System

Abstract

To provide a system that streamlines translation and text draft preparation, in order to improve suitability of an overseas service contract for a Japanese company.SOLUTION: A system for translating a text of an overseas service contract and preparing a text draft for a Japanese company, includes translation means, text draft preparation means for the Japanese company, and legal affairs confirmation means.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] We provide a system that streamlines translation and drafting of contracts for overseas services to improve their suitability for Japanese companies. [Means for solving the problem]

[0005] A system for translating the text of contracts for overseas services and preparing draft text for Japanese companies, the system including a translation means, a means for preparing draft text for Japanese companies, and a legal confirmation means. [Brief description of the drawings]

[0006] [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

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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).

[0010] 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 a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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.

[0012] 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), Bluetooth (registered trademark), etc.

[0013] 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."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 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.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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 wide area network (WAN) and / or a local area network (LAN).

[0017] 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.

[0018] 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 indicative of 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 indicative of the user input.

[0019] The 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). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact 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.

[0020] 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.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] 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.

[0023] 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.

[0024] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] 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 ChatGPT server) 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 (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. In addition, the system is equipped with a means for creating a draft of the text for Japanese companies, and converts the translated text into a format suitable for Japanese companies. It also includes a legal confirmation means, and the created draft of the text can be confirmed by the legal department. (Example 2) One embodiment of the present invention is a system for improving the suitability of overseas service contracts for Japanese companies. The system uses a machine learning algorithm as a translation means to translate the contract text with high accuracy. In addition, the system includes a means for creating a draft text for Japanese companies, which edits the translated text to conform to the business culture and legal requirements of Japanese companies. The system also includes a legal review means, which allows the legal department to review the created draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. The system uses natural language processing technology as a translation means to accurately translate the text of the contract. In addition, the system is provided with a means for creating a text proposal for Japanese companies, and converts the translated text into a format suitable for the terminology and expressions specific to the industry of Japanese companies. In addition, the system includes a legal confirmation means, and the created text proposal can be reviewed by the legal department. As a specific example, when translating a contract of an overseas service provider and creating a text proposal suitable for Japanese companies, important elements such as contract terms and scope of responsibility are appropriately translated and edited.

[0027] The process flow of each embodiment will be described below.

[0028] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Editing using tools for drafting texts for Japanese companies - Edit the translated text into a format suitable for Japanese companies. - The method of drafting the text will involve selecting appropriate expressions and terms based on the business culture and legal requirements of Japanese companies. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the contents of contracts and legal regulations to conduct appropriate reviews. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Editing using tools for drafting texts for Japanese companies - Editing the translated text to ensure it complies with Japanese business culture and legal requirements. - The method of drafting a document involves understanding the contents of the contract and selecting appropriate expressions and terms. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the legal aspects and risks of the contract to conduct appropriate review. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Editing using tools for drafting texts for Japanese companies - Converting the translated text into a format that is suitable for the industry-specific terminology and expressions of Japanese companies. - The method of drafting sentences is to utilize industry knowledge and technical terms to select appropriate expressions. Step 3: Legal verification procedures - Review the draft text with the legal department to ensure it meets legal requirements. - Legal review means have knowledge of the legal aspects and risks of the contract and conduct appropriate review. The above is the flow of processing in the embodiment of the present invention.

[0029] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0030] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine analyzes keywords and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains keywords that express emotions such as "satisfied," "dissatisfied," and "neutral," the emotion engine recognizes them and creates a draft text by selecting expressions that increase satisfaction or expressions that reduce dissatisfaction. (Example 2) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses a machine learning algorithm as a translation means to translate the text of the contract with high accuracy. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and writing styles in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and terms are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "anxiety," and "neutral," the emotion engine recognizes it and selects expressions that emphasize happiness or expressions that reduce anxiety to create a draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses natural language processing technology as a translation means to accurately translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "dissatisfaction," and "neutral," the emotion engine recognizes it and creates a draft text by selecting expressions that emphasize happiness or expressions that reduce dissatisfaction. The above is the flow of processing in an embodiment of the present invention, and by combining it with an emotion engine, it is possible to improve the suitability of contracts for Japanese companies.

