system

Abstract

The system according to the embodiment aims to accurately read the habits and psychological state of the other person from their words, actions and facial expressions, and make effective suggestions. [Solution] A system according to an embodiment includes a sensor unit, an analysis unit, an estimation unit, and a proposal unit. The sensor unit collects the other person's words, actions, or facial expressions using a camera or microphone. The analysis unit analyzes the data collected by the sensor unit using deep learning. The estimation unit estimates the other person's habits and psychological state based on the data analyzed by the analysis unit. The proposal unit makes a proposal based on the habits and psychological state estimated by the estimation unit.

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 technology has made it difficult to accurately read the habits and psychological state of others from their words, actions, and facial expressions, making it difficult to negotiate or persuade them effectively.

[0005] The system according to the embodiment aims to accurately read the habits and psychological state of the other person from their words, actions and facial expressions, and make effective suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, an estimation unit, and a proposal unit. The sensor unit collects the other person's words, actions, or facial expressions using a camera or microphone. The analysis unit analyzes the data collected by the sensor unit using deep learning. The estimation unit estimates the other person's habits and psychological state based on the data analyzed by the analysis unit. The proposal unit makes a proposal based on the habits and psychological state estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately read the habits and psychological state of the other person from their words, actions and facial expressions, and make effective suggestions. [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 communication support system according to an embodiment of the present invention uses artificial intelligence (AI) to interpret the other party's behavior and facial expressions to predict their habits and psychological state, and then makes suggestions to improve the success of negotiations, persuasion, and explanations. This communication support system is adaptable to different races, cultures, and personality types, and also suggests communication styles based on the information it reads. First, a camera and microphone are used to collect the other party's behavior and facial expressions in real time, which the AI ​​analyzes using deep learning. Next, the other party's habits and psychological state are estimated based on the analysis results, and appropriate suggestions are made based on the estimated habits and psychological state. Furthermore, the system is adaptable to different cultures and personality types, and the AI ​​determines the other party's cultural background and personality type based on a database collected in advance, and suggests an appropriate communication style. For example, the system may include a sensor unit that collects the other party's behavior and facial expressions using a camera and microphone, an analysis unit that analyzes the data collected by the sensor unit using deep learning, an estimation unit that estimates the other party's habits and psychological state based on the data analyzed by the analysis unit, a suggestion unit that makes appropriate suggestions based on the estimated habits and psychological state, and a database unit that is adaptable to different cultures and personality types. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit suggests a communication style that matches that cultural background and personality type.This allows the communication support system to analyze the other party's words, behavior, and facial expressions and make appropriate suggestions, thereby enabling successful negotiations and persuasion.

[0029] A communication support system according to an embodiment includes a sensor unit, an analysis unit, an estimation unit, and a proposal unit. The sensor unit collects the other party's behavior or facial expression using a camera or a microphone. For example, the sensor unit collects the other party's facial expressions in real time using a camera. The sensor unit can also collect the other party's tone of voice and language use using a microphone. The sensor unit can also collect gestures and movements. For example, the sensor unit collects the other party's hand movements and body movements using a camera. The analysis unit analyzes the collected data using deep learning. For example, the analysis unit inputs the collected facial expression data into a deep learning model to analyze the other party's emotional state. The analysis unit can also analyze the collected tone of voice and language use to estimate the other party's psychological state. The analysis unit can also analyze the collected gesture and movement data to identify the other party's habits and behavioral patterns. The estimation unit estimates the other party's habits and psychological state based on the analysis results. For example, the estimation unit estimates the other party's level of tension or relaxation based on the analyzed emotional state data. The estimation unit can also estimate the intentions and goals of the other party based on the analyzed psychological state data. Furthermore, the estimation unit can identify the other party's habits and routines based on the analyzed behavioral pattern data. The suggestion unit makes appropriate suggestions based on the estimated habits and psychological state. For example, if the other party is nervous, the suggestion unit makes suggestions to help them relax. Furthermore, if the other party is relaxed, the suggestion unit can also make proactive suggestions. Furthermore, the suggestion unit can also suggest a communication style that suits the other party's habits and routines. As a result, the communication support system according to the embodiment can analyze the other party's words, actions, and facial expressions and make appropriate suggestions, thereby enabling negotiations and persuasion to proceed smoothly.

[0030] The communication support system includes a database unit that can adapt to different cultures and personality types. The database unit determines the cultural background and personality type of the other party based on data collected in advance. For example, the database unit collects cultural data by country and determines the cultural background based on the other party's nationality. The database unit can also determine the other party's personality type based on the results of a personality diagnosis. Furthermore, the database unit can identify the other party's communication style based on past communication history. This allows the database unit to provide basic data for making suggestions according to the other party's cultural background and personality type. For example, if the other party is from an Asian cultural region, the database unit can suggest a communication style suitable for Asian culture. Furthermore, if the other party has an introverted personality type, the database unit can also suggest a communication style suitable for introverts. This allows the communication support system to make suggestions according to different cultures and personality types.

[0031] The database unit can determine the cultural background and personality type of the other party based on data collected in advance. The database unit can determine the cultural background of the other party based on, for example, questionnaire results. For example, the database unit can analyze the results of a questionnaire answered by the other party to identify the other party's cultural background. The database unit can also determine the other party's personality type based on their behavioral history. For example, the database unit can analyze the other party's past behavioral history to identify the other party's personality type. By determining the other party's cultural background and personality type, the database unit can make more appropriate suggestions. For example, if the other party is from a Western cultural sphere, the database unit can suggest a communication style suitable for Western culture. Furthermore, if the other party has an extroverted personality type, the database unit can suggest a communication style suitable for extroverts. This allows the communication support system to make suggestions according to the other party's cultural background and personality type.

[0032] The suggestion unit can suggest a communication style according to the cultural background and personality type of the other party. For example, if the other party is from an Asian cultural region, the suggestion unit can suggest a communication style suitable for Asian culture. For example, the suggestion unit can suggest a way of expressing courtesy and respect that is emphasized in Asian culture. Furthermore, if the other party is from a Western cultural region, the suggestion unit can also suggest a communication style suitable for Western culture. For example, the suggestion unit can suggest a direct way of expression and a way of asserting oneself that is emphasized in Western culture. Furthermore, if the other party has an introverted personality type, the suggestion unit can also suggest a communication style suitable for introverts. For example, the suggestion unit can suggest a conversation in a calm and relaxed tone to an introverted person. Furthermore, if the other party has an extroverted personality type, the suggestion unit can also suggest a communication style suitable for extroverts. For example, the suggestion unit can suggest a conversation in an active and energetic tone to an extroverted person. In this way, the suggestion unit can suggest a communication style according to the other party's cultural background and personality type, thereby enabling successful negotiations and persuasion.

[0033] The sensor unit can estimate the emotions of the other party and adjust the type of data to be collected based on the estimated emotions. For example, if the other party is nervous, the sensor unit can focus on collecting subtle changes in facial expressions. For example, the sensor unit can use a camera to collect detailed images of the other party's facial expressions and estimate the level of nervousness. Furthermore, if the other party is relaxed, the sensor unit can also collect detailed patterns of speech and behavior. For example, the sensor unit can use a microphone to collect the other party's tone of voice and phrasing and estimate the level of relaxation. Furthermore, if the other party is angry, the sensor unit can focus on collecting the tone and volume of voice. For example, the sensor unit can use a microphone to collect detailed images of the other party's tone and volume of voice and estimate the level of anger. This allows the sensor unit to adjust the data collection method according to the other party's emotions, enabling more accurate data collection. For example, if the other party is nervous, the sensor unit can use a high-resolution camera to collect subtle changes in facial expressions. Furthermore, if the other party is relaxed, the sensor unit can use a high-sensitivity microphone to collect patterns of speech and behavior. This allows the sensor unit to collect data according to the emotions of the other person.

[0034] The sensor unit can select a collection method by referring to the other party's past speech, behavior, and facial expression data. For example, the sensor unit identifies the other party's most common facial expression from past data and focuses on collecting that facial expression. For example, the sensor unit can analyze past conversation logs and facial expression data to identify the other party's most common facial expression. The sensor unit can also identify the other party's frequently used words and phrases from past data and focus on collecting those words and behavior. For example, the sensor unit can analyze past conversation logs to identify the other party's frequently used words and phrases. The sensor unit can also identify changes in the other party's tone and volume of voice from past data and focus on collecting those changes. For example, the sensor unit can analyze past voice data to identify changes in the other party's tone and volume of voice. This allows the sensor unit to collect data more effectively by referring to past data. For example, the sensor unit can identify patterns in the other party's facial expression and speech, based on past data, and collect data based on those patterns. The sensor unit can also identify changes in the other party's tone and volume of voice based on past data, and collect data based on those changes. This allows the sensor unit to select the most appropriate collection method by referring to the other person's past behavior and facial expression data.

[0035] The sensor unit simultaneously collects the other party's environmental sounds and background information during data collection, which can be used for analysis. The sensor unit, for example, collects the other party's surrounding environmental sounds and estimates their stress level. For example, the sensor unit uses a microphone to collect the other party's surrounding environmental sounds and analyzes the type and volume of the sounds to estimate the other party's stress level. The sensor unit can also collect the other party's background information and grasp their current situation. For example, the sensor unit uses a camera to collect the other party's background information and analyzes that information to grasp their current situation. The sensor unit can also collect the voices of people around the other party and estimate their social situation. For example, the sensor unit uses a microphone to collect the voices of people around the other party and analyzes the tone and content of the voices to estimate the other party's social situation. This allows the sensor unit to collect environmental sounds and background information, enabling more detailed analysis. For example, the sensor unit collects the other party's surrounding environmental sounds and analyzes the type and volume of the sounds to estimate the other party's stress level. The sensor unit can also collect the other party's background information and analyze that information to grasp their current situation. This allows the sensor unit to collect the other person's environmental sounds and background information and use this information for analysis.

