system

Abstract

The system according to the embodiment aims to make it possible to easily grasp the condition, mood, and stress level of a user in online communication. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a reflection unit. The collection unit collects text information or task volume of a user. The analysis unit analyzes the information collected by the collection unit and determines the user's condition, mood, and stress level. The reflection unit changes an icon based on the results determined by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to grasp a user's condition, mood, and stress level during online communication.

[0005] The system according to the embodiment aims to make it possible to easily grasp the condition, mood, and stress level of a user in online communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a reflection unit. The collection unit collects text information or task volume of a user. The analysis unit analyzes the information collected by the collection unit and determines the user's condition, mood, and stress level. The reflection unit changes an icon based on the results determined by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to easily grasp the condition, mood, and stress level of a user in online communication. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is designed to facilitate online internal communication. This system uses AI to easily assess a user's condition, mood, and stress level, which are often difficult to convey in text during face-to-face communication. This assessment, however, is often overlooked in text, such as "That person doesn't seem well / in a bad mood, so let's avoid them for now" or "They seem fine today, so let's talk." This system uses AI to change icons to easily assess a user's condition, mood, and stress level. A bot integrated into a chat tool makes assessments based on text information and task volume, and reflects the assessment results in an icon. For example, a bot integrated into a chat tool collects information such as the content of messages sent by the user and the progress of current tasks. Next, AI analyzes the collected information to assess the user's condition, mood, and stress level. For example, the system analyzes the user's emotions based on the content of messages and estimates the user's stress level based on the progress of tasks. Finally, the user's icon changes based on the assessment results. For example, if the user is feeling stressed, the icon may change to red. This system facilitates online communication and makes it easy to understand a user's condition and mood. For example, if a user is feeling stressed, other members can be considerate of that user, preventing communication problems. Also, if a user is in good spirits, they can proactively communicate, revitalizing the team. This allows a system that facilitates online internal communication to easily grasp the user's condition, mood, and stress level.

[0029] A communication support system according to an embodiment includes a collection unit, an analysis unit, and a reflection unit. The collection unit collects text information or task volume of a user. For example, the collection unit can collect the content of messages sent by the user and the progress status of current tasks. The collection unit can collect the content of users' messages in real time, for example, using a bot integrated into a chat tool. The collection unit can also automatically acquire the user's task progress status by linking with a task management tool. The analysis unit analyzes the information collected by the collection unit to determine the user's condition, mood, and stress level. For example, the analysis unit can analyze the user's emotions from the message content using natural language processing technology. The analysis unit can also estimate the user's stress level from the task progress status. For example, if task progress is delayed, it can be determined that the user is feeling stressed. The reflection unit changes an icon based on the result determined by the analysis unit. For example, the reflection unit can display the icon in red if the user is feeling stressed. The reflection unit can also display the icon in green if the user is feeling energetic. For example, the reflection unit can change the color of an icon depending on the user's emotions. This allows the communication support system according to the embodiment to easily grasp the user's condition, mood, and stress level. Some or all of the above-described processing by the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can change the icon using an AI model that receives the results determined by the analysis unit as input and outputs changes to the icon.

[0030] The collection unit can collect the content of messages sent by the user or the progress of current tasks. The collection unit, for example, collects the content of messages sent by the user. For example, the collection unit collects the content of users' messages in real time using a bot built into a chat tool. The collection unit can also collect the progress of current tasks. For example, the collection unit works in conjunction with a task management tool to automatically acquire the user's task progress. This allows for more accurate analysis by collecting the user's message content and task progress. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's message content into AI and have the AI ​​analyze the message content.

[0031] The analysis unit can analyze the collected information, analyze the user's emotions, and estimate the stress level from the task progress. The analysis unit, for example, analyzes the collected information and analyzes the user's emotions. For example, the analysis unit can use natural language processing technology to analyze the user's emotions from the message content. The analysis unit can also estimate the stress level from the task progress. For example, if the task progress is delayed, it can be determined that the user is feeling stressed. This allows for accurate estimation of the user's emotions and stress level, enabling appropriate icon changes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform emotion analysis and stress level estimation.

