System

Abstract

An object of a system according to an embodiment is to make an effective call to increase motivation on the basis of personal data and a life pattern of a user.SOLUTION: A system includes a collection unit, an analysis unit, a talking unit, an evaluation unit, and a generation unit. The collection unit collects personal data and life patterns of a user. The analysis unit analyzes the data collected by the collection unit and generates an effective remark for increasing the user's motivation. The speaking part speaks to the user on the basis of the speech generated by the analysis part. The evaluation section monitors a reaction of the user to the speaking performed by the speaking section and evaluates an effect of the speaking. The generator generates a next remark on the basis of the evaluation result obtained by the evaluator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been sufficient in providing effective encouragement to motivate users based on their personal data and lifestyle patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to provide effective encouragement to increase motivation based on the user's personal data and lifestyle patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prompting unit, an evaluation unit, and a generation unit. The collection unit collects personal data and lifestyle patterns of a user. The analysis unit analyzes the data collected by the collection unit and generates effective prompts to increase the user's motivation. The prompting unit prompts the user based on the prompts generated by the analysis unit. The evaluation unit monitors the user's reaction to the prompts made by the prompting unit and evaluates the effectiveness of the prompts. The generation unit generates the next prompt based on the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide effective encouragement to increase motivation based on the user's personal data and lifestyle patterns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention learns personal data, lifestyle patterns, and various counseling data to provide motivational encouragement to people who lack motivation but have something they want to do. The AI ​​system collects a user's personal data and lifestyle patterns, analyzes them, and generates optimal encouragement to motivate the user. Furthermore, the AI ​​monitors the user's reactions, evaluates the effectiveness of the encouragement, and generates the next encouragement. For example, the AI ​​system collects data such as the user's age, gender, occupation, hobbies, and daily behavior patterns. The AI ​​then analyzes the collected data and generates optimal encouragement to motivate the user. For example, if the user is tired, the AI ​​system might encourage the user by saying, "Take a short break and refresh yourself." Furthermore, the AI ​​monitors the user's reactions and evaluates the effectiveness of the encouragement. For example, the AI ​​records the user's response to the encouragement and reflects it in the next encouragement. This allows the AI ​​system to provide individually optimized encouragement to people who lack motivation but have something they want to do, thereby increasing their motivation. This allows the AI ​​system to provide individually optimized encouragement to people who lack motivation but have something they want to do, thereby increasing their motivation. For example, if a user wants to achieve a specific goal, the system will encourage the user by saying something like, "Let's make some progress today," to motivate the user.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a prompting unit, an evaluation unit, and a generation unit. The collection unit collects personal data and lifestyle patterns of a user. The collection unit collects data such as the user's age, gender, occupation, hobbies, and daily behavioral patterns. The collection unit can also collect data using sensors and app usage history. The analysis unit analyzes the data collected by the collection unit and generates effective prompts to motivate the user. The analysis unit, for example, learns past counseling data and psychological knowledge and generates appropriate prompts according to the user's situation. The prompting unit prompts the user based on the prompts generated by the analysis unit. For example, if the user is tired, the prompting unit may suggest, "Take a short break and refresh yourself." The evaluation unit monitors the user's reaction to the prompts made by the prompting unit and evaluates the effectiveness of the prompts. For example, the evaluation unit records the user's reaction and reflects it in the next prompt. The generation unit generates the next prompt based on the evaluation result obtained by the evaluation unit. The generation unit adjusts the content of the next prompt based on the evaluation result, for example. As a result, the AI ​​system according to the embodiment can provide individually optimized prompts based on the user's personal data and lifestyle patterns, thereby increasing the user's motivation.

[0030] The collection unit can collect data including the user's age, gender, occupation, hobbies, and daily behavioral patterns. The collection unit can collect the user's age, gender, occupation, hobbies, and daily behavioral patterns, for example, using a questionnaire. The collection unit can also collect the user's daily behavioral patterns using a sensor. For example, the collection unit can analyze the user's smartphone usage history to understand the user's daily behavioral patterns. The collection unit can also analyze social media activity to collect data related to the user's hobbies. This allows the collection unit to understand basic information about the user.

[0031] The analysis unit can learn from past counseling data and psychological knowledge and generate effective prompts that match the user's situation. The analysis unit, for example, learns from past counseling data and generates appropriate prompts that match the user's situation. For example, the analysis unit analyzes counseling records and session content to understand the user's psychological state. The analysis unit can also learn from psychological knowledge and generate prompts that match the user's situation. For example, the analysis unit generates prompts to increase the user's motivation based on psychological theory and research results. This allows the analysis unit to provide appropriate prompts that match the user's situation.

[0032] If the user is tired, the voice prompting unit can say, "Take a break and refresh yourself." For example, the voice prompting unit can detect that the user is tired using a sensor and say, "Take a break and refresh yourself." The voice prompting unit can also make similar suggestions when the user reports that they are tired. For example, the voice prompting unit can analyze the user's facial expression and tone of voice to determine that they are tired. This allows the voice prompting unit to make appropriate suggestions according to the user's situation.

[0033] The evaluation unit can record the user's reaction and reflect it in the next call. For example, the evaluation unit can record the user's reaction and reflect it in the next call. For example, the evaluation unit can record changes in the user's facial expression and tone of voice and reflect it in the next call. The evaluation unit can also collect user feedback and reflect it in the next call. For example, the evaluation unit can adjust the content of the next call based on the feedback provided by the user. This allows the evaluation unit to make more effective calls to the user.

