System

Abstract

An object of a system according to an embodiment is to understand a user's emotion and situation in real time and provide appropriate advice and support.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires the emotion or situation of the user in real time. The analysis unit analyzes the information acquired by the collection unit and understands the emotion or situation of the user. The provision unit provides advice or support to the user on the basis of the information understood by the analysis unit.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 technologies have had the problem of making it difficult to grasp a user's emotions and situation in real time and provide appropriate advice and support.

[0005] The system according to the embodiment aims to understand the user's emotions and situation in real time and provide appropriate advice and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires a user's emotions or situation in real time. The analysis unit analyzes the information acquired by the collection unit to understand the user's emotions or situation. The provision unit provides advice or support to the user based on the information understood by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can understand the user's emotions and situation in real time and provide appropriate advice and support. [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) An AI interactive service according to an embodiment of the present invention is a system that understands a user's emotions and situation in real time and provides appropriate advice and support. This system acquires the user's emotions and situation in real time, and a generation AI analyzes the acquired information to understand the user's emotions and situation. The generation AI then provides the user with appropriate advice and support. For example, the generation AI collects data such as the user's speech, facial expressions, and tone of voice, and analyzes it to accurately grasp the user's emotions and situation. The generation AI then suggests ways for the user to relax or to consult with a specialist. It also provides personalized support based on the user's situation. This allows corporate employees to recognize mental health issues early and take appropriate measures. This allows the AI ​​interactive service to understand a user's emotions and situation in real time and provide appropriate advice and support. For example, this allows corporate employees to recognize mental health issues early and take appropriate measures. This improves work performance and helps employees maintain a healthy mental state.

[0029] An AI interactive service according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires a user's emotions or situation in real time. The collection unit collects data such as the user's speech content, facial expressions, and tone of voice. The collection unit can also acquire the user's emotions or situation in real time using a generation AI. The analysis unit analyzes the information acquired by the collection unit to understand the user's emotions or situation. The analysis unit uses the generation AI to learn past data and patterns to accurately understand the user's emotions and situation. For example, the analysis unit learns under what circumstances the user felt stressed in the past and compares this with the current situation to detect signs of stress. The provision unit provides advice or support to the user based on the information understood by the analysis unit. If the user is feeling stressed, the provision unit uses the generation AI to suggest ways to relax or to consult with a specialist. The provision unit also provides personalized support according to the user's situation. For example, if the user is feeling stressed about a specific task, the provision unit provides advice on how to perform that task efficiently. As a result, the AI ​​interactive service according to the embodiment can understand the user's emotions and situation in real time and provide appropriate advice and support.

[0030] The collection unit can collect data on the user's utterances, facial expressions, and voice tone. The collection unit, for example, collects the user's utterances. The utterances include oral utterances and text messages. The collection unit can also collect the user's facial expressions. The facial expressions include smiling and angry faces. The collection unit can also collect the user's voice tone. The voice tone includes high and low tones, intonation, and the like. In this way, the collection unit can accurately grasp the user's emotions and situation by collecting data such as the user's utterances, facial expressions, and voice tone. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's utterances into the generation AI and have the generation AI analyze the utterances.

[0031] The analysis unit can learn past data or patterns and understand the user's emotions or situation. The analysis unit, for example, learns past data. Past data includes past speech history and behavior history. The analysis unit can also learn past patterns. Patterns include behavioral patterns and emotional patterns. By learning past data and patterns, the analysis unit can accurately understand the user's emotions and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI analyze the data.

[0032] The providing unit can suggest relaxation methods or consultation with a specialist when the user is feeling stressed. For example, the providing unit can suggest relaxation methods when the user is feeling stressed. Relaxation methods include deep breathing, meditation, listening to music, etc. The providing unit can also suggest consultation with a specialist when the user is feeling stressed. Experts include psychological counselors and doctors. This allows the providing unit to suggest appropriate relaxation methods or consultation with a specialist when the user is feeling stressed. Some or all of the above-described processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's stress state into the generation AI and cause the generation AI to suggest relaxation methods.