[0031] The process flow of each embodiment will be described below.

[0032] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine analyzes keywords and context in text and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The method of drafting the contract involves selecting appropriate expressions and writing styles according to the emotions, and editing the content of the contract into a format suitable for Japanese companies. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts emotions from the expressions and writing style in a sentence and classifies the emotions into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and terms according to the sentiment, and editing the contents of the contract to conform to the business culture and legal requirements of Japanese companies. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts sentiment from expressions and context in a sentence and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and writing styles according to the emotions, and converting the contents of the contract into a format suitable for the terminology and expressions specific to the industry of Japanese companies. The above is the flow of processing in an embodiment of the present invention. Combining a translation means and an emotion engine has the effect of improving the suitability of contracts for Japanese companies.

[0033] 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.

[0034] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. 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 is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0035] In the above embodiment, an example has been given in which the specific process is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0036] [Second embodiment]

[0037] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0038] 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.

[0039] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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 wide area network (WAN) and / or a local area network (LAN).

[0040] 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.

[0041] 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.

[0042] 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).

[0043] 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. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0044] 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.

[0045] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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.

[0046] 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.

[0047] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0048] 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 ChatGPT server) 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 (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0049] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. In addition, the system is equipped with a means for creating a draft of the text for Japanese companies, and converts the translated text into a format suitable for Japanese companies. It also includes a legal confirmation means, and the created draft of the text can be confirmed by the legal department. (Example 2) One embodiment of the present invention is a system for improving the suitability of overseas service contracts for Japanese companies. The system uses a machine learning algorithm as a translation means to translate the contract text with high accuracy. In addition, the system includes a means for creating a draft text for Japanese companies, which edits the translated text to conform to the business culture and legal requirements of Japanese companies. The system also includes a legal review means, which allows the legal department to review the created draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. The system uses natural language processing technology as a translation means to accurately translate the text of the contract. In addition, the system is provided with a means for creating a text proposal for Japanese companies, and converts the translated text into a format suitable for the terminology and expressions specific to the industry of Japanese companies. In addition, the system includes a legal confirmation means, and the created text proposal can be reviewed by the legal department. As a specific example, when translating a contract of an overseas service provider and creating a text proposal suitable for Japanese companies, important elements such as contract terms and scope of responsibility are appropriately translated and edited.

[0050] The process flow of each embodiment will be described below.

[0051] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Editing using tools for drafting texts for Japanese companies - Edit the translated text into a format suitable for Japanese companies. - The method of drafting the text will involve selecting appropriate expressions and terms based on the business culture and legal requirements of Japanese companies. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the contents of contracts and legal regulations to conduct appropriate reviews. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Editing using tools for drafting texts for Japanese companies - Editing the translated text to ensure it complies with Japanese business culture and legal requirements. - The method of drafting a document involves understanding the contents of the contract and selecting appropriate expressions and terms. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the legal aspects and risks of the contract to conduct appropriate review. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Editing using tools for drafting texts for Japanese companies - Converting the translated text into a format that is suitable for the industry-specific terminology and expressions of Japanese companies. - The method of drafting sentences is to utilize industry knowledge and technical terms to select appropriate expressions. Step 3: Legal verification procedures - Review the draft text with the legal department to ensure it meets legal requirements. - Legal review means have knowledge of the legal aspects and risks of the contract and conduct appropriate review. The above is the flow of processing in the embodiment of the present invention.

[0052] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0053] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine analyzes keywords and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains keywords that express emotions such as "satisfied," "dissatisfied," and "neutral," the emotion engine recognizes them and creates a draft text by selecting expressions that increase satisfaction or expressions that reduce dissatisfaction. (Example 2) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses a machine learning algorithm as a translation means to translate the text of the contract with high accuracy. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and writing styles in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and terms are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "anxiety," and "neutral," the emotion engine recognizes it and selects expressions that emphasize happiness or expressions that reduce anxiety to create a draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses natural language processing technology as a translation means to accurately translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "dissatisfaction," and "neutral," the emotion engine recognizes it and creates a draft text by selecting expressions that emphasize happiness or expressions that reduce dissatisfaction. The above is the flow of processing in an embodiment of the present invention, and by combining it with an emotion engine, it is possible to improve the suitability of contracts for Japanese companies.

