System

Abstract

To provide a system that analyzes content of diaries, finds personal growth, and provides positive feedback.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a growth detection unit, a feedback generation unit, and a provision unit. The reception unit receives the input of diaries. The analysis unit analyzes the content of the diaries received by the reception unit. The growth detection unit finds growth points on the basis of the content analyzed by the analysis unit. The feedback generation unit generates feedback on the basis of the growth points found by the growth detection unit. The provision unit provides the feedback generated by the feedback generation 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, one issue was that it was difficult to objectively grasp one's own growth and receive positive feedback through a diary.

[0005] The system according to the embodiment aims to analyze the contents of the diary, find ways to improve oneself, and provide positive feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a growth detection unit, a feedback generation unit, and a provision unit. The reception unit receives diary input. The analysis unit analyzes the contents of the diary received by the reception unit. The growth detection unit finds growth points based on the contents analyzed by the analysis unit. The feedback generation unit generates feedback based on the growth points found by the growth detection unit. The provision unit provides the feedback generated by the feedback generation unit. Effect of the Invention

[0007] The system according to the embodiment can analyze the contents of the diary, find out personal growth, and provide positive feedback. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The diary service system according to the embodiment of the present invention is a system that celebrates the daily growth of a user. In this system, the user only needs to input a diary entry, and the AI ​​analyzes the contents, finds points of growth, and provides positive feedback. For example, when a user records in the diary entry that "I took on a new challenge today," this information is input to the AI. Next, the AI ​​analyzes the contents of the input diary entry and compares it with the user's past diary entries to find points of growth. For example, it finds growth such as "I was not good at it before, but today I took on a new challenge." The AI ​​generates positive feedback based on the growth points that it has found. For example, it provides feedback such as "It's great that you took on a new challenge! Your skills are improving." This feedback is provided to the user, and the user can increase his or her self-affirmation by receiving the positive feedback. In addition, by comparing with past diaries, the user can realize his or her own growth. In this way, the diary service system can celebrate the user's daily growth, thereby increasing self-affirmation and enjoying daily accumulation. In this way, the diary service system can celebrate the user's daily growth, thereby increasing self-affirmation and enjoying daily accumulation.

[0029] The diary service system according to the embodiment includes a reception unit, an analysis unit, a growth detection unit, a feedback generation unit, and a provision unit. The reception unit receives input of a diary of a user. The diary of a user includes, for example, text, images, and voice, but is not limited to such examples. The reception unit receives, for example, text data input by a user. The reception unit can also receive image data and voice data uploaded by a user. The analysis unit analyzes the contents of the diary received by the reception unit. The analysis is performed by, for example, a method such as text analysis, emotion analysis, and topic modeling, but is not limited to such examples. For example, the analysis unit analyzes the contents of the diary using text analysis. The analysis unit can also analyze the emotions of a user using emotion analysis. The analysis unit can also analyze the topics of the diary using topic modeling. The growth detection unit finds growth points based on the contents analyzed by the analysis unit. Growth points include, for example, positive changes and skill improvements, but are not limited to such examples. For example, the growth detection unit analyzes the contents of the user's diary and finds positive changes. The growth detection unit may also find an improvement in the user's skills. The feedback generation unit generates positive feedback based on the growth points found by the growth detection unit. The feedback may include, but is not limited to, a text message, a graph, advice, or the like. For example, the feedback generation unit generates a text message and provides it to the user. The feedback generation unit may also generate a graph and provide it to the user. The feedback generation unit may also generate advice and provide it to the user. The providing unit provides the feedback generated by the feedback generation unit to the user. The feedback may be provided by, but is not limited to, a web application, a mobile application, or an e-mail. For example, the providing unit provides the feedback through a web application. The providing unit may also provide the feedback through a mobile application. The providing unit may also provide the feedback through an e-mail.As a result, the diary service system according to the embodiment can analyze the user's diary, find areas for growth, and provide positive feedback, thereby enhancing self-esteem.

[0030] The reception unit accepts the input of the user's diary. The user's diary includes, for example, text, images, and audio, but is not limited to such examples. The reception unit accepts, for example, text data input by the user. The reception unit can also accept image data and audio data uploaded by the user. Specifically, the reception unit provides an interface for the user to input the diary, and has a function of saving the input content in real time when the user inputs text. For image data, a drag-and-drop function and a file selection dialog are provided so that the user can easily upload from a smartphone or a PC. For audio data, a function is provided for the user to directly record using a microphone and a function to upload an existing audio file. Furthermore, the reception unit uses encryption technology to safely save the data input by the user and protects privacy. For example, the transmission and reception of data is encrypted using the SSL / TLS protocol, and the database is encrypted with AES encryption to prevent unauthorized access by a third party. The reception unit also provides an auto-save function and an input completion function to improve the convenience of the user when inputting the diary. The auto-save function automatically saves data at regular intervals to prevent the user from losing data while inputting. The input completion function predicts words and phrases that the user frequently uses and assists in input. This allows the reception unit to provide a stress-free environment for the user to input diary entries, and promotes continuous diary use.

[0031] The analysis unit analyzes the contents of the diary accepted by the acceptance unit. The analysis is performed by, for example, a method such as text analysis, emotion analysis, topic modeling, etc., but is not limited to such examples. For example, the analysis unit analyzes the contents of the diary using text analysis. The analysis unit can also analyze the user's emotions using emotion analysis. The analysis unit can also analyze the topics of the diary using topic modeling. Specifically, in the text analysis, the diary contents are grammatically analyzed using natural language processing technology, and keywords are extracted and sentence structure is analyzed. In the emotion analysis, a machine learning model is used to classify emotions contained in the user's diary into categories such as positive, negative, and neutral. For example, if a user writes "I had a lot of fun today," the emotion analysis model classifies this as a positive emotion. In the topic modeling, the diary contents are divided into multiple topics and keywords related to each topic are extracted. For example, if a user's diary is divided into topics such as "work," "hobbies," and "family," keywords related to each topic are identified and the diary contents are analyzed in more detail. Furthermore, the analysis unit integrates these analysis results and grasps the overall trends and patterns of the user's diary. For example, if a user expresses a lot of positive emotions in a certain period of time, the events and topics from that period are identified and the user's growth and change are evaluated. The analysis unit can also track fluctuations in the user's emotions and topics over the long term by comparing them with past data, and find signs of growth and issues. This allows the analysis unit to analyze the user's diary from multiple angles and grasp the user's growth and change in detail.

[0032] The growth detection unit finds growth points based on the contents analyzed by the analysis unit. Growth points include, for example, positive changes and skill improvements, but are not limited to such examples. For example, the growth detection unit analyzes the contents of the user's diary to find positive changes. The growth detection unit can also find improvements in the user's skills. Specifically, the growth detection unit evaluates changes in the user's emotions and fluctuations in topics based on the results of emotion analysis and topic modeling provided by the analysis unit. For example, if the user expressed a lot of negative emotions in the past but has recently expressed more positive emotions, the growth detection unit recognizes this as a positive change. In addition, if the user frequently mentions a specific skill and the contents are specific and detailed, the growth detection unit evaluates this as an improvement in the skill. Furthermore, the growth detection unit analyzes the contents of the user's diary in chronological order to track long-term growth and changes. For example, if the user had low self-esteem when he or she first started writing a diary, but has improved self-esteem by continuously recording the diary, the growth detection unit identifies this as a growth point. The growth detection unit can also evaluate the goals set by the user and the results achieved by the user and record them as evidence of the user's growth. This allows the growth detection unit to provide detailed evaluation of the growth and changes obtained through the user's diary and provide specific feedback to enhance the user's self-esteem.

[0033] The feedback generating unit generates positive feedback based on the growth points found by the growth detecting unit. The feedback includes, but is not limited to, text messages, graphs, advice, and the like. For example, the feedback generating unit generates a text message and provides it to the user. The feedback generating unit can also generate a graph and provide it to the user. The feedback generating unit can also generate advice and provide it to the user. Specifically, the feedback generating unit generates specific and positive feedback for the user based on the growth points provided by the growth detecting unit. For example, if the user shows a positive change, the feedback generating unit generates a text message such as "I can see a positive change in your recent diary. That's great!". If the user's skills are improving, the feedback generating unit provides advice such as "Your skills are steadily improving. Keep it up!". Furthermore, the feedback generating unit can generate graphs and charts to visually show the user's growth. For example, the change in the user's emotions is displayed in a line graph to visually show that positive emotions are increasing. The improvement in the user's skills is displayed in a bar graph to allow the user to visually confirm specific progress. This allows the feedback generation unit to provide specific and positive feedback to the user, thereby increasing the user's motivation. Furthermore, the feedback generation unit can collect the user's reaction to the feedback and use this information to improve the content of the feedback. For example, if the user has a positive reaction to the feedback, more effective feedback is generated based on that content. Also, if the user has a negative reaction to the feedback, the cause is analyzed and the feedback content is improved. This allows the feedback generation unit to always provide optimal feedback to the user, supporting the user's growth.

[0034] The providing unit provides the feedback generated by the feedback generating unit to the user. The feedback is provided by, for example, a web application, a mobile application, an email, or the like, but is not limited to such examples. For example, the providing unit provides the feedback through a web application. The providing unit can also provide the feedback through a mobile application. The providing unit can also provide the feedback through email. Specifically, the providing unit supports multiple platforms to provide the feedback in a manner that is most convenient for the user. In the web application, a dashboard is provided where the user can log in and check the feedback, allowing the user to view the feedback history and details. In the mobile application, the feedback is provided in real time using push notifications, allowing the user to check the feedback anytime and anywhere. In the email, the feedback is sent periodically so that the user does not miss the feedback. Furthermore, the providing unit can track the user's reaction to the feedback and evaluate the effect of the feedback. For example, the user's behavior after receiving the feedback and the contents of the diary are analyzed to evaluate how the feedback contributes to the user's growth. In addition, the providing unit can collect feedback on the feedback from the user and use it to improve the feedback content. As a result, the providing unit can provide feedback to the user in an optimal manner and support the user's growth.

[0035] The diary service system includes a storage unit that stores the contents of the diary. The storage unit stores the contents of the diary of the user. The storage is performed by, for example, a method such as cloud storage, local storage, or a database, but is not limited to such examples. For example, the storage unit stores the contents of the diary using cloud storage. The storage unit can also store the contents of the diary using local storage. The storage unit can also store the contents of the diary using a database. In this way, by storing the contents of the diary, past records can be referenced. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the contents of the diary into AI and determine the priority order of storage.

[0036] The diary service system includes a comparison unit that compares a past diary. The comparison unit compares a user's past diary with a current diary. The comparison is performed, for example, by a method such as text similarity, emotion change, or topic match, but is not limited to such examples. For example, the comparison unit compares the past diary with the current diary using text similarity. The comparison unit can also compare the past diary with the current diary using emotion change. The comparison unit can also compare the past diary with the current diary using topic match. This makes it possible to find points of growth by comparing with the past diary. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the past diary and the current diary into AI and adjust the comparison criteria.

