System

Abstract

An object of a system according to an embodiment is to detect a negative utterance in a conversation and provide advice for paraphrasing the negative utterance into a positive utterance.SOLUTION: A system includes a conversation analysis unit, a notification unit, and an advice unit. The conversation analysis unit analyzes a conversation. The notification unit detects a negative utterance from the conversation analyzed by the conversation analysis unit, and notifies the user of the negative utterance with a small sound audible only to the user. The advice unit provides advice for paraphrasing the negative utterance notified by the notification unit into a positive utterance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 the problem of making it difficult to detect negative comments in real time during a conversation and deal with them appropriately.

[0005] The system according to the embodiment aims to detect negative comments in a conversation and provide advice on how to rephrase them into positive comments. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation analysis unit, a notification unit, and an advice unit. The conversation analysis unit analyzes a conversation. The notification unit detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the person in question with a low sound that only the person in question can hear. The advice unit provides advice for rephrasing the negative comments notified by the notification unit into positive comments. [Effects of the Invention]

[0007] The system according to the embodiment can detect negative comments in a conversation and provide advice on how to rephrase them into positive comments. [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 positive communication support system according to an embodiment of the present invention is a system that includes Gemini Glasses, an eyeglass-type device with a built-in earphone microphone, and the Good Communication app. This allows the positive communication support system to analyze user conversations and promote positive communication.

[0029] A positive communication support system according to an embodiment includes a conversation analysis unit, a notification unit, and an advice unit. The conversation analysis unit analyzes conversations. For example, the conversation analysis unit analyzes conversations picked up by a microphone in real time to determine whether a comment is positive or negative. The conversation analysis unit can also analyze the content of the conversation using a generation AI (e.g., a text generation AI or a multimodal generation AI). The notification unit detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the user of the negative comment with a low volume that only the user can hear. For example, when a negative comment is detected, the notification unit notifies the user with a low volume through earphones. The notification unit can also adjust the volume and timing of the notification. The advice unit provides advice for rephrasing negative comments notified by the notification unit into positive comments. For example, the advice unit uses a generation AI to suggest rephrasing negative comments into positive comments. The advice unit can also learn the user's past comment history and make rephrasing suggestions optimized for each individual user. This enables the positive communication support system to analyze the user's conversations and promote positive communication. For example, even if a user makes a negative comment, they can refer to the system's notifications and advice to rephrase it into a positive comment.

[0030] The conversation analysis unit can analyze ambient sounds and adjust the volume and content of notifications according to the noise level and background music. For example, the conversation analysis unit adds a function to Gemini Glasses that analyzes ambient sounds and adjusts the volume of notifications according to the noise level. For example, if the noise is loud, the notification volume is increased. The conversation analysis unit can also analyze background music and adjust the content of notifications according to its content. For example, if relaxing music is playing, the notification content is made gentler. The conversation analysis unit can also analyze ambient sounds in real time and dynamically adjust the volume and content of notifications. For example, the notification volume is reduced in quiet environments and increased in noisy environments. This enables appropriate notifications to be delivered according to the environment.

[0031] The conversation analysis unit can analyze a user's performance during sports or fitness training and provide feedback. For example, the conversation analysis unit uses Gemini Glasses during sports training to analyze the user's performance in real time. For example, it analyzes speed and heart rate while running and provides appropriate feedback. Gemini Glasses can also be used during fitness training to analyze the user's movements and provide feedback. For example, it can point out areas for improvement in form. The conversation analysis unit can also analyze the user's performance data during sports or fitness training and provide motivational feedback. For example, it can display a message such as "Keep trying a little harder!" This contributes to improving performance during training.

