System

Abstract

An object of the system according to the embodiment is to objectively support communication between husband and wife and relationship improvement.SOLUTION: A system according to an embodiment includes an interaction unit, an analysis unit, an advice providing unit, and a follow-up unit. The dialogue unit enables each of the husband and wife to find out his / her personality, sense of values, current situation of the husband and wife relationship, and issues through a dialogue with the AI chat bot. The analysis unit analyzes the dialogue content acquired by the dialogue unit and clarifies the compatibility of the husband and wife and potential problems. The advice providing unit provides advice suitable for each personality or situation based on the compatibility or the problem clarified by the analysis unit. The follow-up unit periodically follows up the situation of the husband and wife to check the progress of the relationship improvement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of lacking objective support when it comes to communication between couples and improving their relationships.

[0005] The system according to the embodiment aims to objectively support communication between couples and improving their relationship. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, an advice provision unit, and a follow-up unit. The dialogue unit allows each spouse to open up about their own personality and values, as well as the current state and challenges of their marital relationship, through dialogue with an AI chatbot. The analysis unit analyzes the dialogue content acquired by the dialogue unit to identify the compatibility and potential problems between the spouses. The advice provision unit provides advice tailored to each spouse's personality and situation based on the compatibility and problems identified by the analysis unit. The follow-up unit periodically follows up on the couple's situation and checks the progress of improving the relationship. [Effects of the Invention]

[0007] The system according to the embodiment can objectively support communication between couples and improving their relationship. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​service for preventing middle-aged divorce according to an embodiment of the present invention is a system that analyzes the personalities, values, and lifestyles of each spouse and proposes appropriate advice and communication methods. As a result, the AI ​​service for preventing middle-aged divorce can provide support to resolve couples' problems and build a stronger bond.

[0029] An AI service for preventing middle-aged divorce according to an embodiment includes a dialogue unit, an analysis unit, an advice-providing unit, and a follow-up unit. The dialogue unit allows each spouse to open up about their personality, values, current state of their marital relationship, and challenges through dialogue with an AI chatbot. For example, when a user inputs content such as "I'm not communicating well with my husband," the dialogue unit uses a generation AI (e.g., LLM) to generate appropriate questions and engage in dialogue with the user. The analysis unit analyzes the dialogue content acquired by the dialogue unit to identify the couple's compatibility and potential problems. For example, if the generation AI asks, "What values ​​do you value?" and the user answers, "I value time with my family," the analysis unit performs analysis based on that information. The advice-providing unit provides advice tailored to each spouse's personality and situation based on the compatibility and problems identified by the analysis unit. For example, the advice-providing unit provides specific advice such as, "To improve communication with your partner, try planning a date once a week." The follow-up unit periodically follows up on the couple's situation and checks the progress of relationship improvement. For example, the AI ​​service for preventing divorce among middle-aged couples according to the embodiment periodically asks questions such as, "How is your communication going lately?" to check the progress. This enables the AI ​​service for preventing divorce among middle-aged couples according to the embodiment to provide support for resolving marital problems and building a stronger bond.

[0030] The dialogue unit can learn the user's past statements and behavioral patterns based on the dialogue history and conduct personalized dialogue. For example, the dialogue unit's generation AI analyzes the dialogue history and learns the user's past statements. For example, it remembers the hobbies and interests that the user has previously mentioned and generates questions related to them in the next dialogue. The dialogue unit also learns the user's behavioral patterns based on the dialogue history and conducts personalized dialogue. For example, if the user prefers to have a dialogue at a specific time of day, the dialogue unit will start a dialogue at that time of day. This makes it possible to conduct more personalized dialogue by learning the user's past statements and behavioral patterns.

[0031] The analysis unit analyzes public information on the user's social media or blog to understand the user's personality and values ​​in more detail. In the analysis unit, for example, the generation AI analyzes the user's social media posts to understand the user's personality and values. For example, the user's interests and concerns are identified from the content and frequency of posts. In addition, the analysis unit analyzes the user's blog to understand the user's personality and values. For example, the user's values ​​are identified from the content and theme of the blog. In this way, the user's personality and values ​​can be understood in more detail by analyzing the user's public information.