[0034] The generation unit can generate the next call based on the evaluation result obtained by the evaluation unit. The generation unit generates the next call based on, for example, the evaluation result obtained by the evaluation unit. For example, the generation unit adjusts the content of the next call based on the evaluation result. The generation unit can also adjust the timing of the next call based on the evaluation result. For example, the generation unit preferentially selects the content of the call that has received a good response from the user. This allows the generation unit to generate the next call more effectively.

[0035] The collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavioral patterns and selects the optimal data collection method. For example, the collection unit determines the timing of data collection based on behaviors the user frequently performed in the past. The collection unit can also select the most efficient data collection method from the user's past behavioral patterns. For example, the collection unit analyzes the user's behavioral patterns and adjusts the frequency of data collection. This allows the collection unit to select the optimal data collection method based on the user's past behavioral patterns.

[0036] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also collect only necessary data depending on the user's living situation. For example, the collection unit filters unnecessary data based on the user's areas of interest. This allows the collection unit to collect only necessary data based on the user's current living situation and areas of interest.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. For example, if the user prefers voice input, the collection unit may preferentially collect voice data. Also, if the user prefers text input, the collection unit may preferentially collect text data. For example, if the user prefers image input, the collection unit may preferentially collect image data. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0038] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, the collection unit filters highly relevant data based on the user's location information. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit analyzes the user's social media activities and collects relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and select an optimal data collection method. For example, the collection unit filters highly relevant data based on the user's social media activities. This allows the collection unit to collect relevant data based on the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the user in the past. The collection unit can also select an optimal data collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency and timing of data collection based on the user's feedback. This allows the collection unit to customize the collection method based on the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis based on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a behavior analysis algorithm to user behavior data. The analysis unit can also apply an emotion analysis algorithm to user emotion data. For example, the analysis unit applies a lifestyle pattern analysis algorithm to user lifestyle pattern data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also perform simplified analysis on older data. For example, the analysis unit determines the analysis priority based on the time of data collection. This allows the analysis unit to determine the analysis priority based on the time of data collection.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also perform simplified analysis on less relevant data. For example, the analysis unit determines the order of analysis based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the analysis unit can also provide analysis results in simple language. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] The voice-calling unit can customize the content of the voice message based on the user's current situation when speaking to the user. For example, the voice-calling unit customizes the content of the voice message based on the user's current situation when speaking to the user. For example, if the user is tired, the voice-calling unit can say, "Take a short break and refresh yourself." Also, if the user is concentrating, the voice-calling unit can say, "Keep up the good work." For example, if the user is feeling stressed, the voice-calling unit can say, "Take a deep breath and relax." In this way, the voice-calling unit can customize the content of the voice message according to the user's current situation.

[0048] The calling unit can improve the accuracy of the call by referring to the user's past responses when calling out. For example, the calling unit can improve the accuracy of the call by referring to the user's past responses when calling out. For example, the calling unit prioritizes calls to which the user has responded favorably in the past. The calling unit can also analyze the user's past responses and select the optimal calling method. For example, the calling unit can adjust the content of the call by reflecting the user's past responses. In this way, the calling unit can improve the accuracy of the call by referring to the user's past responses.

[0049] The calling unit can select the optimal timing to call out to the user based on the user's lifestyle rhythm when calling out to the user. For example, the calling unit selects the optimal timing to call out to the user based on the user's lifestyle rhythm when calling out to the user. For example, if the user has a morning-type lifestyle rhythm, the calling unit can call out to the user in the morning hours. Also, if the user has a night-type lifestyle rhythm, the calling unit can call out to the user in the evening hours. For example, the calling unit selects the optimal timing to call out to the user based on the user's lifestyle rhythm. This allows the calling unit to select the optimal timing to call out to the user based on the user's lifestyle rhythm.

[0050] The voice-calling unit can select optimal voice content in consideration of the user's geographical location information when calling out to the user. For example, the voice-calling unit selects optimal voice content in consideration of the user's geographical location information when calling out to the user. For example, when the user is at home, the voice-calling unit can select voice content that helps the user to relax. Furthermore, when the user is at work, the voice-calling unit can also select voice content that helps the user to concentrate on their work. For example, the voice-calling unit selects optimal voice content based on the user's geographical location information. This allows the voice-calling unit to select optimal voice content based on the user's geographical location information.

[0051] The message-making unit can analyze the user's social media activity and customize the message content when sending a message. For example, the message-making unit analyzes the user's social media activity and customizes the message content when sending a message. For example, the message-making unit sends a relevant message based on information shared by the user on social media. The message-making unit can also analyze the user's social media activity and select optimal message content. For example, the message-making unit customizes the message content based on the user's social media activity. This allows the message-making unit to customize the message content based on the user's social media activity.

[0052] The calling unit can improve the calling method by reflecting the user's past feedback when calling out. For example, the calling unit improves the calling method by reflecting the user's past feedback when calling out. For example, the calling unit adjusts the calling method based on feedback provided by the user in the past. The calling unit can also select the optimal calling method by reflecting the user's past feedback. For example, the calling unit adjusts the frequency and timing of calling out based on the user's feedback. This allows the calling unit to improve the calling method based on the user's past feedback.

[0053] The evaluation unit can optimize the evaluation algorithm by referring to the user's past reaction data during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm by referring to the user's past reaction data during evaluation. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past reaction data. The evaluation unit can also select an optimal evaluation method by referring to the user's past reaction data. For example, the evaluation unit optimizes the evaluation algorithm by reflecting the user's past reaction data. This allows the evaluation unit to optimize the evaluation algorithm based on the user's past reaction data.