[0032] The reflection unit can display the icon in green when the user is in good spirits. For example, the reflection unit can change the color of the icon to green based on the energy level determined by the analysis unit. This visually indicates that the user is in good spirits, thereby promoting active communication. Some or all of the above-described processing in the reflection unit can be performed using AI, for example, or can be performed without using AI. For example, the reflection unit can input the energy level determined by the analysis unit into AI and cause the AI ​​to change the color of the icon.

[0033] The collection unit can analyze the user's past message history and select the optimal collection method. The collection unit, for example, analyzes the user's past message history and selects the optimal collection method. For example, the collection unit can select collection targets based on keywords frequently used by the user in the past. The collection unit can also analyze the tone of the user's past messages and determine an appropriate collection method. For example, the collection unit can adjust the collection timing based on the time period in which the user sent past messages. In this way, a more appropriate collection method can be selected by analyzing the past message history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message history into AI and have the AI ​​select the optimal collection method.

[0034] The collection unit may filter the text information and task volume based on the user's current project and areas of interest when collecting the text information and task volume. For example, the collection unit may filter the text information and task volume based on the user's current project and areas of interest. For example, the collection unit may collect only information related to the project the user is currently working on. The collection unit may also preferentially collect highly relevant information based on the user's areas of interest. For example, the collection unit may filter and collect information related to the user's current task. This allows preferential collection of information related to the user's current project and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input information related to the user's project and areas of interest into AI and have the AI ​​perform the filtering.

[0035] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting text information or task volume. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting text information or task volume. For example, the collection unit can prioritize collecting information related to the user's current location. The collection unit can also collect region-specific information based on the user's geographical location. For example, the collection unit can collect highly relevant information by referring to the user's movement history. This allows highly relevant information to be collected preferentially based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0036] The collection unit can analyze the user's social media activities and collect related information when collecting text information and task volume. For example, the collection unit can analyze the user's social media activities and collect related information when collecting text information and task volume. For example, the collection unit can collect related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect appropriate information. For example, the collection unit can collect highly relevant information by referring to information on accounts the user follows. This makes it possible to collect highly relevant information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into AI and cause the AI ​​to collect related information.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the text information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the text information during analysis. For example, the analysis unit can perform a detailed analysis on text information with high importance. The analysis unit can also perform a simplified analysis on text information with low importance. For example, the analysis unit can adjust the depth of the analysis according to the importance of the text information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the text information to AI and have the AI ​​adjust the level of detail of the analysis.

[0038] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes progress to a project management task. The analysis unit can also apply an algorithm that emphasizes emotion analysis to a communication task. For example, the analysis unit can apply an analysis algorithm that evaluates creativity to a creative task. In this way, by applying an analysis algorithm depending on the task category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the task category into AI and have the AI ​​apply different analysis algorithms.

[0039] The analysis unit can determine the analysis priority based on the submission time of the text information during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text information during analysis. For example, the analysis unit can prioritize analysis of recently submitted text information. The analysis unit can also postpone analysis of text information that was submitted earlier. For example, the analysis unit can adjust the order of analysis based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the text information into AI and have the AI ​​determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the text information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the text information during analysis. For example, the analysis unit can prioritize analysis of highly relevant text information. The analysis unit can also postpone analysis of less relevant text information. For example, the analysis unit can adjust the order of analysis based on the relevance of the text information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the text information into AI and have the AI ​​adjust the order of analysis.

[0041] The reflection unit can analyze the user's past icon change history and select the optimal change method when reflecting the icon change. For example, the reflection unit can analyze the user's past icon change history and select the optimal change method when reflecting the icon change. For example, the reflection unit can select the optimal change method by referring to the icon change history when the user felt stressed in the past. The reflection unit can also select the optimal change method by referring to the icon change history when the user felt relaxed in the past. For example, the reflection unit can analyze the user's past icon change history and select the most effective change method. In this way, the optimal icon change method can be selected by analyzing the past icon change history. Some or all of the above-described processing in the reflection unit can be performed, for example, using AI, or can be performed without using AI. For example, the reflection unit can input the user's past icon change history into AI and have the AI ​​select the optimal change method.