[0037] The analysis unit can analyze the contents of the diary and find growth points. The analysis unit can analyze the contents of the diary using, for example, text analysis. For example, the analysis unit can analyze the contents of the diary using text analysis and find positive changes. The analysis unit can also analyze the user's emotions using emotion analysis. For example, the analysis unit can analyze the contents of the diary using emotion analysis and find changes in the user's emotions. The analysis unit can also analyze the topics of the diary using topic modeling. For example, the analysis unit can analyze the contents of the diary using topic modeling and find topic matches. This makes it possible to analyze the contents of the user's diary and find growth points. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the contents of the diary to the generation AI and cause the generation AI to detect growth points.

[0038] The growth detection unit can find growth points by comparing with the diary. The growth detection unit can find growth points by, for example, comparing a past diary with a current diary. For example, the growth detection unit can compare a past diary with a current diary to find positive changes. The growth detection unit can also compare a past diary with a current diary to find improvements in skills. This makes it possible to find growth points by comparing with a past diary. Some or all of the above-mentioned processing in the growth detection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the growth detection unit can input a past diary and a current diary into the generation AI and cause the generation AI to detect growth points.

[0039] The feedback generation unit can generate feedback based on the growth points. The feedback generation unit generates a text message based on the growth points, for example. For example, the feedback generation unit generates a text message such as "It's great that you've taken on a new challenge! Your skills are improving" based on the growth points. The feedback generation unit can also generate a graph based on the growth points. For example, the feedback generation unit generates a graph that visually shows the user's growth based on the growth points. The feedback generation unit can also generate advice based on the growth points. For example, the feedback generation unit generates advice such as "As a next step, let's continue to take on more challenges" based on the growth points. This makes it possible to generate positive feedback based on the growth points. A part or all of the above-mentioned processing in the feedback generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback generation unit can input the growth points to the generation AI and cause the generation AI to generate feedback.

[0040] The reception unit can analyze the past input history and suggest an input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, the reception unit preferentially suggests a voice input that the user has frequently used in the past. The reception unit can also preferentially suggest a text input that the user has frequently used in the past. The reception unit can also analyze the tendency of the contents that the user has input in the past and automatically display related input items. For example, the reception unit analyzes the tendency of the contents that the user has input in the past and automatically displays related input items. The reception unit can also find out a tendency to input in a specific time period from the user's past input history and send a notification encouraging input in that time period. For example, the reception unit finds out a tendency to input in a specific time period from the user's past input history and sends a notification such as "Now is a good time to write a diary" in that time period. Thereby, the efficiency of input is improved by analyzing the user's past input history and suggesting the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and have the generation AI suggest input methods.

[0041] The reception unit can guide the input contents based on the current living situation and the field of interest when inputting the diary. For example, the reception unit presents questions related to the project the user is currently working on and guides the input of the diary. For example, the reception unit presents questions related to the project the user is currently working on and provides guidance such as "Please tell us about the progress of the project." The reception unit can also suggest related topics based on the user's field of interest and encourage the input of the diary. For example, the reception unit suggests topics such as "Please tell us about what you are interested in recently" based on the user's field of interest. The reception unit can also suggest appropriate input items based on the user's living situation (work, family, hobbies, etc.). For example, the reception unit suggests input items such as "Please tell us about the progress of your work" based on the user's living situation. As a result, more appropriate content can be input by guiding the input contents based on the user's living situation and the field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and the field of interest to the generation AI and cause the generation AI to execute the guide of the input contents.

[0042] The reception unit can suggest highly relevant input contents in consideration of the geographical location information of the user when inputting the diary. For example, when the user is traveling, the reception unit suggests that the user input events and impressions at the travel destination. For example, when the user is traveling, the reception unit makes a suggestion such as "Let's write events and impressions at the travel destination in the diary." In addition, the reception unit can also suggest that the user input events and impressions related to the location when the user frequently visits a specific location. For example, when the user frequently visits a specific location, the reception unit makes a suggestion such as "Let's write events and impressions at the location in the diary." In addition, the reception unit can also suggest that the user input events and impressions at home when the user is at home. For example, when the user is at home, the reception unit makes a suggestion such as "Let's write events and impressions at home in the diary." As a result, by suggesting highly relevant input contents in consideration of the geographical location information of the user, more appropriate contents can be input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and have the generation AI execute suggestions for the input content.

[0043] The reception unit can analyze the social media activity of the user when inputting the diary, and suggest related input contents. The reception unit, for example, suggests that the user input events and impressions shared on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the events and impressions shared on social media in the diary" so that the user inputs the events and impressions shared on social media into the diary. The reception unit can also suggest that the user input events and impressions related to topics followed on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the events and impressions related to the topics followed in the diary" so that the user inputs events and impressions related to the topics followed on social media into the diary. The reception unit can also suggest that the user input comments and feedback received on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the comments and feedback received in the diary" so that the user inputs comments and feedback received on social media into the diary. In this way, by analyzing the social media activity of the user and suggesting related input contents, more appropriate contents can be input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to execute suggestions for input contents.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the diary during the analysis. For example, in the case of a diary about an important event, the analysis unit performs a detailed analysis to provide deep insight. For example, in the case of a diary about an important event, the analysis unit performs a detailed text analysis to provide deep insight. In addition, in the case of a diary about a daily event, the analysis unit can perform a simplified analysis to provide a result that covers the main points. For example, in the case of a diary about a daily event, the analysis unit performs a simplified text analysis to provide a result that covers the main points. In addition, in the case of a diary about a specific theme, the analysis unit can perform a detailed analysis related to the theme. For example, in the case of a diary about a specific theme, the analysis unit performs a detailed text analysis related to the theme. As a result, by adjusting the level of detail of the analysis based on the importance of the diary, a more appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the diary to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the diary during analysis. For example, in the case of a diary related to emotions, the analysis unit applies an emotion analysis algorithm. For example, in the case of a diary related to emotions, the analysis unit applies an emotion analysis algorithm to analyze the user's emotions. In addition, in the case of a diary related to goal setting, the analysis unit can also apply an algorithm to evaluate the degree of goal achievement. For example, in the case of a diary related to goal setting, the analysis unit applies an algorithm to evaluate the degree of goal achievement to analyze the degree of goal achievement of the user. In addition, in the case of a diary related to retrospection, the analysis unit can also apply an algorithm to make a comparison with past events. For example, in the case of a diary related to retrospection, the analysis unit applies an algorithm to make a comparison with past events to analyze the growth of the user. In this way, by applying different analysis algorithms depending on the category of the diary, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input diary category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0046] The analysis unit can determine the priority of the analysis based on the submission time of the diary during the analysis. The analysis unit, for example, preferentially analyzes the diary submitted recently. For example, the analysis unit preferentially analyzes the diary submitted recently to provide the user with quick feedback. The analysis unit can also preferentially analyze the diary submitted in a specific period. For example, the analysis unit preferentially analyzes the diary submitted in a specific period to find the growth points of the period. The analysis unit can also preferentially analyze the diary submitted in a period that the user particularly considers important. For example, the analysis unit preferentially analyzes the diary submitted in a period that the user particularly considers important to find the growth points of the period. In this way, by determining the priority of the analysis based on the submission time of the diary, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the diary to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the diaries during analysis. The analysis unit, for example, analyzes diaries related to the same theme together. For example, the analysis unit analyzes diaries related to the same theme together to find growth points for each theme. The analysis unit can also preferentially analyze diaries related to the user's field of interest. For example, the analysis unit preferentially analyzes diaries related to the user's field of interest to find growth points in that field. The analysis unit can also preferentially analyze diaries related to the user's goals. For example, the analysis unit preferentially analyzes diaries related to the user's goals to find growth points related to goal achievement. This allows the order of analysis to be adjusted based on the relevance of the diaries, thereby providing more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input diary relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0048] The growth detection unit can improve the accuracy of the detection by considering the interrelationships between diaries when detecting growth. The growth detection unit, for example, compares the contents of a plurality of diaries and detects common growth points. For example, the growth detection unit compares the contents of a plurality of diaries and detects a growth point such as "a growth point commonly seen in a plurality of diaries has been found". The growth detection unit can also compare the contents of the diary with past growth points and detect a new growth point. For example, the growth detection unit compares the contents of the diary with past growth points and detects a growth point such as "a new growth point has been found by comparing with past growth points". The growth detection unit can also compare the contents of the diary with the user's goal and detect a growth point related to the achievement of the goal. For example, the growth detection unit compares the contents of the diary with the user's goal and detects a growth point such as "a growth point related to the achievement of the goal has been found". As a result, the accuracy of detection can be improved by considering the interrelationships between the diaries, and more appropriate growth points can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth detection unit can input diary interrelationship data to the generation AI and cause the generation AI to detect growth points.

[0049] The growth detection unit can perform the detection while taking into consideration the attribute information of the submitter of the diary at the time of growth detection. The growth detection unit detects an appropriate growth point based on, for example, the age and sex of the user. For example, the growth detection unit detects a growth point such as "I found a growth point according to the age and sex" based on the age and sex of the user. The growth detection unit can also detect related growth points based on the user's occupation and hobby. For example, the growth detection unit detects a growth point such as "I found a growth point related to the occupation and hobby" based on the user's occupation and hobby. The growth detection unit can also detect an appropriate growth point based on the user's living environment. For example, the growth detection unit detects a growth point such as "I found a growth point according to the living environment" based on the user's living environment. As a result, by performing the detection while taking into consideration the attribute information of the submitter of the diary, a more appropriate growth point can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the growth detection unit can input the attribute information of the submitter of the diary to the generation AI and cause the generation AI to execute the detection of the growth point.

[0050] The growth detection unit can perform the detection while taking into consideration the geographical distribution of the diary when detecting the growth. The growth detection unit detects, for example, a growth point related to the user's activity in a specific area. For example, the growth detection unit detects a growth point related to the user's activity in a specific area, and detects a growth point such as "I found a growth point related to the activity in the specific area." The growth detection unit can also detect a growth point at a travel destination based on a diary recorded by the user during a trip. For example, the growth detection unit detects a growth point such as "I found a growth point at the travel destination" based on a diary recorded by the user during a trip. The growth detection unit can also detect a growth point related to the area where the user lives. For example, the growth detection unit detects a growth point related to the area where the user lives, and detects a growth point such as "I found a growth point related to the area where I live." As a result, by performing the detection while taking into consideration the geographical distribution of the diary, a more appropriate growth point can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the growth detection unit can input geographical distribution data of the diary to the generation AI and cause the generation AI to execute the detection of the growth point.

[0051] The growth detection unit can improve the accuracy of the detection by referring to the literature related to the diary at the time of growth detection. The growth detection unit, for example, refers to academic papers related to the contents of the diary and detects growth points. For example, the growth detection unit refers to academic papers related to the contents of the diary and detects growth points such as "Growth points were found based on the academic papers." The growth detection unit can also refer to books related to the contents of the diary and detect growth points. For example, the growth detection unit refers to books related to the contents of the diary and detects growth points such as "Growth points were found based on the books." The growth detection unit can also refer to online articles related to the contents of the diary and detect growth points. For example, the growth detection unit refers to online articles related to the contents of the diary and detects growth points such as "Growth points were found based on the online articles." As a result, by improving the accuracy of detection by referring to the literature related to the diary, more appropriate growth points can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using AI, for example, or may be performed without using AI. For example, the growth detection unit can input the literature data related to the diary to the generation AI and cause the generation AI to execute the detection of growth points.

