Trademark examination method and device, electronic equipment and storage medium

Abstract

The invention provides a trademark review method and device, electronic equipment and a storage medium, and the method comprises the steps: building an AI text model based on a multi-version trademark review guide file, wherein the trademark review guide file is used for indicating a similar commodity and service distinguishing table; performing automatic cross retrieval on commodities and services in the trademark to be examined based on the AI text model to obtain a retrieval result; performing automatic commodity comparison on commodities and services in the trademark to be examined based on the AI text model to obtain a comparison result; performing integrated analysis on the retrieval result and the comparison result to obtain a trademark review result; and generating an examination result notification corresponding to the trademark examination result based on a pre-configured notification template. Therefore, the integration of the multi-version trademark review guidance files can be realized in the process of constructing the AI text model, and the automatic processing of trademark review is realized, so that the trademark review efficiency is effectively improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of trademark examination, and in particular to a trademark examination method and device, an electronic device and a storage medium. BACKGROUND

[0002] In the technical field of trademark examination, there is a problem that it is time-consuming and laborious to maintain basic data, that is, the Similar Goods and Services Classification Table is usually processed by manual data, which requires high labor cost and time cost. Furthermore, because of the lack of basic data, the comparison range can only be limited in the Similar Goods and Services Classification Table of the current year, resulting in poor trademark examination effect. SUMMARY

[0003] The present disclosure aims to at least partially solve one of the technical problems in the related art.

[0004] To this end, the present disclosure aims to provide a trademark examination method and device, an electronic device and a storage medium, so that the integration of multiple versions of trademark examination guidance files can be realized in the process of constructing an AI text model, and the automation of trademark examination can be realized, thereby effectively improving the trademark examination efficiency.

[0005] To achieve the above-mentioned purpose, the trademark examination method according to the first aspect of the present disclosure comprises: constructing an AI text model based on multiple versions of trademark examination guidance files, wherein the trademark examination guidance files are used to indicate the Similar Goods and Services Classification Table; automatically cross-retrieving the goods and services in the trademark to be examined based on the AI text model to obtain a retrieval result; automatically comparing the goods in the trademark to be examined based on the AI text model to obtain a comparison result; integrating and analyzing the retrieval result and the comparison result to obtain a trademark examination result; generating an examination result notice corresponding to the trademark examination result based on a pre-configured notice template.

[0006] To achieve the above-mentioned purpose, the trademark examination device according to the second aspect of the present disclosure comprises: a model construction module configured to construct an AI text model based on multiple versions of trademark examination guidance files, wherein the trademark examination guidance files are used to indicate the Similar Goods and Services Classification Table; a retrieval module configured to automatically cross-retrieve the goods and services in the trademark to be examined based on the AI text model to obtain a retrieval result; a comparison module configured to automatically compare the goods in the trademark to be examined based on the AI text model to obtain a comparison result; The analysis module is used to integrate and analyze the search results and the comparison results to obtain the trademark examination results; The generation module is used to generate an examination result notification corresponding to the trademark examination result based on a pre-configured notification template.

[0007] The electronic device proposed in the third aspect of this disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the trademark examination method proposed in the first aspect of this disclosure.

[0008] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the trademark examination method as proposed in the first aspect of this disclosure.

[0009] The fifth aspect of this disclosure provides a computer program product that, when instructions in the computer program product are executed by a processor, performs a trademark examination method as proposed in the first aspect of this disclosure.

[0010] The trademark examination method, apparatus, electronic device, and storage medium disclosed herein construct an AI text model based on multiple versions of trademark examination guidance documents, which are used to indicate similar goods and services distinction tables. The AI ​​text model is used to automatically cross-reference the goods and services in the trademark under examination to obtain search results. The AI ​​text model is also used to automatically compare the goods and services in the trademark under examination to obtain comparison results. The search results and comparison results are integrated and analyzed to obtain the trademark examination result. Finally, based on a pre-configured notification template, an examination result notification corresponding to the trademark examination result is generated. Therefore, the integration of multiple versions of trademark examination guidance documents during the construction of the AI ​​text model and the automation of trademark examination processing can be achieved, thereby effectively improving the efficiency of trademark examination.

