System

Abstract

To provide a system capable of confirming whether or not a contract or a service provision violates the law even without having expertise.SOLUTION: A system includes a selection reception part, an input reception part, a selection part, an analysis part and a providing part. The selection reception part receives law selection from a user; the input reception part receives the input of a contract or service provision from the user; the selection part selects a prompt corresponding to the law received by the reception part; the analysis part inputs the prompt selected by the selection part and the contract or the service provision received by the input receptionist part into generation AI to analyze the contract or the service provision in the generation AI; and the providing part provides the result analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of checking whether contracts and service terms and conditions violated the law was complicated and difficult to carry out without specialized knowledge.

[0005] The system according to the embodiment aims to check whether contracts and service terms and conditions violate the law, even without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection receiving unit, an input receiving unit, a selection unit, an analysis unit, and a provision unit. The selection receiving unit receives a selection of law from a user. The input receiving unit receives input of a contract or service terms from a user. The selection unit selects a prompt corresponding to the law received by the reception unit. The analysis unit inputs the prompt selected by the selection unit and the contract or service terms received by the input receiving unit to a generation AI, and analyzes the contract or service terms to the generation AI. The provision unit provides the results analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can check whether a contract or service terms and conditions violate the law, even if the user does not have specialized knowledge. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is a system that checks whether a contract or service terms and conditions violate the law as part of a digital transformation strategy for the legal department. This system operates by a user selecting a law to check and then inputting the contract or service terms and conditions to check. The system selects a prompt corresponding to the selected law and causes the generation AI to analyze the contract or service terms and conditions input. The analysis result is provided to the user and used for legal consultation with an expert. For example, the user selects the law to check. For example, the user selects from the Radio Law, the Telecommunications Business Law, the Equipment Regulations, etc. This selection is accepted by a selection acceptance unit (which may include AI processing). Next, the user inputs the contract or service terms and conditions to check. This input is accepted by an input acceptance unit (which may include AI processing). Then, the user selects a prompt corresponding to the law accepted by the selection acceptance unit. This selection is made by a selection unit (which may include AI processing). The selected prompt and the input contract or service terms and conditions are input to the generation AI, and the generation AI analyzes the contract or service terms and conditions. This analysis is made by an analysis unit (which may include AI processing). Finally, the analysis results are provided to the user by the providing unit (which may include AI processing). The user consults with an expert on legal matters based on the analysis results. This system improves the work efficiency of the legal department, and enables quick confirmation as to whether contracts and service terms and conditions violate the law. This system improves the work efficiency of the legal department, and enables quick confirmation as to whether contracts and service terms and conditions violate the law.

[0029] The legal confirmation system according to the embodiment includes a selection receiving unit, an input receiving unit, a selection unit, an analysis unit, and a provision unit. The selection receiving unit receives a selection of a law from a user. For example, when a user selects a law to be confirmed, the selection receiving unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, and enables the user to select. The selection receiving unit can also present a selection of laws using AI. The input receiving unit receives input of a contract or service terms from a user. For example, when a user inputs a contract or service terms to be confirmed, the input receiving unit can receive digital data such as text format or PDF format. The input receiving unit can also convert the input data into a format that is easy to analyze using AI. The selection unit selects a prompt corresponding to the law accepted by the selection receiving unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input receiving unit to the generation AI, and causes the generation AI to analyze the contract or service terms. For example, the analysis unit inputs a prompt and a contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI analyzes the contract or service terms using a text generation AI (e.g., LLM). The provision unit provides the result analyzed by the analysis unit to the user. For example, the provision unit displays the analysis result to the user. The provision unit can also provide the analysis result to the user using AI. As a result, the legal confirmation system according to the embodiment analyzes the contract or service terms based on the law selected by the user and provides the result, thereby improving the work efficiency of the legal department.

[0030] The selection reception unit accepts the selection of a law from the user. For example, when the user selects a law he or she wants to check, the selection reception unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, allowing the user to make a selection. The selection reception unit can also use AI to present options for laws. Specifically, the selection reception unit has a function of preferentially presenting laws with high relevance based on the user's past selection history and business content. For example, if the user has frequently checked the Radio Law in the past, the selection reception unit will preferentially display the Radio Law. In addition, the selection reception unit can automatically filter laws specific to the user's business content and industry and provide appropriate options. In addition, the selection reception unit also has a function of obtaining the latest legal amendment information and precedents related to the law selected by the user in real time and notifying the user. This allows the user to always perform confirmation work based on the latest legal information. The selection reception unit can also record the user's selection and use it for future analysis and improvement. For example, data such as which laws are selected most frequently and which laws are considered important in which industry can be collected and used to improve the system and develop new functions. This enables the selection receiving unit to support flexible and efficient legal selection according to the user's needs, thereby improving the accuracy and efficiency of legal confirmation work.

[0031] The input acceptance unit accepts input of a contract or terms of service from a user. For example, when a user inputs a contract or terms of service that he or she wants to confirm, the input acceptance unit can accept digital data in text format, PDF format, or other formats. The input acceptance unit can also use AI to convert the input data into a format that is easy to analyze. Specifically, the input acceptance unit uses OCR (optical character recognition) technology to convert PDF-format contracts and terms of service into text data. This makes it possible to handle paper contracts and scanned documents as digital data. The input acceptance unit also uses natural language processing technology to analyze the grammar and structure of the input text data and format the data so that the analysis unit can process it efficiently. For example, it has a function to automatically identify each clause of a contract, divide it into clauses, and save it in a database. In addition, the input acceptance unit also has a function to check the content of the data entered by the user in advance, detect missing information or data in an inappropriate format, and notify the user. This allows the user to prevent input errors and provide accurate data. The input acceptance unit records the user's input history, which also contributes to the efficiency of future data input. For example, templates of contracts or service terms that have been input in the past can be saved and reused when inputting similar documents. This allows the input reception unit to streamline the data input work of the user and improve the performance of the entire legal confirmation system.

[0032] The selection unit selects a prompt corresponding to the law accepted by the selection acceptance unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. Specifically, the selection unit generates an optimal prompt based on the content of the law selected by the user or a specific clause. For example, when checking a contract related to the Radio Law, the selection unit generates a specific prompt such as "Please confirm the legality of the contract based on Article ○ of the Radio Law." The selection unit also has a function to continuously improve the accuracy of the prompt based on past analysis results and user feedback. This allows the selection unit to always generate optimal prompts and improve the analysis accuracy of the generation AI. Furthermore, the selection unit can generate multiple prompts simultaneously and have the generation AI perform parallel processing. This allows the contract or service terms to be analyzed from multiple perspectives, resulting in more comprehensive analysis results. The selection unit also has a function to set the priority of prompts according to the user's selection and business content. For example, prompts related to clauses of high importance or high-risk content are generated preferentially and input to the generation AI. This enables the selection unit to support flexible and efficient prompt selection according to the user's needs, thereby improving the accuracy and efficiency of legal confirmation work.

[0033] The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input acceptance unit to the generation AI, and has the generation AI analyze the contract or service terms. For example, the analysis unit inputs the prompt and the contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI analyzes the contract or service terms using a text generation AI (e.g., LLM). Specifically, the generation AI uses natural language processing technology to analyze each clause of the contract or service terms in detail and identify legal risks and deficiencies. For example, the generation AI detects inconsistencies and unclear expressions between clauses of the contract and makes specific suggestions for correction to the user. The generation AI can also refer to past precedents and legal documents to evaluate the legality of the contract or service terms. This allows the analysis unit to quickly and accurately evaluate the legal risks of the document provided by the user and propose appropriate countermeasures. Furthermore, the analysis unit also has a function of providing a specific action plan to the user based on the analysis results of the generation AI. For example, if a specific clause in a contract needs to be revised, the system will provide instructions on how to make the revision and provide reference examples. The analysis unit also builds a feedback loop to continuously learn from the analysis results of the generative AI and improve the accuracy of the analysis. This allows the analysis unit to always provide highly accurate analysis based on the latest legal information, improving the reliability and efficiency of the entire legal confirmation system.

