system

Abstract

An object of the system according to the embodiment is to provide appropriate support to young people and students at an early stage.SOLUTION: A system includes an analysis part, a support part, a notification part, and a data cooperation part. The analysis unit analyzes an utterance or a search history of a user. The support unit provides active support based on the content detected by the analysis unit. The notification unit transmits an SOS notification to a counselor or an expert when it is difficult for the support unit to deal with the problem. The data cooperation unit acquires necessary data in cooperation with another data source.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide appropriate support early on in preventing suicide among young people and students.

[0005] The system according to the embodiment aims to provide early and appropriate support to young people and students. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a support unit, a notification unit, and a data linkage unit. The analysis unit analyzes the user's comments or search history. The support unit provides active support based on the content detected by the analysis unit. The notification unit sends an SOS notification to a counselor or expert when the support unit is unable to handle the issue. The data linkage unit links with other data sources to obtain necessary data. [Effects of the Invention]

[0007] The system according to the embodiment can provide early and appropriate support to young people and students. [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) In an embodiment of the present invention, a support system analyzes a user's statements and search history, and a generation AI provides active support. If the situation is difficult to resolve, the system sends an SOS notification to a counselor or expert. When a user seeks advice, the generation AI counselor analyzes the content and provides appropriate active support. For example, if a user searches for or speaks a specific word, the generation AI detects the content and provides immediate support. Furthermore, if the situation is difficult to resolve, an SOS notification is sent to a counselor or expert, providing backup support. For example, if a user speaks specific words such as "painful" or "I want to die," the generation AI detects the content and provides immediate support. The generation AI understands the user's emotions and situation and provides appropriate advice and support. The generation AI counselor then provides active support based on the user's search history and statements. For example, if a user searches for a dangerous word such as "suicide method," the generation AI detects the content and provides immediate support. The generation AI then takes appropriate action, such as sending a message encouraging the user to seek professional help. Furthermore, if the situation is difficult to resolve, the system also has a function to send an SOS notification to a counselor or expert. For example, if the generated AI analyzes the user's situation and determines that it is urgent, it will send an SOS notification to a counselor or expert to provide live support. This allows the support system to address the serious social problem of the increasing number of suicides among young people and students and save the lives of those in need of support. This provides a backup system to protect the user's life. For example, if a user is feeling distressed, the generated AI counselor will respond immediately and provide appropriate support to ease the user's feelings. Furthermore, if the situation is urgent, live support from a counselor or expert can be provided to save the user's life.

[0029] The support system according to the embodiment includes an analysis unit, a support unit, a notification unit, and a data linking unit. The analysis unit analyzes a user's statements or search history. Examples of the user's statements or search history include, but are not limited to, text messages, voice input, and web search history. For example, if a user utters specific words such as "painful" or "I want to die," the analysis unit detects the content of the utterance. The analysis unit can also analyze the user's search history to detect dangerous words. For example, if a user searches for dangerous words such as "how to commit suicide," the analysis unit detects the content of the search. The support unit provides active support based on the content detected by the analysis unit. Examples of active support include, but are not limited to, real-time chat support and the provision of guidelines. For example, if a user utters "painful," the support unit generates a generation AI to provide support in kind words. If a user searches for "I want to die," the support unit can send a message encouraging the user to seek professional advice. If a user utters "help," the support unit can provide support in reassuring words. The notification unit sends an SOS notification to a counselor or expert when the support unit is unable to handle the situation. For example, if the generation AI analyzes the user's situation and determines that the situation is urgent, the notification unit sends an SOS notification to a counselor or expert. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on the estimated user emotions. For example, if the user is sad, the generation AI detects the emotion and sends an SOS notification in gentle words. The notification unit can also adjust the level of detail in the notification based on the user's level of urgency. For example, if the user's level of urgency is high, the generation AI sends a detailed SOS notification. The data linkage unit links with other data sources to obtain necessary data. The data linkage unit links with data sources such as social media and medical databases. The data linkage unit can also estimate the user's emotions and adjust the method of data linkage based on the estimated user emotions. For example, if the user is sad, the generation AI detects the emotion and carefully links data.As a result, the support system according to the embodiment has a backup system in place to protect the user's life. For example, if the user is feeling distressed, the AI ​​counselor can immediately respond and provide appropriate support to ease the user's feelings. In addition, in cases of high urgency, the user's life can be protected by providing human support from a counselor or expert.

[0030] The analysis unit can analyze the user's past comments and search history to improve the detection accuracy of specific words. The analysis unit can, for example, cause the generation AI to improve its detection accuracy based on specific words frequently used by the user in the past. The analysis unit can also analyze the user's past comments and search history to cause the generation AI to optimize its detection algorithm for specific words. The analysis unit can also cause the generation AI to improve its detection accuracy for specific words based on the user's past behavioral patterns. Thus, by analyzing the past comments and search history, the detection accuracy for specific words is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past comments and search history into the generation AI and cause the generation AI to improve its detection accuracy for specific words.

[0031] When analyzing comments and search history, the analysis unit can perform analysis based on the user's living environment and background information. For example, if the user has problems in their home environment, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. Furthermore, if the user is experiencing stress at school, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. Furthermore, if the user has problems at work, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. This enables more appropriate analysis by taking the user's living environment and background information into consideration. 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 living environment and background information into the generation AI and have the generation AI perform the analysis.

