System

Abstract

An object of the system according to the embodiment is to efficiently provide details of an application system related to a life plan or a life event of a family and a handling method.SOLUTION: A system includes a reception unit, a generation unit, a provision unit, and a posting unit. The reception unit inputs information of a user. The generation unit creates a chronology based on the information input by the reception unit. A providing part provides the details of the related application system and a coping method based on the chronology prepared by the generation part. The posting unit allows the user to post the information provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently provide information on application systems related to family life plans and life events.

[0005] The system according to the embodiment aims to efficiently provide details of application systems related to family life plans and life events, as well as methods of responding to such applications. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a posting unit. The reception unit inputs user information. The generation unit creates a chronology based on the information input by the reception unit. The provision unit provides details of related application systems and methods of response based on the chronology created by the generation unit. The posting unit allows users to post the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide details of application systems related to family life plans and life events, as well as methods of responding to such applications. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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 life plan proposal system according to an embodiment of the present invention creates a timeline based on user information and provides details and procedures for related application systems. The life plan proposal system allows users to input information via smartphone or PC and uses a generation AI to create the timeline. The timeline includes details and procedures for related application systems. Furthermore, the detailed information and timeline are submitted by users, and points are awarded to information providers. For example, in the life plan proposal system, a user inputs information about their family structure and life events. For example, the user inputs information such as "I plan to get married in 2023" or "I plan to have a child in 2025." This information is sent to the system. The life plan proposal system then uses a generation AI to create a timeline based on the user's information. The generation AI analyzes the input information and identifies related application systems. For example, it identifies application systems related to marriage and childbirth, and incorporates the details and procedures into the timeline. This allows users to see at a glance which application systems best fit their life plan. Furthermore, the life plan proposal system allows users to submit the detailed information and timeline. Users can post timelines created based on their life plans and share them with other users. In addition, points are awarded to information providers. For example, they can review their own life plans by referring to timelines posted by other users. This allows users to share information with each other and create better life plans. This allows the life plan proposal system to easily identify the application system that best suits the user's life plan. For example, a user can check the details and procedures for application systems related to marriage and childbirth in the timeline, and smoothly proceed with the necessary procedures. In addition, points are awarded to information providers, creating an incentive to actively share information. This allows users to cooperate with each other and create better life plans.

[0029] A life plan proposal system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a posting unit. The reception unit inputs user information. The user information includes, but is not limited to, personal information, family structure, and life events. The reception unit allows the user to input information via, for example, a smartphone or PC. The generation unit uses a generation AI to create a chronology based on the information input by the reception unit. The generation unit, for example, analyzes the input information and identifies relevant application systems. The generation unit uses the generation AI to identify, for example, application systems related to marriage and childbirth, and incorporates details and response methods into the chronology. The provision unit provides details and response methods for relevant application systems based on the chronology created by the generation unit. The provision unit provides, for example, details and response methods for the identified application systems to the user. The provision unit uses the generation AI to provide information such as application procedures, required documents, and application deadlines. The posting unit allows the user to post the information provided by the provision unit. The posting unit allows the user to post the created chronology and share it with other users. The posting unit uses a generation AI to, for example, analyze information posted by users and award points. This enables the life plan proposal system according to the embodiment to efficiently input user information, create a chronology, provide application systems, and post the information. For example, the reception unit inputs user information, and the generation unit analyzes the input information and creates a chronology. The provision unit provides details of related application systems and how to respond based on the created chronology, and the posting unit allows users to post the provided information and share it with other users. This allows users to easily identify the application systems that best fit their life plans.

[0030] The reception unit can input information about family structure and life events. Examples of family structure and life events include, but are not limited to, marriage, childbirth, and job changes. The reception unit can, for example, allow a user to input information about family structure and life events. For example, a user can input information such as "I plan to get married in 2023" or "I plan to have a child in 2025." This allows the user to input information about family structure and life events. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the information input by the user to a generation AI and have the generation AI analyze the information.

[0031] The generation unit can analyze the input information and identify the relevant application system. Examples of analysis include, but are not limited to, data mining, statistical analysis, and machine learning. The generation unit can, for example, analyze the input information and identify the relevant application system. For example, the generation unit can identify application systems related to marriage and application systems related to childbirth. This allows the input information to be analyzed and the relevant application system to be identified. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the input information into a generation AI and have the generation AI identify the application system.

[0032] The provision unit can provide details of the identified application system and how to respond. The details and how to respond include, for example, procedural steps, required documents, contact information, etc., but are not limited to these examples. The provision unit can provide, for example, details of the identified application system and how to respond. For example, the provision unit can provide information such as application procedures, required documents, and application deadlines. This makes it possible to provide details of the identified application system and how to respond. Some or all of the above-mentioned processing in the provision unit may be performed using, or without using, the generation AI. For example, the provision unit can input details of the identified application system and how to respond to the application into the generation AI and have the generation AI provide the information.

