system

Abstract

An object of a system according to an embodiment is to analyze a diary or an SNS post of a user and make a personalized proposal.SOLUTION: A system according to an embodiment includes an emotion analysis unit, an action analysis unit, and an advice generation unit. The emotion analysis unit analyzes a diary or an SNS post of the user. The behavior analysis unit specifies the wave of the emotion and the pattern of the behavior analyzed by the emotion analysis unit. The advice generation unit makes a personalized proposal on the basis of the result specified by the behavior 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 technologies have had the problem of not being able to efficiently analyze emotional and behavioral patterns from users' diaries and social media posts and provide appropriate advice.

[0005] The system according to the embodiment aims to analyze a user's diary entries and posts on social media and provide personalized suggestions. [Means for solving the problem]

[0006] The system according to the embodiment includes a sentiment analysis unit, a behavior analysis unit, and an advice generation unit. The sentiment analysis unit analyzes a user's diary or posts on social media. The behavior analysis unit identifies the emotional waves and behavioral patterns analyzed by the sentiment analysis unit. The advice generation unit makes personalized suggestions based on the results identified by the behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's diary entries and posts on social media and make personalized suggestions. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Insight Diary AI system according to an embodiment of the present invention automatically analyzes a user's diary and social media posts, and the generation AI analyzes their emotional and behavioral tendencies to provide personalized suggestions. This allows the Insight Diary AI system to analyze the user's emotional and behavioral tendencies and provide specific advice to improve the quality of life.

[0029] The Insight Diary AI system according to the embodiment includes a sentiment analysis unit, a behavior analysis unit, and an advice generation unit. The sentiment analysis unit analyzes a user's diary or social media posts. For example, the sentiment analysis unit uses natural language processing technology to analyze text data posted by the user and identify emotional trends. The sentiment analysis unit can also analyze image and video posts to identify emotions from visual information. The behavior analysis unit identifies the emotional trends and behavioral patterns analyzed by the sentiment analysis unit. For example, the behavior analysis unit identifies the frequency and time periods of behavior from the content of the user's posts and analyzes behavioral patterns. The behavior analysis unit can also extract specific keywords and phrases contained in the user's posts and analyze their impact on emotions and behavior. The advice generation unit makes personalized suggestions based on the results identified by the behavior analysis unit. For example, if a user is feeling stressed, the advice generation unit can provide specific advice such as "Try yoga to relax." The advice generation unit can also provide real-time advice based on the user's emotional fluctuations to provide immediate support. As a result, the Insight Diary AI system according to the embodiment can analyze the user's emotional and behavioral tendencies and provide specific advice to improve the quality of life. For example, the user can understand their own emotions and behaviors and receive specific advice to improve the quality of life based on that understanding. Furthermore, the user can realize their own growth through the feedback provided by the generation AI.

[0030] When analyzing user posted content, the sentiment analysis unit also analyzes image or video posts, and can identify patterns of emotions and behavior from visual information. For example, when the generative AI analyzes user posted content, the sentiment analysis unit also analyzes image or video posts. For example, image recognition technology is used to identify patterns of emotions and behavior from posted images. This allows for a deeper understanding of user emotions and behavior by identifying patterns of emotions and behavior from visual information.

[0031] The sentiment analysis unit can integrate and analyze posts from different SNS platforms and evaluate the consistency between a user's emotions and actions. For example, the sentiment analysis unit integrates and analyzes posts from different SNS platforms and evaluates the consistency between a user's emotions and actions. For example, it integrates posts from Twitter and Instagram and analyzes emotional fluctuations. This makes it possible to evaluate the consistency between a user's emotions and actions by integrating and analyzing posts from different SNS platforms.

[0032] When analyzing the user's emotional and behavioral tendencies, the advice generation unit can evaluate the effectiveness of past advice and identify patterns of effective advice. For example, the advice generation unit uses a generation AI to analyze the user's emotional and behavioral tendencies and evaluate the effectiveness of past advice. For example, it analyzes how advice provided in the past affected the user's emotions. This allows the effectiveness of past advice to be evaluated and patterns of effective advice to be identified, thereby enabling more appropriate advice to be provided.

[0033] The advice generation unit can take into account life events when analyzing the content posted by a user and provide advice related to specific events. For example, the advice generation unit uses a generation AI to analyze the content posted by a user and provide advice that takes life events into consideration. For example, on a birthday, advice such as "Enjoy your special day" is provided. In this way, advice that takes life events into consideration is provided to make the user's special day even better.

