System

Abstract

An object of a system according to an embodiment is to analyze contents of a diary of a user and provide personalized information and advice.SOLUTION: A system according to an embodiment includes a diary analysis unit, a hobby and preference understanding unit, a related information providing unit, and an advice providing unit. The diary analysis unit analyzes a diary of a user. A hobby and taste understanding part understands the hobby and taste of the user on the basis of the diary contents analyzed by the diary analysis part. The related information providing unit provides related information and events based on the hobbies and preferences understood by the hobby and preference understanding unit. The advice providing unit provides advice based on the diary contents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided personalized information and advice based on the contents of a user's diary, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the contents of a user's diary and provide personalized information and advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a diary analysis unit, a hobby and preference understanding unit, a related information providing unit, and an advice providing unit. The diary analysis unit analyzes a user's diary. The hobby and preference understanding unit understands the user's hobby and preference based on the diary content analyzed by the diary analysis unit. The related information providing unit provides related information and events based on the hobby and preference understood by the hobby and preference understanding unit. The advice providing unit provides advice based on the diary content. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the contents of a user's diary and provide personalized information and advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The MNS (Myself Networking Service) according to an embodiment of the present invention is a system in which a personalized AI responds to a user's daily diary entries based on their hobbies and preferences. This system provides related information and events and advice based on the information written in the diary. This allows the MNS to provide a detox from today's social networking services while exposing users to information that will benefit them.

[0029] The MNS according to the embodiment includes a diary analysis unit, a hobby and preference understanding unit, a related information providing unit, and an advice providing unit. The diary analysis unit analyzes a user's diary. For example, the diary analysis unit analyzes the contents of the diary using natural language processing technology. The diary analysis unit can also analyze emotions in the diary using emotion analysis technology. The diary analysis unit can also analyze diary entries in image format using image analysis technology. The hobby and preference understanding unit understands the user's hobby and preference based on the diary content analyzed by the diary analysis unit. For example, the hobby and preference understanding unit extracts hobbies and preferences from the user's diary content and identifies the user's interests. The hobby and preference understanding unit can also analyze long-term changes in the user's hobby and preference by referring to the user's past diary content. The hobby and preference understanding unit can also more accurately understand the user's hobby and preference by referring to the user's behavioral history and purchase history. The related information providing unit provides related information and events based on the hobby and preference understood by the hobby and preference understanding unit. For example, the related information providing unit may suggest the latest research papers and specialized books related to the user's interests. The related information providing unit may also introduce online communities and forums related to the user's interests. The related information providing unit may also provide past events and happenings related to the user's interests in timeline format. The advice providing unit may provide advice based on the diary content. For example, the advice providing unit may analyze the user's health condition and lifestyle habits and provide health management advice. The advice providing unit may also estimate the user's stress level and provide specific advice on stress management. The advice providing unit may also provide step-by-step advice for achieving the user's goals. In this way, the MNS according to the embodiment understands the user's hobbies and preferences based on the diary content and provides related information and advice, thereby deepening the user's self-understanding. For example, the user can organize their emotions and thoughts through their diary and receive feedback from the generation AI, thereby deepening self-understanding. Furthermore, the user can organize their emotions and thoughts without relying on social media.

[0030] The hobby and preference understanding unit can track the user's hobby and preferences over time based on the diary content and analyze long-term changes in interests. The hobby and preference understanding unit, for example, analyzes the user's diary content and tracks changes in hobbies and preferences over time. For example, it can analyze the diary entries from the past year to identify what hobbies the user continues to be interested in or has recently become interested in. The hobby and preference understanding unit can also analyze data by date in order to analyze changes in the user's hobby and preferences over time. The hobby and preference understanding unit can also analyze changes in interests over time. In this way, by analyzing changes in the user's hobby and preferences over time, it is possible to understand long-term changes in interests.

[0031] When analyzing the diary contents, the hobby / preference understanding unit also refers to the behavioral history and purchase history, thereby enabling a more accurate understanding of the hobby / preferences. The hobby / preference understanding unit, for example, integrates the user's diary contents with the user's past behavioral history to analyze the hobby / preferences. For example, it compares the contents written in the diary with the user's actual behavioral history (places visited and events attended). The hobby / preference understanding unit can also refer to the purchase history to analyze the user's hobby / preferences. For example, it analyzes the user's online shopping history and in-store purchase history to identify the user's interests. The hobby / preference understanding unit can also understand the user's hobby / preferences with a higher degree of accuracy by referring to the behavioral history and purchase history. In this way, by referring to the behavioral history and purchase history, a more accurate understanding of the hobby / preferences becomes possible.

[0032] The related information providing unit can suggest the latest research papers and specialized books related to the user's interests based on the diary content. The related information providing unit, for example, analyzes the diary content and suggests the latest research papers related to the user's interests. For example, the related information providing unit automatically searches for the latest academic papers on a specific topic and provides them to the user. The related information providing unit can also suggest specialized books related to the user's interests. For example, it can recommend specialized books on a specific field. The related information providing unit can also automatically collect and provide related literature to suggest the latest research papers and specialized books related to the user's interests. This allows the user's knowledge to be deepened by suggesting the latest research papers and specialized books related to the user's interests.