[0033] The providing unit can provide individualized support according to the user's situation. The providing unit, for example, provides personalized advice according to the user's situation. The personalized advice includes specific advice tailored to the user's characteristics and situation. The providing unit can also provide a customized plan according to the user's situation. The customized plan includes a specific plan tailored to the user's goals and needs. This enables the providing unit to provide personalized support tailored to the user's situation, thereby enabling more effective assistance. Some or all of the above-described processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's situation into the generation AI and cause the generation AI to provide personalized advice.

[0034] The providing unit can provide advice to help a user to efficiently perform a specific task when the user is feeling stressed by the task. For example, when the user is feeling stressed by the task, the providing unit provides advice to help the user to efficiently perform the task. The advice includes task management methods and efficient work procedures. This allows the providing unit to provide advice to help the user to efficiently perform a specific task when the user is feeling stressed by the task. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's work situation into the generation AI and have the generation AI execute a proposal for an efficient work procedure.

[0035] The collection unit can analyze the user's past statements or behavioral history and select an appropriate data collection method. For example, the collection unit prioritizes the selection of a data collection method (voice, text, etc.) that the user has frequently used in the past. The collection unit can also suggest the optimal data collection method for a specific time period based on the user's past behavioral history. The collection unit can also analyze the user's past statements and select the most effective data collection method. This allows the collection unit to select the optimal data collection method by analyzing the user's past statements and behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past statements into the generation AI and have the generation AI select the data collection method.

[0036] The collection unit can perform filtering based on the user's current activity or environment when collecting data. For example, if the user is in a meeting, the collection unit causes the generation AI to collect only data related to the content of the meeting. If the user is exercising, the collection unit can also cause the generation AI to preferentially collect data related to exercise. If the user is relaxing, the collection unit can also cause the generation AI to collect data related to relaxation. This allows the collection unit to collect highly relevant data by filtering data based on the user's current activity or environment. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's current activity data into the generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit causes the generation AI to prioritize collecting voice data. When the user uses text input, the collection unit can also cause the generation AI to prioritize collecting text data. When the user expresses emotions through facial expressions, the collection unit can also cause the generation AI to prioritize collecting facial expression data. This allows the collection unit to efficiently collect data by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input data to the generation AI and have the generation AI select the collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. When the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. When the user is at home, the collection unit can also prioritize collecting data related to the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect data related to places where the user has checked in on social media. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and customize the collection method. This allows the collection unit to customize the optimal data collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI adjust the collection method.

[0041] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the analysis unit allows the generation AI to perform a detailed analysis of data with high importance. The analysis unit can also allow the generation AI to perform a simplified analysis of data with low importance. The analysis unit can also allow the generation AI to dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0042] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the generation AI can apply an emotion analysis algorithm to emotion data. The analysis unit can also apply a behavior analysis algorithm to behavior data. The analysis unit can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply different analysis algorithms depending on the data category, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit causes the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also learn the user's past analysis results and cause the generation AI to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can set analysis priorities based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data, and the generation AI quickly provides results. The analysis unit can also have the generation AI prioritize analyzing the most recent data, leaving older data for later. The analysis unit can also have the generation AI dynamically adjust the analysis priorities according to the time when the data was collected. This allows the analysis unit to prioritize analyzing the most recent data by determining the analysis priorities based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI set the analysis priorities.

[0045] During analysis, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data, and the generation AI quickly provides results. The analysis unit can also have the generation AI prioritize analysis of highly relevant data, leaving less relevant data for later. The analysis unit can also have the generation AI dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI set the order of analysis.