[0032] The conversation analysis unit understands the context of the conversation and can make a positive or negative judgment based on the intention and background of the statement. For example, the conversation analysis unit uses a generative AI to analyze the context of the conversation and make a positive or negative judgment taking into account the intention and background of the statement. For example, a statement such as "I'm tired today, but it was fun" is judged as positive. In order to understand the context of the conversation, the unit analyzes the preceding and following statements and the situation, and makes a judgment taking into account the intention of the statement. For example, a statement such as "I don't think it's possible, but I'll give it a try" is judged as positive. The unit also analyzes the background information of the statement, understands its intention, and makes a positive or negative judgment. For example, a statement such as "It's raining today, but I'm in a good mood" is judged as positive. This makes it possible to make judgments that take into account the intention and background of the statement.

[0033] The conversation analysis unit learns a user's past speech history and can make positive / negative judgments that are optimized for each individual user. For example, the conversation analysis unit uses a generative AI to learn a user's past speech history and make positive / negative judgments that are optimized for each individual user. For example, it learns expressions that a particular user frequently uses. It also analyzes a user's speech history and makes positive / negative judgments based on the individual user's speech patterns. For example, it responds when a particular phrase has a positive meaning. It also learns a user's speech tendencies based on past speech history and makes optimized judgments. For example, it detects negative expressions that a particular user frequently uses. This makes it possible to make judgments that are optimized for each user.

[0034] The conversation analysis unit can analyze utterances from different languages ​​and cultural spheres and make positive / negative judgments from a global perspective. For example, the conversation analysis unit analyzes utterances from different languages ​​using a generative AI and makes positive / negative judgments from a global perspective. For example, it analyzes utterances in English and French. It also analyzes utterances from different cultural spheres and makes positive / negative judgments taking into account their cultural background. For example, it compares utterances in Japanese with utterances in English. It also analyzes utterances from a global perspective and makes judgments that correspond to different languages ​​and cultural spheres. For example, it detects expressions that are considered positive in a particular culture. This makes it possible to make judgments from a global perspective.

[0035] The conversation analysis unit can also analyze text messages and social media posts to make positive / negative judgments to improve the quality of online communication. For example, the generative AI in the conversation analysis unit analyzes text messages and makes positive / negative judgments. For example, it analyzes messages in chat apps. It also analyzes social media posts and makes positive / negative judgments. For example, it analyzes posts on Twitter and Facebook. The generative AI also analyzes text messages and social media posts to improve the quality of online communication. For example, it detects and notifies users of negative posts. This improves the quality of online communication.

[0036] The advice unit can learn the user's past speech history and make optimized paraphrase suggestions for each individual user. For example, the advice unit uses a generation AI to learn the user's past speech history and make optimized paraphrase suggestions for each individual user. For example, it learns expressions frequently used by a specific user and suggests appropriate paraphrases based on that. It also analyzes the user's speech history and suggests optimal paraphrases based on the individual user's speech patterns. For example, it responds when a specific phrase has a positive meaning. It also learns the user's speech tendencies based on the past speech history and makes optimized paraphrase suggestions. For example, it suggests rephrasing negative expressions frequently used by a specific user in a positive way. This makes it possible to make paraphrase suggestions optimized for the user.

[0037] The advice unit can understand the context of the conversation and suggest the most appropriate rephrasing by taking into consideration the intention and background of the utterance. For example, the generative AI analyzes the context of the conversation and suggests the most appropriate rephrasing by taking into consideration the intention and background of the utterance. For example, the statement "I'm tired today, but it was fun" is rephrased as "Today was fulfilling." In addition, to understand the context of the conversation, the AI ​​analyzes the preceding and following statements and the situation, and suggests the most appropriate rephrasing by taking into consideration the intention of the utterance. For example, the AI ​​rephrases the statement "I think it's impossible, but I'll give it a try" as "It's worth trying." The AI ​​also analyzes the background information of the utterance, understands the intention of the utterance, and suggests the most appropriate rephrasing. For example, the AI ​​rephrases the statement "It's raining today, but I'm in a good mood" as "Even though it's raining, my mood is bright." This makes it possible to suggest the most appropriate rephrasing by taking into consideration the intention and background of the utterance.