[0011] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0012] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which: Figure 1 This is a schematic flowchart of a trademark examination method proposed in one embodiment of this disclosure; Figure 2 This is a schematic diagram of a commodity review process based on this disclosure; Figure 3This is a schematic diagram of the structure of a trademark examination device according to an embodiment of this disclosure; Figure 4 This is a block diagram of an electronic device according to an embodiment of the present application. Detailed Implementation

[0013] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0014] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0015] Figure 1 This is a schematic flowchart of a trademark examination method proposed in one embodiment of this disclosure.

[0016] It should be noted that the subject of the trademark examination method in this embodiment is a trademark examination device, which can be implemented by software and / or hardware. The device can be configured in an electronic device, which may include, but is not limited to, a terminal, a server, etc., such as a mobile phone, a PDA, etc.

[0017] like Figure 1 As shown, the trademark examination method includes: S101: Build an AI text model based on multiple versions of trademark examination guidance documents, which are used to indicate the classification table of similar goods and services.

[0018] Among them, trademark examination guidance documents can refer to documents used for trademark examination, such as the "Classification of Similar Goods and Services".

[0019] Among them, AI text model can refer to a model used for intelligent examination of trademarks pending review.

[0020] It is understood that trademark examination guidance documents may be updated over time. Therefore, in this embodiment of the disclosure, an AI text model can be built based on multiple versions of trademark examination guidance documents, thereby effectively improving the indicative effect of the obtained AI text model.

[0021] Optionally, in some embodiments, the AI ​​text model is configured with a prompt word engineering, which includes hierarchical parsing logic and approximation relationship judgment logic. The approximation relationship judgment logic includes: by default, goods and services within the same similarity group are generally considered similar; if not all goods and services within the same similarity group are similar, they are divided into several parts according to similarity relationships, with goods and services within the same part generally considered similar, and goods and services between different parts generally not considered similar; if other situations exist, a custom similarity association is configured. Thus, this rule transforms the complex human review experience in the similarity goods and services distinction table into clear, structured, and executable machine instructions, laying the logical foundation for the AI ​​model to perform automated approximation judgments. It enables AI to understand and apply the inherent hierarchy and exception relationships of "similarity groups" like human reviewers, thereby achieving a qualitative leap from simple data retrieval to intelligent judgment with logical reasoning capabilities, providing key and reliable core decision-making capabilities for subsequent fully automated review processes.

[0022] S102: Based on the AI ​​text model, automatically cross-reference the goods and services in the trademark under review to obtain the search results.

[0023] The search results can be used to indicate the similarity between the goods and services in the pending trademark and the goods and services in existing trademarks.

[0024] Optionally, in some embodiments, an AI text model is used to automatically cross-reference the goods and services in the trademark under review, based on at least one of the following retrieval algorithms: Chinese character retrieval algorithm; Pinyin retrieval algorithm; English retrieval algorithm; number retrieval algorithm; initial character retrieval algorithm; image retrieval algorithm; and sound retrieval algorithm. Therefore, the reliability and accuracy of the retrieval results can be effectively improved by using multiple retrieval algorithms.

[0025] S103: Based on an AI text model, automatically compare the goods and services in the trademark under examination to obtain the comparison results.

[0026] Among them, the comparison results can be used to indicate the comparison results between the goods in the trademark under examination and existing goods.

[0027] Optionally, in some embodiments, the comparison results may also include: the results of comparing non-standard commodity data with the goods and services in the trademark to be examined. This can further enhance the indicative effect of the obtained comparison results.

