system

Abstract

The system according to the embodiment aims to eliminate the gap in domain knowledge in business negotiations and realize smooth communication. [Solution] A system according to an embodiment includes a speech recognition unit, an analysis unit, a translation unit, and a provision unit. The speech recognition unit recognizes speech during a business negotiation in real time and converts it into text data. The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. The translation unit translates the technical terms and non-technical terms identified by the analysis unit. The provision unit provides the results of the translation by the translation unit to participants in the business negotiation in real time.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem that smooth communication is difficult due to differences in domain knowledge during business negotiations.

[0005] The system according to the embodiment aims to eliminate the gap in domain knowledge in business negotiations and realize smooth communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, an analysis unit, a translation unit, and a provision unit. The speech recognition unit recognizes speech during a business negotiation in real time and converts it into text data. The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. The translation unit translates the technical terms and non-technical terms identified by the analysis unit. The provision unit provides the results of the translation by the translation unit to participants in the business negotiation in real time. [Effects of the Invention]

[0007] The system according to the embodiment can eliminate the gap in domain knowledge in business negotiations and realize smooth communication. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] 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.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. 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 process 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). 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 for eliminating domain knowledge gaps during business negotiations. This system recognizes speech during business negotiations in real time and converts it into text data. Next, a generative AI analyzes the text data and translates technical and non-technical terminology. The translation results are provided to business negotiation participants in real time. This system eliminates the problem of domain knowledge gaps during business negotiations, realizing smooth communication. For example, by replacing technical terminology with general language, even clients who are not familiar with technology can easily understand. Conversely, by replacing general language with technical terminology, communication between engineers can also be facilitated. As a result, the system can significantly improve the efficiency of business negotiations.

[0029] A business negotiation support system according to an embodiment includes a speech recognition unit, an analysis unit, a translation unit, and a provision unit. The speech recognition unit recognizes speech during a business negotiation in real time and converts it into text data. For example, the speech recognition unit collects conversations during the business negotiation using a microphone and converts them into text data using speech recognition technology. The speech recognition unit can also improve speech recognition accuracy by using noise cancellation technology to remove background noise. The speech recognition unit can also learn the characteristics of a speaker's voice and use an individually optimized speech recognition model. The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. For example, the analysis unit analyzes the text data using natural language processing technology and classifies technical terms and non-technical terms. The analysis unit can also identify technical terms and non-technical terms by taking contextual information into account. The analysis unit can also improve identification accuracy by referring to past business negotiation data. The translation unit translates the technical terms and non-technical terms identified by the analysis unit. For example, the translation unit translates technical terms into general language using a generation AI. The translation unit can also translate general words into technical terms. Furthermore, the translation unit can estimate the user's emotions and adjust the way the translation is expressed based on the estimated user's emotions. The providing unit provides the results translated by the translation unit to the participants in the business negotiation in real time. For example, the providing unit displays the translation results on a display. The providing unit can also provide the translation results by voice using speech synthesis technology. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is displayed based on the estimated user's emotions. As a result, the business negotiation support system according to the embodiment can eliminate discrepancies in domain knowledge during business negotiations and achieve smooth communication.

[0030] The speech recognition unit may have a function to automatically remove background noise during speech recognition. For example, the speech recognition unit may filter noises generated during a business negotiation in real time to improve the accuracy of speech recognition. The speech recognition unit may also learn the environmental sounds of the location where the business negotiation is taking place in advance and remove specific noises. The speech recognition unit may also detect sudden noises (for example, the sound of a door opening and closing) during speech recognition and automatically remove them. This removes background noise, improving the accuracy of speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, AI, for example. For example, the speech recognition unit may input speech data during a business negotiation into a generation AI and have the generation AI remove background noise.

[0031] During speech recognition, the speech recognition unit can learn the characteristics of a speaker's voice and use an individually optimized speech recognition model. For example, the speech recognition unit learns the characteristics of the voice of each participant in a business meeting and applies an individually optimized speech recognition model. The speech recognition unit can also analyze the tone and pitch of the speaker's voice and select the optimal speech recognition model. The speech recognition unit can also learn the pronunciation habits of the speaker and improve the accuracy of speech recognition based on that. As a result, the accuracy of speech recognition is improved by using a speech recognition model optimized for each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaker's voice data to a generation AI and have the generation AI learn the speaker's voice characteristics.

[0032] The speech recognition unit can switch recognition algorithms depending on the language or dialect of the speaker during speech recognition. For example, when a speaker speaks different languages, the speech recognition unit automatically applies a speech recognition algorithm corresponding to that language. The speech recognition unit can also detect the speaker's dialect and use a speech recognition algorithm optimized for that dialect. When a speaker speaks a mixture of multiple languages, the speech recognition unit can also switch between speech recognition algorithms corresponding to each language. This improves the accuracy of speech recognition by using a recognition algorithm corresponding to the speaker's language or dialect. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaker's speech data to a generation AI and have the generation AI recognize the language and dialect.

