System

Abstract

An object of a system according to an embodiment is to enable people who are in the middle of a change in lifestyle to continuously record a diary.SOLUTION: A system includes a question generation unit, an answer analysis unit, a summary generation unit, and a reminder notification unit. The question generation unit presents a question to the user. The answer analysis unit analyzes the user's answer to the question presented by the question generation unit. The summary generation unit summarizes the answer analyzed by the answer analysis unit. The prompt notification unit prompts the diary entry.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 techniques have had the problem that it is difficult for people who are in the midst of lifestyle changes to continue recording their diaries.

[0005] The system according to the embodiment aims to enable people who are in the midst of a lifestyle change to keep a diary. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, an answer analysis unit, a summary generation unit, and a prompt notification unit. The question generation unit presents a question to a user. The answer analysis unit analyzes the user's answer to the question presented by the question generation unit. The summary generation unit summarizes the answer analyzed by the answer analysis unit. The prompt notification unit prompts the user to write a diary entry. [Effects of the Invention]

[0007] Systems according to embodiments can enable people going through lifestyle changes to keep a diary. [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 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 diary recording support system according to the embodiment of the present invention is a system that allows a user to easily record a diary and continuously write entries in the diary. As a result, the diary recording support system allows a user to continuously record a diary.

[0029] A diary recording support system according to an embodiment includes a question generation unit, an answer analysis unit, a summary generation unit, and a prompt notification unit. The question generation unit presents a question to a user. For example, questions such as "What was the most memorable event of your day?" or "How did you feel about that event?" are displayed. The answer analysis unit analyzes the user's answer to the question presented by the question generation unit. For example, a generation AI analyzes the user's answer and generates a more specific question. The summary generation unit summarizes the answer analyzed by the answer analysis unit. For example, the generation AI generates a summary based on the user's answer and creates a coherent sentence. The prompt notification unit prompts the user to write a diary entry. For example, if there is no entry after 8:00 p.m., the system sends a notification to prompt the user to write a diary entry. This allows the diary recording support system according to an embodiment to easily record a diary and to continue writing entries. For example, the user can develop the habit of writing a diary entry.

[0030] The question generation unit can generate customized questions based on the user's past answer history. For example, the question generation unit analyzes the content of the user's past answers and generates new questions based on those answers. For example, if a user previously answered about "work stress," the question generation unit presents the user with a question such as "How is work going recently?" This allows the user to be presented with personalized questions, thereby eliciting more specific answers.

[0031] The question generator can randomly change the order of questions. For example, by randomly changing the order of questions, the question generator can allow the user to answer questions in a different order each time. For example, the same set of questions can be displayed in a different order each time. This allows the user to answer questions with a fresh feeling.

[0032] The question generator can accommodate not only text input, but also voice input and image input. For example, the question generator can accommodate not only text input but also voice input as the question format. For example, the question generator can allow users to input answers by voice and convert them into text using voice recognition technology. This makes it possible to support a variety of ways of expression by users.

[0033] The question generation unit can automatically translate the content of the question into different languages. For example, the question generation unit can introduce a function to automatically translate the content of the question into different languages, enabling diary recording in multiple languages. For example, the question generation unit can support multiple languages ​​such as English, French, and Chinese. This allows diary recording in multiple languages.

[0034] The summary generation unit can generate a consistent story by referring to the contents of the user's past diary entries. The summary generation unit can generate a consistent story by referring to the contents of the user's past diary entries, for example. For example, the summary generation unit can indicate the relevance of current events to past events and emotions based on past events and emotions. This makes it possible to generate a consistent story by referring to the contents of the user's past diary entries.

[0035] The summary generation unit can provide an interface that allows the user to make additional comments or corrections to the summary. The summary generation unit can provide an interface that allows the user to make additional comments or corrections to the summary, for example, by editing part of the summary or adding a comment. This allows the user to make additional comments or corrections to the summary.

[0036] The summary generation unit can convert the summarized diary into a visual note or infographic to make it easier to understand visually. The summary generation unit, for example, converts the summarized diary into a visual note and visually displays it. For example, it shows important points with diagrams or icons. This makes it easier to understand the summarized diary visually.

[0037] The summary generator can convert the summary into a different format (for example, a poem or a story) to provide the user with a new perspective. For example, the summary generator can convert the summary into a poem to provide the user with a new perspective. For example, the content of a diary entry can be reconstructed in poetic expression. By converting the summary into a different format, the user can be provided with a new perspective.

[0038] The prompt notification unit can customize the content of the prompt notification based on the user's past behavioral patterns and send the notification at the optimal timing. The prompt notification unit, for example, analyzes the user's past behavioral patterns and sends the prompt notification at the optimal timing. For example, if the user tends to write in their diary at night, the notification is sent at night. This makes it possible to send the prompt notification at the optimal timing based on the user's behavioral patterns.