[0042] The reflection unit can customize the icon change means based on the user's current living situation at the time of reflection. The reflection unit, for example, customizes the icon change means based on the user's current living situation at the time of reflection. For example, the reflection unit can provide a simple icon change when the user is busy. The reflection unit can also provide a detailed icon change when the user is relaxed. For example, the reflection unit can customize the optimal icon change means based on the user's living situation. This makes it possible to provide the optimal icon change means based on the user's living situation. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's living situation into AI and have the AI ​​customize the icon change means.

[0043] The reflection unit can select the optimal icon change method by taking into account the user's geographical location information at the time of reflection. For example, the reflection unit selects the optimal icon change method by taking into account the user's geographical location information at the time of reflection. For example, when the user is in the office, the reflection unit can provide an icon change that is appropriate for the office. Furthermore, when the user is at home, the reflection unit can provide an icon change that is appropriate for the home. For example, the reflection unit can select the optimal icon change method based on the user's geographical location information. This makes it possible to provide the optimal icon change method based on the user's geographical location information. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal icon change method.

[0044] The reflection unit can analyze the user's social media activity and suggest a means for changing the icon when reflecting the changes. For example, the reflection unit can analyze the user's social media activity and suggest a means for changing the icon when reflecting the changes. For example, the reflection unit can suggest a means for changing the icon based on information shared by the user on social media. The reflection unit can also analyze the user's social media activity history and suggest an appropriate means for changing the icon. For example, the reflection unit can suggest a means for changing the icon based on information about accounts the user follows. This makes it possible to provide an optimal means for changing the icon based on the user's social media activity. Some or all of the above-described processing in the reflection unit can be performed using, for example, AI, or can be performed without using AI. For example, the reflection unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of a means for changing the icon.

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

[0046] The communication support system may further include a behavior prediction unit that analyzes the user's past behavior history and predicts future behavior. The behavior prediction unit can predict future behavior patterns based on data such as the user's past message sending times and task completion times. For example, if a user has sent many messages during a specific time period in the past, it can predict that the user is likely to send many messages during that time period again. Also, if a user has taken a long time to complete a specific task in the past, it can predict that a similar task will also take a long time. This makes it possible to predict the user's behavior patterns and communicate at the appropriate time.

[0047] The communication support system can further include a device analysis unit that monitors and analyzes the user's device usage. The device analysis unit can estimate the user's condition by analyzing the type of device used by the user, the amount of time used, and the application usage status. For example, if a user uses a computer for a long period of time, it can be estimated that the user is feeling tired. Also, if a user frequently uses a specific application, it can be determined that the user is concentrating on tasks related to that application. Furthermore, the device analysis unit can collect this data in real time and immediately reflect the user's condition. This makes it possible to understand the user's condition using device usage status.

[0048] The communication support system may further include an environmental sound analysis unit that collects and analyzes the user's environmental sounds. The environmental sound analysis unit can estimate the user's environmental situation by collecting and analyzing the sounds around the user. For example, if the surroundings are noisy, it can be determined that the user is in an environment where it is difficult to concentrate. Also, if the environment is quiet, it can be estimated that the user is relaxed. Furthermore, the environmental sound analysis unit can collect this data in real time and instantly reflect the user's state. This makes it possible to understand the user's state using environmental sounds, enabling more appropriate communication.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The collection unit collects text information or task volume of the user. For example, the collection unit can collect the content of messages sent by the user and the progress status of current tasks. The collection unit collects the content of users' messages in real time using a bot built into the chat tool. The collection unit can also work with a task management tool to automatically obtain the user's task progress status. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the user's condition, mood, and stress level. For example, the analysis unit can use natural language processing technology to analyze the user's emotions from the message content. The analysis unit can also estimate the user's stress level from the progress of the task. For example, if the progress of the task is delayed, it can be determined that the user is feeling stressed. Step 3: The reflection unit changes the icon based on the result determined by the analysis unit. For example, the reflection unit can display the icon in red if the user is feeling stressed. The reflection unit can also display the icon in green if the user is feeling energetic. The reflection unit can change the color of the icon depending on the user's emotions. This makes it easy to understand the user's condition, mood, and stress level. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can change the icon using an AI model that inputs the result determined by the analysis unit and outputs a change in the icon.