[0038] The advice unit can analyze utterances in different languages ​​and cultural spheres and suggest optimal paraphrases from a global perspective. For example, the generative AI in the advice unit analyzes utterances in different languages ​​and suggests optimal paraphrases from a global perspective. For example, it analyzes utterances in English and French and suggests paraphrases appropriate for each language. It also analyzes utterances in different cultural spheres and suggests optimal paraphrases taking into account their cultural background. For example, it compares utterances in Japanese and English and suggests paraphrases appropriate for each culture. It also analyzes utterances from a global perspective and suggests optimal paraphrases that correspond to different languages ​​and cultural spheres. For example, it detects expressions that are considered positive in a particular culture and suggests paraphrases based on that. This makes it possible to provide optimal paraphrases from a global perspective.

[0039] The advice unit can also analyze text messages and social media posts and suggest rephrasing to improve the quality of online communication. For example, the advice unit uses a generation AI to analyze text messages and suggest positive rephrasing. For example, it analyzes messages in chat apps and suggests rephrasing negative expressions in positive ways. It also analyzes social media posts and suggests positive rephrasing. For example, it analyzes posts on Twitter and Facebook and suggests rephrasing negative posts in positive ways. In addition, to improve the quality of online communication, the generation AI analyzes text messages and social media posts and suggests appropriate rephrasing. For example, it detects and notifies users of negative posts and suggests positive rephrasing. This improves the quality of online communication.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The positive communication support system can further include a health management unit that monitors the user's health condition. For example, the health management unit measures the user's heart rate and blood pressure and notifies the user if any abnormalities are detected. The health management unit can also analyze the user's sleep patterns and provide appropriate sleep advice. Furthermore, the health management unit can manage the user's dietary records and provide advice on nutritional balance. This allows for comprehensive support of the user's health condition.

[0042] The positive communication support system may further include an activity recording unit that records the user's activity history. For example, the activity recording unit may record the user's number of steps and distance traveled, and visualize the amount of daily activity. The activity recording unit may also analyze the user's exercise habits and provide appropriate exercise advice. Furthermore, the activity recording unit may evaluate the user's goal achievement level based on the user's activity data and provide feedback to increase motivation. This may improve the user's activity habits and support a healthy lifestyle.

[0043] The positive communication support system can further include a learning support unit that supports the user's learning situation. For example, the learning support unit records the user's learning progress and supports the user in creating a learning plan. The learning support unit can also evaluate the user's level of understanding and provide appropriate learning advice. Furthermore, the learning support unit can suggest effective learning methods based on the user's learning data. This can improve the user's learning efficiency and support the achievement of goals.

[0044] The positive communication support system can further include a hobby support unit that supports the user's hobbies and interests. For example, the hobby support unit collects information about the user's hobbies and suggests related events and activities. The hobby support unit can also suggest new hobbies and activities based on the user's interests. Furthermore, the hobby support unit can introduce communities related to the user's hobbies and provide opportunities for interaction. This can help the user deepen their hobbies and interests and support a fulfilling life.

[0045] The positive communication support system can further include a time management unit that supports the user's time management. For example, the time management unit manages the user's schedule and reminds the user of important appointments. The time management unit can also analyze how the user uses their time and provide advice on efficient time management. Furthermore, the time management unit can suggest how to allocate time to achieve the user's goals. This can improve the user's time management ability and support goal achievement.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The conversation analysis unit analyzes the conversation. For example, the conversation analysis unit analyzes the conversation picked up by the microphone in real time and determines whether the remarks are positive or negative. The conversation analysis unit can also analyze the content of the conversation using generative AI (for example, text generation AI or multimodal generation AI). Step 2: The notification unit detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the user with a quiet sound that only the user can hear. For example, if a negative comment is detected, the notification unit notifies the user with a quiet sound through earphones. The notification unit can also adjust the volume and timing of the notification. Step 3: The advice unit provides advice for rephrasing the negative comments notified by the notification unit into positive ones. For example, the advice unit uses a generation AI to suggest rephrasing negative comments into positive ones. The advice unit can also learn the user's past comment history and make rephrasing suggestions optimized for each individual user.