[0039] The reminder notification unit can cite positive events that the user has recorded in the past to increase the motivation to record. The reminder notification unit can, for example, cite positive events that the user has recorded in the past in the reminder notification to increase the motivation to record. For example, it sends a notification such as, "Remember the wonderful events you recorded in the past and write in your diary today too!" In this way, the motivation to record can be increased by citing positive events that the user has recorded in the past.

[0040] The prompt notification unit may enable the prompt notification to be sent not only as a text message but also as a voice message or a video message. For example, the prompt notification unit may enable the prompt notification to be sent not only as a text message but also as a voice message. For example, the user may receive the prompt notification as a voice message. This allows the prompt notification to be sent in various formats.

[0041] The prompt notification unit can automatically adjust the frequency and content of the prompt notification based on user feedback. For example, the prompt notification unit adds a function to automatically adjust the frequency and content of the prompt notification based on user feedback. For example, the prompt notification unit allows the user to adjust the frequency of notifications. This allows the frequency and content of the prompt notification to be automatically adjusted based on user feedback.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The diary recording support system can also be equipped with a health management unit that monitors the user's health status. For example, by recording the user's heart rate and sleep patterns and correlating them with the contents of the diary, the user's health status can be grasped comprehensively. This makes it easier for the user to understand the relationship between their health status and daily events.

[0044] The diary recording support system can further include a hobby suggestion unit that provides customized content based on the user's hobbies and interests. For example, the system can suggest new hobbies and activities based on the hobbies and interests recorded by the user in the past. This can provide an opportunity for the user to discover new interests.

[0045] The diary recording support system can also include a theme summarization unit that analyzes the user's past records and generates a diary summary based on a specific theme. For example, it can summarize the contents of a user's past travel records and provide them as a single travel journal. This allows the user to enjoy a diary summary based on a specific theme.

[0046] The diary recording support system can further include a period summarization unit that analyzes the user's past records and generates a diary summary based on a specific period. For example, it can provide an annual report by summarizing the contents recorded by the user over the past year. This allows the user to enjoy a diary summary based on a specific period.

[0047] The diary recording support system can further include a person summary unit that analyzes the user's past records and generates a diary summary based on a specific person. For example, it can summarize the family-related content that the user has previously recorded and provide it as a family diary. This allows the user to enjoy a diary summary based on a specific person.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The question generator presents a question to the user. For example, questions such as "What was the most memorable event today?" or "How did you feel about that event?" are displayed. Step 2: The answer analysis unit analyzes the user's answer to the question posed by the question generation unit. For example, a generation AI analyzes the user's answer and generates a more specific question. Step 3: The summary generation unit summarizes the answers analyzed by the answer analysis unit. For example, the generation AI generates a summary based on the user's answers and creates a coherent sentence. Step 4: The reminder notification unit reminds the user to write in the diary. For example, if there is no entry after 8 p.m., the system sends a notification to remind the user to write in the diary.

[0050] (Example 2) The diary recording support system according to the embodiment of the present invention is a system that allows a user to easily record a diary and continuously write entries in the diary. As a result, the diary recording support system allows a user to continuously record a diary.

[0051] A diary recording support system according to an embodiment includes a question generation unit, an answer analysis unit, a summary generation unit, and a prompt notification unit. The question generation unit presents a question to a user. For example, questions such as "What was the most memorable event of your day?" or "How did you feel about that event?" are displayed. The answer analysis unit analyzes the user's answer to the question presented by the question generation unit. For example, a generation AI analyzes the user's answer and generates a more specific question. The summary generation unit summarizes the answer analyzed by the answer analysis unit. For example, the generation AI generates a summary based on the user's answer and creates a coherent sentence. The prompt notification unit prompts the user to write a diary entry. For example, if there is no entry after 8:00 p.m., the system sends a notification to prompt the user to write a diary entry. This allows the diary recording support system according to an embodiment to easily record a diary and to continue writing entries. For example, the user can develop the habit of writing a diary entry.

[0052] The question generation unit can generate customized questions based on the user's past answer history. For example, the question generation unit analyzes the content of the user's past answers and generates new questions based on those answers. For example, if a user previously answered about "work stress," the question generation unit presents the user with a question such as "How is work going recently?" This allows the user to be presented with personalized questions, thereby eliciting more specific answers.

[0053] The question generator can randomly change the order of questions. For example, by randomly changing the order of questions, the question generator can allow the user to answer questions in a different order each time. For example, the same set of questions can be displayed in a different order each time. This allows the user to answer questions with a fresh feeling.