[0046] During analysis, the analysis unit can set the use of technical terms for analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can have the generation AI use technical terms to provide analysis results. If the user does not have technical expertise, the analysis unit can also have the generation AI provide analysis results in simple language. The analysis unit can also have the generation AI dynamically adjust the use of technical terms for analysis according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easy to understand by adjusting the use of technical terms for analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0047] When providing advice or support, the providing unit can select an appropriate method by analyzing the user's past behavioral history. For example, the providing unit allows the generation AI to select the optimal method based on advice that was effective for the user in the past. The providing unit can also provide the advice that is optimal for a specific situation from the user's past behavioral history. The providing unit can also analyze the user's past behavioral history and allow the generation AI to select the most effective support method. In this way, the providing unit can select the optimal advice or support method by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI and cause the generation AI to select the optimal method.

[0048] When providing advice or support, the providing unit can adjust the content based on the user's current situation. For example, if the user is feeling stressed, the providing unit can have the generating AI suggest ways to relax. If the user is having difficulty with a particular task, the providing unit can also provide advice to help the generating AI perform that task efficiently. If the user is tired, the providing unit can also have the generating AI suggest taking a rest. This allows the providing unit to provide more effective support by customizing the content based on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's current situation into the generating AI and have the generating AI adjust the content.

[0049] The providing unit can improve the method by reflecting user feedback when providing advice or support. For example, when a user provides feedback on the provided advice, the providing unit causes the generation AI to improve the advice method based on that feedback. The providing unit can also analyze the user's feedback, and the generation AI can propose an optimal support method. The providing unit can also cause the generation AI to customize the content of the advice by referring to the user's feedback. In this way, the providing unit can improve the advice or support method by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the method.

[0050] When providing advice or support, the providing unit can select an appropriate method by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide advice related to that location. If the user is traveling, the providing unit can also provide advice related to the user's destination. If the user is at home, the providing unit can also provide advice that can be performed at home. This allows the providing unit to select the optimal advice or support method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal method.

[0051] When providing advice or support, the providing unit can analyze the user's social media activity and provide suggestions. For example, the providing unit can analyze the content posted by the user on social media and provide related advice. The providing unit can also provide related advice by referring to the activity of the user's friends on social media. The providing unit can also provide advice related to places the user has checked in to on social media. In this way, the providing unit can provide relevant advice or support by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to provide suggestions.

[0052] The providing unit can adjust the method by reflecting the user's past feedback when providing advice or support. For example, the providing unit selects the optimal advice method based on feedback provided by the user in the past. The providing unit can also preferentially use a specific advice method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the advice method. In this way, the providing unit can customize the optimal advice or support method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the generation AI can perform a detailed analysis of data with high importance. The generation AI can also perform a simplified analysis of data with low importance. The generation AI can also dynamically adjust the level of detail of the analysis depending on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0055] When providing advice or support, the providing unit can select an appropriate method by analyzing the user's past behavioral history. For example, the generation AI selects the optimal method based on advice that was effective for the user in the past. The optimal advice for a specific situation can also be provided from the user's past behavioral history. The generation AI can also analyze the user's past behavioral history and select the most effective support method. In this way, the providing unit can select the optimal advice or support method by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI and have the generation AI select the optimal method.

[0056] The collection unit can analyze the user's past statements or behavioral history and select an appropriate data collection method. For example, it can prioritize the selection of a data collection method (voice, text, etc.) that the user has frequently used in the past. It can also suggest the optimal data collection method for a specific time period based on the user's past behavioral history. It can also analyze the user's past statements and select the most effective data collection method. In this way, the collection unit can select the optimal data collection method by analyzing the user's past statements and behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past statements into the generation AI and have the generation AI select the data collection method.

