodically updates the information in the case order in the system's temporary memory. An input module that receives text or shapes entered by the user of the instruction manual, converts the text of the instruction manual into a string for marking processing, transmits the string information, and records the input language of the string information in temporary memory. A semantic analysis module that receives string information, analyzes and segments it using a natural language database, creates semantic analysis results, and transmits them. A classification module analyzes the trademark category classification code based on the semantic analysis results, connects to a database module, and determines and creates at least one set of trademark classification codes. A category recommendation module, which is a calculation model for a natural language model, trademark classification table, and detailed training, analyzes string information with ambiguous meaning or inaccurate descriptions into trademark category recommendation information and ultimately combines it with a semantic analysis module and a classification module to create recommended application trademark categories. A search document creation module including a text search means that receives the input text, searches for past cases in the database module, and ranks the similarity to create a risk assessment report; a graphic search means that receives the input graphic and searches for past cases in the database module; a conversion means; and a graphic comparison means. A content learning module, which is a large language model, further includes a mode learning means for learning corresponding trademark content from the database module for different trademark categories, A risk management module further comprising text creation means and pattern creation means, which recreates text and/or figures based on learning of the content learning module and past cases matched by the figure search means and text search means in the database module, Includes, The pattern creation means, after receiving a trademark name or a brainstorming concept of a figure from the user via the input module, combines with the semantic analysis module to analyze the brainstorming concept and translate it into a pattern creation language, creates a pattern code corresponding to the brainstorming concept using the pattern creation language, then creates a recreated figure corresponding to the brainstorming concept using the compiler means, and the figure search means, during the recreation of the figure, compares the similarity between the recreated figure and past examples that have been searched in the past so that the similarity between the recreated figure and past examples is less than a predetermined value. The text creation means combines with the semantic analysis module to analyze the brainstorming concept after the user inputs the trademark name and the brainstorming concept of the figure via the input module, and then combines with the content learning module to recreate the text; and simultaneously, the text search means compares the similarity between the recreated text and the past examples that have been searched in the past, such that the similarity between the recreated text and the past examples is within the range of the predetermined value. Trademark risk management system.
The trademark risk management system according to claim 5, wherein the graphic search means, upon receiving the input graphic, first converts the graphic into a vector representation using the conversion means, and then performs a matching search in the database module using the graphic comparison means to find past examples.
The trademark risk management system according to claim 5, further comprising a language determination module that determines whether the input language of the string information is the same as the official language of the first target country.
The trademark risk management system according to claim 7, wherein the language determination module determines that the input language of the string information is different from the official language of the first target country, and then translates the string information using the translation module, and also translates the language of the trademark design in the final search document into the input language of the string information using the translation module.
The system further includes a case processing module that includes means for extracting user identification information authenticated through login authentication and incorporating it into application data, or for creating application data by having the user directly input application data into fields using an electronic device, receiving the application data via an input module, and applying a format template. The trademark risk management system according to claim 5, wherein the user's case order is completed after the case processing module has created the application form.
The trademark risk management system according to claim 5, wherein the conversion means converts the figure from pixels to vectors.
The trademark risk management system according to claim 10, wherein the figure comparison means calculates the similarity using edit distance, cosine similarity, or Jaccard similarity during the comparison process, and filters out figures whose similarity exceeds a predetermined value.
The trademark risk management system according to claim 5, further comprising a keyword extraction module that extracts keywords from the input content, creates multiple keywords, and trains the model using a machine learning algorithm with the keywords.
The trademark risk management system according to claim 8, further comprising a multi-language translation module, wherein, after a second target country is selected by the user, the language determination module first determines whether the input language of the string information is the same as the official language of the second target country, and if not, translates the trademark name into the official language of the second target country before being formatted by the translation module.
(1) The user logs into an electronic device and authenticates their ID and identification information, (2) When the user creates an incident order using an electronic device and selects a first target country, the information in the incident order is periodically updated in temporary memory, (3) The user inputs explanatory text or figures via the input module of the electronic device, performs a marking process to convert the explanatory text into a string, creates string information, and records the input language of the string information in temporary memory. (4) The semantic analysis module performs semantic analysis on the string information, and the classification module performs trademark classification on the string information. Includes, In step 4, further, (411) The semantic analysis module includes the steps of analyzing and splitting string information to create semantic analysis results, (412) The classification module includes the step of classifying string information based on semantic analysis results so as to create at least one set of trademark classification codes, (413) The category recommendation module includes the steps of analyzing string information into trademark category recommendation information and combining it with the semantic analysis module and the classification module to ultimately create the application trademark category to recommend, (421) The graphic search means and the text search means include the steps of receiving text and/or graphics entered by the user, performing a search in a database module to compare and search past examples of trademarks, creating a search file, and further filtering past examples whose similarity is higher than the risk value, In step 421, further, (5) The risk management module includes the step of recreating text and/or figures based on past cases matched by the figure search means and text search means in the database module, based on the learning of the content learning module. Trademark risk management methods.