[0048] (Example 2) A positive communication support system according to an embodiment of the present invention is a system that includes Gemini Glasses, an eyeglass-type device with a built-in earphone microphone, and the Good Communication app. This allows the positive communication support system to analyze user conversations and promote positive communication.

[0049] A positive communication support system according to an embodiment includes a conversation analysis unit, a notification unit, and an advice unit. The conversation analysis unit analyzes conversations. For example, the conversation analysis unit analyzes conversations picked up by a microphone in real time to determine whether a comment is positive or negative. The conversation analysis unit can also analyze the content of the conversation using a generation AI (e.g., a text generation AI or a multimodal generation AI). The notification unit detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the user of the negative comment with a low volume that only the user can hear. For example, when a negative comment is detected, the notification unit notifies the user with a low volume through earphones. The notification unit can also adjust the volume and timing of the notification. The advice unit provides advice for rephrasing negative comments notified by the notification unit into positive comments. For example, the advice unit uses a generation AI to suggest rephrasing negative comments into positive comments. The advice unit can also learn the user's past comment history and make rephrasing suggestions optimized for each individual user. This enables the positive communication support system to analyze the user's conversations and promote positive communication. For example, even if a user makes a negative comment, they can refer to the system's notifications and advice to rephrase it into a positive comment.

[0050] The conversation analysis unit can analyze the tone and speed of conversation and estimate and notify the user's emotional state. The conversation analysis unit, for example, analyzes the tone and speed of conversation to estimate the user's emotional state. For example, if the conversation speed is fast and the tone is high, it is estimated that the user is excited. Furthermore, by analyzing the tone and speed of conversation, the user's emotional state can be estimated and notified in real time. For example, if the tone is low and the speed is slow, it is estimated that the user is tired. Furthermore, the generation AI analyzes the tone and speed of conversation to estimate and notify the user's emotional state. For example, if the tone fluctuates and the speed is irregular, it is estimated that the user is feeling anxious. This makes it possible to grasp the user's emotional state in real time.

[0051] The conversation analysis unit captures the user's gaze and facial expression with a camera and analyzes it together with the content of the conversation, enabling more accurate emotion estimation. For example, the conversation analysis unit uses a camera installed in Gemini Glasses to capture the user's gaze and facial expression and analyzes it together with the content of the conversation. For example, if the gaze is directed downward and the expression is stiff, it is assumed that the user is nervous. In addition, the gaze and facial expression data captured by the camera are integrated with the content of the conversation, allowing the generation AI to more accurately estimate emotions. For example, if the gaze is unsteady and the expression does not change, it is assumed that the user is confused. In addition, the gaze and facial expression data is analyzed together with the content of the conversation to estimate the user's emotional state with high accuracy. For example, if the gaze is not directed at the other person and the expression is clouded, it is assumed that the user is dissatisfied. This improves the accuracy of emotion estimation.

[0052] The notification unit can automatically provide a message of relaxation or encouragement when the user feels a specific emotion. For example, the notification unit uses an emotion estimation function to provide a message of relaxation when the user feels stressed. For example, it displays a message such as "Take a deep breath and relax." Furthermore, the notification unit automatically provides a message of encouragement when the user feels joy. For example, it displays a positive message such as "Keep it up!" Furthermore, the emotion estimation function can be used to provide an appropriate message when the user feels a specific emotion. For example, if the user feels stressed, it displays a message such as "Take a short break." In this way, it is possible to provide an appropriate message according to the user's emotions.

[0053] The conversation analysis unit can analyze ambient sounds and adjust the volume and content of notifications according to the noise level and background music. For example, the conversation analysis unit adds a function to Gemini Glasses that analyzes ambient sounds and adjusts the volume of notifications according to the noise level. For example, if the noise is loud, the notification volume is increased. The conversation analysis unit can also analyze background music and adjust the content of notifications according to its content. For example, if relaxing music is playing, the notification content is made gentler. The conversation analysis unit can also analyze ambient sounds in real time and dynamically adjust the volume and content of notifications. For example, the notification volume is reduced in quiet environments and increased in noisy environments. This enables appropriate notifications to be delivered according to the environment.

