system

Abstract

An object of a system according to an embodiment is to enable a user to effectively utilize past experiences and future goals.SOLUTION: A system includes an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes a diary or an SNS post of the user. The generation unit generates a future goal and a past study based on the data analyzed by the analysis unit. The providing unit provides the user with the future goal and the past learning generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for users to effectively utilize their past experiences and future goals.

[0005] The system according to the embodiment aims to enable a user to effectively utilize past experiences and future goals. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes a user's diary and posts on social media. The generation unit generates future goals and past learnings based on the data analyzed by the analysis unit. The provision unit provides the user with the future goals and past learnings generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to effectively utilize past experiences and future goals. [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) A time traveler's diary AI system according to an embodiment of the present invention allows a user to virtually communicate with their future self. This system analyzes the user's diary and social media posts to generate goals and dreams that the future self would like to achieve, as well as lessons learned and advice from the past self. This allows the time traveler's diary AI system to virtually communicate with the future and past selves and gain new insights into their current actions and decision-making.

[0029] A time traveler's diary AI system according to an embodiment includes an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes a user's diary and social media posts. For example, the analysis unit collects diaries and social media posts written by the user in the past and analyzes their contents. The generation unit generates future goals and past lessons learned based on the data analyzed by the analysis unit. For example, the generation unit generates goals and dreams that the user's future self wants to achieve based on the analyzed data. The generation unit also generates lessons learned and advice from the user's past self based on the diary and posts written by the user in the past. The provision unit provides the user with the future goals and past lessons learned generated by the generation unit. For example, the provision unit generates diary entries that appear to have been written by the user's future or past self and provides them to the user. In this way, the time traveler's diary AI system according to an embodiment allows the user to virtually communicate with their future or past selves and gain new insights into their current actions and decision-making.

[0030] The analysis unit performs topic modeling of the posted content and can extract the user's interests and values. For example, the analysis unit performs topic modeling of the posted content and extracts the user's interests and values. For example, it analyzes keywords and phrases frequently mentioned by the user and identifies major topics. This allows the user's interests and values ​​to be extracted and future goals and dreams to be generated based on them.

[0031] The analysis unit can perform multimodal data analysis, including the user's voice memos and video logs as analysis targets. For example, the analysis unit can include the user's voice memos and video logs as analysis targets, integrating them with text data for analysis. For example, the analysis unit can convert the voice memos into text using voice recognition technology and analyze facial expressions and gestures from the video logs. This allows for more accurate data analysis by including the user's voice memos and video logs as analysis targets.

[0032] The analysis unit can integrate and analyze posts from different SNS platforms to generate a more comprehensive user profile. For example, the analysis unit collects posts from different SNS platforms and performs an integrated analysis. For example, posts from Twitter, Facebook, Instagram, etc. are unified and the user's overall interests and emotions are analyzed. This makes it possible to generate a more comprehensive user profile by integrating and analyzing posts from different SNS platforms.

[0033] The generation unit can analyze the user's past successful experiences and set specific future goals based on them. For example, the generation unit uses a generation AI to analyze the user's past successful experiences and set specific future goals based on them. For example, it analyzes projects and goals achieved in the past and suggests future goals based on similar successful experiences. This allows the user to set specific future goals based on their past successful experiences.

[0034] The generation unit can generate multiple scenarios of future goals based on keywords extracted from the user's past posts, and provide the user with options. The generation unit generates multiple scenarios of future goals based on keywords extracted from the user's past posts, for example. For example, different goal scenarios are proposed based on keywords such as "health," "career," and "travel." This allows the user to be provided with multiple future goal scenarios, expanding their options.

[0035] The generation unit can also analyze posts from the user's friends and family and generate future goals that take social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and generate future goals that take social influence into consideration. For example, the generation unit can set the user's future goals based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and generate future goals that take social influence into consideration.

[0036] The generation unit can refer to data of users from different cultural spheres and regions and generate future goals from a global perspective. The generation unit, for example, refers to data of users from different cultural spheres and regions and generates future goals from a global perspective. For example, it analyzes success stories and goal setting from different countries and regions and suggests them to the user. This makes it possible to refer to data from different cultural spheres and regions and generate future goals from a global perspective.

[0037] The generation unit can analyze the user's past failure experiences in detail and generate specific advice to help them overcome them. For example, the generation unit uses a generation AI to analyze the user's past failure experiences in detail and generate specific advice to help them overcome them. For example, it analyzes the causes and background of failures and suggests steps to succeed in similar situations. This makes it possible to generate specific advice based on the user's past failure experiences.