[0028] In this embodiment of the disclosure, when an automatic comparison of goods and services in a trademark under examination is performed based on an AI text model to obtain the comparison results, reliable reference information can be provided for obtaining the trademark examination results subsequently.

[0029] S104: Integrate and analyze the search results and comparison results to obtain the trademark examination results.

[0030] The results of trademark examination can be used to indicate whether a trademark under examination can pass the examination.

[0031] Optionally, in some embodiments, when integrating and analyzing the search results and comparison results to obtain the trademark examination result, if the search results indicate no similar citations, the trademark examination result for the corresponding trademark under examination is determined to be preliminary approval; if the search results indicate citations under the completely identical algorithm, and the comparison results indicate a similar relationship, the trademark examination result for the corresponding trademark under examination is determined to be rejection; if the search results indicate non-completely identical similar citations, and no completely identical citations, the search results are retained for manual examination. This allows for accurate analysis of trademark examination results in various scenarios.

[0032] S105: Generate an examination result notification corresponding to the trademark examination result based on a pre-configured notification template.

[0033] Optionally, in some embodiments, the review result notification may undergo content review; after the content review is passed, the review result notification is issued. Thus, the review process for the review result notification can be implemented before the notification is issued.

[0034] In this embodiment, an AI text model is constructed based on multiple versions of trademark examination guidance documents, which serve as indicators for similar goods and services distinction tables. The AI ​​text model is used to automatically cross-reference the goods and services in the trademark under examination to obtain search results. The AI ​​text model is also used to automatically compare the goods and services in the trademark under examination to obtain comparison results. The search results and comparison results are integrated and analyzed to obtain the trademark examination result. Finally, based on a pre-configured notification template, an examination result notification corresponding to the trademark examination result is generated. Therefore, the integration of multiple versions of trademark examination guidance documents can be achieved during the construction of the AI ​​text model, and the trademark examination process can be automated, thereby effectively improving the efficiency of trademark examination.

[0035] In summary, as described in the above embodiments, Figure 2 As shown, Figure 2 This is a schematic diagram of a commodity review process based on this disclosure, wherein: (i) The "Similar Goods and Services Differentiation Table" is processed by an AI model. That is, using an AI text model, a prompt word engineering comparison and association logic is set up. When the comparison function runs, the AI ​​model will first parse the corresponding data level of the "Similar Goods and Services Differentiation Table", and then make a similarity judgment based on the established prompt word engineering logic. First, the approximate association judgment logic rule should follow the principle that "goods and service items within the same similar group are generally regarded as similar goods and services. If not all goods and service items within the same similar group are judged as similar, then the goods and service items are divided into several parts according to the similar relationship, represented by Chinese serial numbers (1), (2) …… Goods and service items within the same part are generally judged as similar, while those between different parts are generally not judged as similar. For some special cases, detailed explanations are provided in the form of "notes" after the similar group. If there are exceptions, the similar association relationship can be customized and configured." Second, the approximate association relationships between this version and cross - versions clarified in the "notes" are also set as comparison logics in the prompt word engineering. When the AI runs the comparison logic, it first compares the approximate relationships of this version and then the cross - version approximate association relationships in the second order.

[0036] (2) The original [Goods Comparison] function only made approximate comparisons using the current year's "Distinction Table of Similar Goods and Services". This application expands the comparison scope to all versions of the "Distinction Table of Similar Goods and Services", and the comparison scope should also include non - standard goods.