[0054] The conversation analysis unit can analyze a user's performance during sports or fitness training and provide feedback. For example, the conversation analysis unit uses Gemini Glasses during sports training to analyze the user's performance in real time. For example, it analyzes speed and heart rate while running and provides appropriate feedback. Gemini Glasses can also be used during fitness training to analyze the user's movements and provide feedback. For example, it can point out areas for improvement in form. The conversation analysis unit can also analyze the user's performance data during sports or fitness training and provide motivational feedback. For example, it can display a message such as "Keep trying a little harder!" This contributes to improving performance during training.

[0055] The conversation analysis unit can automatically play music or video content to adjust the user's emotions when the user feels a particular emotion. For example, the conversation analysis unit uses the emotion estimation function to automatically play relaxing music when the user feels stressed. For example, classical music or nature sounds can be played. Also, when the user feels joy, the conversation analysis unit automatically plays video content that elicits more positive emotions. For example, fun videos or moving videos can be displayed. Also, the emotion estimation function can automatically play appropriate music or video content when the user feels a particular emotion. For example, uplifting music can be played when the user feels sad. This allows the user's emotions to be adjusted appropriately.

[0056] The conversation analysis unit understands the context of the conversation and can make a positive or negative judgment based on the intention and background of the statement. For example, the conversation analysis unit uses a generative AI to analyze the context of the conversation and make a positive or negative judgment taking into account the intention and background of the statement. For example, a statement such as "I'm tired today, but it was fun" is judged as positive. In order to understand the context of the conversation, the unit analyzes the preceding and following statements and the situation, and makes a judgment taking into account the intention of the statement. For example, a statement such as "I don't think it's possible, but I'll give it a try" is judged as positive. The unit also analyzes the background information of the statement, understands its intention, and makes a positive or negative judgment. For example, a statement such as "It's raining today, but I'm in a good mood" is judged as positive. This makes it possible to make judgments that take into account the intention and background of the statement.

[0057] The conversation analysis unit learns a user's past speech history and can make positive / negative judgments that are optimized for each individual user. For example, the conversation analysis unit uses a generative AI to learn a user's past speech history and make positive / negative judgments that are optimized for each individual user. For example, it learns expressions that a particular user frequently uses. It also analyzes a user's speech history and makes positive / negative judgments based on the individual user's speech patterns. For example, it responds when a particular phrase has a positive meaning. It also learns a user's speech tendencies based on past speech history and makes optimized judgments. For example, it detects negative expressions that a particular user frequently uses. This makes it possible to make judgments that are optimized for each user.

[0058] The conversation analysis unit can use the emotion estimation function to dynamically adjust the positive / negative judgment of a comment based on the user's emotional state. For example, the conversation analysis unit uses the emotion estimation function to dynamically adjust the positive / negative judgment of a comment based on the user's emotional state. For example, if the user is feeling stressed, it will judge negative comments more strictly. It also analyzes the user's emotional state in real time and adjusts the judgment of comments based on the results. For example, if the user is relaxed, it will detect more positive comments. Furthermore, a system can be built that dynamically adjusts the positive / negative judgment of a comment based on the emotion estimation data. For example, the judgment criteria can be updated every time the user's emotions change. This enables flexible judgment according to the user's emotional state.

[0059] The conversation analysis unit can analyze utterances from different languages ​​and cultural spheres and make positive / negative judgments from a global perspective. For example, the conversation analysis unit analyzes utterances from different languages ​​using a generative AI and makes positive / negative judgments from a global perspective. For example, it analyzes utterances in English and French. It also analyzes utterances from different cultural spheres and makes positive / negative judgments taking into account their cultural background. For example, it compares utterances in Japanese with utterances in English. It also analyzes utterances from a global perspective and makes judgments that correspond to different languages ​​and cultural spheres. For example, it detects expressions that are considered positive in a particular culture. This makes it possible to make judgments from a global perspective.