[0032] The analysis unit can automatically generate psychological tests and questionnaires based on the user's answers and analyze personality and values ​​from multiple angles. In the analysis unit, for example, the generation AI automatically generates psychological tests based on the user's answers and analyzes personality and values. For example, the generation AI generates a personality diagnostic test and has the user answer it. In addition, the analysis unit can automatically generate questionnaires based on the user's answers and analyze personality and values. For example, the generation AI generates a questionnaire about values ​​and has the user answer it. In this way, personality and values ​​can be analyzed from multiple angles using psychological tests and questionnaires.

[0033] The advice providing unit can learn the user's past actions and reactions and provide more effective advice. For example, the generation AI of the advice providing unit learns the user's past actions and provides effective advice. For example, advice is generated based on behavioral patterns that have been successful in the past. The advice providing unit also learns the user's past reactions and provides effective advice. For example, advice that has previously elicited a positive response is provided again. In this way, more effective advice can be provided by learning past actions and reactions.

[0034] The advice providing unit can provide actionable advice by taking into account the user's lifestyle and schedule. For example, the generation AI in the advice providing unit analyzes the user's lifestyle and provides actionable advice. For example, advice that can be done in a short time is provided to a busy user. The advice providing unit also analyzes the user's schedule by using the generation AI and provides actionable advice. For example, advice is provided that matches the user's free time. In this way, actionable advice can be provided by taking into account the user's lifestyle and schedule.

[0035] The follow-up unit learns the user's past follow-up data and can perform follow-up at the optimal timing. In the follow-up unit, for example, the generation AI learns the user's past follow-up data and performs follow-up at the optimal timing. For example, follow-up is performed based on timing that was effective in the past. In addition, the follow-up unit learns the user's past follow-up data and performs follow-up at the optimal timing. For example, follow-up is performed at a timing that suits the user's situation. In this way, by learning past follow-up data, follow-up can be performed at the optimal timing.

[0036] The follow-up unit can provide appropriate follow-up by taking into account the user's life events and schedule. For example, the generation AI in the follow-up unit analyzes the user's life events and provides appropriate follow-up. For example, follow-up is performed to coincide with the user's birthday or anniversary. The generation AI in the follow-up unit also analyzes the user's schedule and provides appropriate follow-up. For example, follow-up is performed to coincide with the user's free time. In this way, appropriate follow-up can be provided by taking into account the user's life events and schedule.

[0037] The follow-up unit can introduce success stories of other users based on the user's follow-up history and suggest them for reference. In the follow-up unit, for example, the generation AI analyzes the user's follow-up history and introduces success stories of other users. For example, it can suggest success stories of users who solved the same problem. In addition, the follow-up unit can introduce success stories of other users based on the user's follow-up history and suggest them for reference. For example, it can clarify the specific content and evaluation criteria of success stories and suggest them to the user. In this way, the user can refer to the success stories of other users by being introduced to them.

[0038] The follow-up unit collects feedback on the user's follow-up and can improve the accuracy of the follow-up. For example, the generation AI collects feedback on the user's follow-up and improves the accuracy of the follow-up. For example, the follow-up is improved based on the user's evaluation. The follow-up unit also collects feedback on the user's follow-up and improves the accuracy of the follow-up. For example, the generation AI collects surveys and user comments and improves the content of the follow-up. In this way, the accuracy of the follow-up can be improved by collecting feedback.

[0039] The advice providing unit can analyze success cases and failure cases based on the user's advice history and provide optimal advice. For example, the generation AI analyzes the user's advice history and provides optimal advice based on success cases. For example, it provides advice that was successful in the past again. The advice providing unit can also analyze success cases and failure cases based on the user's advice history and provide optimal advice. For example, it refers to success cases and failure cases of other users and provides optimal advice. In this way, optimal advice can be provided by analyzing success cases and failure cases.

[0040] The advice providing unit can collect feedback on the user's advice and improve the accuracy of the advice. For example, the generation AI of the advice providing unit collects feedback on the user's advice and improves the accuracy of the advice. For example, the advice providing unit improves the accuracy of the advice based on the user's evaluation. The generation AI of the advice providing unit also collects feedback on the user's advice and improves the accuracy of the advice. For example, the advice providing unit collects surveys and user comments and improves the content of the advice. In this way, the accuracy of the advice can be improved by collecting feedback.