[0034] The advice generation unit can integrate health data when analyzing the user's emotional and behavioral tendencies and provide comprehensive advice. For example, the advice generation unit uses a generation AI to analyze the user's emotional and behavioral tendencies and integrate health data to provide comprehensive advice. For example, advice such as "It's important to get enough sleep" can be provided based on sleep data. By integrating health data, more comprehensive advice can be provided.

[0035] The advice generation unit can suggest activities based on the user's hobbies and interests when analyzing the content posted by the user. For example, the advice generation unit uses a generation AI to analyze the content posted by the user and suggest activities based on the user's hobbies and interests. For example, the unit may suggest, "Since you like music, why not go to a concert." This improves the user's quality of life by suggesting activities based on the user's hobbies and interests.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] When analyzing user posts, the Insight Diary AI system can also provide region-specific advice by taking into account the user's geographic location. For example, if a user is in a specific area, the system can provide advice based on the local weather and events. This allows the system to provide specific advice tailored to the user's living environment. For example, on a rainy day, the system can provide advice such as "It's raining today, so try some indoor activities." Based on local event information, the system can also suggest "Try attending a nearby festival." Furthermore, the system can take into account local health information and provide health advice such as "There is a lot of pollen in this area, so please wear a mask when going out."

[0038] When analyzing a user's posts, the Insight Diary AI system can provide advice based on the user's lifestyle. For example, if the user is health-conscious, the system can provide focused health advice. This allows the system to provide specific advice tailored to the user's lifestyle. For example, if the user is interested in fitness, the system can provide advice such as, "Consider going to the gym to maintain daily exercise." If the user is interested in diet, the system can suggest, "Try a new recipe once a week to maintain a balanced diet." Furthermore, if the user is feeling stressed at work, the system can provide specific advice such as, "Take short breaks between work sessions to maintain concentration."

[0039] When analyzing user posts, the Insight Diary AI system can provide advice related to the user's occupation or studies. For example, if the user is a student, it can provide academic advice. This allows the system to provide specific advice tailored to the user's occupation or studies. For example, a student user could be given advice such as, "It's important to get enough rest before exams." A working user could also be given a suggestion such as, "Try using a task management tool to improve your work efficiency." Furthermore, it can provide specific advice to freelance users such as, "Perform regular self-evaluations to manage project progress."

[0040] When analyzing user posts, the Insight Diary AI system can suggest learning resources based on the user's hobbies and interests. For example, if a user is interested in a particular field, it can provide learning resources related to that field. This allows it to provide specific advice tailored to the user's interests. For example, if a user is interested in programming, it can provide advice such as "Try taking an online programming course." If a user is interested in art, it can also suggest "Try attending a local art class." Furthermore, if a user is interested in cooking, it can provide specific advice such as "Try a new recipe."

[0041] The processing flow of the first embodiment will be briefly explained below.

[0042] Step 1: The sentiment analysis unit analyzes the user's diary or social media posts. For example, the sentiment analysis unit uses natural language processing technology to analyze the text data posted by the user and identify emotional trends. The sentiment analysis unit can also analyze image and video posts and identify emotions from visual information. Step 2: The behavioral analysis unit identifies the emotional waves and behavioral patterns analyzed by the emotion analysis unit. For example, the behavioral analysis unit identifies the frequency and time period of behavior from the content of the user's posts and analyzes the behavioral patterns. The behavioral analysis unit can also extract specific keywords and phrases contained in the user's posts and analyze their impact on emotions and behavior. Step 3: The advice generator makes personalized suggestions based on the results identified by the behavioral analysis unit. For example, if the user is feeling stressed, the advice generator may provide specific advice such as "Try yoga to relax." The advice generator may also provide real-time advice based on the user's emotional fluctuations, providing immediate support.

[0043] (Example 2) The Insight Diary AI system according to an embodiment of the present invention automatically analyzes a user's diary and social media posts, and the generation AI analyzes their emotional and behavioral tendencies to provide personalized suggestions. This allows the Insight Diary AI system to analyze the user's emotional and behavioral tendencies and provide specific advice to improve the quality of life.

