System

Abstract

An object of a system according to an embodiment is to provide an appropriate reward to a user.SOLUTION: A system includes an input unit, an analysis unit, and a generation unit. The input unit receives input information from a user. The analysis unit analyzes the input information received by the input unit. The generation unit generates a reward on the basis of the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks opportunities for social praise and recognition, and there is room for improvement in terms of increasing individual satisfaction and motivation.

[0005] The system according to the embodiment aims to provide appropriate compliments to users. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, an analysis unit, and a generation unit. The input unit receives input information from a user. The analysis unit analyzes the input information received by the input unit. The generation unit generates a compliment based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate compliments to the user. [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) The generative AI service according to an embodiment of the present invention is a system that compensates for the lack of recognition that users feel in their daily lives and business situations, and creates a culture of mutual recognition and praise. As a result, the generative AI service allows users to receive compliments and messages of recognition anytime, anywhere, and as many times as they like.

[0029] A generation AI service according to an embodiment includes an input unit, an analysis unit, and a generation unit. The input unit receives input information from a user. For example, the input unit can receive text input. The input unit can also receive voice input. The input unit can also receive image input. The analysis unit analyzes the input information received by the input unit. For example, the analysis unit analyzes text using natural language processing technology. The analysis unit can also analyze voice using voice recognition technology. The analysis unit can also analyze images using image recognition technology. The generation unit generates a compliment based on the information analyzed by the analysis unit. For example, the generation unit generates a compliment using a template-based generation method. The generation unit can also generate a compliment using a machine learning model. The generation unit can also generate a customized compliment based on the user's input information. This allows the generation AI service to generate an appropriate compliment based on the user's input information.

[0030] The analysis unit can analyze the user's past behavioral history and generate compliments based on specific behavioral patterns. For example, the generation AI in the analysis unit analyzes the user's past behavioral history and generates compliments based on specific behavioral patterns. For example, if the user continues to jog every day, a message praising the user's persistence is generated. The analysis unit can also detect specific behavioral patterns based on the user's past behavioral history and generate compliments for that behavior. For example, if the user regularly participates in volunteer activities, a message praising the user's social contributions is generated. The analysis unit can also analyze the user's behavioral history and generate compliments based on specific behavioral patterns. For example, if the user achieves their monthly goal, a message sharing the user's sense of accomplishment is generated. This makes it possible to generate appropriate compliments based on the user's behavioral patterns.

[0031] The analysis unit can analyze the user's tone of voice and facial expression, and generate compliments that correspond to their emotional state. For example, the generation AI in the analysis unit analyzes the user's tone of voice and generates compliments that correspond to their emotional state. For example, if the user sounds tired, it generates words of encouragement. The analysis unit can also analyze the user's facial expression and generate compliments that correspond to their emotional state. For example, if the user speaks with a smile, it generates a message praising their smile. The analysis unit can also analyze the user's tone of voice and facial expression, and generate compliments that correspond to their emotional state. For example, if the user is nervous, it generates compliments to relax them. This makes it possible to generate appropriate compliments that correspond to the user's emotional state.

[0032] The analysis unit can analyze a user's SNS posts and generate compliments based on the content of the posts. In the analysis unit, for example, a generation AI analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a photo of a trip, a message praising the beauty of the photo is generated. The analysis unit also analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a photo of a dish, a message praising the cooking quality is generated. In addition, the analysis unit analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a goal they have achieved, a message praising their efforts is generated. This makes it possible to generate appropriate compliments based on a user's SNS posts.

[0033] The analysis unit can analyze the user's health data and generate health-related compliments. For example, the generation AI in the analysis unit analyzes data from the user's fitness tracker and generates health-related compliments. For example, if the user achieves a target number of steps, a message praising the achievement is generated. The analysis unit also analyzes the user's health data and generates health-related compliments. For example, if the user continues to exercise regularly, a message praising the user's persistence is generated. The analysis unit also analyzes the user's health data and generates health-related compliments. For example, if the user achieves a weight loss goal, a message praising the user's efforts is generated. This makes it possible to generate appropriate compliments based on the user's health data.

[0034] The analysis unit can analyze an employee's work performance data and generate words of praise based on specific achievements. For example, the generation AI in the analysis unit analyzes an employee's work performance data and generates words of praise based on specific achievements. For example, if an employee achieves a sales target, a message praising the employee's achievement is generated. The analysis unit also generates words of praise for specific achievements based on the employee's work performance data. For example, if an employee successfully completes a project, a message praising the employee's efforts is generated. The analysis unit also analyzes an employee's work performance data and generates words of praise based on specific achievements. For example, if an employee proposes a new idea and it is adopted, a message praising the employee's creativity is generated. This makes it possible to generate appropriate words of praise based on an employee's achievements.