[0054] The evaluation unit can update the evaluation data to reflect user feedback during evaluation. For example, the evaluation unit updates the evaluation data to reflect user feedback during evaluation. For example, the evaluation unit updates the evaluation data based on feedback provided by the user. The evaluation unit can also adjust the evaluation algorithm to reflect user feedback. For example, the evaluation unit updates the evaluation data based on user feedback. This allows the evaluation unit to update the evaluation data based on user feedback.

[0055] The evaluation unit can analyze the user's lifestyle rhythm at the time of evaluation and adjust the timing of the evaluation. The evaluation unit, for example, analyzes the user's lifestyle rhythm at the time of evaluation and adjusts the timing of the evaluation. For example, if the user has a morning-type lifestyle rhythm, the evaluation unit can perform the evaluation in the morning hours. Also, if the user has a nocturnal lifestyle rhythm, the evaluation unit can perform the evaluation in the evening hours. For example, the evaluation unit adjusts the timing of the evaluation based on the user's lifestyle rhythm. This allows the evaluation unit to adjust the timing of the evaluation based on the user's lifestyle rhythm.

[0056] The evaluation unit can weight the evaluation data based on the time of data collection during evaluation. The evaluation unit, for example, weights the evaluation data based on the time of data collection during evaluation. For example, the evaluation unit assigns a higher weight to the most recent data. The evaluation unit can also assign a lower weight to older data. For example, the evaluation unit adjusts the weighting of the evaluation data based on the time of data collection. This allows the evaluation unit to weight the evaluation data based on the time of data collection.

[0057] The evaluation unit can improve the accuracy of the evaluation by analyzing the user's social media activity during the evaluation. For example, the evaluation unit can analyze the user's social media activity during the evaluation to improve the accuracy of the evaluation. For example, the evaluation unit can analyze the user's social media activity and reflect it in the evaluation data. The evaluation unit can also adjust the evaluation algorithm based on the user's social media activity. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the user's social media activity. This allows the evaluation unit to improve the accuracy of the evaluation based on the user's social media activity.

[0058] The evaluation unit can adjust the evaluation algorithm by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the evaluation algorithm by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the evaluation algorithm based on feedback provided by the user. The evaluation unit can also optimize the evaluation algorithm by reflecting the user's past feedback. For example, the evaluation unit adjusts the evaluation algorithm based on the user's feedback. This allows the evaluation unit to adjust the evaluation algorithm based on the user's past feedback.

[0059] The generation unit can optimize the next call algorithm by referring to the user's past reaction data at the time of generation. The generation unit, for example, optimizes the next call algorithm by referring to the user's past reaction data at the time of generation. For example, the generation unit adjusts the next call algorithm based on the user's past reaction data. The generation unit can also select an optimal call method by referring to the user's past reaction data. For example, the generation unit optimizes the next call algorithm by reflecting the user's past reaction data. This allows the generation unit to optimize the next call algorithm based on the user's past reaction data.

[0060] The generation unit can update the next call content by reflecting user feedback at the time of generation. For example, the generation unit updates the next call content by reflecting user feedback at the time of generation. For example, the generation unit updates the next call content based on feedback provided by the user. The generation unit can also select an optimal call method by reflecting user feedback. For example, the generation unit updates the next call content based on user feedback. In this way, the generation unit can update the next call content based on user feedback.

[0061] The generation unit can analyze the user's lifestyle rhythm at the time of generation and adjust the timing of the next call. For example, the generation unit analyzes the user's lifestyle rhythm at the time of generation and adjust the timing of the next call. For example, if the user has a morning-type lifestyle rhythm, the generation unit can call out to the user in the morning hours. Also, if the user has a night-type lifestyle rhythm, the generation unit can call out to the user in the evening hours. For example, the generation unit adjusts the timing of the next call based on the user's lifestyle rhythm. This allows the generation unit to adjust the timing of the next call based on the user's lifestyle rhythm.

[0062] The generation unit can weight the next call content based on the time of data collection at the time of generation. The generation unit, for example, weights the next call content based on the time of data collection at the time of generation. For example, the generation unit increases the weighting for the most recent data. The generation unit can also decrease the weighting for older data. For example, the generation unit adjusts the weighting for the next call content based on the time of data collection. This allows the generation unit to weight the next call content based on the time of data collection.

[0063] The generation unit can analyze the user's social media activity at the time of generation to customize the content of the next message. For example, the generation unit analyzes the user's social media activity at the time of generation to customize the content of the next message. For example, the generation unit performs a relevant message based on information shared by the user on social media. The generation unit can also analyze the user's social media activity to select the optimal message content. For example, the generation unit customizes the message content based on the user's social media activity. This allows the generation unit to customize the content of the next message based on the user's social media activity.

[0064] The generation unit can adjust the next voice-call algorithm by reflecting the user's past feedback at the time of generation. For example, the generation unit adjusts the next voice-call algorithm by reflecting the user's past feedback at the time of generation. For example, the generation unit adjusts the next voice-call algorithm based on feedback provided by the user. The generation unit can also optimize the next voice-call algorithm by reflecting the user's past feedback. For example, the generation unit adjusts the next voice-call algorithm based on the user's feedback. This allows the generation unit to adjust the next voice-call algorithm based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. For example, the collection unit determines the timing of data collection based on the user's frequent behavior in the past. The collection unit can also select the most efficient data collection method from the user's past behavioral patterns. For example, the collection unit analyzes the user's behavioral patterns and adjusts the frequency of data collection. This allows the collection unit to select the optimal data collection method based on the user's past behavioral patterns.