[0052] The feedback generating unit can adjust the level of detail of the feedback based on the importance of the growth point when generating the feedback. The feedback generating unit provides detailed feedback for an important growth point, for example. For example, the feedback generating unit provides detailed feedback such as "This growth point is very important. We will provide detailed feedback" for an important growth point. The feedback generating unit can also provide simplified feedback for an everyday growth point. For example, the feedback generating unit provides simplified feedback such as "This growth point is everyday. We will provide simplified feedback" for an everyday growth point. The feedback generating unit can also provide detailed feedback related to a specific theme for a growth point related to the theme. For example, the feedback generating unit provides detailed feedback such as "This is a growth point related to this theme. We will provide detailed feedback" for a growth point related to a specific theme. In this way, by adjusting the level of detail of the feedback based on the importance of the growth point, more appropriate feedback can be provided. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback generation unit can input growth point importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0053] The feedback generating unit can apply different feedback algorithms depending on the category of the growth point when generating the feedback. For example, the feedback generating unit provides feedback to which an emotion analysis algorithm is applied to the growth point related to emotions. For example, the feedback generating unit applies an emotion analysis algorithm to the growth point related to emotions and provides feedback such as "Growth points have been found based on changes in emotions." The feedback generating unit can also provide feedback to which an algorithm for evaluating the degree of goal achievement is applied to the growth point related to goal setting. For example, the feedback generating unit applies an algorithm for evaluating the degree of goal achievement to the growth point related to goal setting and provides feedback such as "You have succeeded in achieving your goal." The feedback generating unit can also provide feedback to which an algorithm for comparing with past events is applied to the growth point related to reflection. For example, the feedback generating unit applies an algorithm for comparing with past events to the growth point related to reflection and provides feedback such as "You have grown compared to past events." This allows more appropriate feedback to be provided by applying different feedback algorithms depending on the category of the growth point. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback generation unit can input growth point category data to the generation AI and cause the generation AI to apply a feedback algorithm.

[0054] The feedback generating unit can determine the priority of the feedback based on the submission time of the growth point when generating the feedback. The feedback generating unit, for example, provides feedback preferentially to the growth point submitted recently. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the recent growth point" for the growth point submitted recently. The feedback generating unit can also provide feedback preferentially to the growth point submitted in a specific period. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the specific period" for the growth point submitted in a specific period. The feedback generating unit can also provide feedback preferentially to the growth point submitted in a period that the user particularly values. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the important period" for the growth point submitted in a period that the user particularly values. In this way, by determining the priority of the feedback based on the submission time of the growth point, more appropriate feedback can be provided. Part or all of the above-mentioned processing in the feedback generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback generating unit can input the submission time data of the growth point to the generation AI and cause the generation AI to execute the determination of the priority of the feedback.

[0055] The feedback generating unit can adjust the order of feedback based on the relevance of the growth points when generating feedback. The feedback generating unit, for example, provides feedback for growth points related to the same theme collectively. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the same theme" for growth points related to the same theme. The feedback generating unit can also provide feedback preferentially for growth points related to the user's field of interest. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the field of interest" for growth points related to the user's field of interest. The feedback generating unit can also provide feedback preferentially for growth points related to the user's goal. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the goal" for growth points related to the user's goal. This allows more appropriate feedback to be provided by adjusting the order of feedback based on the relevance of the growth points. A part or all of the above-mentioned processing in the feedback generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback generating unit can input relevance data of the growth points to the generating AI and cause the generating AI to adjust the order of the feedback.

[0056] The providing unit can select the optimal providing method by referring to the user's past feedback history when providing feedback. The providing unit, for example, preferentially provides the feedback method that the user has received in the past. For example, the providing unit preferentially provides the feedback method that the user has received in the past, and provides feedback such as "We will provide feedback in the method that you have received in the past." The providing unit can also provide feedback at the optimal timing from the user's past feedback history. For example, the providing unit provides feedback such as "We will provide feedback at the optimal timing" from the user's past feedback history. The providing unit can also analyze the user's past feedback history and select the most effective providing method. For example, the providing unit analyzes the user's past feedback history and provides feedback such as "We will provide feedback in the most effective method." As a result, more appropriate feedback can be provided by selecting the optimal providing method by referring to the user's past feedback history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data to the generating AI and cause the generating AI to select the optimal providing method.

[0057] The providing unit can select the optimal providing method in consideration of the device information of the user when providing feedback. For example, when the user is using a smartphone, the providing unit provides feedback according to the screen size. For example, when the user is using a smartphone, the providing unit provides feedback such as "We will provide feedback according to the screen size of the smartphone." In addition, when the user is using a tablet, the providing unit can also provide feedback optimized for a large screen. For example, when the user is using a tablet, the providing unit provides feedback such as "We will provide feedback optimized for the large screen of the tablet." In addition, the providing unit can also provide concise and highly visible feedback in the case of a user using a smartwatch. For example, when the user is using a smartwatch, the providing unit provides feedback such as "We will provide concise and highly visible feedback optimized for the smartwatch." In this way, by selecting the optimal providing method in consideration of the device information of the user, more appropriate feedback can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0058] The storage unit can adjust the level of detail of the storage based on the importance of the diary when storing. For example, in the case of a diary about an important event, the storage unit stores the diary including detailed information. For example, in the case of a diary about an important event, the storage unit stores the diary including detailed information about the important event. In addition, in the case of a diary about a daily event, the storage unit can store the diary including simplified information. For example, in the case of a diary about a daily event, the storage unit stores the diary including simplified information about the daily event. In addition, in the case of a diary about a specific theme, the storage unit can store the diary including detailed information related to the theme. For example, in the case of a diary about a specific theme, the storage unit stores the diary including detailed information related to the specific theme. In this way, by adjusting the level of detail of the storage based on the importance of the diary, more appropriate storage is possible. Part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the importance data of the diary to the generation AI and cause the generation AI to adjust the level of detail of the storage.

[0059] The storage unit can determine the priority of storage based on the submission time of the diary at the time of storage. The storage unit, for example, prioritizes storage of recently submitted diaries. For example, the storage unit prioritizes storage of recently submitted diaries, and performs storage such as "preferentially save recently submitted diaries". The storage unit can also prioritize storage of diaries submitted in a specific period. For example, the storage unit prioritizes storage of diaries submitted in a specific period, and performs storage such as "preferentially save diaries submitted in a specific period". The storage unit can also prioritize storage of diaries submitted in a period that the user considers particularly important. For example, the storage unit prioritizes storage of diaries submitted in a period that the user considers particularly important, and performs storage such as "preferentially save diaries submitted in an important period". As a result, more appropriate storage is possible by determining the priority of storage based on the submission time of the diary. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the submission time of a diary entry to the generation AI and have the generation AI determine the priority of storage.

[0060] The storage unit can adjust the order of storage based on the relevance of the diaries when storing. The storage unit, for example, stores diaries related to the same theme together. For example, the storage unit stores diaries related to the same theme together, and stores the diaries such as "save diaries related to the same theme together". The storage unit can also store diaries related to the user's field of interest preferentially. For example, the storage unit stores diaries related to the user's field of interest preferentially, and stores the diaries such as "save diaries related to the field of interest preferentially". The storage unit can also store diaries related to the user's goals preferentially. For example, the storage unit stores diaries related to the user's goals preferentially, and stores the diaries such as "save diaries related to the goals preferentially". This allows more appropriate storage by adjusting the order of storage based on the relevance of the diaries. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input diary relevance data to the generation AI and cause the generation AI to adjust the order of storage.

[0061] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between the diaries. For example, the comparison unit compares the contents of a plurality of diaries and finds common growth points. For example, the comparison unit compares the contents of a plurality of diaries and finds growth points such as "We have found growth points that are common to a plurality of diaries". The comparison unit can also compare the contents of the diary with past growth points and find new growth points. For example, the comparison unit compares the contents of the diary with past growth points and finds growth points such as "We have found new growth points by comparing with past growth points". The comparison unit can also compare the contents of the diary with the user's goal and find growth points related to goal achievement. For example, the comparison unit compares the contents of the diary with the user's goal and finds growth points such as "We have found growth points related to goal achievement". This improves the accuracy of the comparison by considering the interrelationships between the diaries, making it possible to make a more appropriate comparison. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input diary interrelationship data to the generation AI and cause the generation AI to improve the accuracy of the comparison.

[0062] The comparison unit can make the comparison taking into consideration the geographical distribution of the diaries. For example, the comparison unit compares diaries related to activities in a specific area by the user. For example, the comparison unit compares diaries related to activities in a specific area by the user, and makes a comparison such as "compare diaries related to activities in a specific area". The comparison unit can also compare diaries recorded by the user while traveling. For example, the comparison unit compares diaries recorded by the user while traveling, and makes a comparison such as "compare diaries recorded during the travel". The comparison unit can also compare diaries related to the area in which the user lives. For example, the comparison unit compares diaries related to the area in which the user lives, and makes a comparison such as "compare diaries related to the area in which the user lives". This makes it possible to make a more appropriate comparison by making a comparison taking into consideration the geographical distribution of the diaries. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input geographical distribution data of the diary to the generation AI and cause the generation AI to execute the comparison.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The diary service system can acquire health data of a user and associate it with the contents of the diary. For example, by associating exercise data and sleep data acquired from a user's fitness tracker with the contents of the diary, the correlation between the user's health condition and the contents of the diary can be analyzed. In addition, by associating food data acquired from a food recording app of the user with the contents of the diary, the change in food and emotions can be analyzed. Furthermore, by associating data acquired from a device that measures the user's stress level with the contents of the diary, the relationship between stress and the contents of the diary can be analyzed. In this way, deeper insights can be provided by integrating and analyzing the user's health data and the contents of the diary.

[0065] The diary service system can analyze a user's social media activities and associate them with the contents of the diary. For example, by associating events and emotions shared by the user on social media with the contents of the diary, the correlation between social media activities and the contents of the diary can be analyzed. In addition, by analyzing topics and hashtags followed by the user and associating them with the contents of the diary, the relationship between areas of interest and the contents of the diary can be analyzed. Furthermore, by associating comments and feedback received by the user with the contents of the diary, the relationship between the influence of others and the contents of the diary can be analyzed. In this way, a deeper insight can be provided by comprehensively analyzing the user's social media activities and the contents of the diary.

[0066] The diary service system can acquire the geographical location information of the user and associate it with the contents of the diary. For example, by associating the diary recorded by the user while traveling with the geographical location information, it is possible to analyze events and emotional changes at the travel destination. In addition, if the user frequently visits a specific place, it is also possible to analyze events and emotional changes at that place. Furthermore, by analyzing the diary related to the area where the user lives, it is possible to analyze events and emotional changes specific to the area. In this way, it is possible to provide deeper insights by integrating the analysis of the geographical location information of the user and the contents of the diary.

[0067] The diary service system can analyze the voice input of the user and associate it with the contents of the diary. For example, the diary recorded by the user in voice can be converted into text and emotion analysis can be performed to analyze the difference between the voice input and the text input. In addition, by analyzing the tone and pitch of the voice, the change in the user's emotion can be analyzed in more detail. Furthermore, by comparing the voice input and the text input, the difference in the user's expression method can be analyzed. In this way, deeper insights can be provided by integrating the analysis of the voice input and the text input.