[0032] When analyzing comments and search history, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can prioritize analysis of information related to that area by taking into account the geographical location information. Furthermore, when the user is traveling, the analysis unit can prioritize analysis of information related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the analysis unit can prioritize analysis of information around the home by taking into account the user's geographical location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to analyze highly relevant information.

[0033] The analysis unit can analyze the user's social media activity and analyze related information when analyzing comments and search history. For example, the analysis unit causes the generation AI to analyze related information based on content posted by the user on social media. The analysis unit can also analyze the user's social media activity history and cause the generation AI to analyze related information. The analysis unit can also cause the generation AI to analyze related information by referring to the activities of the user's friends on social media. This allows the analysis of social media activity to more appropriately analyze related information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's social media activity into the generation AI and cause the generation AI to analyze related information.

[0034] When analyzing comments and search history, the analysis unit can customize the analysis method by reflecting the user's past feedback. In the analysis unit, for example, the generation AI customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also analyze the user's past feedback and cause the generation AI to optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. In this way, by reflecting past feedback, the analysis method can be customized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback into the generation AI and cause the generation AI to customize the analysis method.

[0035] When providing support, the support unit can apply different support algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the support unit causes the generation AI to apply a support algorithm according to that category. Furthermore, if the user searches for "I want to die," the support unit can also cause the generation AI to apply a support algorithm according to that category. Furthermore, if the user says "Help me," the support unit can also cause the generation AI to apply a support algorithm according to that category. This allows for more appropriate support to be provided by applying a support algorithm according to the category of the comments or search history. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can input the user's comments or search history into the generation AI and have the generation AI apply a support algorithm.

[0036] When providing support, the support unit can improve the accuracy of the support by referring to the user's past support history. In the support unit, for example, the generation AI improves the accuracy of the support based on the user's past support history. The support unit can also analyze the user's past support history, and the generation AI can suggest the optimal support method. The support unit can also customize the content of the support by referring to the user's past support history. In this way, the accuracy of the support can be improved by referring to the past support history. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past support history into the generation AI and cause the generation AI to improve the accuracy of the support.

[0037] When providing support, the support unit can determine the priority of support based on the time when the user's comments and search history were submitted. For example, the support unit may have the generation AI determine the priority of support based on the content of recent comments made by the user. The support unit may also have the generation AI determine the priority of support based on the content of past comments made by the user. The support unit may also have the generation AI determine the priority of support based on the content of comments made by the user during a specific time period. In this way, more appropriate support can be provided by determining the priority of support based on the time when the comments and search history were submitted. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input the time when the user's comments and search history were submitted into the generation AI and have the generation AI determine the priority of support.

[0038] When providing support, the support unit can adjust the order of support based on the relevance of the user. For example, if the user makes a highly relevant statement, the support unit causes the generation AI to prioritize support for that content. Also, if the user makes a less relevant statement, the support unit can cause the generation AI to postpone that content. The support unit can also analyze the relevance of the user's statement content, and the generation AI can determine the optimal support order. In this way, more appropriate support can be provided by adjusting the support order based on relevance. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the relevance of the user's statement content into the generation AI and cause the generation AI to adjust the support order.

[0039] When providing support, the support unit can adjust the use of technical terminology in the support according to the user's level of expertise. For example, if the user has technical expertise, the support unit can have the generation AI provide support using technical terminology. Furthermore, if the user does not have technical expertise, the support unit can have the generation AI provide support in simple language. Furthermore, the support unit can analyze the user's level of expertise and have the generation AI provide support using the most appropriate language. This allows for more appropriate support to be provided by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the support unit can be performed, for example, using AI, or can be performed without using AI. For example, the support unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0040] When sending an SOS notification, the notification unit can adjust the level of detail of the notification based on the user's level of urgency. For example, if the user's level of urgency is high, the generation AI can send a detailed SOS notification. Furthermore, if the user's level of urgency is medium, the notification unit can also send an SOS notification with moderate level of detail. Furthermore, if the user's level of urgency is low, the generation AI can also send a concise SOS notification. This allows for more appropriate notification by adjusting the level of detail of the notification based on the user's level of urgency. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's level of urgency to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0041] When sending an SOS notification, the notification unit can apply different notification algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the generation AI can apply a notification algorithm depending on that category. Furthermore, if the user searches for "I want to die," the notification unit can also apply a notification algorithm depending on that category. Furthermore, if the user says "Help me," the notification unit can also apply a notification algorithm depending on that category. This allows for more appropriate notifications by applying a notification algorithm depending on the category of the comments or search history. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's comments or search history into the generation AI and have the generation AI apply a notification algorithm.

[0042] When sending an SOS notification, the notification unit can prioritize sending highly relevant notifications by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can consider the geographical location information and prioritize sending notifications related to that area. Furthermore, when the user is traveling, the notification unit can also prioritize sending notifications related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the generation AI can also prioritize sending notifications around the home by taking into account the geographical location information. In this way, by taking into account the geographical location information, highly relevant notifications can be sent preferentially. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information into the generation AI and cause the generation AI to send highly relevant notifications.

[0043] When sending an SOS notification, the notification unit can analyze the user's social media activity and send a related notification. For example, the notification unit can have the generation AI send a related notification based on what the user has said on social media. The notification unit can also analyze the user's social media activity history and have the generation AI send a related notification. The notification unit can also have the generation AI send a related notification based on the activity of the user's friends on social media. In this way, related notifications can be sent by analyzing social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's social media activity into the generation AI and have the generation AI send a related notification.