[0034] The provision unit provides the user with the results analyzed by the analysis unit. For example, the provision unit displays the analysis results to the user. The provision unit can also use AI to provide the analysis results to the user. Specifically, the provision unit provides a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays an overview of the analysis results, important risk items, and correction suggestions at a glance. The provision unit also has a function for displaying the analysis results for each clause and specific correction suggestions so that the user can check the analysis results in detail. Furthermore, the provision unit provides an API for integrating the analysis results into the user's business flow and can link with other systems and tools. For example, the provision unit can link with the legal department's workflow management system and document management system to automatically import the analysis results. This allows the user to efficiently utilize the analysis results and make quick decisions. The provision unit also has a function for collecting user feedback and continuously improving the accuracy of the analysis results and the display method. For example, when the user comments or evaluates the provided analysis results, the system learns the feedback and improves the accuracy of the analysis results from the next time onwards. This enables the providing unit to always provide optimal analysis results to the user, thereby improving the efficiency and accuracy of legal confirmation work.

[0035] The analysis unit can analyze whether the contract or the service terms and conditions conflict with the law accepted by the selection acceptance unit. The analysis unit, for example, analyzes whether the contract or the service terms and conditions conflict with the law. For example, the analysis unit uses a generation AI to analyze the contents of the contract or the service terms and conditions and determines whether they match or do not match the legal provisions. The generation AI analyzes the contents of the contract or the service terms and conditions using a text generation AI (for example, LLM). The generation AI receives a prompt, "Please check whether this contract conflicts with the law," and analyzes the contents of the contract. The generation AI analyzes each clause of the contract and determines whether they match or do not match the legal provisions. For example, the generation AI analyzes whether a clause of the contract conflicts with a specific clause of the Radio Law. The generation AI analyzes whether a clause of the contract conflicts with a specific clause of the Telecommunications Business Act. The generation AI analyzes whether a clause of the contract conflicts with a specific clause of the Equipment Regulations. This allows the analysis unit to reduce legal risks by analyzing whether or not the contract or service terms and conditions violate the law.

[0036] The selection reception unit can analyze the past law selection history and propose an option of law suitable for the user. The selection reception unit, for example, analyzes the past law selection history and proposes the optimal option of law to the user. For example, the selection reception unit automatically displays the law that the user frequently selected in the past as a candidate. The selection reception unit preferentially proposes a law related to a specific business from the user's past selection history. The selection reception unit analyzes the user's selection history and predicts and proposes the law to be used in a specific time period. In this way, the selection reception unit improves the efficiency of the user's selection by proposing the optimal option of law based on the past selection history. Part or all of the above-mentioned processing in the selection reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection reception unit inputs the past law selection history into AI, and the AI ​​proposes the optimal option of law.

[0037] The selection reception unit can perform filtering based on the user's business content and field of interest when selecting a law. The selection reception unit performs filtering based on the user's business content and field of interest when selecting a law, for example. For example, the selection reception unit preferentially displays highly relevant laws based on the user's business content. The selection reception unit filters and presents related laws based on the user's field of interest. The selection reception unit combines the user's business content and field of interest to propose optimal legal options. As a result, the selection reception unit preferentially presents highly relevant laws by filtering laws based on the user's business content and field of interest. A part or all of the above-mentioned processing in the selection reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection reception unit inputs the user's business content and field of interest into AI, and the AI ​​proposes optimal legal options.

[0038] The selection receiving unit can preferentially present a law that is highly relevant in consideration of the geographical location information of the user when selecting a law. For example, the selection receiving unit preferentially presents a law that is highly relevant in consideration of the geographical location information of the user when selecting a law. For example, the selection receiving unit preferentially displays a law specific to a region based on the user's current location. The selection receiving unit filters and presents related laws in consideration of the geographical location information of the user. The selection receiving unit combines the geographical location information of the user and the business content to propose an optimal legal option. As a result, the selection receiving unit presents laws in consideration of the geographical location information of the user, thereby preferentially displaying a law specific to a region. A part or all of the above-mentioned processing in the selection receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection receiving unit inputs the geographical location information of the user to AI, and the AI ​​proposes an optimal legal option.

[0039] The selection reception unit can analyze the user's social media activity and suggest related laws when selecting a law. The selection reception unit, for example, analyzes the user's social media activity and suggests related laws when selecting a law. For example, the selection reception unit identifies laws of interest from the user's social media activity and presents them preferentially. The selection reception unit analyzes the user's social media activity and filters and suggests related laws. The selection reception unit combines the user's social media activity and business content to suggest optimal legal options. In this way, the selection reception unit analyzes the user's social media activity to preferentially present laws of interest. Part or all of the above-mentioned processing in the selection reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection reception unit inputs the user's social media activity into AI, and the AI ​​suggests optimal legal options.

[0040] The input acceptance unit can analyze the past input history and propose the optimal input format to the user. The input acceptance unit, for example, analyzes the past input history and proposes the optimal input format to the user. For example, the input acceptance unit automatically displays the format of the contract or service terms that the user frequently inputs in the past as a candidate. The input acceptance unit preferentially proposes the input method (voice, text, etc.) that the user used in the past. The input acceptance unit predicts and proposes the format of the contract or service terms to be used in a specific time period from the user's past input history. In this way, the input acceptance unit improves input efficiency by proposing the optimal input format based on the past input history. A part or all of the above-mentioned processing in the input acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the input acceptance unit inputs the past input history to AI, and the AI ​​proposes the optimal input format.

[0041] The input reception unit can perform filtering based on the user's business content and field of interest when inputting a contract or service terms. The input reception unit performs filtering based on the user's business content and field of interest when inputting a contract or service terms. For example, the input reception unit preferentially displays a highly relevant contract or service terms format based on the user's business content. The input reception unit filters and presents related contract or service terms formats based on the user's field of interest. The input reception unit combines the user's business content and field of interest to propose an optimal contract or service terms format. As a result, the input reception unit preferentially inputs highly relevant content by filtering the input based on the user's business content and field of interest. Part or all of the above-mentioned processing in the input reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the input reception unit inputs the user's business content and field of interest to AI, and the AI ​​proposes an optimal contract or service terms format.

[0042] The input reception unit can preferentially input highly relevant content in consideration of the geographical location information of the user when inputting a contract or service terms. For example, the input reception unit preferentially inputs highly relevant content in consideration of the geographical location information of the user when inputting a contract or service terms. For example, the input reception unit preferentially inputs the content of a contract or service terms specific to a region based on the user's current location. The input reception unit filters and inputs the content of the related contract or service terms in consideration of the geographical location information of the user. The input reception unit combines the geographical location information of the user and the business content to propose the optimal content of the contract or service terms. Thereby, the input reception unit preferentially inputs the content specific to the region by inputting in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the input reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the input reception unit inputs the geographical location information of the user to AI, and the AI ​​proposes the optimal content of the contract or service terms.

[0043] The input reception unit can analyze the social media activity of the user and input the relevant content when inputting the contract or the service terms. For example, the input reception unit analyzes the social media activity of the user and inputs the relevant content when inputting the contract or the service terms. For example, the input reception unit identifies the content of the contract or the service terms of interest from the user's social media activity and inputs it preferentially. The input reception unit analyzes the user's social media activity and filters and inputs the content of the related contract or the service terms. The input reception unit combines the user's social media activity and the business content to propose the optimal content of the contract or the service terms. Thereby, the input reception unit analyzes the user's social media activity and inputs the content of the interest preferentially. A part or all of the above-mentioned processing in the input reception unit may be performed using AI, for example, or may be performed without using AI. For example, the input reception unit inputs the user's social media activity to AI, and the AI ​​proposes the optimal content of the contract or the service terms.