[0044] The Insight Diary AI system according to the embodiment includes a sentiment analysis unit, a behavior analysis unit, and an advice generation unit. The sentiment analysis unit analyzes a user's diary or social media posts. For example, the sentiment analysis unit uses natural language processing technology to analyze text data posted by the user and identify emotional trends. The sentiment analysis unit can also analyze image and video posts to identify emotions from visual information. The behavior analysis unit identifies the emotional trends and behavioral patterns analyzed by the sentiment analysis unit. For example, the behavior analysis unit identifies the frequency and time periods of behavior from the content of the user's posts and analyzes behavioral patterns. The behavior analysis unit can also extract specific keywords and phrases contained in the user's posts and analyze their impact on emotions and behavior. The advice generation unit makes personalized suggestions based on the results identified by the behavior analysis unit. For example, if a user is feeling stressed, the advice generation unit can provide specific advice such as "Try yoga to relax." The advice generation unit can also provide real-time advice based on the user's emotional fluctuations to provide immediate support. As a result, the Insight Diary AI system according to the embodiment can analyze the user's emotional and behavioral tendencies and provide specific advice to improve the quality of life. For example, the user can understand their own emotions and behaviors and receive specific advice to improve the quality of life based on that understanding. Furthermore, the user can realize their own growth through the feedback provided by the generation AI.

[0045] The sentiment analysis unit can identify fluctuations in emotions depending on the time of day or day of the week when a post is made, and analyze the periodicity of a user's emotions. For example, the generative AI analyzes the content of a user's posts and identifies fluctuations in emotions depending on the time of day or day of the week when a post is made. For example, it compares content posted on weekday mornings with content posted on weekend nights to analyze the periodicity of emotions. This makes it easier to predict emotional fluctuations by analyzing the periodicity of a user's emotions.

[0046] The sentiment analysis unit can extract specific keywords or phrases included in user posts and analyze their impact on emotions and behavior. For example, the generative AI extracts specific keywords and phrases included in user posts and analyzes their impact on emotions. For example, it analyzes how keywords such as "stress" and "fun" affect emotions. This allows for a deeper understanding of user emotions and behavior by analyzing the impact of specific keywords and phrases on emotions and behavior.

[0047] The emotion analysis unit can use the emotion estimation function to estimate the intensity of emotions from user posts and identify factors that cause strong emotions. For example, the emotion analysis unit uses the emotion estimation function to estimate the intensity of emotions from user posts and identify factors that cause strong emotions. For example, it analyzes posts that contain strong emotions such as "very happy" or "very sad." This allows the unit to estimate the intensity of emotions and identify factors that cause strong emotions, thereby deepening understanding of user emotions.

[0048] When analyzing user posted content, the sentiment analysis unit also analyzes image or video posts, and can identify patterns of emotions and behavior from visual information. For example, when the generative AI analyzes user posted content, the sentiment analysis unit also analyzes image or video posts. For example, image recognition technology is used to identify patterns of emotions and behavior from posted images. This allows for a deeper understanding of user emotions and behavior by identifying patterns of emotions and behavior from visual information.

[0049] The sentiment analysis unit can integrate and analyze posts from different SNS platforms and evaluate the consistency between a user's emotions and actions. For example, the sentiment analysis unit integrates and analyzes posts from different SNS platforms and evaluates the consistency between a user's emotions and actions. For example, it integrates posts from Twitter and Instagram and analyzes emotional fluctuations. This makes it possible to evaluate the consistency between a user's emotions and actions by integrating and analyzing posts from different SNS platforms.

[0050] The emotion analysis unit can use the emotion estimation function to analyze other users' reactions to a user's post and identify the ripple effect of emotions. For example, the emotion analysis unit can use the emotion estimation function to analyze other users' reactions to a user's post and identify the ripple effect of emotions. For example, the emotion analysis unit can analyze the number of "likes" and comments on a positive post. In this way, the ripple effect of emotions can be identified by analyzing the reactions of other users.

[0051] When analyzing the user's emotional and behavioral tendencies, the advice generation unit can evaluate the effectiveness of past advice and identify patterns of effective advice. For example, the advice generation unit uses a generation AI to analyze the user's emotional and behavioral tendencies and evaluate the effectiveness of past advice. For example, it analyzes how advice provided in the past affected the user's emotions. This allows the effectiveness of past advice to be evaluated and patterns of effective advice to be identified, thereby enabling more appropriate advice to be provided.

[0052] The advice generation unit can take into account life events when analyzing the content posted by a user and provide advice related to specific events. For example, the advice generation unit uses a generation AI to analyze the content posted by a user and provide advice that takes life events into consideration. For example, on a birthday, advice such as "Enjoy your special day" is provided. In this way, advice that takes life events into consideration is provided to make the user's special day even better.

[0053] The advice generation unit uses the emotion estimation function to provide real-time advice in response to the user's emotional fluctuations, thereby enabling immediate support. The advice generation unit, for example, uses the emotion estimation function to provide real-time advice in response to the user's emotional fluctuations. For example, if the user is feeling stressed, the advice generation unit provides advice such as "Take a deep breath and relax." In this way, by providing real-time advice, it is possible to respond immediately to the user's emotional fluctuations.