[0037] (3) It solves the problem that the manual piece - by - piece review under some types of pending review cases causes a long review process time and low efficiency. The original domestic registration substantive review (Initial Substantive Review) was a pure manual review, but now it can be optimized to an automatic review. In the case of automatic cross - checking, the Chinese character retrieval algorithms are exact same, partially same, adding Chinese characters at any position, changing Chinese characters, subtracting Chinese characters, contained in other trademarks, containing other trademarks, changing order, reverse order, same pronunciation; the pinyin retrieval algorithm is that the pinyin trademarks are the same; the English retrieval algorithms are exact same, partially same, adding letters at any position, changing letters, subtracting letters, contained in other trademarks, containing other trademarks, changing order, reverse order, same pronunciation, 20% similarity; the digital retrieval algorithms are exact same, partially same, changing any one digit, changing order, contained in other trademarks, containing other trademarks; the initial character retrieval algorithms are exact same, same order change, (specified) similar glyphs; the graphic retrieval algorithms are all - code retrieval (union), partial - code retrieval (intersection); the sound retrieval algorithms are all - code retrieval (union), partial - code retrieval (intersection).

[0038] The system analyzes the retrieval results by itself. If there are no approximate citation documents, a superscript [Self - Initial] is added to the pending review list, and the preliminary conclusion is preliminary approval.

[0039] The system analyzes the retrieval results by itself. If there are citation documents under the exact - same algorithm and there is an approximate association relationship through the automatic comparison of goods comparison, a superscript [Self - Reject] is added to the pending review list, and the preliminary conclusion is rejection.

[0040] The system will automatically analyze the search results. If there are non-identical similar citations, but no completely identical citations, the search results will be retained without a final conclusion, pending the examiner's decision. For retrieved citations, if a comparison of the goods determines that they are similar, the corresponding goods / services will be highlighted in red for a prominent indication.

[0041] (iv) This application solves the problem of the time-consuming and inefficient manual generation of notices for some types of pending documents. The original Trademark Rejection Notice was generated manually during the examination. This application intends to automatically generate the Trademark Rejection Notice according to the rules. After the Trademark Rejection Notice is generated, the examiner can perform a second check and pre-configure various notice content templates. The examiner can make changes with one click. Once the check is completed, it can proceed to the next stage.

[0042] Based on the above embodiments, this disclosure can achieve at least the following technical effects: (1) Successfully solved the efficiency problem of manual maintenance of basic data, and fundamentally upgraded the data processing mode of the "Similar Goods and Services Classification Table" - bidding farewell to the traditional method of relying on manual entry, verification and updating of each item, and fully switching to AI model automated processing. In the past manual maintenance stage, not only did a lot of manpower cost to complete repetitive data sorting work, but it also faced problems such as long operation cycle, high human error rate and data iteration lag. Especially when faced with the dynamic adjustment of the massive number of goods and services categories in the classification table, manual processing could not balance efficiency and accuracy, which seriously restricted the pace of subsequent business development. The application of AI model, through algorithm to intelligently identify, classify and verify data, not only greatly reduced the time cost of data processing, but also significantly reduced the error caused by human operation. At the same time, it can quickly respond to data update needs, making the maintenance of the classification table more efficient, more accurate and more adaptable to the dynamic needs of business development.

[0043] (2) Successfully solved the problem of limited comparison scope caused by missing basic data. This application completely broke the previous constraint that the comparison scope could only be limited to the current year's "Classification Table of Similar Goods and Services" by making up for the shortcomings of historical version data resources.

[0044] Previously, due to missing basic data, the core data of historical versions of the similar goods and services classification table could not be effectively integrated and reused. This limited comparison work to the corresponding data information of the current year, making it impossible to trace the evolution of goods and services classifications across different periods or to discover similar correlations across years. This resulted in the easy omission of key information due to incomplete comparison dimensions, affecting the accuracy and comprehensiveness of subsequent judgments. This application, however, improves the basic data system through technological innovation, incorporating all historical versions of the "Similar Goods and Services Classification Table" into the data reserve, achieving a leapfrog upgrade in comparison scope from "single year" to "full version coverage." Relying on complete historical data support, this application can conduct comprehensive and multi-dimensional similar correlation comparisons, not only covering differences in goods and services classifications across different periods but also accurately capturing similar correlation logic across versions, making the comparison results more complete, traceable, and valuable for reference.