[0044] The selection unit can analyze the past prompt selection history and propose the most suitable prompt to the user. The selection unit, for example, analyzes the past prompt selection history and proposes the most suitable prompt to the user. For example, the selection unit automatically displays prompts that the user has frequently selected in the past as candidates. The selection unit preferentially proposes prompts related to a specific task from the user's past selection history. The selection unit analyzes the user's selection history and predicts and proposes a prompt to be used in a specific time period. In this way, the selection unit improves the efficiency of the user's selection by proposing the most suitable prompt based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit inputs the past prompt selection history into AI, which proposes the most suitable prompt.

[0045] The selection unit can perform filtering based on the user's business content and field of interest when selecting a prompt. For example, the selection unit performs filtering based on the user's business content and field of interest when selecting a prompt. For example, the selection unit preferentially displays a highly relevant prompt based on the user's business content. The selection unit filters and presents related prompts based on the user's field of interest. The selection unit combines the user's business content and field of interest to propose an optimal prompt. As a result, the selection unit preferentially presents a highly relevant prompt by filtering the prompt based on the user's business content and field of interest. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit inputs the user's business content and field of interest into AI, and the AI ​​proposes an optimal prompt.

[0046] The selection unit can preferentially select a prompt with high relevance in consideration of the geographical location information of the user when selecting a prompt. For example, the selection unit preferentially selects a prompt with high relevance in consideration of the geographical location information of the user when selecting a prompt. For example, the selection unit preferentially displays a region-specific prompt based on the user's current location. The selection unit filters and presents related prompts in consideration of the geographical location information of the user. The selection unit combines the geographical location information of the user and the business content to propose an optimal prompt. As a result, the selection unit selects a prompt in consideration of the geographical location information of the user, thereby preferentially displaying a region-specific prompt. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit inputs the geographical location information of the user to AI, and the AI ​​proposes an optimal prompt.

[0047] The selection unit can analyze the user's social media activity and select a relevant prompt when selecting a prompt. For example, the selection unit analyzes the user's social media activity and selects a relevant prompt when selecting a prompt. For example, the selection unit identifies a prompt of interest from the user's social media activity and presents it preferentially. The selection unit analyzes the user's social media activity and filters and presents relevant prompts. The selection unit combines the user's social media activity and work content to propose an optimal prompt. In this way, the selection unit analyzes the user's social media activity to present a prompt of interest preferentially. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit inputs the user's social media activity into AI, and the AI ​​proposes an optimal prompt.

[0048] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit optimizes the analysis algorithm by referring to past analysis data, for example, during analysis. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit improves the accuracy of the analysis algorithm by referring to past analysis data. The analysis unit analyzes past analysis data and optimizes the analysis algorithm based on a specific pattern. As a result, the analysis unit improves the accuracy of the analysis by optimizing the analysis algorithm based on the past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs past analysis data to AI, and the AI ​​proposes an optimal analysis algorithm.

[0049] The analysis unit can apply different analysis methods depending on the category of the contract or service terms during analysis. The analysis unit applies different analysis methods depending on the category of the contract or service terms during analysis, for example. For example, the analysis unit selects an optimal analysis method depending on the category of the contract. The analysis unit applies different analysis methods based on the category of the service terms. The analysis unit analyzes the category of the contract or service terms and proposes an optimal analysis method. As a result, the analysis unit improves the accuracy of the analysis by applying the optimal analysis method depending on the category of the contract or service terms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the category of the contract or service terms into AI, and the AI ​​proposes the optimal analysis method.

[0050] The analysis unit can determine the priority of the analysis based on the submission time of the contract or the service terms and conditions during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the contract or the service terms and conditions during the analysis. For example, the analysis unit preferentially analyzes the contract or the service terms and conditions whose submission deadline is approaching. The analysis unit determines the priority of the analysis based on the submission time. The analysis unit optimizes the priority of the analysis taking into account the submission time and importance. As a result, the analysis unit determines the priority of the analysis based on the submission time, thereby preferentially analyzing the content whose submission deadline is approaching. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the submission time of the contract or the service terms and conditions into AI, and the AI ​​proposes the optimal analysis priority.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related literature of the contract or service terms during the analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to related literature of the contract or service terms during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related literature. The analysis unit optimizes the analysis method based on related literature of the contract or service terms. The analysis unit analyzes related literature and improves the accuracy of the analysis based on a specific pattern. As a result, the analysis unit improves the accuracy of the analysis by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs related literature of the contract or service terms into AI, and the AI ​​proposes an optimal analysis method.

[0052] The providing unit can select the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method based on the user's past feedback. The providing unit improves the accuracy of the providing method by referring to the user's feedback. The providing unit analyzes the user's past feedback and optimizes the providing method based on a specific pattern. Thereby, the providing unit improves the accuracy of the providing by selecting the optimal providing method based on the past feedback. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's past feedback to AI, and the AI ​​suggests the optimal providing method.

[0053] The providing unit can perform filtering based on the user's business content and field of interest at the time of providing. For example, the providing unit performs filtering based on the user's business content and field of interest at the time of providing. For example, the providing unit preferentially provides highly relevant analysis results based on the user's business content. The providing unit filters and provides related analysis results based on the user's field of interest. The providing unit combines the user's business content and field of interest to provide optimal analysis results. Thereby, the providing unit preferentially provides highly relevant content by filtering the analysis results based on the user's business content and field of interest. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's business content and field of interest into AI, and the AI ​​proposes optimal analysis results.

[0054] The providing unit can provide a highly relevant analysis result preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit provides a highly relevant analysis result preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit preferentially displays a region-specific analysis result based on the user's current location. The providing unit filters and provides the related analysis result in consideration of the geographical location information of the user. The providing unit combines the geographical location information of the user and the business content to provide an optimal analysis result. As a result, the providing unit provides the analysis result in consideration of the geographical location information of the user, thereby preferentially displaying the content specific to the region. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information of the user to AI, and the AI ​​proposes the optimal analysis result.

[0055] The providing unit can analyze the user's social media activity at the time of providing and provide the related analysis result. For example, the providing unit analyzes the user's social media activity at the time of providing and provides the related analysis result. For example, the providing unit identifies an analysis result of interest from the user's social media activity and provides it preferentially. The providing unit analyzes the user's social media activity and filters and provides the related analysis result. The providing unit combines the user's social media activity and the work content to provide the optimal analysis result. Thereby, the providing unit provides the content of interest preferentially by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the user's social media activity into AI, and the AI ​​proposes the optimal analysis result.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The selection receiving unit can automatically customize the legal options based on the user's past selection history. For example, the selection receiving unit preferentially displays laws that the user has frequently selected in the past. The selection receiving unit suggests highly relevant laws based on the user's business operations. The selection receiving unit analyzes the user's selection history and predicts and suggests laws to be used during a specific time period. In this way, the selection receiving unit makes user selections more efficient by suggesting optimal legal options based on the user's past selection history.

[0058] The analysis unit can refer to a database of similar past cases when analyzing the contents of a contract or service terms and conditions. For example, the analysis unit can refer to a database of contracts and service terms and conditions that have been analyzed in the past to identify similar clauses and problems. The analysis unit determines whether there is a match or a mismatch with specific legal provisions based on the results of the past analysis. The analysis unit utilizes past data to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to a database of similar past cases.

[0059] The selection receiving unit can perform filtering based on the user's business content and field of interest when selecting a law. For example, the selection receiving unit preferentially displays highly relevant laws based on the user's business content. The selection receiving unit filters and presents related laws based on the user's field of interest. The selection receiving unit combines the user's business content and field of interest to propose optimal legal options. In this way, the selection receiving unit preferentially presents highly relevant laws by filtering laws based on the user's business content and field of interest.

[0060] The analysis unit can refer to relevant legal precedents when analyzing the contract or the service terms. For example, the analysis unit analyzes how the clauses of the contract are interpreted based on past precedents. The analysis unit determines how the contents of the service terms are evaluated based on past precedents. The analysis unit improves the accuracy of the analysis by referring to relevant precedents. In this way, the analysis unit can improve the accuracy of the analysis of the contract or the service terms by referring to relevant legal precedents.