[0057] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the generation AI can apply an emotion analysis algorithm to emotion data. The generation AI can also apply a behavior analysis algorithm to behavior data. The generation AI can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply different analysis algorithms depending on the data category, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0058] When providing advice or support, the providing unit can adjust the content based on the user's current situation. For example, if the user is feeling stressed, the generating AI can suggest ways to relax. If the user is having difficulty with a particular task, the generating AI can provide advice to efficiently perform that task. If the user is tired, the generating AI can also suggest taking a rest. This allows the providing unit to provide more effective support by customizing the content based on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's current situation into the generating AI and have the generating AI adjust the content.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit acquires the user's emotions or situation in real time. The collection unit collects data such as the user's speech content, facial expressions, and tone of voice. The collection unit can also use a generation AI to acquire the user's emotions and situation in real time. Step 2: The analysis unit analyzes the information acquired by the collection unit and understands the user's emotions or situation. The analysis unit uses generative AI to learn past data and patterns to accurately understand the user's emotions and situation. For example, the analysis unit learns under what circumstances the user felt stressed in the past and compares this with the current situation to detect signs of stress. Step 3: The provision unit provides advice or support to the user based on the information understood by the analysis unit. If the user is feeling stressed, the provision unit uses the generation AI to suggest ways to relax or to consult with a specialist. The provision unit also provides personalized support according to the user's situation. For example, if the user is feeling stressed about a specific task, the provision unit provides advice on how to perform that task efficiently.

[0061] (Example 2) An AI interactive service according to an embodiment of the present invention is a system that understands a user's emotions and situation in real time and provides appropriate advice and support. This system acquires the user's emotions and situation in real time, and a generation AI analyzes the acquired information to understand the user's emotions and situation. The generation AI then provides the user with appropriate advice and support. For example, the generation AI collects data such as the user's speech, facial expressions, and tone of voice, and analyzes it to accurately grasp the user's emotions and situation. The generation AI then suggests ways for the user to relax or to consult with a specialist. It also provides personalized support based on the user's situation. This allows corporate employees to recognize mental health issues early and take appropriate measures. This allows the AI ​​interactive service to understand a user's emotions and situation in real time and provide appropriate advice and support. For example, this allows corporate employees to recognize mental health issues early and take appropriate measures. This improves work performance and helps employees maintain a healthy mental state.

[0062] An AI interactive service according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit acquires a user's emotions or situation in real time. The collection unit collects data such as the user's speech content, facial expressions, and tone of voice. The collection unit can also acquire the user's emotions or situation in real time using a generation AI. The analysis unit analyzes the information acquired by the collection unit to understand the user's emotions or situation. The analysis unit uses the generation AI to learn past data and patterns to accurately understand the user's emotions and situation. For example, the analysis unit learns under what circumstances the user felt stressed in the past and compares this with the current situation to detect signs of stress. The provision unit provides advice or support to the user based on the information understood by the analysis unit. If the user is feeling stressed, the provision unit uses the generation AI to suggest ways to relax or to consult with a specialist. The provision unit also provides personalized support according to the user's situation. For example, if the user is feeling stressed about a specific task, the provision unit provides advice on how to perform that task efficiently. As a result, the AI ​​interactive service according to the embodiment can understand the user's emotions and situation in real time and provide appropriate advice and support.

[0063] The collection unit can collect data on the user's utterances, facial expressions, and voice tone. The collection unit, for example, collects the user's utterances. The utterances include oral utterances and text messages. The collection unit can also collect the user's facial expressions. The facial expressions include smiling and angry faces. The collection unit can also collect the user's voice tone. The voice tone includes high and low tones, intonation, and the like. In this way, the collection unit can accurately grasp the user's emotions and situation by collecting data such as the user's utterances, facial expressions, and voice tone. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's utterances into the generation AI and have the generation AI analyze the utterances.

[0064] The analysis unit can learn past data or patterns and understand the user's emotions or situation. The analysis unit, for example, learns past data. Past data includes past speech history and behavior history. The analysis unit can also learn past patterns. Patterns include behavioral patterns and emotional patterns. By learning past data and patterns, the analysis unit can accurately understand the user's emotions and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past data into the generation AI and have the generation AI analyze the data.