[0036] The sensor unit can estimate the emotions of the other party and determine the priority of data to be collected based on the estimated emotions. For example, if the other party is nervous, the sensor unit prioritizes collecting changes in facial expressions. For example, the sensor unit uses a camera to collect detailed images of the other party's facial expressions and estimate the level of nervousness. Furthermore, if the other party is relaxed, the sensor unit can prioritize collecting patterns of speech and behavior. For example, the sensor unit uses a microphone to collect the other party's tone of voice and phrasing and estimate the level of relaxation. Furthermore, if the other party is angry, the sensor unit can prioritize collecting the tone and volume of voice. For example, the sensor unit uses a microphone to collect detailed images of the other party's tone and volume of voice and estimate the level of anger. In this way, the sensor unit can prioritize collecting important data by determining the priority of data according to the other party's emotions. For example, if the other party is nervous, the sensor unit can use a high-resolution camera to prioritize collecting changes in facial expressions. Furthermore, if the other party is relaxed, the sensor unit can use a high-sensitivity microphone to prioritize collecting patterns of speech and behavior. This allows the sensor unit to collect data according to the emotions of the other person.

[0037] When collecting data, the sensor unit can prioritize collecting highly relevant data by taking into account the geographical location information of the other party. For example, if the other party is in a specific region, the sensor unit prioritizes collecting data related to the culture and customs of that region. For example, the sensor unit identifies the other party's geographical location using GPS data and collects data related to the culture and customs of that region. Furthermore, if the other party is traveling, the sensor unit can prioritize collecting information about the travel destination. For example, the sensor unit collects data related to the culture and customs of the travel destination based on the other party's geographical location information. Furthermore, if the other party is at home, the sensor unit can prioritize collecting data related to the situation at home. For example, the sensor unit collects data related to the situation at home based on the other party's geographical location information. In this way, the sensor unit can collect more relevant data by taking into account the geographical location information. For example, if the other party is in a specific region, the sensor unit prioritizes collecting data related to the culture and customs of that region. Furthermore, if the other party is traveling, the sensor unit can prioritize collecting information about the travel destination. This allows the sensor unit to take into consideration the geographical location information of the other party and to preferentially collect highly relevant data.

[0038] During collection, the sensor unit can analyze the other party's social media activity and collect related data. For example, the sensor unit analyzes the content recently posted by the other party and collects data related to that content. For example, the sensor unit analyzes the other party's social media account and collects related data based on the content of the recent posts. The sensor unit can also analyze hashtags frequently used by the other party and collect data related to those hashtags. For example, the sensor unit can analyze the other party's social media account, identify frequently used hashtags, and collect data related to those hashtags. The sensor unit can also analyze accounts followed by the other party and collect data related to those accounts. For example, the sensor unit can analyze the other party's social media account, identify the accounts followed, and collect data related to those accounts. In this way, the sensor unit can collect more relevant data by analyzing social media activity. For example, the sensor unit analyzes the content recently posted by the other party and collects data related to that content. In addition, the sensor unit can analyze hashtags frequently used by the other party and collect data related to those hashtags. In this way, the sensor unit can analyze the other party's social media activity and collect related data.

[0039] The analysis unit can estimate the emotions of the other party and adjust the analysis algorithm based on the estimated emotions. For example, if the other party is nervous, the analysis unit applies an algorithm that emphasizes subtle changes in facial expressions. For example, the analysis unit uses a deep learning model to analyze the other party's facial expressions in detail and estimate the level of nervousness. Furthermore, if the other party is relaxed, the analysis unit can apply an algorithm that emphasizes patterns of speech and behavior. For example, the analysis unit uses natural language processing technology to analyze the other party's tone of voice and language usage and estimate the level of relaxation. Furthermore, if the other party is angry, the analysis unit can apply an algorithm that emphasizes the tone and volume of voice. For example, the analysis unit uses voice analysis technology to analyze the other party's tone and volume in detail and estimate the level of anger. This allows the analysis unit to adjust the analysis algorithm according to the other party's emotions, enabling more accurate analysis. For example, if the other party is nervous, the analysis unit applies an algorithm that emphasizes subtle changes in facial expressions. Furthermore, when the other person is relaxed, the analysis unit can apply an algorithm that emphasizes patterns of speech and behavior, thereby enabling the analysis unit to perform analysis according to the other person's emotions.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit uses a deep learning model to analyze data with high importance in detail and infer the emotions and psychological state of the other party. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit uses a simple algorithm to analyze data with low importance and infer the emotions and psychological state of the other party. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit uses an algorithm with a medium level of detail to analyze data with medium importance and infer the emotions and psychological state of the other party. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data, thereby enabling efficient analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit uses a deep learning model to analyze the facial expressions of the other person in detail and estimate their emotional state. The analysis unit can also apply a natural language processing algorithm to speech and behavior data. For example, the analysis unit uses natural language processing technology to analyze the tone of voice and phrasing of the other person and estimate their psychological state. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit uses voice analysis technology to analyze the tone and volume of the other person's voice in detail and estimate their emotional state. This allows the analysis unit to apply an analysis algorithm depending on the data category, enabling more accurate analysis. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. The analysis unit can also apply a natural language processing algorithm to speech and behavior data. This allows the analysis unit to perform analysis depending on the data category.

[0042] The analysis unit can estimate the emotions of the other party and determine the priority of analysis based on the estimated emotions. For example, if the other party is nervous, the analysis unit prioritizes the analysis of facial expression data. For example, the analysis unit uses a deep learning model to analyze the other party's facial expressions in detail and estimate the level of nervousness. The analysis unit can also prioritize the analysis of speech and behavior data if the other party is relaxed. For example, the analysis unit uses natural language processing technology to analyze the other party's tone of voice and language usage and estimate the level of relaxation. Furthermore, the analysis unit can prioritize the analysis of voice data if the other party is angry. For example, the analysis unit uses voice analysis technology to analyze the other party's tone of voice and volume in detail and estimate the level of anger. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the other party's emotions. For example, the analysis unit prioritizes the analysis of facial expression data if the other party is nervous. The analysis unit can also prioritize the analysis of speech and behavior data if the other party is relaxed. This allows the analysis unit to perform analysis according to the other party's emotions.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit uses a deep learning model to analyze the most recent data in detail and estimate the emotions and psychological state of the other party. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit analyzes the most recent data based on the past data and estimates the emotions and psychological state of the other party. The analysis unit can also prioritize analyzing data collected during a specific period. For example, the analysis unit analyzes data collected during a specific period in detail and estimates the emotions and psychological state of the other party during that period. In this way, the analysis unit can prioritize analyzing the most recent data by determining the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. In addition, the analysis unit can prioritize analyzing the most recent data while referring to the past data. In this way, the analysis unit can prioritize analyzing the most recent data.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit uses a deep learning model to analyze data with high relevance in detail and infer the other party's emotions and psychological state. The analysis unit can also analyze data with medium relevance next. For example, the analysis unit analyzes data with medium relevance in detail and infer the other party's emotions and psychological state. The analysis unit can also analyze data with low relevance last. For example, the analysis unit analyzes data with low relevance in detail and infer the other party's emotions and psychological state. This allows the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also analyze data with medium relevance next. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0045] The estimation unit can estimate the other person's emotions and adjust the estimation method for habits and psychological states based on the estimated emotions. For example, if the other person is nervous, the estimation unit estimates the other person's habits by focusing on subtle changes in facial expressions. For example, the estimation unit uses a deep learning model to analyze the other person's facial expressions in detail and estimate the level of nervousness. Furthermore, if the other person is relaxed, the estimation unit can estimate the other person's habits by focusing on patterns of speech and behavior. For example, the estimation unit uses natural language processing technology to analyze the other person's tone of voice and language usage and estimate the level of relaxation. Furthermore, if the other person is angry, the estimation unit can estimate the other person's habits by focusing on the tone and volume of voice. For example, the estimation unit uses voice analysis technology to analyze the other person's tone and volume in detail and estimate the level of anger. This allows the estimation unit to adjust the estimation method according to the other person's emotions, enabling more accurate estimation. For example, if the other person is nervous, the estimation unit estimates the other person's habits by focusing on subtle changes in facial expressions. Furthermore, when the other person is relaxed, the estimation unit can estimate the other person's habits by placing emphasis on the other person's speech and behavior patterns. This allows the estimation unit to make estimations according to the other person's emotions.

[0046] The estimation unit can optimize the estimation algorithm by referring to past estimation results during estimation. The estimation unit, for example, adjusts parameters of the algorithm based on past estimation results. For example, the estimation unit analyzes past emotion estimation results and optimizes parameters of the algorithm. The estimation unit can also adjust weighting of the algorithm based on past estimation results. For example, the estimation unit analyzes past behavioral patterns and optimizes weighting of the algorithm. The estimation unit can also update learning data of the algorithm based on past estimation results. For example, the estimation unit updates learning data of the algorithm based on past data to improve estimation accuracy. In this way, the estimation unit improves the accuracy of the estimation algorithm by referring to past estimation results. For example, the estimation unit adjusts parameters of the algorithm based on past estimation results. In addition, the estimation unit can adjust weighting of the algorithm based on past estimation results. In this way, the estimation unit can optimize the estimation algorithm by referring to past estimation results.