[0061] The providing unit can optimize the method of providing the analysis results based on the user's past feedback. For example, the providing unit preferentially uses a display format that the user has preferred in the past. The providing unit analyzes the user's feedback and suggests a specific display format. The providing unit customizes the method of providing the analysis results based on the user's past feedback. In this way, the providing unit improves the accuracy of provision by selecting the optimal providing method based on the user's past feedback.

[0062] The providing unit can provide highly relevant analysis results preferentially, taking into account the geographical location information of the user. For example, the providing unit preferentially displays analysis results specific to a region, based on the user's current location. The providing unit filters and provides related analysis results, taking into account the geographical location information of the user. The providing unit combines the geographical location information of the user with the business content to provide optimal analysis results. As a result, the providing unit provides analysis results taking into account the geographical location information of the user, thereby preferentially displaying content specific to the region.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The selection reception unit receives a selection of law from the user. For example, when the user selects the law he or she wants to check, the selection reception unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, allowing the user to make a selection. The selection reception unit can also use AI to present options for law. Step 2: The input reception unit receives input of the contract or service terms from the user. For example, when the user inputs the contract or service terms that he or she wants to confirm, the input reception unit can receive digital data in text format, PDF format, or other formats. The input reception unit can also use AI to convert the input data into a format that is easier to analyze. Step 3: The selection unit selects a prompt corresponding to the law accepted by the selection acceptance unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. Step 4: The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input acceptance unit to the generation AI, and has the generation AI analyze the contract or service terms. For example, the analysis unit inputs the prompt and the contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI parses the contract or service terms using a text generation AI (e.g., LLM). Step 5: The providing unit provides the result analyzed by the analysis unit to the user. For example, the providing unit displays the analysis result to the user. The providing unit can also provide the analysis result to the user using AI.

[0065] (Example 2) A system according to an embodiment of the present invention is a system that checks whether a contract or service terms and conditions violate the law as part of a digital transformation strategy for the legal department. This system operates by a user selecting a law to check and then inputting the contract or service terms and conditions to check. The system selects a prompt corresponding to the selected law and causes the generation AI to analyze the contract or service terms and conditions input. The analysis result is provided to the user and used for legal consultation with an expert. For example, the user selects the law to check. For example, the user selects from the Radio Law, the Telecommunications Business Law, the Equipment Regulations, etc. This selection is accepted by a selection acceptance unit (which may include AI processing). Next, the user inputs the contract or service terms and conditions to check. This input is accepted by an input acceptance unit (which may include AI processing). Then, the user selects a prompt corresponding to the law accepted by the selection acceptance unit. This selection is made by a selection unit (which may include AI processing). The selected prompt and the input contract or service terms and conditions are input to the generation AI, and the generation AI analyzes the contract or service terms and conditions. This analysis is made by an analysis unit (which may include AI processing). Finally, the analysis results are provided to the user by the providing unit (which may include AI processing). The user consults with an expert on legal matters based on the analysis results. This system improves the work efficiency of the legal department, and enables quick confirmation as to whether contracts and service terms and conditions violate the law. This system improves the work efficiency of the legal department, and enables quick confirmation as to whether contracts and service terms and conditions violate the law.

[0066] The legal confirmation system according to the embodiment includes a selection receiving unit, an input receiving unit, a selection unit, an analysis unit, and a provision unit. The selection receiving unit receives a selection of a law from a user. For example, when a user selects a law to be confirmed, the selection receiving unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, and enables the user to select. The selection receiving unit can also present a selection of laws using AI. The input receiving unit receives input of a contract or service terms from a user. For example, when a user inputs a contract or service terms to be confirmed, the input receiving unit can receive digital data such as text format or PDF format. The input receiving unit can also convert the input data into a format that is easy to analyze using AI. The selection unit selects a prompt corresponding to the law accepted by the selection receiving unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input receiving unit to the generation AI, and causes the generation AI to analyze the contract or service terms. For example, the analysis unit inputs a prompt and a contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI analyzes the contract or service terms using a text generation AI (e.g., LLM). The provision unit provides the result analyzed by the analysis unit to the user. For example, the provision unit displays the analysis result to the user. The provision unit can also provide the analysis result to the user using AI. As a result, the legal confirmation system according to the embodiment analyzes the contract or service terms based on the law selected by the user and provides the result, thereby improving the work efficiency of the legal department.

[0067] The selection reception unit accepts the selection of a law from the user. For example, when the user selects a law he or she wants to check, the selection reception unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, allowing the user to make a selection. The selection reception unit can also use AI to present options for laws. Specifically, the selection reception unit has a function of preferentially presenting laws with high relevance based on the user's past selection history and business content. For example, if the user has frequently checked the Radio Law in the past, the selection reception unit will preferentially display the Radio Law. In addition, the selection reception unit can automatically filter laws specific to the user's business content and industry and provide appropriate options. In addition, the selection reception unit also has a function of obtaining the latest legal amendment information and precedents related to the law selected by the user in real time and notifying the user. This allows the user to always perform confirmation work based on the latest legal information. The selection reception unit can also record the user's selection and use it for future analysis and improvement. For example, data such as which laws are selected most frequently and which laws are considered important in which industry can be collected and used to improve the system and develop new functions. This enables the selection receiving unit to support flexible and efficient legal selection according to the user's needs, thereby improving the accuracy and efficiency of legal confirmation work.

[0068] The input acceptance unit accepts input of a contract or terms of service from a user. For example, when a user inputs a contract or terms of service that he or she wants to confirm, the input acceptance unit can accept digital data in text format, PDF format, or other formats. The input acceptance unit can also use AI to convert the input data into a format that is easy to analyze. Specifically, the input acceptance unit uses OCR (optical character recognition) technology to convert PDF-format contracts and terms of service into text data. This makes it possible to handle paper contracts and scanned documents as digital data. The input acceptance unit also uses natural language processing technology to analyze the grammar and structure of the input text data and format the data so that the analysis unit can process it efficiently. For example, it has a function to automatically identify each clause of a contract, divide it into clauses, and save it in a database. In addition, the input acceptance unit also has a function to check the content of the data entered by the user in advance, detect missing information or data in an inappropriate format, and notify the user. This allows the user to prevent input errors and provide accurate data. The input acceptance unit records the user's input history, which also contributes to the efficiency of future data input. For example, templates of contracts or service terms that have been input in the past can be saved and reused when inputting similar documents. This allows the input reception unit to streamline the data input work of the user and improve the performance of the entire legal confirmation system.

[0069] The selection unit selects a prompt corresponding to the law accepted by the selection acceptance unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. Specifically, the selection unit generates an optimal prompt based on the content of the law selected by the user or a specific clause. For example, when checking a contract related to the Radio Law, the selection unit generates a specific prompt such as "Please confirm the legality of the contract based on Article ○ of the Radio Law." The selection unit also has a function to continuously improve the accuracy of the prompt based on past analysis results and user feedback. This allows the selection unit to always generate optimal prompts and improve the analysis accuracy of the generation AI. Furthermore, the selection unit can generate multiple prompts simultaneously and have the generation AI perform parallel processing. This allows the contract or service terms to be analyzed from multiple perspectives, resulting in more comprehensive analysis results. The selection unit also has a function to set the priority of prompts according to the user's selection and business content. For example, prompts related to clauses of high importance or high-risk content are generated preferentially and input to the generation AI. This enables the selection unit to support flexible and efficient prompt selection according to the user's needs, thereby improving the accuracy and efficiency of legal confirmation work.