[0054] The process flow of each embodiment will be described below.

[0055] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine analyzes keywords and context in text and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The method of drafting the contract involves selecting appropriate expressions and writing styles according to the emotions, and editing the content of the contract into a format suitable for Japanese companies. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts emotions from the expressions and writing style in a sentence and classifies the emotions into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and terms according to the sentiment, and editing the contents of the contract to conform to the business culture and legal requirements of Japanese companies. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts sentiment from expressions and context in a sentence and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and writing styles according to the emotions, and converting the contents of the contract into a format suitable for the terminology and expressions specific to the industry of Japanese companies. The above is the flow of processing in an embodiment of the present invention. Combining a translation means and an emotion engine has the effect of improving the suitability of contracts for Japanese companies.

[0056] 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.

[0057] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. 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 is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0058] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0059] [Third embodiment]

[0060] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0061] 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.

[0062] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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 wide area network (WAN) and / or a local area network (LAN).

[0063] 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.

[0064] 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.

[0065] 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).

[0066] 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. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0067] Fig. 6 shows an example of 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.

[0068] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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.

[0069] 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.

[0070] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0071] 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 ChatGPT server) 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 (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0072] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. In addition, the system is equipped with a means for creating a draft of the text for Japanese companies, and converts the translated text into a format suitable for Japanese companies. It also includes a legal confirmation means, and the created draft of the text can be confirmed by the legal department. (Example 2) One embodiment of the present invention is a system for improving the suitability of overseas service contracts for Japanese companies. The system uses a machine learning algorithm as a translation means to translate the contract text with high accuracy. In addition, the system includes a means for creating a draft text for Japanese companies, which edits the translated text to conform to the business culture and legal requirements of Japanese companies. The system also includes a legal review means, which allows the legal department to review the created draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. The system uses natural language processing technology as a translation means to accurately translate the text of the contract. In addition, the system is provided with a means for creating a text proposal for Japanese companies, and converts the translated text into a format suitable for the terminology and expressions specific to the industry of Japanese companies. In addition, the system includes a legal confirmation means, and the created text proposal can be reviewed by the legal department. As a specific example, when translating a contract of an overseas service provider and creating a text proposal suitable for Japanese companies, important elements such as contract terms and scope of responsibility are appropriately translated and edited.

[0073] The process flow of each embodiment will be described below.

[0074] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Editing using tools for drafting texts for Japanese companies - Edit the translated text into a format suitable for Japanese companies. - The method of drafting the text will involve selecting appropriate expressions and terms based on the business culture and legal requirements of Japanese companies. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the contents of contracts and legal regulations to conduct appropriate reviews. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Editing using tools for drafting texts for Japanese companies - Editing the translated text to ensure it complies with Japanese business culture and legal requirements. - The method of drafting a document involves understanding the contents of the contract and selecting appropriate expressions and terms. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the legal aspects and risks of the contract to conduct appropriate review. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Editing using tools for drafting texts for Japanese companies - Converting the translated text into a format that is suitable for the industry-specific terminology and expressions of Japanese companies. - The method of drafting sentences is to utilize industry knowledge and technical terms to select appropriate expressions. Step 3: Legal verification procedures - Review the draft text with the legal department to ensure it meets legal requirements. - Legal review means have knowledge of the legal aspects and risks of the contract and conduct appropriate review. The above is the flow of processing in the embodiment of the present invention.

[0075] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0076] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine analyzes keywords and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains keywords that express emotions such as "satisfied," "dissatisfied," and "neutral," the emotion engine recognizes them and creates a draft text by selecting expressions that increase satisfaction or expressions that reduce dissatisfaction. (Example 2) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses a machine learning algorithm as a translation means to translate the text of the contract with high accuracy. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and writing styles in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and terms are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "anxiety," and "neutral," the emotion engine recognizes it and selects expressions that emphasize happiness or expressions that reduce anxiety to create a draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses natural language processing technology as a translation means to accurately translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "dissatisfaction," and "neutral," the emotion engine recognizes it and creates a draft text by selecting expressions that emphasize happiness or expressions that reduce dissatisfaction. The above is the flow of processing in an embodiment of the present invention, and by combining it with an emotion engine, it is possible to improve the suitability of contracts for Japanese companies.