[0051] (Example 2) A system according to an embodiment of the present invention is designed to facilitate online internal communication. This system uses AI to easily assess a user's condition, mood, and stress level, which are often difficult to convey in text during face-to-face communication. This assessment, however, is often overlooked in text, such as "That person doesn't seem well / in a bad mood, so let's avoid them for now" or "They seem fine today, so let's talk." This system uses AI to change icons to easily assess a user's condition, mood, and stress level. A bot integrated into a chat tool makes assessments based on text information and task volume, and reflects the assessment results in an icon. For example, a bot integrated into a chat tool collects information such as the content of messages sent by the user and the progress of current tasks. Next, AI analyzes the collected information to assess the user's condition, mood, and stress level. For example, the system analyzes the user's emotions based on the content of messages and estimates the user's stress level based on the progress of tasks. Finally, the user's icon changes based on the assessment results. For example, if the user is feeling stressed, the icon may change to red. This system facilitates online communication and makes it easy to understand a user's condition and mood. For example, if a user is feeling stressed, other members can be considerate of that user, preventing communication problems. Also, if a user is in good spirits, they can proactively communicate, revitalizing the team. This allows a system that facilitates online internal communication to easily grasp the user's condition, mood, and stress level.

[0052] A communication support system according to an embodiment includes a collection unit, an analysis unit, and a reflection unit. The collection unit collects text information or task volume of a user. For example, the collection unit can collect the content of messages sent by the user and the progress status of current tasks. The collection unit can collect the content of users' messages in real time, for example, using a bot integrated into a chat tool. The collection unit can also automatically acquire the user's task progress status by linking with a task management tool. The analysis unit analyzes the information collected by the collection unit to determine the user's condition, mood, and stress level. For example, the analysis unit can analyze the user's emotions from the message content using natural language processing technology. The analysis unit can also estimate the user's stress level from the task progress status. For example, if task progress is delayed, it can be determined that the user is feeling stressed. The reflection unit changes an icon based on the result determined by the analysis unit. For example, the reflection unit can display the icon in red if the user is feeling stressed. The reflection unit can also display the icon in green if the user is feeling energetic. For example, the reflection unit can change the color of an icon depending on the user's emotions. This allows the communication support system according to the embodiment to easily grasp the user's condition, mood, and stress level. Some or all of the above-described processing by the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can change the icon using an AI model that receives the results determined by the analysis unit as input and outputs changes to the icon.

[0053] The collection unit can collect the content of messages sent by the user or the progress of current tasks. The collection unit, for example, collects the content of messages sent by the user. For example, the collection unit collects the content of users' messages in real time using a bot built into a chat tool. The collection unit can also collect the progress of current tasks. For example, the collection unit works in conjunction with a task management tool to automatically acquire the user's task progress. This allows for more accurate analysis by collecting the user's message content and task progress. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's message content into AI and have the AI ​​analyze the message content.

[0054] The analysis unit can analyze the collected information, analyze the user's emotions, and estimate the stress level from the task progress. The analysis unit, for example, analyzes the collected information and analyzes the user's emotions. For example, the analysis unit can use natural language processing technology to analyze the user's emotions from the message content. The analysis unit can also estimate the stress level from the task progress. For example, if the task progress is delayed, it can be determined that the user is feeling stressed. This allows for accurate estimation of the user's emotions and stress level, enabling appropriate icon changes. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into AI and have the AI ​​perform emotion analysis and stress level estimation.

[0055] The reflection unit can display an icon in red when the user is feeling stressed. For example, the reflection unit can change the color of the icon to red based on the stress level determined by the analysis unit. This visually indicates that the user is feeling stressed, allowing other members to be considerate. Some or all of the above-mentioned processing in the reflection unit can be performed using AI, for example, or can be performed without using AI. For example, the reflection unit can input the stress level determined by the analysis unit into AI and cause the AI ​​to change the color of the icon.

[0056] The reflection unit can display the icon in green when the user is in good spirits. For example, the reflection unit can change the color of the icon to green based on the energy level determined by the analysis unit. This visually indicates that the user is in good spirits, thereby promoting active communication. Some or all of the above-described processing in the reflection unit can be performed using AI, for example, or can be performed without using AI. For example, the reflection unit can input the energy level determined by the analysis unit into AI and cause the AI ​​to change the color of the icon.