[0070] The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input acceptance unit to the generation AI, and has the generation AI analyze the contract or service terms. For example, the analysis unit inputs the prompt and the contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI analyzes the contract or service terms using a text generation AI (e.g., LLM). Specifically, the generation AI uses natural language processing technology to analyze each clause of the contract or service terms in detail and identify legal risks and deficiencies. For example, the generation AI detects inconsistencies and unclear expressions between clauses of the contract and makes specific suggestions for correction to the user. The generation AI can also refer to past precedents and legal documents to evaluate the legality of the contract or service terms. This allows the analysis unit to quickly and accurately evaluate the legal risks of the document provided by the user and propose appropriate countermeasures. Furthermore, the analysis unit also has a function of providing a specific action plan to the user based on the analysis results of the generation AI. For example, if a specific clause in a contract needs to be revised, the system will provide instructions on how to make the revision and provide reference examples. The analysis unit also builds a feedback loop to continuously learn from the analysis results of the generative AI and improve the accuracy of the analysis. This allows the analysis unit to always provide highly accurate analysis based on the latest legal information, improving the reliability and efficiency of the entire legal confirmation system.

[0071] The provision unit provides the user with the results analyzed by the analysis unit. For example, the provision unit displays the analysis results to the user. The provision unit can also use AI to provide the analysis results to the user. Specifically, the provision unit provides a dashboard for visually displaying the analysis results in an easy-to-understand manner. The dashboard displays an overview of the analysis results, important risk items, and correction suggestions at a glance. The provision unit also has a function for displaying the analysis results for each clause and specific correction suggestions so that the user can check the analysis results in detail. Furthermore, the provision unit provides an API for integrating the analysis results into the user's business flow and can link with other systems and tools. For example, the provision unit can link with the legal department's workflow management system and document management system to automatically import the analysis results. This allows the user to efficiently utilize the analysis results and make quick decisions. The provision unit also has a function for collecting user feedback and continuously improving the accuracy of the analysis results and the display method. For example, when the user comments or evaluates the provided analysis results, the system learns the feedback and improves the accuracy of the analysis results from the next time onwards. This enables the providing unit to always provide optimal analysis results to the user, thereby improving the efficiency and accuracy of legal confirmation work.

[0072] The analysis unit can analyze whether the contract or the service terms and conditions conflict with the law accepted by the line tau acceptance unit. The analysis unit, for example, analyzes whether the contract or the service terms and conditions conflict with the law. For example, the analysis unit uses a generation AI to analyze the contents of the contract or the service terms and conditions and determines whether they match or do not match the legal provisions. The generation AI analyzes the contents of the contract or the service terms and conditions using a text generation AI (for example, LLM). The generation AI receives a prompt, "Please check whether this contract conflicts with the law," and analyzes the contents of the contract. The generation AI analyzes each clause of the contract and determines whether they match or do not match the legal provisions. For example, the generation AI analyzes whether the clause of the contract conflicts with a specific clause of the Radio Law. The generation AI analyzes whether the clause of the contract conflicts with a specific clause of the Telecommunications Business Act. The generation AI analyzes whether the clause of the contract conflicts with a specific clause of the Equipment Regulations. This allows the analysis unit to reduce legal risks by analyzing whether or not the contract or service terms and conditions violate the law.

[0073] The selection receiving unit can estimate the user's emotion and adjust the order in which the legal options are presented based on the estimated user's emotion. The selection receiving unit, for example, estimates the user's emotion and adjusts the order in which the legal options are presented based on the estimated user's emotion. For example, when the user is stressed, the selection receiving unit preferentially presents the most relevant law. When the user is relaxed, the selection receiving unit provides a detailed list of laws to expand the options. When the user is in a hurry, the selection receiving unit preferentially presents laws that have been frequently selected in the past. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the selection receiving unit improves the user's convenience by adjusting the order in which the legal options are presented according to the user's emotion.

[0074] The selection reception unit can analyze the past law selection history and propose an option of law suitable for the user. The selection reception unit, for example, analyzes the past law selection history and proposes the optimal option of law to the user. For example, the selection reception unit automatically displays the law that the user frequently selected in the past as a candidate. The selection reception unit preferentially proposes a law related to a specific business from the user's past selection history. The selection reception unit analyzes the user's selection history and predicts and proposes the law to be used in a specific time period. In this way, the selection reception unit improves the efficiency of the user's selection by proposing the optimal option of law based on the past selection history. Part or all of the above-mentioned processing in the selection reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection reception unit inputs the past law selection history into AI, and the AI ​​proposes the optimal option of law.

[0075] The selection reception unit can perform filtering based on the user's business content and field of interest when selecting a law. The selection reception unit performs filtering based on the user's business content and field of interest when selecting a law, for example. For example, the selection reception unit preferentially displays highly relevant laws based on the user's business content. The selection reception unit filters and presents related laws based on the user's field of interest. The selection reception unit combines the user's business content and field of interest to propose optimal legal options. As a result, the selection reception unit preferentially presents highly relevant laws by filtering laws based on the user's business content and field of interest. A part or all of the above-mentioned processing in the selection reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection reception unit inputs the user's business content and field of interest into AI, and the AI ​​proposes optimal legal options.

[0076] The selection receiving unit can estimate the user's emotion and narrow down the legal options based on the estimated user's emotion. The selection receiving unit, for example, estimates the user's emotion and narrows down the legal options based on the estimated user's emotion. For example, when the user is stressed, the selection receiving unit narrows down and presents the most relevant laws. When the user is relaxed, the selection receiving unit provides a detailed list of laws and expands the options. When the user is in a hurry, the selection receiving unit narrows down and presents laws that have been frequently selected in the past. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the selection receiving unit narrows down the legal options according to the user's emotion, thereby making the user's selection more efficient.

[0077] The selection receiving unit can preferentially present a law that is highly relevant in consideration of the geographical location information of the user when selecting a law. For example, the selection receiving unit preferentially presents a law that is highly relevant in consideration of the geographical location information of the user when selecting a law. For example, the selection receiving unit preferentially displays a law specific to a region based on the user's current location. The selection receiving unit filters and presents related laws in consideration of the geographical location information of the user. The selection receiving unit combines the geographical location information of the user and the business content to propose an optimal legal option. As a result, the selection receiving unit presents laws in consideration of the geographical location information of the user, thereby preferentially displaying a law specific to a region. A part or all of the above-mentioned processing in the selection receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection receiving unit inputs the geographical location information of the user to AI, and the AI ​​proposes an optimal legal option.

[0078] The selection reception unit can analyze the user's social media activity and suggest related laws when selecting a law. The selection reception unit, for example, analyzes the user's social media activity and suggests related laws when selecting a law. For example, the selection reception unit identifies laws of interest from the user's social media activity and presents them preferentially. The selection reception unit analyzes the user's social media activity and filters and suggests related laws. The selection reception unit combines the user's social media activity and business content to suggest optimal legal options. In this way, the selection reception unit analyzes the user's social media activity to preferentially present laws of interest. Part or all of the above-mentioned processing in the selection reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection reception unit inputs the user's social media activity into AI, and the AI ​​suggests optimal legal options.

[0079] The input reception unit can estimate the user's emotion and adjust the input method of the contract or the service terms and conditions based on the estimated user's emotion. The input reception unit, for example, estimates the user's emotion and adjusts the input method of the contract or the service terms and conditions based on the estimated user's emotion. For example, when the user is stressed, the input reception unit provides a simple interface and minimizes the input procedure. When the user is relaxed, the input reception unit provides detailed input options and suggests a customizable input method. When the user is in a hurry, the input reception unit prioritizes voice input and enables the user to quickly input the contract or the service terms and conditions. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the input reception unit improves the efficiency of input by adjusting the input method according to the user's emotion.

[0080] The input acceptance unit can analyze the past input history and propose the optimal input format to the user. The input acceptance unit, for example, analyzes the past input history and proposes the optimal input format to the user. For example, the input acceptance unit automatically displays the format of the contract or service terms that the user frequently inputs in the past as a candidate. The input acceptance unit preferentially proposes the input method (voice, text, etc.) that the user used in the past. The input acceptance unit predicts and proposes the format of the contract or service terms to be used in a specific time period from the user's past input history. In this way, the input acceptance unit improves input efficiency by proposing the optimal input format based on the past input history. A part or all of the above-mentioned processing in the input acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the input acceptance unit inputs the past input history to AI, and the AI ​​proposes the optimal input format.