[0047] The estimation unit can make estimation taking into account the attribute information of the other party when making estimation. The estimation unit makes estimation taking into account, for example, the age of the other party. For example, the estimation unit estimates the emotion or psychological state based on the age of the other party. The estimation unit can also make estimation taking into account the gender of the other party. For example, the estimation unit estimates the emotion or psychological state based on the gender of the other party. The estimation unit can also make estimation taking into account the occupation of the other party. For example, the estimation unit estimates the emotion or psychological state based on the occupation of the other party. This allows the estimation unit to make more accurate estimation by taking into account the attribute information of the other party. For example, the estimation unit makes estimation taking into account the age of the other party. The estimation unit can also make estimation taking into account the gender of the other party. This allows the estimation unit to make estimation taking into account the attribute information of the other party.

[0048] The estimation unit can estimate the other party's emotions and adjust the display method of the estimation result based on the estimated emotions. For example, if the other party is nervous, the estimation unit provides a simple, highly visible display method. For example, the estimation unit displays the other party's emotional state using simple graphs or icons to increase visibility. Furthermore, if the other party is relaxed, the estimation unit can provide a display method including detailed information. For example, the estimation unit displays the other party's emotional state using detailed text or graphs to provide depth of information. Furthermore, if the other party is in a hurry, the estimation unit can provide a display method that focuses on the main points. For example, the estimation unit displays the other party's emotional state using concise text or icons to emphasize the main points. In this way, the estimation unit can provide more appropriate information by providing a display method that corresponds to the other party's emotions. For example, if the other party is nervous, the estimation unit provides a simple, highly visible display method. Furthermore, if the other party is relaxed, the estimation unit can provide a display method that includes detailed information. In this way, the estimation unit can adjust the display method of the estimation result according to the other party's emotions.

[0049] The estimation unit can make the estimation taking into account the geographical distribution of the other party. For example, if the other party is in a specific region, the estimation unit makes the estimation taking into account the culture and customs of that region. For example, the estimation unit collects data related to the culture and customs of that region based on the other party's geographical location information and reflects the data in the estimation. Furthermore, if the other party is traveling, the estimation unit can make the estimation taking into account information about the travel destination. For example, the estimation unit collects data related to the culture and customs of the travel destination based on the other party's geographical location information and reflects the data in the estimation. Furthermore, if the other party is at home, the estimation unit can make the estimation taking into account the situation within the home. For example, the estimation unit collects data related to the situation within the home based on the other party's geographical location information and reflects the data in the estimation. This enables the estimation unit to make a more accurate estimation by taking into account the geographical distribution. For example, if the other party is in a specific region, the estimation unit makes the estimation taking into account the culture and customs of that region. Furthermore, if the other party is traveling, the estimation unit can make the estimation taking into account information about the travel destination. This allows the estimation unit to make the estimation taking into account the other party's geographical distribution.

[0050] The estimation unit can improve the accuracy of the estimation by referring to related literature during estimation. The estimation unit, for example, improves the estimation algorithm by referring to the latest research papers. For example, the estimation unit adjusts the parameters of the estimation algorithm based on the latest research papers to improve accuracy. The estimation unit can also optimize the estimation algorithm by referring to related patent documents. For example, the estimation unit adjusts the weighting of the estimation algorithm based on the related patent documents to improve accuracy. The estimation unit can also strengthen the estimation algorithm by referring to specialized books. For example, the estimation unit updates the learning data of the estimation algorithm based on the specialized books to improve accuracy. In this way, the estimation unit improves the accuracy of the estimation by referring to related literature. For example, the estimation unit improves the estimation algorithm by referring to the latest research papers. In addition, the estimation unit can optimize the estimation algorithm by referring to related patent documents. In this way, the estimation unit can improve the accuracy of the estimation by referring to related literature.

[0051] The suggestion unit can estimate the emotion of the other party and adjust the way in which the suggestion is expressed based on the estimated emotion. For example, if the other party is nervous, the suggestion unit makes the suggestion in a calm expression manner. For example, the suggestion unit makes the suggestion in a calm tone, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the suggestion unit can also make the suggestion in a cheerful expression manner. For example, the suggestion unit makes the suggestion in a cheerful tone, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the suggestion unit can also make the suggestion in a calm expression manner. For example, the suggestion unit makes the suggestion in a calm tone, taking into account the emotional state of the other party. This enables the suggestion unit to provide a more effective expression manner in accordance with the emotion of the other party. For example, if the other party is nervous, the suggestion unit makes the suggestion in a calm expression manner. Furthermore, if the other party is relaxed, the suggestion unit can also make the suggestion in a cheerful expression manner. This enables the suggestion unit to adjust the way in which the suggestion is expressed in accordance with the emotion of the other party.

[0052] The proposal unit can adjust the level of detail of the proposal based on the importance of the estimation result when making a proposal. For example, the proposal unit makes a detailed proposal for an estimation result with high importance. For example, the proposal unit evaluates the importance of the estimation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for an estimation result with low importance. For example, the proposal unit evaluates the importance of the estimation result and makes a simplified proposal. The proposal unit can also make a proposal with an appropriate level of detail for an estimation result with medium importance. For example, the proposal unit evaluates the importance of the estimation result and makes a proposal with an appropriate level of detail. This allows the proposal unit to adjust the level of detail of the proposal according to the importance of the estimation result, thereby enabling a more effective proposal. For example, the proposal unit makes a detailed proposal for an estimation result with high importance. The proposal unit can also make a simplified proposal for an estimation result with low importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the estimation result.

[0053] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the estimation result. For example, the proposal unit makes a proposal related to facial expression for an estimation result based on facial expression data. For example, the proposal unit analyzes the facial expression data of the other party and makes a proposal related to facial expression. Furthermore, the proposal unit can also make a proposal related to speech and behavior for an estimation result based on speech and behavior data. For example, the proposal unit analyzes the speech and behavior data of the other party and makes a proposal related to speech and behavior. Furthermore, the proposal unit can also make a proposal related to voice for an estimation result based on voice data. For example, the proposal unit analyzes the voice data of the other party and makes a proposal related to voice. In this way, the proposal unit can make a more effective proposal by making a proposal depending on the category of the estimation result. For example, the proposal unit makes a proposal related to facial expression for an estimation result based on facial expression data. Furthermore, the proposal unit can also make a proposal related to speech and behavior for an estimation result based on speech and behavior data. In this way, the proposal unit can apply different proposal algorithms depending on the category of the estimation result.

[0054] The suggestion unit can estimate the emotion of the other party and determine the priority of suggestions based on the estimated emotion. For example, if the other party is nervous, the suggestion unit prioritizes suggestions to relax. For example, the suggestion unit makes suggestions to relax, taking into account the emotional state of the other party. Furthermore, the suggestion unit can also prioritize proactive suggestions when the other party is relaxed. For example, the suggestion unit makes proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the suggestion unit can prioritize suggestions to calm down. For example, the suggestion unit makes suggestions to calm down, taking into account the emotional state of the other party. In this way, the suggestion unit can determine the priority according to the emotion of the other party, thereby enabling more effective suggestions. For example, if the other party is nervous, the suggestion unit prioritizes suggestions to relax. Furthermore, the suggestion unit can also prioritize proactive suggestions when the other party is relaxed. In this way, the suggestion unit can determine the priority of suggestions according to the emotion of the other party.

[0055] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the estimation results were collected. The proposal unit, for example, makes a proposal based on the latest estimation result. For example, the proposal unit determines the priority of the proposal based on the latest estimation result. The proposal unit can also emphasize the latest estimation result while referring to past estimation results. For example, the proposal unit makes a proposal based on past estimation results and emphasize the latest estimation result. Furthermore, the proposal unit can also make a proposal based on estimation results collected during a specific period. For example, the proposal unit determines the priority of the proposal based on estimation results collected during a specific period. This enables the proposal unit to make a more effective proposal by making a proposal based on the time when the estimation results were collected. For example, the proposal unit can make a proposal based on the latest estimation result. The proposal unit can also emphasize the latest estimation result while referring to past estimation results. This allows the proposal unit to determine the priority of the proposal based on the time when the estimation results were collected.

[0056] The proposal unit can adjust the order of proposals based on the relevance of the estimation results when making a proposal. The proposal unit, for example, makes a proposal based on an estimation result with high relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with high relevance. The proposal unit can also make a proposal based on an estimation result with medium relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with medium relevance. The proposal unit can also make a proposal based on an estimation result with low relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with low relevance. This enables the proposal unit to make a more effective proposal by making a proposal based on the relevance of the estimation results. For example, the proposal unit can make a proposal based on the estimation result with high relevance. The proposal unit can also make a proposal based on the estimation result with medium relevance. This allows the proposal unit to adjust the order of proposals based on the relevance of the estimation results.

[0057] The database unit can estimate the emotions of the other party and select a database based on the estimated emotions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. For example, the database unit selects data for relaxing the other party, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. For example, the database unit selects data for making proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the database unit can also preferentially select data for calming the other party. For example, the database unit selects data for calming the other party, taking into account the emotional state of the other party. In this way, the database unit can select a database according to the other party's emotions, thereby enabling more effective suggestions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. In this way, the database unit can select a database according to the other party's emotions.

[0058] When updating the database, the database unit can select optimal data by referring to past data. The database unit, for example, selects the latest data based on past data. For example, the database unit analyzes past data and selects the latest data. The database unit can also select highly relevant data based on past data. For example, the database unit analyzes past data and selects highly relevant data. The database unit can also select highly important data based on past data. For example, the database unit analyzes past data and selects highly important data. This allows the database unit to update the database more effectively by referring to past data. For example, the database unit selects the latest data based on past data. The database unit can also select highly relevant data based on past data. This allows the database unit to select optimal data by referring to past data.