[0035] The analysis unit can analyze communication data within an employee's team and generate compliments related to teamwork. In the analysis unit, for example, the generation AI analyzes communication data within an employee's team and generates compliments related to teamwork. For example, if a team works together to complete a project, it generates a message praising their cooperation. The analysis unit also generates compliments related to teamwork based on communication data within the team. For example, if team members support each other, it generates a message praising that support. The analysis unit also generates compliments related to teamwork based on communication data within an employee's team. For example, if a team overcomes a difficult situation, it generates a message praising their unity. This makes it possible to generate appropriate compliments based on employees' teamwork.

[0036] The analysis unit can analyze an employee's career path data and generate words of praise based on their career progress. In the analysis unit, for example, the generation AI analyzes an employee's career path data and generates words of praise based on their career progress. For example, if an employee is promoted, a message praising their efforts is generated. The analysis unit also generates words of praise for their career progress based on the employee's career path data. For example, if an employee acquires a new skill, a message praising their enthusiasm for learning is generated. The analysis unit also analyzes an employee's career path data and generates words of praise based on their career progress. For example, if an employee uses their many years of experience to successfully complete a project, a message praising their experience is generated. This makes it possible to generate appropriate words of praise based on an employee's career progress.

[0037] The analysis unit can analyze an employee's project management data and generate words of praise based on the progress of the project. In the analysis unit, for example, the generation AI analyzes an employee's project management data and generates words of praise based on the progress of the project. For example, if the project is progressing as planned, a message praising the progress is generated. The analysis unit also generates words of praise for the progress of the project based on the project management data. For example, if the project is completed within budget, a message praising the efficiency is generated. In addition, the analysis unit also generates words of praise for the progress of the project based on the project management data. For example, if the project overcomes a difficult situation, a message praising the effort is generated. In this way, appropriate words of praise can be generated based on the progress of the project.

[0038] The analysis unit can analyze the user's behavioral data at home and generate compliments based on the user's contributions at home. In the analysis unit, for example, the generation AI analyzes the user's behavioral data at home and generates compliments based on the user's contributions at home. For example, if the user helps with housework, the analysis unit generates a message praising the user's contributions. The analysis unit also generates compliments for the user's contributions at home based on the behavioral data at home. For example, if the user takes care of children, the analysis unit generates a message praising the user's efforts. The analysis unit also generates compliments for the user's contributions at home based on the behavioral data at home. For example, if the user takes care of a vegetable garden, the analysis unit generates a message praising the user's efforts. In this way, appropriate compliments can be generated based on the user's contributions at home.

[0039] The analysis unit can analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, the analysis unit uses a generation AI to analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if a user draws a picture, the analysis unit generates a message praising the user's creativity. The analysis unit also generates compliments for those hobbies and interests based on data related to the user's hobbies and interests. For example, if a user plays music, the analysis unit generates a message praising the user's musical skills. The analysis unit also uses a generation AI to analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if a user enjoys gardening, the analysis unit generates a message praising the user's efforts. This makes it possible to generate appropriate compliments based on the user's hobbies and interests.

[0040] The analysis unit can analyze the user's travel data and generate compliments based on the user's behavior at the travel destination. In the analysis unit, for example, the generation AI analyzes the user's travel data and generates compliments based on the user's behavior at the travel destination. For example, if the user explores a new place, it generates a message praising the user's adventurous spirit. The analysis unit also generates compliments for the user's behavior at the travel destination based on the user's travel data. For example, if the user learns about the local culture, it generates a message praising the user's eagerness to learn. The analysis unit also analyzes the user's travel data and generates compliments based on the user's behavior at the travel destination. For example, if the user makes a new friend while traveling, it generates a message praising the user's sociability. This makes it possible to generate appropriate compliments based on the user's behavior at the travel destination.

[0041] The analysis unit can analyze the user's cooking data and generate compliments based on the cooking results. In the analysis unit, for example, a generation AI analyzes the user's cooking data and generates compliments based on the cooking results. For example, if the user tries a new recipe, a message praising the attempt is generated. The analysis unit also generates compliments for the cooking results based on the user's cooking data. For example, if the user cooks a delicious dish, a message praising the dish's quality is generated. In addition, the analysis unit analyzes the user's cooking data and generates compliments based on the cooking results. For example, if the user cooks for their family, a message praising the user's thoughtfulness is generated. This makes it possible to generate appropriate compliments based on the cooking results.

[0042] The analysis unit can analyze a child's learning data and generate words of praise based on their learning achievements. In the analysis unit, for example, a generation AI analyzes a child's learning data and generates words of praise based on their learning achievements. For example, if a child gets a high score on a test, a message praising the child's efforts is generated. The analysis unit also generates words of praise for learning achievements based on the child's learning data. For example, if a child acquires a new skill, a message praising the child's motivation to learn is generated. In addition, the analysis unit analyzes a child's learning data and generates words of praise based on their learning achievements. For example, if a child completes their homework properly, a message praising the child's responsibility is generated. This makes it possible to generate appropriate words of praise based on learning achievements.