[0077] The process flow of each embodiment will be described below.

[0078] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine analyzes keywords and context in text and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The method of drafting the contract involves selecting appropriate expressions and writing styles according to the emotions, and editing the content of the contract into a format suitable for Japanese companies. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts emotions from the expressions and writing style in a sentence and classifies the emotions into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and terms according to the sentiment, and editing the contents of the contract to conform to the business culture and legal requirements of Japanese companies. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts sentiment from expressions and context in a sentence and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and writing styles according to the emotions, and converting the contents of the contract into a format suitable for the terminology and expressions specific to the industry of Japanese companies. The above is the flow of processing in an embodiment of the present invention. Combining a translation means and an emotion engine has the effect of improving the suitability of contracts for Japanese companies.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. 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 is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0081] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314. [Fourth embodiment]

[0082] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0083] 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.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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 wide area network (WAN) and / or a local area network (LAN).

[0085] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0086] 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.

[0087] 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).

[0088] 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. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0089] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[0090] Fig. 8 shows an example of 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.

[0091] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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.

[0092] 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.

[0093] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0094] 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 ChatGPT server) 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 (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0095] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. In addition, the system is equipped with a means for creating a draft of the text for Japanese companies, and converts the translated text into a format suitable for Japanese companies. It also includes a legal confirmation means, and the created draft of the text can be confirmed by the legal department. (Example 2) One embodiment of the present invention is a system for improving the suitability of overseas service contracts for Japanese companies. The system uses a machine learning algorithm as a translation means to translate the contract text with high accuracy. In addition, the system includes a means for creating a draft text for Japanese companies, which edits the translated text to conform to the business culture and legal requirements of Japanese companies. The system also includes a legal review means, which allows the legal department to review the created draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. The system uses natural language processing technology as a translation means to accurately translate the text of the contract. In addition, the system is provided with a means for creating a text proposal for Japanese companies, and converts the translated text into a format suitable for the terminology and expressions specific to the industry of Japanese companies. In addition, the system includes a legal confirmation means, and the created text proposal can be reviewed by the legal department. As a specific example, when translating a contract of an overseas service provider and creating a text proposal suitable for Japanese companies, important elements such as contract terms and scope of responsibility are appropriately translated and edited.

[0096] The process flow of each embodiment will be described below.

[0097] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Editing using tools for drafting texts for Japanese companies - Edit the translated text into a format suitable for Japanese companies. - The method of drafting the text will involve selecting appropriate expressions and terms based on the business culture and legal requirements of Japanese companies. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the contents of contracts and legal regulations to conduct appropriate reviews. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Editing using tools for drafting texts for Japanese companies - Editing the translated text to ensure it complies with Japanese business culture and legal requirements. - The method of drafting a document involves understanding the contents of the contract and selecting appropriate expressions and terms. Step 3: Legal verification procedures - The draft text is reviewed by the legal department to ensure it complies with legal requirements. - Legal review measures utilize knowledge of the legal aspects and risks of the contract to conduct appropriate review. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Editing using tools for drafting texts for Japanese companies - Converting the translated text into a format that is suitable for the industry-specific terminology and expressions of Japanese companies. - The method of drafting sentences is to utilize industry knowledge and technical terms to select appropriate expressions. Step 3: Legal verification procedures - Review the draft text with the legal department to ensure it meets legal requirements. - Legal review means have knowledge of the legal aspects and risks of the contract and conduct appropriate review. The above is the flow of processing in the embodiment of the present invention.