[0044] When linking data, the data linking unit can apply different data linking algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the generation AI can apply a data linking algorithm according to that category. Furthermore, if the user searches for "I want to die," the data linking unit can also apply a data linking algorithm according to that category. Furthermore, if the user says "Help me," the generation AI can also apply a data linking algorithm according to that category. This enables more appropriate data linking by applying a data linking algorithm according to the category of the comments or search history. Some or all of the above-described processing in the data linking unit may be performed using, or without, AI. For example, the data linking unit can input the user's comments or search history into the generation AI and have the generation AI apply a data linking algorithm.

[0045] When linking data, the data linking unit can improve the accuracy of the linking by referring to the user's past data linking history. In the data linking unit, for example, the generation AI improves the accuracy of the linking based on data that the user has linked in the past. The data linking unit can also analyze the user's past data linking history, and the generation AI can propose an optimal data linking method. The data linking unit can also customize the content of the linking by referring to the user's past data linking history. In this way, the accuracy of the linking can be improved by referring to the past data linking history. Some or all of the above-mentioned processing in the data linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the data linking unit can input the user's past data linking history into the generation AI and cause the generation AI to improve the accuracy of the linking.

[0046] During data integration, the data integration unit can perform data integration based on a schedule by referring to the user's calendar information. For example, the data integration unit references the schedule registered in the user's calendar, and the generation AI performs data integration. The data integration unit can also integrate data related to a specific event from the user's calendar information. The data integration unit can also perform optimal data integration based on the schedule based on the user's calendar information. This makes it possible to perform data integration based on a schedule by referring to the calendar information. Some or all of the above-described processing in the data integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the data integration unit can input the user's calendar information into the generation AI and cause the generation AI to perform data integration based on the schedule.

[0047] When linking data, the data linking unit can prioritize linking highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can prioritize linking data related to that area by taking into account the geographical location information. Furthermore, when the user is traveling, the data linking unit can also prioritize linking data related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the generation AI can also prioritize linking data around the home by taking into account the geographical location information. In this way, by taking into account the geographical location information, highly relevant data can be prioritized. Some or all of the above-described processing in the data linking unit may be performed using AI, for example, or may be performed without using AI. For example, the data linking unit can input the user's geographical location information to the generation AI and cause the generation AI to link highly relevant data.

[0048] When linking data, the data linking unit can analyze the user's social media activity and link related data. For example, the data linking unit allows the generation AI to link related data based on content posted by the user on social media. The data linking unit can also analyze the user's social media activity history and allow the generation AI to link related data. The data linking unit can also allow the generation AI to link related data based on the activities of the user's friends on social media. In this way, related data can be linked by analyzing social media activity. Some or all of the above-mentioned processing in the data linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the data linking unit can input the user's social media activity into the generation AI and cause the generation AI to link related data.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] In addition to the user's comments and search history, the analysis unit can also analyze the user's biometric data (e.g., heart rate, electrodermal activity, etc.). This allows the system to grasp the user's stress level and state of tension in real time and provide more appropriate support. For example, if the user's heart rate suddenly rises, the generation AI can detect the change and provide advice on how to relax. Also, if the user's electrodermal activity increases, the generation AI can provide guidelines for reducing stress. Furthermore, it is possible to monitor the user's biometric data over the long term and predict changes in their health condition.

[0051] The analysis unit can infer a user's interests and concerns based on the user's comments and search history and provide related information. For example, if a user frequently searches for information about a particular hobby, the generation AI can provide the latest information and events related to that hobby. Also, if a user shows interest in a particular topic, the generation AI can provide in-depth information on that topic. Furthermore, it can suggest personalized content based on the user's interests and concerns. This makes it possible to provide information tailored to the user's interests and concerns, improving the user experience.

[0052] The analysis unit can analyze the user's social network in addition to the user's living environment and background information. For example, it can analyze the comments and actions of friends and family with whom the user frequently interacts to estimate the user's psychological state and behavioral patterns. It can also analyze the user's social media activity to determine the communities to which the user belongs. Furthermore, it can provide support appropriate to the user based on the user's social network. This makes it possible to perform analysis that takes the user's social background into account, allowing for more appropriate support to be provided.

[0053] The analysis unit can perform analysis that takes into account the culture and customs specific to the region based on the user's geographical location information. For example, if the user is in a specific region, the analysis results are provided based on the culture and customs of that region. Also, if the user is traveling, the analysis results can be provided that take into account the culture and customs of the destination. Furthermore, if the user is in a different cultural region, it is also possible to provide support appropriate for that culture. This makes it possible to perform analysis that takes into account culture and customs based on geographical location information, and to provide more appropriate support.

[0054] The analysis unit can analyze a user's online shopping history in addition to their social media activity. For example, the generation AI can suggest related products and services based on products and services the user has previously purchased. It can also analyze the user's shopping history to understand their purchasing trends. It can also provide personalized shopping advice based on the user's purchasing history. This enables analysis that takes into account the user's online shopping history, allowing for more appropriate support.