[0033] The related information providing unit can analyze the user's interests from the diary content and introduce related online communities and forums. The related information providing unit can, for example, analyze the diary content and introduce online communities related to the user's interests. For example, it can recommend forums and groups related to specific hobbies or interests. The related information providing unit can also introduce expert communities related to the user's interests. For example, it can recommend forums of experts in a specific field. The related information providing unit can also automatically collect and provide related information to introduce online communities and forums related to the user's interests. This can broaden the range of user interaction by introducing online communities and forums related to the user's interests.

[0034] The related information providing unit can provide past events and occurrences related to the user's interests in timeline format based on the diary content. The related information providing unit, for example, analyzes the diary content and provides past events and occurrences related to the user's interests in timeline format. For example, past events and occurrences related to a specific theme are displayed in chronological order. The related information providing unit can also provide past events related to the user's interests in timeline format. For example, historical events or past seminars and workshops are displayed in chronological order. The related information providing unit can also automatically collect and provide related information to provide past events and occurrences related to the user's interests in timeline format. This allows the user's knowledge to be deepened by providing past events and occurrences related to the user's interests in timeline format.

[0035] The related information providing unit can analyze the diary content and provide coupons for products and services related to the user's interests. The related information providing unit, for example, analyzes the diary content and builds a system that provides coupons for products and services related to the user's interests. For example, it automatically provides discount coupons for products related to a specific hobby. The related information providing unit can also provide coupons for services related to the user's interests. For example, it provides discount coupons for specific online services. The related information providing unit can also automatically collect and provide related information to provide coupons for products and services related to the user's interests. This can increase the user's purchasing motivation by providing coupons for products and services related to the user's interests.

[0036] The advice providing unit can estimate the stress level from the diary content and provide specific advice regarding stress management. The advice providing unit, for example, analyzes the diary content to estimate the user's stress level. For example, it extracts negative expressions and keywords related to stress and evaluates the stress level. The advice providing unit can also analyze physiological indicators to estimate the user's stress level. For example, it can analyze heart rate and electrodermal activity to evaluate the stress level. The advice providing unit can also automatically collect and provide related information to estimate the user's stress level and provide specific advice regarding stress management. This makes it possible to support the user in reducing stress by estimating the user's stress level and providing specific advice regarding stress management.

[0037] The advice providing unit can analyze the diary content and provide step-by-step advice to the user toward achieving their goals. The advice providing unit, for example, analyzes the diary content and identifies the user's goals. For example, it extracts goals and plans written in the diary and provides advice toward achieving the goals. The advice providing unit can also set short-term, medium-term, and long-term goals in order to provide step-by-step advice toward achieving the user's goals. For example, a task to be achieved within one week can be set as a short-term goal, and a task to be achieved within one month can be set as a medium-term goal. The advice providing unit can also automatically collect and provide related information in order to provide step-by-step advice toward achieving the user's goals. This allows the user to be supported in achieving their goals by providing step-by-step advice toward achieving their goals.

[0038] The advice providing unit can suggest online sessions with experts or coaches related to the user's interests based on the diary content. The advice providing unit, for example, analyzes the diary content and builds a system that suggests online sessions with experts or coaches related to the user's interests. For example, the advice providing unit recommends experts in a specific field. The advice providing unit can also suggest online sessions with coaches related to the user's interests. For example, the advice providing unit can recommend sessions with a career coach or a health coach. The advice providing unit can also automatically collect and provide related information to suggest online sessions with experts or coaches related to the user's interests. This makes it possible to support the improvement of the user's knowledge and skills by suggesting online sessions with experts or coaches related to the user's interests.

[0039] The advice providing unit can analyze the diary content and introduce workshops and seminars related to the user's interests. The advice providing unit, for example, analyzes the diary content and builds a system that introduces workshops and seminars related to the user's interests. For example, it recommends events related to a specific field. The advice providing unit can also introduce seminars related to the user's interests. For example, it can recommend technical seminars and business workshops. The advice providing unit can also automatically collect and provide related information to introduce workshops and seminars related to the user's interests. This can support the improvement of the user's knowledge and skills by introducing workshops and seminars related to the user's interests.

[0040] The self-understanding promotion unit can automatically generate a reflection journal to deepen the user's self-understanding based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that automatically generates a reflection journal to deepen the user's self-understanding. For example, it automatically generates self-reflection questions on a specific theme. The self-understanding promotion unit can also include self-assessment questions and goal-setting items to automatically generate a reflection journal to deepen the user's self-understanding. For example, self-assessment questions can include questions such as "What was the most memorable thing that happened today?" and "What are your goals for the future?" The self-understanding promotion unit can also automatically collect and provide related information to automatically generate a reflection journal to deepen the user's self-understanding. In this way, the user's self-understanding can be promoted by automatically generating a reflection journal to deepen the user's self-understanding.

[0041] The self-understanding promotion unit can analyze the diary content and provide a list of questions to clarify the user's values ​​and beliefs. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that provides a list of questions to clarify the user's values ​​and beliefs. For example, questions that explore values ​​related to a specific theme are automatically generated. In addition, the self-understanding promotion unit can include questions related to ethics, outlook on life, and religious beliefs in order to provide a list of questions to clarify the user's values ​​and beliefs. For example, questions such as "What values ​​do you hold most important in your life?" and "Have you ever acted based on your beliefs?" In addition, the self-understanding promotion unit can automatically collect and provide related information in order to provide a list of questions to clarify the user's values ​​and beliefs. In this way, the self-understanding promotion unit can promote the user's self-understanding by providing a list of questions to clarify the user's values ​​and beliefs.