[0057] The collection unit can estimate the user's emotions and adjust the timing of collecting text information and task volume based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting text information and task volume based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection frequency to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the collection frequency to acquire more detailed data. For example, if the user is in a hurry, the collection unit can adjust the collection timing and prioritize collecting only important information. This allows the user's burden to be reduced and more detailed data to be acquired by adjusting the collection timing according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's emotions into AI and have the AI ​​adjust the collection timing.

[0058] The collection unit can analyze the user's past message history and select the optimal collection method. The collection unit, for example, analyzes the user's past message history and selects the optimal collection method. For example, the collection unit can select collection targets based on keywords frequently used by the user in the past. The collection unit can also analyze the tone of the user's past messages and determine an appropriate collection method. For example, the collection unit can adjust the collection timing based on the time period in which the user sent past messages. In this way, a more appropriate collection method can be selected by analyzing the past message history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past message history into AI and have the AI ​​select the optimal collection method.

[0059] The collection unit may filter the text information and task volume based on the user's current project and areas of interest when collecting the text information and task volume. For example, the collection unit may filter the text information and task volume based on the user's current project and areas of interest. For example, the collection unit may collect only information related to the project the user is currently working on. The collection unit may also preferentially collect highly relevant information based on the user's areas of interest. For example, the collection unit may filter and collect information related to the user's current task. This allows preferential collection of information related to the user's current project and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input information related to the user's project and areas of interest into AI and have the AI ​​perform the filtering.

[0060] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can postpone collection of less important information. The collection unit can also prioritize collecting detailed information when the user is relaxed. For example, when the user is in a hurry, the collection unit can prioritize collecting only important information. In this way, important information can be prioritized by determining the priority of information according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's emotions into AI and have the AI ​​determine the priority of information.

[0061] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting text information or task volume. For example, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting text information or task volume. For example, the collection unit can prioritize collecting information related to the user's current location. The collection unit can also collect region-specific information based on the user's geographical location. For example, the collection unit can collect highly relevant information by referring to the user's movement history. This allows highly relevant information to be collected preferentially based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI and cause the AI ​​to collect highly relevant information.

[0062] The collection unit can analyze the user's social media activities and collect related information when collecting text information and task volume. For example, the collection unit can analyze the user's social media activities and collect related information when collecting text information and task volume. For example, the collection unit can collect related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect appropriate information. For example, the collection unit can collect highly relevant information by referring to information on accounts the user follows. This makes it possible to collect highly relevant information based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activities into AI and cause the AI ​​to collect related information.

[0063] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can present the analysis results using a simple presentation method. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can present a concise analysis result that focuses on the main points. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's emotions into AI and have the AI ​​adjust the way the analysis is presented.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the text information during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the text information during analysis. For example, the analysis unit can perform a detailed analysis on text information with high importance. The analysis unit can also perform a simplified analysis on text information with low importance. For example, the analysis unit can adjust the depth of the analysis according to the importance of the text information. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the text information to AI and have the AI ​​adjust the level of detail of the analysis.

[0065] The analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the task category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes progress to a project management task. The analysis unit can also apply an algorithm that emphasizes emotion analysis to a communication task. For example, the analysis unit can apply an analysis algorithm that evaluates creativity to a creative task. In this way, by applying an analysis algorithm depending on the task category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the task category into AI and have the AI ​​apply different analysis algorithms.

[0066] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a short and to-the-point analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit can provide a concise analysis result when the user is in a hurry. This allows for adjusting the length of the analysis according to the user's emotions to provide a more appropriate analysis result. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotions into AI and have the AI ​​adjust the length of the analysis.

[0067] The analysis unit can determine the analysis priority based on the submission time of the text information during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text information during analysis. For example, the analysis unit can prioritize analysis of recently submitted text information. The analysis unit can also postpone analysis of text information that was submitted earlier. For example, the analysis unit can adjust the order of analysis based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time of the text information into AI and have the AI ​​determine the analysis priority.