[0033] During analysis, the analysis unit can identify technical terms and non-technical terms based on context information. For example, the analysis unit analyzes the context of a business negotiation and identifies technical terms and non-technical terms based on that context. The analysis unit can also make appropriate identification when the same word has different meanings based on the context information. The analysis unit can also improve the accuracy of identifying technical terms and non-technical terms by taking context information into account. In this way, the accuracy of identifying technical terms and non-technical terms is improved by taking context information into account. 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 can input text data of a business negotiation into a generation AI and have the generation AI analyze the context information.

[0034] During analysis, the analysis unit can improve the accuracy of identifying technical terms and non-technical terms by referring to past sales negotiation data. The analysis unit, for example, analyzes past sales negotiation data to identify frequently used technical terms and non-technical terms. The analysis unit can also preferentially identify terms used in a specific industry or field based on the past sales negotiation data. The analysis unit can also apply an algorithm to refer to the past sales negotiation data and improve the identification accuracy. In this way, by referring to the past sales negotiation data, the accuracy of identifying technical terms and non-technical terms is improved. 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 can input past sales negotiation data into the generation AI and cause the generation AI to improve the accuracy of identifying technical terms and non-technical terms.

[0035] During analysis, the analysis unit can improve the identification accuracy by using an industry-specific terminology dictionary. For example, the analysis unit prepares a terminology dictionary for each specific industry and identifies terminology based on that dictionary. The analysis unit can also improve the identification accuracy by using the industry-specific terminology dictionary. The analysis unit can also periodically update the industry-specific terminology dictionary and identify the latest terms. In this way, the use of the industry-specific terminology dictionary improves the identification accuracy. 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 can input the industry-specific terminology dictionary into the generation AI and cause the generation AI to identify terminology.

[0036] The translation unit may have a function to provide detailed explanations of the meanings of technical terms during translation. For example, the translation unit may add annotations to the translation results of technical terms that provide detailed explanations of their meanings and background information. The translation unit may also add related examples and usage examples to the translation results of technical terms. The translation unit may also add diagrams and illustrations to the translation results of technical terms to make them easier to understand visually. This provides a detailed explanation of the meanings of technical terms, thereby deepening understanding. Some or all of the above-described processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit may input the translation results of technical terms into a generation AI and have the generation AI provide a detailed explanation of the meanings.

[0037] The translation unit can improve translation accuracy based on context information during translation. For example, the translation unit analyzes the context of a business meeting and performs an appropriate translation based on that context. The translation unit can also perform an appropriate translation when the same word has different meanings based on the context information. The translation unit can also improve the translation accuracy of technical terms and non-technical terms by taking context information into account. In this way, taking context information into account improves translation accuracy. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input text data of a business meeting into a generation AI and have the generation AI analyze the context information.

[0038] The translation unit can improve translation accuracy by using an industry-specific terminology dictionary during translation. For example, the translation unit prepares a terminology dictionary for each specific industry and translates terminology based on that dictionary. The translation unit can also improve translation accuracy by using the industry-specific terminology dictionary. The translation unit can also regularly update the industry-specific terminology dictionary and translate the latest terms. In this way, using the industry-specific terminology dictionary improves translation accuracy. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the industry-specific terminology dictionary into a generation AI and have the generation AI translate the terminology.

[0039] The providing unit can adjust the timing of displaying information according to the progress of the business negotiation when providing the information. For example, the providing unit analyzes the progress of the business negotiation in real time and displays the information at an appropriate timing. The providing unit can also prioritize the display of important information according to the progress of the business negotiation. The providing unit can also adjust the timing of displaying information taking into account the progress of the business negotiation. In this way, by adjusting the timing of displaying information according to the progress of the business negotiation, it is possible to provide information at an appropriate timing. Some 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 can input business negotiation progress data to the generation AI and cause the generation AI to adjust the timing of displaying information.

[0040] The providing unit can provide information customized for each participant in the business negotiation at the time of providing the information. For example, the providing unit provides different information for each participant in the business negotiation and displays information according to each participant's needs. The providing unit can also provide customized information according to the position and level of expertise of the participant in the business negotiation. The providing unit can also provide optimal information based on the participant's past speech history. This makes it possible to provide information tailored to the needs of each participant by providing customized information for each participant in the business negotiation. Some 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 can input data of the participants in the business negotiation into a generating AI and cause the generating AI to provide customized information.

[0041] The providing unit can select the optimal information provision method by referring to past negotiation data when providing the information. For example, the providing unit analyzes past negotiation data and selects the optimal information provision method. The providing unit can also select an information provision method that is effective in a specific industry or field based on the past negotiation data. The providing unit can also refer to the past negotiation data and apply an algorithm to optimize the information provision method. In this way, the optimal information provision method can be selected by referring to the past negotiation data. Some 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 can input past negotiation data into the generation AI and cause the generation AI to select the optimal information provision method.