[0067] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, the collection unit filters highly relevant data based on the user's location information. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a behavior analysis algorithm to user behavior data. The analysis unit can also apply an emotion analysis algorithm to user emotion data. For example, the analysis unit applies a lifestyle pattern analysis algorithm to user lifestyle pattern data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0069] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past reaction data. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past reaction data. The evaluation unit can also select an optimal evaluation method by referring to the user's past reaction data. For example, the evaluation unit optimizes the evaluation algorithm by reflecting the user's past reaction data. This allows the evaluation unit to optimize the evaluation algorithm based on the user's past reaction data.

[0070] The generation unit can update the next call content by reflecting the user's feedback when generating the content. For example, the generation unit updates the next call content based on the feedback provided by the user. The generation unit can also select the optimal call method by reflecting the user's feedback. For example, the generation unit updates the next call content based on the user's feedback. In this way, the generation unit can update the next call content based on the user's feedback.

[0071] During evaluation, the evaluation unit can weight the evaluation data based on the time the data was collected. For example, the evaluation unit can assign a higher weight to the most recent data. The evaluation unit can also assign a lower weight to older data. For example, the evaluation unit can adjust the weight of the evaluation data based on the time the data was collected. This allows the evaluation unit to weight the evaluation data based on the time the data was collected.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects personal data and lifestyle patterns of the user. The collection unit collects data such as the user's age, gender, occupation, hobbies, and daily behavior patterns. The collection unit can also collect data using sensors and app usage history. Step 2: The analysis unit analyzes the data collected by the collection unit and generates effective prompts to motivate the user. For example, the analysis unit learns from past counseling data and psychological knowledge and generates appropriate prompts according to the user's situation. Step 3: The prompting unit prompts the user based on the prompt generated by the analysis unit. For example, if the user is tired, the prompting unit prompts the user, saying, "Take a short break and refresh yourself." Step 4: The evaluation unit monitors the user's reaction to the prompt made by the prompt unit and evaluates the effectiveness of the prompt. For example, the evaluation unit records the user's reaction and reflects it in the next prompt. Step 5: The generation unit generates the next prompt based on the evaluation result obtained by the evaluation unit. The generation unit adjusts the content of the next prompt based on the evaluation result, for example.

[0074] (Example 2) An AI system according to an embodiment of the present invention learns personal data, lifestyle patterns, and various counseling data to provide motivational encouragement to people who lack motivation but have something they want to do. The AI ​​system collects a user's personal data and lifestyle patterns, analyzes them, and generates optimal encouragement to motivate the user. Furthermore, the AI ​​monitors the user's reactions, evaluates the effectiveness of the encouragement, and generates the next encouragement. For example, the AI ​​system collects data such as the user's age, gender, occupation, hobbies, and daily behavior patterns. The AI ​​then analyzes the collected data and generates optimal encouragement to motivate the user. For example, if the user is tired, the AI ​​system might encourage the user by saying, "Take a short break and refresh yourself." Furthermore, the AI ​​monitors the user's reactions and evaluates the effectiveness of the encouragement. For example, the AI ​​records the user's response to the encouragement and reflects it in the next encouragement. This allows the AI ​​system to provide individually optimized encouragement to people who lack motivation but have something they want to do, thereby increasing their motivation. This allows the AI ​​system to provide individually optimized encouragement to people who lack motivation but have something they want to do, thereby increasing their motivation. For example, if a user wants to achieve a specific goal, the system will encourage the user by saying something like, "Let's make some progress today," to motivate the user.

[0075] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a prompting unit, an evaluation unit, and a generation unit. The collection unit collects personal data and lifestyle patterns of a user. The collection unit collects data such as the user's age, gender, occupation, hobbies, and daily behavioral patterns. The collection unit can also collect data using sensors and app usage history. The analysis unit analyzes the data collected by the collection unit and generates effective prompts to motivate the user. The analysis unit, for example, learns past counseling data and psychological knowledge and generates appropriate prompts according to the user's situation. The prompting unit prompts the user based on the prompts generated by the analysis unit. For example, if the user is tired, the prompting unit may suggest, "Take a short break and refresh yourself." The evaluation unit monitors the user's reaction to the prompts made by the prompting unit and evaluates the effectiveness of the prompts. For example, the evaluation unit records the user's reaction and reflects it in the next prompt. The generation unit generates the next prompt based on the evaluation result obtained by the evaluation unit. The generation unit adjusts the content of the next prompt based on the evaluation result, for example. As a result, the AI ​​system according to the embodiment can provide individually optimized prompts based on the user's personal data and lifestyle patterns, thereby increasing the user's motivation.

[0076] The collection unit can collect data including the user's age, gender, occupation, hobbies, and daily behavioral patterns. The collection unit can collect the user's age, gender, occupation, hobbies, and daily behavioral patterns, for example, using a questionnaire. The collection unit can also collect the user's daily behavioral patterns using a sensor. For example, the collection unit can analyze the user's smartphone usage history to understand the user's daily behavioral patterns. The collection unit can also analyze social media activity to collect data related to the user's hobbies. This allows the collection unit to understand basic information about the user.