[0033] The posting unit can post a timeline created by a user and share it with other users. Sharing includes, but is not limited to, sharing on a social networking site or within a specific group. The posting unit can post a timeline created by a user and share it with other users. For example, the posting unit can share a timeline created by a user on a social networking site. The posting unit can also share a timeline within a specific group. This allows a user to post a timeline created by a user and share it with other users. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI. For example, the posting unit can input a timeline created by a user into a generation AI and cause the generation AI to share the timeline.

[0034] The posting unit can award points to information providers. Examples of points include, but are not limited to, points based on the number of posts and methods for exchanging points. The posting unit can, for example, award points to information providers. For example, the posting unit can award points based on information posted by users. The posting unit can also provide methods for exchanging points. By awarding points to information providers, incentives for providing information are increased. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI. For example, the posting unit can input information posted by users into a generation AI and cause the generation AI to award points.

[0035] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and select the optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting information. For example, if the user is newly married, the reception unit can prioritize displaying information input items related to marriage. Furthermore, if the user is raising children, the reception unit can add information input items related to the child's further education and childcare. Furthermore, if the user is about to retire, the reception unit can display information input items related to life after retirement. This allows the input items to be customized based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input information related to the user's current living situation and areas of interest into the generation AI and have the generation AI customize the input items.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also extract information using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input information regarding the user's input method to the generation AI and have the generation AI select the optimal input means.

[0038] When inputting information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting application systems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting information related to the user's travel destination. Furthermore, if the user is planning to move, the reception unit can prioritize inputting information related to the user's new address. This allows highly relevant information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0039] The reception unit can analyze the user's social media activity and input related information when inputting information. The reception unit can input information based on, for example, life events shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input information about the user's social media activity to the generation AI and cause the generation AI to input related information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can improve the input interface, for example, based on feedback provided by the user in the past. The reception unit can also optimize the order of input items based on the user's past feedback. The reception unit can also simplify the input procedure by referring to the user's past feedback. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback into the generation AI and have the generation AI customize the input method.

[0041] When generating the chronology, the generation unit can adjust the level of detail of the chronology based on the importance of the life events. For example, the generation unit adds detailed descriptions to important life events and reflects them in the chronology. The generation unit can also briefly describe less important life events. The generation unit can also highlight important life events specified by the user. This allows the level of detail of the chronology to be adjusted based on the importance of the life events. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information regarding the importance of life events into the generation AI and cause the generation AI to adjust the level of detail of the chronology.

[0042] When generating a timeline, the generation unit can apply different generation algorithms depending on the category of the life event. For example, for a life event related to marriage, the generation unit generates a timeline that includes information about the wedding and honeymoon. For a life event related to childbirth, the generation unit can also generate a timeline that includes information about childbirth preparations and childcare. For a life event related to further education, the generation unit can also generate a timeline that includes information about school selection and admission procedures. This allows different generation algorithms to be applied depending on the category of the life event. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the category of the life event into the generation AI and cause the generation AI to apply the generation algorithm.

[0043] When generating a timeline, the generation unit can improve the accuracy of the generation by referring to the user's past chronology results. For example, the generation unit automatically adds similar life events based on chronologies created by the user in the past. The generation unit can also select an optimal generation algorithm based on the user's past chronology results. The generation unit can also analyze the user's past chronology results to improve the accuracy of the generation. This improves the accuracy of the generation by referring to the user's past chronology results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past chronology results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0044] When generating the chronology, the generation unit can determine the priority of the chronology based on the time of occurrence of life events. For example, the generation unit can prioritize and display the most recent life events. The generation unit can also briefly describe past life events. The generation unit can also add detailed descriptions to future life events. This allows the priority of the chronology to be determined based on the time of occurrence of life events. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information regarding the time of occurrence of life events into the generation AI and cause the generation AI to determine the priority of the chronology.

[0045] When generating the chronology, the generation unit can adjust the order of the chronology based on the relevance of the life events. For example, the generation unit can display highly relevant life events consecutively. The generation unit can also display less relevant life events in a separate section. The generation unit can also adjust the order of the chronology based on the relevance specified by the user. This allows the order of the chronology to be adjusted based on the relevance of the life events. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information regarding the relevance of the life events to the generation AI and cause the generation AI to adjust the order of the chronology.

[0046] When generating the chronology, the generation unit can adjust the use of technical terms in the chronology according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a chronology that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can generate a concise chronology that avoids technical terms. Furthermore, the generation unit can adjust the use of optimal technical terms based on the user's past input history. This allows the use of technical terms in the chronology to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] When providing information, the providing unit can adjust the level of detail provided based on the importance of the application system. For example, the providing unit provides a detailed explanation for important application systems. The providing unit can also briefly describe application systems with low importance. The providing unit can also highlight and display important application systems designated by the user. This allows the level of detail provided to be adjusted based on the importance of the application system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information regarding the importance of the application system into the generation AI and have the generation AI adjust the level of detail provided.