[0065] The providing unit can suggest relaxation methods or consultation with a specialist when the user is feeling stressed. For example, the providing unit can suggest relaxation methods when the user is feeling stressed. Relaxation methods include deep breathing, meditation, listening to music, etc. The providing unit can also suggest consultation with a specialist when the user is feeling stressed. Experts include psychological counselors and doctors. This allows the providing unit to suggest appropriate relaxation methods or consultation with a specialist when the user is feeling stressed. Some or all of the above-described processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's stress state into the generation AI and cause the generation AI to suggest relaxation methods.

[0066] The providing unit can provide individualized support according to the user's situation. The providing unit, for example, provides personalized advice according to the user's situation. The personalized advice includes specific advice tailored to the user's characteristics and situation. The providing unit can also provide a customized plan according to the user's situation. The customized plan includes a specific plan tailored to the user's goals and needs. This enables the providing unit to provide personalized support tailored to the user's situation, thereby enabling more effective assistance. Some or all of the above-described processing in the providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's situation into the generation AI and cause the generation AI to provide personalized advice.

[0067] The providing unit can provide advice to help a user to efficiently perform a specific task when the user is feeling stressed by the task. For example, when the user is feeling stressed by the task, the providing unit provides advice to help the user to efficiently perform the task. The advice includes task management methods and efficient work procedures. This allows the providing unit to provide advice to help the user to efficiently perform a specific task when the user is feeling stressed by the task. Some or all of the above-described processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's work situation into the generation AI and have the generation AI execute a proposal for an efficient work procedure.

[0068] The collection unit can estimate the user's emotions and determine the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit allows the generation AI to detect the signs and increase the frequency of data collection to obtain more detailed information. If the user is relaxed, the collection unit can also reduce the frequency of data collection, thereby reducing the user's burden. If the user is in a hurry, the collection unit can also allow the generation AI to collect necessary data in a short period of time and perform analysis quickly. This allows the collection unit to collect more appropriate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0069] The collection unit can analyze the user's past statements or behavioral history and select an appropriate data collection method. For example, the collection unit prioritizes the selection of a data collection method (voice, text, etc.) that the user has frequently used in the past. The collection unit can also suggest the optimal data collection method for a specific time period based on the user's past behavioral history. The collection unit can also analyze the user's past statements and select the most effective data collection method. This allows the collection unit to select the optimal data collection method by analyzing the user's past statements and behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past statements into the generation AI and have the generation AI select the data collection method.

[0070] The collection unit can perform filtering based on the user's current activity or environment when collecting data. For example, if the user is in a meeting, the collection unit causes the generation AI to collect only data related to the content of the meeting. If the user is exercising, the collection unit can also cause the generation AI to preferentially collect data related to exercise. If the user is relaxing, the collection unit can also cause the generation AI to collect data related to relaxation. This allows the collection unit to collect highly relevant data by filtering data based on the user's current activity or environment. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's current activity data into the generation AI and have the generation AI perform data filtering.

[0071] When collecting data, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit causes the generation AI to prioritize collecting voice data. When the user uses text input, the collection unit can also cause the generation AI to prioritize collecting text data. When the user expresses emotions through facial expressions, the collection unit can also cause the generation AI to prioritize collecting facial expression data. This allows the collection unit to efficiently collect data by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's input data to the generation AI and have the generation AI select the collection means.

[0072] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit causes the generation AI to prioritize collecting data related to stress. If the user is relaxed, the collection unit can also cause the generation AI to prioritize collecting data related to relaxation. If the user is in a hurry, the collection unit can also prioritize collecting data that the generation AI can collect quickly. This allows the collection unit to prioritize collecting important data by determining the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI set the data priority.

[0073] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. When the user is traveling, the collection unit can also prioritize collecting data related to the user's destination. When the user is at home, the collection unit can also prioritize collecting data related to the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant data.

[0074] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect data related to places where the user has checked in on social media. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related data.

[0075] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and customize the collection method. This allows the collection unit to customize the optimal data collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and have the generation AI adjust the collection method.