[0038] The generation unit can reconstruct lessons extracted from the user's past posts so that they can be applied to different situations. For example, the generation unit reconstructs lessons extracted from the user's past posts so that they can be applied to different situations. For example, the generation unit provides advice on how to apply lessons learned from a specific failure to other similar situations. This allows the user's past lessons to be reconstructed so that they can be applied to different situations.

[0039] The generation unit can refer to data of users of different age groups and life stages to generate advice from a wider variety of perspectives. The generation unit, for example, refers to data of users of different age groups and life stages to generate advice from a wider variety of perspectives. For example, advice is provided based on the success experiences and lessons learned of young people and the elderly. This allows advice to be generated from a wider variety of perspectives by referring to data of users of different age groups and life stages.

[0040] The generation unit can generate specific scenarios that the user's future self may face based on the user's past posts, and create diary entries based on those scenarios. For example, the generation unit uses a generation AI to generate specific scenarios that the user's future self may face based on the user's past posts. For example, the generation AI predicts future career and life events from the content of past posts, and creates diary entries based on those scenarios. This makes it possible to generate specific scenarios that the user's future self may face based on the user's past posts, and create diary entries based on those scenarios.

[0041] The generation unit can also analyze posts from the user's friends and family and generate diary entries that take social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and generate diary entries that take social influence into consideration. For example, the generation unit creates a user's diary entries based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and generate diary entries that take social influence into consideration.

[0042] The generation unit can generate diary entries from a global perspective by referring to data of users from different cultural spheres or regions. The generation unit, for example, generates diary entries from a global perspective by referring to data of users from different cultural spheres or regions. For example, diary entries that reflect the cultures and values ​​of different countries or regions are created. This allows diary entries to be generated from a global perspective by referring to data from different cultural spheres or regions.

[0043] The generation unit can analyze the user's past experiences of success and failure and, based on that, provide specific advice for current actions and decision-making. For example, the generation unit uses a generation AI to analyze the user's past experiences of success and failure and, based on that, provide specific advice for current actions and decision-making. For example, it analyzes the factors behind past successes and causes of failures and proposes action plans for similar situations. This makes it possible to provide specific advice for current actions and decision-making based on the user's past experiences of success and failure.

[0044] The generation unit can reconstruct lessons extracted from the user's past posts so that they can be applied to the current situation. For example, the generation unit reconstructs lessons extracted from the user's past posts so that they can be applied to the current situation. For example, the generation unit provides advice on how to apply lessons learned from a specific failure to a similar current situation. In this way, the user's past lessons can be reconstructed so that they can be applied to the current situation.

[0045] The generation unit can also analyze posts from the user's friends and family and provide advice that takes social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and provide advice that takes social influence into consideration. For example, it can provide advice on the user's actions and decision-making based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and provide advice that takes social influence into consideration.

[0046] The generation unit can provide advice from a wider variety of perspectives by referring to data on users of different age groups and life stages. The generation unit can provide advice from a wider variety of perspectives by referring to data on users of different age groups and life stages, for example. For example, advice can be provided based on the success experiences and lessons learned by young people and the elderly. This allows advice to be provided from a wider variety of perspectives by referring to data on different age groups and life stages.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit performs topic modeling of user posts to extract the user's interests and values. For example, it analyzes keywords and phrases frequently mentioned by users to identify key topics. This allows the system to extract the user's interests and values ​​and generate future goals and dreams based on them.

[0049] The analysis unit can perform multimodal data analysis, including users' voice memos and video logs. For example, voice memos can be converted into text using voice recognition technology, and facial expressions and gestures can be analyzed from video logs. This allows for more accurate data analysis by including users' voice memos and video logs in the analysis.

[0050] The analysis unit can integrate and analyze posts from different social media platforms to generate more comprehensive user profiles. For example, it can unify posts from Twitter, Facebook, Instagram, etc. and analyze the user's overall interests and emotions. This allows for the integration and analysis of posts from different social media platforms to generate more comprehensive user profiles.

[0051] The generation unit can analyze the user's past successes and set specific future goals based on them. For example, it can analyze projects and goals that have been achieved in the past and suggest future goals based on similar successes. This allows the user to set specific future goals based on the user's past successes.