[0054] The advice generation unit can integrate health data when analyzing the user's emotional and behavioral tendencies and provide comprehensive advice. For example, the advice generation unit uses a generation AI to analyze the user's emotional and behavioral tendencies and integrate health data to provide comprehensive advice. For example, advice such as "It's important to get enough sleep" can be provided based on sleep data. By integrating health data, more comprehensive advice can be provided.

[0055] The advice generation unit can suggest activities based on the user's hobbies and interests when analyzing the content posted by the user. For example, the advice generation unit uses a generation AI to analyze the content posted by the user and suggest activities based on the user's hobbies and interests. For example, the unit may suggest, "Since you like music, why not go to a concert." This improves the user's quality of life by suggesting activities based on the user's hobbies and interests.

[0056] The advice generation unit uses the emotion estimation function to recommend music and video content in accordance with the user's emotional fluctuations, thereby stabilizing the user's emotions. The advice generation unit, for example, uses the emotion estimation function to recommend music in accordance with the user's emotional fluctuations. For example, when the user is feeling stressed, the advice generation unit recommends relaxing music. In this way, the recommendation of music and video content in accordance with the user's emotional fluctuations helps stabilize the user's emotions.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] When analyzing user posts, the Insight Diary AI system can also provide region-specific advice by taking into account the user's geographic location. For example, if a user is in a specific area, the system can provide advice based on the local weather and events. This allows the system to provide specific advice tailored to the user's living environment. For example, on a rainy day, the system can provide advice such as "It's raining today, so try some indoor activities." Based on local event information, the system can also suggest "Try attending a nearby festival." Furthermore, the system can take into account local health information and provide health advice such as "There is a lot of pollen in this area, so please wear a mask when going out."

[0059] When analyzing a user's posts, the Insight Diary AI system takes into account the user's past posting history and can analyze long-term emotional fluctuations. For example, it can analyze posts from the past year to identify seasonal emotional fluctuations. This allows it to understand long-term trends in a user's emotions and provide more appropriate advice. For example, for a user who tends to feel depressed in the winter, the system can provide advice such as, "Increase your exposure to sunlight to prevent winter depression." It can also analyze the long-term impact of specific events and occurrences on emotions and provide specific advice such as, "Since you tend to feel stressed at this time of year, try ways to relax." Furthermore, it can evaluate a user's growth and changes based on past posts and provide feedback such as, "Your emotional stability has improved compared to last year."

[0060] When analyzing a user's posts, the Insight Diary AI system can provide advice based on the user's lifestyle. For example, if the user is health-conscious, the system can provide focused health advice. This allows the system to provide specific advice tailored to the user's lifestyle. For example, if the user is interested in fitness, the system can provide advice such as, "Consider going to the gym to maintain daily exercise." If the user is interested in diet, the system can suggest, "Try a new recipe once a week to maintain a balanced diet." Furthermore, if the user is feeling stressed at work, the system can provide specific advice such as, "Take short breaks between work sessions to maintain concentration."

[0061] When analyzing user posts, the Insight Diary AI system can suggest physical activities based on the user's emotional fluctuations. For example, if a user is feeling stressed, it can suggest relaxing exercises. This allows the system to provide specific advice tailored to the user's emotional fluctuations. For example, a user who is feeling stressed can be advised to "try yoga or meditation." A user who is feeling low in energy can be suggested to "try a light jog or walk." Furthermore, it is possible to provide specific advice to users who are feeling positive, such as "try incorporating fun exercises like dancing or aerobics."

[0062] When analyzing user posts, the Insight Diary AI system can provide advice related to the user's occupation or studies. For example, if the user is a student, it can provide academic advice. This allows the system to provide specific advice tailored to the user's occupation or studies. For example, a student user could be given advice such as, "It's important to get enough rest before exams." A working user could also be given a suggestion such as, "Try using a task management tool to improve your work efficiency." Furthermore, it can provide specific advice to freelance users such as, "Perform regular self-evaluations to manage project progress."

[0063] When analyzing user posts, the Insight Diary AI system can suggest meals based on the user's emotional fluctuations. For example, if a user is feeling stressed, it can suggest a relaxing meal. This allows the system to provide specific advice tailored to the user's emotional fluctuations. For example, a user who is feeling stressed can be advised to "try herbal tea for relaxation." A user who is experiencing low energy levels can be suggested to "try a nutritious smoothie." Furthermore, a user who is feeling positive can be given specific advice such as "enjoy your favorite dessert to boost your celebratory mood."