[0055] The analysis unit can analyze the user's health data in addition to the user's past feedback. For example, the generation AI can customize the analysis method based on the health data the user has provided in the past. The generation AI can also analyze the user's health data and predict changes in health status. It can also provide health management advice based on the user's health data. This allows the analysis method to be customized and accuracy improved by reflecting past feedback and health data.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The analysis unit analyzes the user's statements or search history. The user's statements or search history includes text messages, voice input, web search history, etc. For example, if the user utters specific words such as "painful" or "I want to die," the analysis unit detects the content of those words. Also, if the user searches for dangerous words such as "suicide methods," the analysis unit detects the content of those words. Step 2: The support unit provides active support based on the content detected by the analysis unit. Active support includes real-time chat support and the provision of guidelines. For example, if a user says "I'm suffering," the generation AI will provide support in kind words. Also, if a user searches for "I want to die," the generation AI can send a message encouraging them to seek professional advice. Step 3: The notification unit sends an SOS notification to a counselor or expert if the support unit is unable to handle the situation. For example, if the generation AI analyzes the user's situation and determines that it is urgent, it will send an SOS notification to a counselor or expert. It can also estimate the user's emotions and adjust the method of sending SOS notifications based on the estimated emotions. Step 4: The data linking unit connects with other data sources to obtain the necessary data. For example, it connects with data sources such as social media and medical databases. It can also estimate the user's emotions and adjust the data linking method based on the estimated emotions.

[0058] (Example 2) In an embodiment of the present invention, a support system analyzes a user's statements and search history, and a generation AI provides active support. If the situation is difficult to resolve, the system sends an SOS notification to a counselor or expert. When a user seeks advice, the generation AI counselor analyzes the content and provides appropriate active support. For example, if a user searches for or speaks a specific word, the generation AI detects the content and provides immediate support. Furthermore, if the situation is difficult to resolve, an SOS notification is sent to a counselor or expert, providing backup support. For example, if a user speaks specific words such as "painful" or "I want to die," the generation AI detects the content and provides immediate support. The generation AI understands the user's emotions and situation and provides appropriate advice and support. The generation AI counselor then provides active support based on the user's search history and statements. For example, if a user searches for a dangerous word such as "suicide method," the generation AI detects the content and provides immediate support. The generation AI then takes appropriate action, such as sending a message encouraging the user to seek professional help. Furthermore, if the situation is difficult to resolve, the system also has a function to send an SOS notification to a counselor or expert. For example, if the generated AI analyzes the user's situation and determines that it is urgent, it will send an SOS notification to a counselor or expert to provide live support. This allows the support system to address the serious social problem of the increasing number of suicides among young people and students and save the lives of those in need of support. This provides a backup system to protect the user's life. For example, if a user is feeling distressed, the generated AI counselor will respond immediately and provide appropriate support to ease the user's feelings. Furthermore, if the situation is urgent, live support from a counselor or expert can be provided to save the user's life.

[0059] The support system according to the embodiment includes an analysis unit, a support unit, a notification unit, and a data linking unit. The analysis unit analyzes a user's statements or search history. Examples of the user's statements or search history include, but are not limited to, text messages, voice input, and web search history. For example, if a user utters specific words such as "painful" or "I want to die," the analysis unit detects the content of the utterance. The analysis unit can also analyze the user's search history to detect dangerous words. For example, if a user searches for dangerous words such as "how to commit suicide," the analysis unit detects the content of the search. The support unit provides active support based on the content detected by the analysis unit. Examples of active support include, but are not limited to, real-time chat support and the provision of guidelines. For example, if a user utters "painful," the support unit generates a generation AI to provide support in kind words. If a user searches for "I want to die," the support unit can send a message encouraging the user to seek professional advice. If a user utters "help," the support unit can provide support in reassuring words. The notification unit sends an SOS notification to a counselor or expert when the support unit is unable to handle the situation. For example, if the generation AI analyzes the user's situation and determines that the situation is urgent, the notification unit sends an SOS notification to a counselor or expert. The notification unit can also estimate the user's emotions and adjust the method of sending the SOS notification based on the estimated user emotions. For example, if the user is sad, the generation AI detects the emotion and sends an SOS notification in gentle words. The notification unit can also adjust the level of detail in the notification based on the user's level of urgency. For example, if the user's level of urgency is high, the generation AI sends a detailed SOS notification. The data linkage unit links with other data sources to obtain necessary data. The data linkage unit links with data sources such as social media and medical databases. The data linkage unit can also estimate the user's emotions and adjust the method of data linkage based on the estimated user emotions. For example, if the user is sad, the generation AI detects the emotion and carefully links data.As a result, the support system according to the embodiment has a backup system in place to protect the user's life. For example, if the user is feeling distressed, the AI ​​counselor can immediately respond and provide appropriate support to ease the user's feelings. In addition, in cases of high urgency, the user's life can be protected by providing human support from a counselor or expert.

[0060] The analysis unit can estimate the user's emotions and adjust the analysis method of the user's comments and search history based on the estimated user emotions. For example, if the user is sad, the analysis unit allows the generation AI to detect the emotion and carefully analyze the user's comments and search history. Alternatively, if the user is angry, the analysis unit can allow the generation AI to detect the emotion and quickly analyze the user's comments and search history. Alternatively, if the user is anxious, the analysis unit can allow the generation AI to detect the emotion and carefully analyze the user's comments and search history. This enables more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's comments and search history into the generation AI and have the generation AI adjust the analysis method.

[0061] The analysis unit can analyze the user's past comments and search history to improve the detection accuracy of specific words. The analysis unit can, for example, cause the generation AI to improve its detection accuracy based on specific words frequently used by the user in the past. The analysis unit can also analyze the user's past comments and search history to cause the generation AI to optimize its detection algorithm for specific words. The analysis unit can also cause the generation AI to improve its detection accuracy for specific words based on the user's past behavioral patterns. Thus, by analyzing the past comments and search history, the detection accuracy for specific words is improved. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past comments and search history into the generation AI and cause the generation AI to improve its detection accuracy for specific words.