[0045] (3) Successfully solved the industry pain points of long process time and low efficiency caused by manual review of some types of documents under review, and completely changed the inefficiency of the traditional review model through technological innovation.

[0046] Previously, the manual review process required reviewers to meticulously check, judge, and record each item. This not only incurred significant manpower costs for repetitive tasks but was also hampered by limited staff capacity, varying experience, and subjective judgment biases. With a massive volume of review items, the review cycle lengthened considerably, often resulting in "queues for review," severely slowing down the overall business process. Furthermore, manual processing was prone to omissions and errors, affecting consistency and increasing subsequent review workload, creating a vicious cycle of "time-consuming, inefficient, and repetitive" processes. This solution, however, optimizes the review mechanism, replacing the traditional manual review process for certain types of items. It significantly reduces the time required for single-item reviews and enables parallel processing of multiple items. While improving review efficiency, it ensures consistent review standards and accurate results, making the review process smoother, more efficient, and adaptable to the processing needs of high-volume business scenarios.

[0047] (4) Successfully broke through the efficiency bottleneck of manually generating notification letters for some types of pending documents, completely solved the core pain point of long time and low efficiency in the traditional mode of notification letter production, and realized the efficient upgrade of the notification letter generation process.

[0048] Previously, the manual generation of notifications, one by one, required staff to complete multiple repetitive steps: first, retrieving the review results and basic information of the corresponding business, then manually filling them into a fixed notification template, followed by spending a significant amount of time verifying the accuracy of the information and adjusting the format. This model was not only labor-intensive but also affected by factors such as staff energy and operational proficiency. When faced with batch notification requests, queues were likely to occur, significantly lengthening the overall production cycle and severely slowing down business processes. Furthermore, manual operation inevitably resulted in issues such as missing information, inconsistent formats, and data mismatches, requiring additional costs for secondary verification and correction, further reducing overall work efficiency and impacting the experience of business stakeholders. This solution, through process optimization and technological empowerment, replaces the traditional manual one-by-one processing model. It automatically retrieves business data, intelligently matches templates, and completes information filling, while simultaneously achieving standardized format verification and rapid output. This significantly reduces the generation time of a single notification and avoids human error, making notification generation more efficient, accurate, and standardized, fully adapting to the high-efficiency workflow requirements of batch business scenarios.

[0049] Figure 3 This is a schematic diagram of the structure of a trademark examination device according to an embodiment of this disclosure.

[0050] like Figure 3 As shown, the trademark examination device 30 includes: Model building module 301 is used to build an AI text model based on multiple versions of trademark examination guidance documents, which are used to indicate the classification table of similar goods and services. The retrieval module 302 is used to automatically perform cross-referencing of goods and services in the trademark under review based on an AI text model in order to obtain retrieval results; The comparison module 303 is used to automatically compare the goods and services in the trademark under examination based on the AI ​​text model in order to obtain the comparison results; Analysis module 304 is used to integrate and analyze the search results and comparison results to obtain the trademark examination results; The generation module 305 is used to generate an examination result notification corresponding to the trademark examination result based on a pre-configured notification template.

[0051] It should be noted that the foregoing explanation of the trademark examination method also applies to the trademark examination device of this embodiment, and will not be repeated here.

[0052] In this embodiment, an AI text model is constructed based on multiple versions of trademark examination guidance documents, which serve as indicators for similar goods and services distinction tables. The AI ​​text model is used to automatically cross-reference the goods and services in the trademark under examination to obtain search results. The AI ​​text model is also used to automatically compare the goods and services in the trademark under examination to obtain comparison results. The search results and comparison results are integrated and analyzed to obtain the trademark examination result. Finally, based on a pre-configured notification template, an examination result notification corresponding to the trademark examination result is generated. Therefore, the integration of multiple versions of trademark examination guidance documents can be achieved during the construction of the AI ​​text model, and the trademark examination process can be automated, thereby effectively improving the efficiency of trademark examination.