[0048] When providing information, the providing unit can apply different providing algorithms depending on the category of the application system. For example, the providing unit provides information including information on weddings and honeymoons to an application system related to marriage. The providing unit can also provide information including information on childbirth preparations and childcare to an application system related to childbirth. The providing unit can also provide information including information on school selection and admission procedures to an application system related to further education. This allows different providing algorithms to be applied depending on the category of the application system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information related to the category of the application system into the generation AI and cause the generation AI to apply the providing algorithm.

[0049] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past provision results. For example, the providing unit automatically provides similar application systems based on information the user has received in the past. The providing unit can also select an optimal provision algorithm from the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the information provided. This improves the accuracy of the information provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0050] When providing information, the provision unit can determine the priority of provision based on the timing of the application system. For example, the provision unit can prioritize providing the most recent application system. The provision unit can also briefly describe past application systems. The provision unit can also add detailed descriptions to future application systems. This makes it possible to determine the priority of provision based on the timing of the application system. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the provision unit can input information regarding the timing of the application system into the generation AI and have the generation AI determine the priority of provision.

[0051] When providing information, the providing unit can adjust the order of provision based on the relevance of the application systems. For example, the providing unit can provide highly relevant application systems consecutively. The providing unit can also provide less relevant application systems in separate sections. The providing unit can also adjust the order of provision based on the relevance specified by the user. This makes it possible to adjust the order of provision based on the relevance of the application systems. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information regarding the relevance of the application systems into the generation AI and have the generation AI adjust the order of provision.

[0052] When providing information, the providing unit can adjust the use of technical terminology provided according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user is a beginner, the providing unit can also provide concise information that avoids technical terminology. Furthermore, the providing unit can adjust the use of optimal technical terminology based on the user's past input history. This makes it possible to adjust the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input information regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] The posting unit can adjust the level of detail of a post based on the importance of the content of the post when posting. For example, the posting unit adds a detailed explanation to important posts and posts them. The posting unit can also briefly describe posts with low importance. The posting unit can also highlight and display important posts designated by the user. This makes it possible to adjust the level of detail of a post based on the importance of the content of the post. Some or all of the above-mentioned processing in the posting unit may be performed using, or without, a generation AI. For example, the posting unit can input information regarding the importance of the content of the post to the generation AI and cause the generation AI to adjust the level of detail of the post.

[0054] The posting unit can apply different posting algorithms depending on the category of the post content when posting. For example, the posting unit may post information about the wedding and honeymoon for a post related to marriage. The posting unit may also post information about childbirth preparations and child-rearing for a post related to childbirth. The posting unit may also post information about school selection and admission procedures for a post related to further education. This allows different posting algorithms to be applied depending on the category of the post content. Some or all of the above-mentioned processing in the posting unit may be performed using, or without, a generation AI, for example. For example, the posting unit may input information about the category of the post content into the generation AI and cause the generation AI to apply the posting algorithm.

[0055] When posting, the posting unit can improve the accuracy of the post by referring to the user's past posting results. For example, the posting unit automatically adds similar posts based on posts created by the user in the past. The posting unit can also select the optimal posting algorithm from the user's past posting results. The posting unit can also analyze the user's past posting results and improve the accuracy of the post. This improves the accuracy of the post by referring to the user's past posting results. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input the user's past posting results into the generation AI and cause the generation AI to improve the accuracy of the post.

[0056] The posting unit can determine the priority of posts based on the time when the posted content was made at the time of posting. For example, the posting unit can prioritize displaying the most recent posts. The posting unit can also briefly describe past posts. The posting unit can also add detailed explanations to future posts. This allows the priority of posts to be determined based on the time when the posted content was made. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input information regarding the time when the posted content was made into the generation AI and have the generation AI determine the priority of posts.

[0057] The posting unit can adjust the order of posts based on the relevance of the posted content when posting. For example, the posting unit displays highly relevant posts consecutively. The posting unit can also display less relevant posts in a separate section. The posting unit can also adjust the order of posts based on the relevance specified by the user. This makes it possible to adjust the order of posts based on the relevance of the posted content. Some or all of the above-described processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input information regarding the relevance of the posted content to the generation AI and cause the generation AI to adjust the order of posts.

[0058] The posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, if the user has specialized knowledge, the posting unit can encourage the user to post using a lot of technical terminology. Furthermore, if the user is a beginner, the posting unit can encourage the user to post concisely and avoid technical terminology. Furthermore, the posting unit can adjust the use of optimal technical terminology based on the user's past input history. This allows the use of technical terminology in a post to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI, for example. For example, the posting unit can input information regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also suggest specific events and services in related areas based on the user's input. For example, if the user lives in a specific area, it can suggest events held in that area and services available there. If the user is planning a trip, it can also suggest tourist spots and local services at the travel destination. Furthermore, if the user is planning to move, it can also provide information useful for living in the new area. This makes it possible to provide information based on the user's area.