[0068] The analysis unit can adjust the order of analysis based on the relevance of the text information during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the text information during analysis. For example, the analysis unit can prioritize analysis of highly relevant text information. The analysis unit can also postpone analysis of less relevant text information. For example, the analysis unit can adjust the order of analysis based on the relevance of the text information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the text information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the text information into AI and have the AI ​​adjust the order of analysis.

[0069] The reflection unit can estimate the user's emotions and adjust the icon change method based on the estimated user's emotions. The reflection unit, for example, estimates the user's emotions and adjusts the icon change method based on the estimated user's emotions. For example, the reflection unit can display the icon in red if the user is stressed. The reflection unit can also display the icon in green if the user is relaxed. For example, the reflection unit can display the icon in yellow if the user is in a hurry. This allows for more appropriate icon change by adjusting the icon change method according to the user's emotions. Some or all of the above-described processing in the reflection unit may be performed using AI, for example, or may be performed without using AI. For example, the reflection unit can input the user's emotions into AI and have the AI ​​adjust the icon change method.

[0070] The reflection unit can analyze the user's past icon change history and select the optimal change method when reflecting the icon change. For example, the reflection unit can analyze the user's past icon change history and select the optimal change method when reflecting the icon change. For example, the reflection unit can select the optimal change method by referring to the icon change history when the user felt stressed in the past. The reflection unit can also select the optimal change method by referring to the icon change history when the user felt relaxed in the past. For example, the reflection unit can analyze the user's past icon change history and select the most effective change method. In this way, the optimal icon change method can be selected by analyzing the past icon change history. Some or all of the above-described processing in the reflection unit can be performed, for example, using AI, or can be performed without using AI. For example, the reflection unit can input the user's past icon change history into AI and have the AI ​​select the optimal change method.

[0071] The reflection unit can customize the icon change means based on the user's current living situation at the time of reflection. The reflection unit, for example, customizes the icon change means based on the user's current living situation at the time of reflection. For example, the reflection unit can provide a simple icon change when the user is busy. The reflection unit can also provide a detailed icon change when the user is relaxed. For example, the reflection unit can customize the optimal icon change means based on the user's living situation. This makes it possible to provide the optimal icon change means based on the user's living situation. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's living situation into AI and have the AI ​​customize the icon change means.

[0072] The reflection unit can estimate the user's emotions and determine the priority of icon changes based on the estimated user emotions. The reflection unit, for example, estimates the user's emotions and determines the priority of icon changes based on the estimated user emotions. For example, the reflection unit can prioritize icon changes when the user is feeling stressed. The reflection unit can also postpone icon changes when the user is relaxed. For example, the reflection unit can determine the priority of icon changes based on the user's emotions. This enables more appropriate icon changes by determining the priority of icon changes according to the user's emotions. Some or all of the above-described processing in the reflection unit may be performed using, or without, AI. For example, the reflection unit can input the user's emotions into AI and have the AI ​​determine the priority of icon changes.

[0073] The reflection unit can select the optimal icon change method by taking into account the user's geographical location information at the time of reflection. For example, the reflection unit selects the optimal icon change method by taking into account the user's geographical location information at the time of reflection. For example, when the user is in the office, the reflection unit can provide an icon change that is appropriate for the office. Furthermore, when the user is at home, the reflection unit can provide an icon change that is appropriate for the home. For example, the reflection unit can select the optimal icon change method based on the user's geographical location information. This makes it possible to provide the optimal icon change method based on the user's geographical location information. Some or all of the above-described processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can input the user's geographical location information into AI and cause the AI ​​to select the optimal icon change method.