[0059] The database unit can estimate the emotions of the other party and determine the priority of databases based on the estimated emotions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. For example, the database unit selects data for relaxing the other party, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. For example, the database unit selects data for making proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the database unit can also preferentially select data for calming the other party. For example, the database unit selects data for calming the other party, taking into account the emotional state of the other party. In this way, the database unit can determine the priority of databases according to the emotions of the other party, thereby enabling more effective suggestions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. In this way, the database unit can determine the priority of databases according to the emotions of the other party.

[0060] When updating the database, the database unit can weight the data based on the time the data was collected. For example, the database unit assigns a high weight to the most recent data. For example, the database unit assigns a high weight based on the most recent data. The database unit can also assign a low weight to past data. For example, the database unit assigns a low weight based on past data. The database unit can also assign a medium weight to data collected during a specific period. For example, the database unit assigns a medium weight based on data collected during a specific period. In this way, the database unit can weight the data based on the time the data was collected, thereby enabling more effective database updates. For example, the database unit assigns a high weight to the most recent data. The database unit can also assign a low weight to past data. In this way, the database unit can weight the data based on the time the data was collected.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The communication support system may further include a feedback unit. The feedback unit collects the results of communication conducted by the user and provides feedback to the analysis unit. For example, the feedback unit collects the results of negotiations and persuasion conducted by the user and transmits the results to the analysis unit. The feedback unit may also analyze the success rate and failure rate of communication conducted by the user and provide the data to the analysis unit. The feedback unit may also identify areas for improvement in the communication conducted by the user and provide the information to the analysis unit. This allows the analysis unit to perform more accurate analysis based on the data provided from the feedback unit. For example, the analysis unit may retrain the deep learning model based on the data provided from the feedback unit. The analysis unit may also optimize the analysis algorithm based on the data provided from the feedback unit. This allows the communication support system to utilize user feedback to make more effective suggestions.

[0063] The communication support system may further include a learning unit. The learning unit learns the user's communication style based on the user's past communication history. For example, the learning unit collects the user's past negotiation and persuasion histories and analyzes the data. The learning unit may also analyze the success and failure rates of the user's past communications and identify the user's communication style based on the data. The learning unit may also identify areas for improvement in the user's past communications and provide the information to the suggestion unit. This allows the suggestion unit to make suggestions tailored to the user's communication style based on the data provided from the learning unit. For example, the suggestion unit may make suggestions based on the user's past success patterns based on the data provided from the learning unit. The suggestion unit may also make suggestions to avoid the user's past failure patterns based on the data provided from the learning unit. This allows the communication support system to make more effective suggestions by utilizing the user's past communication history.

[0064] The communication support system can further include a prediction unit. The prediction unit predicts the other party's future behavior and reaction. For example, the prediction unit predicts the other party's future behavior pattern based on the other party's past behavioral data. The prediction unit can also predict the other party's future reaction based on the other party's current psychological state. The prediction unit can also predict the other party's future behavior and reaction based on the other party's cultural background and personality type. This allows the suggestion unit to make suggestions based on the other party's future behavior and reaction based on the data provided from the prediction unit. For example, the suggestion unit predicts how the other party will react in the future based on the data provided from the prediction unit, and makes suggestions based on that reaction. The suggestion unit can also predict how the other party will act in the future based on the data provided from the prediction unit, and make suggestions based on that behavior. This allows the communication support system to predict the other party's future behavior and reaction and make more effective suggestions.

[0065] The communication support system may further include a simulation unit. The simulation unit simulates communication that the user plans to have in advance. For example, the simulation unit inputs a scenario of negotiation or persuasion that the user plans to have and performs a simulation based on the scenario. The simulation unit may also provide simulation results taking into account the cultural background and personality type of the other party. The simulation unit may also predict the success rate and failure rate of the communication that the user plans to have and provide the result to the proposal unit. This allows the proposal unit to make suggestions according to the communication that the user plans to have based on the data provided from the simulation unit. For example, the proposal unit may make suggestions to increase the success rate of the negotiation that the user plans to have based on the data provided from the simulation unit. The proposal unit may also make suggestions to reduce the failure rate of the persuasion that the user plans to have based on the data provided from the simulation unit. This allows the communication support system to simulate communication that the user plans to have in advance and provide more effective suggestions.

[0066] The communication support system may further include a monitoring unit. The monitoring unit monitors communication conducted by the user in real time. For example, the monitoring unit may monitor the progress of negotiations or persuasion conducted by the user in real time and provide the data to the analysis unit. The monitoring unit may also identify problems that arise in the communication conducted by the user and provide the information to the proposal unit. The monitoring unit may also collect responses of the other party in the communication conducted by the user in real time and provide the data to the analysis unit. This allows the analysis unit to perform analysis in real time based on the data provided by the monitoring unit. For example, the analysis unit may analyze the progress of the communication conducted by the user based on the data provided by the monitoring unit and provide appropriate feedback. The analysis unit may also analyze problems that arise in the communication conducted by the user based on the data provided by the monitoring unit and propose solutions to those problems. This allows the communication support system to monitor communication conducted by the user in real time and make more effective proposals.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The sensor unit uses a camera or microphone to collect the other person's behavior or facial expressions. For example, a camera can be used to collect the other person's facial expressions in real time, and a microphone can be used to collect the other person's tone of voice and choice of words. Gestures and actions can also be collected, and a camera can be used to collect the other person's hand movements and body movements. Step 2: The analysis unit uses deep learning to analyze the collected data. For example, collected facial expression data can be input into a deep learning model to analyze the other person's emotional state. It can also analyze collected voice tone and vocabulary to estimate the other person's psychological state. It can also analyze collected gesture and movement data to identify the other person's habits and behavioral patterns. Step 3: The estimation unit estimates the other person's habits and psychological state based on the analysis results. For example, the other person's level of tension or relaxation can be estimated based on the analyzed emotional state data. The other person's intentions and goals can also be estimated based on the analyzed psychological state data. Furthermore, the other person's habits and customs can be identified based on the analyzed behavioral pattern data. Step 4: The suggestion unit makes appropriate suggestions based on the estimated habits and psychological state. For example, if the other person is nervous, the suggestion unit will make suggestions to help them relax. If the other person is relaxed, the suggestion unit can also make proactive suggestions. It can also suggest a communication style that suits the other person's habits and customs.

[0069] (Example 2) A communication support system according to an embodiment of the present invention uses artificial intelligence (AI) to interpret the other party's behavior and facial expressions to predict their habits and psychological state, and then makes suggestions to improve the success of negotiations, persuasion, and explanations. This communication support system is adaptable to different races, cultures, and personality types, and also suggests communication styles based on the information it reads. First, a camera and microphone are used to collect the other party's behavior and facial expressions in real time, which the AI ​​analyzes using deep learning. Next, the other party's habits and psychological state are estimated based on the analysis results, and appropriate suggestions are made based on the estimated habits and psychological state. Furthermore, the system is adaptable to different cultures and personality types, and the AI ​​determines the other party's cultural background and personality type based on a database collected in advance, and suggests an appropriate communication style. For example, the system may include a sensor unit that collects the other party's behavior and facial expressions using a camera and microphone, an analysis unit that analyzes the data collected by the sensor unit using deep learning, an estimation unit that estimates the other party's habits and psychological state based on the data analyzed by the analysis unit, a suggestion unit that makes appropriate suggestions based on the estimated habits and psychological state, and a database unit that is adaptable to different cultures and personality types. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit suggests a communication style that matches that cultural background and personality type.This allows the communication support system to analyze the other party's words, behavior, and facial expressions and make appropriate suggestions, thereby enabling successful negotiations and persuasion.

[0070] A communication support system according to an embodiment includes a sensor unit, an analysis unit, an estimation unit, and a proposal unit. The sensor unit collects the other party's behavior or facial expression using a camera or a microphone. For example, the sensor unit collects the other party's facial expressions in real time using a camera. The sensor unit can also collect the other party's tone of voice and language use using a microphone. The sensor unit can also collect gestures and movements. For example, the sensor unit collects the other party's hand movements and body movements using a camera. The analysis unit analyzes the collected data using deep learning. For example, the analysis unit inputs the collected facial expression data into a deep learning model to analyze the other party's emotional state. The analysis unit can also analyze the collected tone of voice and language use to estimate the other party's psychological state. The analysis unit can also analyze the collected gesture and movement data to identify the other party's habits and behavioral patterns. The estimation unit estimates the other party's habits and psychological state based on the analysis results. For example, the estimation unit estimates the other party's level of tension or relaxation based on the analyzed emotional state data. The estimation unit can also estimate the intentions and goals of the other party based on the analyzed psychological state data. Furthermore, the estimation unit can identify the other party's habits and routines based on the analyzed behavioral pattern data. The suggestion unit makes appropriate suggestions based on the estimated habits and psychological state. For example, if the other party is nervous, the suggestion unit makes suggestions to help them relax. Furthermore, if the other party is relaxed, the suggestion unit can also make proactive suggestions. Furthermore, the suggestion unit can also suggest a communication style that suits the other party's habits and routines. As a result, the communication support system according to the embodiment can analyze the other party's words, actions, and facial expressions and make appropriate suggestions, thereby enabling negotiations and persuasion to proceed smoothly.

