system

Abstract

The system according to the embodiment aims to provide necessary information at appropriate timing in response to a user's life events. [Solution] A system according to an embodiment includes an input unit, a recording unit, a notification unit, and a providing unit. The input unit inputs information related to a user's life events. The recording unit records the information input by the input unit. The notification unit issues a notification at a pre-set timing based on the information recorded by the recording unit. The providing unit provides additional information based on the information notified by the notification unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided necessary information at appropriate times in response to user life events, and there is room for improvement.

[0005] The system according to the embodiment aims to provide necessary information at appropriate timing in response to a user's life events. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a recording unit, a notification unit, and a providing unit. The input unit inputs information related to a user's life events. The recording unit records the information input by the input unit. The notification unit issues a notification at a predetermined timing based on the information recorded by the recording unit. The providing unit provides additional information based on the information notified by the notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide necessary information at appropriate timing in response to a life event of the user. [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 event management system according to an embodiment of the present invention records information about a user's life events and provides them with the information they need at that time (personal information) and additional information. The life event management system records the life event information entered by the user and notifies them at the appropriate time, providing additional information. For example, it notifies them of to-do items (TODOs) that they need to complete when they reach a certain age, such as when their pension or insurance maturity date arrives. Users pay a subscription fee to use the service, and monetization is achieved by providing them with information. For example, the user enters information such as the age at which they will start receiving their pension and the maturity date of their insurance, which the system records. Based on the recorded information, the system then notifies them of the necessary procedures and paperwork one year before their pension starts. Furthermore, when their insurance maturity date approaches, it notifies them of post-maturity procedures and new insurance proposals. Furthermore, based on the user's personal information, the system also provides additional information, such as tax considerations associated with the start of their pension and advice on asset management after their insurance maturity. This allows the life event management system to provide users with the information they need at the appropriate time, helping them hedge against future risks. This allows the life event management system to provide users with the necessary information for their life events at the right time, helping them hedge against future risks. For example, the system can quickly and accurately record information written by users and notify them at the right time, reducing the burden on users. In addition, users can learn specific information about their own life events, enabling them to respond in a planned manner.

[0029] A life event management system according to an embodiment includes an input unit, a recording unit, a notification unit, and a providing unit. The input unit inputs information related to a user's life events. For example, the user can input information such as the age at which pension benefits begin and the maturity date of an insurance policy. The input unit receives the information input by the user in digital form and transmits it to the system. The input unit can also support multiple input methods, such as voice input and text input. The recording unit records the information input by the input unit. For example, the recording unit stores the information input by the user, such as the age at which pension benefits begin and the maturity date of an insurance policy, in a database. The recording unit also has a function of periodically backing up the recorded information. The notification unit provides notifications at appropriate times based on the information recorded by the recording unit. For example, the notification unit notifies the user about the necessary procedures and document preparations one year before the start of pension benefits. The notification unit can also notify the user of post-maturity procedures and new insurance proposals when the maturity date of an insurance policy approaches. The providing unit provides additional information based on the information notified by the notification unit. For example, the providing unit may provide information on tax matters that accompany the start of pension benefits. The providing unit may also provide advice on asset management after insurance maturity. As a result, the life event management system according to the embodiment notifies the user of information about life events at the appropriate time and provides additional information, which is useful for hedging future risks.

[0030] The input unit can input the user's pension start age and insurance maturity date. Examples of pension start age include, but are not limited to, the statutory age and a personally set age. The input unit, for example, provides an interface for the user to input the pension start age. The input unit can also provide an interface for the user to input the insurance maturity date. For example, the input unit provides an interface that allows the user to select the pension start age and the insurance maturity date in a calendar format. The input unit can also support multiple input methods, such as voice input and text input. This allows the user to input information such as the pension start age and the insurance maturity date, so that notifications can be sent at appropriate times.

[0031] The notification unit can notify the user about the preparation of necessary procedures and documents one year before the start of pension benefits. The necessary procedures and documents include, but are not limited to, application forms and certificates. For example, the notification unit notifies the user about the preparation of necessary procedures and documents one year before the start of pension benefits. The notification unit can also provide an interface through which the user receives the notification. For example, the notification unit provides an interface through which the user can receive notifications by email, SMS, or the like. The notification unit can also provide an interface through which the user can set the timing for receiving the notification. In this way, by notifying the user about the preparation of necessary procedures and documents one year before the start of pension benefits, the user can make appropriate preparations.