[0062] The analysis unit can optimize the analysis algorithm based on the user's current psychological state when analyzing comments and search history. For example, if the user is feeling stressed, the analysis unit causes the generation AI to take the user's psychological state into account and adjust the analysis algorithm. Furthermore, if the user is relaxed, the analysis unit can also cause the generation AI to take the user's psychological state into account and quickly adjust the analysis algorithm. This enables optimization of the analysis algorithm by taking the user's psychological state into account. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's psychological state into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0063] When analyzing comments and search history, the analysis unit can perform analysis based on the user's living environment and background information. For example, if the user has problems in their home environment, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. Furthermore, if the user is experiencing stress at school, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. Furthermore, if the user has problems at work, the analysis unit can have the generation AI take the living environment into consideration and perform analysis. This enables more appropriate analysis by taking the user's living environment and background information into consideration. 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 living environment and background information into the generation AI and have the generation AI perform the analysis.

[0064] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and prioritize displaying important analysis results. Alternatively, if the user is angry, the generation AI can detect the emotion and prioritize displaying analysis results that are more urgent. Alternatively, if the user is anxious, the generation AI can detect the emotion and prioritize displaying analysis results that provide a sense of security. This prioritizes the analysis results based on the user's emotions, allowing important information to be provided preferentially. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotions into the generation AI and have the generation AI determine the priority of the analysis results.

[0065] When analyzing comments and search history, the analysis unit can prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can prioritize analysis of information related to that area by taking into account the geographical location information. Furthermore, when the user is traveling, the analysis unit can prioritize analysis of information related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the analysis unit can prioritize analysis of information around the home by taking into account the user's geographical location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to analyze highly relevant information.

[0066] The analysis unit can analyze the user's social media activity and analyze related information when analyzing comments and search history. For example, the analysis unit causes the generation AI to analyze related information based on content posted by the user on social media. The analysis unit can also analyze the user's social media activity history and cause the generation AI to analyze related information. The analysis unit can also cause the generation AI to analyze related information by referring to the activities of the user's friends on social media. This allows the analysis of social media activity to more appropriately analyze related information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's social media activity into the generation AI and cause the generation AI to analyze related information.

[0067] When analyzing comments and search history, the analysis unit can customize the analysis method by reflecting the user's past feedback. In the analysis unit, for example, the generation AI customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also analyze the user's past feedback and cause the generation AI to optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. In this way, by reflecting past feedback, the analysis method can be customized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past feedback into the generation AI and cause the generation AI to customize the analysis method.

[0068] The support unit can estimate the user's emotions and adjust the way support is expressed based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and provide support in kind words. Alternatively, if the user is angry, the generation AI can detect the emotion and provide support in calm words. Alternatively, if the user is anxious, the generation AI can detect the emotion and provide support in reassuring words. This allows for more appropriate support to be provided by adjusting the way support is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's emotions into the generation AI and have the generation AI adjust the way support is expressed.

[0069] When providing support, the support unit can adjust the level of detail of the support based on the user's psychological state. For example, if the user is feeling stressed, the generation AI can provide concise support by taking the user's psychological state into consideration. Furthermore, if the user is relaxed, the generation AI can provide detailed support by taking the user's psychological state into consideration. Furthermore, if the user is feeling impatient, the generation AI can provide quick support by taking the user's psychological state into consideration. This allows for more appropriate support to be provided by adjusting the level of detail of the support based on the user's psychological state. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the user's psychological state into the generation AI and have the generation AI adjust the level of detail of the support.

[0070] When providing support, the support unit can apply different support algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the support unit causes the generation AI to apply a support algorithm according to that category. Furthermore, if the user searches for "I want to die," the support unit can also cause the generation AI to apply a support algorithm according to that category. Furthermore, if the user says "Help me," the support unit can also cause the generation AI to apply a support algorithm according to that category. This allows for more appropriate support to be provided by applying a support algorithm according to the category of the comments or search history. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can input the user's comments or search history into the generation AI and have the generation AI apply a support algorithm.

[0071] When providing support, the support unit can improve the accuracy of the support by referring to the user's past support history. In the support unit, for example, the generation AI improves the accuracy of the support based on the user's past support history. The support unit can also analyze the user's past support history, and the generation AI can suggest the optimal support method. The support unit can also customize the content of the support by referring to the user's past support history. In this way, the accuracy of the support can be improved by referring to the past support history. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the user's past support history into the generation AI and cause the generation AI to improve the accuracy of the support.

[0072] The support unit can estimate the user's emotions and adjust the length of the support based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and provide longer support. Also, if the user is angry, the generation AI can detect the emotion and provide shorter support. Also, if the user is anxious, the generation AI can detect the emotion and provide support of an appropriate length. This allows for more appropriate support by adjusting the length of support based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the support unit can be performed using, for example, an AI, or without an AI. For example, the support unit can input the user's emotions into the generation AI and have the generation AI adjust the length of the support.

[0073] When providing support, the support unit can determine the priority of support based on the time when the user's comments and search history were submitted. For example, the support unit may have the generation AI determine the priority of support based on the content of recent comments made by the user. The support unit may also have the generation AI determine the priority of support based on the content of past comments made by the user. The support unit may also have the generation AI determine the priority of support based on the content of comments made by the user during a specific time period. In this way, more appropriate support can be provided by determining the priority of support based on the time when the comments and search history were submitted. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input the time when the user's comments and search history were submitted into the generation AI and have the generation AI determine the priority of support.