[0043] The analysis unit can analyze the activity data of the elderly person and generate compliments based on the efforts made in daily life. For example, the generation AI in the analysis unit analyzes the activity data of the elderly person and generates compliments based on the efforts made in daily life. For example, if an elderly person continues to take walks every day, a message praising their persistence is generated. The analysis unit also generates compliments for the efforts made in daily life based on the activity data of the elderly person. For example, if an elderly person enjoys a hobby, a message praising their initiative is generated. The analysis unit also generates compliments based on the efforts made in daily life based on the activity data of the elderly person. For example, if an elderly person cooks for their family, a message praising their thoughtfulness is generated. This makes it possible to generate appropriate compliments based on the efforts made in daily life.

[0044] The analysis unit can analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, the analysis unit uses a generation AI to analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, if a student achieves excellent grades, it generates a message praising the student's efforts. The analysis unit also generates words of praise for achievements in educational settings based on data from schools and educational institutions. For example, if a student acquires a new skill, it generates a message praising the student's motivation to learn. The analysis unit also uses a generation AI to analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, if a student successfully completes a project, it generates a message praising the student's creativity. This makes it possible to generate appropriate words of praise based on achievements in educational settings.

[0045] The analysis unit can analyze local community data and generate compliments based on local activities. For example, the generation AI in the analysis unit analyzes local community data and generates compliments based on local activities. For example, if a resident participates in local cleanup activities, a message praising their contribution is generated. The analysis unit also generates compliments for local activities based on local community data. For example, if a resident plans a local event, a message praising their leadership is generated. The analysis unit also analyzes local community data and generates compliments based on local activities. For example, if a resident volunteers for local children, a message praising their consideration is generated. This makes it possible to generate appropriate compliments based on local activities.

[0046] The analysis unit can analyze the user's past behavioral history and generate a praise song based on a specific behavioral pattern. For example, the analysis unit uses a generation AI to analyze the user's past behavioral history and generate a praise song based on a specific behavioral pattern. For example, if a user continues to jog every day, a song praising that user's continuity is generated. The analysis unit also detects a specific behavioral pattern based on the user's past behavioral history and generates a praise song for that behavior. For example, if a user regularly participates in volunteer activities, a song praising that user's social contributions is generated. The analysis unit also uses a generation AI to analyze the user's behavioral history and generate a praise song based on a specific behavioral pattern. For example, if a user achieves their monthly goal, a song sharing that sense of accomplishment is generated. This makes it possible to generate appropriate praise songs based on specific behavioral patterns.

[0047] The analysis unit can analyze the user's tone of voice and facial expression, and generate a praise song that matches their emotional state. For example, the generation AI in the analysis unit analyzes the user's tone of voice and generates a praise song that matches their emotional state. For example, if the user speaks in a tired voice, it generates an encouraging song. The analysis unit can also analyze the user's facial expression and generate a praise song that matches their emotional state. For example, if the user speaks with a smile, it generates a song that praises that smile. The analysis unit can also analyze the user's tone of voice and facial expression, and generate a praise song that matches their emotional state. For example, if the user is nervous, it generates a praise song to relax them. This makes it possible to generate appropriate praise songs that match the user's emotional state.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if the user draws a picture, a message praising the user's creativity can be generated. Also, if the user plays music, a message praising the user's skills can be generated. Furthermore, if the user enjoys gardening, a message praising the user's efforts can be generated. In this way, appropriate compliments can be generated based on the user's hobbies and interests.

[0050] The analysis unit can analyze the user's travel data and generate compliments based on the user's behavior at the travel destination. For example, if the user explores a new place, a message praising the user's adventurous spirit can be generated. Also, if the user learns about the local culture, a message praising the user's eagerness to learn can be generated. Furthermore, if the user makes new friends during the trip, a message praising the user's sociability can be generated. In this way, appropriate compliments can be generated based on the user's behavior at the travel destination.

[0051] The analysis unit can analyze the user's cooking data and generate compliments based on the cooking results. For example, if the user tries a new recipe, a message praising the attempt can be generated. Also, if the user makes a delicious dish, a message praising the cooking results can be generated. Furthermore, if the user cooks for their family, a message praising the thoughtfulness of the cook can be generated. In this way, appropriate compliments can be generated based on the cooking results.

[0052] The analysis unit can analyze the user's behavioral data within the home and generate compliments based on the user's contributions within the home. For example, if the user helps with housework, a message praising the user's contributions can be generated. Also, if the user takes care of children, a message praising the user's efforts can be generated. Furthermore, if the user takes care of a vegetable garden, a message praising the user's efforts can be generated. In this way, appropriate compliments can be generated based on the user's contributions within the home.

[0053] The analysis unit can analyze a user's SNS posts and generate compliments based on the content of the posts. For example, if a user posts a photo of a trip, a message praising the beauty of the photo can be generated. Also, if a user posts a photo of cooking, a message praising the cooking can be generated. Furthermore, if a user posts a goal they have achieved, a message praising their efforts can be generated. In this way, appropriate compliments can be generated based on the user's SNS posts.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The input unit receives input information from a user, such as text input, voice input, or image input. Step 2: The analysis unit analyzes the input information received by the input unit. For example, the analysis unit can analyze text using natural language processing technology, speech using speech recognition technology, and images using image recognition technology. Step 3: The generator generates a compliment based on the information analyzed by the analyzer. For example, the generator may generate a compliment using a template-based generation method or a machine learning model, and may generate a customized compliment based on the user's input information.