[0060] The conversation analysis unit can also analyze text messages and social media posts to make positive / negative judgments to improve the quality of online communication. For example, the generative AI in the conversation analysis unit analyzes text messages and makes positive / negative judgments. For example, it analyzes messages in chat apps. It also analyzes social media posts and makes positive / negative judgments. For example, it analyzes posts on Twitter and Facebook. The generative AI also analyzes text messages and social media posts to improve the quality of online communication. For example, it detects and notifies users of negative posts. This improves the quality of online communication.

[0061] The conversation analysis unit uses the emotion estimation function to adjust the positive / negative judgment of a comment in real time when the user feels a specific emotion. For example, the conversation analysis unit uses the emotion estimation function to adjust the judgment of a comment in real time when the user feels a specific emotion. For example, if the user is feeling angry, it will judge negative comments more strictly. It also analyzes the user's emotional state in real time and adjusts the judgment of comments based on the results. For example, if the user is feeling happy, it will detect more positive comments. Furthermore, a system can be built that adjusts the positive / negative judgment of a comment in real time based on the emotion estimation data. For example, the judgment criteria can be updated every time the user's emotion changes. This makes it possible to make judgments in real time according to the user's emotions.

[0062] The advice unit can learn the user's past speech history and make optimized paraphrase suggestions for each individual user. For example, the advice unit uses a generation AI to learn the user's past speech history and make optimized paraphrase suggestions for each individual user. For example, it learns expressions frequently used by a specific user and suggests appropriate paraphrases based on that. It also analyzes the user's speech history and suggests optimal paraphrases based on the individual user's speech patterns. For example, it responds when a specific phrase has a positive meaning. It also learns the user's speech tendencies based on the past speech history and makes optimized paraphrase suggestions. For example, it suggests rephrasing negative expressions frequently used by a specific user in a positive way. This makes it possible to make paraphrase suggestions optimized for the user.

[0063] The advice unit can understand the context of the conversation and suggest the most appropriate rephrasing by taking into consideration the intention and background of the utterance. For example, the generative AI analyzes the context of the conversation and suggests the most appropriate rephrasing by taking into consideration the intention and background of the utterance. For example, the statement "I'm tired today, but it was fun" is rephrased as "Today was fulfilling." In addition, to understand the context of the conversation, the AI ​​analyzes the preceding and following statements and the situation, and suggests the most appropriate rephrasing by taking into consideration the intention of the utterance. For example, the AI ​​rephrases the statement "I think it's impossible, but I'll give it a try" as "It's worth trying." The AI ​​also analyzes the background information of the utterance, understands the intention of the utterance, and suggests the most appropriate rephrasing. For example, the AI ​​rephrases the statement "It's raining today, but I'm in a good mood" as "Even though it's raining, my mood is bright." This makes it possible to suggest the most appropriate rephrasing by taking into consideration the intention and background of the utterance.

[0064] The advice unit uses the emotion estimation function to suggest optimal paraphrases based on the user's emotional state, leading to a positive emotion. The advice unit, for example, uses the emotion estimation function to suggest optimal paraphrases based on the user's emotional state, leading to a positive emotion. For example, if the user is feeling stressed, the advice unit suggests paraphrases that will help the user relax. The advice unit also analyzes the user's emotional state in real time and suggests optimal paraphrases based on the results. For example, if the user is relaxed, the advice unit suggests more positive paraphrases. The advice unit also builds a system that suggests optimal paraphrases based on the emotion estimation data. For example, the paraphrase suggestions are updated every time the user's emotions change. This makes it possible to provide optimal paraphrases based on the user's emotional state.