[0074] When providing support, the support unit can adjust the order of support based on the relevance of the user. For example, if the user makes a highly relevant statement, the support unit causes the generation AI to prioritize support for that content. Also, if the user makes a less relevant statement, the support unit can cause the generation AI to postpone that content. The support unit can also analyze the relevance of the user's statement content, and the generation AI can determine the optimal support order. In this way, more appropriate support can be provided by adjusting the support order based on relevance. Some or all of the above-described processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the relevance of the user's statement content into the generation AI and cause the generation AI to adjust the support order.

[0075] When providing support, the support unit can adjust the use of technical terminology in the support according to the user's level of expertise. For example, if the user has technical expertise, the support unit can have the generation AI provide support using technical terminology. Furthermore, if the user does not have technical expertise, the support unit can have the generation AI provide support in simple language. Furthermore, the support unit can analyze the user's level of expertise and have the generation AI provide support using the most appropriate language. This allows for more appropriate support to be provided by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the support unit can be performed, for example, using AI, or can be performed without using AI. For example, the support unit can input the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terminology.

[0076] The notification unit can estimate the user's emotions and adjust the method of sending an SOS notification based on the estimated user's emotions. For example, if the user is sad, the generation AI can detect the emotion and send an SOS notification in kind words. Alternatively, if the user is angry, the generation AI can detect the emotion and send an SOS notification in calm words. Alternatively, if the user is anxious, the generation AI can detect the emotion and send an SOS notification in reassuring words. This allows for more appropriate notifications by adjusting the method of sending an SOS notification based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotions into the generation AI and have the generation AI adjust the method of sending an SOS notification.

[0077] When sending an SOS notification, the notification unit can adjust the level of detail of the notification based on the user's level of urgency. For example, if the user's level of urgency is high, the generation AI can send a detailed SOS notification. Furthermore, if the user's level of urgency is medium, the notification unit can also send an SOS notification with moderate level of detail. Furthermore, if the user's level of urgency is low, the generation AI can also send a concise SOS notification. This allows for more appropriate notification by adjusting the level of detail of the notification based on the user's level of urgency. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's level of urgency to the generation AI and cause the generation AI to adjust the level of detail of the notification.

[0078] When sending an SOS notification, the notification unit can apply different notification algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the generation AI can apply a notification algorithm depending on that category. Furthermore, if the user searches for "I want to die," the notification unit can also apply a notification algorithm depending on that category. Furthermore, if the user says "Help me," the notification unit can also apply a notification algorithm depending on that category. This allows for more appropriate notifications by applying a notification algorithm depending on the category of the comments or search history. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input the user's comments or search history into the generation AI and have the generation AI apply a notification algorithm.

[0079] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. For example, if the user is sad, the generation AI of the notification unit can detect the emotion and prioritize sending important notifications. Alternatively, if the user is angry, the generation AI can detect the emotion and prioritize sending notifications with high urgency. Alternatively, if the user is anxious, the generation AI can detect the emotion and prioritize sending notifications that provide a sense of security. This enables more appropriate notifications by determining the priority of notifications based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or without an AI. For example, the notification unit can input the user's emotions into the generation AI and have the generation AI determine the priority of notifications.

[0080] When sending an SOS notification, the notification unit can prioritize sending highly relevant notifications by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can consider the geographical location information and prioritize sending notifications related to that area. Furthermore, when the user is traveling, the notification unit can also prioritize sending notifications related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the generation AI can also prioritize sending notifications around the home by taking into account the geographical location information. In this way, by taking into account the geographical location information, highly relevant notifications can be sent preferentially. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's geographical location information into the generation AI and cause the generation AI to send highly relevant notifications.

[0081] When sending an SOS notification, the notification unit can analyze the user's social media activity and send a related notification. For example, the notification unit can have the generation AI send a related notification based on what the user has said on social media. The notification unit can also analyze the user's social media activity history and have the generation AI send a related notification. The notification unit can also have the generation AI send a related notification based on the activity of the user's friends on social media. In this way, related notifications can be sent by analyzing social media activity. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input the user's social media activity into the generation AI and have the generation AI send a related notification.

[0082] The data linking unit can estimate the user's emotions and adjust the data linking method based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and carefully link the data. Also, if the user is angry, the generation AI can detect the emotion and quickly link the data. Also, if the user is anxious, the generation AI can detect the emotion and carefully link the data. This allows for more appropriate data linking by adjusting the data linking method based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the data linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data linking unit can input the user's emotions into the generation AI and have the generation AI adjust the data linking method.

[0083] When linking data, the data linking unit can apply different data linking algorithms depending on the category of the user's comments or search history. For example, if the user says "I'm sad," the generation AI can apply a data linking algorithm according to that category. Furthermore, if the user searches for "I want to die," the data linking unit can also apply a data linking algorithm according to that category. Furthermore, if the user says "Help me," the generation AI can also apply a data linking algorithm according to that category. This enables more appropriate data linking by applying a data linking algorithm according to the category of the comments or search history. Some or all of the above-described processing in the data linking unit may be performed using, or without, AI. For example, the data linking unit can input the user's comments or search history into the generation AI and have the generation AI apply a data linking algorithm.