[0032] The notification unit can notify the user of post-maturity procedures and new insurance product proposals when the insurance maturity date approaches. Examples of new insurance products include, but are not limited to, life insurance and medical insurance. For example, the notification unit can notify the user of post-maturity procedures and new insurance product proposals when the insurance maturity date approaches. The notification unit can also provide an interface for the user to receive the notification. For example, the notification unit can provide an interface that allows the user to receive notifications by email, SMS, or the like. The notification unit can also provide an interface that allows the user to set the timing for receiving notifications. In this way, the user can take appropriate action by being notified of post-maturity procedures and new insurance proposals when the insurance maturity date approaches.

[0033] The providing unit can provide tax points to be aware of when pension benefits begin. Examples of tax points to be aware of include, but are not limited to, tax rates and deductions. The providing unit can also provide, for example, tax points to be aware of when pension benefits begin to begin to a user. The providing unit can also provide an interface for the user to check the tax points to be aware of. For example, the providing unit can provide an interface for the user to check the tax points to be aware of through a website or an application. The providing unit can also provide a link for the user to obtain detailed information about the tax points to be aware of. In this way, by providing tax points to be aware of when pension benefits begin to begin to begin to begin, the user can take appropriate tax measures.

[0034] The providing unit can advise the user on a specific method of asset management after the insurance expires. Examples of asset management methods include, but are not limited to, investment trusts and stock investments. The providing unit, for example, advises the user on a specific method of asset management after the insurance expires. The providing unit can also provide an interface for the user to check the asset management advice. For example, the providing unit provides an interface for the user to check the asset management advice through a website or an application. The providing unit can also provide a link for the user to obtain detailed information about asset management. In this way, by providing advice on asset management after the insurance expires, the user can manage their assets appropriately.

[0035] The input unit can analyze the user's past input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input and text input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also simplify the input procedure based on information the user has previously input. The input unit can also send a notification prompting input at a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The input unit can filter the information based on the user's current living situation and areas of interest during input. Examples of living situations include, but are not limited to, family structure and income status. Examples of areas of interest include, but are not limited to, hobbies and occupations. The input unit, for example, preferentially suggests input of related life events based on the user's current living situation. The input unit can also prompt the user to input related information based on the user's areas of interest. The input unit can also customize the input procedure based on the user's living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The input unit can select the optimal input means depending on the user's input method at the time of input. Input methods include, but are not limited to, voice input, text input, and image input, for example. For example, if the user prefers voice input, the input unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the input unit can also preferentially suggest text input. Furthermore, if the user prefers image input, the input unit can also preferentially suggest image input. In this way, by selecting the optimal input means depending on the user's input method, it is possible to provide an input method that is easy for the user to use. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI or without using AI.

[0038] The input unit can prioritize input of highly relevant information by taking into account the user's geographical location information at the time of input. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if the user lives in a specific area, the input unit can prioritize input of information related to that area. Furthermore, if the user is traveling, the input unit can prioritize input of information related to the user's travel destination. Furthermore, if the user is planning to move, the input unit can prioritize input of information related to the user's new residence. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0039] The input unit can analyze the user's social media activity at the time of input and input relevant information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The input unit can prompt the user to input relevant information based on, for example, life events shared by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest the input of relevant information. The input unit can also prompt the user to input relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant information can be input preferentially. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting data. Past feedback includes, but is not limited to, for example, survey results and user comments. The input unit can improve the input procedure, for example, based on feedback provided by the user in the past. The input unit can also customize the input interface by referring to the user's past feedback. The input unit can also optimize the input method based on the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI or without using AI.