[0053] According to embodiments of this application, this application also provides an electronic device and a readable storage medium.

[0054] Figure 4 This is a block diagram of an electronic device according to an embodiment of the present application.

[0055] like Figure 4 As shown, the electronic device includes: The memory 401, the processor 402, and the computer instructions stored in the memory 401 and executable on the processor 402.

[0056] When processor 402 executes instructions, it implements the trademark examination method provided in the above embodiments.

[0057] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0058] The memory 401 is used to store computer instructions that can be executed on the processor 402.

[0059] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0060] The processor 402 is used to implement the trademark examination method of the above embodiments when executing the program.

[0061] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0062] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0063] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0064] This application also proposes a computer program product that implements the trademark examination method of the embodiments of this application when the instruction processor in the computer program product is executed.

[0065] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0066] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0067] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0068] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning paper or other media, followed by editing, interpreting or otherwise processing as necessary, and then stored in computer memory.

[0069] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0070] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0071] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0072] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A trademark examination method, characterized in that, include: An AI text model is constructed based on multiple versions of trademark examination guidance documents, which are used to indicate the classification table of similar goods and services. The AI ​​text model is used to automatically cross-reference the goods and services in the trademark under review to obtain the search results; The AI ​​text model is used to automatically compare the goods and services in the trademarks to be examined to obtain the comparison results. The search results and comparison results are integrated and analyzed to obtain the trademark examination results; Based on a pre-configured notification template, an examination result notification corresponding to the trademark examination result is generated.

2. The method as described in claim 1, characterized in that, The AI ​​text model is configured with a prompt word project, which includes hierarchical parsing logic and approximation relationship judgment logic. The approximation relationship judgment logic includes: By default, goods and services within the same similar group are generally considered similar. If the goods and services within the same similar group are not all similar, they shall be divided into several parts according to the similarity relationship. Goods and services within the same part shall be judged to be similar in principle, while goods and services between different parts shall not be judged to be similar in principle. If other conditions exist, a similar relationship can be configured in a custom way.

3. The method as described in claim 1, characterized in that, The AI ​​text model is used to perform automatic cross-referencing of goods and services in the trademark under review, based on at least one of the following retrieval algorithms: Chinese character retrieval algorithm; Pinyin search algorithm; English search algorithm; Number retrieval algorithm; First character search algorithm; Image retrieval algorithm; Sound retrieval algorithm.

4. The method as described in claim 1, characterized in that, The process of integrating and analyzing the search results and the comparison results to obtain the trademark examination results includes: If the search results indicate that there are no similar citations, then the trademark examination result corresponding to the trademark under examination is determined to be a preliminary approval; If the search results indicate that there are cited documents under the exact same algorithm, and the comparison results indicate that there is a similar relationship, then the trademark examination result corresponding to the trademark under examination is determined to be a rejection; If the search results indicate that there are not completely identical similar citations, but no completely identical citations, then the search results will be retained for manual review.

5. The method as described in claim 1, characterized in that, The comparison results also include the results of comparing non-standard commodity data with the goods and services in the trademark under examination.

6. The method as described in claim 1, characterized in that, The method further includes: The content of the review result notification shall be reviewed; After the content review is approved, the review result notification will be issued.

7. A trademark examination device, characterized in that, include: The model building module is used to build an AI text model based on multiple versions of trademark examination guidance documents, which are used to indicate the similar goods and services distinction table. The retrieval module is used to automatically perform cross-referencing of goods and services in the trademark under review based on an AI text model to obtain retrieval results; The comparison module is used to automatically compare the goods and services in the trademark under examination based on an AI text model to obtain the comparison results; The analysis module is used to integrate and analyze the search results and the comparison results to obtain the trademark examination results; The generation module is used to generate an examination result notification corresponding to the trademark examination result based on a pre-configured notification template.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

Images