[0077] The analysis unit can learn from past counseling data and psychological knowledge and generate effective prompts that match the user's situation. The analysis unit, for example, learns from past counseling data and generates appropriate prompts that match the user's situation. For example, the analysis unit analyzes counseling records and session content to understand the user's psychological state. The analysis unit can also learn from psychological knowledge and generate prompts that match the user's situation. For example, the analysis unit generates prompts to increase the user's motivation based on psychological theory and research results. This allows the analysis unit to provide appropriate prompts that match the user's situation.

[0078] If the user is tired, the voice prompting unit can say, "Take a break and refresh yourself." For example, the voice prompting unit can detect that the user is tired using a sensor and say, "Take a break and refresh yourself." The voice prompting unit can also make similar suggestions when the user reports that they are tired. For example, the voice prompting unit can analyze the user's facial expression and tone of voice to determine that they are tired. This allows the voice prompting unit to make appropriate suggestions according to the user's situation.

[0079] The evaluation unit can record the user's reaction and reflect it in the next call. For example, the evaluation unit can record the user's reaction and reflect it in the next call. For example, the evaluation unit can record changes in the user's facial expression and tone of voice and reflect it in the next call. The evaluation unit can also collect user feedback and reflect it in the next call. For example, the evaluation unit can adjust the content of the next call based on the feedback provided by the user. This allows the evaluation unit to make more effective calls to the user.

[0080] The generation unit can generate the next call based on the evaluation result obtained by the evaluation unit. The generation unit generates the next call based on, for example, the evaluation result obtained by the evaluation unit. For example, the generation unit adjusts the content of the next call based on the evaluation result. The generation unit can also adjust the timing of the next call based on the evaluation result. For example, the generation unit preferentially selects the content of the call that has received a good response from the user. This allows the generation unit to generate the next call more effectively.

[0081] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects data during a time period when the user is relaxing. Furthermore, if the user is concentrating, the collection unit can also collect data by avoiding that time period. For example, if the user is tired, the collection unit collects data after the user has rested. This allows the collection unit to collect data at the optimal timing depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavioral patterns and selects the optimal data collection method. For example, the collection unit determines the timing of data collection based on behaviors the user frequently performed in the past. The collection unit can also select the most efficient data collection method from the user's past behavioral patterns. For example, the collection unit analyzes the user's behavioral patterns and adjusts the frequency of data collection. This allows the collection unit to select the optimal data collection method based on the user's past behavioral patterns.

[0083] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit performs filtering based on the user's current living situation and areas of interest when collecting data. For example, the collection unit preferentially collects data related to areas in which the user is currently interested. The collection unit can also collect only necessary data depending on the user's living situation. For example, the collection unit filters unnecessary data based on the user's areas of interest. This allows the collection unit to collect only necessary data based on the user's current living situation and areas of interest.

[0084] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. For example, if the user prefers voice input, the collection unit may preferentially collect voice data. Also, if the user prefers text input, the collection unit may preferentially collect text data. For example, if the user prefers image input, the collection unit may preferentially collect image data. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0085] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, if the user is feeling stressed, the collection unit prioritizes collecting data related to stress reduction. Also, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. For example, if the user is excited, the collection unit prioritizes collecting data related to excitement. In this way, the collection unit can determine the priority of data to be collected according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, the collection unit filters highly relevant data based on the user's location information. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0087] The collection unit can analyze the user's social media activities and collect relevant data when collecting data. For example, the collection unit analyzes the user's social media activities and collects relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and select an optimal data collection method. For example, the collection unit filters highly relevant data based on the user's social media activities. This allows the collection unit to collect relevant data based on the user's social media activities.

[0088] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the user in the past. The collection unit can also select an optimal data collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency and timing of data collection based on the user's feedback. This allows the collection unit to customize the collection method based on the user's past feedback.

[0089] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the way the analysis is presented based on the estimated user's emotion. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the analysis unit to adjust the way the analysis is presented depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis based on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0091] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a behavior analysis algorithm to user behavior data. The analysis unit can also apply an emotion analysis algorithm to user emotion data. For example, the analysis unit applies a lifestyle pattern analysis algorithm to user lifestyle pattern data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0092] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The analysis unit can determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also perform simplified analysis on older data. For example, the analysis unit determines the analysis priority based on the time of data collection. This allows the analysis unit to determine the analysis priority based on the time of data collection.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also perform simplified analysis on less relevant data. For example, the analysis unit determines the order of analysis based on the relevance of the data. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0096] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical knowledge, the analysis unit can also provide analysis results in simple language. For example, the analysis unit adjusts the way in which the analysis results are expressed according to the user's level of expertise. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0097] The voice-calling unit can estimate the user's emotion and adjust the way in which the voice is expressed based on the estimated user's emotion. The voice-calling unit, for example, estimates the user's emotion and adjusts the way in which the voice-calling unit expresses the user's emotion based on the estimated user's emotion. For example, if the user is nervous, the voice-calling unit can speak to the user in a calm voice. Also, if the user is relaxed, the voice-calling unit can speak to the user in a cheerful voice. For example, if the user is excited, the voice-calling unit can speak to the user in a cheerful voice. In this way, the voice-calling unit can adjust the way in which the voice-calling unit expresses the user's emotion according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The voice-calling unit can customize the content of the voice message based on the user's current situation when speaking to the user. For example, the voice-calling unit customizes the content of the voice message based on the user's current situation when speaking to the user. For example, if the user is tired, the voice-calling unit can say, "Take a short break and refresh yourself." Also, if the user is concentrating, the voice-calling unit can say, "Keep up the good work." For example, if the user is feeling stressed, the voice-calling unit can say, "Take a deep breath and relax." In this way, the voice-calling unit can customize the content of the voice message according to the user's current situation.