[0068] The diary service system can analyze the user's life rhythm and associate it with the contents of the diary. For example, by analyzing the user's sleep patterns and activity levels and associating them with the contents of the diary, it is possible to analyze changes in the life rhythm and emotions. In addition, by analyzing the user's meal times and exercise times, it is possible to analyze the relationship between the life rhythm and the contents of the diary. Furthermore, by analyzing the user's work and school schedules, it is possible to analyze the relationship between the life rhythm and the contents of the diary. In this way, a deeper insight can be provided by comprehensively analyzing the user's life rhythm and the contents of the diary.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The reception unit receives the input of a diary entry of a user. The diary entry of a user includes text, images, voice, etc. For example, the reception unit receives text data input by the user, and image data and voice data uploaded by the user. Step 2: The analysis unit analyzes the contents of the diary accepted by the acceptance unit. The analysis is performed by a method such as text analysis, sentiment analysis, topic modeling, etc. For example, the diary contents are analyzed using text analysis, the user's sentiment is analyzed using sentiment analysis, and the diary topics are analyzed using topic modeling. Step 3: The growth detection unit finds growth points based on the content analyzed by the analysis unit. Growth points include positive changes and skill improvements. For example, the content of the user's diary is analyzed to find positive changes and skill improvements. Step 4: The feedback generating unit generates positive feedback based on the growth points found by the growth detecting unit. The feedback may include a text message, a graph, advice, etc. For example, a text message, a graph, or advice may be generated and provided to the user. Step 5: The providing unit provides the feedback generated by the feedback generating unit to the user. The provision is performed by a method such as a web application, a mobile application, or an email. For example, the feedback is provided through a web application, a mobile application, or an email.

[0071] (Example 2) The diary service system according to the embodiment of the present invention is a system that celebrates the daily growth of a user. In this system, the user only needs to input a diary entry, and the AI ​​analyzes the contents, finds points of growth, and provides positive feedback. For example, when a user records in the diary entry that "I took on a new challenge today," this information is input to the AI. Next, the AI ​​analyzes the contents of the input diary entry and compares it with the user's past diary entries to find points of growth. For example, it finds growth such as "I was not good at it before, but today I took on a new challenge." The AI ​​generates positive feedback based on the growth points that it has found. For example, it provides feedback such as "It's great that you took on a new challenge! Your skills are improving." This feedback is provided to the user, and the user can increase his or her self-affirmation by receiving the positive feedback. In addition, by comparing with past diaries, the user can realize his or her own growth. In this way, the diary service system can celebrate the user's daily growth, thereby increasing self-affirmation and enjoying daily accumulation. In this way, the diary service system can celebrate the user's daily growth, thereby increasing self-affirmation and enjoying daily accumulation.

[0072] The diary service system according to the embodiment includes a reception unit, an analysis unit, a growth detection unit, a feedback generation unit, and a provision unit. The reception unit receives input of a diary of a user. The diary of a user includes, for example, text, images, and voice, but is not limited to such examples. The reception unit receives, for example, text data input by a user. The reception unit can also receive image data and voice data uploaded by a user. The analysis unit analyzes the contents of the diary received by the reception unit. The analysis is performed by, for example, a method such as text analysis, emotion analysis, and topic modeling, but is not limited to such examples. For example, the analysis unit analyzes the contents of the diary using text analysis. The analysis unit can also analyze the emotions of a user using emotion analysis. The analysis unit can also analyze the topics of the diary using topic modeling. The growth detection unit finds growth points based on the contents analyzed by the analysis unit. Growth points include, for example, positive changes and skill improvements, but are not limited to such examples. For example, the growth detection unit analyzes the contents of the user's diary and finds positive changes. The growth detection unit may also find an improvement in the user's skills. The feedback generation unit generates positive feedback based on the growth points found by the growth detection unit. The feedback may include, but is not limited to, a text message, a graph, advice, or the like. For example, the feedback generation unit generates a text message and provides it to the user. The feedback generation unit may also generate a graph and provide it to the user. The feedback generation unit may also generate advice and provide it to the user. The providing unit provides the feedback generated by the feedback generation unit to the user. The feedback may be provided by, but is not limited to, a web application, a mobile application, or an e-mail. For example, the providing unit provides the feedback through a web application. The providing unit may also provide the feedback through a mobile application. The providing unit may also provide the feedback through an e-mail.As a result, the diary service system according to the embodiment can analyze the user's diary, find areas for growth, and provide positive feedback, thereby enhancing self-esteem.

[0073] The reception unit accepts the input of the user's diary. The user's diary includes, for example, text, images, and audio, but is not limited to such examples. The reception unit accepts, for example, text data input by the user. The reception unit can also accept image data and audio data uploaded by the user. Specifically, the reception unit provides an interface for the user to input the diary, and has a function of saving the input content in real time when the user inputs text. For image data, a drag-and-drop function and a file selection dialog are provided so that the user can easily upload from a smartphone or a PC. For audio data, a function is provided for the user to directly record using a microphone and a function to upload an existing audio file. Furthermore, the reception unit uses encryption technology to safely save the data input by the user and protects privacy. For example, the transmission and reception of data is encrypted using the SSL / TLS protocol, and the database is encrypted with AES encryption to prevent unauthorized access by a third party. The reception unit also provides an auto-save function and an input completion function to improve the convenience of the user when inputting the diary. The auto-save function automatically saves data at regular intervals to prevent the user from losing data while inputting. The input completion function predicts words and phrases that the user frequently uses and assists in input. This allows the reception unit to provide a stress-free environment for the user to input diary entries, and promotes continuous diary use.

[0074] The analysis unit analyzes the contents of the diary accepted by the acceptance unit. The analysis is performed by, for example, a method such as text analysis, emotion analysis, topic modeling, etc., but is not limited to such examples. For example, the analysis unit analyzes the contents of the diary using text analysis. The analysis unit can also analyze the user's emotions using emotion analysis. The analysis unit can also analyze the topics of the diary using topic modeling. Specifically, in the text analysis, the diary contents are grammatically analyzed using natural language processing technology, and keywords are extracted and sentence structure is analyzed. In the emotion analysis, a machine learning model is used to classify emotions contained in the user's diary into categories such as positive, negative, and neutral. For example, if a user writes "I had a lot of fun today," the emotion analysis model classifies this as a positive emotion. In the topic modeling, the diary contents are divided into multiple topics and keywords related to each topic are extracted. For example, if a user's diary is divided into topics such as "work," "hobbies," and "family," keywords related to each topic are identified and the diary contents are analyzed in more detail. Furthermore, the analysis unit integrates these analysis results and grasps the overall trends and patterns of the user's diary. For example, if a user expresses a lot of positive emotions in a certain period of time, the events and topics from that period are identified and the user's growth and change are evaluated. The analysis unit can also track fluctuations in the user's emotions and topics over the long term by comparing them with past data, and find signs of growth and issues. This allows the analysis unit to analyze the user's diary from multiple angles and grasp the user's growth and change in detail.

[0075] The growth detection unit finds growth points based on the contents analyzed by the analysis unit. Growth points include, for example, positive changes and skill improvements, but are not limited to such examples. For example, the growth detection unit analyzes the contents of the user's diary to find positive changes. The growth detection unit can also find improvements in the user's skills. Specifically, the growth detection unit evaluates changes in the user's emotions and fluctuations in topics based on the results of emotion analysis and topic modeling provided by the analysis unit. For example, if the user expressed a lot of negative emotions in the past but has recently expressed more positive emotions, the growth detection unit recognizes this as a positive change. In addition, if the user frequently mentions a specific skill and the contents are specific and detailed, the growth detection unit evaluates this as an improvement in the skill. Furthermore, the growth detection unit analyzes the contents of the user's diary in chronological order to track long-term growth and changes. For example, if the user had low self-esteem when he or she first started writing a diary, but has improved self-esteem by continuously recording the diary, the growth detection unit identifies this as a growth point. The growth detection unit can also evaluate the goals set by the user and the results achieved by the user and record them as evidence of the user's growth. This allows the growth detection unit to provide detailed evaluation of the growth and changes obtained through the user's diary and provide specific feedback to enhance the user's self-esteem.

[0076] The feedback generating unit generates positive feedback based on the growth points found by the growth detecting unit. The feedback includes, but is not limited to, text messages, graphs, advice, and the like. For example, the feedback generating unit generates a text message and provides it to the user. The feedback generating unit can also generate a graph and provide it to the user. The feedback generating unit can also generate advice and provide it to the user. Specifically, the feedback generating unit generates specific and positive feedback for the user based on the growth points provided by the growth detecting unit. For example, if the user shows a positive change, the feedback generating unit generates a text message such as "I can see a positive change in your recent diary. That's great!". If the user's skills are improving, the feedback generating unit provides advice such as "Your skills are steadily improving. Keep it up!". Furthermore, the feedback generating unit can generate graphs and charts to visually show the user's growth. For example, the change in the user's emotions is displayed in a line graph to visually show that positive emotions are increasing. The improvement in the user's skills is displayed in a bar graph to allow the user to visually confirm specific progress. This allows the feedback generation unit to provide specific and positive feedback to the user, thereby increasing the user's motivation. Furthermore, the feedback generation unit can collect the user's reaction to the feedback and use this information to improve the content of the feedback. For example, if the user has a positive reaction to the feedback, more effective feedback is generated based on that content. Also, if the user has a negative reaction to the feedback, the cause is analyzed and the feedback content is improved. This allows the feedback generation unit to always provide optimal feedback to the user, supporting the user's growth.

[0077] The providing unit provides the feedback generated by the feedback generating unit to the user. The feedback is provided by, for example, a web application, a mobile application, an email, or the like, but is not limited to such examples. For example, the providing unit provides the feedback through a web application. The providing unit can also provide the feedback through a mobile application. The providing unit can also provide the feedback through email. Specifically, the providing unit supports multiple platforms to provide the feedback in a manner that is most convenient for the user. In the web application, a dashboard is provided where the user can log in and check the feedback, allowing the user to view the feedback history and details. In the mobile application, the feedback is provided in real time using push notifications, allowing the user to check the feedback anytime and anywhere. In the email, the feedback is sent periodically so that the user does not miss the feedback. Furthermore, the providing unit can track the user's reaction to the feedback and evaluate the effect of the feedback. For example, the user's behavior after receiving the feedback and the contents of the diary are analyzed to evaluate how the feedback contributes to the user's growth. In addition, the providing unit can collect feedback on the feedback from the user and use it to improve the feedback content. As a result, the providing unit can provide feedback to the user in an optimal manner and support the user's growth.

[0078] The diary service system includes a storage unit that stores the contents of the diary. The storage unit stores the contents of the diary of the user. The storage is performed by, for example, a method such as cloud storage, local storage, or a database, but is not limited to such examples. For example, the storage unit stores the contents of the diary using cloud storage. The storage unit can also store the contents of the diary using local storage. The storage unit can also store the contents of the diary using a database. In this way, by storing the contents of the diary, past records can be referenced. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the contents of the diary into AI and determine the priority order of storage.

[0079] The diary service system includes a comparison unit that compares a past diary. The comparison unit compares a user's past diary with a current diary. The comparison is performed, for example, by a method such as text similarity, emotion change, or topic match, but is not limited to such examples. For example, the comparison unit compares the past diary with the current diary using text similarity. The comparison unit can also compare the past diary with the current diary using emotion change. The comparison unit can also compare the past diary with the current diary using topic match. This makes it possible to find points of growth by comparing with the past diary. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input the past diary and the current diary into AI and adjust the comparison criteria.