[0042] The providing unit can provide information taking into consideration the geographical location information of the business negotiation participants when providing the information. For example, the providing unit provides information specific to a region based on the geographical location information of the business negotiation participants. The providing unit can also provide appropriate information taking into consideration the geographical location information of the business negotiation participants. The providing unit can also provide information tailored to region-specific needs based on the geographical location information of the business negotiation participants. In this way, region-specific information can be provided by taking into consideration the geographical location information of the business negotiation participants. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location data of the business negotiation participants into the generation AI and cause the generation AI to provide the information.

[0043] When providing information, the providing unit can provide information by referring to the past speech history of the business negotiation participants. For example, the providing unit analyzes the past speech history of the business negotiation participants and provides frequently used words and phrases preferentially. The providing unit can also provide specific technical terms or industry terms based on the past speech history of the business negotiation participants. The providing unit can also refer to the past speech history of the business negotiation participants and apply an algorithm to improve the accuracy of information provision. In this way, more appropriate information can be provided by referring to the past speech history of the business negotiation participants. Some 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 can input the past speech history data of the business negotiation participants to the generation AI and have the generation AI provide the information.

[0044] The providing unit can adjust the information display method according to the expertise level of the business negotiation participants when providing the information. For example, if the business negotiation participants are experts, the providing unit uses a display method that prioritizes displaying technical terms. Furthermore, if the business negotiation participants are beginners, the providing unit can also use a display method that prioritizes displaying general words and phrases. Furthermore, the providing unit can set the expertise level of the business negotiation participants in advance and apply an information display method according to that level. This makes it possible to provide more appropriate information by using an information display method according to the expertise level of the business negotiation participants. Some 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 can input expertise level data of the business negotiation participants to the generation AI and cause the generation AI to adjust the information display method.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The speech recognition unit can also dynamically adjust the speech recognition parameters according to the progress of the negotiation. For example, the sensitivity of the speech recognition can be increased in the early stages of the negotiation to avoid missing important information. The sensitivity can also be returned to normal in the middle of the negotiation to recognize natural conversation. Furthermore, the speed of speech recognition can be increased in the final stages of the negotiation to quickly convert the speech into text data. This allows for more effective speech recognition by adjusting the speech recognition parameters according to the progress of the negotiation.

[0047] The speech recognition unit can further apply different speech recognition models to each participant in a business meeting. For example, a speech recognition model specialized in technical terms is used for engineers, and a speech recognition model specialized in general words is used for clients. The speech recognition unit can also select the optimal speech recognition model depending on the position and level of expertise of each participant in a business meeting. Furthermore, the speech recognition unit can also apply the optimal speech recognition model based on the past speech history of each participant in a business meeting. This improves the accuracy of speech recognition by using a speech recognition model optimized for each participant in a business meeting.

[0048] The speech recognition unit can further adjust the speech recognition algorithm by taking into account the geographical location information of the business meeting participants. For example, it can apply a speech recognition algorithm that corresponds to the dialect or accent of a particular region. In addition, if the business meeting participants are from different regions, the speech recognition unit can switch the speech recognition algorithm that corresponds to each region. Furthermore, the speech recognition unit can remove noise specific to each region based on the geographical location information of the business meeting participants. In this way, the accuracy of speech recognition can be improved by taking into account the geographical location information of the business meeting participants.

[0049] The analysis unit can also dynamically adjust the accuracy of identifying technical and non-technical terms according to the progress of the sales negotiation. For example, in the early stages of a sales negotiation, the accuracy of identifying technical terms can be increased to accurately identify important information. In addition, in the middle stages of a sales negotiation, the accuracy of identifying non-technical terms can be increased to identify natural conversations. Furthermore, in the final stages of a sales negotiation, the speed at which technical and non-technical terms are identified can be increased to identify them more quickly. In this way, by adjusting the accuracy of identifying technical and non-technical terms according to the progress of the sales negotiation, more accurate identification is possible.

[0050] The analysis unit can further improve the accuracy of identifying technical terms and non-technical terms by referring to the past speech history of business negotiation participants. For example, it can analyze past business negotiation data to identify frequently used technical terms and non-technical terms. The analysis unit can also preferentially identify terms used in specific industries or fields based on past business negotiation data. Furthermore, the analysis unit can also refer to past business negotiation data and apply an algorithm to improve the identification accuracy. In this way, by referring to past business negotiation data, the accuracy of identifying technical terms and non-technical terms is improved.