[0099] The calling unit can improve the accuracy of the call by referring to the user's past responses when calling out. For example, the calling unit can improve the accuracy of the call by referring to the user's past responses when calling out. For example, the calling unit prioritizes calls to which the user has responded favorably in the past. The calling unit can also analyze the user's past responses and select the optimal calling method. For example, the calling unit can adjust the content of the call by reflecting the user's past responses. In this way, the calling unit can improve the accuracy of the call by referring to the user's past responses.

[0100] The calling unit can select the optimal timing to call out to the user based on the user's lifestyle rhythm when calling out to the user. For example, the calling unit selects the optimal timing to call out to the user based on the user's lifestyle rhythm when calling out to the user. For example, if the user has a morning-type lifestyle rhythm, the calling unit can call out to the user in the morning hours. Also, if the user has a night-type lifestyle rhythm, the calling unit can call out to the user in the evening hours. For example, the calling unit selects the optimal timing to call out to the user based on the user's lifestyle rhythm. This allows the calling unit to select the optimal timing to call out to the user based on the user's lifestyle rhythm.

[0101] The voice-calling unit can estimate the user's emotion and adjust the frequency of voice calls based on the estimated user's emotion. The voice-calling unit, for example, estimates the user's emotion and adjusts the frequency of voice calls based on the estimated user's emotion. For example, the voice-calling unit reduces the frequency of voice calls when the user is stressed. The voice-calling unit can also increase the frequency of voice calls when the user is relaxed. For example, the voice-calling unit adjusts the frequency of voice calls when the user is excited. In this way, the voice-calling unit can adjust the frequency of voice calls according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The voice-calling unit can select optimal voice content in consideration of the user's geographical location information when calling out to the user. For example, the voice-calling unit selects optimal voice content in consideration of the user's geographical location information when calling out to the user. For example, when the user is at home, the voice-calling unit can select voice content that helps the user to relax. Furthermore, when the user is at work, the voice-calling unit can also select voice content that helps the user to concentrate on their work. For example, the voice-calling unit selects optimal voice content based on the user's geographical location information. This allows the voice-calling unit to select optimal voice content based on the user's geographical location information.

[0103] The message-making unit can analyze the user's social media activity and customize the message content when sending a message. For example, the message-making unit analyzes the user's social media activity and customizes the message content when sending a message. For example, the message-making unit sends a relevant message based on information shared by the user on social media. The message-making unit can also analyze the user's social media activity and select optimal message content. For example, the message-making unit customizes the message content based on the user's social media activity. This allows the message-making unit to customize the message content based on the user's social media activity.

[0104] The calling unit can improve the calling method by reflecting the user's past feedback when calling out. For example, the calling unit improves the calling method by reflecting the user's past feedback when calling out. For example, the calling unit adjusts the calling method based on feedback provided by the user in the past. The calling unit can also select the optimal calling method by reflecting the user's past feedback. For example, the calling unit adjusts the frequency and timing of calling out based on the user's feedback. This allows the calling unit to improve the calling method based on the user's past feedback.

[0105] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. For example, the evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user's emotion. For example, if the user is nervous, the evaluation unit provides a simple, highly visible evaluation method. The evaluation unit can also provide a detailed evaluation method if the user is relaxed. For example, if the user is excited, the evaluation unit provides a visually stimulating evaluation method. This allows the evaluation unit to adjust the evaluation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The evaluation unit can optimize the evaluation algorithm by referring to the user's past reaction data during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm by referring to the user's past reaction data during evaluation. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past reaction data. The evaluation unit can also select an optimal evaluation method by referring to the user's past reaction data. For example, the evaluation unit optimizes the evaluation algorithm by reflecting the user's past reaction data. This allows the evaluation unit to optimize the evaluation algorithm based on the user's past reaction data.

[0107] The evaluation unit can update the evaluation data to reflect user feedback during evaluation. For example, the evaluation unit updates the evaluation data to reflect user feedback during evaluation. For example, the evaluation unit updates the evaluation data based on feedback provided by the user. The evaluation unit can also adjust the evaluation algorithm to reflect user feedback. For example, the evaluation unit updates the evaluation data based on user feedback. This allows the evaluation unit to update the evaluation data based on user feedback.

[0108] The evaluation unit can analyze the user's lifestyle rhythm at the time of evaluation and adjust the timing of the evaluation. The evaluation unit, for example, analyzes the user's lifestyle rhythm at the time of evaluation and adjusts the timing of the evaluation. For example, if the user has a morning-type lifestyle rhythm, the evaluation unit can perform the evaluation in the morning hours. Also, if the user has a nocturnal lifestyle rhythm, the evaluation unit can perform the evaluation in the evening hours. For example, the evaluation unit adjusts the timing of the evaluation based on the user's lifestyle rhythm. This allows the evaluation unit to adjust the timing of the evaluation based on the user's lifestyle rhythm.