[0084] When linking data, the data linking unit can improve the accuracy of the linking by referring to the user's past data linking history. In the data linking unit, for example, the generation AI improves the accuracy of the linking based on data that the user has linked in the past. The data linking unit can also analyze the user's past data linking history, and the generation AI can propose an optimal data linking method. The data linking unit can also customize the content of the linking by referring to the user's past data linking history. In this way, the accuracy of the linking can be improved by referring to the past data linking history. Some or all of the above-mentioned processing in the data linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the data linking unit can input the user's past data linking history into the generation AI and cause the generation AI to improve the accuracy of the linking.

[0085] During data integration, the data integration unit can perform data integration based on a schedule by referring to the user's calendar information. For example, the data integration unit references the schedule registered in the user's calendar, and the generation AI performs data integration. The data integration unit can also integrate data related to a specific event from the user's calendar information. The data integration unit can also perform optimal data integration based on the schedule based on the user's calendar information. This makes it possible to perform data integration based on a schedule by referring to the calendar information. Some or all of the above-described processing in the data integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the data integration unit can input the user's calendar information into the generation AI and cause the generation AI to perform data integration based on the schedule.

[0086] The data linking unit can estimate the user's emotions and determine the priority of data linking based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the user's emotions and prioritize important data linking. Alternatively, if the user is angry, the generation AI can detect the user's emotions and prioritize urgent data linking. Alternatively, if the user is anxious, the generation AI can detect the user's emotions and prioritize data linking that provides a sense of security. This enables more appropriate data linking by determining the priority of data linking based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the data linking unit can input the user's emotions into the generation AI and have the generation AI determine the priority of data linking.

[0087] When linking data, the data linking unit can prioritize linking highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI can prioritize linking data related to that area by taking into account the geographical location information. Furthermore, when the user is traveling, the data linking unit can also prioritize linking data related to the travel destination by taking into account the geographical location information. Furthermore, when the user is at home, the generation AI can also prioritize linking data around the home by taking into account the geographical location information. In this way, by taking into account the geographical location information, highly relevant data can be prioritized. Some or all of the above-described processing in the data linking unit may be performed using AI, for example, or may be performed without using AI. For example, the data linking unit can input the user's geographical location information to the generation AI and cause the generation AI to link highly relevant data.

[0088] When linking data, the data linking unit can analyze the user's social media activity and link related data. For example, the data linking unit allows the generation AI to link related data based on content posted by the user on social media. The data linking unit can also analyze the user's social media activity history and allow the generation AI to link related data. The data linking unit can also allow the generation AI to link related data based on the activities of the user's friends on social media. In this way, related data can be linked by analyzing social media activity. Some or all of the above-mentioned processing in the data linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the data linking unit can input the user's social media activity into the generation AI and cause the generation AI to link related data. === Hard Collateral 1-1 === Each of the multiple elements including the above-described analysis unit, support unit, notification unit, and data linkage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the process of analyzing a user's comments and search history is performed by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision of real-time chat support and guidelines is performed by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The notification unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the process of sending an SOS notification to a counselor or expert in the event of a high urgency is performed by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. The data linking unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the process of linking with other data sources and acquiring necessary data is performed by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, support unit, notification unit, and data linkage unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the process of analyzing a user's comments and search history is performed by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision of real-time chat support and guidelines is performed by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the process of sending an SOS notification to a counselor or expert in the event of a high urgency is performed by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. The data linking unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the process of linking with other data sources and acquiring necessary data is performed by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-described analysis unit, support unit, notification unit, and data linkage unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the process of analyzing a user's comments and search history is performed by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision of real-time chat support and guidelines is performed by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the process of sending an SOS notification to a counselor or specialist in the event of a high urgency is performed by the processor 46 of the headset terminal 314 or the processor 28 of the data processing device 12. The data linking unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the process of linking with other data sources and obtaining necessary data is performed by the processor 46 of the headset terminal 314 or the processor 28 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-described analysis unit, support unit, notification unit, and data linkage unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the process of analyzing a user's comments and search history is performed by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision of real-time chat support and guidelines is performed by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The notification unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the process of sending an SOS notification to a counselor or expert in the event of a high urgency is performed by the processor 46 of the robot 414 or the processor 28 of the data processing device 12. The data linking unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the process of linking with other data sources and acquiring necessary data is performed by the processor 46 of the robot 414 or the processor 28 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] In addition to the user's comments and search history, the analysis unit can also analyze the user's biometric data (e.g., heart rate, electrodermal activity, etc.). This allows the system to grasp the user's stress level and state of tension in real time and provide more appropriate support. For example, if the user's heart rate suddenly rises, the generation AI can detect the change and provide advice on how to relax. Also, if the user's electrodermal activity increases, the generation AI can provide guidelines for reducing stress. Furthermore, it is possible to monitor the user's biometric data over the long term and predict changes in their health condition.

[0091] When estimating a user's emotions, the analysis unit can more accurately estimate emotions by combining the user's voice tone and facial expression analysis. For example, if a user speaks in a sad voice, the generation AI can analyze the voice tone and estimate their emotions. The accuracy of emotion estimation can also be improved by capturing the user's facial expression with a camera and analyzing it. This makes it possible to capture the user's emotions from multiple angles and provide more appropriate support. For example, if the user is smiling, the generation AI can detect that expression and provide positive feedback.