[0041] The advice providing unit can make personalized suggestions to deepen bonds by taking into account the user's lifestyle and values. In the advice providing unit, for example, the generation AI analyzes the user's lifestyle and makes personalized suggestions to deepen bonds. For example, if the user is busy, it will suggest activities that can be done in a short amount of time. In addition, the advice providing unit analyzes the user's values ​​and makes personalized suggestions to deepen bonds. For example, if the user values ​​time with family, it will suggest activities to spend time with family. This makes it possible to make personalized suggestions to deepen bonds by taking into account lifestyle and values.

[0042] The advice providing unit can introduce success stories of other users based on the user's bond deepening history and suggest that they use them as reference. For example, the generation AI analyzes the user's bond deepening history and introduces success stories of other users. For example, it suggests success stories of users who solved the same problem. The advice providing unit also suggests that the generation AI introduce success stories of other users based on the user's bond deepening history and suggest that they use them as reference. For example, it clarifies the specific content and evaluation criteria of success stories and suggests them to the user. In this way, the user can use the success stories of other users as reference by being introduced to them.

[0043] The advice providing unit can collect feedback on the deepening of the user's bond and improve the accuracy of the proposal. For example, the generation AI collects feedback on the deepening of the user's bond and improves the accuracy of the proposal. For example, the proposal is improved based on the user's evaluation. The advice providing unit can also collect feedback on the deepening of the user's bond and improve the accuracy of the proposal. For example, the generation AI collects surveys and user comments and improves the content of the proposal. In this way, the accuracy of the proposal can be improved by collecting feedback.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The AI ​​service for preventing middle-aged divorce can also be equipped with a health management section. The health management section monitors the health status of each spouse and provides health advice. For example, it can suggest regular exercise and a balanced diet. The health management section can also provide advice on relaxation methods for stress management and how to improve sleep quality. This will support the health of couples and help them live better lives.

[0046] The AI ​​service for preventing middle-aged divorce can further include a hobby suggestion unit. The hobby suggestion unit suggests new hobbies and activities based on the interests of each spouse. For example, it can provide information on workshops to find common hobbies and hobby-related events. The hobby suggestion unit can also suggest activities that couples can enjoy together, increasing opportunities to spend time together. This can provide new ways for couples to deepen their bond.

[0047] The AI ​​service for preventing middle-aged divorce can further include a travel suggestion unit. The travel suggestion unit proposes travel plans based on the couple's preferences and budget. For example, it can suggest a relaxing hot spring trip or an active outdoor trip. The travel suggestion unit can also provide travel plans tailored to the couple's anniversaries and special events, helping them create special memories. This can refresh the couple's relationship and provide an opportunity to share new memories.

[0048] The AI ​​service for preventing middle-aged divorces can further include an education suggestion unit. The education suggestion unit suggests learning programs tailored to each spouse's interests and careers. For example, it can provide information on online courses to acquire new skills or hobby workshops. The education suggestion unit can also suggest programs that couples can study together, helping them deepen their bond by having a common goal. This can support the couple's growth and provide opportunities for them to pursue common interests.

[0049] The AI ​​service for preventing middle-aged divorce can also be equipped with a household finances management section. The household finances management section manages the couple's income and expenses and supports the establishment of a healthy household finances. For example, it can provide advice on saving money and investment suggestions. The household finances management section can also provide savings plans tailored to the couple's future goals and support financial stability. This can reduce financial stress for couples and help them live with peace of mind.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: In the dialogue section, each spouse opens up to the AI ​​chatbot about their personality, values, current state of their relationship, and any issues they may have. For example, if a user uses a generation AI (e.g., LLM) to input information such as "I'm not communicating well with my husband," the generation AI will generate appropriate questions and continue the dialogue with the user. Step 2: The analysis unit analyzes the dialogue content obtained by the dialogue unit to identify the couple's compatibility and potential problems. For example, if the generation AI asks, "What values ​​do you value?" and the user answers, "I value spending time with my family," the analysis will be based on that information. Step 3: The advice section provides advice tailored to each individual's personality and situation based on the compatibility and problems identified by the analysis section. For example, it may provide specific advice such as, "To improve communication with your partner, try planning a date once a week." Step 4: The follow-up department periodically checks with the couple to see how their relationship is progressing. For example, they regularly ask questions like, "How is your communication going these days?" to see how things are going.