[0061] The generation unit can also predict future life events based on the user's input and reflect the prediction results in the timeline. For example, if the user hopes to be promoted at their current workplace, the possibility of promotion and the steps required to achieve it can be added to the timeline. Also, if the user wants to acquire a new skill, a learning plan for acquiring that skill can be incorporated into the timeline. Furthermore, if the user places importance on health management, regular health checks and exercise plans can be reflected in the timeline. This makes the user's plans for future life events more concrete.

[0062] The provider can also introduce relevant experts and service providers based on the user's input. For example, if the user is planning a wedding, the provider can introduce wedding planners and marriage counseling agencies. If the user is planning to give birth, the provider can introduce obstetricians and childcare support services. Furthermore, if the user is considering changing jobs, the provider can introduce career consultants and job placement agencies. This allows the user to easily access the experts and services they need.

[0063] The posting unit can also provide feedback and advice to other users based on the information posted by the user. For example, if a user posts a timeline of their marriage, they can receive advice and experiences from other users. Also, if a user posts a timeline of their childbirth, they can receive advice and support regarding child-rearing. Furthermore, if a user posts a timeline of their job changes, they can receive feedback and advice regarding their career. This allows users to share information with each other and create better life plans.

[0064] The reception unit can also provide guidance on related legal procedures and necessary documents based on the user's input. For example, if the user is planning to get married, the reception unit can provide guidance on the legal procedures and documents required for marriage. If the user is expecting a child, the reception unit can provide guidance on birth registration and childcare allowance application procedures. Furthermore, if the user is considering changing jobs, the reception unit can provide guidance on resignation procedures and necessary documents for the new workplace. This allows the user to smoothly prepare the necessary legal procedures and documents.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit inputs user information. The user information includes, for example, personal information, family structure, and life events. The reception unit allows the user to input information using a smartphone or PC. Step 2: The generation unit creates a timeline based on the information entered by the reception unit. Using the generation AI, the generation unit analyzes the entered information and identifies related application systems. For example, details of application systems related to marriage and childbirth and how to respond to them are incorporated into the timeline. Step 3: The provision unit provides details of the relevant application system and how to respond based on the timeline created by the generation unit. The provision unit uses the generation AI to provide users with information such as application procedures, required documents, and application deadlines. Step 4: The posting unit allows users to post the information provided by the providing unit. The posting unit posts the timeline created by the user and allows it to be shared with other users. The posting unit uses a generating AI to analyze the information posted by the user and award points.

[0067] (Example 2) A life plan proposal system according to an embodiment of the present invention creates a timeline based on user information and provides details and procedures for related application systems. The life plan proposal system allows users to input information via smartphone or PC and uses a generation AI to create the timeline. The timeline includes details and procedures for related application systems. Furthermore, the detailed information and timeline are submitted by users, and points are awarded to information providers. For example, in the life plan proposal system, a user inputs information about their family structure and life events. For example, the user inputs information such as "I plan to get married in 2023" or "I plan to have a child in 2025." This information is sent to the system. The life plan proposal system then uses a generation AI to create a timeline based on the user's information. The generation AI analyzes the input information and identifies related application systems. For example, it identifies application systems related to marriage and childbirth, and incorporates the details and procedures into the timeline. This allows users to see at a glance which application systems best fit their life plan. Furthermore, the life plan proposal system allows users to submit the detailed information and timeline. Users can post timelines created based on their life plans and share them with other users. In addition, points are awarded to information providers. For example, they can review their own life plans by referring to timelines posted by other users. This allows users to share information with each other and create better life plans. This allows the life plan proposal system to easily identify the application system that best suits the user's life plan. For example, a user can check the details and procedures for application systems related to marriage and childbirth in the timeline, and smoothly proceed with the necessary procedures. In addition, points are awarded to information providers, creating an incentive to actively share information. This allows users to cooperate with each other and create better life plans.

[0068] A life plan proposal system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a posting unit. The reception unit inputs user information. The user information includes, but is not limited to, personal information, family structure, and life events. The reception unit allows the user to input information via, for example, a smartphone or PC. The generation unit uses a generation AI to create a chronology based on the information input by the reception unit. The generation unit, for example, analyzes the input information and identifies relevant application systems. The generation unit uses the generation AI to identify, for example, application systems related to marriage and childbirth, and incorporates details and response methods into the chronology. The provision unit provides details and response methods for relevant application systems based on the chronology created by the generation unit. The provision unit provides, for example, details and response methods for the identified application systems to the user. The provision unit uses the generation AI to provide information such as application procedures, required documents, and application deadlines. The posting unit allows the user to post the information provided by the provision unit. The posting unit allows the user to post the created chronology and share it with other users. The posting unit uses a generation AI to, for example, analyze information posted by users and award points. This enables the life plan proposal system according to the embodiment to efficiently input user information, create a chronology, provide application systems, and post the information. For example, the reception unit inputs user information, and the generation unit analyzes the input information and creates a chronology. The provision unit provides details of related application systems and how to respond based on the created chronology, and the posting unit allows users to post the provided information and share it with other users. This allows users to easily identify the application systems that best fit their life plans.