[0052] The generation unit can generate multiple scenarios of future goals based on keywords extracted from the user's past posts, and provide the user with options. For example, different goal scenarios can be proposed based on keywords such as "health," "career," and "travel." This allows the user to be provided with multiple future goal scenarios, expanding their options.

[0053] The generation unit can also analyze posts from the user's friends and family to generate future goals that take social influence into account. For example, the generation unit can set the user's future goals based on the goals and values ​​shared by friends and family. This allows the generation of future goals that take social influence into account by analyzing posts from the user's friends and family.

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

[0055] Step 1: The analysis unit analyzes the user's diary entries and social media posts. For example, the analysis unit collects diary entries and social media posts written by the user in the past and analyzes their contents. Step 2: The generation unit generates future goals and past learnings based on the data analyzed by the analysis unit. For example, the generation unit generates goals and dreams that the future self wants to achieve based on the analyzed data. The generation unit also generates lessons and advice from the past self based on diaries and posts written by the past self. Step 3: The providing unit provides the future goals and past learnings generated by the generating unit to the user. For example, the providing unit generates diary entries that look as if they were written by the future or past self, and provides them to the user.

[0056] (Example 2) A time traveler's diary AI system according to an embodiment of the present invention allows a user to virtually communicate with their future self. This system analyzes the user's diary and social media posts to generate goals and dreams that the future self would like to achieve, as well as lessons learned and advice from the past self. This allows the time traveler's diary AI system to virtually communicate with the future and past selves and gain new insights into their current actions and decision-making.

[0057] A time traveler's diary AI system according to an embodiment includes an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes a user's diary and social media posts. For example, the analysis unit collects diaries and social media posts written by the user in the past and analyzes their contents. The generation unit generates future goals and past lessons learned based on the data analyzed by the analysis unit. For example, the generation unit generates goals and dreams that the user's future self wants to achieve based on the analyzed data. The generation unit also generates lessons learned and advice from the user's past self based on the diary and posts written by the user in the past. The provision unit provides the user with the future goals and past lessons learned generated by the generation unit. For example, the provision unit generates diary entries that appear to have been written by the user's future or past self and provides them to the user. In this way, the time traveler's diary AI system according to an embodiment allows the user to virtually communicate with their future or past selves and gain new insights into their current actions and decision-making.

[0058] The analysis unit can analyze emotions contained in user posts and track changes in emotions over time. For example, the analysis unit can analyze emotions contained in user posts and track changes in emotions over time. For example, emotions such as joy, sadness, and anger can be extracted from the diary entries and social media posts posted by the user, and these changes can be graphed. This makes it possible to track changes in the user's emotions and provide advice based on the emotions.

[0059] The analysis unit performs topic modeling of the posted content and can extract the user's interests and values. For example, the analysis unit performs topic modeling of the posted content and extracts the user's interests and values. For example, it analyzes keywords and phrases frequently mentioned by the user and identifies major topics. This allows the user's interests and values ​​to be extracted and future goals and dreams to be generated based on them.

[0060] The analysis unit uses the emotion estimation function to analyze the emotion of the user at the time of posting, and can prioritize analysis of posts with strong positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user at the time of posting. For example, it analyzes the text of the post content and distinguishes between positive emotions (joy, gratitude, excitement, etc.) and negative emotions (sadness, anger, anxiety, etc.). This makes it possible to prioritize analysis of posts with strong positive emotions and extract information that is useful for user motivation and goal setting.

[0061] The analysis unit can perform multimodal data analysis, including the user's voice memos and video logs as analysis targets. For example, the analysis unit can include the user's voice memos and video logs as analysis targets, integrating them with text data for analysis. For example, the analysis unit can convert the voice memos into text using voice recognition technology and analyze facial expressions and gestures from the video logs. This allows for more accurate data analysis by including the user's voice memos and video logs as analysis targets.

[0062] The analysis unit can integrate and analyze posts from different SNS platforms to generate a more comprehensive user profile. For example, the analysis unit collects posts from different SNS platforms and performs an integrated analysis. For example, posts from Twitter, Facebook, Instagram, etc. are unified and the user's overall interests and emotions are analyzed. This makes it possible to generate a more comprehensive user profile by integrating and analyzing posts from different SNS platforms.

[0063] The analysis unit uses the emotion estimation function to provide real-time emotion feedback when a user posts, thereby encouraging positive posts. The analysis unit, for example, uses the emotion estimation function to provide real-time emotion feedback when a user posts. For example, the analysis unit displays an emotion score while the user is entering content to post, and provides advice to increase positive emotions. In this way, by providing real-time emotion feedback, it is possible to encourage users to post more positive posts.