[0056] (Example 2) The generative AI service according to an embodiment of the present invention is a system that compensates for the lack of recognition that users feel in their daily lives and business situations, and creates a culture of mutual recognition and praise. As a result, the generative AI service allows users to receive compliments and messages of recognition anytime, anywhere, and as many times as they like.

[0057] A generation AI service according to an embodiment includes an input unit, an analysis unit, and a generation unit. The input unit receives input information from a user. For example, the input unit can receive text input. The input unit can also receive voice input. The input unit can also receive image input. The analysis unit analyzes the input information received by the input unit. For example, the analysis unit analyzes text using natural language processing technology. The analysis unit can also analyze voice using voice recognition technology. The analysis unit can also analyze images using image recognition technology. The generation unit generates a compliment based on the information analyzed by the analysis unit. For example, the generation unit generates a compliment using a template-based generation method. The generation unit can also generate a compliment using a machine learning model. The generation unit can also generate a customized compliment based on the user's input information. This allows the generation AI service to generate an appropriate compliment based on the user's input information.

[0058] The analysis unit can analyze the user's past behavioral history and generate compliments based on specific behavioral patterns. For example, the generation AI in the analysis unit analyzes the user's past behavioral history and generates compliments based on specific behavioral patterns. For example, if the user continues to jog every day, a message praising the user's persistence is generated. The analysis unit can also detect specific behavioral patterns based on the user's past behavioral history and generate compliments for that behavior. For example, if the user regularly participates in volunteer activities, a message praising the user's social contributions is generated. The analysis unit can also analyze the user's behavioral history and generate compliments based on specific behavioral patterns. For example, if the user achieves their monthly goal, a message sharing the user's sense of accomplishment is generated. This makes it possible to generate appropriate compliments based on the user's behavioral patterns.

[0059] The analysis unit can analyze the user's tone of voice and facial expression, and generate compliments that correspond to their emotional state. For example, the generation AI in the analysis unit analyzes the user's tone of voice and generates compliments that correspond to their emotional state. For example, if the user sounds tired, it generates words of encouragement. The analysis unit can also analyze the user's facial expression and generate compliments that correspond to their emotional state. For example, if the user speaks with a smile, it generates a message praising their smile. The analysis unit can also analyze the user's tone of voice and facial expression, and generate compliments that correspond to their emotional state. For example, if the user is nervous, it generates compliments to relax them. This makes it possible to generate appropriate compliments that correspond to the user's emotional state.

[0060] The analysis unit can use the emotion estimation function to estimate the user's emotion in real time and generate a compliment that best suits that emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotion in real time and generate a compliment that best suits that emotion. For example, if the user is sad, it generates words of comfort. The analysis unit can also estimate the user's emotion in real time and generate a compliment that best suits that emotion. For example, if the user is happy, it generates a message sharing that joy. The analysis unit can also use the emotion estimation function to estimate the user's emotion in real time and generate a compliment that best suits that emotion. For example, if the user is angry, it generates a compliment encouraging the user to stay calm. This makes it possible to estimate the user's emotion in real time and generate an appropriate compliment.

[0061] The analysis unit can analyze a user's SNS posts and generate compliments based on the content of the posts. In the analysis unit, for example, a generation AI analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a photo of a trip, a message praising the beauty of the photo is generated. The analysis unit also analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a photo of a dish, a message praising the cooking quality is generated. In addition, the analysis unit analyzes a user's SNS posts and generates compliments based on the content of the posts. For example, if a user posts a goal they have achieved, a message praising their efforts is generated. This makes it possible to generate appropriate compliments based on a user's SNS posts.

[0062] The analysis unit can analyze the user's health data and generate health-related compliments. For example, the generation AI in the analysis unit analyzes data from the user's fitness tracker and generates health-related compliments. For example, if the user achieves a target number of steps, a message praising the achievement is generated. The analysis unit also analyzes the user's health data and generates health-related compliments. For example, if the user continues to exercise regularly, a message praising the user's persistence is generated. The analysis unit also analyzes the user's health data and generates health-related compliments. For example, if the user achieves a weight loss goal, a message praising the user's efforts is generated. This makes it possible to generate appropriate compliments based on the user's health data.

[0063] The analysis unit can use the emotion estimation function to analyze the emotion a user feels when praising another user and generate a compliment based on that emotion. For example, the analysis unit uses the emotion estimation function to analyze the emotion a user feels when praising another user and generate a compliment based on that emotion. For example, if the user feels grateful, a message expressing that gratitude is generated. The analysis unit also analyzes the emotion a user feels when praising another user and generates a compliment based on that emotion. For example, if the user feels respect, a message expressing that respect is generated. The analysis unit also uses the emotion estimation function to analyze the emotion a user feels when praising another user and generate a compliment based on that emotion. For example, if the user feels joy, a message sharing that joy is generated. This makes it possible to generate an appropriate compliment based on the emotion a user feels when praising another user.