[0052] (Example 2) The AI ​​service for preventing middle-aged divorce according to an embodiment of the present invention is a system that analyzes the personalities, values, and lifestyles of each spouse and proposes appropriate advice and communication methods. As a result, the AI ​​service for preventing middle-aged divorce can provide support to resolve couples' problems and build a stronger bond.

[0053] An AI service for preventing middle-aged divorce according to an embodiment includes a dialogue unit, an analysis unit, an advice-providing unit, and a follow-up unit. The dialogue unit allows each spouse to open up about their personality, values, current state of their marital relationship, and challenges through dialogue with an AI chatbot. For example, when a user inputs content such as "I'm not communicating well with my husband," the dialogue unit uses a generation AI (e.g., LLM) to generate appropriate questions and engage in dialogue with the user. The analysis unit analyzes the dialogue content acquired by the dialogue unit to identify the couple's compatibility and potential problems. For example, if the generation AI asks, "What values ​​do you value?" and the user answers, "I value time with my family," the analysis unit performs analysis based on that information. The advice-providing unit provides advice tailored to each spouse's personality and situation based on the compatibility and problems identified by the analysis unit. For example, the advice-providing unit provides specific advice such as, "To improve communication with your partner, try planning a date once a week." The follow-up unit periodically follows up on the couple's situation and checks the progress of relationship improvement. For example, the AI ​​service for preventing divorce among middle-aged couples according to the embodiment periodically asks questions such as, "How is your communication going lately?" to check the progress. This enables the AI ​​service for preventing divorce among middle-aged couples according to the embodiment to provide support for resolving marital problems and building a stronger bond.

[0054] The dialogue unit can analyze the user's facial expressions and tone of voice in real time, and provide questions and advice according to their emotional state. For example, the generation AI analyzes the user's facial expressions with a camera during a conversation to grasp their emotional state in real time. For example, if the user is smiling, it will generate positive questions, and if they look sad, it will generate comforting questions. The dialogue unit also analyzes the user's tone of voice during a conversation to grasp their emotional state in real time. For example, if the voice tone is high, it will determine that the user is excited and generate calming questions. This allows for more effective dialogue by providing questions and advice according to the user's emotional state.

[0055] The dialogue unit can learn the user's past statements and behavioral patterns based on the dialogue history and conduct personalized dialogue. For example, the dialogue unit's generation AI analyzes the dialogue history and learns the user's past statements. For example, it remembers the hobbies and interests that the user has previously mentioned and generates questions related to them in the next dialogue. The dialogue unit also learns the user's behavioral patterns based on the dialogue history and conducts personalized dialogue. For example, if the user prefers to have a dialogue at a specific time of day, the dialogue unit will start a dialogue at that time of day. This makes it possible to conduct more personalized dialogue by learning the user's past statements and behavioral patterns.

[0056] The dialogue unit can use the emotion estimation function to estimate the user's emotions in real time and generate dialogue content that corresponds to the emotions. In the dialogue unit, for example, the generation AI uses the emotion estimation function to estimate the user's emotions in real time. For example, if the user is angry, it generates questions to calm the user, and if the user is happy, it generates questions to share those emotions. In addition, the dialogue unit uses the emotion estimation function to generate dialogue content that corresponds to the user's emotions. For example, if the user is feeling anxious, it generates dialogue content that reassures the user. This allows for more effective dialogue by generating dialogue content that corresponds to the user's emotions.

[0057] The analysis unit analyzes public information on the user's social media or blog to understand the user's personality and values ​​in more detail. In the analysis unit, for example, the generation AI analyzes the user's social media posts to understand the user's personality and values. For example, the user's interests and concerns are identified from the content and frequency of posts. In addition, the analysis unit analyzes the user's blog to understand the user's personality and values. For example, the user's values ​​are identified from the content and theme of the blog. In this way, the user's personality and values ​​can be understood in more detail by analyzing the user's public information.