[0080] The analysis unit can analyze the contents of the diary and find growth points. The analysis unit can analyze the contents of the diary using, for example, text analysis. For example, the analysis unit can analyze the contents of the diary using text analysis and find positive changes. The analysis unit can also analyze the user's emotions using emotion analysis. For example, the analysis unit can analyze the contents of the diary using emotion analysis and find changes in the user's emotions. The analysis unit can also analyze the topics of the diary using topic modeling. For example, the analysis unit can analyze the contents of the diary using topic modeling and find topic matches. This makes it possible to analyze the contents of the user's diary and find growth points. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the contents of the diary to the generation AI and cause the generation AI to detect growth points.

[0081] The growth detection unit can find growth points by comparing with the diary. The growth detection unit can find growth points by, for example, comparing a past diary with a current diary. For example, the growth detection unit can compare a past diary with a current diary to find positive changes. The growth detection unit can also compare a past diary with a current diary to find improvements in skills. This makes it possible to find growth points by comparing with a past diary. Some or all of the above-mentioned processing in the growth detection unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the growth detection unit can input a past diary and a current diary into the generation AI and cause the generation AI to detect growth points.

[0082] The feedback generation unit can generate feedback based on the growth points. The feedback generation unit generates a text message based on the growth points, for example. For example, the feedback generation unit generates a text message such as "It's great that you've taken on a new challenge! Your skills are improving" based on the growth points. The feedback generation unit can also generate a graph based on the growth points. For example, the feedback generation unit generates a graph that visually shows the user's growth based on the growth points. The feedback generation unit can also generate advice based on the growth points. For example, the feedback generation unit generates advice such as "As a next step, let's continue to take on more challenges" based on the growth points. This makes it possible to generate positive feedback based on the growth points. A part or all of the above-mentioned processing in the feedback generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the feedback generation unit can input the growth points to the generation AI and cause the generation AI to generate feedback.

[0083] The reception unit can estimate the emotion and adjust the timing of diary entry based on the estimated emotion. For example, when the user is feeling stressed, the reception unit sends a notification to prompt the user to enter the diary during a time when the user can relax. For example, when the user is feeling stressed, the reception unit sends a notification such as "Now is a good time to write a diary" during a time when the user can relax. The reception unit can also send a notification to prompt the user to enter the diary when the user is relaxed. For example, when the user is relaxed, the reception unit sends a notification such as "Let's write a diary in a relaxed state." The reception unit can also provide a simplified input form so that the user can enter the diary in a short time when the user is busy. For example, when the user is busy, the reception unit sends a notification such as "We have prepared a diary form that can be entered in a short time." This allows the diary entry timing to be adjusted based on the user's emotion, so that the diary entry can be made at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of diary entry.

[0084] The reception unit can analyze the past input history and suggest an input method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, the reception unit preferentially suggests a voice input that the user has frequently used in the past. The reception unit can also preferentially suggest a text input that the user has frequently used in the past. The reception unit can also analyze the tendency of the contents that the user has input in the past and automatically display related input items. For example, the reception unit analyzes the tendency of the contents that the user has input in the past and automatically displays related input items. The reception unit can also find out a tendency to input in a specific time period from the user's past input history and send a notification encouraging input in that time period. For example, the reception unit finds out a tendency to input in a specific time period from the user's past input history and sends a notification such as "Now is a good time to write a diary" in that time period. Thereby, the efficiency of input is improved by analyzing the user's past input history and suggesting the optimal input method. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history to the generation AI and have the generation AI suggest input methods.

[0085] The reception unit can guide the input contents based on the current living situation and the field of interest when inputting the diary. For example, the reception unit presents questions related to the project the user is currently working on and guides the input of the diary. For example, the reception unit presents questions related to the project the user is currently working on and provides guidance such as "Please tell us about the progress of the project." The reception unit can also suggest related topics based on the user's field of interest and encourage the input of the diary. For example, the reception unit suggests topics such as "Please tell us about what you are interested in recently" based on the user's field of interest. The reception unit can also suggest appropriate input items based on the user's living situation (work, family, hobbies, etc.). For example, the reception unit suggests input items such as "Please tell us about the progress of your work" based on the user's living situation. As a result, more appropriate content can be input by guiding the input contents based on the user's living situation and the field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and the field of interest to the generation AI and cause the generation AI to execute the guide of the input contents.

[0086] The reception unit can estimate the emotion and determine the priority of diary entries to be entered based on the estimated emotion. For example, when the user is feeling stressed, the reception unit preferentially suggests diary entries related to stress relief. For example, when the user is feeling stressed, the reception unit makes a suggestion such as "Let's write in your diary about an event that helps relieve stress." In addition, the reception unit can also suggest diary entries related to reflection and goal setting in a relaxed state when the user is relaxed. For example, when the user is relaxed, the reception unit makes a suggestion such as "Let's write in your diary about reflection and goal setting in a relaxed state." In addition, the reception unit can also preferentially suggest diary items that can be entered in a short time when the user is busy. For example, when the user is busy, the reception unit makes a suggestion such as "We have prepared diary items that can be entered in a short time." In this way, by determining the priority of diary entries to be entered based on the user's emotion, more important content can be preferentially entered. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the diary.

[0087] The reception unit can suggest highly relevant input contents in consideration of the geographical location information of the user when inputting the diary. For example, when the user is traveling, the reception unit suggests that the user input events and impressions at the travel destination. For example, when the user is traveling, the reception unit makes a suggestion such as "Let's write events and impressions at the travel destination in the diary." In addition, the reception unit can also suggest that the user input events and impressions related to the location when the user frequently visits a specific location. For example, when the user frequently visits a specific location, the reception unit makes a suggestion such as "Let's write events and impressions at the location in the diary." In addition, the reception unit can also suggest that the user input events and impressions at home when the user is at home. For example, when the user is at home, the reception unit makes a suggestion such as "Let's write events and impressions at home in the diary." As a result, by suggesting highly relevant input contents in consideration of the geographical location information of the user, more appropriate contents can be input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and have the generation AI execute suggestions for the input content.

[0088] The reception unit can analyze the social media activity of the user when inputting the diary, and suggest related input contents. The reception unit, for example, suggests that the user input events and impressions shared on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the events and impressions shared on social media in the diary" so that the user inputs the events and impressions shared on social media into the diary. The reception unit can also suggest that the user input events and impressions related to topics followed on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the events and impressions related to the topics followed in the diary" so that the user inputs events and impressions related to the topics followed on social media into the diary. The reception unit can also suggest that the user input comments and feedback received on social media into the diary. For example, the reception unit makes a suggestion such as "Let's write the comments and feedback received in the diary" so that the user inputs comments and feedback received on social media into the diary. In this way, by analyzing the social media activity of the user and suggesting related input contents, more appropriate contents can be input. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to execute suggestions for input contents.

[0089] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated emotion. For example, when the user is stressed, the analysis unit provides a simple and visually easy-to-understand analysis result. For example, when the user is stressed, the analysis unit provides an analysis result using a simple and visually easy-to-understand graph or chart. In addition, when the user is relaxed, the analysis unit can provide a detailed analysis result to encourage deep insight. For example, when the user is relaxed, the analysis unit provides a detailed text analysis result to encourage deep insight. In addition, the analysis unit can provide a visually stimulating analysis result when the user is excited. For example, when the user is excited, the analysis unit provides an analysis result using a visually stimulating graph or chart. Thereby, by adjusting the expression method of the analysis based on the user's emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the diary during the analysis. For example, in the case of a diary about an important event, the analysis unit performs a detailed analysis to provide deep insight. For example, in the case of a diary about an important event, the analysis unit performs a detailed text analysis to provide deep insight. In addition, in the case of a diary about a daily event, the analysis unit can perform a simplified analysis to provide a result that covers the main points. For example, in the case of a diary about a daily event, the analysis unit performs a simplified text analysis to provide a result that covers the main points. In addition, in the case of a diary about a specific theme, the analysis unit can perform a detailed analysis related to the theme. For example, in the case of a diary about a specific theme, the analysis unit performs a detailed text analysis related to the theme. As a result, by adjusting the level of detail of the analysis based on the importance of the diary, a more appropriate analysis result can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance data of the diary to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the diary during analysis. For example, in the case of a diary related to emotions, the analysis unit applies an emotion analysis algorithm. For example, in the case of a diary related to emotions, the analysis unit applies an emotion analysis algorithm to analyze the user's emotions. In addition, in the case of a diary related to goal setting, the analysis unit can also apply an algorithm to evaluate the degree of goal achievement. For example, in the case of a diary related to goal setting, the analysis unit applies an algorithm to evaluate the degree of goal achievement to analyze the degree of goal achievement of the user. In addition, in the case of a diary related to retrospection, the analysis unit can also apply an algorithm to make a comparison with past events. For example, in the case of a diary related to retrospection, the analysis unit applies an algorithm to make a comparison with past events to analyze the growth of the user. In this way, by applying different analysis algorithms depending on the category of the diary, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input diary category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0092] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. For example, when the user is in a hurry, the analysis unit provides a short and to the point text analysis result. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user is relaxed, the analysis unit provides a detailed text analysis result. The analysis unit can also provide a visually stimulating analysis result when the user is excited. For example, when the user is excited, the analysis unit provides an analysis result using a visually stimulating graph or chart. This allows the length of the analysis to be adjusted based on the user's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0093] The analysis unit can determine the priority of the analysis based on the submission time of the diary during the analysis. The analysis unit, for example, preferentially analyzes the diary submitted recently. For example, the analysis unit preferentially analyzes the diary submitted recently to provide the user with quick feedback. The analysis unit can also preferentially analyze the diary submitted in a specific period. For example, the analysis unit preferentially analyzes the diary submitted in a specific period to find the growth points of the period. The analysis unit can also preferentially analyze the diary submitted in a period that the user particularly considers important. For example, the analysis unit preferentially analyzes the diary submitted in a period that the user particularly considers important to find the growth points of the period. In this way, by determining the priority of the analysis based on the submission time of the diary, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the submission time data of the diary to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the diaries during analysis. The analysis unit, for example, analyzes diaries related to the same theme together. For example, the analysis unit analyzes diaries related to the same theme together to find growth points for each theme. The analysis unit can also preferentially analyze diaries related to the user's field of interest. For example, the analysis unit preferentially analyzes diaries related to the user's field of interest to find growth points in that field. The analysis unit can also preferentially analyze diaries related to the user's goals. For example, the analysis unit preferentially analyzes diaries related to the user's goals to find growth points related to goal achievement. This allows the order of analysis to be adjusted based on the relevance of the diaries, thereby providing more appropriate analysis results. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input diary relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0095] The growth detection unit can estimate the user's emotion and adjust the detection criteria for growth points based on the estimated emotion. For example, when the user is feeling stressed, the growth detection unit preferentially detects growth points related to stress relief. For example, when the user is feeling stressed, the growth detection unit preferentially detects growth points related to stress relief and finds a growth point such as "I succeeded in relieving stress." In addition, the growth detection unit can also detect growth points in a relaxed state when the user is relaxed. For example, when the user is relaxed, the growth detection unit detects growth points in a relaxed state and finds a growth point such as "I relaxed and acquired a new skill." In addition, the growth detection unit can also detect growth points in an excited state when the user is excited. For example, when the user is excited, the growth detection unit detects growth points in an excited state and finds a growth point such as "I was excited and succeeded in a new challenge." In this way, by adjusting the detection criteria for growth points based on the user's emotion, more appropriate growth points can be detected. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the growth detection unit may be performed, for example, using AI or may be performed without using AI. For example, the growth detection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the detection criteria for growth points.