[0069] The reception unit can input information about family structure and life events. Examples of family structure and life events include, but are not limited to, marriage, childbirth, and job changes. The reception unit can, for example, allow a user to input information about family structure and life events. For example, a user can input information such as "I plan to get married in 2023" or "I plan to have a child in 2025." This allows the user to input information about family structure and life events. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the information input by the user to a generation AI and have the generation AI analyze the information.

[0070] The generation unit can analyze the input information and identify the relevant application system. Examples of analysis include, but are not limited to, data mining, statistical analysis, and machine learning. The generation unit can, for example, analyze the input information and identify the relevant application system. For example, the generation unit can identify application systems related to marriage and application systems related to childbirth. This allows the input information to be analyzed and the relevant application system to be identified. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the input information into a generation AI and have the generation AI identify the application system.

[0071] The provision unit can provide details of the identified application system and how to respond. The details and how to respond include, for example, procedural steps, required documents, contact information, etc., but are not limited to these examples. The provision unit can provide, for example, details of the identified application system and how to respond. For example, the provision unit can provide information such as application procedures, required documents, and application deadlines. This makes it possible to provide details of the identified application system and how to respond. Some or all of the above-mentioned processing in the provision unit may be performed using, or without using, the generation AI. For example, the provision unit can input details of the identified application system and how to respond to the application into the generation AI and have the generation AI provide the information.

[0072] The posting unit can post a timeline created by a user and share it with other users. Sharing includes, but is not limited to, sharing on a social networking site or within a specific group. The posting unit can post a timeline created by a user and share it with other users. For example, the posting unit can share a timeline created by a user on a social networking site. The posting unit can also share a timeline within a specific group. This allows a user to post a timeline created by a user and share it with other users. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI. For example, the posting unit can input a timeline created by a user into a generation AI and cause the generation AI to share the timeline.

[0073] The posting unit can award points to information providers. Examples of points include, but are not limited to, points based on the number of posts and methods for exchanging points. The posting unit can, for example, award points to information providers. For example, the posting unit can award points based on information posted by users. The posting unit can also provide methods for exchanging points. By awarding points to information providers, incentives for providing information are increased. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI. For example, the posting unit can input information posted by users into a generation AI and cause the generation AI to award points.

[0074] The reception unit can estimate the user's emotions and adjust the timing of information input based on the emotions. For example, if the user is feeling stressed, the reception unit temporarily suspends input and displays a relaxing interface. Furthermore, if the user is relaxed, the reception unit can prompt the user to input information continuously. Furthermore, if the user is in a hurry, the reception unit can simplify the input items so that the input can be completed in the shortest time. This allows the timing of information input to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This makes it possible to analyze the user's past input history and select the optimal input method. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into the generation AI and have the generation AI select the optimal input method.

[0076] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting information. For example, if the user is newly married, the reception unit can prioritize displaying information input items related to marriage. Furthermore, if the user is raising children, the reception unit can add information input items related to the child's further education and childcare. Furthermore, if the user is about to retire, the reception unit can display information input items related to life after retirement. This allows the input items to be customized based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input information related to the user's current living situation and areas of interest into the generation AI and have the generation AI customize the input items.

[0077] The reception unit can select the optimal input means depending on the user's input method when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also extract information using image recognition technology. This makes it possible to select the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input information regarding the user's input method to the generation AI and have the generation AI select the optimal input means.

[0078] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of important information and postpone input of detailed information. The reception unit can also encourage the user to input detailed information when the user is relaxed. The reception unit can also prioritize input of only the most important information when the user is in a hurry. This allows the priority of information to be input to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] When inputting information, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting application systems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting information related to the user's travel destination. Furthermore, if the user is planning to move, the reception unit can prioritize inputting information related to the user's new address. This allows highly relevant information to be prioritized in consideration of the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input highly relevant information.

[0080] The reception unit can analyze the user's social media activity and input related information when inputting information. The reception unit can input information based on, for example, life events shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related information. The reception unit can also input related information by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be input. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input information about the user's social media activity to the generation AI and cause the generation AI to input related information.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. The reception unit can improve the input interface, for example, based on feedback provided by the user in the past. The reception unit can also optimize the order of input items based on the user's past feedback. The reception unit can also simplify the input procedure by referring to the user's past feedback. This allows the input method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback into the generation AI and have the generation AI customize the input method.

[0082] The generation unit can estimate the user's emotions and adjust the way the timeline is presented based on the emotions. For example, if the user is relaxed, the generation unit can generate a visually rich timeline. If the user is in a hurry, the generation unit can also generate a concise and to-the-point timeline. If the user is excited, the generation unit can also generate a timeline with a visually stimulating effect. This allows the way the timeline is presented to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the timeline is presented.