[0041] During recording, the recording unit can analyze the user's past recording history and select the optimal recording method. Optimal recording methods include, but are not limited to, text recording and audio recording. The recording unit can, for example, suggest the optimal recording method based on recording methods used by the user in the past. The recording unit can also send a notification prompting the user to record at a specific time period based on the user's past recording history. The recording unit can also analyze the user's past recording history and simplify the recording procedure. In this way, the optimal recording method can be suggested by analyzing the user's past recording history. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0042] The recording unit can customize the recording method based on the user's current living situation when recording. Examples of living situations include, but are not limited to, family structure and income status. The recording unit can, for example, suggest a relevant recording method based on the user's current living situation. The recording unit can also customize the recording procedure according to the user's living situation. The recording unit can also adjust the recording frequency based on the user's living situation. In this way, by customizing the recording method based on the user's current living situation, highly relevant information can be preferentially recorded. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0043] The recording unit can improve the recording method by reflecting user feedback during recording. Feedback includes, but is not limited to, for example, survey results and user comments. The recording unit improves the recording method, for example, based on feedback provided by the user. The recording unit can also customize the recording interface by referring to the user feedback. The recording unit can also optimize the recording procedure based on the user feedback. In this way, the recording method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or without using AI.

[0044] When recording, the recording unit can select the optimal recording method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if the user lives in a specific area, the recording unit can prioritize recording information related to that area. Furthermore, if the user is traveling, the recording unit can prioritize recording information related to the travel destination. Furthermore, if the user is planning to move, the recording unit can prioritize recording information related to the user's new residence. In this way, by taking the user's geographical location information into account, highly relevant information can be preferentially recorded. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or without using AI.

[0045] The recording unit can analyze the user's social media activity during recording and suggest a means of recording. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The recording unit can, for example, prompt the user to record related information based on life events shared by the user on social media. The recording unit can also analyze the content of the user's social media posts and suggest recording related information. The recording unit can also prompt the user to record related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be recorded preferentially. Some or all of the above-described processing in the recording unit can be performed, for example, using AI or without using AI.

[0046] The recording unit can customize the recording method by reflecting the user's past feedback during recording. Past feedback includes, but is not limited to, for example, survey results and user comments. The recording unit can improve the recording procedure, for example, based on feedback provided by the user in the past. The recording unit can also customize the recording interface by referring to the user's past feedback. The recording unit can also optimize the recording method based on the user's past feedback. In this way, the recording method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0047] The notification unit can adjust the level of detail of the notification based on the importance of the information when sending a notification. The importance of the information includes, but is not limited to, for example, urgency and impact. For example, the notification unit can send a detailed notification for information of high importance. The notification unit can also send a simplified notification for information of low importance. The notification unit can also adjust the level of detail of the notification depending on the importance of the information. In this way, important information can be notified in detail by adjusting the level of detail of the notification based on the importance of the information. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0048] The notification unit can apply different notification algorithms depending on the category of information when sending a notification. Information categories include, but are not limited to, financial information and health information, for example. For example, in the case of pension information, the notification unit can send a notification including detailed procedures. In addition, in the case of insurance information, the notification unit can also send a notification including new insurance proposals. In addition, in the case of tax information, the notification unit can also send a notification including important points. In this way, by applying different notification algorithms depending on the category of information, appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. Past notification results include, but are not limited to, for example, the notification open rate and the user's response. The notification unit can, for example, analyze the user's response to notifications received in the past and improve the content of the notification. The notification unit can also optimize the timing of notifications based on the user's past notification results. The notification unit can also customize the notification method by referring to the user's past notification results. In this way, the accuracy of notifications can be improved by referring to the user's past notification results. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0050] The notification unit can determine the priority of notifications based on the time of submission of information at the time of notification. The time of submission of information includes, but is not limited to, a submission deadline or the occurrence of an event. For example, the notification unit can prioritize sending notifications for information with an approaching submission deadline. Furthermore, the notification unit can also postpone sending notifications for information with a distant submission deadline. Furthermore, the notification unit can adjust the priority of notifications depending on the time of submission. In this way, by determining the priority of notifications based on the time of submission of information, important information can be prioritized. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0051] The notification unit can adjust the order of notifications based on the relevance of the information when notifying the user. The relevance of the information includes, but is not limited to, similarity of content and related topics, for example. The notification unit, for example, prioritizes notifying information related to the user's current situation. The notification unit can also prioritize notifying highly relevant information based on the user's past behavior. The notification unit can also adjust the order of notifications according to the relevance of the information. In this way, by adjusting the order of notifications based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI.