[0042] The self-understanding promotion unit can propose a personalized study plan to promote the user's self-growth based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that proposes a personalized study plan to promote the user's self-growth. For example, a study plan to improve specific skills or knowledge is provided. The self-understanding promotion unit can also include suggestions of individual learning goals and learning resources to propose a personalized study plan to promote the user's self-growth. For example, an individual learning goal such as "master a new programming language within one month" can be set, and online courses and learning materials can be provided as learning resources. The self-understanding promotion unit can also automatically collect and provide related information to propose a personalized study plan to promote the user's self-growth. This allows the user's growth to be supported by proposing a personalized study plan to promote the user's self-growth.

[0043] The self-understanding promotion unit can introduce online courses and teaching materials to deepen the user's self-understanding based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that introduces online courses and teaching materials to deepen the user's self-understanding. For example, it recommends online courses on a specific theme. The self-understanding promotion unit can also introduce teaching materials to deepen the user's self-understanding. For example, it can recommend video lectures and e-books. The self-understanding promotion unit can also automatically collect and provide related information to introduce online courses and teaching materials to deepen the user's self-understanding. This can support the user's growth by introducing online courses and teaching materials to deepen the user's self-understanding.

[0044] The self-understanding promotion unit can analyze the diary content and suggest a mentoring program to support the user's self-growth. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that suggests a mentoring program to support the user's self-growth. For example, the self-understanding promotion unit recommends a mentor in a specific field. The self-understanding promotion unit can also include regular mentoring sessions and goal setting and feedback to suggest a mentoring program to support the user's self-growth. For example, the regular mentoring session can be set up as a monthly online meeting, and advice from the mentor can be provided as goal setting and feedback. The self-understanding promotion unit can also automatically collect and provide related information to suggest a mentoring program to support the user's self-growth. This allows the user's growth to be supported by suggesting a mentoring program to support the user's self-growth.

[0045] The SNS detox promotion unit can suggest hobbies and activities that can replace the user's SNS use based on the diary content. For example, the SNS detox promotion unit analyzes the diary content and builds a system that suggests hobbies and activities that can replace the user's SNS use. For example, it recommends activities related to specific hobbies or interests. Furthermore, the SNS detox promotion unit can include activities such as sports, reading, and handicrafts to suggest hobbies and activities that can replace the user's SNS use. For example, it can recommend sports clubs or reading groups that the user is interested in. Furthermore, the SNS detox promotion unit can automatically collect and provide related information to suggest hobbies and activities that can replace the user's SNS use. This allows the user's SNS addiction to be reduced by suggesting hobbies and activities that can replace the user's SNS use.

[0046] The SNS detox promotion unit can recommend digital well-being apps to reduce the user's SNS use based on the diary content. For example, the SNS detox promotion unit analyzes the diary content and builds a system to recommend digital well-being apps to reduce the user's SNS use. For example, the SNS detox promotion unit recommends specific apps. The SNS detox promotion unit can also recommend digital well-being apps to reduce the user's SNS use, including screen time management apps and meditation apps. For example, the SNS detox promotion unit can recommend apps to help the user manage smartphone usage time. The SNS detox promotion unit can also automatically collect and provide related information to recommend digital well-being apps to reduce the user's SNS use. This can reduce the user's SNS addiction by recommending digital well-being apps to reduce the user's SNS use.

[0047] The SNS detox promotion unit can analyze diary content and provide a self-management tool for the user regarding SNS usage. The SNS detox promotion unit, for example, analyzes diary content and builds a system that provides a self-management tool for the user regarding SNS usage. For example, it provides a tool for managing SNS usage time. The SNS detox promotion unit can also include a time management tool or a goal setting tool to provide a self-management tool for the user regarding SNS usage. For example, it can provide a timer function for the user to limit the amount of time they spend using SNS. The SNS detox promotion unit can also automatically collect and provide related information to provide a self-management tool for the user regarding SNS usage. This can reduce the user's SNS addiction by providing a self-management tool for the user regarding SNS usage.

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

[0049] The MNS can further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit analyzes the user's sleep patterns using the user's diary entries, behavioral history, and data acquired from the wearable device. For example, it analyzes the diary entries about bedtimes, wake-up times, and sleep quality to identify the user's sleep patterns. The sleep analysis unit can also perform more accurate sleep analysis by referring to the user's behavioral history and heart rate and activity data acquired from the wearable device. This allows the system to analyze the user's sleep patterns and provide appropriate advice for improving their sleep.

[0050] The MNS can further include a dietary analysis unit that analyzes the user's dietary patterns. The dietary analysis unit analyzes the user's dietary patterns using data acquired from the user's diary entries and a dietary record app. For example, the dietary analysis unit analyzes the dietary patterns of the user by analyzing the dietary contents, meal times, and meal frequency recorded in the diary. The dietary analysis unit can also perform more accurate dietary analysis by referencing data acquired from the user's dietary record app. This allows the user's dietary patterns to be analyzed and appropriate dietary improvement advice to be provided.