[0083] When generating the chronology, the generation unit can adjust the level of detail of the chronology based on the importance of the life events. For example, the generation unit adds detailed descriptions to important life events and reflects them in the chronology. The generation unit can also briefly describe less important life events. The generation unit can also highlight important life events specified by the user. This allows the level of detail of the chronology to be adjusted based on the importance of the life events. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information regarding the importance of life events into the generation AI and cause the generation AI to adjust the level of detail of the chronology.

[0084] When generating a timeline, the generation unit can apply different generation algorithms depending on the category of the life event. For example, for a life event related to marriage, the generation unit generates a timeline that includes information about the wedding and honeymoon. For a life event related to childbirth, the generation unit can also generate a timeline that includes information about childbirth preparations and childcare. For a life event related to further education, the generation unit can also generate a timeline that includes information about school selection and admission procedures. This allows different generation algorithms to be applied depending on the category of the life event. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input information about the category of the life event into the generation AI and cause the generation AI to apply the generation algorithm.

[0085] When generating a timeline, the generation unit can improve the accuracy of the generation by referring to the user's past chronology results. For example, the generation unit automatically adds similar life events based on chronologies created by the user in the past. The generation unit can also select an optimal generation algorithm based on the user's past chronology results. The generation unit can also analyze the user's past chronology results to improve the accuracy of the generation. This improves the accuracy of the generation by referring to the user's past chronology results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the user's past chronology results into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0086] The generation unit can estimate the user's emotions and adjust the length of the timeline based on the emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise timeline. If the user is relaxed, the generation unit can generate a longer timeline with detailed explanations. If the user is excited, the generation unit can generate a timeline with visually stimulating effects. This allows the length of the timeline to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the timeline.

[0087] When generating the chronology, the generation unit can determine the priority of the chronology based on the time of occurrence of life events. For example, the generation unit can prioritize and display the most recent life events. The generation unit can also briefly describe past life events. The generation unit can also add detailed descriptions to future life events. This allows the priority of the chronology to be determined based on the time of occurrence of life events. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information regarding the time of occurrence of life events into the generation AI and cause the generation AI to determine the priority of the chronology.

[0088] When generating the chronology, the generation unit can adjust the order of the chronology based on the relevance of the life events. For example, the generation unit can display highly relevant life events consecutively. The generation unit can also display less relevant life events in a separate section. The generation unit can also adjust the order of the chronology based on the relevance specified by the user. This allows the order of the chronology to be adjusted based on the relevance of the life events. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input information regarding the relevance of the life events to the generation AI and cause the generation AI to adjust the order of the chronology.

[0089] When generating the chronology, the generation unit can adjust the use of technical terms in the chronology according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can generate a chronology that uses a lot of technical terms. Furthermore, if the user is a beginner, the generation unit can generate a concise chronology that avoids technical terms. Furthermore, the generation unit can adjust the use of optimal technical terms based on the user's past input history. This allows the use of technical terms in the chronology to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0090] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. This allows the way the information is presented to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the information is presented.

[0091] When providing information, the providing unit can adjust the level of detail provided based on the importance of the application system. For example, the providing unit provides a detailed explanation for important application systems. The providing unit can also briefly describe application systems with low importance. The providing unit can also highlight and display important application systems designated by the user. This allows the level of detail provided to be adjusted based on the importance of the application system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information regarding the importance of the application system into the generation AI and have the generation AI adjust the level of detail provided.

[0092] When providing information, the providing unit can apply different providing algorithms depending on the category of the application system. For example, the providing unit provides information including information on weddings and honeymoons to an application system related to marriage. The providing unit can also provide information including information on childbirth preparations and childcare to an application system related to childbirth. The providing unit can also provide information including information on school selection and admission procedures to an application system related to further education. This allows different providing algorithms to be applied depending on the category of the application system. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information related to the category of the application system into the generation AI and cause the generation AI to apply the providing algorithm.

[0093] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past provision results. For example, the providing unit automatically provides similar application systems based on information the user has received in the past. The providing unit can also select an optimal provision algorithm from the user's past provision results. The providing unit can also analyze the user's past provision results and improve the accuracy of the information provided. This improves the accuracy of the information provided by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the user's past provision results into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0094] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide longer information with detailed explanations. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. This allows the length of the information to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the information.

[0095] When providing information, the provision unit can determine the priority of provision based on the timing of the application system. For example, the provision unit can prioritize providing the most recent application system. The provision unit can also briefly describe past application systems. The provision unit can also add detailed descriptions to future application systems. This makes it possible to determine the priority of provision based on the timing of the application system. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the provision unit can input information regarding the timing of the application system into the generation AI and have the generation AI determine the priority of provision.