[0081] The input reception unit can perform filtering based on the user's business content and field of interest when inputting a contract or service terms. The input reception unit performs filtering based on the user's business content and field of interest when inputting a contract or service terms. For example, the input reception unit preferentially displays a highly relevant contract or service terms format based on the user's business content. The input reception unit filters and presents related contract or service terms formats based on the user's field of interest. The input reception unit combines the user's business content and field of interest to propose an optimal contract or service terms format. As a result, the input reception unit preferentially inputs highly relevant content by filtering the input based on the user's business content and field of interest. Part or all of the above-mentioned processing in the input reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the input reception unit inputs the user's business content and field of interest to AI, and the AI ​​proposes an optimal contract or service terms format.

[0082] The input reception unit can estimate the user's emotion and determine the priority order of the contract or service terms based on the estimated user's emotion. The input reception unit, for example, estimates the user's emotion and determines the priority order of the contract or service terms based on the estimated user's emotion. For example, when the user is stressed, the input reception unit preferentially inputs the most important contract or service terms. When the user is relaxed, the input reception unit prioritizes input of detailed contract or service terms. When the user is in a hurry, the input reception unit prioritizes input of the contract or service terms that can be input most quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the input reception unit preferentially inputs important content by determining the priority order according to the user's emotion.

[0083] The input reception unit can preferentially input highly relevant content in consideration of the geographical location information of the user when inputting a contract or service terms. For example, the input reception unit preferentially inputs highly relevant content in consideration of the geographical location information of the user when inputting a contract or service terms. For example, the input reception unit preferentially inputs the content of a contract or service terms specific to a region based on the user's current location. The input reception unit filters and inputs the content of the related contract or service terms in consideration of the geographical location information of the user. The input reception unit combines the geographical location information of the user and the business content to propose the optimal content of the contract or service terms. Thereby, the input reception unit preferentially inputs the content specific to the region by inputting in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the input reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the input reception unit inputs the geographical location information of the user to AI, and the AI ​​proposes the optimal content of the contract or service terms.

[0084] The input reception unit can analyze the social media activity of the user and input the relevant content when inputting the contract or the service terms. For example, the input reception unit analyzes the social media activity of the user and inputs the relevant content when inputting the contract or the service terms. For example, the input reception unit identifies the content of the contract or the service terms of interest from the user's social media activity and inputs it preferentially. The input reception unit analyzes the user's social media activity and filters and inputs the content of the related contract or the service terms. The input reception unit combines the user's social media activity and the business content to propose the optimal content of the contract or the service terms. Thereby, the input reception unit analyzes the user's social media activity and inputs the content of the interest preferentially. A part or all of the above-mentioned processing in the input reception unit may be performed using AI, for example, or may be performed without using AI. For example, the input reception unit inputs the user's social media activity to AI, and the AI ​​proposes the optimal content of the contract or the service terms.

[0085] The selection unit can estimate the user's emotion and adjust the prompt selection method based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the prompt selection method based on the estimated user's emotion. For example, the selection unit preferentially selects a simple prompt when the user is stressed. The selection unit provides detailed prompts and expands options when the user is relaxed. The selection unit preferentially selects prompts that have been frequently selected in the past when the user is in a hurry. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the selection unit improves user convenience by adjusting the prompt selection method according to the user's emotion.

[0086] The selection unit can analyze the past prompt selection history and propose the most suitable prompt to the user. The selection unit, for example, analyzes the past prompt selection history and proposes the most suitable prompt to the user. For example, the selection unit automatically displays prompts that the user has frequently selected in the past as candidates. The selection unit preferentially proposes prompts related to a specific task from the user's past selection history. The selection unit analyzes the user's selection history and predicts and proposes a prompt to be used in a specific time period. In this way, the selection unit improves the efficiency of the user's selection by proposing the most suitable prompt based on the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit inputs the past prompt selection history into AI, which proposes the most suitable prompt.

[0087] The selection unit can perform filtering based on the user's business content and field of interest when selecting a prompt. For example, the selection unit performs filtering based on the user's business content and field of interest when selecting a prompt. For example, the selection unit preferentially displays a highly relevant prompt based on the user's business content. The selection unit filters and presents related prompts based on the user's field of interest. The selection unit combines the user's business content and field of interest to propose an optimal prompt. As a result, the selection unit preferentially presents a highly relevant prompt by filtering the prompt based on the user's business content and field of interest. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit inputs the user's business content and field of interest into AI, and the AI ​​proposes an optimal prompt.

[0088] The selection unit can estimate the user's emotion and determine the priority of the prompts based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and determines the priority of the prompts based on the estimated user's emotion. For example, when the user is stressed, the selection unit preferentially presents the most relevant prompt. When the user is relaxed, the selection unit provides a detailed prompt list and expands the options. When the user is in a hurry, the selection unit preferentially presents prompts that have been frequently selected in the past. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the selection unit makes the user's selection more efficient by determining the priority of the prompts according to the user's emotion.

[0089] The selection unit can preferentially select a prompt with high relevance in consideration of the geographical location information of the user when selecting a prompt. For example, the selection unit preferentially selects a prompt with high relevance in consideration of the geographical location information of the user when selecting a prompt. For example, the selection unit preferentially displays a region-specific prompt based on the user's current location. The selection unit filters and presents related prompts in consideration of the geographical location information of the user. The selection unit combines the geographical location information of the user and the business content to propose an optimal prompt. As a result, the selection unit selects a prompt in consideration of the geographical location information of the user, thereby preferentially displaying a region-specific prompt. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit inputs the geographical location information of the user to AI, and the AI ​​proposes an optimal prompt.

[0090] The selection unit can analyze the user's social media activity and select a relevant prompt when selecting a prompt. For example, the selection unit analyzes the user's social media activity and selects a relevant prompt when selecting a prompt. For example, the selection unit identifies a prompt of interest from the user's social media activity and presents it preferentially. The selection unit analyzes the user's social media activity and filters and presents relevant prompts. The selection unit combines the user's social media activity and work content to propose an optimal prompt. In this way, the selection unit analyzes the user's social media activity to present a prompt of interest preferentially. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit inputs the user's social media activity into AI, and the AI ​​proposes an optimal prompt.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple and highly visible display method when the user is stressed. When the user is relaxed, the analysis unit provides detailed analysis results and expands options. When the user is in a hurry, the analysis unit provides analysis results that focus on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the analysis unit improves user convenience by adjusting the display method of the analysis result according to the user's emotion.

[0092] The analysis unit can optimize the analysis algorithm by referring to past analysis data during analysis. The analysis unit optimizes the analysis algorithm by referring to past analysis data, for example, during analysis. For example, the analysis unit selects an optimal analysis algorithm based on past analysis data. The analysis unit improves the accuracy of the analysis algorithm by referring to past analysis data. The analysis unit analyzes past analysis data and optimizes the analysis algorithm based on a specific pattern. As a result, the analysis unit improves the accuracy of the analysis by optimizing the analysis algorithm based on the past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs past analysis data to AI, and the AI ​​proposes an optimal analysis algorithm.

[0093] The analysis unit can apply different analysis methods depending on the category of the contract or service terms during analysis. The analysis unit applies different analysis methods depending on the category of the contract or service terms during analysis, for example. For example, the analysis unit selects an optimal analysis method depending on the category of the contract. The analysis unit applies different analysis methods based on the category of the service terms. The analysis unit analyzes the category of the contract or service terms and proposes an optimal analysis method. As a result, the analysis unit improves the accuracy of the analysis by applying the optimal analysis method depending on the category of the contract or service terms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the category of the contract or service terms into AI, and the AI ​​proposes the optimal analysis method.

[0094] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the priority of the analysis results based on the estimated user's emotion. For example, when the user is stressed, the analysis unit preferentially displays the most important analysis results. When the user is relaxed, the analysis unit provides detailed analysis results and expands the options. When the user is in a hurry, the analysis unit preferentially displays analysis results that focus on the main points. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the analysis unit preferentially displays important analysis results by determining the priority of the analysis results according to the user's emotion.