[0051] The MNS can further include a motion analysis unit that analyzes the user's exercise patterns. The motion analysis unit analyzes the user's exercise patterns using data obtained from the user's diary entries and fitness apps. For example, it analyzes the exercise content, exercise time, and exercise frequency recorded in the diary to identify the user's exercise patterns. The motion analysis unit can also refer to data obtained from the user's fitness app to perform more accurate motion analysis. This allows the MNS to analyze the user's exercise patterns and provide appropriate exercise improvement advice.

[0052] The MNS can further include a stress analysis unit that analyzes the user's stress level. The stress analysis unit analyzes the user's stress level using the user's diary content and physiological indicator data. For example, it analyzes stress-related descriptions written in the diary and physiological indicator data (heart rate and electrodermal activity) to identify the user's stress level. The stress analysis unit can also refer to the user's behavioral history and physiological indicator data to perform more accurate stress analysis. This allows the system to analyze the user's stress level and provide appropriate stress management advice.

[0053] The MNS can further include a learning analysis unit that analyzes the user's learning patterns. The learning analysis unit analyzes the user's learning patterns using the contents of the user's diary and data obtained from the learning app. For example, it analyzes the learning content, study time, and study frequency recorded in the diary to identify the user's learning patterns. The learning analysis unit can also refer to data obtained from the user's learning app to perform more accurate learning analysis. This allows the system to analyze the user's learning patterns and provide appropriate advice for improving their learning.

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

[0055] Step 1: The diary analysis unit analyzes the user's diary. For example, it can analyze the contents of the diary using natural language processing technology, and analyze the emotions in the diary using emotion analysis technology. It can also analyze diary entries in image format using image analysis technology. Step 2: The hobby and preference understanding unit understands the user's hobby and preferences based on the diary content analyzed by the diary analysis unit. For example, it extracts the user's hobby and preferences from the diary content and identifies the user's interests. It can also refer to past diary content to analyze long-term changes in hobby and preferences. It can also refer to the user's behavioral history and purchase history to understand the user's hobby and preferences more accurately. Step 3: The related information provider provides related information and events based on the user's interests and preferences. For example, it can suggest the latest research papers and specialized books related to the user's interests, introduce online communities and forums, and provide past events and happenings in a timeline format. Step 4: The advice provider provides advice based on the diary contents. For example, it analyzes the user's health condition and lifestyle habits and provides advice on health management. It can also estimate stress levels and provide specific advice on stress management. It can also provide step-by-step advice on achieving goals.

[0056] (Example 2) The MNS (Myself Networking Service) according to an embodiment of the present invention is a system in which a personalized AI responds to a user's daily diary entries based on their hobbies and preferences. This system provides related information and events and advice based on the information written in the diary. This allows the MNS to provide a detox from today's social networking services while exposing users to information that will benefit them.

[0057] The MNS according to the embodiment includes a diary analysis unit, a hobby and preference understanding unit, a related information providing unit, and an advice providing unit. The diary analysis unit analyzes a user's diary. For example, the diary analysis unit analyzes the contents of the diary using natural language processing technology. The diary analysis unit can also analyze emotions in the diary using emotion analysis technology. The diary analysis unit can also analyze diary entries in image format using image analysis technology. The hobby and preference understanding unit understands the user's hobby and preference based on the diary content analyzed by the diary analysis unit. For example, the hobby and preference understanding unit extracts hobbies and preferences from the user's diary content and identifies the user's interests. The hobby and preference understanding unit can also analyze long-term changes in the user's hobby and preference by referring to the user's past diary content. The hobby and preference understanding unit can also more accurately understand the user's hobby and preference by referring to the user's behavioral history and purchase history. The related information providing unit provides related information and events based on the hobby and preference understood by the hobby and preference understanding unit. For example, the related information providing unit may suggest the latest research papers and specialized books related to the user's interests. The related information providing unit may also introduce online communities and forums related to the user's interests. The related information providing unit may also provide past events and happenings related to the user's interests in timeline format. The advice providing unit may provide advice based on the diary content. For example, the advice providing unit may analyze the user's health condition and lifestyle habits and provide health management advice. The advice providing unit may also estimate the user's stress level and provide specific advice on stress management. The advice providing unit may also provide step-by-step advice for achieving the user's goals. In this way, the MNS according to the embodiment understands the user's hobbies and preferences based on the diary content and provides related information and advice, thereby deepening the user's self-understanding. For example, the user can organize their emotions and thoughts through their diary and receive feedback from the generation AI, thereby deepening self-understanding. Furthermore, the user can organize their emotions and thoughts without relying on social media.

[0058] The hobby and preference understanding unit can track the user's hobby and preferences over time based on the diary content and analyze long-term changes in interests. The hobby and preference understanding unit, for example, analyzes the user's diary content and tracks changes in hobbies and preferences over time. For example, it can analyze the diary entries from the past year to identify what hobbies the user continues to be interested in or has recently become interested in. The hobby and preference understanding unit can also analyze data by date in order to analyze changes in the user's hobby and preferences over time. The hobby and preference understanding unit can also analyze changes in interests over time. In this way, by analyzing changes in the user's hobby and preferences over time, it is possible to understand long-term changes in interests.