[0096] When providing information, the providing unit can adjust the order of provision based on the relevance of the application systems. For example, the providing unit can provide highly relevant application systems consecutively. The providing unit can also provide less relevant application systems in separate sections. The providing unit can also adjust the order of provision based on the relevance specified by the user. This makes it possible to adjust the order of provision based on the relevance of the application systems. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input information regarding the relevance of the application systems into the generation AI and have the generation AI adjust the order of provision.

[0097] When providing information, the providing unit can adjust the use of technical terminology provided according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user is a beginner, the providing unit can also provide concise information that avoids technical terminology. Furthermore, the providing unit can adjust the use of optimal technical terminology based on the user's past input history. This makes it possible to adjust the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input information regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0098] The posting unit can estimate the user's emotions and adjust the way the post is expressed based on the emotions. For example, if the user is relaxed, the posting unit can encourage visually rich posts. Furthermore, if the user is in a hurry, the posting unit can encourage concise and to-the-point posts. Furthermore, if the user is excited, the posting unit can encourage posts with visually stimulating effects. This allows the way the post is expressed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the posting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the posting unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the post is expressed.

[0099] The posting unit can adjust the level of detail of a post based on the importance of the content of the post when posting. For example, the posting unit adds a detailed explanation to important posts and posts them. The posting unit can also briefly describe posts with low importance. The posting unit can also highlight and display important posts designated by the user. This makes it possible to adjust the level of detail of a post based on the importance of the content of the post. Some or all of the above-mentioned processing in the posting unit may be performed using, or without, a generation AI. For example, the posting unit can input information regarding the importance of the content of the post to the generation AI and cause the generation AI to adjust the level of detail of the post.

[0100] The posting unit can apply different posting algorithms depending on the category of the post content when posting. For example, the posting unit may post information about the wedding and honeymoon for a post related to marriage. The posting unit may also post information about childbirth preparations and child-rearing for a post related to childbirth. The posting unit may also post information about school selection and admission procedures for a post related to further education. This allows different posting algorithms to be applied depending on the category of the post content. Some or all of the above-mentioned processing in the posting unit may be performed using, or without, a generation AI, for example. For example, the posting unit may input information about the category of the post content into the generation AI and cause the generation AI to apply the posting algorithm.

[0101] When posting, the posting unit can improve the accuracy of the post by referring to the user's past posting results. For example, the posting unit automatically adds similar posts based on posts created by the user in the past. The posting unit can also select the optimal posting algorithm from the user's past posting results. The posting unit can also analyze the user's past posting results and improve the accuracy of the post. This improves the accuracy of the post by referring to the user's past posting results. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input the user's past posting results into the generation AI and cause the generation AI to improve the accuracy of the post.

[0102] The posting unit can estimate the user's emotions and adjust the length of the post based on the emotions. For example, if the user is in a hurry, the posting unit can encourage the user to post short, to-the-point posts. Furthermore, if the user is relaxed, the posting unit can encourage the user to post longer posts with detailed explanations. Furthermore, if the user is excited, the posting unit can encourage the user to post posts with visually stimulating effects. This allows the length of the post to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the posting unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the posting unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the post.

[0103] The posting unit can determine the priority of posts based on the time when the posted content was made at the time of posting. For example, the posting unit can prioritize displaying the most recent posts. The posting unit can also briefly describe past posts. The posting unit can also add detailed explanations to future posts. This allows the priority of posts to be determined based on the time when the posted content was made. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input information regarding the time when the posted content was made into the generation AI and have the generation AI determine the priority of posts.

[0104] The posting unit can adjust the order of posts based on the relevance of the posted content when posting. For example, the posting unit displays highly relevant posts consecutively. The posting unit can also display less relevant posts in a separate section. The posting unit can also adjust the order of posts based on the relevance specified by the user. This makes it possible to adjust the order of posts based on the relevance of the posted content. Some or all of the above-described processing in the posting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the posting unit can input information regarding the relevance of the posted content to the generation AI and cause the generation AI to adjust the order of posts.

[0105] The posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, if the user has specialized knowledge, the posting unit can encourage the user to post using a lot of technical terminology. Furthermore, if the user is a beginner, the posting unit can encourage the user to post concisely and avoid technical terminology. Furthermore, the posting unit can adjust the use of optimal technical terminology based on the user's past input history. This allows the use of technical terminology in a post to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the posting unit can be performed using, or without, a generation AI, for example. For example, the posting unit can input information regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, and posting unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input user information using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a chronology using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides details of related application systems and response methods. The posting unit is realized by the control unit 46A of the smart device 14 and allows the user to post the created chronology and share it with other users. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, and posting unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a chronology using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides details of related application systems and response methods. The posting unit is realized by the control unit 46A of the smart glasses 214 and allows users to post the created chronology and share it with other users. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and posting unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a chronology using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides details of related application systems and methods for responding. The posting unit is realized by the control unit 46A of the headset-type terminal 314 and allows users to post the created chronology and share it with other users. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and posting unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a chronology using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides details of related application systems and response methods. The posting unit is realized by the control unit 46A of the robot 414 and allows the user to post the created chronology and share it with other users.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also suggest specific events and services in related areas based on the user's input. For example, if the user lives in a specific area, it can suggest events held in that area and services available there. If the user is planning a trip, it can also suggest tourist spots and local services at the travel destination. Furthermore, if the user is planning to move, it can also provide information useful for living in the new area. This makes it possible to provide information based on the user's area.