[0096] The growth detection unit can improve the accuracy of the detection by considering the interrelationships between diaries when detecting growth. The growth detection unit, for example, compares the contents of a plurality of diaries and detects common growth points. For example, the growth detection unit compares the contents of a plurality of diaries and detects a growth point such as "a growth point commonly seen in a plurality of diaries has been found". The growth detection unit can also compare the contents of the diary with past growth points and detect a new growth point. For example, the growth detection unit compares the contents of the diary with past growth points and detects a growth point such as "a new growth point has been found by comparing with past growth points". The growth detection unit can also compare the contents of the diary with the user's goal and detect a growth point related to the achievement of the goal. For example, the growth detection unit compares the contents of the diary with the user's goal and detects a growth point such as "a growth point related to the achievement of the goal has been found". As a result, the accuracy of detection can be improved by considering the interrelationships between the diaries, and more appropriate growth points can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the growth detection unit can input diary interrelationship data to the generation AI and cause the generation AI to detect growth points.

[0097] The growth detection unit can perform the detection while taking into consideration the attribute information of the submitter of the diary at the time of growth detection. The growth detection unit detects an appropriate growth point based on, for example, the age and sex of the user. For example, the growth detection unit detects a growth point such as "I found a growth point according to the age and sex" based on the age and sex of the user. The growth detection unit can also detect related growth points based on the user's occupation and hobby. For example, the growth detection unit detects a growth point such as "I found a growth point related to the occupation and hobby" based on the user's occupation and hobby. The growth detection unit can also detect an appropriate growth point based on the user's living environment. For example, the growth detection unit detects a growth point such as "I found a growth point according to the living environment" based on the user's living environment. As a result, by performing the detection while taking into consideration the attribute information of the submitter of the diary, a more appropriate growth point can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the growth detection unit can input the attribute information of the submitter of the diary to the generation AI and cause the generation AI to execute the detection of the growth point.

[0098] The growth detection unit can estimate the user's emotion and adjust the display method of the growth points based on the estimated emotion. For example, when the user is stressed, the growth detection unit provides a simple and visually easy-to-understand display method. For example, when the user is stressed, the growth detection unit provides a display method using a simple and visually easy-to-understand graph or chart. In addition, the growth detection unit can also provide a display method including detailed information when the user is relaxed. For example, when the user is relaxed, the growth detection unit provides a display method including detailed text information. In addition, the growth detection unit can also provide a visually stimulating display method when the user is excited. For example, when the user is excited, the growth detection unit provides a display method using a visually stimulating graph or chart. This allows for more appropriate display by adjusting the display method of the growth points based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the growth detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the growth detection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way in which growth points are displayed.

[0099] The growth detection unit can perform the detection while taking into consideration the geographical distribution of the diary when detecting the growth. The growth detection unit detects, for example, a growth point related to the user's activity in a specific area. For example, the growth detection unit detects a growth point related to the user's activity in a specific area, and detects a growth point such as "I found a growth point related to the activity in the specific area." The growth detection unit can also detect a growth point at a travel destination based on a diary recorded by the user during a trip. For example, the growth detection unit detects a growth point such as "I found a growth point at the travel destination" based on a diary recorded by the user during a trip. The growth detection unit can also detect a growth point related to the area where the user lives. For example, the growth detection unit detects a growth point related to the area where the user lives, and detects a growth point such as "I found a growth point related to the area where I live." As a result, by performing the detection while taking into consideration the geographical distribution of the diary, a more appropriate growth point can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the growth detection unit can input geographical distribution data of the diary to the generation AI and cause the generation AI to execute the detection of the growth point.

[0100] The growth detection unit can improve the accuracy of the detection by referring to the literature related to the diary at the time of growth detection. The growth detection unit, for example, refers to academic papers related to the contents of the diary and detects growth points. For example, the growth detection unit refers to academic papers related to the contents of the diary and detects growth points such as "Growth points were found based on the academic papers." The growth detection unit can also refer to books related to the contents of the diary and detect growth points. For example, the growth detection unit refers to books related to the contents of the diary and detects growth points such as "Growth points were found based on the books." The growth detection unit can also refer to online articles related to the contents of the diary and detect growth points. For example, the growth detection unit refers to online articles related to the contents of the diary and detects growth points such as "Growth points were found based on the online articles." As a result, by improving the accuracy of detection by referring to the literature related to the diary, more appropriate growth points can be detected. A part or all of the above-mentioned processing in the growth detection unit may be performed using AI, for example, or may be performed without using AI. For example, the growth detection unit can input the literature data related to the diary to the generation AI and cause the generation AI to execute the detection of growth points.

[0101] The feedback generating unit can estimate the user's emotion and adjust the way of expressing the feedback based on the estimated emotion. For example, when the user is stressed, the feedback generating unit provides feedback including encouraging words. For example, when the user is stressed, the feedback generating unit provides feedback including encouraging words such as "You are great. You are overcoming stress." In addition, when the user is relaxed, the feedback generating unit can provide detailed feedback to encourage deep insight. For example, when the user is relaxed, the feedback generating unit provides detailed feedback such as "You have acquired a new skill in a relaxed state." In addition, the feedback generating unit can also provide visually stimulating feedback when the user is excited. For example, when the user is excited, the feedback generating unit provides visually stimulating feedback such as "You were excited and succeeded in a new challenge." In this way, by adjusting the way of expressing the feedback based on the user's emotion, more appropriate feedback can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback generating unit may input the user's emotion data to the generating AI and cause the generating AI to adjust the feedback expression method.

[0102] The feedback generating unit can adjust the level of detail of the feedback based on the importance of the growth point when generating the feedback. The feedback generating unit provides detailed feedback for an important growth point, for example. For example, the feedback generating unit provides detailed feedback such as "This growth point is very important. We will provide detailed feedback" for an important growth point. The feedback generating unit can also provide simplified feedback for an everyday growth point. For example, the feedback generating unit provides simplified feedback such as "This growth point is everyday. We will provide simplified feedback" for an everyday growth point. The feedback generating unit can also provide detailed feedback related to a specific theme for a growth point related to the theme. For example, the feedback generating unit provides detailed feedback such as "This is a growth point related to this theme. We will provide detailed feedback" for a growth point related to a specific theme. In this way, by adjusting the level of detail of the feedback based on the importance of the growth point, more appropriate feedback can be provided. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback generation unit can input growth point importance data to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0103] The feedback generating unit can apply different feedback algorithms depending on the category of the growth point when generating the feedback. For example, the feedback generating unit provides feedback to which an emotion analysis algorithm is applied to the growth point related to emotions. For example, the feedback generating unit applies an emotion analysis algorithm to the growth point related to emotions and provides feedback such as "Growth points have been found based on changes in emotions." The feedback generating unit can also provide feedback to which an algorithm for evaluating the degree of goal achievement is applied to the growth point related to goal setting. For example, the feedback generating unit applies an algorithm for evaluating the degree of goal achievement to the growth point related to goal setting and provides feedback such as "You have succeeded in achieving your goal." The feedback generating unit can also provide feedback to which an algorithm for comparing with past events is applied to the growth point related to reflection. For example, the feedback generating unit applies an algorithm for comparing with past events to the growth point related to reflection and provides feedback such as "You have grown compared to past events." This allows more appropriate feedback to be provided by applying different feedback algorithms depending on the category of the growth point. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback generation unit can input growth point category data to the generation AI and cause the generation AI to apply a feedback algorithm.

[0104] The feedback generating unit can estimate the user's emotion and adjust the length of the feedback based on the estimated emotion. For example, when the user is in a hurry, the feedback generating unit provides short and to the point feedback. For example, when the user is in a hurry, the feedback generating unit provides short and to the point text feedback. The feedback generating unit can also provide detailed feedback when the user is relaxed. For example, when the user is relaxed, the feedback generating unit provides detailed text feedback. The feedback generating unit can also provide visually stimulating feedback when the user is excited. For example, when the user is excited, the feedback generating unit provides feedback using visually stimulating graphs and charts. This allows more appropriate feedback to be provided by adjusting the length of the feedback based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback generation unit can input user emotion data to the generation AI and cause the generation AI to adjust the length of the feedback.

[0105] The feedback generating unit can determine the priority of the feedback based on the submission time of the growth point when generating the feedback. The feedback generating unit, for example, provides feedback preferentially to the growth point submitted recently. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the recent growth point" for the growth point submitted recently. The feedback generating unit can also provide feedback preferentially to the growth point submitted in a specific period. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the specific period" for the growth point submitted in a specific period. The feedback generating unit can also provide feedback preferentially to the growth point submitted in a period that the user particularly values. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the important period" for the growth point submitted in a period that the user particularly values. In this way, by determining the priority of the feedback based on the submission time of the growth point, more appropriate feedback can be provided. Part or all of the above-mentioned processing in the feedback generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback generating unit can input the submission time data of the growth point to the generation AI and cause the generation AI to execute the determination of the priority of the feedback.

[0106] The feedback generating unit can adjust the order of feedback based on the relevance of the growth points when generating feedback. The feedback generating unit, for example, provides feedback for growth points related to the same theme collectively. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the same theme" for growth points related to the same theme. The feedback generating unit can also provide feedback preferentially for growth points related to the user's field of interest. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the field of interest" for growth points related to the user's field of interest. The feedback generating unit can also provide feedback preferentially for growth points related to the user's goal. For example, the feedback generating unit provides feedback such as "We will provide feedback based on the goal" for growth points related to the user's goal. This allows more appropriate feedback to be provided by adjusting the order of feedback based on the relevance of the growth points. A part or all of the above-mentioned processing in the feedback generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback generating unit can input relevance data of the growth points to the generating AI and cause the generating AI to adjust the order of the feedback.

[0107] The providing unit can estimate the user's emotion and adjust the method of providing feedback based on the estimated emotion. For example, when the user is stressed, the providing unit provides feedback in a manner that allows the user to relax. For example, when the user is stressed, the providing unit provides feedback such as "We will provide feedback in a manner that allows the user to relax." In addition, the providing unit can also provide detailed feedback when the user is relaxed. For example, when the user is relaxed, the providing unit provides feedback such as "We will provide detailed feedback." In addition, the providing unit can also provide feedback in a visually stimulating manner when the user is excited. For example, when the user is excited, the providing unit provides feedback such as "We will provide feedback in a visually stimulating manner." In this way, by adjusting the method of providing feedback based on the user's emotion, more appropriate feedback can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI and cause the generating AI to adjust the method of providing feedback.