[0064] When analyzing user posts, the Insight Diary AI system can suggest learning resources based on the user's hobbies and interests. For example, if a user is interested in a particular field, it can provide learning resources related to that field. This allows it to provide specific advice tailored to the user's interests. For example, if a user is interested in programming, it can provide advice such as "Try taking an online programming course." If a user is interested in art, it can also suggest "Try attending a local art class." Furthermore, if a user is interested in cooking, it can provide specific advice such as "Try a new recipe."

[0065] When analyzing user posts, the Insight Diary AI system can suggest relaxation techniques based on the user's emotional fluctuations. For example, if a user is feeling stressed, it will suggest relaxation techniques. This allows the system to provide specific advice tailored to the user's emotional fluctuations. For example, a user who is feeling stressed can be advised to "try deep breathing or meditation." A user who is feeling anxious can be suggested to "try aromatherapy." Furthermore, it is possible to provide specific advice to a user who wants to relax, such as "take a warm bath and relax."

[0066] When analyzing user posts, the Insight Diary AI system can suggest social activities based on the user's emotional fluctuations. For example, if a user feels lonely, it will suggest social activities. This allows the system to provide specific advice tailored to the user's emotional fluctuations. For example, a user who feels lonely can be advised to "try spending more time with friends." A user who wants to enjoy social activities can be suggested to "try participating in local events." Furthermore, a user who wants to stabilize their emotions can be given specific advice such as "join an online community and make new friends."

[0067] When analyzing user posts, the Insight Diary AI system can suggest creative activities based on the user's emotional fluctuations. For example, if a user is feeling stressed, it can suggest creative activities. This allows the system to provide specific advice tailored to the user's emotional fluctuations. For example, a user who is feeling stressed can be offered advice such as "Try drawing a picture or doing some needlework." A user who wants to express their emotions can be offered suggestions such as "Try writing a diary or composing poetry." Furthermore, a user who wants to relax can be offered specific advice such as "Try making music or playing an instrument."

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The sentiment analysis unit analyzes the user's diary or social media posts. For example, the sentiment analysis unit uses natural language processing technology to analyze the text data posted by the user and identify emotional trends. The sentiment analysis unit can also analyze image and video posts and identify emotions from visual information. Step 2: The behavioral analysis unit identifies the emotional waves and behavioral patterns analyzed by the emotion analysis unit. For example, the behavioral analysis unit identifies the frequency and time period of behavior from the content of the user's posts and analyzes the behavioral patterns. The behavioral analysis unit can also extract specific keywords and phrases contained in the user's posts and analyze their impact on emotions and behavior. Step 3: The advice generator makes personalized suggestions based on the results identified by the behavioral analysis unit. For example, if the user is feeling stressed, the advice generator may provide specific advice such as "Try yoga to relax." The advice generator may also provide real-time advice based on the user's emotional fluctuations, providing immediate support.

[0070] 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.

[0071] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0083] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0084] 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.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0136] 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]

[0137] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sentiment analysis unit that analyzes users' diary entries or social media posts; a behavior analysis unit that identifies patterns of emotions and behaviors analyzed by the emotion analysis unit; an advice generation unit that makes personalized suggestions based on the results identified by the behavior analysis unit; A system characterized by:

2. The emotion analysis unit Using an emotion estimation function, the intensity of the emotion is estimated from the user's post, and factors that cause the strong emotion are identified. The system of claim 1 .

3. The emotion analysis unit When analyzing the content posted by the user, images or videos are also included in the analysis, and patterns of emotions and behavior are identified from visual information. The system of claim 1 .

4. The advice generation unit Evaluating the effectiveness of past advice and identifying patterns of effective advice when analyzing the user's sentiment and behavioral trends. The system of claim 1 .

5. The advice generation unit Using an emotion estimation function, real-time advice is provided in response to the user's emotional fluctuations, providing immediate support. The system of claim 1 .

6. The emotion analysis unit Identifying fluctuations in sentiment by time of day or day of the week of posts and analyzing the periodicity of the user's sentiment The system of claim 1 .

7. The emotion analysis unit Using an emotion estimation function, the reactions of other users to the user's posts are analyzed to identify the ripple effect of the emotions. The system of claim 1 .

8. The advice generation unit Using an emotion estimation function, music and video content is recommended according to the fluctuations in the user's emotions, thereby stabilizing the emotions. The system of claim 1 .

Images