[0109] The evaluation unit can estimate the user's emotion and adjust the frequency of evaluations based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion and adjusts the frequency of evaluations based on the estimated user's emotion. For example, the evaluation unit reduces the frequency of evaluations when the user is stressed. The evaluation unit can also increase the frequency of evaluations when the user is relaxed. For example, the evaluation unit adjusts the frequency of evaluations when the user is excited. This allows the evaluation unit to adjust the frequency of evaluations according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The evaluation unit can weight the evaluation data based on the time of data collection during evaluation. The evaluation unit, for example, weights the evaluation data based on the time of data collection during evaluation. For example, the evaluation unit assigns a higher weight to the most recent data. The evaluation unit can also assign a lower weight to older data. For example, the evaluation unit adjusts the weighting of the evaluation data based on the time of data collection. This allows the evaluation unit to weight the evaluation data based on the time of data collection.

[0111] The evaluation unit can improve the accuracy of the evaluation by analyzing the user's social media activity during the evaluation. For example, the evaluation unit can analyze the user's social media activity during the evaluation to improve the accuracy of the evaluation. For example, the evaluation unit can analyze the user's social media activity and reflect it in the evaluation data. The evaluation unit can also adjust the evaluation algorithm based on the user's social media activity. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the user's social media activity. This allows the evaluation unit to improve the accuracy of the evaluation based on the user's social media activity.

[0112] The evaluation unit can adjust the evaluation algorithm by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the evaluation algorithm by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the evaluation algorithm based on feedback provided by the user. The evaluation unit can also optimize the evaluation algorithm by reflecting the user's past feedback. For example, the evaluation unit adjusts the evaluation algorithm based on the user's feedback. This allows the evaluation unit to adjust the evaluation algorithm based on the user's past feedback.

[0113] The generation unit can estimate the user's emotions and adjust the content of the next message based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the content of the next message based on the estimated user emotions. For example, if the user is relaxed, the generation unit issues a message encouraging the user to relax. Furthermore, if the user is feeling stressed, the generation unit can also issue a message encouraging the user to reduce stress. For example, if the user is excited, the generation unit issues a message to calm the user. This allows the generation unit to adjust the content of the next message according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0114] The generation unit can optimize the next call algorithm by referring to the user's past reaction data at the time of generation. The generation unit, for example, optimizes the next call algorithm by referring to the user's past reaction data at the time of generation. For example, the generation unit adjusts the next call algorithm based on the user's past reaction data. The generation unit can also select an optimal call method by referring to the user's past reaction data. For example, the generation unit optimizes the next call algorithm by reflecting the user's past reaction data. This allows the generation unit to optimize the next call algorithm based on the user's past reaction data.

[0115] The generation unit can update the next call content by reflecting user feedback at the time of generation. For example, the generation unit updates the next call content by reflecting user feedback at the time of generation. For example, the generation unit updates the next call content based on feedback provided by the user. The generation unit can also select an optimal call method by reflecting user feedback. For example, the generation unit updates the next call content based on user feedback. In this way, the generation unit can update the next call content based on user feedback.

[0116] The generation unit can analyze the user's lifestyle rhythm at the time of generation and adjust the timing of the next call. For example, the generation unit analyzes the user's lifestyle rhythm at the time of generation and adjust the timing of the next call. For example, if the user has a morning-type lifestyle rhythm, the generation unit can call out to the user in the morning hours. Also, if the user has a night-type lifestyle rhythm, the generation unit can call out to the user in the evening hours. For example, the generation unit adjusts the timing of the next call based on the user's lifestyle rhythm. This allows the generation unit to adjust the timing of the next call based on the user's lifestyle rhythm.

[0117] The generation unit can estimate the user's emotion and adjust the frequency of the next call based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the frequency of the next call based on the estimated user emotion. For example, the generation unit reduces the frequency of calls when the user is stressed. The generation unit can also increase the frequency of calls when the user is relaxed. For example, the generation unit adjusts the frequency of calls when the user is excited. In this way, the generation unit can adjust the frequency of the next call according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0118] The generation unit can weight the next call content based on the time of data collection at the time of generation. The generation unit, for example, weights the next call content based on the time of data collection at the time of generation. For example, the generation unit increases the weighting for the most recent data. The generation unit can also decrease the weighting for older data. For example, the generation unit adjusts the weighting for the next call content based on the time of data collection. This allows the generation unit to weight the next call content based on the time of data collection.

[0119] The generation unit can analyze the user's social media activity at the time of generation to customize the content of the next message. For example, the generation unit analyzes the user's social media activity at the time of generation to customize the content of the next message. For example, the generation unit performs a relevant message based on information shared by the user on social media. The generation unit can also analyze the user's social media activity to select the optimal message content. For example, the generation unit customizes the message content based on the user's social media activity. This allows the generation unit to customize the content of the next message based on the user's social media activity.

[0120] The generation unit can adjust the next voice-call algorithm by reflecting the user's past feedback at the time of generation. For example, the generation unit adjusts the next voice-call algorithm by reflecting the user's past feedback at the time of generation. For example, the generation unit adjusts the next voice-call algorithm based on feedback provided by the user. The generation unit can also optimize the next voice-call algorithm by reflecting the user's past feedback. For example, the generation unit adjusts the next voice-call algorithm based on the user's feedback. This allows the generation unit to adjust the next voice-call algorithm based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, prompting unit, evaluation unit, and generation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal data and lifestyle patterns of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate prompts to motivate the user. The prompting unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated prompts to the user. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the user's reaction to evaluate the effectiveness of the prompts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the next prompt based on the evaluation result. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prompting unit, evaluation unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal data and lifestyle patterns of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate prompts to motivate the user. The prompting unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated prompts to the user. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the user's reaction to evaluate the effectiveness of the prompts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the next prompt based on the evaluation result. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, prompting unit, evaluation unit, and generation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects personal data and lifestyle patterns of the user using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate prompts to motivate the user. The prompting unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated prompts to the user. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and monitors the user's reaction to evaluate the effectiveness of the prompts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates the next prompt based on the evaluation result. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prompting unit, evaluation unit, and generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal data and lifestyle patterns of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate prompts to motivate the user. The prompting unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated prompts to the user. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors the user's reaction to evaluate the effectiveness of the prompts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the next prompt based on the evaluation result.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing data related to stress reduction. Also, if the user is relaxed, the analysis unit can prioritize analyzing data related to relaxation. For example, if the user is excited, the analysis unit can prioritize analyzing data related to excitement. In this way, the analysis unit can determine the priority of analysis according to the user's emotions.