[0108] The generation unit can also predict future life events based on the user's input and reflect the prediction results in the timeline. For example, if the user hopes to be promoted at their current workplace, the possibility of promotion and the steps required to achieve it can be added to the timeline. Also, if the user wants to acquire a new skill, a learning plan for acquiring that skill can be incorporated into the timeline. Furthermore, if the user places importance on health management, regular health checks and exercise plans can be reflected in the timeline. This makes the user's plans for future life events more concrete.

[0109] The provider can also introduce relevant experts and service providers based on the user's input. For example, if the user is planning a wedding, the provider can introduce wedding planners and marriage counseling agencies. If the user is planning to give birth, the provider can introduce obstetricians and childcare support services. Furthermore, if the user is considering changing jobs, the provider can introduce career consultants and job placement agencies. This allows the user to easily access the experts and services they need.

[0110] The posting unit can also provide feedback and advice to other users based on the information posted by the user. For example, if a user posts a timeline of their marriage, they can receive advice and experiences from other users. Also, if a user posts a timeline of their childbirth, they can receive advice and support regarding child-rearing. Furthermore, if a user posts a timeline of their job changes, they can receive feedback and advice regarding their career. This allows users to share information with each other and create better life plans.

[0111] The reception unit can also provide guidance on related legal procedures and necessary documents based on the user's input. For example, if the user is planning to get married, the reception unit can provide guidance on the legal procedures and documents required for marriage. If the user is expecting a child, the reception unit can provide guidance on birth registration and childcare allowance application procedures. Furthermore, if the user is considering changing jobs, the reception unit can provide guidance on resignation procedures and necessary documents for the new workplace. This allows the user to smoothly prepare the necessary legal procedures and documents.

[0112] The reception unit can also estimate the user's emotions and customize the input interface based on the emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, an interface that encourages the user to input detailed information can be provided. Furthermore, if the user is excited, a visually stimulating interface can be provided. This makes it possible to provide an optimal input experience according to the user's emotions.

[0113] The generation unit can also estimate the user's emotions and adjust the timeline design based on the emotions. For example, if the user is relaxed, a colorful and visually appealing timeline can be generated. If the user is in a hurry, a simple and to-the-point timeline can be generated. Furthermore, if the user is excited, a timeline with animations and effects can be generated. This makes it possible to provide a timeline design that matches the user's emotions.

[0114] The providing unit can also estimate the user's emotions and adjust the way information is provided based on the emotions. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that focuses on the main points can be provided. This makes it possible to provide optimal information according to the user's emotions.

[0115] The posting unit can also estimate the user's emotions and adjust the posted feedback based on the emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise and to the point feedback can be provided. Furthermore, if the user is excited, visually stimulating feedback can be provided. In this way, optimal feedback can be provided according to the user's emotions.

[0116] The providing unit can also estimate the user's emotions and adjust the priority of information based on the emotions. For example, if the user is feeling stressed, important information can be provided first, and detailed information can be postponed. Also, if the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, only the most important information can be provided. In this way, the priority of information can be adjusted according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit inputs user information. The user information includes, for example, personal information, family structure, and life events. The reception unit allows the user to input information using a smartphone or PC. Step 2: The generation unit creates a timeline based on the information entered by the reception unit. Using the generation AI, the generation unit analyzes the entered information and identifies related application systems. For example, details of application systems related to marriage and childbirth and how to respond to them are incorporated into the timeline. Step 3: The provision unit provides details of the relevant application system and how to respond based on the timeline created by the generation unit. The provision unit uses the generation AI to provide users with information such as application procedures, required documents, and application deadlines. Step 4: The posting unit allows users to post the information provided by the providing unit. The posting unit posts the timeline created by the user and allows it to be shared with other users. The posting unit uses a generating AI to analyze the information posted by the user and award points.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting user information; a generation unit that generates a chronology based on the information input by the reception unit; a providing unit that provides details of related application systems and corresponding methods based on the chronology created by the generating unit; a posting unit through which a user posts the information provided by the providing unit. A system characterized by:

2. The reception unit Enter information about your family and life events 2. The system of claim 1.

3. The generation unit Analyze the entered information and identify the relevant application system 2. The system of claim 1.

4. The providing unit Provide details of the identified application schemes and how to respond 2. The system of claim 1.

5. The posting unit: Users can post their own timelines and share them with other users.

2. The system of claim 1.

6. The posting unit: Award points to informants 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

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