[0064] The analysis unit can analyze an employee's work performance data and generate words of praise based on specific achievements. For example, the generation AI in the analysis unit analyzes an employee's work performance data and generates words of praise based on specific achievements. For example, if an employee achieves a sales target, a message praising the employee's achievement is generated. The analysis unit also generates words of praise for specific achievements based on the employee's work performance data. For example, if an employee successfully completes a project, a message praising the employee's efforts is generated. The analysis unit also analyzes an employee's work performance data and generates words of praise based on specific achievements. For example, if an employee proposes a new idea and it is adopted, a message praising the employee's creativity is generated. This makes it possible to generate appropriate words of praise based on an employee's achievements.

[0065] The analysis unit can analyze communication data within an employee's team and generate compliments related to teamwork. In the analysis unit, for example, the generation AI analyzes communication data within an employee's team and generates compliments related to teamwork. For example, if a team works together to complete a project, it generates a message praising their cooperation. The analysis unit also generates compliments related to teamwork based on communication data within the team. For example, if team members support each other, it generates a message praising that support. The analysis unit also generates compliments related to teamwork based on communication data within an employee's team. For example, if a team overcomes a difficult situation, it generates a message praising their unity. This makes it possible to generate appropriate compliments based on employees' teamwork.

[0066] The analysis unit can use the emotion estimation function to estimate the emotional state of an employee in real time and generate a compliment that best suits that emotion. The analysis unit, for example, uses the emotion estimation function to estimate the emotional state of an employee in real time and generate a compliment that best suits that emotion. For example, if an employee is feeling stressed, it generates words of encouragement. The analysis unit can also estimate the emotional state of an employee in real time and generate a compliment that best suits that emotion. For example, if an employee is happy, it generates a message sharing that joy. The analysis unit can also use the emotion estimation function to estimate the emotional state of an employee in real time and generate a compliment that best suits that emotion. For example, if an employee is tired, it generates a compliment that encourages them to refresh themselves. This makes it possible to generate appropriate compliments based on the emotional state of an employee.

[0067] The analysis unit can analyze an employee's career path data and generate words of praise based on their career progress. In the analysis unit, for example, the generation AI analyzes an employee's career path data and generates words of praise based on their career progress. For example, if an employee is promoted, a message praising their efforts is generated. The analysis unit also generates words of praise for their career progress based on the employee's career path data. For example, if an employee acquires a new skill, a message praising their enthusiasm for learning is generated. The analysis unit also analyzes an employee's career path data and generates words of praise based on their career progress. For example, if an employee uses their many years of experience to successfully complete a project, a message praising their experience is generated. This makes it possible to generate appropriate words of praise based on an employee's career progress.

[0068] The analysis unit can analyze an employee's project management data and generate words of praise based on the progress of the project. In the analysis unit, for example, the generation AI analyzes an employee's project management data and generates words of praise based on the progress of the project. For example, if the project is progressing as planned, a message praising the progress is generated. The analysis unit also generates words of praise for the progress of the project based on the project management data. For example, if the project is completed within budget, a message praising the efficiency is generated. In addition, the analysis unit also generates words of praise for the progress of the project based on the project management data. For example, if the project overcomes a difficult situation, a message praising the effort is generated. In this way, appropriate words of praise can be generated based on the progress of the project.

[0069] The analysis unit can use the emotion estimation function to analyze the emotion an employee feels when praising another employee and generate a compliment based on that emotion. For example, the analysis unit uses the emotion estimation function to analyze the emotion an employee feels when praising another employee and generate a compliment based on that emotion. For example, if the employee feels grateful, a message expressing that gratitude is generated. The analysis unit also analyzes the emotion an employee feels when praising another employee and generates a compliment based on that emotion. For example, if the employee feels respect, a message expressing that respect is generated. The analysis unit also uses the emotion estimation function to analyze the emotion an employee feels when praising another employee and generate a compliment based on that emotion. For example, if the employee feels happy, a message sharing that happiness is generated. This makes it possible to generate appropriate compliments based on the emotion an employee feels when praising another employee.

[0070] The analysis unit can analyze the user's behavioral data at home and generate compliments based on the user's contributions at home. In the analysis unit, for example, the generation AI analyzes the user's behavioral data at home and generates compliments based on the user's contributions at home. For example, if the user helps with housework, the analysis unit generates a message praising the user's contributions. The analysis unit also generates compliments for the user's contributions at home based on the behavioral data at home. For example, if the user takes care of children, the analysis unit generates a message praising the user's efforts. The analysis unit also generates compliments for the user's contributions at home based on the behavioral data at home. For example, if the user takes care of a vegetable garden, the analysis unit generates a message praising the user's efforts. In this way, appropriate compliments can be generated based on the user's contributions at home.