[0064] The generation unit can analyze the user's past successful experiences and set specific future goals based on them. For example, the generation unit uses a generation AI to analyze the user's past successful experiences and set specific future goals based on them. For example, it analyzes projects and goals achieved in the past and suggests future goals based on similar successful experiences. This allows the user to set specific future goals based on their past successful experiences.

[0065] The generation unit can generate multiple scenarios of future goals based on keywords extracted from the user's past posts, and provide the user with options. The generation unit generates multiple scenarios of future goals based on keywords extracted from the user's past posts, for example. For example, different goal scenarios are proposed based on keywords such as "health," "career," and "travel." This allows the user to be provided with multiple future goal scenarios, expanding their options.

[0066] The generation unit can use the emotion estimation function to identify the future goal for which the user feels the most positive emotion and present that goal preferentially. The generation unit, for example, uses the emotion estimation function to identify the future goal for which the user feels the most positive emotion. For example, it extracts goals for which positive emotion is strongest from past posts and sets future goals based on those. This makes it possible to identify the future goal for which the user feels the most positive emotion and present it preferentially.

[0067] The generation unit can also analyze posts from the user's friends and family and generate future goals that take social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and generate future goals that take social influence into consideration. For example, the generation unit can set the user's future goals based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and generate future goals that take social influence into consideration.

[0068] The generation unit can refer to data of users from different cultural spheres and regions and generate future goals from a global perspective. The generation unit, for example, refers to data of users from different cultural spheres and regions and generates future goals from a global perspective. For example, it analyzes success stories and goal setting from different countries and regions and suggests them to the user. This makes it possible to refer to data from different cultural spheres and regions and generate future goals from a global perspective.

[0069] The generation unit uses the emotion estimation function to analyze the emotions of the user when setting future goals in real time, and can support optimal goal setting. The generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when setting future goals in real time. For example, the generation unit analyzes the facial expressions and voice of the user while setting goals and calculates an emotion score. This allows the generation unit to analyze the emotions of the user when setting future goals in real time, and can support optimal goal setting.

[0070] The generation unit can analyze the user's past failure experiences in detail and generate specific advice to help them overcome them. For example, the generation unit uses a generation AI to analyze the user's past failure experiences in detail and generate specific advice to help them overcome them. For example, it analyzes the causes and background of failures and suggests steps to succeed in similar situations. This makes it possible to generate specific advice based on the user's past failure experiences.

[0071] The generation unit can reconstruct lessons extracted from the user's past posts so that they can be applied to different situations. For example, the generation unit reconstructs lessons extracted from the user's past posts so that they can be applied to different situations. For example, the generation unit provides advice on how to apply lessons learned from a specific failure to other similar situations. This allows the user's past lessons to be reconstructed so that they can be applied to different situations.

[0072] The generation unit can use the emotion estimation function to identify the moment in the past when the user felt the most positive emotion, and provide the actions and thoughts at that time as advice. The generation unit can, for example, use the emotion estimation function to identify the moment in the past when the user felt the most positive emotion. For example, it can extract moments of strong positive emotion from past posts and analyze the actions and thoughts at that time. This makes it possible to identify the moment in the past when the user felt the most positive emotion, and provide those actions and thoughts as advice.

[0073] The generation unit can refer to data of users of different age groups and life stages to generate advice from a wider variety of perspectives. The generation unit, for example, refers to data of users of different age groups and life stages to generate advice from a wider variety of perspectives. For example, advice is provided based on the success experiences and lessons learned of young people and the elderly. This allows advice to be generated from a wider variety of perspectives by referring to data of users of different age groups and life stages.

[0074] The generation unit uses the emotion estimation function to analyze the emotions of the user when receiving past advice in real time, and can provide optimal advice. The generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving past advice in real time. For example, the generation unit analyzes facial expressions and voice when receiving advice and calculates an emotion score. This allows the generation unit to analyze the emotions of the user when receiving past advice in real time, and can provide optimal advice.

[0075] The generation unit can generate specific scenarios that the user's future self may face based on the user's past posts, and create diary entries based on those scenarios. For example, the generation unit uses a generation AI to generate specific scenarios that the user's future self may face based on the user's past posts. For example, the generation AI predicts future career and life events from the content of past posts, and creates diary entries based on those scenarios. This makes it possible to generate specific scenarios that the user's future self may face based on the user's past posts, and create diary entries based on those scenarios.