[0076] The analysis unit can estimate the user's emotions and set the analysis presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can have the generation AI provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also have the generation AI provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a key analysis result. This allows the analysis unit to adjust the analysis presentation method based on the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis presentation method.

[0077] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the analysis unit allows the generation AI to perform a detailed analysis of data with high importance. The analysis unit can also allow the generation AI to perform a simplified analysis of data with low importance. The analysis unit can also allow the generation AI to dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0078] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the generation AI can apply an emotion analysis algorithm to emotion data. The analysis unit can also apply a behavior analysis algorithm to behavior data. The analysis unit can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply different analysis algorithms depending on the data category, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit causes the generation AI to improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also cause the generation AI to adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also learn the user's past analysis results and cause the generation AI to improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and set the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can have the generation AI provide a short, concise analysis result. If the user is relaxed, the analysis unit can also have the generation AI provide a detailed analysis result. If the user is in a hurry, the analysis unit can have the generation AI perform the analysis quickly and provide the result in a short time. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0081] During analysis, the analysis unit can set analysis priorities based on the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data, and the generation AI quickly provides results. The analysis unit can also have the generation AI prioritize analyzing the most recent data, leaving older data for later. The analysis unit can also have the generation AI dynamically adjust the analysis priorities according to the time when the data was collected. This allows the analysis unit to prioritize analyzing the most recent data by determining the analysis priorities based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI set the analysis priorities.

[0082] During analysis, the analysis unit can set the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data, and the generation AI quickly provides results. The analysis unit can also have the generation AI prioritize analysis of highly relevant data, leaving less relevant data for later. The analysis unit can also have the generation AI dynamically adjust the order of analysis according to the relevance of the data. This allows the analysis unit to prioritize analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI set the order of analysis.

[0083] During analysis, the analysis unit can set the use of technical terms for analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can have the generation AI use technical terms to provide analysis results. If the user does not have technical expertise, the analysis unit can also have the generation AI provide analysis results in simple language. The analysis unit can also have the generation AI dynamically adjust the use of technical terms for analysis according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easy to understand by adjusting the use of technical terms for analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and have the generation AI use technical terms.

[0084] The providing unit can estimate the user's emotions and set the way to express advice or support based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can have the generation AI provide gentle advice. If the user is relaxed, the providing unit can also have the generation AI provide detailed advice. If the user is in a hurry, the providing unit can also have the generation AI provide concise and quick advice. This allows the providing unit to adjust the way to express advice or support based on the user's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way to express advice or support.

[0085] When providing advice or support, the providing unit can select an appropriate method by analyzing the user's past behavioral history. For example, the providing unit allows the generation AI to select the optimal method based on advice that was effective for the user in the past. The providing unit can also provide the advice that is optimal for a specific situation from the user's past behavioral history. The providing unit can also analyze the user's past behavioral history and allow the generation AI to select the most effective support method. In this way, the providing unit can select the optimal advice or support method by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI and cause the generation AI to select the optimal method.

[0086] When providing advice or support, the providing unit can adjust the content based on the user's current situation. For example, if the user is feeling stressed, the providing unit can have the generating AI suggest ways to relax. If the user is having difficulty with a particular task, the providing unit can also provide advice to help the generating AI perform that task efficiently. If the user is tired, the providing unit can also have the generating AI suggest taking a rest. This allows the providing unit to provide more effective support by customizing the content based on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's current situation into the generating AI and have the generating AI adjust the content.

[0087] The providing unit can improve the method by reflecting user feedback when providing advice or support. For example, when a user provides feedback on the provided advice, the providing unit causes the generation AI to improve the advice method based on that feedback. The providing unit can also analyze the user's feedback, and the generation AI can propose an optimal support method. The providing unit can also cause the generation AI to customize the content of the advice by referring to the user's feedback. In this way, the providing unit can improve the advice or support method by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the method.