[0059] The hobby and preference understanding unit can infer emotions from the diary content and predict changes in the hobby and preference based on emotional fluctuations. The hobby and preference understanding unit, for example, analyzes the diary content to infer the user's emotions. For example, it classifies diaries into those containing mostly positive emotions and those containing mostly negative emotions, and tracks emotional fluctuations. The hobby and preference understanding unit can also analyze temporal changes in the emotion score to predict changes in the hobby and preference based on emotional fluctuations. The hobby and preference understanding unit can also analyze the intensity of emotions and predict changes in the hobby and preference. This makes it possible to grasp changes in the user's interests by predicting changes in the hobby and preference based on emotional fluctuations.

[0060] When analyzing the diary contents, the hobby / preference understanding unit also refers to the behavioral history and purchase history, thereby enabling a more accurate understanding of the hobby / preferences. The hobby / preference understanding unit, for example, integrates the user's diary contents with the user's past behavioral history to analyze the hobby / preferences. For example, it compares the contents written in the diary with the user's actual behavioral history (places visited and events attended). The hobby / preference understanding unit can also refer to the purchase history to analyze the user's hobby / preferences. For example, it analyzes the user's online shopping history and in-store purchase history to identify the user's interests. The hobby / preference understanding unit can also understand the user's hobby / preferences with a higher degree of accuracy by referring to the behavioral history and purchase history. In this way, by referring to the behavioral history and purchase history, a more accurate understanding of the hobby / preferences becomes possible.

[0061] The related information providing unit can suggest the latest research papers and specialized books related to the user's interests based on the diary content. The related information providing unit, for example, analyzes the diary content and suggests the latest research papers related to the user's interests. For example, the related information providing unit automatically searches for the latest academic papers on a specific topic and provides them to the user. The related information providing unit can also suggest specialized books related to the user's interests. For example, it can recommend specialized books on a specific field. The related information providing unit can also automatically collect and provide related literature to suggest the latest research papers and specialized books related to the user's interests. This allows the user's knowledge to be deepened by suggesting the latest research papers and specialized books related to the user's interests.

[0062] The related information providing unit can analyze the user's interests from the diary content and introduce related online communities and forums. The related information providing unit can, for example, analyze the diary content and introduce online communities related to the user's interests. For example, it can recommend forums and groups related to specific hobbies or interests. The related information providing unit can also introduce expert communities related to the user's interests. For example, it can recommend forums of experts in a specific field. The related information providing unit can also automatically collect and provide related information to introduce online communities and forums related to the user's interests. This can broaden the range of user interaction by introducing online communities and forums related to the user's interests.

[0063] The related information providing unit can provide past events and occurrences related to the user's interests in timeline format based on the diary content. The related information providing unit, for example, analyzes the diary content and provides past events and occurrences related to the user's interests in timeline format. For example, past events and occurrences related to a specific theme are displayed in chronological order. The related information providing unit can also provide past events related to the user's interests in timeline format. For example, historical events or past seminars and workshops are displayed in chronological order. The related information providing unit can also automatically collect and provide related information to provide past events and occurrences related to the user's interests in timeline format. This allows the user's knowledge to be deepened by providing past events and occurrences related to the user's interests in timeline format.

[0064] The related information providing unit can analyze the diary content and provide coupons for products and services related to the user's interests. The related information providing unit, for example, analyzes the diary content and builds a system that provides coupons for products and services related to the user's interests. For example, it automatically provides discount coupons for products related to a specific hobby. The related information providing unit can also provide coupons for services related to the user's interests. For example, it provides discount coupons for specific online services. The related information providing unit can also automatically collect and provide related information to provide coupons for products and services related to the user's interests. This can increase the user's purchasing motivation by providing coupons for products and services related to the user's interests.

[0065] The related information providing unit can use the emotion estimation function to analyze emotions regarding the diary content and suggest related information and events based on the emotions. For example, the related information providing unit can use the emotion estimation function to analyze the user's emotions regarding the diary and suggest related information based on the emotions. For example, the related information providing unit can recommend events related to a diary entry that contains strong positive emotions. The related information providing unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and suggest events based on the emotions. For example, the related information providing unit can recommend a relaxation event for a diary entry that contains strong negative emotions. The related information providing unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and automatically collect and provide related information to suggest related information and events based on the emotions. This makes it possible to provide appropriate information according to the user's emotions by suggesting related information and events based on the emotions.

[0066] The advice providing unit can estimate the stress level from the diary content and provide specific advice regarding stress management. The advice providing unit, for example, analyzes the diary content to estimate the user's stress level. For example, it extracts negative expressions and keywords related to stress and evaluates the stress level. The advice providing unit can also analyze physiological indicators to estimate the user's stress level. For example, it can analyze heart rate and electrodermal activity to evaluate the stress level. The advice providing unit can also automatically collect and provide related information to estimate the user's stress level and provide specific advice regarding stress management. This makes it possible to support the user in reducing stress by estimating the user's stress level and providing specific advice regarding stress management.