[0076] The generation unit can generate emotionally rich diary entries by reflecting emotions extracted from the user's past posts. The generation unit can, for example, generate emotionally rich diary entries by reflecting emotions extracted from the user's past posts. For example, emotions such as joy, sadness, and surprise can be extracted from the content of the past posts and reflected in the diary entries. In this way, it is possible to generate emotionally rich diary entries by reflecting emotions extracted from the user's past posts.

[0077] The generation unit can use the emotion estimation function to generate a diary entry that the user can most emotionally empathize with. The generation unit, for example, uses the emotion estimation function to generate a diary entry that the user can most emotionally empathize with. For example, the generation unit calculates an emotion score from content posted in the past and selects content that is easy to emotionally empathize with. This makes it possible to generate a diary entry that the user can most emotionally empathize with.

[0078] The generation unit can also analyze posts from the user's friends and family and generate diary entries that take social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and generate diary entries that take social influence into consideration. For example, the generation unit creates a user's diary entries based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and generate diary entries that take social influence into consideration.

[0079] The generation unit can generate diary entries from a global perspective by referring to data of users from different cultural spheres or regions. The generation unit, for example, generates diary entries from a global perspective by referring to data of users from different cultural spheres or regions. For example, diary entries that reflect the cultures and values ​​of different countries or regions are created. This allows diary entries to be generated from a global perspective by referring to data from different cultural spheres or regions.

[0080] The generation unit uses the emotion estimation function to analyze the emotion of the user when reading the diary entry in real time, and can provide the optimal entry. The generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when reading the diary entry in real time. For example, the generation unit analyzes the user's facial expression and voice and calculates an emotion score. This allows the generation unit to analyze the emotion of the user when reading the diary entry in real time, and can provide the optimal entry.

[0081] The generation unit can analyze the user's past experiences of success and failure and, based on that, provide specific advice for current actions and decision-making. For example, the generation unit uses a generation AI to analyze the user's past experiences of success and failure and, based on that, provide specific advice for current actions and decision-making. For example, it analyzes the factors behind past successes and causes of failures and proposes action plans for similar situations. This makes it possible to provide specific advice for current actions and decision-making based on the user's past experiences of success and failure.

[0082] The generation unit can reconstruct lessons extracted from the user's past posts so that they can be applied to the current situation. For example, the generation unit reconstructs lessons extracted from the user's past posts so that they can be applied to the current situation. For example, the generation unit provides advice on how to apply lessons learned from a specific failure to a similar current situation. In this way, the user's past lessons can be reconstructed so that they can be applied to the current situation.

[0083] The generation unit can use the emotion estimation function to identify actions and decisions that the user feels most positive about and prioritize suggesting them. The generation unit, for example, uses the emotion estimation function to identify actions and decisions that the user feels most positive about. For example, it extracts actions and decisions that evoke strong positive emotions from past posts and makes suggestions based on those. This makes it possible to identify actions and decisions that the user feels most positive about and prioritize suggesting them.

[0084] The generation unit can also analyze posts from the user's friends and family and provide advice that takes social influence into consideration. For example, the generation AI can analyze posts from the user's friends and family and provide advice that takes social influence into consideration. For example, it can provide advice on the user's actions and decision-making based on the goals and values ​​shared by friends and family. This makes it possible to analyze posts from the user's friends and family and provide advice that takes social influence into consideration.

[0085] The generation unit can provide advice from a wider variety of perspectives by referring to data on users of different age groups and life stages. The generation unit can provide advice from a wider variety of perspectives by referring to data on users of different age groups and life stages, for example. For example, advice can be provided based on the success experiences and lessons learned by young people and the elderly. This allows advice to be provided from a wider variety of perspectives by referring to data on different age groups and life stages.

[0086] The generation unit uses the emotion estimation function to analyze the user's emotions regarding their current behavior or decision-making in real time, and can provide optimal advice. The generation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding their current behavior or decision-making in real time. For example, the generation unit analyzes the user's facial expressions and voice during behavior or decision-making to calculate an emotion score. This allows the generation unit to analyze the user's emotions regarding their current behavior or decision-making in real time, and can provide optimal advice.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The analysis unit analyzes the emotions contained in users' posts and can track changes in emotions over time. For example, it can extract emotions such as joy, sadness, and anger from the contents of diaries and social media posts posted by users and graph these changes. This makes it possible to track changes in a user's emotions and provide advice based on their emotions.