[0088] The providing unit can estimate the user's emotions and prioritize advice or support based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can cause the generation AI to prioritize advice for stress reduction. If the user is relaxed, the providing unit can also cause the generation AI to prioritize advice related to long-term goals. If the user is in a hurry, the providing unit can also prioritize advice that the generation AI can implement quickly. This allows the providing unit to prioritize advice and support based on the user's emotions, thereby providing important support preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize advice and support.

[0089] When providing advice or support, the providing unit can select an appropriate method by taking into account the user's geographical location information. For example, if the user is in a specific location, the providing unit can provide advice related to that location. If the user is traveling, the providing unit can also provide advice related to the user's destination. If the user is at home, the providing unit can also provide advice that can be performed at home. This allows the providing unit to select the optimal advice or support method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal method.

[0090] When providing advice or support, the providing unit can analyze the user's social media activity and provide suggestions. For example, the providing unit can analyze the content posted by the user on social media and provide related advice. The providing unit can also provide related advice by referring to the activity of the user's friends on social media. The providing unit can also provide advice related to places the user has checked in to on social media. In this way, the providing unit can provide relevant advice or support by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using or without the generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to provide suggestions.

[0091] The providing unit can adjust the method by reflecting the user's past feedback when providing advice or support. For example, the providing unit selects the optimal advice method based on feedback provided by the user in the past. The providing unit can also preferentially use a specific advice method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the advice method. In this way, the providing unit can customize the optimal advice or support method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to adjust the method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as the user's speech content, facial expressions, and tone of voice using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the user's emotions and situation in real time using a generation AI on the cloud. The analysis unit can be realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the collection unit to understand the user's emotions and situation. The provision unit can be realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides advice or support to the user based on the information understood by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as the user's speech content, facial expressions, and voice tone using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the user's emotions and situation in real time using a generation AI on the cloud. The analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the collection unit to understand the user's emotions and situation. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides advice or support to the user based on the information understood by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data such as the user's speech content, facial expressions, and voice tone using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the user's emotions and situation in real time using a generation AI on the cloud. The analysis unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the collection unit to understand the user's emotions and situation. The provision unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides advice or support to the user based on the information understood by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as the user's speech content, facial expressions, and voice tone using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can acquire the user's emotions and situation in real time using generative AI on the cloud. The analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and analyzes the information acquired by the collection unit to understand the user's emotions and situation. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides advice or support to the user based on the information understood by the analysis unit.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and set analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize analyzing data related to stress. If the user is relaxed, the generation AI can prioritize analyzing data related to relaxation. If the user is in a hurry, the generation AI can prioritize analyzing data that can be analyzed quickly. This allows the analysis unit to prioritize important data by adjusting the analysis priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI set the analysis priorities.

[0094] The providing unit can estimate the user's emotions and set the way to express advice or support based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide gentle advice. If the user is relaxed, the generation AI can provide detailed advice. If the user is in a hurry, the generation AI can provide concise and quick advice. This allows the providing unit to adjust the way to express advice or support based on the user's emotions, thereby providing more appropriate support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way to express advice or support.

[0095] The collection unit can estimate the user's emotions and determine the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can detect the signs and increase the frequency of data collection to obtain more detailed information. If the user is relaxed, the generation AI can reduce the frequency of data collection to reduce the user's burden. If the user is in a hurry, the generation AI can collect necessary data in a short time and perform analysis quickly. This allows the collection unit to collect more appropriate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0096] The analysis unit can estimate the user's emotions and set the analysis presentation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple, highly visible analysis result. If the user is relaxed, the generation AI can provide a detailed analysis result. If the user is in a hurry, the generation AI can provide a concise analysis result. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis presentation method.

[0097] The providing unit can estimate the user's emotions and prioritize advice or support based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize advice for stress reduction. If the user is relaxed, the generation AI can also provide advice related to long-term goals. If the user is in a hurry, the generation AI can prioritize advice that can be implemented quickly. This allows the providing unit to prioritize advice and support based on the user's emotions, thereby providing important support preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI prioritize advice and support.