[0071] The analysis unit can analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, the analysis unit uses a generation AI to analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if a user draws a picture, the analysis unit generates a message praising the user's creativity. The analysis unit also generates compliments for those hobbies and interests based on data related to the user's hobbies and interests. For example, if a user plays music, the analysis unit generates a message praising the user's musical skills. The analysis unit also uses a generation AI to analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if a user enjoys gardening, the analysis unit generates a message praising the user's efforts. This makes it possible to generate appropriate compliments based on the user's hobbies and interests.

[0072] The analysis unit can use the emotion estimation function to estimate the user's emotional state in real time and generate a compliment that best suits that emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotional state in real time and generate a compliment that best suits that emotion. For example, if the user is sad, the analysis unit generates words of comfort. The analysis unit can also estimate the user's emotional state in real time and generate a compliment that best suits that emotion. For example, if the user is happy, the analysis unit generates a message sharing that joy. The analysis unit can also use the emotion estimation function to estimate the user's emotional state in real time and generate a compliment that best suits that emotion. For example, if the user is angry, the analysis unit generates a compliment encouraging the user to stay calm. This makes it possible to generate appropriate compliments based on the user's emotional state.

[0073] The analysis unit can analyze the user's travel data and generate compliments based on the user's behavior at the travel destination. In the analysis unit, for example, the generation AI analyzes the user's travel data and generates compliments based on the user's behavior at the travel destination. For example, if the user explores a new place, it generates a message praising the user's adventurous spirit. The analysis unit also generates compliments for the user's behavior at the travel destination based on the user's travel data. For example, if the user learns about the local culture, it generates a message praising the user's eagerness to learn. The analysis unit also analyzes the user's travel data and generates compliments based on the user's behavior at the travel destination. For example, if the user makes a new friend while traveling, it generates a message praising the user's sociability. This makes it possible to generate appropriate compliments based on the user's behavior at the travel destination.

[0074] The analysis unit can analyze the user's cooking data and generate compliments based on the cooking results. In the analysis unit, for example, a generation AI analyzes the user's cooking data and generates compliments based on the cooking results. For example, if the user tries a new recipe, a message praising the attempt is generated. The analysis unit also generates compliments for the cooking results based on the user's cooking data. For example, if the user cooks a delicious dish, a message praising the dish's quality is generated. In addition, the analysis unit analyzes the user's cooking data and generates compliments based on the cooking results. For example, if the user cooks for their family, a message praising the user's thoughtfulness is generated. This makes it possible to generate appropriate compliments based on the cooking results.

[0075] The analysis unit can use the emotion estimation function to analyze the emotion a user feels when praising another family member and generate a compliment based on that emotion. For example, the analysis unit can use the emotion estimation function to analyze the emotion a user feels when praising another family member and generate a compliment based on that emotion. For example, if the user feels grateful, the analysis unit generates a message expressing that gratitude. The analysis unit can also analyze the emotion a user feels when praising another family member and generate a compliment based on that emotion. For example, if the user feels respect, the analysis unit generates a message expressing that respect. The analysis unit can also use the emotion estimation function to analyze the emotion a user feels when praising another family member and generate a compliment based on that emotion. For example, if the user feels joy, the analysis unit generates a message sharing that joy. This makes it possible to generate appropriate compliments based on the emotion a user feels when praising another family member.

[0076] The analysis unit can analyze a child's learning data and generate words of praise based on their learning achievements. In the analysis unit, for example, a generation AI analyzes a child's learning data and generates words of praise based on their learning achievements. For example, if a child gets a high score on a test, a message praising the child's efforts is generated. The analysis unit also generates words of praise for learning achievements based on the child's learning data. For example, if a child acquires a new skill, a message praising the child's motivation to learn is generated. In addition, the analysis unit analyzes a child's learning data and generates words of praise based on their learning achievements. For example, if a child completes their homework properly, a message praising the child's responsibility is generated. This makes it possible to generate appropriate words of praise based on learning achievements.

[0077] The analysis unit can analyze the activity data of the elderly person and generate compliments based on the efforts made in daily life. For example, the generation AI in the analysis unit analyzes the activity data of the elderly person and generates compliments based on the efforts made in daily life. For example, if an elderly person continues to take walks every day, a message praising their persistence is generated. The analysis unit also generates compliments for the efforts made in daily life based on the activity data of the elderly person. For example, if an elderly person enjoys a hobby, a message praising their initiative is generated. The analysis unit also generates compliments based on the efforts made in daily life based on the activity data of the elderly person. For example, if an elderly person cooks for their family, a message praising their thoughtfulness is generated. This makes it possible to generate appropriate compliments based on the efforts made in daily life.