[0065] The advice unit can analyze utterances in different languages ​​and cultural spheres and suggest optimal paraphrases from a global perspective. For example, the generative AI in the advice unit analyzes utterances in different languages ​​and suggests optimal paraphrases from a global perspective. For example, it analyzes utterances in English and French and suggests paraphrases appropriate for each language. It also analyzes utterances in different cultural spheres and suggests optimal paraphrases taking into account their cultural background. For example, it compares utterances in Japanese and English and suggests paraphrases appropriate for each culture. It also analyzes utterances from a global perspective and suggests optimal paraphrases that correspond to different languages ​​and cultural spheres. For example, it detects expressions that are considered positive in a particular culture and suggests paraphrases based on that. This makes it possible to provide optimal paraphrases from a global perspective.

[0066] The advice unit can also analyze text messages and social media posts and suggest rephrasing to improve the quality of online communication. For example, the advice unit uses a generation AI to analyze text messages and suggest positive rephrasing. For example, it analyzes messages in chat apps and suggests rephrasing negative expressions in positive ways. It also analyzes social media posts and suggests positive rephrasing. For example, it analyzes posts on Twitter and Facebook and suggests rephrasing negative posts in positive ways. In addition, to improve the quality of online communication, the generation AI analyzes text messages and social media posts and suggests appropriate rephrasing. For example, it detects and notifies users of negative posts and suggests positive rephrasing. This improves the quality of online communication.

[0067] The advice unit uses the emotion estimation function to suggest optimal paraphrases in real time when the user feels a specific emotion, thereby guiding the emotion to a positive one. The advice unit, for example, uses the emotion estimation function to suggest optimal paraphrases in real time when the user feels a specific emotion, thereby guiding the emotion to a positive one. For example, if the user is feeling angry, the advice unit suggests paraphrases that will help the user to remain calm. The advice unit also analyzes the user's emotional state in real time and suggests optimal paraphrases based on the results. For example, if the user is feeling happy, the advice unit suggests more positive paraphrases. Furthermore, a system is constructed that suggests optimal paraphrases in real time according to the user's emotional state based on the emotion estimation data. For example, the paraphrase suggestions are updated every time the user's emotion changes. This makes it possible to suggest paraphrases in real time according to the user's emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The positive communication support system can further include a health management unit that monitors the user's health condition. For example, the health management unit measures the user's heart rate and blood pressure and notifies the user if any abnormalities are detected. The health management unit can also analyze the user's sleep patterns and provide appropriate sleep advice. Furthermore, the health management unit can manage the user's dietary records and provide advice on nutritional balance. This allows for comprehensive support of the user's health condition.

[0070] The positive communication support system may further include an activity recording unit that records the user's activity history. For example, the activity recording unit may record the user's number of steps and distance traveled, and visualize the amount of daily activity. The activity recording unit may also analyze the user's exercise habits and provide appropriate exercise advice. Furthermore, the activity recording unit may evaluate the user's goal achievement level based on the user's activity data and provide feedback to increase motivation. This may improve the user's activity habits and support a healthy lifestyle.

[0071] The positive communication support system can further include a learning support unit that supports the user's learning situation. For example, the learning support unit records the user's learning progress and supports the user in creating a learning plan. The learning support unit can also evaluate the user's level of understanding and provide appropriate learning advice. Furthermore, the learning support unit can suggest effective learning methods based on the user's learning data. This can improve the user's learning efficiency and support the achievement of goals.

[0072] The positive communication support system can further include a hobby support unit that supports the user's hobbies and interests. For example, the hobby support unit collects information about the user's hobbies and suggests related events and activities. The hobby support unit can also suggest new hobbies and activities based on the user's interests. Furthermore, the hobby support unit can introduce communities related to the user's hobbies and provide opportunities for interaction. This can help the user deepen their hobbies and interests and support a fulfilling life.

[0073] The positive communication support system can further include a time management unit that supports the user's time management. For example, the time management unit manages the user's schedule and reminds the user of important appointments. The time management unit can also analyze how the user uses their time and provide advice on efficient time management. Furthermore, the time management unit can suggest how to allocate time to achieve the user's goals. This can improve the user's time management ability and support goal achievement.