[0095] The analysis unit can determine the priority of the analysis based on the submission time of the contract or the service terms and conditions during the analysis. The analysis unit, for example, determines the priority of the analysis based on the submission time of the contract or the service terms and conditions during the analysis. For example, the analysis unit preferentially analyzes the contract or the service terms and conditions whose submission deadline is approaching. The analysis unit determines the priority of the analysis based on the submission time. The analysis unit optimizes the priority of the analysis taking into account the submission time and importance. As a result, the analysis unit determines the priority of the analysis based on the submission time, thereby preferentially analyzing the content whose submission deadline is approaching. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the submission time of the contract or the service terms and conditions into AI, and the AI ​​proposes the optimal analysis priority.

[0096] The analysis unit can improve the accuracy of the analysis by referring to related literature of the contract or service terms during the analysis. The analysis unit, for example, improves the accuracy of the analysis by referring to related literature of the contract or service terms during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to related literature. The analysis unit optimizes the analysis method based on related literature of the contract or service terms. The analysis unit analyzes related literature and improves the accuracy of the analysis based on a specific pattern. As a result, the analysis unit improves the accuracy of the analysis by referring to related literature. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs related literature of the contract or service terms into AI, and the AI ​​proposes an optimal analysis method.

[0097] The providing unit can estimate the user's emotion and adjust the method of providing the analysis result based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing the analysis result based on the estimated user's emotion. For example, when the user is stressed, the providing unit selects a simple and highly visible providing method. When the user is relaxed, the providing unit provides detailed analysis results and expands the options. When the user is in a hurry, the providing unit provides analysis results that focus on the main points. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the providing unit improves the convenience of the user by adjusting the method of providing the analysis result according to the user's emotion.

[0098] The providing unit can select the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past feedback at the time of providing. For example, the providing unit selects the optimal providing method based on the user's past feedback. The providing unit improves the accuracy of the providing method by referring to the user's feedback. The providing unit analyzes the user's past feedback and optimizes the providing method based on a specific pattern. Thereby, the providing unit improves the accuracy of the providing by selecting the optimal providing method based on the past feedback. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's past feedback to AI, and the AI ​​suggests the optimal providing method.

[0099] The providing unit can perform filtering based on the user's business content and field of interest at the time of providing. For example, the providing unit performs filtering based on the user's business content and field of interest at the time of providing. For example, the providing unit preferentially provides highly relevant analysis results based on the user's business content. The providing unit filters and provides related analysis results based on the user's field of interest. The providing unit combines the user's business content and field of interest to provide optimal analysis results. Thereby, the providing unit preferentially provides highly relevant content by filtering the analysis results based on the user's business content and field of interest. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's business content and field of interest into AI, and the AI ​​proposes optimal analysis results.

[0100] The providing unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display order of the analysis results based on the estimated user's emotion. For example, when the user is stressed, the providing unit preferentially displays the most important analysis results. When the user is relaxed, the providing unit provides detailed analysis results and expands the options. When the user is in a hurry, the providing unit preferentially displays analysis results that focus on the main points. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the providing unit preferentially displays important content by adjusting the display order of the analysis results according to the user's emotion.

[0101] The providing unit can provide a highly relevant analysis result preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit provides a highly relevant analysis result preferentially in consideration of the geographical location information of the user at the time of providing. For example, the providing unit preferentially displays a region-specific analysis result based on the user's current location. The providing unit filters and provides the related analysis result in consideration of the geographical location information of the user. The providing unit combines the geographical location information of the user and the business content to provide an optimal analysis result. As a result, the providing unit provides the analysis result in consideration of the geographical location information of the user, thereby preferentially displaying the content specific to the region. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the geographical location information of the user to AI, and the AI ​​proposes the optimal analysis result.

[0102] The providing unit can analyze the user's social media activity at the time of providing and provide the related analysis result. For example, the providing unit analyzes the user's social media activity at the time of providing and provides the related analysis result. For example, the providing unit identifies an analysis result of interest from the user's social media activity and provides it preferentially. The providing unit analyzes the user's social media activity and filters and provides the related analysis result. The providing unit combines the user's social media activity and the work content to provide the optimal analysis result. Thereby, the providing unit provides the content of interest preferentially by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the user's social media activity into AI, and the AI ​​proposes the optimal analysis result.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The selection receiving unit can automatically customize the legal options based on the user's past selection history. For example, the selection receiving unit preferentially displays laws that the user has frequently selected in the past. The selection receiving unit suggests highly relevant laws based on the user's business operations. The selection receiving unit analyzes the user's selection history and predicts and suggests laws to be used during a specific time period. In this way, the selection receiving unit makes user selections more efficient by suggesting optimal legal options based on the user's past selection history.

[0105] The analysis unit can refer to a database of similar past cases when analyzing the contents of a contract or service terms and conditions. For example, the analysis unit can refer to a database of contracts and service terms and conditions that have been analyzed in the past to identify similar clauses and problems. The analysis unit determines whether there is a match or a mismatch with specific legal provisions based on the results of the past analysis. The analysis unit utilizes past data to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to a database of similar past cases.

[0106] The selection receiving unit can estimate the user's emotions and adjust the order in which legal options are presented based on the estimated user's emotions. For example, when the user is stressed, the selection receiving unit preferentially presents the most relevant laws. When the user is relaxed, the selection receiving unit provides a detailed list of laws to expand the options. When the user is in a hurry, the selection receiving unit preferentially presents laws that have been frequently selected in the past. In this way, the selection receiving unit improves user convenience by adjusting the order in which legal options are presented according to the user's emotions.

[0107] The selection receiving unit can perform filtering based on the user's business content and field of interest when selecting a law. For example, the selection receiving unit preferentially displays highly relevant laws based on the user's business content. The selection receiving unit filters and presents related laws based on the user's field of interest. The selection receiving unit combines the user's business content and field of interest to propose optimal legal options. In this way, the selection receiving unit preferentially presents highly relevant laws by filtering laws based on the user's business content and field of interest.

[0108] The selection receiving unit can estimate the user's emotions and narrow down legal options based on the estimated user's emotions. For example, when the user is stressed, the selection receiving unit narrows down and presents the most relevant laws. When the user is relaxed, the selection receiving unit provides a detailed list of laws to expand the options. When the user is in a hurry, the selection receiving unit narrows down and presents laws that have been frequently selected in the past. In this way, the selection receiving unit makes the user's selection more efficient by narrowing down the legal options according to the user's emotions.

[0109] The analysis unit can refer to relevant legal precedents when analyzing the contract or the service terms. For example, the analysis unit analyzes how the clauses of the contract are interpreted based on past precedents. The analysis unit determines how the contents of the service terms are evaluated based on past precedents. The analysis unit improves the accuracy of the analysis by referring to relevant precedents. In this way, the analysis unit can improve the accuracy of the analysis of the contract or the service terms by referring to relevant legal precedents.

[0110] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit provides a simple and highly visible display method. When the user is relaxed, the analysis unit provides detailed analysis results and expands options. When the user is in a hurry, the analysis unit provides analysis results that focus on the main points. In this way, the analysis unit improves user convenience by adjusting the display method of the analysis results according to the user's emotions.

[0111] The providing unit can optimize the method of providing the analysis results based on the user's past feedback. For example, the providing unit preferentially uses a display format that the user has preferred in the past. The providing unit analyzes the user's feedback and suggests a specific display format. The providing unit customizes the method of providing the analysis results based on the user's past feedback. In this way, the providing unit improves the accuracy of provision by selecting the optimal providing method based on the user's past feedback.

[0112] The providing unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit prioritizes displaying the most important analysis results. When the user is relaxed, the providing unit provides detailed analysis results to expand options. When the user is in a hurry, the providing unit prioritizes displaying analysis results that focus on the main points. In this way, the providing unit prioritizes displaying important content by adjusting the display order of the analysis results according to the user's emotions.