[0071] The communication support system includes a database unit that can adapt to different cultures and personality types. The database unit determines the cultural background and personality type of the other party based on data collected in advance. For example, the database unit collects cultural data by country and determines the cultural background based on the other party's nationality. The database unit can also determine the other party's personality type based on the results of a personality diagnosis. Furthermore, the database unit can identify the other party's communication style based on past communication history. This allows the database unit to provide basic data for making suggestions according to the other party's cultural background and personality type. For example, if the other party is from an Asian cultural region, the database unit can suggest a communication style suitable for Asian culture. Furthermore, if the other party has an introverted personality type, the database unit can also suggest a communication style suitable for introverts. This allows the communication support system to make suggestions according to different cultures and personality types.

[0072] The database unit can determine the cultural background and personality type of the other party based on data collected in advance. The database unit can determine the cultural background of the other party based on, for example, questionnaire results. For example, the database unit can analyze the results of a questionnaire answered by the other party to identify the other party's cultural background. The database unit can also determine the other party's personality type based on their behavioral history. For example, the database unit can analyze the other party's past behavioral history to identify the other party's personality type. By determining the other party's cultural background and personality type, the database unit can make more appropriate suggestions. For example, if the other party is from a Western cultural sphere, the database unit can suggest a communication style suitable for Western culture. Furthermore, if the other party has an extroverted personality type, the database unit can suggest a communication style suitable for extroverts. This allows the communication support system to make suggestions according to the other party's cultural background and personality type.

[0073] The suggestion unit can suggest a communication style according to the cultural background and personality type of the other party. For example, if the other party is from an Asian cultural region, the suggestion unit can suggest a communication style suitable for Asian culture. For example, the suggestion unit can suggest a way of expressing courtesy and respect that is emphasized in Asian culture. Furthermore, if the other party is from a Western cultural region, the suggestion unit can also suggest a communication style suitable for Western culture. For example, the suggestion unit can suggest a direct way of expression and a way of asserting oneself that is emphasized in Western culture. Furthermore, if the other party has an introverted personality type, the suggestion unit can also suggest a communication style suitable for introverts. For example, the suggestion unit can suggest a conversation in a calm and relaxed tone to an introverted person. Furthermore, if the other party has an extroverted personality type, the suggestion unit can also suggest a communication style suitable for extroverts. For example, the suggestion unit can suggest a conversation in an active and energetic tone to an extroverted person. In this way, the suggestion unit can suggest a communication style according to the other party's cultural background and personality type, thereby enabling successful negotiations and persuasion.

[0074] The sensor unit can estimate the emotions of the other party and adjust the type of data to be collected based on the estimated emotions. For example, if the other party is nervous, the sensor unit can focus on collecting subtle changes in facial expressions. For example, the sensor unit can use a camera to collect detailed images of the other party's facial expressions and estimate the level of nervousness. Furthermore, if the other party is relaxed, the sensor unit can also collect detailed patterns of speech and behavior. For example, the sensor unit can use a microphone to collect the other party's tone of voice and phrasing and estimate the level of relaxation. Furthermore, if the other party is angry, the sensor unit can focus on collecting the tone and volume of voice. For example, the sensor unit can use a microphone to collect detailed images of the other party's tone and volume of voice and estimate the level of anger. This allows the sensor unit to adjust the data collection method according to the other party's emotions, enabling more accurate data collection. For example, if the other party is nervous, the sensor unit can use a high-resolution camera to collect subtle changes in facial expressions. Furthermore, if the other party is relaxed, the sensor unit can use a high-sensitivity microphone to collect patterns of speech and behavior. This allows the sensor unit to collect data according to the emotions of the other person.

[0075] The sensor unit can select a collection method by referring to the other party's past speech, behavior, and facial expression data. For example, the sensor unit identifies the other party's most common facial expression from past data and focuses on collecting that facial expression. For example, the sensor unit can analyze past conversation logs and facial expression data to identify the other party's most common facial expression. The sensor unit can also identify the other party's frequently used words and phrases from past data and focus on collecting those words and behavior. For example, the sensor unit can analyze past conversation logs to identify the other party's frequently used words and phrases. The sensor unit can also identify changes in the other party's tone and volume of voice from past data and focus on collecting those changes. For example, the sensor unit can analyze past voice data to identify changes in the other party's tone and volume of voice. This allows the sensor unit to collect data more effectively by referring to past data. For example, the sensor unit can identify patterns in the other party's facial expression and speech, based on past data, and collect data based on those patterns. The sensor unit can also identify changes in the other party's tone and volume of voice based on past data, and collect data based on those changes. This allows the sensor unit to select the most appropriate collection method by referring to the other person's past behavior and facial expression data.

[0076] The sensor unit simultaneously collects the other party's environmental sounds and background information during data collection, which can be used for analysis. The sensor unit, for example, collects the other party's surrounding environmental sounds and estimates their stress level. For example, the sensor unit uses a microphone to collect the other party's surrounding environmental sounds and analyzes the type and volume of the sounds to estimate the other party's stress level. The sensor unit can also collect the other party's background information and grasp their current situation. For example, the sensor unit uses a camera to collect the other party's background information and analyzes that information to grasp their current situation. The sensor unit can also collect the voices of people around the other party and estimate their social situation. For example, the sensor unit uses a microphone to collect the voices of people around the other party and analyzes the tone and content of the voices to estimate the other party's social situation. This allows the sensor unit to collect environmental sounds and background information, enabling more detailed analysis. For example, the sensor unit collects the other party's surrounding environmental sounds and analyzes the type and volume of the sounds to estimate the other party's stress level. The sensor unit can also collect the other party's background information and analyze that information to grasp their current situation. This allows the sensor unit to collect the other person's environmental sounds and background information and use this information for analysis.

[0077] The sensor unit can estimate the emotions of the other party and determine the priority of data to be collected based on the estimated emotions. For example, if the other party is nervous, the sensor unit prioritizes collecting changes in facial expressions. For example, the sensor unit uses a camera to collect detailed images of the other party's facial expressions and estimate the level of nervousness. Furthermore, if the other party is relaxed, the sensor unit can prioritize collecting patterns of speech and behavior. For example, the sensor unit uses a microphone to collect the other party's tone of voice and phrasing and estimate the level of relaxation. Furthermore, if the other party is angry, the sensor unit can prioritize collecting the tone and volume of voice. For example, the sensor unit uses a microphone to collect detailed images of the other party's tone and volume of voice and estimate the level of anger. In this way, the sensor unit can prioritize collecting important data by determining the priority of data according to the other party's emotions. For example, if the other party is nervous, the sensor unit can use a high-resolution camera to prioritize collecting changes in facial expressions. Furthermore, if the other party is relaxed, the sensor unit can use a high-sensitivity microphone to prioritize collecting patterns of speech and behavior. This allows the sensor unit to collect data according to the emotions of the other person.

[0078] When collecting data, the sensor unit can prioritize collecting highly relevant data by taking into account the geographical location information of the other party. For example, if the other party is in a specific region, the sensor unit prioritizes collecting data related to the culture and customs of that region. For example, the sensor unit identifies the other party's geographical location using GPS data and collects data related to the culture and customs of that region. Furthermore, if the other party is traveling, the sensor unit can prioritize collecting information about the travel destination. For example, the sensor unit collects data related to the culture and customs of the travel destination based on the other party's geographical location information. Furthermore, if the other party is at home, the sensor unit can prioritize collecting data related to the situation at home. For example, the sensor unit collects data related to the situation at home based on the other party's geographical location information. In this way, the sensor unit can collect more relevant data by taking into account the geographical location information. For example, if the other party is in a specific region, the sensor unit prioritizes collecting data related to the culture and customs of that region. Furthermore, if the other party is traveling, the sensor unit can prioritize collecting information about the travel destination. This allows the sensor unit to take into consideration the geographical location information of the other party and to preferentially collect highly relevant data.

[0079] During collection, the sensor unit can analyze the other party's social media activity and collect related data. For example, the sensor unit analyzes the content recently posted by the other party and collects data related to that content. For example, the sensor unit analyzes the other party's social media account and collects related data based on the content of the recent posts. The sensor unit can also analyze hashtags frequently used by the other party and collect data related to those hashtags. For example, the sensor unit can analyze the other party's social media account, identify frequently used hashtags, and collect data related to those hashtags. The sensor unit can also analyze accounts followed by the other party and collect data related to those accounts. For example, the sensor unit can analyze the other party's social media account, identify the accounts followed, and collect data related to those accounts. In this way, the sensor unit can collect more relevant data by analyzing social media activity. For example, the sensor unit analyzes the content recently posted by the other party and collects data related to that content. In addition, the sensor unit can analyze hashtags frequently used by the other party and collect data related to those hashtags. In this way, the sensor unit can analyze the other party's social media activity and collect related data.