[0074] The positive communication support system can suggest appropriate relaxation methods based on the user's emotional state. For example, if the user is feeling stressed, it can suggest deep breathing or meditation. If the user is tired, it can also suggest light stretching or relaxing music. Furthermore, if the user is feeling anxious, it can suggest relaxing video content. In this way, it can provide appropriate relaxation methods according to the user's emotional state and support refreshing the mind and body.

[0075] The positive communication support system can suggest appropriate exercise methods based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing yoga or light jogging. If the user has too much energy, it can suggest high-intensity training or dancing. Furthermore, if the user is feeling down, it can suggest light exercise to lift their spirits. This allows the system to provide appropriate exercise methods according to the user's emotional state and support their physical and mental health.

[0076] The positive communication support system can provide appropriate dietary advice based on the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing herbal tea or light snacks. If the user is tired, it can suggest a balanced meal to replenish energy. Furthermore, if the user is feeling anxious, it can suggest recipes using ingredients that will help them relax. This allows the system to provide appropriate dietary advice according to the user's emotional state and support their physical and mental health.

[0077] The positive communication support system can suggest appropriate rest methods based on the user's emotional state. For example, if the user is feeling stressed, it can suggest taking a short nap or reading something relaxing. If the user is tired, it can also provide advice on how to get quality sleep. Furthermore, if the user is feeling anxious, it can provide advice on creating a relaxing environment. In this way, it can provide appropriate rest methods according to the user's emotional state and support refreshing the mind and body.

[0078] The positive communication support system can suggest appropriate communication methods based on the user's emotional state. For example, if the user is feeling stressed, it can suggest topics and tones that will help them relax. If the user is tired, it can also suggest concise and clear communication methods. Furthermore, if the user is feeling anxious, it can suggest communication methods that will give the user a sense of security. This makes it possible to provide appropriate communication methods according to the user's emotional state and support smooth dialogue.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The conversation analysis unit analyzes the conversation. For example, the conversation analysis unit analyzes the conversation picked up by the microphone in real time and determines whether the remarks are positive or negative. The conversation analysis unit can also analyze the content of the conversation using generative AI (for example, text generation AI or multimodal generation AI). Step 2: The notification unit detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the user with a quiet sound that only the user can hear. For example, if a negative comment is detected, the notification unit notifies the user with a quiet sound through earphones. The notification unit can also adjust the volume and timing of the notification. Step 3: The advice unit provides advice for rephrasing the negative comments notified by the notification unit into positive ones. For example, the advice unit uses a generation AI to suggest rephrasing negative comments into positive ones. The advice unit can also learn the user's past comment history and make rephrasing suggestions optimized for each individual user.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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. [Explanation of symbols]

[0148] 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 conversation analysis unit that analyzes a conversation; a notification unit that detects negative comments from the conversation analyzed by the conversation analysis unit and notifies the person in question with a small sound that can only be heard by the person in question; an advice unit that provides advice on how to rephrase the negative comments notified by the notification unit into positive comments. A system characterized by:

2. The conversation analysis unit Analyzes the tone and speed of conversation to estimate and notify the user's emotional state 2. The system of claim 1.

3. The conversation analysis unit By capturing the user's gaze and facial expressions with a camera and analyzing them together with the content of the conversation, more accurate emotion estimation is possible.

2. The system of claim 1.

4. The notification unit Automatically provide relaxing or encouraging messages when users experience certain emotions 2. The system of claim 1.

5. The conversation analysis unit Analyzes surrounding environmental sounds and adjusts notification volume and content according to noise levels and background music 2. The system of claim 1.

6. The conversation analysis unit Analyze your performance and provide feedback during sports and fitness training 2. The system of claim 1.

7. The conversation analysis unit When a user feels a certain emotion, music or video content is automatically played to adjust the emotion.

2. The system of claim 1.

8. The conversation analysis unit Understand the context of a conversation and make positive or negative judgments based on the intention and background of the statement 2. The system of claim 1.

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