[0092] The analysis unit can infer a user's interests and concerns based on the user's comments and search history and provide related information. For example, if a user frequently searches for information about a particular hobby, the generation AI can provide the latest information and events related to that hobby. Also, if a user shows interest in a particular topic, the generation AI can provide in-depth information on that topic. Furthermore, it can suggest personalized content based on the user's interests and concerns. This makes it possible to provide information tailored to the user's interests and concerns, improving the user experience.

[0093] The analysis unit can visually display the analysis results of comments and search history based on the user's psychological state. For example, if the user is feeling stressed, the generation AI can visually display the analysis results in graphs and charts, providing them in a format that is easy for the user to understand. Alternatively, if the user is relaxed, the generation AI can provide the analysis results in a simple text format. Furthermore, if the user is feeling anxious, the generation AI can quickly display the analysis results so that the user can respond immediately. This makes it possible to provide more appropriate information by adjusting the way the analysis results are displayed according to the user's psychological state.

[0094] The analysis unit can analyze the user's social network in addition to the user's living environment and background information. For example, it can analyze the comments and actions of friends and family with whom the user frequently interacts to estimate the user's psychological state and behavioral patterns. It can also analyze the user's social media activity to determine the communities to which the user belongs. Furthermore, it can provide support appropriate to the user based on the user's social network. This makes it possible to perform analysis that takes the user's social background into account, allowing for more appropriate support to be provided.

[0095] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and provide feedback in kind words. Alternatively, if the user is angry, the generation AI can detect the emotion and provide feedback in calm words. Alternatively, if the user is feeling anxious, the generation AI can detect the emotion and provide feedback in reassuring words. This allows for more appropriate feedback to be provided by adjusting the feedback method based on the user's emotions.

[0096] The analysis unit can perform analysis that takes into account the culture and customs specific to the region based on the user's geographical location information. For example, if the user is in a specific region, the analysis results are provided based on the culture and customs of that region. Also, if the user is traveling, the analysis results can be provided that take into account the culture and customs of the destination. Furthermore, if the user is in a different cultural region, it is also possible to provide support appropriate for that culture. This makes it possible to perform analysis that takes into account culture and customs based on geographical location information, and to provide more appropriate support.

[0097] The analysis unit can analyze a user's online shopping history in addition to their social media activity. For example, the generation AI can suggest related products and services based on products and services the user has previously purchased. It can also analyze the user's shopping history to understand their purchasing trends. It can also provide personalized shopping advice based on the user's purchasing history. This enables analysis that takes into account the user's online shopping history, allowing for more appropriate support.

[0098] The analysis unit can analyze the user's health data in addition to the user's past feedback. For example, the generation AI can customize the analysis method based on the health data the user has provided in the past. The generation AI can also analyze the user's health data and predict changes in health status. It can also provide health management advice based on the user's health data. This allows the analysis method to be customized and accuracy improved by reflecting past feedback and health data.

[0099] The support unit can estimate the user's emotions and adjust the timing of support based on the estimated user emotions. For example, if the user is sad, the generation AI can detect the emotion and provide support at the appropriate time. Alternatively, if the user is angry, the generation AI can detect the emotion and wait until the user has calmed down before providing support. Alternatively, if the user is feeling anxious, the generation AI can detect the emotion and provide support at a time that will give the user a sense of security. This allows for more appropriate support to be provided by adjusting the timing of support based on the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The analysis unit analyzes the user's statements or search history. The user's statements or search history includes text messages, voice input, web search history, etc. For example, if the user utters specific words such as "painful" or "I want to die," the analysis unit detects the content of those words. Also, if the user searches for dangerous words such as "suicide methods," the analysis unit detects the content of those words. Step 2: The support unit provides active support based on the content detected by the analysis unit. Active support includes real-time chat support and the provision of guidelines. For example, if a user says "I'm suffering," the generation AI will provide support in kind words. Also, if a user searches for "I want to die," the generation AI can send a message encouraging them to seek professional advice. Step 3: The notification unit sends an SOS notification to a counselor or expert if the support unit is unable to handle the situation. For example, if the generation AI analyzes the user's situation and determines that it is urgent, it will send an SOS notification to a counselor or expert. It can also estimate the user's emotions and adjust the method of sending SOS notifications based on the estimated emotions. Step 4: The data linking unit connects with other data sources to obtain the necessary data. For example, it connects with data sources such as social media and medical databases. It can also estimate the user's emotions and adjust the data linking method based on the estimated emotions.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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. an analysis unit that analyzes a user's comments or search history; a support unit that provides active support based on the content detected by the analysis unit; a notification unit that sends an SOS notification to a counselor or an expert when the support unit is unable to handle the situation; A data linking unit that links with other data sources to acquire necessary data. A system characterized by:

2. The analysis unit Estimate user sentiment and adjust analysis of user comments and search history based on the estimated sentiment The system of claim 1 .

3. The analysis unit Analyze users' past comments and search history to improve the accuracy of detecting specific words The system of claim 1 .

4. The analysis unit When analyzing comments and search history, the analysis algorithm is optimized based on the user's current psychological state. The system of claim 1 .

5. The analysis unit When analyzing comments and search history, the analysis is based on the user's living environment and background information. The system of claim 1 .

6. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The system of claim 1 .

7. The analysis unit When analyzing comments and search history, the system takes into account the user's geographic location information to prioritize relevant information. The system of claim 1 .

8. The analysis unit Analyzing users' social media activities and analyzing related information when analyzing comments and search history The system of claim 1 .

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