[0058] The analysis unit can automatically generate psychological tests and questionnaires based on the user's answers and analyze personality and values ​​from multiple angles. In the analysis unit, for example, the generation AI automatically generates psychological tests based on the user's answers and analyzes personality and values. For example, the generation AI generates a personality diagnostic test and has the user answer it. In addition, the analysis unit can automatically generate questionnaires based on the user's answers and analyze personality and values. For example, the generation AI generates a questionnaire about values ​​and has the user answer it. In this way, personality and values ​​can be analyzed from multiple angles using psychological tests and questionnaires.

[0059] The analysis unit can use the emotion estimation function to track changes in the user's emotions and analyze personality and values ​​based on the emotions. In the analysis unit, for example, the generation AI uses the emotion estimation function to track changes in the user's emotions. For example, the emotion score during the conversation is recorded and used to analyze personality and values. In addition, the analysis unit uses the emotion estimation function to analyze personality and values ​​based on the user's emotions. For example, the evaluation of personality and values ​​is updated according to changes in emotions. In this way, personality and values ​​can be analyzed more accurately by tracking changes in emotions.

[0060] The advice providing unit can learn the user's past actions and reactions and provide more effective advice. For example, the generation AI of the advice providing unit learns the user's past actions and provides effective advice. For example, advice is generated based on behavioral patterns that have been successful in the past. The advice providing unit also learns the user's past reactions and provides effective advice. For example, advice that has previously elicited a positive response is provided again. In this way, more effective advice can be provided by learning past actions and reactions.

[0061] The advice providing unit can provide actionable advice by taking into account the user's lifestyle and schedule. For example, the generation AI in the advice providing unit analyzes the user's lifestyle and provides actionable advice. For example, advice that can be done in a short time is provided to a busy user. The advice providing unit also analyzes the user's schedule by using the generation AI and provides actionable advice. For example, advice is provided that matches the user's free time. In this way, actionable advice can be provided by taking into account the user's lifestyle and schedule.

[0062] The advice providing unit can use the emotion estimation function to provide advice in real time according to the user's emotions. For example, the generation AI in the advice providing unit uses the emotion estimation function to provide advice in real time according to the user's emotions. For example, if the user is feeling anxious, the advice providing unit provides reassuring advice. The generation AI also uses the emotion estimation function to provide advice in real time according to the user's emotions. For example, if the user is happy, the advice providing unit provides advice to share that emotion. This allows for more effective support by providing advice in real time according to emotions.

[0063] The follow-up unit learns the user's past follow-up data and can perform follow-up at the optimal timing. In the follow-up unit, for example, the generation AI learns the user's past follow-up data and performs follow-up at the optimal timing. For example, follow-up is performed based on timing that was effective in the past. In addition, the follow-up unit learns the user's past follow-up data and performs follow-up at the optimal timing. For example, follow-up is performed at a timing that suits the user's situation. In this way, by learning past follow-up data, follow-up can be performed at the optimal timing.

[0064] The follow-up unit can provide appropriate follow-up by taking into account the user's life events and schedule. For example, the generation AI in the follow-up unit analyzes the user's life events and provides appropriate follow-up. For example, follow-up is performed to coincide with the user's birthday or anniversary. The generation AI in the follow-up unit also analyzes the user's schedule and provides appropriate follow-up. For example, follow-up is performed to coincide with the user's free time. In this way, appropriate follow-up can be provided by taking into account the user's life events and schedule.

[0065] The follow-up unit can use the emotion estimation function to provide follow-up based on the user's emotions in real time. For example, the generation AI in the follow-up unit uses the emotion estimation function to provide follow-up based on the user's emotions in real time. For example, if the user is feeling anxious, a follow-up to reassure them is performed. The generation AI in the follow-up unit also uses the emotion estimation function to provide follow-up based on the user's emotions in real time. For example, if the user is happy, a follow-up to share those emotions is performed. This allows for more effective support by providing follow-up based on emotions in real time.

[0066] The follow-up unit can introduce success stories of other users based on the user's follow-up history and suggest them for reference. In the follow-up unit, for example, the generation AI analyzes the user's follow-up history and introduces success stories of other users. For example, it can suggest success stories of users who solved the same problem. In addition, the follow-up unit can introduce success stories of other users based on the user's follow-up history and suggest them for reference. For example, it can clarify the specific content and evaluation criteria of success stories and suggest them to the user. In this way, the user can refer to the success stories of other users by being introduced to them.