[0067] The advice providing unit can analyze the diary content and provide step-by-step advice to the user toward achieving their goals. The advice providing unit, for example, analyzes the diary content and identifies the user's goals. For example, it extracts goals and plans written in the diary and provides advice toward achieving the goals. The advice providing unit can also set short-term, medium-term, and long-term goals in order to provide step-by-step advice toward achieving the user's goals. For example, a task to be achieved within one week can be set as a short-term goal, and a task to be achieved within one month can be set as a medium-term goal. The advice providing unit can also automatically collect and provide related information in order to provide step-by-step advice toward achieving the user's goals. This allows the user to be supported in achieving their goals by providing step-by-step advice toward achieving their goals.

[0068] The advice providing unit can suggest online sessions with experts or coaches related to the user's interests based on the diary content. The advice providing unit, for example, analyzes the diary content and builds a system that suggests online sessions with experts or coaches related to the user's interests. For example, the advice providing unit recommends experts in a specific field. The advice providing unit can also suggest online sessions with coaches related to the user's interests. For example, the advice providing unit can recommend sessions with a career coach or a health coach. The advice providing unit can also automatically collect and provide related information to suggest online sessions with experts or coaches related to the user's interests. This makes it possible to support the improvement of the user's knowledge and skills by suggesting online sessions with experts or coaches related to the user's interests.

[0069] The advice providing unit can analyze the diary content and introduce workshops and seminars related to the user's interests. The advice providing unit, for example, analyzes the diary content and builds a system that introduces workshops and seminars related to the user's interests. For example, it recommends events related to a specific field. The advice providing unit can also introduce seminars related to the user's interests. For example, it can recommend technical seminars and business workshops. The advice providing unit can also automatically collect and provide related information to introduce workshops and seminars related to the user's interests. This can support the improvement of the user's knowledge and skills by introducing workshops and seminars related to the user's interests.

[0070] The self-understanding promotion unit can automatically generate a reflection journal to deepen the user's self-understanding based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that automatically generates a reflection journal to deepen the user's self-understanding. For example, it automatically generates self-reflection questions on a specific theme. The self-understanding promotion unit can also include self-assessment questions and goal-setting items to automatically generate a reflection journal to deepen the user's self-understanding. For example, self-assessment questions can include questions such as "What was the most memorable thing that happened today?" and "What are your goals for the future?" The self-understanding promotion unit can also automatically collect and provide related information to automatically generate a reflection journal to deepen the user's self-understanding. In this way, the user's self-understanding can be promoted by automatically generating a reflection journal to deepen the user's self-understanding.

[0071] The self-understanding promotion unit can analyze the diary content and provide a list of questions to clarify the user's values ​​and beliefs. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that provides a list of questions to clarify the user's values ​​and beliefs. For example, questions that explore values ​​related to a specific theme are automatically generated. In addition, the self-understanding promotion unit can include questions related to ethics, outlook on life, and religious beliefs in order to provide a list of questions to clarify the user's values ​​and beliefs. For example, questions such as "What values ​​do you hold most important in your life?" and "Have you ever acted based on your beliefs?" In addition, the self-understanding promotion unit can automatically collect and provide related information in order to provide a list of questions to clarify the user's values ​​and beliefs. In this way, the self-understanding promotion unit can promote the user's self-understanding by providing a list of questions to clarify the user's values ​​and beliefs.

[0072] The self-understanding promotion unit can propose a personalized study plan to promote the user's self-growth based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that proposes a personalized study plan to promote the user's self-growth. For example, a study plan to improve specific skills or knowledge is provided. The self-understanding promotion unit can also include suggestions of individual learning goals and learning resources to propose a personalized study plan to promote the user's self-growth. For example, an individual learning goal such as "master a new programming language within one month" can be set, and online courses and learning materials can be provided as learning resources. The self-understanding promotion unit can also automatically collect and provide related information to propose a personalized study plan to promote the user's self-growth. This allows the user's growth to be supported by proposing a personalized study plan to promote the user's self-growth.

[0073] The self-understanding promotion unit can introduce online courses and teaching materials to deepen the user's self-understanding based on the diary content. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that introduces online courses and teaching materials to deepen the user's self-understanding. For example, it recommends online courses on a specific theme. The self-understanding promotion unit can also introduce teaching materials to deepen the user's self-understanding. For example, it can recommend video lectures and e-books. The self-understanding promotion unit can also automatically collect and provide related information to introduce online courses and teaching materials to deepen the user's self-understanding. This can support the user's growth by introducing online courses and teaching materials to deepen the user's self-understanding.

[0074] The self-understanding promotion unit can analyze the diary content and suggest a mentoring program to support the user's self-growth. The self-understanding promotion unit, for example, analyzes the diary content and builds a system that suggests a mentoring program to support the user's self-growth. For example, the self-understanding promotion unit recommends a mentor in a specific field. The self-understanding promotion unit can also include regular mentoring sessions and goal setting and feedback to suggest a mentoring program to support the user's self-growth. For example, the regular mentoring session can be set up as a monthly online meeting, and advice from the mentor can be provided as goal setting and feedback. The self-understanding promotion unit can also automatically collect and provide related information to suggest a mentoring program to support the user's self-growth. This allows the user's growth to be supported by suggesting a mentoring program to support the user's self-growth.