[0054] The question generation unit uses the emotion estimation function to generate questions according to the user's emotional state, thereby eliciting positive emotions. The question generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate questions based on the results. For example, if the user is feeling stressed, the question generation unit presents a question such as, "What have you done recently to relax?" In this way, positive emotions can be elicited by generating questions according to the user's emotional state.

[0055] The question generator can accommodate not only text input, but also voice input and image input. For example, the question generator can accommodate not only text input but also voice input as the question format. For example, the question generator can allow users to input answers by voice and convert them into text using voice recognition technology. This makes it possible to support a variety of ways of expression by users.

[0056] The question generation unit can automatically translate the content of the question into different languages. For example, the question generation unit can introduce a function to automatically translate the content of the question into different languages, enabling diary recording in multiple languages. For example, the question generation unit can support multiple languages ​​such as English, French, and Chinese. This allows diary recording in multiple languages.

[0057] The question generation unit can use the emotion estimation function to analyze the emotion of the user when answering a question in real time and provide appropriate feedback. The question generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when answering a question in real time. For example, the question generation unit analyzes the user's facial expression and voice and calculates an emotion score. This provides feedback according to the user's emotion, thereby improving user satisfaction.

[0058] The summary generation unit can generate a consistent story by referring to the contents of the user's past diary entries. The summary generation unit can generate a consistent story by referring to the contents of the user's past diary entries, for example. For example, the summary generation unit can indicate the relevance of current events to past events and emotions based on past events and emotions. This makes it possible to generate a consistent story by referring to the contents of the user's past diary entries.

[0059] The summary generation unit can provide an interface that allows the user to make additional comments or corrections to the summary. The summary generation unit can provide an interface that allows the user to make additional comments or corrections to the summary, for example, by editing part of the summary or adding a comment. This allows the user to make additional comments or corrections to the summary.

[0060] The summary generation unit can reflect the user's emotions in the summary using the emotion estimation function. The summary generation unit, for example, uses the emotion estimation function to reflect the user's emotions in the summary. For example, for an event that moved the user, the emotion is emphasized. In this way, by reflecting the user's emotions in the summary, emotional empathy can be increased.

[0061] The summary generation unit can convert the summarized diary into a visual note or infographic to make it easier to understand visually. The summary generation unit, for example, converts the summarized diary into a visual note and visually displays it. For example, it shows important points with diagrams or icons. This makes it easier to understand the summarized diary visually.

[0062] The summary generator can convert the summary into a different format (for example, a poem or a story) to provide the user with a new perspective. For example, the summary generator can convert the summary into a poem to provide the user with a new perspective. For example, the content of a diary entry can be reconstructed in poetic expression. By converting the summary into a different format, the user can be provided with a new perspective.

[0063] The summary generation unit can use the emotion estimation function to collect users' emotional reactions to the summary sentences and improve the accuracy of the summaries. For example, the summary generation unit collects users' emotional reactions to the summary sentences in real time and improves the accuracy of the summaries based on that data. For example, it preferentially adopts summary sentences with a large number of positive reactions. In this way, by collecting users' emotional reactions to the summary sentences, the accuracy of the summaries can be improved.

[0064] The prompt notification unit can customize the content of the prompt notification based on the user's past behavioral patterns and send the notification at the optimal timing. The prompt notification unit, for example, analyzes the user's past behavioral patterns and sends the prompt notification at the optimal timing. For example, if the user tends to write in their diary at night, the notification is sent at night. This makes it possible to send the prompt notification at the optimal timing based on the user's behavioral patterns.

[0065] The reminder notification unit can cite positive events that the user has recorded in the past to increase the motivation to record. The reminder notification unit can, for example, cite positive events that the user has recorded in the past in the reminder notification to increase the motivation to record. For example, it sends a notification such as, "Remember the wonderful events you recorded in the past and write in your diary today too!" In this way, the motivation to record can be increased by citing positive events that the user has recorded in the past.

[0066] The prompt notification unit uses the emotion estimation function to generate a prompt notification according to the user's emotional state, thereby reducing stress. The prompt notification unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generates a prompt notification based on the results. For example, if the user is feeling stressed, the prompt notification unit sends a notification including advice on how to relax. In this way, generating a prompt notification according to the user's emotional state can reduce stress.

[0067] The prompt notification unit may enable the prompt notification to be sent not only as a text message but also as a voice message or a video message. For example, the prompt notification unit may enable the prompt notification to be sent not only as a text message but also as a voice message. For example, the user may receive the prompt notification as a voice message. This allows the prompt notification to be sent in various formats.

[0068] The prompt notification unit can automatically adjust the frequency and content of the prompt notification based on user feedback. For example, the prompt notification unit adds a function to automatically adjust the frequency and content of the prompt notification based on user feedback. For example, the prompt notification unit allows the user to adjust the frequency of notifications. This allows the frequency and content of the prompt notification to be automatically adjusted based on user feedback.