[0067] The follow-up unit collects feedback on the user's follow-up and can improve the accuracy of the follow-up. For example, the generation AI collects feedback on the user's follow-up and improves the accuracy of the follow-up. For example, the follow-up is improved based on the user's evaluation. The follow-up unit also collects feedback on the user's follow-up and improves the accuracy of the follow-up. For example, the generation AI collects surveys and user comments and improves the content of the follow-up. In this way, the accuracy of the follow-up can be improved by collecting feedback.

[0068] The follow-up unit can use the emotion estimation function to evaluate the effectiveness of follow-up based on the user's emotions and reflect the results in the next follow-up. In the follow-up unit, for example, the generation AI uses the emotion estimation function to evaluate the effectiveness of follow-up based on the user's emotions. For example, the emotion score after the follow-up is recorded and reflected in the next follow-up. In addition, the follow-up unit can use the emotion estimation function to evaluate the effectiveness of follow-up based on the user's emotions and reflect the results in the next follow-up. For example, the user's satisfaction and progress in improving the relationship are evaluated and reflected in the next follow-up. In this way, the effectiveness of follow-up based on emotions can be evaluated and reflected in the next follow-up.

[0069] The advice providing unit can analyze success cases and failure cases based on the user's advice history and provide optimal advice. For example, the generation AI analyzes the user's advice history and provides optimal advice based on success cases. For example, it provides advice that was successful in the past again. The advice providing unit can also analyze success cases and failure cases based on the user's advice history and provide optimal advice. For example, it refers to success cases and failure cases of other users and provides optimal advice. In this way, optimal advice can be provided by analyzing success cases and failure cases.

[0070] The advice providing unit can collect feedback on the user's advice and improve the accuracy of the advice. For example, the generation AI of the advice providing unit collects feedback on the user's advice and improves the accuracy of the advice. For example, the advice providing unit improves the accuracy of the advice based on the user's evaluation. The generation AI of the advice providing unit also collects feedback on the user's advice and improves the accuracy of the advice. For example, the advice providing unit collects surveys and user comments and improves the content of the advice. In this way, the accuracy of the advice can be improved by collecting feedback.

[0071] The advice providing unit can use the emotion estimation function to evaluate the effectiveness of advice based on the user's emotions and reflect it in the next advice. In the advice providing unit, for example, the generation AI uses the emotion estimation function to evaluate the effectiveness of advice based on the user's emotions. For example, the emotion score after the advice is provided is recorded and reflected in the next advice. In addition, the advice providing unit can use the emotion estimation function to evaluate the effectiveness of advice based on the user's emotions and reflect it in the next advice. For example, the generation AI evaluates the user's satisfaction and progress in improving relationships and reflects it in the next advice. In this way, the effectiveness of advice based on emotions can be evaluated and reflected in the next advice.

[0072] The advice providing unit can make personalized suggestions to deepen bonds by taking into account the user's lifestyle and values. In the advice providing unit, for example, the generation AI analyzes the user's lifestyle and makes personalized suggestions to deepen bonds. For example, if the user is busy, it will suggest activities that can be done in a short amount of time. In addition, the advice providing unit analyzes the user's values ​​and makes personalized suggestions to deepen bonds. For example, if the user values ​​time with family, it will suggest activities to spend time with family. This makes it possible to make personalized suggestions to deepen bonds by taking into account lifestyle and values.

[0073] The advice providing unit can use the emotion estimation function to provide advice to deepen bonds based on the user's emotions in real time. For example, the generation AI uses the emotion estimation function to provide advice to deepen bonds based on the user's emotions in real time. For example, if the user is feeling anxious, the advice providing unit provides reassuring advice. The generation AI also uses the emotion estimation function to provide advice to deepen bonds based on the user's emotions in real time. For example, if the user is happy, the advice providing unit provides advice to share those emotions. This makes it possible to provide emotion-based advice in real time, thereby enabling effective support for deepening bonds.