[0080] The analysis unit can estimate the emotions of the other party and adjust the analysis algorithm based on the estimated emotions. For example, if the other party is nervous, the analysis unit applies an algorithm that emphasizes subtle changes in facial expressions. For example, the analysis unit uses a deep learning model to analyze the other party's facial expressions in detail and estimate the level of nervousness. Furthermore, if the other party is relaxed, the analysis unit can apply an algorithm that emphasizes patterns of speech and behavior. For example, the analysis unit uses natural language processing technology to analyze the other party's tone of voice and language usage and estimate the level of relaxation. Furthermore, if the other party is angry, the analysis unit can apply an algorithm that emphasizes the tone and volume of voice. For example, the analysis unit uses voice analysis technology to analyze the other party's tone and volume in detail and estimate the level of anger. This allows the analysis unit to adjust the analysis algorithm according to the other party's emotions, enabling more accurate analysis. For example, if the other party is nervous, the analysis unit applies an algorithm that emphasizes subtle changes in facial expressions. Furthermore, when the other person is relaxed, the analysis unit can apply an algorithm that emphasizes patterns of speech and behavior, thereby enabling the analysis unit to perform analysis according to the other person's emotions.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit uses a deep learning model to analyze data with high importance in detail and infer the emotions and psychological state of the other party. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit uses a simple algorithm to analyze data with low importance and infer the emotions and psychological state of the other party. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit uses an algorithm with a medium level of detail to analyze data with medium importance and infer the emotions and psychological state of the other party. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data, thereby enabling efficient analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected data.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit uses a deep learning model to analyze the facial expressions of the other person in detail and estimate their emotional state. The analysis unit can also apply a natural language processing algorithm to speech and behavior data. For example, the analysis unit uses natural language processing technology to analyze the tone of voice and phrasing of the other person and estimate their psychological state. The analysis unit can also apply a voice analysis algorithm to voice data. For example, the analysis unit uses voice analysis technology to analyze the tone and volume of the other person's voice in detail and estimate their emotional state. This allows the analysis unit to apply an analysis algorithm depending on the data category, enabling more accurate analysis. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. The analysis unit can also apply a natural language processing algorithm to speech and behavior data. This allows the analysis unit to perform analysis depending on the data category.

[0083] The analysis unit can estimate the emotions of the other party and determine the priority of analysis based on the estimated emotions. For example, if the other party is nervous, the analysis unit prioritizes the analysis of facial expression data. For example, the analysis unit uses a deep learning model to analyze the other party's facial expressions in detail and estimate the level of nervousness. The analysis unit can also prioritize the analysis of speech and behavior data if the other party is relaxed. For example, the analysis unit uses natural language processing technology to analyze the other party's tone of voice and language usage and estimate the level of relaxation. Furthermore, the analysis unit can prioritize the analysis of voice data if the other party is angry. For example, the analysis unit uses voice analysis technology to analyze the other party's tone of voice and volume in detail and estimate the level of anger. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the other party's emotions. For example, the analysis unit prioritizes the analysis of facial expression data if the other party is nervous. The analysis unit can also prioritize the analysis of speech and behavior data if the other party is relaxed. This allows the analysis unit to perform analysis according to the other party's emotions.

[0084] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit uses a deep learning model to analyze the most recent data in detail and estimate the emotions and psychological state of the other party. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit analyzes the most recent data based on the past data and estimates the emotions and psychological state of the other party. The analysis unit can also prioritize analyzing data collected during a specific period. For example, the analysis unit analyzes data collected during a specific period in detail and estimates the emotions and psychological state of the other party during that period. In this way, the analysis unit can prioritize analyzing the most recent data by determining the priority of analysis based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. In addition, the analysis unit can prioritize analyzing the most recent data while referring to the past data. In this way, the analysis unit can prioritize analyzing the most recent data.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. For example, the analysis unit uses a deep learning model to analyze data with high relevance in detail and infer the other party's emotions and psychological state. The analysis unit can also analyze data with medium relevance next. For example, the analysis unit analyzes data with medium relevance in detail and infer the other party's emotions and psychological state. The analysis unit can also analyze data with low relevance last. For example, the analysis unit analyzes data with low relevance in detail and infer the other party's emotions and psychological state. This allows the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also analyze data with medium relevance next. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0086] The estimation unit can estimate the other person's emotions and adjust the estimation method for habits and psychological states based on the estimated emotions. For example, if the other person is nervous, the estimation unit estimates the other person's habits by focusing on subtle changes in facial expressions. For example, the estimation unit uses a deep learning model to analyze the other person's facial expressions in detail and estimate the level of nervousness. Furthermore, if the other person is relaxed, the estimation unit can estimate the other person's habits by focusing on patterns of speech and behavior. For example, the estimation unit uses natural language processing technology to analyze the other person's tone of voice and language usage and estimate the level of relaxation. Furthermore, if the other person is angry, the estimation unit can estimate the other person's habits by focusing on the tone and volume of voice. For example, the estimation unit uses voice analysis technology to analyze the other person's tone and volume in detail and estimate the level of anger. This allows the estimation unit to adjust the estimation method according to the other person's emotions, enabling more accurate estimation. For example, if the other person is nervous, the estimation unit estimates the other person's habits by focusing on subtle changes in facial expressions. Furthermore, when the other person is relaxed, the estimation unit can estimate the other person's habits by placing emphasis on the other person's speech and behavior patterns. This allows the estimation unit to make estimations according to the other person's emotions.

[0087] The estimation unit can optimize the estimation algorithm by referring to past estimation results during estimation. The estimation unit, for example, adjusts parameters of the algorithm based on past estimation results. For example, the estimation unit analyzes past emotion estimation results and optimizes parameters of the algorithm. The estimation unit can also adjust weighting of the algorithm based on past estimation results. For example, the estimation unit analyzes past behavioral patterns and optimizes weighting of the algorithm. The estimation unit can also update learning data of the algorithm based on past estimation results. For example, the estimation unit updates learning data of the algorithm based on past data to improve estimation accuracy. In this way, the estimation unit improves the accuracy of the estimation algorithm by referring to past estimation results. For example, the estimation unit adjusts parameters of the algorithm based on past estimation results. In addition, the estimation unit can adjust weighting of the algorithm based on past estimation results. In this way, the estimation unit can optimize the estimation algorithm by referring to past estimation results.

[0088] The estimation unit can make estimation taking into account the attribute information of the other party when making estimation. The estimation unit makes estimation taking into account, for example, the age of the other party. For example, the estimation unit estimates the emotion or psychological state based on the age of the other party. The estimation unit can also make estimation taking into account the gender of the other party. For example, the estimation unit estimates the emotion or psychological state based on the gender of the other party. The estimation unit can also make estimation taking into account the occupation of the other party. For example, the estimation unit estimates the emotion or psychological state based on the occupation of the other party. This allows the estimation unit to make more accurate estimation by taking into account the attribute information of the other party. For example, the estimation unit makes estimation taking into account the age of the other party. The estimation unit can also make estimation taking into account the gender of the other party. This allows the estimation unit to make estimation taking into account the attribute information of the other party.

[0089] The estimation unit can estimate the other party's emotions and adjust the display method of the estimation result based on the estimated emotions. For example, if the other party is nervous, the estimation unit provides a simple, highly visible display method. For example, the estimation unit displays the other party's emotional state using simple graphs or icons to increase visibility. Furthermore, if the other party is relaxed, the estimation unit can provide a display method including detailed information. For example, the estimation unit displays the other party's emotional state using detailed text or graphs to provide depth of information. Furthermore, if the other party is in a hurry, the estimation unit can provide a display method that focuses on the main points. For example, the estimation unit displays the other party's emotional state using concise text or icons to emphasize the main points. In this way, the estimation unit can provide more appropriate information by providing a display method that corresponds to the other party's emotions. For example, if the other party is nervous, the estimation unit provides a simple, highly visible display method. Furthermore, if the other party is relaxed, the estimation unit can provide a display method that includes detailed information. In this way, the estimation unit can adjust the display method of the estimation result according to the other party's emotions.

[0090] The estimation unit can make the estimation taking into account the geographical distribution of the other party. For example, if the other party is in a specific region, the estimation unit makes the estimation taking into account the culture and customs of that region. For example, the estimation unit collects data related to the culture and customs of that region based on the other party's geographical location information and reflects the data in the estimation. Furthermore, if the other party is traveling, the estimation unit can make the estimation taking into account information about the travel destination. For example, the estimation unit collects data related to the culture and customs of the travel destination based on the other party's geographical location information and reflects the data in the estimation. Furthermore, if the other party is at home, the estimation unit can make the estimation taking into account the situation within the home. For example, the estimation unit collects data related to the situation within the home based on the other party's geographical location information and reflects the data in the estimation. This enables the estimation unit to make a more accurate estimation by taking into account the geographical distribution. For example, if the other party is in a specific region, the estimation unit makes the estimation taking into account the culture and customs of that region. Furthermore, if the other party is traveling, the estimation unit can make the estimation taking into account information about the travel destination. This allows the estimation unit to make the estimation taking into account the other party's geographical distribution.

[0091] The estimation unit can improve the accuracy of the estimation by referring to related literature during estimation. The estimation unit, for example, improves the estimation algorithm by referring to the latest research papers. For example, the estimation unit adjusts the parameters of the estimation algorithm based on the latest research papers to improve accuracy. The estimation unit can also optimize the estimation algorithm by referring to related patent documents. For example, the estimation unit adjusts the weighting of the estimation algorithm based on the related patent documents to improve accuracy. The estimation unit can also strengthen the estimation algorithm by referring to specialized books. For example, the estimation unit updates the learning data of the estimation algorithm based on the specialized books to improve accuracy. In this way, the estimation unit improves the accuracy of the estimation by referring to related literature. For example, the estimation unit improves the estimation algorithm by referring to the latest research papers. In addition, the estimation unit can optimize the estimation algorithm by referring to related patent documents. In this way, the estimation unit can improve the accuracy of the estimation by referring to related literature.

[0092] The suggestion unit can estimate the emotion of the other party and adjust the way in which the suggestion is expressed based on the estimated emotion. For example, if the other party is nervous, the suggestion unit makes the suggestion in a calm expression manner. For example, the suggestion unit makes the suggestion in a calm tone, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the suggestion unit can also make the suggestion in a cheerful expression manner. For example, the suggestion unit makes the suggestion in a cheerful tone, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the suggestion unit can also make the suggestion in a calm expression manner. For example, the suggestion unit makes the suggestion in a calm tone, taking into account the emotional state of the other party. This enables the suggestion unit to provide a more effective expression manner in accordance with the emotion of the other party. For example, if the other party is nervous, the suggestion unit makes the suggestion in a calm expression manner. Furthermore, if the other party is relaxed, the suggestion unit can also make the suggestion in a cheerful expression manner. This enables the suggestion unit to adjust the way in which the suggestion is expressed in accordance with the emotion of the other party.