[0051] The translation department can also dynamically adjust the way the translation is presented depending on the progress of the negotiation. For example, in the early stages of a negotiation, concise and easy-to-understand expressions are used to emphasize important information. In the middle stages of a negotiation, expressions with detailed explanations can be used to deepen understanding. Furthermore, in the final stages of a negotiation, a quick translation that covers the main points can be provided. In this way, by adjusting the way the translation is presented depending on the progress of the negotiation, more appropriate translations can be provided.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The speech recognition unit recognizes the speech of the business negotiation in real time and converts it into text data. For example, the speech recognition unit collects the conversation during the business negotiation using a microphone and converts it into text data using speech recognition technology. It can also use noise canceling technology to remove background noise and improve the accuracy of speech recognition. It can also learn the characteristics of the speaker's voice and use an individually optimized speech recognition model. Step 2: The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. For example, the analysis unit uses natural language processing technology to analyze the text data and classify it into technical terms and non-technical terms. It can also identify technical terms and non-technical terms by taking contextual information into account. It can also improve the accuracy of identification by referring to past business negotiation data. Step 3: The translation unit translates the technical and non-technical terms identified by the analysis unit. For example, the translation unit can use generative AI to translate technical terms into general language, or vice versa. It can also estimate the user's emotions and adjust the translation's expression based on the estimated user emotions. Step 4: The providing unit provides the results of the translation by the translation unit to the participants in the business negotiation in real time. For example, the providing unit displays the translation results on a display. The providing unit can also provide the translation results by voice using speech synthesis technology. Furthermore, the providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions.

[0054] (Example 2) A system according to an embodiment of the present invention is a system for eliminating domain knowledge gaps during business negotiations. This system recognizes speech during business negotiations in real time and converts it into text data. Next, a generative AI analyzes the text data and translates technical and non-technical terminology. The translation results are provided to business negotiation participants in real time. This system eliminates the problem of domain knowledge gaps during business negotiations, realizing smooth communication. For example, by replacing technical terminology with general language, even clients who are not familiar with technology can easily understand. Conversely, by replacing general language with technical terminology, communication between engineers can also be facilitated. As a result, the system can significantly improve the efficiency of business negotiations.

[0055] A business negotiation support system according to an embodiment includes a speech recognition unit, an analysis unit, a translation unit, and a provision unit. The speech recognition unit recognizes speech during a business negotiation in real time and converts it into text data. For example, the speech recognition unit collects conversations during the business negotiation using a microphone and converts them into text data using speech recognition technology. The speech recognition unit can also improve speech recognition accuracy by using noise cancellation technology to remove background noise. The speech recognition unit can also learn the characteristics of a speaker's voice and use an individually optimized speech recognition model. The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. For example, the analysis unit analyzes the text data using natural language processing technology and classifies technical terms and non-technical terms. The analysis unit can also identify technical terms and non-technical terms by taking contextual information into account. The analysis unit can also improve identification accuracy by referring to past business negotiation data. The translation unit translates the technical terms and non-technical terms identified by the analysis unit. For example, the translation unit translates technical terms into general language using a generation AI. The translation unit can also translate general words into technical terms. Furthermore, the translation unit can estimate the user's emotions and adjust the way the translation is expressed based on the estimated user's emotions. The providing unit provides the results translated by the translation unit to the participants in the business negotiation in real time. For example, the providing unit displays the translation results on a display. The providing unit can also provide the translation results by voice using speech synthesis technology. Furthermore, the providing unit can estimate the user's emotions and adjust the way the information is displayed based on the estimated user's emotions. As a result, the business negotiation support system according to the embodiment can eliminate discrepancies in domain knowledge during business negotiations and achieve smooth communication.

[0056] The speech recognition unit estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated user emotions. For example, if the user is nervous, the speech recognition unit increases the sensitivity of speech recognition to recognize speech more accurately. Furthermore, if the user is relaxed, the speech recognition unit can return the sensitivity to normal and recognize natural conversations. Furthermore, if the user is in a hurry, the speech recognition unit can increase the speech recognition speed and quickly convert the speech into text data. This enables more accurate speech recognition by adjusting the accuracy of speech recognition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speech recognition unit may input the user's voice data into the generation AI and have the generation AI estimate the user's emotions.

[0057] The speech recognition unit may have a function to automatically remove background noise during speech recognition. For example, the speech recognition unit may filter noises generated during a business negotiation in real time to improve the accuracy of speech recognition. The speech recognition unit may also learn the environmental sounds of the location where the business negotiation is taking place in advance and remove specific noises. The speech recognition unit may also detect sudden noises (for example, the sound of a door opening and closing) during speech recognition and automatically remove them. This removes background noise, improving the accuracy of speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, AI, for example. For example, the speech recognition unit may input speech data during a business negotiation into a generation AI and have the generation AI remove background noise.