[0074] The reflection unit can analyze the user's social media activity and suggest a means for changing the icon when reflecting the changes. For example, the reflection unit can analyze the user's social media activity and suggest a means for changing the icon when reflecting the changes. For example, the reflection unit can suggest a means for changing the icon based on information shared by the user on social media. The reflection unit can also analyze the user's social media activity history and suggest an appropriate means for changing the icon. For example, the reflection unit can suggest a means for changing the icon based on information about accounts the user follows. This makes it possible to provide an optimal means for changing the icon based on the user's social media activity. Some or all of the above-described processing in the reflection unit can be performed using, for example, AI, or can be performed without using AI. For example, the reflection unit can input the user's social media activity into AI and have the AI ​​execute the suggestion of a means for changing the icon. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and reflection 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 the user's message content and task progress status in real time using a bot incorporated in a chat tool of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the user's condition, mood, and stress level. The reflection unit is realized, for example, by the control unit 46A of the smart device 14, and can change an icon based on the results determined by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and reflection 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 the user's message content and task progress status in real time using a bot incorporated into the chat tool of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the user's condition, mood, and stress level. The reflection unit is realized, for example, by the control unit 46A of the smart glasses 214, and can change an icon based on the results determined by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and reflection 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 the user's message content and task progress status in real time using a bot incorporated into a chat tool of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the user's condition, mood, and stress level. The reflection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and can change an icon based on the results determined by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and reflection 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 the user's message content and task progress status in real time using a bot incorporated into the chat tool of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to determine the user's condition, mood, and stress level. The reflection unit is realized, for example, by the control unit 46A of the robot 414, and can change an icon based on the results determined by the analysis unit.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The communication support system can further include a voice analysis unit that collects and analyzes the user's voice data. The voice analysis unit can analyze the tone, speed, volume, etc. of the user's voice to estimate emotions and stress levels. For example, if the user speaks quickly and in a high-pitched voice, it can be estimated that the user is feeling stressed. On the other hand, if the user speaks slowly and in a low-pitched voice, it can be determined that the user is relaxed. Furthermore, the voice analysis unit can monitor changes in the user's voice in real time and immediately reflect changes in emotions. This enables emotion analysis using voice data, allowing for a more accurate understanding of the user's state.

[0077] The communication support system may further include a biometric information analysis unit that collects and analyzes the user's biometric information. The biometric information analysis unit monitors the user's heart rate, electrodermal activity, body temperature, and the like, and can estimate the user's stress level and emotional state. For example, if the heart rate is elevated, it can be determined that the user is nervous. Also, if the electrodermal activity is high, it can be determined that the user is feeling stressed. Furthermore, the biometric information analysis unit can collect this data in real time and instantly reflect the user's state. This enables emotion analysis using biometric information, enabling a more accurate understanding of the user's state.

[0078] The communication support system may further include a behavior prediction unit that analyzes the user's past behavior history and predicts future behavior. The behavior prediction unit can predict future behavior patterns based on data such as the user's past message sending times and task completion times. For example, if a user has sent many messages during a specific time period in the past, it can predict that the user is likely to send many messages during that time period again. Also, if a user has taken a long time to complete a specific task in the past, it can predict that a similar task will also take a long time. This makes it possible to predict the user's behavior patterns and communicate at the appropriate time.

[0079] The communication support system can further include a device analysis unit that monitors and analyzes the user's device usage. The device analysis unit can estimate the user's condition by analyzing the type of device used by the user, the amount of time used, and the application usage status. For example, if a user uses a computer for a long period of time, it can be estimated that the user is feeling tired. Also, if a user frequently uses a specific application, it can be determined that the user is concentrating on tasks related to that application. Furthermore, the device analysis unit can collect this data in real time and immediately reflect the user's condition. This makes it possible to understand the user's condition using device usage status.

[0080] The communication support system may further include an environmental sound analysis unit that collects and analyzes the user's environmental sounds. The environmental sound analysis unit can estimate the user's environmental situation by collecting and analyzing the sounds around the user. For example, if the surroundings are noisy, it can be determined that the user is in an environment where it is difficult to concentrate. Also, if the environment is quiet, it can be estimated that the user is relaxed. Furthermore, the environmental sound analysis unit can collect this data in real time and instantly reflect the user's state. This makes it possible to understand the user's state using environmental sounds, enabling more appropriate communication.

[0081] The communication support system may further include a message generation unit that estimates the user's emotions and automatically generates an appropriate message based on the estimated emotions. The message generation unit can generate a message with an appropriate tone and content according to the user's emotional state. For example, if the user is feeling stressed, a message showing encouragement or support can be generated. Alternatively, if the user is relaxed, a message with light conversation or relaxed content can be generated. Furthermore, the message generation unit can adjust the content of the message in real time according to changes in the user's emotions. This enables appropriate communication according to the user's emotions.