In step (3), further, (31) The language determination module includes the step of determining whether the input language of the string information is the same as the official language of the first target country, (32) If so, the step of performing semantic analysis as is, (33) Otherwise, the process includes the steps of first translating the string information into the official language of the first target country using a translation module, and then performing semantic analysis, The trademark risk management method according to claim 14, wherein in step (413), the created recommended application trademark category is translated again into the input language of the string information.
After step (421), further, (422) The shape search means, after receiving an input shape, includes the step of converting the input shape into a vector representation, (423) The figure comparison means then compares figures in the database module, filters out figures whose similarity exceeds a predetermined value, and the filtered figures are considered to be past examples of trademarks. A trademark risk management method according to claim 14, including the following :
In step (5), further, (51) The figure creation means, after a user inputs a trademark name or a brainstorming concept of a figure via an input module, analyzes the brainstorming concept and combines it with a semantic analysis module to translate it into a figure creation language, the figure creation language creates a figure code corresponding to the brainstorming concept, the figure code is then compiled by a compiler means to create a recreated figure corresponding to the brainstorming concept, and the figure search means, in the figure recreation process, compares the recreated figure with past examples that have been searched in the past to ensure that the recreated figure and the past examples have a similarity lower than a predetermined value, (52) The text creation means analyzes the concept of brainstorming, then combines it with the semantic analysis module to recreate the text based on the content learning module, and the text creation means further compares the recreated text with past cases that have been searched in the past to ensure that the recreated text and the past cases have a similarity lower than a predetermined value, A trademark risk management method according to claim 14, including the following:

Description

This invention relates to a trademark risk management system and its method, and more particularly to the reproduction of trademark text or graphics. Traditional patent application processes in China and internationally require the printing of paper documents and the completion of multiple forms. Many of these documents are paper-based, posing significant management and classification challenges. This is not only environmentally unfriendly but also leads to massive paper waste and potential errors in the patent application process, potentially resulting in patent invalidation and serious losses. In addition to traditional document preparation, the patent application process requires communication between personnel. Differences in their professional backgrounds, language use, cultural differences, and other unpredictable factors can lead to inaccurate or misunderstood information transmission. This results in misunderstandings between applicants, offices, agents, and government agencies. Consequently, applicants may be unable to achieve their intended results. Furthermore, patent applications aim not only as a defensive weapon in patent infringement litigation, but also as a symbol of the company's image. In effect, value creation through patent rights may not only generate revenue through licensing fees obtained by permitting others to use the patent, but also through the right to prevent infringement and the right to claim damages, which may also generate settlement revenue. For companies, protecting the results of their research and development through patent applications has traditionally been a necessary and important part of the commercial process. Some companies seem to consider patent applications as the responsibility of a professional patent firm. Professional patent firms can draft patent documents that accurately describe the technology, fully disclose its contents, and broadly cover the scope of the invention, by inviting contractors to discuss the technical content of the patent application. In practice, patent applications are not only a collaborative task between a company and a patent firm, but also require communication between different departments within the company, particularly between the development department and the intellectual property department, regarding internal patent applications. This requires development engineers in the development department to provide relevant technical information. This information may include a specification outlining the basic background of the technology, defects or problems requiring improvement in the prior art, and characteristics of the new technology. Furthermore, it is necessary to pre-search the proposed technical content in patent databases of numerous countries to identify similar prior art, facilitating internal discussions of patent proposals. In many companies, the number of people in the development department far exceeds the number of people in the internal intellectual property department, which increases the workload of the intellectual property department. However, despite many medium-sized and large enterprises having appropriate patent proposal systems, in reality, communication between the development and intellectual property departments incurs significant time costs, ranging from days to months, due to specialized differences between them. While the development department is familiar with the technology, it typically cannot meet the requirements of the intellectual property department for patent proposals (patent disclosures). Conversely, when the intellectual property department discusses with the development department based on prior art search reports, it often fails to clearly and comprehensively explain the differences in a way that the development department can easily understand. Therefore, the progress of internal patent proposal (patent disclosure) discussions is extremely laborious and time-consuming. Creating patent disclosures, including drawings and claims, internally within a company typically takes at least several days to several weeks. Many companies outsource the drafting of patent specifications and filing of patent applications to third-party patent and trademark offices or law firms without patent publication, resulting in increased communication and understanding costs, such as developers having to communicate the technology again, delaying patent applications and impacting the company's technical rights. Furthermore, for small and medium-sized enterprises (SMEs) and venture companies without intellectual property departments, patent searches are entirely entrusted to third-party patent and trademark offices and law firms. However, since there are no patent proposals (patent disclosures), this process is primarily conducted through presentations or chat. This model generally causes inventors to expend considerable effort and time on technical communication. The cost of communication and understanding in th