[0058] During speech recognition, the speech recognition unit can learn the characteristics of a speaker's voice and use an individually optimized speech recognition model. For example, the speech recognition unit learns the characteristics of the voice of each participant in a business meeting and applies an individually optimized speech recognition model. The speech recognition unit can also analyze the tone and pitch of the speaker's voice and select the optimal speech recognition model. The speech recognition unit can also learn the pronunciation habits of the speaker and improve the accuracy of speech recognition based on that. As a result, the accuracy of speech recognition is improved by using a speech recognition model optimized for each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaker's voice data to a generation AI and have the generation AI learn the speaker's voice characteristics.

[0059] The speech recognition unit can switch recognition algorithms depending on the language or dialect of the speaker during speech recognition. For example, when a speaker speaks different languages, the speech recognition unit automatically applies a speech recognition algorithm corresponding to that language. The speech recognition unit can also detect the speaker's dialect and use a speech recognition algorithm optimized for that dialect. When a speaker speaks a mixture of multiple languages, the speech recognition unit can also switch between speech recognition algorithms corresponding to each language. This improves the accuracy of speech recognition by using a recognition algorithm corresponding to the speaker's language or dialect. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input the speaker's speech data to a generation AI and have the generation AI recognize the language and dialect.

[0060] The analysis unit can estimate the user's emotions and adjust the accuracy of identifying technical terms and non-technical terms based on the estimated user emotions. For example, if the user is nervous, the analysis unit can increase the accuracy of identifying technical terms and more accurately identify them. Furthermore, if the user is relaxed, the analysis unit can increase the accuracy of identifying non-technical terms and identify natural conversations. Furthermore, if the user is in a hurry, the analysis unit can increase the speed at which technical terms and non-technical terms are identified and quickly identify them. This enables more accurate identification by adjusting the accuracy of identifying technical terms and non-technical terms according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's text data into the generation AI and cause the generation AI to adjust the accuracy of identifying technical terms and non-technical terms.

[0061] During analysis, the analysis unit can identify technical terms and non-technical terms based on context information. For example, the analysis unit analyzes the context of a business negotiation and identifies technical terms and non-technical terms based on that context. The analysis unit can also make appropriate identification when the same word has different meanings based on the context information. The analysis unit can also improve the accuracy of identifying technical terms and non-technical terms by taking context information into account. In this way, the accuracy of identifying technical terms and non-technical terms is improved by taking context information into account. 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 can input text data of a business negotiation into a generation AI and have the generation AI analyze the context information.

[0062] During analysis, the analysis unit can improve the accuracy of identifying technical terms and non-technical terms by referring to past sales negotiation data. The analysis unit, for example, analyzes past sales negotiation data to identify frequently used technical terms and non-technical terms. The analysis unit can also preferentially identify terms used in a specific industry or field based on the past sales negotiation data. The analysis unit can also apply an algorithm to refer to the past sales negotiation data and improve the identification accuracy. In this way, by referring to the past sales negotiation data, the accuracy of identifying technical terms and non-technical terms is improved. 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 can input past sales negotiation data into the generation AI and cause the generation AI to improve the accuracy of identifying technical terms and non-technical terms.

[0063] During analysis, the analysis unit can improve the identification accuracy by using an industry-specific terminology dictionary. For example, the analysis unit prepares a terminology dictionary for each specific industry and identifies terminology based on that dictionary. The analysis unit can also improve the identification accuracy by using the industry-specific terminology dictionary. The analysis unit can also periodically update the industry-specific terminology dictionary and identify the latest terms. In this way, the use of the industry-specific terminology dictionary improves the identification accuracy. 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 can input the industry-specific terminology dictionary into the generation AI and cause the generation AI to identify terminology.

[0064] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is nervous, the translation unit uses a concise and easy-to-understand expression. Furthermore, if the user is relaxed, the translation unit can use an expression that includes detailed explanations. Furthermore, if the user is in a hurry, the translation unit can provide a quick translation that focuses on the main points. This allows the translation expression to be adjusted according to the user's emotions, resulting in a more appropriate translation. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the user's text data into the generation AI and have the generation AI adjust the translation expression.

[0065] The translation unit may have a function to provide detailed explanations of the meanings of technical terms during translation. For example, the translation unit may add annotations to the translation results of technical terms that provide detailed explanations of their meanings and background information. The translation unit may also add related examples and usage examples to the translation results of technical terms. The translation unit may also add diagrams and illustrations to the translation results of technical terms to make them easier to understand visually. This provides a detailed explanation of the meanings of technical terms, thereby deepening understanding. Some or all of the above-described processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit may input the translation results of technical terms into a generation AI and have the generation AI provide a detailed explanation of the meanings.