[0082] The communication support system may further include a task allocation unit that estimates the user's emotions and assigns appropriate tasks based on the estimated emotions. The task allocation unit can assign appropriate tasks according to the user's emotional state. For example, if the user is feeling stressed, it can assign a less burdensome task. On the other hand, if the user is relaxed, it can assign a more difficult task. Furthermore, the task allocation unit can adjust the task allocation in real time according to changes in the user's emotions. This makes it possible to assign appropriate tasks according to the user's emotions.

[0083] The communication support system may further include a break suggestion unit that estimates the user's emotions and suggests appropriate break timing based on the estimated emotions. The break suggestion unit can suggest appropriate break timing according to the user's emotional state. For example, if the user is feeling stressed, it can suggest an earlier break. Also, if the user is relaxed, it can suggest a normal break timing. Furthermore, the break suggestion unit can adjust the break timing in real time according to changes in the user's emotions. This makes it possible to suggest appropriate break timing according to the user's emotions.

[0084] The communication support system may further include a feedback providing unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. The feedback providing unit can provide appropriate feedback according to the user's emotional state. For example, if the user is feeling stressed, positive feedback can be provided. Alternatively, if the user is relaxed, detailed feedback can be provided. Furthermore, the feedback providing unit can adjust the content of the feedback in real time according to changes in the user's emotions. This makes it possible to provide appropriate feedback according to the user's emotions.

[0085] The communication support system may further include a learning content providing unit that estimates the user's emotions and provides appropriate learning content based on the estimated emotions. The learning content providing unit can provide appropriate learning content according to the user's emotional state. For example, if the user is feeling stressed, learning content that helps the user relax can be provided. Also, if the user is relaxed, learning content that helps the user improve concentration can be provided. Furthermore, the learning content providing unit can adjust the learning content in real time according to changes in the user's emotions. This makes it possible to provide appropriate learning content according to the user's emotions.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The collection unit collects text information or task volume of the user. For example, the collection unit can collect the content of messages sent by the user and the progress status of current tasks. The collection unit collects the content of users' messages in real time using a bot built into the chat tool. The collection unit can also work with a task management tool to automatically obtain the user's task progress status. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the user's condition, mood, and stress level. For example, the analysis unit can use natural language processing technology to analyze the user's emotions from the message content. The analysis unit can also estimate the user's stress level from the progress of the task. For example, if the progress of the task is delayed, it can be determined that the user is feeling stressed. Step 3: The reflection unit changes the icon based on the result determined by the analysis unit. For example, the reflection unit can display the icon in red if the user is feeling stressed. The reflection unit can also display the icon in green if the user is feeling energetic. The reflection unit can change the color of the icon depending on the user's emotions. This makes it easy to understand the user's condition, mood, and stress level. Some or all of the above-mentioned processing in the reflection unit may be performed using, for example, AI, or may be performed without using AI. For example, the reflection unit can change the icon using an AI model that inputs the result determined by the analysis unit and outputs a change in the icon.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0090] 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.

[0091] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[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] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] [Explanation of symbols]

[0160] 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 collects user text information or task volume, An analysis unit analyzes the information collected by the aforementioned collection unit to determine the user's condition or mood and stress level, The system includes a reflection unit that changes the icon based on the results determined by the analysis unit. A system characterized by:

2. The collecting unit Collect the content of messages sent by the user or the progress of the current task. The system of claim 1 .

3. The analysis unit The collected information is analyzed to understand the user's emotions and estimate their stress level based on task progress. The system of claim 1 .

4. The reflection unit The icon turns red when the user is feeling stressed. The system of claim 1 .

5. The reflection unit The icon will turn green if the user is feeling well. The system of claim 1 .

6. The collecting unit It estimates the user's emotions and adjusts the timing of text information and task volume collection based on the estimated user emotions. The system of claim 1 .

7. The collecting unit Analyze the user's past message history and select the optimal collection method. The system of claim 1 .

8. The collecting unit When collecting text information or task volume, filter it based on the user's current project or area of ​​interest. The system of claim 1 .

Images