[0108] The providing unit can select the optimal providing method by referring to the user's past feedback history when providing feedback. The providing unit, for example, preferentially provides the feedback method that the user has received in the past. For example, the providing unit preferentially provides the feedback method that the user has received in the past, and provides feedback such as "We will provide feedback in the method that you have received in the past." The providing unit can also provide feedback at the optimal timing from the user's past feedback history. For example, the providing unit provides feedback such as "We will provide feedback at the optimal timing" from the user's past feedback history. The providing unit can also analyze the user's past feedback history and select the most effective providing method. For example, the providing unit analyzes the user's past feedback history and provides feedback such as "We will provide feedback in the most effective method." As a result, more appropriate feedback can be provided by selecting the optimal providing method by referring to the user's past feedback history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data to the generating AI and cause the generating AI to select the optimal providing method.

[0109] The providing unit can estimate the user's emotion and adjust the timing of providing the feedback based on the estimated emotion. For example, when the user is stressed, the providing unit provides feedback during a time period when the user can relax. For example, when the user is stressed, the providing unit provides feedback such as "feedback will be provided during a time period when the user can relax." The providing unit can also provide feedback when the user is relaxed. For example, when the user is relaxed, the providing unit provides feedback such as "feedback will be provided in a relaxed state." The providing unit can also provide feedback that can be received in a short time when the user is busy. For example, when the user is busy, the providing unit provides feedback such as "feedback that can be received in a short time." This allows the feedback to be provided at a more appropriate timing by adjusting the timing of providing the feedback based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input a user's emotional data to the generating AI and cause the generating AI to adjust the timing of providing feedback.

[0110] The providing unit can select the optimal providing method in consideration of the device information of the user when providing feedback. For example, when the user is using a smartphone, the providing unit provides feedback according to the screen size. For example, when the user is using a smartphone, the providing unit provides feedback such as "We will provide feedback according to the screen size of the smartphone." In addition, when the user is using a tablet, the providing unit can also provide feedback optimized for a large screen. For example, when the user is using a tablet, the providing unit provides feedback such as "We will provide feedback optimized for the large screen of the tablet." In addition, the providing unit can also provide concise and highly visible feedback in the case of a user using a smartwatch. For example, when the user is using a smartwatch, the providing unit provides feedback such as "We will provide concise and highly visible feedback optimized for the smartwatch." In this way, by selecting the optimal providing method in consideration of the device information of the user, more appropriate feedback can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0111] The storage unit can estimate the user's emotion and determine the priority of the diary to be saved based on the estimated emotion. For example, when the user is stressed, the storage unit preferentially saves a diary related to stress relief. For example, when the user is stressed, the storage unit performs saving such as "preferentially save a diary related to stress relief". In addition, when the user is relaxed, the storage unit can also preferentially save events that occurred in the relaxed state. For example, when the user is relaxed, the storage unit performs saving such as "preferentially save events that occurred in the relaxed state". In addition, when the user is excited, the storage unit can also preferentially save events that occurred in the excited state. For example, when the user is excited, the storage unit performs saving such as "preferentially save events that occurred in the excited state". In this way, by determining the priority of the diary to be saved based on the user's emotion, more important contents can be preferentially saved. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the diary to be stored.

[0112] The storage unit can adjust the level of detail of the storage based on the importance of the diary when storing. For example, in the case of a diary about an important event, the storage unit stores the diary including detailed information. For example, in the case of a diary about an important event, the storage unit stores the diary including detailed information about the important event. In addition, in the case of a diary about a daily event, the storage unit can store the diary including simplified information. For example, in the case of a diary about a daily event, the storage unit stores the diary including simplified information about the daily event. In addition, in the case of a diary about a specific theme, the storage unit can store the diary including detailed information related to the theme. For example, in the case of a diary about a specific theme, the storage unit stores the diary including detailed information related to the specific theme. In this way, by adjusting the level of detail of the storage based on the importance of the diary, more appropriate storage is possible. Part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the importance data of the diary to the generation AI and cause the generation AI to adjust the level of detail of the storage.

[0113] The storage unit can determine the priority of storage based on the submission time of the diary at the time of storage. The storage unit, for example, prioritizes storage of recently submitted diaries. For example, the storage unit prioritizes storage of recently submitted diaries, and performs storage such as "preferentially save recently submitted diaries". The storage unit can also prioritize storage of diaries submitted in a specific period. For example, the storage unit prioritizes storage of diaries submitted in a specific period, and performs storage such as "preferentially save diaries submitted in a specific period". The storage unit can also prioritize storage of diaries submitted in a period that the user considers particularly important. For example, the storage unit prioritizes storage of diaries submitted in a period that the user considers particularly important, and performs storage such as "preferentially save diaries submitted in an important period". As a result, more appropriate storage is possible by determining the priority of storage based on the submission time of the diary. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the submission time of a diary entry to the generation AI and have the generation AI determine the priority of storage.

[0114] The storage unit can adjust the order of storage based on the relevance of the diaries when storing. The storage unit, for example, stores diaries related to the same theme together. For example, the storage unit stores diaries related to the same theme together, and stores the diaries such as "save diaries related to the same theme together". The storage unit can also store diaries related to the user's field of interest preferentially. For example, the storage unit stores diaries related to the user's field of interest preferentially, and stores the diaries such as "save diaries related to the field of interest preferentially". The storage unit can also store diaries related to the user's goals preferentially. For example, the storage unit stores diaries related to the user's goals preferentially, and stores the diaries such as "save diaries related to the goals preferentially". This allows more appropriate storage by adjusting the order of storage based on the relevance of the diaries. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input diary relevance data to the generation AI and cause the generation AI to adjust the order of storage.

[0115] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated emotion. For example, when the user is stressed, the comparison unit performs the comparison based on a criterion related to stress relief. For example, when the user is stressed, the comparison unit performs the comparison such as "Comparison based on a criterion related to stress relief". In addition, when the user is relaxed, the comparison unit can also perform the comparison based on a criterion in the relaxed state. For example, when the user is relaxed, the comparison unit performs the comparison such as "Comparison based on a criterion in the relaxed state". In addition, when the user is excited, the comparison unit can also perform the comparison based on a criterion in the excited state. For example, when the user is excited, the comparison unit performs the comparison such as "Comparison based on a criterion in the excited state". This allows for more appropriate comparison by adjusting the comparison criteria based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the comparison unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the comparison criteria.

[0116] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between the diaries. For example, the comparison unit compares the contents of a plurality of diaries and finds common growth points. For example, the comparison unit compares the contents of a plurality of diaries and finds growth points such as "We have found growth points that are common to a plurality of diaries". The comparison unit can also compare the contents of the diary with past growth points and find new growth points. For example, the comparison unit compares the contents of the diary with past growth points and finds growth points such as "We have found new growth points by comparing with past growth points". The comparison unit can also compare the contents of the diary with the user's goal and find growth points related to goal achievement. For example, the comparison unit compares the contents of the diary with the user's goal and finds growth points such as "We have found growth points related to goal achievement". This improves the accuracy of the comparison by considering the interrelationships between the diaries, making it possible to make a more appropriate comparison. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input diary interrelationship data to the generation AI and cause the generation AI to improve the accuracy of the comparison.

[0117] The comparison unit can estimate the user's emotion and adjust the order of displaying the comparison results based on the estimated emotion. For example, when the user is stressed, the comparison unit preferentially displays results related to stress relief. For example, when the user is stressed, the comparison unit displays a result such as "results related to stress relief will be preferentially displayed." In addition, when the user is relaxed, the comparison unit can also preferentially display results in a relaxed state. For example, when the user is relaxed, the comparison unit displays a result such as "results in a relaxed state will be preferentially displayed." In addition, when the user is excited, the comparison unit can also preferentially display results in an excited state. For example, when the user is excited, the comparison unit displays a result such as "results in an excited state will be preferentially displayed." This allows for more appropriate result display by adjusting the order of displaying the comparison results based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the comparison unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the comparison unit can input user emotion data to the generation AI and cause the generation AI to adjust the display order of the comparison results.

[0118] The comparison unit can make the comparison taking into consideration the geographical distribution of the diaries. For example, the comparison unit compares diaries related to activities in a specific area by the user. For example, the comparison unit compares diaries related to activities in a specific area by the user, and makes a comparison such as "compare diaries related to activities in a specific area". The comparison unit can also compare diaries recorded by the user while traveling. For example, the comparison unit compares diaries recorded by the user while traveling, and makes a comparison such as "compare diaries recorded during the travel". The comparison unit can also compare diaries related to the area in which the user lives. For example, the comparison unit compares diaries related to the area in which the user lives, and makes a comparison such as "compare diaries related to the area in which the user lives". This makes it possible to make a more appropriate comparison by making a comparison taking into consideration the geographical distribution of the diaries. A part or all of the above-mentioned processing in the comparison unit may be performed, for example, using AI, or may be performed without using AI. For example, the comparison unit can input geographical distribution data of the diary to the generation AI and cause the generation AI to execute the comparison.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The diary service system can acquire health data of a user and associate it with the contents of the diary. For example, by associating exercise data and sleep data acquired from a user's fitness tracker with the contents of the diary, the correlation between the user's health condition and the contents of the diary can be analyzed. In addition, by associating food data acquired from a food recording app of the user with the contents of the diary, the change in food and emotions can be analyzed. Furthermore, by associating data acquired from a device that measures the user's stress level with the contents of the diary, the relationship between stress and the contents of the diary can be analyzed. In this way, deeper insights can be provided by integrating and analyzing the user's health data and the contents of the diary.

[0121] The diary service system can analyze a user's social media activities and associate them with the contents of the diary. For example, by associating events and emotions shared by the user on social media with the contents of the diary, the correlation between social media activities and the contents of the diary can be analyzed. In addition, by analyzing topics and hashtags followed by the user and associating them with the contents of the diary, the relationship between areas of interest and the contents of the diary can be analyzed. Furthermore, by associating comments and feedback received by the user with the contents of the diary, the relationship between the influence of others and the contents of the diary can be analyzed. In this way, a deeper insight can be provided by comprehensively analyzing the user's social media activities and the contents of the diary.

[0122] The diary service system can acquire the geographical location information of the user and associate it with the contents of the diary. For example, by associating the diary recorded by the user while traveling with the geographical location information, it is possible to analyze events and emotional changes at the travel destination. In addition, if the user frequently visits a specific place, it is also possible to analyze events and emotional changes at that place. Furthermore, by analyzing the diary related to the area where the user lives, it is possible to analyze events and emotional changes specific to the area. In this way, it is possible to provide deeper insights by integrating the analysis of the geographical location information of the user and the contents of the diary.

[0123] The diary service system can analyze the voice input of the user and associate it with the contents of the diary. For example, the diary recorded by the user in voice can be converted into text and emotion analysis can be performed to analyze the difference between the voice input and the text input. In addition, by analyzing the tone and pitch of the voice, the change in the user's emotion can be analyzed in more detail. Furthermore, by comparing the voice input and the text input, the difference in the user's expression method can be analyzed. In this way, deeper insights can be provided by integrating the analysis of the voice input and the text input.

[0124] The diary service system can analyze the user's life rhythm and associate it with the contents of the diary. For example, by analyzing the user's sleep patterns and activity levels and associating them with the contents of the diary, it is possible to analyze changes in the life rhythm and emotions. In addition, by analyzing the user's meal times and exercise times, it is possible to analyze the relationship between the life rhythm and the contents of the diary. Furthermore, by analyzing the user's work and school schedules, it is possible to analyze the relationship between the life rhythm and the contents of the diary. In this way, a deeper insight can be provided by comprehensively analyzing the user's life rhythm and the contents of the diary.