[0098] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the data. For example, the generation AI can perform a detailed analysis of data with high importance. The generation AI can also perform a simplified analysis of data with low importance. The generation AI can also dynamically adjust the level of detail of the analysis depending on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI set the level of detail of the analysis.

[0099] When providing advice or support, the providing unit can select an appropriate method by analyzing the user's past behavioral history. For example, the generation AI selects the optimal method based on advice that was effective for the user in the past. The optimal advice for a specific situation can also be provided from the user's past behavioral history. The generation AI can also analyze the user's past behavioral history and select the most effective support method. In this way, the providing unit can select the optimal advice or support method by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past behavioral history into the generation AI and have the generation AI select the optimal method.

[0100] The collection unit can analyze the user's past statements or behavioral history and select an appropriate data collection method. For example, it can prioritize the selection of a data collection method (voice, text, etc.) that the user has frequently used in the past. It can also suggest the optimal data collection method for a specific time period based on the user's past behavioral history. It can also analyze the user's past statements and select the most effective data collection method. In this way, the collection unit can select the optimal data collection method by analyzing the user's past statements and behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past statements into the generation AI and have the generation AI select the data collection method.

[0101] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the generation AI can apply an emotion analysis algorithm to emotion data. The generation AI can also apply a behavior analysis algorithm to behavior data. The generation AI can also apply a voice analysis algorithm to voice data. This allows the analysis unit to apply different analysis algorithms depending on the data category, enabling more accurate analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0102] When providing advice or support, the providing unit can adjust the content based on the user's current situation. For example, if the user is feeling stressed, the generating AI can suggest ways to relax. If the user is having difficulty with a particular task, the generating AI can provide advice to efficiently perform that task. If the user is tired, the generating AI can also suggest taking a rest. This allows the providing unit to provide more effective support by customizing the content based on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using the generating AI, or may be performed without using the generating AI. For example, the providing unit can input the user's current situation into the generating AI and have the generating AI adjust the content.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit acquires the user's emotions or situation in real time. The collection unit collects data such as the user's speech content, facial expressions, and tone of voice. The collection unit can also use a generation AI to acquire the user's emotions and situation in real time. Step 2: The analysis unit analyzes the information acquired by the collection unit and understands the user's emotions or situation. The analysis unit uses generative AI to learn past data and patterns to accurately understand the user's emotions and situation. For example, the analysis unit learns under what circumstances the user felt stressed in the past and compares this with the current situation to detect signs of stress. Step 3: The provision unit provides advice or support to the user based on the information understood by the analysis unit. If the user is feeling stressed, the provision unit uses the generation AI to suggest ways to relax or to consult with a specialist. The provision unit also provides personalized support according to the user's situation. For example, if the user is feeling stressed about a specific task, the provision unit provides advice on how to perform that task efficiently.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

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

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the 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.

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

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 collection unit that acquires the user's emotions or situations in real time; an analysis unit that analyzes the information acquired by the collection unit and understands the user's emotions or situation; a providing unit that provides advice or support to a user based on the information understood by the analyzing unit. A system characterized by:

2. The collecting unit Collect data on what the user says, their facial expressions, or their tone of voice 2. The system of claim 1.

3. The analysis unit Learn from past data or patterns to understand user sentiment or situations 2. The system of claim 1.

4. The providing unit If the user is feeling stressed, suggest ways to relax or seek professional help.

2. The system of claim 1.

5. The providing unit Provide personalized support based on the user's situation 2. The system of claim 1.

6. The providing unit If a user is feeling stressed about a particular task, provide advice on how to perform that task more effectively.

2. The system of claim 1.

7. The collecting unit Estimate user emotions and determine the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze users' past comments or behavioral history to select the appropriate data collection method 2. The system of claim 1.

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