[0075] The self-understanding promotion unit can use the emotion estimation function to analyze emotions regarding the diary content and promote self-understanding based on emotions. The self-understanding promotion unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the diary and promote self-understanding based on emotions. For example, advice to increase self-esteem is provided for a diary entry containing strong positive emotions. The self-understanding promotion unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and provide self-assessment questions and feedback to promote self-understanding based on emotions. For example, self-assessment questions are generated based on emotion scores and feedback is provided. The self-understanding promotion unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and automatically collect and provide related information to promote self-understanding based on emotions. In this way, promoting self-understanding based on emotions can deepen the user's self-understanding.

[0076] The SNS detox promotion unit can suggest hobbies and activities that can replace the user's SNS use based on the diary content. For example, the SNS detox promotion unit analyzes the diary content and builds a system that suggests hobbies and activities that can replace the user's SNS use. For example, it recommends activities related to specific hobbies or interests. Furthermore, the SNS detox promotion unit can include activities such as sports, reading, and handicrafts to suggest hobbies and activities that can replace the user's SNS use. For example, it can recommend sports clubs or reading groups that the user is interested in. Furthermore, the SNS detox promotion unit can automatically collect and provide related information to suggest hobbies and activities that can replace the user's SNS use. This allows the user's SNS addiction to be reduced by suggesting hobbies and activities that can replace the user's SNS use.

[0077] The SNS detox promotion unit can recommend digital well-being apps to reduce the user's SNS use based on the diary content. For example, the SNS detox promotion unit analyzes the diary content and builds a system to recommend digital well-being apps to reduce the user's SNS use. For example, the SNS detox promotion unit recommends specific apps. The SNS detox promotion unit can also recommend digital well-being apps to reduce the user's SNS use, including screen time management apps and meditation apps. For example, the SNS detox promotion unit can recommend apps to help the user manage smartphone usage time. The SNS detox promotion unit can also automatically collect and provide related information to recommend digital well-being apps to reduce the user's SNS use. This can reduce the user's SNS addiction by recommending digital well-being apps to reduce the user's SNS use.

[0078] The SNS detox promotion unit can analyze diary content and provide a self-management tool for the user regarding SNS usage. The SNS detox promotion unit, for example, analyzes diary content and builds a system that provides a self-management tool for the user regarding SNS usage. For example, it provides a tool for managing SNS usage time. The SNS detox promotion unit can also include a time management tool or a goal setting tool to provide a self-management tool for the user regarding SNS usage. For example, it can provide a timer function for the user to limit the amount of time they spend using SNS. The SNS detox promotion unit can also automatically collect and provide related information to provide a self-management tool for the user regarding SNS usage. This can reduce the user's SNS addiction by providing a self-management tool for the user regarding SNS usage.

[0079] The SNS detox promotion unit can use the emotion estimation function to analyze emotions regarding the diary content and provide emotion-based SNS detox advice. For example, the SNS detox promotion unit can use the emotion estimation function to analyze the user's emotions regarding the diary and provide emotion-based SNS detox advice. For example, advice to reduce SNS use can be provided for entries containing strong positive emotions. The SNS detox promotion unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and provide emotion-based SNS detox advice, which can include limiting SNS usage time and suggesting alternative activities. For example, relaxation activities can be suggested for entries containing strong negative emotions. The SNS detox promotion unit can also use the emotion estimation function to analyze the user's emotions regarding the diary and automatically collect and provide related information to provide emotion-based SNS detox advice. As a result, providing emotion-based SNS detox advice can reduce the user's SNS addiction.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The MNS can further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit analyzes the user's sleep patterns using the user's diary entries, behavioral history, and data acquired from the wearable device. For example, it analyzes the diary entries about bedtimes, wake-up times, and sleep quality to identify the user's sleep patterns. The sleep analysis unit can also perform more accurate sleep analysis by referring to the user's behavioral history and heart rate and activity data acquired from the wearable device. This allows the system to analyze the user's sleep patterns and provide appropriate advice for improving their sleep.

[0082] The MNS can further include a dietary analysis unit that analyzes the user's dietary patterns. The dietary analysis unit analyzes the user's dietary patterns using data acquired from the user's diary entries and a dietary record app. For example, the dietary analysis unit analyzes the dietary patterns of the user by analyzing the dietary contents, meal times, and meal frequency recorded in the diary. The dietary analysis unit can also perform more accurate dietary analysis by referencing data acquired from the user's dietary record app. This allows the user's dietary patterns to be analyzed and appropriate dietary improvement advice to be provided.

[0083] The MNS can further include a motion analysis unit that analyzes the user's exercise patterns. The motion analysis unit analyzes the user's exercise patterns using data obtained from the user's diary entries and fitness apps. For example, it analyzes the exercise content, exercise time, and exercise frequency recorded in the diary to identify the user's exercise patterns. The motion analysis unit can also refer to data obtained from the user's fitness app to perform more accurate motion analysis. This allows the MNS to analyze the user's exercise patterns and provide appropriate exercise improvement advice.