[0113] The providing unit can provide highly relevant analysis results preferentially, taking into account the geographical location information of the user. For example, the providing unit preferentially displays analysis results specific to a region, based on the user's current location. The providing unit filters and provides related analysis results, taking into account the geographical location information of the user. The providing unit combines the geographical location information of the user with the business content to provide optimal analysis results. As a result, the providing unit provides analysis results taking into account the geographical location information of the user, thereby preferentially displaying content specific to the region.

[0114] The process flow of the second embodiment will be briefly described below.

[0115] Step 1: The selection reception unit receives a selection of law from the user. For example, when the user selects the law he or she wants to check, the selection reception unit presents laws such as the Radio Law, the Telecommunications Business Law, and the Equipment Regulations, allowing the user to make a selection. The selection reception unit can also use AI to present options for law. Step 2: The input reception unit receives input of the contract or service terms from the user. For example, when the user inputs the contract or service terms that he or she wants to confirm, the input reception unit can receive digital data in text format, PDF format, or other formats. The input reception unit can also use AI to convert the input data into a format that is easier to analyze. Step 3: The selection unit selects a prompt corresponding to the law accepted by the selection acceptance unit. For example, the selection unit automatically selects a prompt to be input to the generation AI based on the law selected by the user. The selection unit can also select a prompt using AI. Step 4: The analysis unit inputs the prompt selected by the selection unit and the contract or service terms accepted by the input acceptance unit to the generation AI, and has the generation AI analyze the contract or service terms. For example, the analysis unit inputs the prompt and the contract or service terms to the generation AI, and the generation AI analyzes the contract or service terms. The generation AI parses the contract or service terms using a text generation AI (e.g., LLM). Step 5: The providing unit provides the result analyzed by the analysis unit to the user. For example, the providing unit displays the analysis result to the user. The providing unit can also provide the analysis result to the user using AI.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0118] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0119] For example, the selection reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the input reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0127] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] For example, the selection receiving unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the input receiving unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] For example, the selection receiving unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the input receiving unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0159] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] For example, the selection receiving unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the input receiving unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0170] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0174] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0181] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0185] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0187] (Appendix 1) a selection receiving unit that receives a selection of law from a user; an input receiving unit that receives input of a contract or service terms from the user; a selection unit for selecting a prompt corresponding to the law accepted by the acceptance unit; an analysis unit that inputs the prompt selected by the selection unit and the contract or the service terms and conditions accepted by the input acceptance unit into a generation AI and analyzes the contract or the service terms and conditions in the generation AI; A providing unit that provides the results analyzed by the analyzing unit. A system characterized by: (Appendix 2) The analysis unit is Analyzing whether the contract or the service terms are in violation of the law accepted by the acceptance unit 2. The system of claim 1, (Appendix 3) The selection receiving unit is Using a method for estimating user sentiment, the order in which legal options are presented is adjusted based on the estimated user sentiment. 2. The system of claim 1, (Appendix 4) The selection receiving unit is Analyze past legal choices and suggest legal options that are appropriate for the user 2. The system of claim 1, (Appendix 5) The selection receiving unit is When selecting law, filter based on your practice and interests 2. The system of claim 1, (Appendix 6) The selection receiving unit is Inferring user sentiment and narrowing down legal options based on the inferred user sentiment 2. The system of claim 1, (Appendix 7) The selection receiving unit is When selecting laws, the system takes into account the user's geographic location to prioritize relevant laws. 2. The system of claim 1, (Appendix 8) The selection receiving unit is When selecting laws, analyze users' social media activity and suggest relevant laws 2. The system of claim 1, (Appendix 9) The input receiving unit is To estimate a user's emotion and adjust a method of inputting a contract or terms of service based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The input receiving unit is Analyzes past input history and suggests optimal input format for users 2. The system of claim 1, (Appendix 11) The input receiving unit is Filtering users based on their business or interests when entering contracts or terms of service 2. The system of claim 1, (Appendix 12) The input receiving unit is To estimate a user's sentiment and prioritize contracts or terms of service based on the estimated user's sentiment 2. The system of claim 1, (Appendix 13) The input receiving unit is When entering contracts or terms of service, the system takes into account the user's geographic location to prioritize the most relevant content 2. The system of claim 1, (Appendix 14) The input receiving unit is Analyze users' social media activity and enter relevant content when entering contracts or service terms 2. The system of claim 1, (Appendix 15) The selection unit is Inferring a user's emotion and adjusting a prompt selection method based on the inferred user emotion 2. The system of claim 1, (Appendix 16) The selection unit is Analyze past prompt selection history and suggest the most suitable prompts to the user 2. The system of claim 1, (Appendix 17) The selection unit is Filter prompt selections based on the user's job or interests 2. The system of claim 1, (Appendix 18) The selection unit is Inferring a user's emotion and prioritizing prompts based on the inferred user emotion 2. The system of claim 1, (Appendix 19) The selection unit is When selecting prompts, consider the user's geographic location to prioritize the most relevant prompts 2. The system of claim 1, (Appendix 20) The selection unit is When prompts are selected, analyze the user's social media activity and select relevant prompts 2. The system of claim 1, (Appendix 21) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The analysis unit is During analysis, the analysis algorithm is optimized by referencing past analysis data. 2. The system of claim 1, (Appendix 23) The analysis unit is During analysis, apply different analysis methods depending on the category of contract or service terms 2. The system of claim 1, (Appendix 24) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 25) The analysis unit is During analysis, prioritize analysis based on when the contract or service agreement was submitted 2. The system of claim 1, (Appendix 26) The analysis unit is During analysis, refer to relevant documents such as contracts or service terms to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 27) The providing unit is Inferring user emotions and adjusting the way analysis results are presented based on the inferred user emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past feedback. 2. The system of claim 1, (Appendix 29) The providing unit is Filtering at the time of delivery based on the user's job duties and areas of interest 2. The system of claim 1, (Appendix 30) The providing unit is Infer user emotions and adjust the display order of analysis results based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing analytics, the app takes into account the user's geographic location to prioritize relevant analytics results. 2. The system of claim 1, (Appendix 32) The providing unit is When provided, analyze your social media activity and provide you with relevant analytics 2. The system of claim 1, [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection receiving unit that receives a selection of law from a user; an input receiving unit that receives input of a contract or service terms from the user; a selection unit that selects a prompt corresponding to the law accepted by the input acceptance unit; an analysis unit that inputs the prompt selected by the selection unit and the contract or the service terms and conditions accepted by the input acceptance unit into a generation AI and analyzes the contract or the service terms and conditions in the generation AI; A providing unit that provides the results analyzed by the analyzing unit. A system characterized by:

2. The analysis unit is Analyzing whether the contract or the service terms are in violation of the law accepted by the acceptance unit 2. The system of claim 1 .

3. The selection receiving unit is Using the method for estimating the user's sentiment, the order in which legal options are presented is adjusted based on the estimated sentiment of the user.

2. The system of claim 1 .

4. The selection receiving unit is Analyze the user's past legal choices and suggest legal options suitable for the user 2. The system of claim 1 .

5. The selection receiving unit is When selecting laws, filter based on the user's business activities and interests 2. The system of claim 1 .

6. The selection receiving unit is Inferring a sentiment of the user and narrowing down legal options based on the inferred sentiment of the user.

2. The system of claim 1 .

7. The selection receiving unit is When selecting a law, the user's geographic location information is taken into consideration to preferentially present relevant laws.

2. The system of claim 1 .

8. The selection receiving unit is When selecting a law, analyze the user's social media activity and suggest relevant laws.

2. The system of claim 1 .

9. The input receiving unit is Estimating the user's emotion and adjusting the input method of the contract or the service terms and conditions based on the estimated user's emotion.

2. The system of claim 1 .

10. The input receiving unit is Analyzes past input history and suggests optimal input format for users 2. The system of claim 1 .

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