[0074] The advice providing unit can introduce success stories of other users based on the user's bond deepening history and suggest that they use them as reference. For example, the generation AI analyzes the user's bond deepening history and introduces success stories of other users. For example, it suggests success stories of users who solved the same problem. The advice providing unit also suggests that the generation AI introduce success stories of other users based on the user's bond deepening history and suggest that they use them as reference. For example, it clarifies the specific content and evaluation criteria of success stories and suggests them to the user. In this way, the user can use the success stories of other users as reference by being introduced to them.

[0075] The advice providing unit can collect feedback on the deepening of the user's bond and improve the accuracy of the proposal. For example, the generation AI collects feedback on the deepening of the user's bond and improves the accuracy of the proposal. For example, the proposal is improved based on the user's evaluation. The advice providing unit can also collect feedback on the deepening of the user's bond and improve the accuracy of the proposal. For example, the generation AI collects surveys and user comments and improves the content of the proposal. In this way, the accuracy of the proposal can be improved by collecting feedback.

[0076] The advice providing unit can use the emotion estimation function to evaluate the effect of deepening bonds based on the user's emotions and reflect it in the next proposal. In the advice providing unit, for example, the generation AI uses the emotion estimation function to evaluate the effect of deepening bonds based on the user's emotions. For example, the emotion score after deepening the bond is recorded and reflected in the next proposal. In addition, the advice providing unit can use the emotion estimation function to evaluate the effect of deepening bonds based on the user's emotions and reflect it in the next proposal. For example, the user's satisfaction and progress in improving the relationship are evaluated and reflected in the next proposal. In this way, the effect of deepening bonds based on emotions can be evaluated and reflected in the next proposal.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The AI ​​service for preventing middle-aged divorce can also be equipped with a health management section. The health management section monitors the health status of each spouse and provides health advice. For example, it can suggest regular exercise and a balanced diet. The health management section can also provide advice on relaxation methods for stress management and how to improve sleep quality. This will support the health of couples and help them live better lives.

[0079] The AI ​​service for preventing middle-aged divorce can further include a hobby suggestion unit. The hobby suggestion unit suggests new hobbies and activities based on the interests of each spouse. For example, it can provide information on workshops to find common hobbies and hobby-related events. The hobby suggestion unit can also suggest activities that couples can enjoy together, increasing opportunities to spend time together. This can provide new ways for couples to deepen their bond.

[0080] The AI ​​service for preventing middle-aged divorce can further include a travel suggestion unit. The travel suggestion unit proposes travel plans based on the couple's preferences and budget. For example, it can suggest a relaxing hot spring trip or an active outdoor trip. The travel suggestion unit can also provide travel plans tailored to the couple's anniversaries and special events, helping them create special memories. This can refresh the couple's relationship and provide an opportunity to share new memories.

[0081] The AI ​​service for preventing middle-aged divorces can further include an education suggestion unit. The education suggestion unit suggests learning programs tailored to each spouse's interests and careers. For example, it can provide information on online courses to acquire new skills or hobby workshops. The education suggestion unit can also suggest programs that couples can study together, helping them deepen their bond by having a common goal. This can support the couple's growth and provide opportunities for them to pursue common interests.

[0082] The AI ​​service for preventing middle-aged divorce can also be equipped with a household finances management section. The household finances management section manages the couple's income and expenses and supports the establishment of a healthy household finances. For example, it can provide advice on saving money and investment suggestions. The household finances management section can also provide savings plans tailored to the couple's future goals and support financial stability. This can reduce financial stress for couples and help them live with peace of mind.

[0083] The middle-aged divorce prevention AI service can also use its emotion estimation function to suggest relaxation methods based on the couple's emotional state. For example, if they are feeling stressed, it can suggest relaxation music or meditation. The emotion estimation function can also be used to suggest relaxation methods according to the couple's emotional state. For example, if they are tired, it can suggest relaxing aromatherapy or massage. This makes it possible to provide relaxation methods according to the couple's emotional state, helping them reduce stress and spend more time relaxing.

[0084] The AI ​​service for preventing middle-aged divorce can also use its emotion estimation function to suggest communication methods based on the emotional state of the couple. For example, the emotion estimation function can be used to suggest communication methods based on the emotional state of the couple. For example, if one partner is angry, it can suggest communication methods to help them stay calm, and if one partner is happy, it can suggest communication methods to help them share their emotions. The emotion estimation function can also be used to suggest communication methods based on the emotional state of the couple. For example, if one partner is sad, it can suggest communication methods to comfort them. This allows for more effective dialogue by providing communication methods that suit the emotional state of the couple.