[0098] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0099] (Example 1) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses AI as a translation means to automatically translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine analyzes keywords and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains keywords that express emotions such as "satisfied," "dissatisfied," and "neutral," the emotion engine recognizes them and creates a draft text by selecting expressions that increase satisfaction or expressions that reduce dissatisfaction. (Example 2) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses a machine learning algorithm as a translation means to translate the text of the contract with high accuracy. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and writing styles in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and terms are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "anxiety," and "neutral," the emotion engine recognizes it and selects expressions that emphasize happiness or expressions that reduce anxiety to create a draft text. (Example 3) One embodiment of the present invention is a system for improving the suitability of contracts for overseas services for Japanese companies. This system uses natural language processing technology as a translation means to accurately translate the text of the contract. Furthermore, by combining an emotion engine, the system recognizes the user's emotions contained in the text of the contract and creates a draft text based on the emotions. The emotion engine extracts emotions from expressions and contexts in the text and classifies the user's emotions into positive, negative, and neutral categories. Then, appropriate expressions and writing styles are selected to create a draft text for Japanese companies. As a specific example, if the text of the contract contains expressions expressing emotions such as "happiness," "dissatisfaction," and "neutral," the emotion engine recognizes it and creates a draft text by selecting expressions that emphasize happiness or expressions that reduce dissatisfaction. The above is the flow of processing in an embodiment of the present invention, and by combining it with an emotion engine, it is possible to improve the suitability of contracts for Japanese companies.

[0100] The process flow of each embodiment will be described below.

[0101] (Example 1) Step 1: Translating the contract using translation tools - Use AI to translate contract text. - The translation method utilizes natural language processing technology to provide highly accurate translation. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine analyzes keywords and context in text and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The method of drafting the contract involves selecting appropriate expressions and writing styles according to the emotions, and editing the content of the contract into a format suitable for Japanese companies. (Example 2) Step 1: Translating the contract using translation tools - Translate contract text using machine learning algorithms. - The translation means learns from a large amount of data and achieves high translation accuracy. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts emotions from the expressions and writing style in a sentence and classifies the emotions into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and terms according to the sentiment, and editing the contents of the contract to conform to the business culture and legal requirements of Japanese companies. (Example 3) Step 1: Translating the contract using translation tools - Accurately translate contract text using natural language processing technology. - The translation tool takes into account context and terminology to produce appropriate translation results. Step 2: Emotion recognition by the emotion engine - Input the translated text into the emotion engine to recognize the user's emotions. - The sentiment engine extracts sentiment from expressions and context in a sentence and classifies sentiment into positive, negative, and neutral categories. Step 3: Editing using tools for drafting texts for Japanese companies - Combining the translated text with sentiment information from the sentiment engine to create text suggestions for Japanese companies. - The drafting method involves selecting appropriate expressions and writing styles according to the emotions, and converting the contents of the contract into a format suitable for the terminology and expressions specific to the industry of Japanese companies. The above is the flow of processing in an embodiment of the present invention. Combining a translation means and an emotion engine has the effect of improving the suitability of contracts for Japanese companies.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. 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 is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0104] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0109] 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.

[0110] 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."

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 replacements may be made to the above description and illustrations, 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 above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0121] 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, or standard was specifically and individually indicated to be incorporated by reference.

[0122] The following is further disclosed regarding the above embodiment.

[0123] (Appendix 1) A system for translating contracts for overseas services and creating drafts for Japanese companies. The system includes a translation means, a means for creating draft documents for Japanese companies, and a means for legal review. (Appendix 2) 2. The system according to claim 1, wherein the translation means performs translation using AI. (Appendix 3) 2. The system of claim 1, wherein the means for creating a draft of text for Japanese companies includes means for converting the translated text into a format suitable for Japanese companies.

[0124] (Appendix 4) A system for improving the suitability of overseas service contracts for Japanese companies, the system including an emotion engine. (Appendix 5) 5. The system according to claim 4, wherein the emotion engine recognizes the user's emotion contained in the contract text and creates a draft text based on the emotion. (Appendix 6) A system as described in Appendix 4 or 5, characterized in that the emotion engine classifies the user's emotions contained in the contract text into one of the categories of positive, negative, or neutral, and selects an expression or writing style accordingly to create a draft text. [Explanation of symbols]

[0125] 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 system for translating contracts for overseas services and creating drafts for Japanese companies. The system includes a translation means, a means for creating draft documents for Japanese companies, and a means for legal review.

2. 2. The system according to claim 1, wherein the translation means performs translation using AI.

3. 2. The system of claim 1, wherein the means for creating a draft for Japanese companies includes means for converting the translated text into a format suitable for Japanese companies.

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