[0078] The analysis unit can use the emotion estimation function to estimate the emotional state of a child or elderly person in real time and generate a compliment that best suits that emotion. For example, the analysis unit can use the emotion estimation function to estimate the emotional state of a child or elderly person in real time and generate a compliment that best suits that emotion. For example, if a child is sad, the analysis unit generates words of comfort. The analysis unit can also estimate the emotional state of a child or elderly person in real time and generate a compliment that best suits that emotion. For example, if an elderly person is happy, the analysis unit generates a message sharing that joy. The analysis unit can also use the emotion estimation function to estimate the emotional state of a child or elderly person in real time and generate a compliment that best suits that emotion. For example, if a child is angry, the analysis unit generates a compliment encouraging the child to stay calm. This makes it possible to generate appropriate compliments based on the emotional state of a child or elderly person.

[0079] The analysis unit can analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, the analysis unit uses a generation AI to analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, if a student achieves excellent grades, it generates a message praising the student's efforts. The analysis unit also generates words of praise for achievements in educational settings based on data from schools and educational institutions. For example, if a student acquires a new skill, it generates a message praising the student's motivation to learn. The analysis unit also uses a generation AI to analyze data from schools and educational institutions and generate words of praise based on achievements in educational settings. For example, if a student successfully completes a project, it generates a message praising the student's creativity. This makes it possible to generate appropriate words of praise based on achievements in educational settings.

[0080] The analysis unit can analyze local community data and generate compliments based on local activities. For example, the generation AI in the analysis unit analyzes local community data and generates compliments based on local activities. For example, if a resident participates in local cleanup activities, a message praising their contribution is generated. The analysis unit also generates compliments for local activities based on local community data. For example, if a resident plans a local event, a message praising their leadership is generated. The analysis unit also analyzes local community data and generates compliments based on local activities. For example, if a resident volunteers for local children, a message praising their consideration is generated. This makes it possible to generate appropriate compliments based on local activities.

[0081] The analysis unit can use the emotion estimation function to analyze the emotions felt by children or elderly people when praising others and generate compliments based on those emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions felt by children or elderly people when praising others and generate compliments based on those emotions. For example, if the child or elderly person feels grateful, a message expressing that gratitude is generated. The analysis unit also analyzes the emotions felt by children or elderly people when praising others and generates compliments based on those emotions. For example, if the child or elderly person feels respect, a message expressing that respect is generated. The analysis unit also uses the emotion estimation function to analyze the emotions felt by children or elderly people when praising others and generate compliments based on those emotions. For example, if the child or elderly person feels joy, a message sharing that joy is generated. This makes it possible to generate appropriate compliments based on the emotions felt by children or elderly people when praising others.

[0082] The analysis unit can analyze the user's past behavioral history and generate a praise song based on a specific behavioral pattern. For example, the analysis unit uses a generation AI to analyze the user's past behavioral history and generate a praise song based on a specific behavioral pattern. For example, if a user continues to jog every day, a song praising that user's continuity is generated. The analysis unit also detects a specific behavioral pattern based on the user's past behavioral history and generates a praise song for that behavior. For example, if a user regularly participates in volunteer activities, a song praising that user's social contributions is generated. The analysis unit also uses a generation AI to analyze the user's behavioral history and generate a praise song based on a specific behavioral pattern. For example, if a user achieves their monthly goal, a song sharing that sense of accomplishment is generated. This makes it possible to generate appropriate praise songs based on specific behavioral patterns.

[0083] The analysis unit can analyze the user's tone of voice and facial expression, and generate a praise song that matches their emotional state. For example, the generation AI in the analysis unit analyzes the user's tone of voice and generates a praise song that matches their emotional state. For example, if the user speaks in a tired voice, it generates an encouraging song. The analysis unit can also analyze the user's facial expression and generate a praise song that matches their emotional state. For example, if the user speaks with a smile, it generates a song that praises that smile. The analysis unit can also analyze the user's tone of voice and facial expression, and generate a praise song that matches their emotional state. For example, if the user is nervous, it generates a praise song to relax them. This makes it possible to generate appropriate praise songs that match the user's emotional state.

[0084] The analysis unit can use the emotion estimation function to estimate the user's emotion in real time and generate a compliment song that best suits that emotion. The analysis unit, for example, uses the emotion estimation function to estimate the user's emotion in real time and generate a compliment song that best suits that emotion. For example, if the user is sad, it generates a comforting song. The analysis unit can also estimate the user's emotion in real time and generate a compliment song that best suits that emotion. For example, if the user is happy, it generates a song that shares that joy. The analysis unit can also use the emotion estimation function to estimate the user's emotion in real time and generate a compliment song that best suits that emotion. For example, if the user is angry, it generates a compliment song that encourages the user to stay calm. This makes it possible to generate appropriate compliment songs based on the user's emotions.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The analysis unit can analyze data related to the user's hobbies and interests and generate compliments based on those hobbies and interests. For example, if the user draws a picture, a message praising the user's creativity can be generated. Also, if the user plays music, a message praising the user's skills can be generated. Furthermore, if the user enjoys gardening, a message praising the user's efforts can be generated. In this way, appropriate compliments can be generated based on the user's hobbies and interests.