[0066] The translation unit can improve translation accuracy based on context information during translation. For example, the translation unit analyzes the context of a business meeting and performs an appropriate translation based on that context. The translation unit can also perform an appropriate translation when the same word has different meanings based on the context information. The translation unit can also improve the translation accuracy of technical terms and non-technical terms by taking context information into account. In this way, taking context information into account improves translation accuracy. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input text data of a business meeting into a generation AI and have the generation AI analyze the context information.

[0067] The translation unit can improve translation accuracy by using an industry-specific terminology dictionary during translation. For example, the translation unit prepares a terminology dictionary for each specific industry and translates terminology based on that dictionary. The translation unit can also improve translation accuracy by using the industry-specific terminology dictionary. The translation unit can also regularly update the industry-specific terminology dictionary and translate the latest terms. In this way, using the industry-specific terminology dictionary improves translation accuracy. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the industry-specific terminology dictionary into a generation AI and have the generation AI translate the terminology.

[0068] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This enables more appropriate information to be provided by adjusting the information display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information display method.

[0069] The providing unit can adjust the timing of displaying information according to the progress of the business negotiation when providing the information. For example, the providing unit analyzes the progress of the business negotiation in real time and displays the information at an appropriate timing. The providing unit can also prioritize the display of important information according to the progress of the business negotiation. The providing unit can also adjust the timing of displaying information taking into account the progress of the business negotiation. In this way, by adjusting the timing of displaying information according to the progress of the business negotiation, it is possible to provide information at an appropriate timing. Some 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 can input business negotiation progress data to the generation AI and cause the generation AI to adjust the timing of displaying information.

[0070] The providing unit can provide information customized for each participant in the business negotiation at the time of providing the information. For example, the providing unit provides different information for each participant in the business negotiation and displays information according to each participant's needs. The providing unit can also provide customized information according to the position and level of expertise of the participant in the business negotiation. The providing unit can also provide optimal information based on the participant's past speech history. This makes it possible to provide information tailored to the needs of each participant by providing customized information for each participant in the business negotiation. Some 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 can input data of the participants in the business negotiation into a generating AI and cause the generating AI to provide customized information.

[0071] The providing unit can select the optimal information provision method by referring to past negotiation data when providing the information. For example, the providing unit analyzes past negotiation data and selects the optimal information provision method. The providing unit can also select an information provision method that is effective in a specific industry or field based on the past negotiation data. The providing unit can also refer to the past negotiation data and apply an algorithm to optimize the information provision method. In this way, the optimal information provision method can be selected by referring to the past negotiation data. Some 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 can input past negotiation data into the generation AI and cause the generation AI to select the optimal information provision method.

[0072] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing information that covers the main points. In this way, by determining the priority of information according to the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0073] The providing unit can provide information taking into consideration the geographical location information of the business negotiation participants when providing the information. For example, the providing unit provides information specific to a region based on the geographical location information of the business negotiation participants. The providing unit can also provide appropriate information taking into consideration the geographical location information of the business negotiation participants. The providing unit can also provide information tailored to region-specific needs based on the geographical location information of the business negotiation participants. In this way, region-specific information can be provided by taking into consideration the geographical location information of the business negotiation participants. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location data of the business negotiation participants into the generation AI and cause the generation AI to provide the information.

[0074] When providing information, the providing unit can provide information by referring to the past speech history of the business negotiation participants. For example, the providing unit analyzes the past speech history of the business negotiation participants and provides frequently used words and phrases preferentially. The providing unit can also provide specific technical terms or industry terms based on the past speech history of the business negotiation participants. The providing unit can also refer to the past speech history of the business negotiation participants and apply an algorithm to improve the accuracy of information provision. In this way, more appropriate information can be provided by referring to the past speech history of the business negotiation participants. Some 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 can input the past speech history data of the business negotiation participants to the generation AI and have the generation AI provide the information.