[0085] The AI ​​service for preventing middle-aged divorce can also use its emotion estimation function to suggest date plans based on the emotional state of the couple. For example, the emotion estimation function can be used to suggest date plans that match the emotional state of the couple. For example, if the couple is feeling stressed, it can suggest a relaxing date plan, and if the couple is excited, it can suggest an active date plan. The emotion estimation function can also be used to suggest date plans based on the emotional state of the couple. For example, if the couple is tired, it can suggest a date plan to a relaxing spa or hot spring. This allows for more effective refreshing by providing date plans that match the emotional state of the couple.

[0086] The middle-aged divorce prevention AI service can also use its emotion estimation function to suggest surprise events based on the emotional state of the couple. For example, the emotion estimation function can be used to suggest surprise events based on the couple's emotional state. For example, if the couple is happy, it can suggest a surprise event to further enhance that emotion, and if the couple is sad, it can suggest a surprise event to cheer them up. The emotion estimation function can also be used to suggest surprise events based on the couple's emotional state. For example, if the couple is tired, it can suggest a surprise event to help them relax. This allows for more effective support by providing surprise events that match the couple's emotional state.

[0087] The middle-aged divorce prevention AI service can also use its emotion estimation function to suggest gifts based on the emotional state of the couple. For example, using the emotion estimation function, it can suggest gifts that correspond to the emotional state of the couple. For example, if the couple is happy, it can suggest a gift that will further enhance that emotion, and if the couple is sad, it can suggest a gift that will cheer them up. The emotion estimation function can also be used to suggest gifts based on the emotional state of the couple. For example, if the couple is tired, it can suggest a gift that will help them relax. This allows for more effective support by providing gifts that correspond to the emotional state of the couple.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: In the dialogue section, each spouse opens up to the AI ​​chatbot about their personality, values, current state of their relationship, and any issues they may have. For example, if a user uses a generation AI (e.g., LLM) to input information such as "I'm not communicating well with my husband," the generation AI will generate appropriate questions and continue the dialogue with the user. Step 2: The analysis unit analyzes the dialogue content obtained by the dialogue unit to identify the couple's compatibility and potential problems. For example, if the generation AI asks, "What values ​​do you value?" and the user answers, "I value spending time with my family," the analysis will be based on that information. Step 3: The advice section provides advice tailored to each individual's personality and situation based on the compatibility and problems identified by the analysis section. For example, it may provide specific advice such as, "To improve communication with your partner, try planning a date once a week." Step 4: The follow-up department periodically checks with the couple to see how their relationship is progressing. For example, they regularly ask questions like, "How is your communication going these days?" to see how things are going.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

[0106] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

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

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

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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. In the dialogue section, each couple opens up to the AI ​​chatbot about their personalities, values, current state of their relationship, and challenges. an analysis unit that analyzes the dialogue content acquired by the dialogue unit and identifies the compatibility and potential problems between the couple; an advice providing unit that provides advice tailored to each person's personality and situation based on the compatibility and problems identified by the analysis unit; A follow-up department that periodically follows up on the couple's situation and checks the progress of improving the relationship is provided. A system characterized by:

2. The dialogue unit Analyzes the user's facial expressions and tone of voice in real time and provides questions and advice according to their emotional state.

2. The system of claim 1.

3. The analysis unit Analyzing public information on the user's SNS or blog to understand the user's personality and values ​​in more detail 2. The system of claim 1.

4. The advice providing unit Learn from the user's past actions and reactions to provide more effective advice 2. The system of claim 1.

5. The follow-up unit Learn the user's past follow-up data and perform the follow-up at the optimal time 2. The system of claim 1.

6. The dialogue unit An emotion estimation function is used to estimate the user's emotion in real time, and the dialogue content is generated according to the emotion.

2. The system of claim 1.

7. The analysis unit Track changes in the user's emotions using an emotion estimation function and analyze the user's personality and values ​​based on the emotions.

2. The system of claim 1.

8. The advice providing unit Using emotion estimation function, the advice is provided in real time according to the user's emotions.

2. The system of claim 1.

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