[0069] The prompt notification unit uses the emotion estimation function to analyze the emotional response of the user when receiving the prompt notification, and can continuously improve the optimal notification method. The prompt notification unit, for example, uses the emotion estimation function to analyze the emotional response of the user when receiving the prompt notification in real time. For example, it analyzes the user's facial expression and voice and calculates an emotion score. In this way, by analyzing the user's emotional response, the optimal notification method can be continuously improved.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The diary recording support system can also be equipped with a health management unit that monitors the user's health status. For example, by recording the user's heart rate and sleep patterns and correlating them with the contents of the diary, the user's health status can be grasped comprehensively. This makes it easier for the user to understand the relationship between their health status and daily events.

[0072] The diary recording support system can further include a hobby suggestion unit that provides customized content based on the user's hobbies and interests. For example, the system can suggest new hobbies and activities based on the hobbies and interests recorded by the user in the past. This can provide an opportunity for the user to discover new interests.

[0073] The diary recording support system can further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods based on those emotions. For example, if the user is feeling stressed, the system can suggest deep breathing or meditation. This allows the user to practice relaxation methods that suit their emotional state.

[0074] The diary recording support system can further include a music recommendation unit that estimates the user's emotions and recommends appropriate music based on those emotions. For example, if the user is feeling sad, relaxing music will be recommended. This allows the user to enjoy music that suits their emotional state.

[0075] The diary recording support system can further include an exercise suggestion unit that estimates the user's emotions and suggests appropriate exercises based on those emotions. For example, if the user is tired, it will suggest light stretching. This allows the user to exercise according to their emotional state.

[0076] The diary recording support system can further include a reading suggestion unit that estimates the user's emotions and suggests appropriate reading based on those emotions. For example, if the user wants to relax, the system will suggest books that will help them relax. This allows the user to enjoy reading according to their emotional state.

[0077] The diary recording support system can further include a movie recommendation unit that estimates the user's emotions and recommends appropriate movies based on those emotions. For example, if the user wants to cheer up, an uplifting movie will be recommended. This allows the user to enjoy a movie that suits their emotional state.

[0078] The diary recording support system can also include a theme summarization unit that analyzes the user's past records and generates a diary summary based on a specific theme. For example, it can summarize the contents of a user's past travel records and provide them as a single travel journal. This allows the user to enjoy a diary summary based on a specific theme.

[0079] The diary recording support system can further include a period summarization unit that analyzes the user's past records and generates a diary summary based on a specific period. For example, it can provide an annual report by summarizing the contents recorded by the user over the past year. This allows the user to enjoy a diary summary based on a specific period.

[0080] The diary recording support system can further include a person summary unit that analyzes the user's past records and generates a diary summary based on a specific person. For example, it can summarize the family-related content that the user has previously recorded and provide it as a family diary. This allows the user to enjoy a diary summary based on a specific person.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The question generator presents a question to the user. For example, questions such as "What was the most memorable event today?" or "How did you feel about that event?" are displayed. Step 2: The answer analysis unit analyzes the user's answer to the question posed by the question generation unit. For example, a generation AI analyzes the user's answer and generates a more specific question. Step 3: The summary generation unit summarizes the answers analyzed by the answer analysis unit. For example, the generation AI generates a summary based on the user's answers and creates a coherent sentence. Step 4: The reminder notification unit reminds the user to write in the diary. For example, if there is no entry after 8 p.m., the system sends a notification to remind the user to write in the diary.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[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 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.

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

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

[0111] 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.

[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 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.

[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 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[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 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.

[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 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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 question generator that presents questions to a user; an answer analysis unit that analyzes answers from users to questions presented by the question generation unit; a summary generation unit that summarizes the answers analyzed by the answer analysis unit; A reminder notification unit that reminds the user to write a diary entry. A system characterized by:

2. The question generation unit Generate customized questions based on the user's past answer history 2. The system of claim 1.

3. The question generation unit Supports not only text input but also voice and image input.

2. The system of claim 1.

4. The summary generation unit Reference the user's past diary entries to generate a consistent story 2. The system of claim 1.

5. The prompt notification unit The content of the reminder notification is customized based on the user's past behavioral patterns, and the notification is sent at the optimal timing.

2. The system of claim 1.

6. The question generation unit Using an emotion estimation function, questions are generated according to the user's emotional state, and positive emotions are elicited.

2. The system of claim 1.

7. The summary generation unit Using emotion estimation function, the emotion of the user is reflected in the summary text.

2. The system of claim 1.

8. The prompt notification unit Using an emotion estimation function, a reminder notification is generated according to the user's emotional state, thereby reducing stress.

2. The system of claim 1.

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