[0125] The diary service system can estimate the user's emotions and automatically classify the diary contents based on the estimated emotions. For example, if the user is feeling stressed, the diary entries related to stress can be automatically classified. Also, if the user is relaxed, the diary entries related to relaxation can be automatically classified. Furthermore, if the user is excited, the diary entries related to excitement can be automatically classified. In this way, more appropriate feedback can be provided by automatically classifying the diary contents based on the user's emotions.

[0126] The diary service system can estimate the user's emotions and automatically summarize the diary contents based on the estimated emotions. For example, if the user is feeling stressed, the stress-related contents can be summarized. Also, if the user is relaxed, the relaxation-related contents can be summarized. Furthermore, if the user is excited, the excitement-related contents can be summarized. In this way, more appropriate feedback can be provided by automatically summarizing the diary contents based on the user's emotions.

[0127] The diary service system can estimate the user's emotions and automatically translate the diary contents based on the estimated emotions. For example, when the user is feeling stressed, the stress-related contents can be translated into another language. When the user is feeling relaxed, the relaxation-related contents can be translated into another language. When the user is excited, the excitement-related contents can be translated into another language. This makes it possible to provide more appropriate feedback by automatically translating the diary contents based on the user's emotions.

[0128] The diary service system can estimate the user's emotions and automatically edit the diary contents based on the estimated emotions. For example, when the user is feeling stressed, the diary contents can be edited to emphasize the stress-related content. When the user is feeling relaxed, the diary contents can be edited to emphasize the relaxation-related content. When the user is excited, the diary contents can be edited to emphasize the excitement-related content. In this way, the diary contents can be automatically edited based on the user's emotions, thereby providing more appropriate feedback.

[0129] The diary service system can estimate the user's emotions and automatically analyze the diary contents based on the estimated emotions. For example, if the user is feeling stressed, the stress-related contents can be analyzed in detail. Also, if the user is relaxed, the relaxation-related contents can be analyzed in detail. Furthermore, if the user is excited, the excitement-related contents can be analyzed in detail. In this way, more appropriate feedback can be provided by automatically analyzing the diary contents based on the user's emotions.

[0130] The process flow of the second embodiment will be briefly described below.

[0131] Step 1: The reception unit receives the input of a diary entry of a user. The diary entry of a user includes text, images, voice, etc. For example, the reception unit receives text data input by the user, and image data and voice data uploaded by the user. Step 2: The analysis unit analyzes the contents of the diary accepted by the acceptance unit. The analysis is performed by a method such as text analysis, sentiment analysis, topic modeling, etc. For example, the diary contents are analyzed using text analysis, the user's sentiment is analyzed using sentiment analysis, and the diary topics are analyzed using topic modeling. Step 3: The growth detection unit finds growth points based on the content analyzed by the analysis unit. Growth points include positive changes and skill improvements. For example, the content of the user's diary is analyzed to find positive changes and skill improvements. Step 4: The feedback generating unit generates positive feedback based on the growth points found by the growth detecting unit. The feedback may include a text message, a graph, advice, etc. For example, a text message, a graph, or advice may be generated and provided to the user. Step 5: The providing unit provides the feedback generated by the feedback generating unit to the user. The provision is performed by a method such as a web application, a mobile application, or an email. For example, the feedback is provided through a web application, a mobile application, or an email.

[0132] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0134] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements including the above-mentioned reception unit, analysis unit, growth detection unit, feedback generation unit, provision unit, storage unit, and comparison unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives the input of the user's diary. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the diary. The growth detection unit is realized by the specific processing unit 290 of the data processing device 12 and finds growth points. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates positive feedback. The provision unit is realized by the control unit 46A of the smart device 14 and provides feedback to the user. The storage unit is realized by the database 24 of the data processing device 12 and stores the contents of the diary. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares the past diary with the current diary. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] 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, the RAM 30, and the 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.

[0139] 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.

[0140] 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 the voice according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0142] 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 performed in a secure state.

[0143] Fig. 4 shows an example of 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.

[0144] 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.

[0145] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements including the above-mentioned reception unit, analysis unit, growth detection unit, feedback generation unit, provision unit, storage unit, and comparison unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the input of the user's diary. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the diary. The growth detection unit is realized by the specific processing unit 290 of the data processing device 12 and finds growth points. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates positive feedback. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides feedback to the user. The storage unit is realized by the database 24 of the data processing device 12 and stores the contents of the diary. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares the past diary with the current diary. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0154] 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, the RAM 30, and the 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.

[0155] 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.

[0156] 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 the voice according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0158] 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 performed in a secure state.

[0159] 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.

[0160] 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.

[0161] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] 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 voice indicating a user input for 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.

[0165] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements including the above-mentioned reception unit, analysis unit, growth detection unit, feedback generation unit, provision unit, storage unit, and comparison unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives the input of the user's diary. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the diary. The growth detection unit is realized by the specific processing unit 290 of the data processing device 12 and finds growth points. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates positive feedback. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides feedback to the user. The storage unit is realized by the database 24 of the data processing device 12 and stores the contents of the diary. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares the past diary with the current diary. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 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.

[0170] 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, the RAM 30, and the 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.

[0171] 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.

[0172] 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 the voice according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0174] 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 performed in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0176] 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.

[0177] 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.

[0178] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0179] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0180] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0181] 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 a voice indicating a user input for 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.

[0182] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0183] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0184] Each of the multiple elements including the above-mentioned reception unit, analysis unit, growth detection unit, feedback generation unit, provision unit, storage unit, and comparison unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives the input of the user's diary. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the diary. The growth detection unit is realized by the specific processing unit 290 of the data processing device 12 and finds growth points. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates positive feedback. The provision unit is realized by the control unit 46A of the robot 414 and provides feedback to the user. The storage unit is realized by the database 24 of the data processing device 12 and stores the contents of the diary. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares the past diary with the current diary. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0186] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0188] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0190] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0192] 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.

[0193] 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 Universal Serial Bus (USB) 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.

[0194] In addition, 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 upon request from the data processing device 12.

[0195] 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.

[0196] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0197] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0198] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0199] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0201] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0203] (Appendix 1) A reception unit that receives diary entries; an analysis unit that analyzes the contents of the diary accepted by the acceptance unit; a growth detection unit that finds growth points based on the content analyzed by the analysis unit; a feedback generator that generates feedback based on the growth points found by the growth detector; a providing unit that provides the feedback generated by the feedback generating unit; A system characterized by: (Appendix 2) Equipped with a section for storing diary contents 2. The system of claim 1, (Appendix 3) Includes a section for comparing with past diaries 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the diary contents and find areas for growth 2. The system of claim 1, (Appendix 5) The growth detection unit is Compare with your diary to find areas for improvement 2. The system of claim 1, (Appendix 6) The feedback generating unit is Generate feedback based on growth points 2. The system of claim 1, (Appendix 7) The reception unit is Estimate emotions and adjust the timing of diary entries based on the estimated emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes past input history and suggests input methods 2. The system of claim 1, (Appendix 9) The reception unit is Guide your diary entries based on your current life situation and interests 2. The system of claim 1, (Appendix 10) The reception unit is Estimate emotions and prioritize diary entries based on the estimated emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering diary entries, the system takes into account the user's geographic location to suggest relevant entries. 2. The system of claim 1, (Appendix 12) The reception unit is When you enter a diary entry, the app analyzes your social media activity and suggests related entries. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing, adjust the level of detail based on the importance of the diary. 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the diary category. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the analysis priority is determined based on the time of diary submission. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on diary relevance 2. The system of claim 1, (Appendix 19) The growth detection unit is Estimating a user's emotion and adjusting a growth point detection criterion based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The growth detection unit is When detecting growth, improve the accuracy of detection by considering diary correlations. 2. The system of claim 1, (Appendix 21) The growth detection unit is When detecting growth, the attribute information of the diary submitter is taken into consideration. 2. The system of claim 1, (Appendix 22) The growth detection unit is Inferring a user's emotions and adjusting the way growth points are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 23) The growth detection unit is When detecting growth, the geographical distribution of diaries is taken into account. 2. The system of claim 1, (Appendix 24) The growth detection unit is When detecting growth, improve the accuracy of detection by referring to related literature in the diary 2. The system of claim 1, (Appendix 25) The feedback generating unit is Infer user emotions and adjust feedback presentation based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The feedback generating unit is When generating feedback, adjust the level of detail in the feedback based on the importance of the growth point 2. The system of claim 1, (Appendix 27) The feedback generating unit is When generating feedback, apply different feedback algorithms depending on the category of growth points. 2. The system of claim 1, (Appendix 28) The feedback generating unit is Estimate the user's emotions and adjust the length of the feedback based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The feedback generating unit is When feedback is generated, it is prioritized based on when growth points were submitted 2. The system of claim 1, (Appendix 30) The feedback generating unit is When generating feedback, adjust the order of feedback based on the relevance of growth points 2. The system of claim 1, (Appendix 31) The providing unit is Inferring a user's emotions and adjusting the way feedback is provided based on the inferred user emotions 2. The system of claim 1, (Appendix 32) The providing unit is When providing feedback, the system selects the best way to provide it by referring to the user's past feedback history. 2. The system of claim 1, (Appendix 33) The providing unit is The system estimates the user's emotions and adjusts the timing of providing feedback based on the estimated user emotions. 2. The system of claim 1, (Appendix 34) The providing unit is When providing feedback, the optimal method of providing it is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 35) The storage unit includes: The system estimates the user's emotions and prioritizes the diary entries to be saved based on the estimated emotions. 2. The system of claim 1, (Appendix 36) The storage unit includes: When saving, adjust the detail of the save based on the importance of the diary 2. The system of claim 1, (Appendix 37) The storage unit includes: When saving, prioritize saving based on when the diary was submitted 2. The system of claim 1, (Appendix 38) The storage unit includes: When saving, adjust the order of saves based on diary relevance 2. The system of claim 1, (Appendix 39) The comparison unit is Inferring a user's emotions and adjusting the comparison criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 40) The comparison unit is When comparing, the accuracy of the comparison is improved by taking into account the correlation between diaries. 2. The system of claim 1, (Appendix 41) The comparison unit is Inferring user sentiment and adjusting the order in which comparison results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 42) The comparison unit is When making comparisons, the geographical distribution of diaries is taken into account. 2. The system of claim 1, [Explanation of symbols]

[0204] 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 reception unit that receives diary entries; an analysis unit that analyzes the contents of the diary accepted by the acceptance unit; a growth detection unit that finds growth points based on the content analyzed by the analysis unit; a feedback generator that generates feedback based on the growth points found by the growth detector; a providing unit that provides the feedback generated by the feedback generating unit; A system characterized by:

2. Equipped with a storage section for storing diary contents 2. The system of claim 1 .

3. Equipped with a comparison section for comparing with past diaries 2. The system of claim 1 .

4. The feedback generating unit is Generate feedback based on growth points 2. The system of claim 1 .

5. The reception unit is Estimate emotions and adjust the timing of diary entries based on the estimated emotions 2. The system of claim 1 .

6. The reception unit is Analyzes past input history and suggests input methods 2. The system of claim 1 .

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