[0084] The MNS can further include a stress analysis unit that analyzes the user's stress level. The stress analysis unit analyzes the user's stress level using the user's diary content and physiological indicator data. For example, it analyzes stress-related descriptions written in the diary and physiological indicator data (heart rate and electrodermal activity) to identify the user's stress level. The stress analysis unit can also refer to the user's behavioral history and physiological indicator data to perform more accurate stress analysis. This allows the system to analyze the user's stress level and provide appropriate stress management advice.

[0085] The MNS can further include a learning analysis unit that analyzes the user's learning patterns. The learning analysis unit analyzes the user's learning patterns using the contents of the user's diary and data obtained from the learning app. For example, it analyzes the learning content, study time, and study frequency recorded in the diary to identify the user's learning patterns. The learning analysis unit can also refer to data obtained from the user's learning app to perform more accurate learning analysis. This allows the system to analyze the user's learning patterns and provide appropriate advice for improving their learning.

[0086] The determination unit can estimate the user's emotions and suggest appropriate relaxation methods based on the estimated user emotions. For example, for a diary entry containing strong negative emotions, it can suggest relaxation methods such as meditation, deep breathing, and yoga. The determination unit can also automatically collect and provide related information to estimate the user's emotions and suggest relaxation methods based on the estimated user emotions. This can help reduce the user's stress by suggesting appropriate relaxation methods according to the user's emotions.

[0087] The determination unit can estimate the user's emotions and suggest appropriate entertainment content based on the estimated user emotions. For example, comedy movies or fun music can be suggested for a diary entry containing a strong positive emotion. The determination unit can also automatically collect and provide related information to suggest entertainment content based on the estimated user emotions. This can improve the user's mood by suggesting appropriate entertainment content according to the user's emotions.

[0088] The determination unit can estimate the user's emotions and suggest an appropriate exercise program based on the estimated user emotions. For example, it can suggest yoga or stretching exercises that have a relaxing effect for a diary entry that contains strong negative emotions. The determination unit can also automatically collect and provide related information to estimate the user's emotions and suggest an exercise program based on the estimated user emotions. This can support the user in reducing stress and maintaining health by suggesting an appropriate exercise program based on the user's emotions.

[0089] The determination unit can estimate the user's emotions and suggest appropriate reading content based on the estimated user emotions. For example, it can suggest self-help books or fictional novels for diaries with strong positive emotions. The determination unit can also automatically collect and provide related information to estimate the user's emotions and suggest reading content based on the estimated user emotions. This can improve the user's knowledge and emotional richness by suggesting appropriate reading content according to the user's emotions.

[0090] The determination unit can estimate the user's emotions and suggest appropriate travel destinations based on the estimated user emotions. For example, for a diary entry containing a strong positive emotion, it can suggest relaxing beach resorts and natural tourist spots. The determination unit can also automatically collect and provide related information to estimate the user's emotions and suggest travel destinations based on the estimated user emotions. This can support the user's refreshment and relaxation by suggesting appropriate travel destinations according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The diary analysis unit analyzes the user's diary. For example, it can analyze the contents of the diary using natural language processing technology, and analyze the emotions in the diary using emotion analysis technology. It can also analyze diary entries in image format using image analysis technology. Step 2: The hobby and preference understanding unit understands the user's hobby and preferences based on the diary content analyzed by the diary analysis unit. For example, it extracts the user's hobby and preferences from the diary content and identifies the user's interests. It can also refer to past diary content to analyze long-term changes in hobby and preferences. It can also refer to the user's behavioral history and purchase history to understand the user's hobby and preferences more accurately. Step 3: The related information provider provides related information and events based on the user's interests and preferences. For example, it can suggest the latest research papers and specialized books related to the user's interests, introduce online communities and forums, and provide past events and happenings in a timeline format. Step 4: The advice provider provides advice based on the diary contents. For example, it analyzes the user's health condition and lifestyle habits and provides advice on health management. It can also estimate stress levels and provide specific advice on stress management. It can also provide step-by-step advice on achieving goals.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] 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.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] 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.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 diary analysis unit that analyzes a user's diary; a hobby / preference understanding unit that understands the user's hobby / preference based on the diary content analyzed by the diary analysis unit; a related information providing unit that provides related information and events based on the hobbies and preferences understood by the hobbies and preferences understanding unit; an advice providing unit that provides advice based on the diary content; A system characterized by:

2. The hobby and preference understanding unit Based on the diary contents, the user's hobbies and preferences are tracked over time, and long-term changes in interests are analyzed.

2. The system of claim 1.

3. The related information providing unit Based on the diary contents, the system suggests the latest research papers and specialized books related to the user's interests.

2. The system of claim 1.

4. The advice providing unit Analyzing the user's health condition and lifestyle habits based on the diary content and providing advice on health management 2. The system of claim 1.

5. The SNS detox promotion unit: Based on the diary content, the system analyzes the user's SNS usage time and provides a specific action plan for SNS detox.

2. The system of claim 1.

6. The hobby and preference understanding unit Emotions are estimated from the diary contents, and changes in hobbies and preferences are predicted based on the fluctuations in the emotions.

2. The system of claim 1.

7. The related information providing unit Analyzing emotions regarding the diary content and suggesting related information and events based on the emotions 2. The system of claim 1.

8. The Self-Understanding Promotion Department Analyzing emotions regarding the diary contents and promoting self-understanding based on those emotions 2. The system of claim 1.

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