[0093] The proposal unit can adjust the level of detail of the proposal based on the importance of the estimation result when making a proposal. For example, the proposal unit makes a detailed proposal for an estimation result with high importance. For example, the proposal unit evaluates the importance of the estimation result and makes a detailed proposal. The proposal unit can also make a simplified proposal for an estimation result with low importance. For example, the proposal unit evaluates the importance of the estimation result and makes a simplified proposal. The proposal unit can also make a proposal with an appropriate level of detail for an estimation result with medium importance. For example, the proposal unit evaluates the importance of the estimation result and makes a proposal with an appropriate level of detail. This allows the proposal unit to adjust the level of detail of the proposal according to the importance of the estimation result, thereby enabling a more effective proposal. For example, the proposal unit makes a detailed proposal for an estimation result with high importance. The proposal unit can also make a simplified proposal for an estimation result with low importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the estimation result.

[0094] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the estimation result. For example, the proposal unit makes a proposal related to facial expression for an estimation result based on facial expression data. For example, the proposal unit analyzes the facial expression data of the other party and makes a proposal related to facial expression. Furthermore, the proposal unit can also make a proposal related to speech and behavior for an estimation result based on speech and behavior data. For example, the proposal unit analyzes the speech and behavior data of the other party and makes a proposal related to speech and behavior. Furthermore, the proposal unit can also make a proposal related to voice for an estimation result based on voice data. For example, the proposal unit analyzes the voice data of the other party and makes a proposal related to voice. In this way, the proposal unit can make a more effective proposal by making a proposal depending on the category of the estimation result. For example, the proposal unit makes a proposal related to facial expression for an estimation result based on facial expression data. Furthermore, the proposal unit can also make a proposal related to speech and behavior for an estimation result based on speech and behavior data. In this way, the proposal unit can apply different proposal algorithms depending on the category of the estimation result.

[0095] The suggestion unit can estimate the emotion of the other party and determine the priority of suggestions based on the estimated emotion. For example, if the other party is nervous, the suggestion unit prioritizes suggestions to relax. For example, the suggestion unit makes suggestions to relax, taking into account the emotional state of the other party. Furthermore, the suggestion unit can also prioritize proactive suggestions when the other party is relaxed. For example, the suggestion unit makes proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the suggestion unit can prioritize suggestions to calm down. For example, the suggestion unit makes suggestions to calm down, taking into account the emotional state of the other party. In this way, the suggestion unit can determine the priority according to the emotion of the other party, thereby enabling more effective suggestions. For example, if the other party is nervous, the suggestion unit prioritizes suggestions to relax. Furthermore, the suggestion unit can also prioritize proactive suggestions when the other party is relaxed. In this way, the suggestion unit can determine the priority of suggestions according to the emotion of the other party.

[0096] When making a proposal, the proposal unit can determine the priority of the proposal based on the time when the estimation results were collected. The proposal unit, for example, makes a proposal based on the latest estimation result. For example, the proposal unit determines the priority of the proposal based on the latest estimation result. The proposal unit can also emphasize the latest estimation result while referring to past estimation results. For example, the proposal unit makes a proposal based on past estimation results and emphasize the latest estimation result. Furthermore, the proposal unit can also make a proposal based on estimation results collected during a specific period. For example, the proposal unit determines the priority of the proposal based on estimation results collected during a specific period. This enables the proposal unit to make a more effective proposal by making a proposal based on the time when the estimation results were collected. For example, the proposal unit can make a proposal based on the latest estimation result. The proposal unit can also emphasize the latest estimation result while referring to past estimation results. This allows the proposal unit to determine the priority of the proposal based on the time when the estimation results were collected.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the estimation results when making a proposal. The proposal unit, for example, makes a proposal based on an estimation result with high relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with high relevance. The proposal unit can also make a proposal based on an estimation result with medium relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with medium relevance. The proposal unit can also make a proposal based on an estimation result with low relevance. For example, the proposal unit evaluates the relevance of the estimation results and makes a proposal based on the estimation result with low relevance. This enables the proposal unit to make a more effective proposal by making a proposal based on the relevance of the estimation results. For example, the proposal unit can make a proposal based on the estimation result with high relevance. The proposal unit can also make a proposal based on the estimation result with medium relevance. This allows the proposal unit to adjust the order of proposals based on the relevance of the estimation results.

[0098] The database unit can estimate the emotions of the other party and select a database based on the estimated emotions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. For example, the database unit selects data for relaxing the other party, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. For example, the database unit selects data for making proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the database unit can also preferentially select data for calming the other party. For example, the database unit selects data for calming the other party, taking into account the emotional state of the other party. In this way, the database unit can select a database according to the other party's emotions, thereby enabling more effective suggestions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. In this way, the database unit can select a database according to the other party's emotions.

[0099] When updating the database, the database unit can select optimal data by referring to past data. The database unit, for example, selects the latest data based on past data. For example, the database unit analyzes past data and selects the latest data. The database unit can also select highly relevant data based on past data. For example, the database unit analyzes past data and selects highly relevant data. The database unit can also select highly important data based on past data. For example, the database unit analyzes past data and selects highly important data. This allows the database unit to update the database more effectively by referring to past data. For example, the database unit selects the latest data based on past data. The database unit can also select highly relevant data based on past data. This allows the database unit to select optimal data by referring to past data.

[0100] The database unit can estimate the emotions of the other party and determine the priority of databases based on the estimated emotions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. For example, the database unit selects data for relaxing the other party, taking into account the emotional state of the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. For example, the database unit selects data for making proactive suggestions, taking into account the emotional state of the other party. Furthermore, if the other party is angry, the database unit can also preferentially select data for calming the other party. For example, the database unit selects data for calming the other party, taking into account the emotional state of the other party. In this way, the database unit can determine the priority of databases according to the emotions of the other party, thereby enabling more effective suggestions. For example, if the other party is nervous, the database unit preferentially selects data for relaxing the other party. Furthermore, if the other party is relaxed, the database unit can also preferentially select data for making proactive suggestions. In this way, the database unit can determine the priority of databases according to the emotions of the other party.

[0101] When updating the database, the database unit can weight the data based on the time the data was collected. For example, the database unit assigns a high weight to the most recent data. For example, the database unit assigns a high weight based on the most recent data. The database unit can also assign a low weight to past data. For example, the database unit assigns a low weight based on past data. The database unit can also assign a medium weight to data collected during a specific period. For example, the database unit assigns a medium weight based on data collected during a specific period. In this way, the database unit can weight the data based on the time the data was collected, thereby enabling more effective database updates. For example, the database unit assigns a high weight to the most recent data. The database unit can also assign a low weight to past data. In this way, the database unit can weight the data based on the time the data was collected. === Hard Collateral 1-1 === Each of the multiple elements, including the sensor unit, analysis unit, estimation unit, suggestion unit, and database unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit collects the other party's words, actions, and facial expressions using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the collected data using deep learning by the specific processing unit 290 of the data processing device 12. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the other party's habits and psychological state based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that makes appropriate suggestions based on the estimated habits and psychological state. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that suggests a communication style according to the other party's cultural background and personality type. === Hard Collateral 1-2 === Each of the multiple elements, including the sensor unit, analysis unit, estimation unit, suggestion unit, and database unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit collects the other party's words, actions, and facial expressions using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the collected data using deep learning by the specific processing unit 290 of the data processing device 12. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the other party's habits and psychological state based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that makes appropriate suggestions based on the estimated habits and psychological state. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that suggests a communication style according to the other party's cultural background and personality type. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, analysis unit, estimation unit, suggestion unit, and database 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 sensor unit collects the other party's words, actions, and facial expressions using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit analyzes the collected data using deep learning by the specific processing unit 290 of the data processing device 12. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the other party's habits and psychological state based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that makes appropriate suggestions based on the estimated habits and psychological state. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that suggests a communication style according to the other party's cultural background and personality type. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, analysis unit, estimation unit, suggestion unit, and database unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit collects the other party's words, actions, and facial expressions using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the collected data using deep learning by the specific processing unit 290 of the data processing device 12. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that estimates the other party's habits and psychological state based on the analyzed data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that makes appropriate suggestions based on the estimated habits and psychological state. The database unit determines the other party's cultural background and personality type based on data collected in advance, and the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 as a processing unit that suggests a communication style according to the other party's cultural background and personality type.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The communication support system may further include a feedback unit. The feedback unit collects the results of communication conducted by the user and provides feedback to the analysis unit. For example, the feedback unit collects the results of negotiations and persuasion conducted by the user and transmits the results to the analysis unit. The feedback unit may also analyze the success rate and failure rate of communication conducted by the user and provide the data to the analysis unit. The feedback unit may also identify areas for improvement in the communication conducted by the user and provide the information to the analysis unit. This allows the analysis unit to perform more accurate analysis based on the data provided from the feedback unit. For example, the analysis unit may retrain the deep learning model based on the data provided from the feedback unit. The analysis unit may also optimize the analysis algorithm based on the data provided from the feedback unit. This allows the communication support system to utilize user feedback to make more effective suggestions.