[0089] The analysis unit performs topic modeling of user posts to extract the user's interests and values. For example, it analyzes keywords and phrases frequently mentioned by users to identify key topics. This allows the system to extract the user's interests and values ​​and generate future goals and dreams based on them.

[0090] The analysis unit uses the emotion estimation function to analyze the emotions expressed by users when they post, and prioritizes analyzing posts that express strong positive emotions. For example, it analyzes the text of the post and distinguishes between positive emotions (joy, gratitude, excitement, etc.) and negative emotions (sadness, anger, anxiety, etc.). This allows it to prioritize analyzing posts that express strong positive emotions and extract information that is useful for user motivation and goal setting.

[0091] The analysis unit can perform multimodal data analysis, including users' voice memos and video logs. For example, voice memos can be converted into text using voice recognition technology, and facial expressions and gestures can be analyzed from video logs. This allows for more accurate data analysis by including users' voice memos and video logs in the analysis.

[0092] The analysis unit can integrate and analyze posts from different social media platforms to generate more comprehensive user profiles. For example, it can unify posts from Twitter, Facebook, Instagram, etc. and analyze the user's overall interests and emotions. This allows for the integration and analysis of posts from different social media platforms to generate more comprehensive user profiles.

[0093] The analysis unit uses the emotion estimation function to provide real-time emotional feedback when users post, encouraging them to post more positively. For example, it displays an emotion score while users are entering content to post, and gives advice to increase positive emotions. This provides real-time emotional feedback, encouraging users to post more positively.

[0094] The generation unit can analyze the user's past successes and set specific future goals based on them. For example, it can analyze projects and goals that have been achieved in the past and suggest future goals based on similar successes. This allows the user to set specific future goals based on the user's past successes.

[0095] The generation unit can generate multiple scenarios of future goals based on keywords extracted from the user's past posts, and provide the user with options. For example, different goal scenarios can be proposed based on keywords such as "health," "career," and "travel." This allows the user to be provided with multiple future goal scenarios, expanding their options.

[0096] The generation unit can use the emotion estimation function to identify future goals that evoke the most positive emotions in the user and prioritize presenting those goals. For example, it can extract goals that evoke the most positive emotions from past posts and set future goals based on those. This allows it to identify future goals that evoke the most positive emotions in the user and prioritize presenting them.

[0097] The generation unit can also analyze posts from the user's friends and family to generate future goals that take social influence into account. For example, the generation unit can set the user's future goals based on the goals and values ​​shared by friends and family. This allows the generation of future goals that take social influence into account by analyzing posts from the user's friends and family.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The analysis unit analyzes the user's diary entries and social media posts. For example, the analysis unit collects diary entries and social media posts written by the user in the past and analyzes their contents. Step 2: The generation unit generates future goals and past learnings based on the data analyzed by the analysis unit. For example, the generation unit generates goals and dreams that the future self wants to achieve based on the analyzed data. The generation unit also generates lessons and advice from the past self based on diaries and posts written by the past self. Step 3: The providing unit provides the future goals and past learnings generated by the generating unit to the user. For example, the providing unit generates diary entries that look as if they were written by the future or past self, and provides them to the user.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis part that analyzes users' diaries and social media posts, A generation unit that generates future goals and past learnings based on the data analyzed by the analysis unit; a providing unit that provides the user with the future goals and past learnings generated by the generating unit. A system characterized by:

2. The analysis unit Analyzing the emotions contained in the user's posts and tracking the transition of the emotions over time The system of claim 1 .

3. The analysis unit The user's voice memos and video logs will also be included in the analysis, and multimodal data analysis will be performed. The system of claim 1 .

4. The generation unit Analyzing the user's past success experiences and setting specific future goals based on the results The system of claim 1 .

5. The generation unit Analyze the user's past failure experiences in detail and generate specific advice to overcome them The system of claim 1 .

6. The generation unit Based on the user's past posts, a specific scenario that the user may face in the future is generated, and a diary entry is created based on the scenario. The system of claim 1 .

7. The generation unit Analyzing the user's past successes and failures and providing specific advice on current actions and decisions based on that analysis The system of claim 1 .

8. The analysis unit Using an emotion estimation function, the emotion expressed by the user at the time of posting is analyzed, and posts with a strong positive emotion are analyzed preferentially. The system of claim 1 .

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