[0087] The analysis unit can analyze the user's travel data and generate compliments based on the user's behavior at the travel destination. For example, if the user explores a new place, a message praising the user's adventurous spirit can be generated. Also, if the user learns about the local culture, a message praising the user's eagerness to learn can be generated. Furthermore, if the user makes new friends during the trip, a message praising the user's sociability can be generated. In this way, appropriate compliments can be generated based on the user's behavior at the travel destination.

[0088] The analysis unit can analyze the user's cooking data and generate compliments based on the cooking results. For example, if the user tries a new recipe, a message praising the attempt can be generated. Also, if the user makes a delicious dish, a message praising the cooking results can be generated. Furthermore, if the user cooks for their family, a message praising the thoughtfulness of the cook can be generated. In this way, appropriate compliments can be generated based on the cooking results.

[0089] The analysis unit can analyze the user's behavioral data within the home and generate compliments based on the user's contributions within the home. For example, if the user helps with housework, a message praising the user's contributions can be generated. Also, if the user takes care of children, a message praising the user's efforts can be generated. Furthermore, if the user takes care of a vegetable garden, a message praising the user's efforts can be generated. In this way, appropriate compliments can be generated based on the user's contributions within the home.

[0090] The analysis unit can analyze a user's SNS posts and generate compliments based on the content of the posts. For example, if a user posts a photo of a trip, a message praising the beauty of the photo can be generated. Also, if a user posts a photo of cooking, a message praising the cooking can be generated. Furthermore, if a user posts a goal they have achieved, a message praising their efforts can be generated. In this way, appropriate compliments can be generated based on the user's SNS posts.

[0091] The analysis unit can use the emotion estimation function to estimate the user's emotions in real time and generate the most appropriate compliment for that emotion. For example, if the user is sad, it can generate words of comfort. If the user is happy, it can also generate a message sharing that joy. Furthermore, if the user is angry, it can also generate a compliment encouraging the user to stay calm. This makes it possible to estimate the user's emotions in real time and generate appropriate compliments.

[0092] The analysis unit can use the emotion estimation function to analyze the emotion a user feels when praising another user and generate a compliment based on that emotion. For example, if a user feels grateful, a message expressing that gratitude can be generated. Also, if a user feels respect, a message expressing that respect can be generated. Furthermore, if a user feels joy, a message sharing that joy can be generated. This makes it possible to generate appropriate compliments based on the emotion a user feels when praising another user.

[0093] The analysis unit uses the emotion estimation function to estimate the emotional state of an employee in real time and generate a compliment that best suits that emotion. For example, if an employee is feeling stressed, it can generate words of encouragement. If an employee is happy, it can also generate a message sharing that joy. Furthermore, if an employee is tired, it can also generate a compliment encouraging them to refresh themselves. This makes it possible to generate appropriate compliments based on the employee's emotional state.

[0094] The analysis unit uses the emotion estimation function to estimate the emotional state of a child or elderly person in real time and generate a compliment that best suits that emotion. For example, if a child is sad, it can generate comforting words. Also, if an elderly person is happy, it can generate a message sharing that joy. Furthermore, if a child is angry, it can generate a compliment encouraging them to stay calm. This makes it possible to generate appropriate compliments based on the emotional state of a child or elderly person.

[0095] The analysis unit uses the emotion estimation function to analyze the emotions felt by children and elderly people when they compliment others, and can generate compliments based on those emotions. For example, if they feel grateful, it can generate a message expressing that gratitude. Also, if they feel respect, it can generate a message expressing that respect. Furthermore, if they feel joy, it can generate a message sharing that joy. This makes it possible to generate appropriate compliments based on the emotions felt by children and elderly people when they compliment others.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The input unit receives input information from a user, such as text input, voice input, or image input. Step 2: The analysis unit analyzes the input information received by the input unit. For example, the analysis unit can analyze text using natural language processing technology, speech using speech recognition technology, and images using image recognition technology. Step 3: The generator generates a compliment based on the information analyzed by the analyzer. For example, the generator may generate a compliment using a template-based generation method or a machine learning model, and may generate a customized compliment based on the user's input information.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0124] 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.

[0125] 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.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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, in order to avoid confusion and to 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.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit that receives input information from a user; an analysis unit that analyzes the input information received by the input unit; a generation unit that generates a compliment based on the information analyzed by the analysis unit. A system characterized by:

2. The analysis unit Analyzes the user's past behavior history and generates compliments based on specific behavioral patterns 2. The system of claim 1.

3. The analysis unit Analyzes the user's tone of voice and facial expressions and generates compliments that correspond to their emotional state 2. The system of claim 1.

4. The analysis unit Estimates the user's emotions in real time and generates the most appropriate compliment for that emotion.

2. The system of claim 1.

5. The analysis unit Analyzes users' social media posts and generates compliments based on the content of the posts 2. The system of claim 1.

6. The analysis unit Analyzes user's health data and generates health-related compliments 2. The system of claim 1.

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