[0104] The communication support system may further include a learning unit. The learning unit learns the user's communication style based on the user's past communication history. For example, the learning unit collects the user's past negotiation and persuasion histories and analyzes the data. The learning unit may also analyze the success and failure rates of the user's past communications and identify the user's communication style based on the data. The learning unit may also identify areas for improvement in the user's past communications and provide the information to the suggestion unit. This allows the suggestion unit to make suggestions tailored to the user's communication style based on the data provided from the learning unit. For example, the suggestion unit may make suggestions based on the user's past success patterns based on the data provided from the learning unit. The suggestion unit may also make suggestions to avoid the user's past failure patterns based on the data provided from the learning unit. This allows the communication support system to make more effective suggestions by utilizing the user's past communication history.

[0105] The communication support system can further include a prediction unit. The prediction unit predicts the other party's future behavior and reaction. For example, the prediction unit predicts the other party's future behavior pattern based on the other party's past behavioral data. The prediction unit can also predict the other party's future reaction based on the other party's current psychological state. The prediction unit can also predict the other party's future behavior and reaction based on the other party's cultural background and personality type. This allows the suggestion unit to make suggestions based on the other party's future behavior and reaction based on the data provided from the prediction unit. For example, the suggestion unit predicts how the other party will react in the future based on the data provided from the prediction unit, and makes suggestions based on that reaction. The suggestion unit can also predict how the other party will act in the future based on the data provided from the prediction unit, and make suggestions based on that behavior. This allows the communication support system to predict the other party's future behavior and reaction and make more effective suggestions.

[0106] The communication support system may further include a simulation unit. The simulation unit simulates communication that the user plans to have in advance. For example, the simulation unit inputs a scenario of negotiation or persuasion that the user plans to have and performs a simulation based on the scenario. The simulation unit may also provide simulation results taking into account the cultural background and personality type of the other party. The simulation unit may also predict the success rate and failure rate of the communication that the user plans to have and provide the result to the proposal unit. This allows the proposal unit to make suggestions according to the communication that the user plans to have based on the data provided from the simulation unit. For example, the proposal unit may make suggestions to increase the success rate of the negotiation that the user plans to have based on the data provided from the simulation unit. The proposal unit may also make suggestions to reduce the failure rate of the persuasion that the user plans to have based on the data provided from the simulation unit. This allows the communication support system to simulate communication that the user plans to have in advance and provide more effective suggestions.

[0107] The communication support system may further include a monitoring unit. The monitoring unit monitors communication conducted by the user in real time. For example, the monitoring unit may monitor the progress of negotiations or persuasion conducted by the user in real time and provide the data to the analysis unit. The monitoring unit may also identify problems that arise in the communication conducted by the user and provide the information to the proposal unit. The monitoring unit may also collect responses of the other party in the communication conducted by the user in real time and provide the data to the analysis unit. This allows the analysis unit to perform analysis in real time based on the data provided by the monitoring unit. For example, the analysis unit may analyze the progress of the communication conducted by the user based on the data provided by the monitoring unit and provide appropriate feedback. The analysis unit may also analyze problems that arise in the communication conducted by the user based on the data provided by the monitoring unit and propose solutions to those problems. This allows the communication support system to monitor communication conducted by the user in real time and make more effective proposals.

[0108] The communication support system may further include an emotion feedback unit. The emotion feedback unit monitors the user's emotional state in real time and provides the data to the analysis unit. For example, the emotion feedback unit collects the user's facial expressions and tone of voice in real time and transmits the data to the analysis unit. The emotion feedback unit may also analyze the user's emotional state and provide the result to the suggestion unit. The emotion feedback unit may also provide feedback according to the user's emotional state and provide the information to the analysis unit. This allows the analysis unit to analyze the user's emotional state based on the data provided from the emotion feedback unit and make appropriate suggestions. For example, if the user is nervous, the analysis unit may make suggestions to help the user relax based on the data provided from the emotion feedback unit. Furthermore, if the user is relaxed, the analysis unit may make proactive suggestions based on the data provided from the emotion feedback unit. This allows the communication support system to monitor the user's emotional state in real time and make more effective suggestions.

[0109] The communication support system may further include an emotion history unit. The emotion history unit records the user's past emotional states and provides the data to the analysis unit. For example, the emotion history unit collects emotional states the user has experienced in the past and transmits the data to the analysis unit. The emotion history unit may also analyze the user's past emotional states and provide the results to the suggestion unit. The emotion history unit may also predict the user's future emotional state based on the user's past emotional states and provide the predicted information to the analysis unit. This allows the analysis unit to analyze the user's emotional state based on the data provided from the emotion history unit and make appropriate suggestions. For example, the analysis unit may identify situations in which the user felt tense in the past based on the data provided from the emotion history unit and make suggestions appropriate to those situations. The analysis unit may also identify situations in which the user felt relaxed in the past based on the data provided from the emotion history unit and make suggestions appropriate to those situations. This allows the communication support system to record the user's past emotional states and make more effective suggestions.

[0110] The communication support system may further include an emotion prediction unit. The emotion prediction unit predicts the user's future emotional state and provides the data to the analysis unit. For example, the emotion prediction unit predicts the user's future emotional state based on the user's past emotion data. The emotion prediction unit can also predict the user's future emotional state based on the user's current psychological state. The emotion prediction unit can also predict the user's future emotional state based on the user's cultural background and personality type. This allows the analysis unit to analyze the user's future emotional state based on the data provided from the emotion prediction unit and make appropriate suggestions. For example, the analysis unit can identify situations in which the user is likely to become nervous in the future based on the data provided from the emotion prediction unit and make suggestions according to the situations. The analysis unit can also identify situations in which the user is likely to become relaxed in the future based on the data provided from the emotion prediction unit and make suggestions according to the situations. This allows the communication support system to predict the user's future emotional state and make more effective suggestions.

[0111] The communication support system may further include an emotion simulation unit. The emotion simulation unit simulates in advance emotional changes in communication that the user plans to have. For example, the emotion simulation unit inputs a scenario of negotiation or persuasion that the user plans to have and simulates emotional changes based on the scenario. The emotion simulation unit can also simulate emotional changes taking into account the cultural background and personality type of the other party. The emotion simulation unit can also predict emotional changes in communication that the user plans to have and provide the result to the suggestion unit. This allows the suggestion unit to make suggestions according to the communication that the user plans to have based on the data provided from the emotion simulation unit. For example, the suggestion unit can predict emotional changes in negotiation that the user plans to have based on the data provided from the emotion simulation unit and make suggestions according to the changes. The suggestion unit can also predict emotional changes in persuasion that the user plans to have based on the data provided from the emotion simulation unit and make suggestions according to the changes. This allows the communication support system to simulate emotional changes in communication that the user plans to have in advance and make more effective suggestions.

[0112] The communication support system may further include an emotion monitoring unit. The emotion monitoring unit monitors the user's emotional state in real time and provides the data to the analysis unit. For example, the emotion monitoring unit collects the user's facial expressions and tone of voice in real time and transmits the data to the analysis unit. The emotion monitoring unit may also analyze the user's emotional state and provide the results to the suggestion unit. The emotion monitoring unit may also provide feedback according to the user's emotional state and provide the information to the analysis unit. This allows the analysis unit to analyze the user's emotional state based on the data provided from the emotion monitoring unit and make appropriate suggestions. For example, if the user is nervous, the analysis unit may make suggestions to help the user relax based on the data provided from the emotion monitoring unit. Furthermore, if the user is relaxed, the analysis unit may make proactive suggestions based on the data provided from the emotion monitoring unit. This allows the communication support system to monitor the user's emotional state in real time and make more effective suggestions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The sensor unit uses a camera or microphone to collect the other person's behavior or facial expressions. For example, a camera can be used to collect the other person's facial expressions in real time, and a microphone can be used to collect the other person's tone of voice and choice of words. Gestures and actions can also be collected, and a camera can be used to collect the other person's hand movements and body movements. Step 2: The analysis unit uses deep learning to analyze the collected data. For example, collected facial expression data can be input into a deep learning model to analyze the other person's emotional state. It can also analyze collected voice tone and vocabulary to estimate the other person's psychological state. It can also analyze collected gesture and movement data to identify the other person's habits and behavioral patterns. Step 3: The estimation unit estimates the other person's habits and psychological state based on the analysis results. For example, the other person's level of tension or relaxation can be estimated based on the analyzed emotional state data. The other person's intentions and goals can also be estimated based on the analyzed psychological state data. Furthermore, the other person's habits and customs can be identified based on the analyzed behavioral pattern data. Step 4: The suggestion unit makes appropriate suggestions based on the estimated habits and psychological state. For example, if the other person is nervous, the suggestion unit will make suggestions to help them relax. If the other person is relaxed, the suggestion unit can also make proactive suggestions. It can also suggest a communication style that suits the other person's habits and customs.

[0115] 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.

[0116] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 sensor unit that uses a camera or a microphone to collect the other person's words, actions, or facial expressions; an analysis unit that analyzes data collected by the sensor unit using deep learning; an estimation unit that estimates the habits and psychological state of the other person based on the data analyzed by the analysis unit; a suggestion unit that makes a suggestion based on the habits and psychological state estimated by the estimation unit; Equipped with A system characterized by:

2. Equipped with a database that adapts to different cultures and personality types 2. The system of claim 1.

3. The database unit Determine the other person's cultural background and personality type based on data collected in advance 3. The system of claim 2.

4. The proposal unit Suggest a communication style that suits the other person's cultural background and personality type 3. The system of claim 2.

5. The sensor unit Inferring someone's emotions and adjusting the type of data you collect based on those emotions 2. The system of claim 1.

6. The sensor unit Select the collection method by referring to the other person's past behavior and facial expression data.

2. The system of claim 1.

7. The sensor unit When collecting information, the other party's environmental sounds and background information are also collected at the same time, which is useful for analysis.

2. The system of claim 1.

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