[0123] The collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. For example, the collection unit determines the timing of data collection based on the user's frequent behavior in the past. The collection unit can also select the most efficient data collection method from the user's past behavioral patterns. For example, the collection unit analyzes the user's behavioral patterns and adjusts the frequency of data collection. This allows the collection unit to select the optimal data collection method based on the user's past behavioral patterns.

[0124] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit provides a simple, highly visible evaluation method. Also, if the user is relaxed, the evaluation unit can provide a detailed evaluation method. For example, if the user is excited, the evaluation unit provides a visually stimulating evaluation method. This allows the evaluation unit to adjust the evaluation method according to the user's emotions.

[0125] The voice-calling unit can estimate the user's emotions and adjust the way the voice-calling unit expresses itself based on the estimated user's emotions. For example, if the user is nervous, the voice-calling unit can speak to the user in a calm voice. Also, if the user is relaxed, the voice-calling unit can speak to the user in a cheerful voice. For example, if the user is excited, the voice-calling unit can speak to the user in a cheerful voice. This allows the voice-calling unit to adjust the way the voice-calling unit expresses itself based on the user's emotions.

[0126] The generation unit can estimate the user's emotions and adjust the content of the next message based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can issue a message encouraging the user to relax. Furthermore, if the user is feeling stressed, the generation unit can issue a message encouraging the user to reduce stress. For example, if the user is excited, the generation unit can issue a message to calm the user's excitement. This allows the generation unit to adjust the content of the next message according to the user's emotions.

[0127] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, the collection unit filters highly relevant data based on the user's location information. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0128] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a behavior analysis algorithm to user behavior data. The analysis unit can also apply an emotion analysis algorithm to user emotion data. For example, the analysis unit applies a lifestyle pattern analysis algorithm to user lifestyle pattern data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0129] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past reaction data. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past reaction data. The evaluation unit can also select an optimal evaluation method by referring to the user's past reaction data. For example, the evaluation unit optimizes the evaluation algorithm by reflecting the user's past reaction data. This allows the evaluation unit to optimize the evaluation algorithm based on the user's past reaction data.

[0130] The generation unit can update the next call content by reflecting the user's feedback when generating the content. For example, the generation unit updates the next call content based on the feedback provided by the user. The generation unit can also select the optimal call method by reflecting the user's feedback. For example, the generation unit updates the next call content based on the user's feedback. In this way, the generation unit can update the next call content based on the user's feedback.

[0131] During evaluation, the evaluation unit can weight the evaluation data based on the time the data was collected. For example, the evaluation unit can assign a higher weight to the most recent data. The evaluation unit can also assign a lower weight to older data. For example, the evaluation unit can adjust the weight of the evaluation data based on the time the data was collected. This allows the evaluation unit to weight the evaluation data based on the time the data was collected.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The collection unit collects personal data and lifestyle patterns of the user. The collection unit collects data such as the user's age, gender, occupation, hobbies, and daily behavior patterns. The collection unit can also collect data using sensors and app usage history. Step 2: The analysis unit analyzes the data collected by the collection unit and generates effective prompts to motivate the user. For example, the analysis unit learns from past counseling data and psychological knowledge and generates appropriate prompts according to the user's situation. Step 3: The prompting unit prompts the user based on the prompt generated by the analysis unit. For example, if the user is tired, the prompting unit prompts the user, saying, "Take a short break and refresh yourself." Step 4: The evaluation unit monitors the user's reaction to the prompt made by the prompt unit and evaluates the effectiveness of the prompt. For example, the evaluation unit records the user's reaction and reflects it in the next prompt. Step 5: The generation unit generates the next prompt based on the evaluation result obtained by the evaluation unit. The generation unit adjusts the content of the next prompt based on the evaluation result, for example.

[0134] 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.

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

[0136] 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.

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

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects personal data and lifestyle patterns of users; an analysis unit that analyzes the data collected by the collection unit and generates effective prompts to increase the user's motivation; a voice prompting unit that prompts the user based on the prompt generated by the analysis unit; an evaluation unit that monitors a user's reaction to the call made by the call unit and evaluates the effectiveness of the call; a generation unit that generates a next prompt based on the evaluation result obtained by the evaluation unit. A system characterized by:

2. The collecting unit Collect data including the user's age, gender, occupation, hobbies, and daily behavior patterns 2. The system of claim 1.

3. The analysis unit Learns from past counseling data and psychological knowledge to generate effective prompts tailored to the user's situation 2. The system of claim 1.

4. The evaluation unit Record the user's response and use it in the next prompt 2. The system of claim 1.

5. The generation unit A next prompt is generated based on the evaluation result obtained by the evaluation unit.

2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past behavioral patterns and select the most appropriate data collection method 2. The system of claim 1.

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