[0075] The providing unit can adjust the information display method according to the expertise level of the business negotiation participants when providing the information. For example, if the business negotiation participants are experts, the providing unit uses a display method that prioritizes displaying technical terms. Furthermore, if the business negotiation participants are beginners, the providing unit can also use a display method that prioritizes displaying general words and phrases. Furthermore, the providing unit can set the expertise level of the business negotiation participants in advance and apply an information display method according to that level. This makes it possible to provide more appropriate information by using an information display method according to the expertise level of the business negotiation participants. Some 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 can input expertise level data of the business negotiation participants to the generation AI and cause the generation AI to adjust the information display method. === Hard Collateral 1-1 === Each of the multiple elements, including the speech recognition unit, analysis unit, translation unit, and provision unit, described above, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the speech recognition unit collects business negotiation speech using the microphone 38B of the smart device 14 and converts it into text data using the control unit 46A. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the text data to identify technical terms and non-technical terms. The translation unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, translates technical terms into general language using a generation AI. The provision unit displays the translation result using, for example, the display 40A of the smart device 14. The speech recognition unit can also estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned speech recognition unit, analysis unit, translation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the speech recognition unit collects business negotiation voice using the microphone 238 of the smart glasses 214 and converts it into text data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text data to identify technical terms and non-technical terms. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates technical terms into general language using a generation AI. The provision unit displays the translation result, for example, using the display of the smart glasses 214. In addition, the speech recognition unit can estimate the user's emotion and adjust the accuracy of speech recognition based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the speech recognition unit, analysis unit, translation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the speech recognition unit collects speech during business negotiations using the microphone 238 of the headset-type terminal 314 and converts it into text data by the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the text data to identify technical terms and non-technical terms. The translation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and translates technical terms into general language using a generation AI. The provision unit displays the translation result, for example, using the display 343 of the headset-type terminal 314. The speech recognition unit can also estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the speech recognition unit, analysis unit, translation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the speech recognition unit collects speech during business negotiations using the microphone 238 of the robot 414 and converts it into text data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the text data to identify technical terms and non-technical terms. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates technical terms into general language using a generation AI. The provision unit displays the translation results, for example, using a display of the robot 414. The speech recognition unit can also estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The business negotiation support system may further include an emotion support unit that estimates the user's emotions and supports the progress of the business negotiation based on the estimated emotions. For example, if the user feels nervous, the emotion support unit may provide advice or reminders to relax. If the user feels relaxed, the emotion support unit may also emphasize important points in the business negotiation to help the user maintain concentration. Furthermore, if the user feels rushed, the emotion support unit may make suggestions to speed up the progress of the business negotiation. In this way, the efficiency and effectiveness of business negotiations can be improved by providing support according to the user's emotions.

[0078] The speech recognition unit can also analyze the tone and pitch of the speaker's voice to detect changes in emotion in real time. For example, if the speaker's voice gets higher, it can be determined that the speaker is nervous, and the sensitivity of the speech recognition can be adjusted accordingly. Alternatively, if the speaker's voice gets lower, it can be determined that the speaker is relaxed, and the sensitivity can be returned to normal. Furthermore, if the speaker's voice speed increases, it can be determined that the speaker is in a hurry, and the speech recognition speed can be increased. This allows for more accurate speech recognition by dynamically adjusting the accuracy of speech recognition based on the speaker's vocal characteristics.

[0079] The speech recognition unit can also dynamically adjust the speech recognition parameters according to the progress of the negotiation. For example, the sensitivity of the speech recognition can be increased in the early stages of the negotiation to avoid missing important information. The sensitivity can also be returned to normal in the middle of the negotiation to recognize natural conversation. Furthermore, the speed of speech recognition can be increased in the final stages of the negotiation to quickly convert the speech into text data. This allows for more effective speech recognition by adjusting the speech recognition parameters according to the progress of the negotiation.

[0080] The speech recognition unit can further apply different speech recognition models to each participant in a business meeting. For example, a speech recognition model specialized in technical terms is used for engineers, and a speech recognition model specialized in general words is used for clients. The speech recognition unit can also select the optimal speech recognition model depending on the position and level of expertise of each participant in a business meeting. Furthermore, the speech recognition unit can also apply the optimal speech recognition model based on the past speech history of each participant in a business meeting. This improves the accuracy of speech recognition by using a speech recognition model optimized for each participant in a business meeting.

[0081] The speech recognition unit can further adjust the speech recognition algorithm by taking into account the geographical location information of the business meeting participants. For example, it can apply a speech recognition algorithm that corresponds to the dialect or accent of a particular region. In addition, if the business meeting participants are from different regions, the speech recognition unit can switch the speech recognition algorithm that corresponds to each region. Furthermore, the speech recognition unit can remove noise specific to each region based on the geographical location information of the business meeting participants. In this way, the accuracy of speech recognition can be improved by taking into account the geographical location information of the business meeting participants.

[0082] The analysis unit can also dynamically adjust the accuracy of identifying technical and non-technical terms according to the progress of the sales negotiation. For example, in the early stages of a sales negotiation, the accuracy of identifying technical terms can be increased to accurately identify important information. In addition, in the middle stages of a sales negotiation, the accuracy of identifying non-technical terms can be increased to identify natural conversations. Furthermore, in the final stages of a sales negotiation, the speed at which technical and non-technical terms are identified can be increased to identify them more quickly. In this way, by adjusting the accuracy of identifying technical and non-technical terms according to the progress of the sales negotiation, more accurate identification is possible.

[0083] The analysis unit can further estimate the emotions of the participants in the business negotiation and adjust the method for identifying technical terms and non-technical terms based on the estimated emotions. For example, if the user is nervous, the accuracy of identifying technical terms can be increased to identify them more accurately. Also, if the user is relaxed, the accuracy of identifying non-technical terms can be increased to identify natural conversations. Furthermore, if the user is in a hurry, the speed at which technical terms and non-technical terms are identified can be increased to identify them more quickly. In this way, by adjusting the method for identifying technical terms and non-technical terms according to the user's emotions, more accurate identification is possible.

[0084] The analysis unit can further improve the accuracy of identifying technical terms and non-technical terms by referring to the past speech history of business negotiation participants. For example, it can analyze past business negotiation data to identify frequently used technical terms and non-technical terms. The analysis unit can also preferentially identify terms used in specific industries or fields based on past business negotiation data. Furthermore, the analysis unit can also refer to past business negotiation data and apply an algorithm to improve the identification accuracy. In this way, by referring to past business negotiation data, the accuracy of identifying technical terms and non-technical terms is improved.

[0085] The translation department can also dynamically adjust the way the translation is presented depending on the progress of the negotiation. For example, in the early stages of a negotiation, concise and easy-to-understand expressions are used to emphasize important information. In the middle stages of a negotiation, expressions with detailed explanations can be used to deepen understanding. Furthermore, in the final stages of a negotiation, a quick translation that covers the main points can be provided. In this way, by adjusting the way the translation is presented depending on the progress of the negotiation, more appropriate translations can be provided.

[0086] The translation unit can further estimate the emotions of the participants in the business negotiation and adjust the way the translation is expressed based on the estimated emotions. For example, if the user is nervous, a concise and easy-to-understand way of expression can be used. If the user is relaxed, a way of expression that includes detailed explanations can be used. Furthermore, if the user is in a hurry, a quick translation that focuses on the main points can be performed. In this way, by adjusting the way the translation is expressed according to the user's emotions, a more appropriate translation can be provided.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The speech recognition unit recognizes the speech of the business negotiation in real time and converts it into text data. For example, the speech recognition unit collects the conversation during the business negotiation using a microphone and converts it into text data using speech recognition technology. It can also use noise canceling technology to remove background noise and improve the accuracy of speech recognition. It can also learn the characteristics of the speaker's voice and use an individually optimized speech recognition model. Step 2: The analysis unit identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit. For example, the analysis unit uses natural language processing technology to analyze the text data and classify it into technical terms and non-technical terms. It can also identify technical terms and non-technical terms by taking contextual information into account. It can also improve the accuracy of identification by referring to past business negotiation data. Step 3: The translation unit translates the technical and non-technical terms identified by the analysis unit. For example, the translation unit can use generative AI to translate technical terms into general language, or vice versa. It can also estimate the user's emotions and adjust the translation's expression based on the estimated user emotions. Step 4: The providing unit provides the results of the translation by the translation unit to the participants in the business negotiation in real time. For example, the providing unit displays the translation results on a display. The providing unit can also provide the translation results by voice using speech synthesis technology. Furthermore, the providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions.

[0089] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0090] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with 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 specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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, RAM 30, and 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.

[0096] 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.

[0097] 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 audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] 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 carried out in a secure state.

[0100] Fig. 4 shows an example of the 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.

[0101] 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.

[0102] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0106] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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, RAM 30, and 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.

[0112] 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.

[0113] 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 audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] 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 carried out in a secure state.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0120] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0122] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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, RAM 30, and 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.

[0128] 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.

[0129] 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 audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] 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 carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0133] 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.

[0134] 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.

[0135] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0137] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] 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 voice indicating a user input regarding 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.

[0139] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] 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.

[0150] 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 USB (Universal Serial Bus) 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.

[0151] Alternatively, 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 in response to a request from the data processing device 12.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0155] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0160] [Explanation of symbols]

[0161] 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 speech recognition unit that recognizes the voice of business negotiations in real time and converts it into text data; an analysis unit that identifies technical terms and non-technical terms based on the text data recognized by the speech recognition unit; a translation unit that translates the technical terms and non-technical terms identified by the analysis unit; a providing unit that provides the results of the translation by the translation unit to participants in the business negotiation in real time. A system characterized by:

2. The voice recognition unit Estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions.

2. The system of claim 1.

3. The voice recognition unit Equipped with a function to automatically remove background noise during voice recognition 2. The system of claim 1.

4. The voice recognition unit During speech recognition, the system learns the characteristics of the speaker's voice and uses an individually optimized speech recognition model.

2. The system of claim 1.

5. The voice recognition unit During speech recognition, the recognition algorithm is switched depending on the speaker's language and dialect.

2. The system of claim 1.

6. The analysis unit Estimate user sentiment and adjust the accuracy of identifying technical and non-technical terms based on the estimated user sentiment.

2. The system of claim 1.

7. The analysis unit During analysis, identify technical and non-technical terms based on contextual information 2. The system of claim 1.

8. The analysis unit During analysis, refer to past sales data to improve the accuracy of identifying technical and non-technical terms 2. The system of claim 1.

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