[0052] When sending a notification, the notification unit can adjust the use of technical terms in the notification depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether or not the user has a qualification or past experience. For example, if the user has technical expertise, the notification unit can send a notification using technical terms. Furthermore, if the user does not have technical expertise, the notification unit can send a notification that explains the information in simple terms. Furthermore, the notification unit can adjust the content of the notification depending on the user's level of expertise. By adjusting the use of technical terms in the notification depending on the user's level of expertise, the notification can be made easier for the user to understand. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The importance of the information includes, for example, urgency and impact, but is not limited to these examples. For example, the providing unit provides detailed information when the information is highly important. Furthermore, the providing unit can also provide simplified information when the information is low important. Furthermore, the providing unit can adjust the level of detail of the information provided depending on the importance of the information. In this way, important information can be provided in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0054] The providing unit can apply different providing algorithms depending on the category of information when providing the information. Information categories include, but are not limited to, financial information and health information, for example. For example, in the case of pension information, the providing unit can provide information including detailed procedures. Furthermore, in the case of insurance information, the providing unit can also provide information including suggestions for new insurance. Furthermore, in the case of tax information, the providing unit can also provide information including important points to note. In this way, by applying different providing algorithms depending on the category of information, appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0055] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results when providing the information. Past provision results include, but are not limited to, for example, the acceptance rate of the information provided and the user's reactions. For example, the providing unit analyzes the user's reactions to information received in the past and improves the content of the information provided. The providing unit can also optimize the timing of the information provided based on the user's past provision results. The providing unit can also customize the method of providing the information by referring to the user's past provision results. In this way, the accuracy of the information provided can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0056] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, a submission deadline or when an event occurs, but is not limited to such examples. For example, the providing unit provides information with a close submission deadline preferentially. Furthermore, the providing unit can also postpone the provision of information with a distant submission deadline. Furthermore, the providing unit can adjust the priority of provision according to the time of submission. In this way, by determining the priority of provision based on the time of submission of information, important information can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0057] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. The relevance of the information includes, but is not limited to, for example, similarity of content and related topics. For example, the providing unit can preferentially provide information related to the user's current situation. The providing unit can also preferentially provide highly relevant information based on the user's past behavior. The providing unit can also adjust the order of providing the information according to the relevance of the information. In this way, by adjusting the order of providing the information based on the relevance of the information, highly relevant information can be preferentially provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0058] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the presence or absence of qualifications and past experience. For example, if the user has technical expertise, the providing unit can provide information using technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information explained in simple language. Furthermore, the providing unit can adjust the content of the provided information depending on the user's level of expertise. By adjusting the use of technical terminology in the provided information depending on the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[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 life event management system may further include a health management unit that monitors the user's health condition. The health management unit acquires the user's health data (e.g., heart rate, blood pressure, body temperature, etc.) and predicts health risks associated with life events. For example, the health management unit may send a notification recommending a health check before the start of pension benefits. In addition, when proposing a new insurance product after an insurance policy expires, the system may suggest the most suitable insurance product based on the user's health condition. This enables life event management that takes the user's health condition into consideration, enabling more personalized services to be provided.

[0061] The life event management system can further include a hobby suggestion unit that suggests events based on the user's hobbies and interests. The hobby suggestion unit analyzes information about the hobbies and interests entered by the user and suggests related events and activities. For example, if the user's hobby is traveling, the unit can suggest recommended travel destinations and travel plans after the user begins receiving pension benefits. Also, if the user is interested in sports, the unit can suggest sporting events and clubs that the user can participate in after the insurance matures. This makes it possible to make suggestions that take into account the hobbies and interests related to the user's life events, thereby improving the user's quality of life.

[0062] The life event management system may further include a family management unit that manages the life events of the entire family based on the user's family structure. The family management unit inputs the user's family structure (e.g., spouse, children, parents, etc.) and centrally manages the life events of the entire family. For example, it can record events such as a child's entry into higher education or a spouse's retirement and send notifications at the appropriate time. It can also integrate information on insurance and pensions for the entire family and propose optimal plans. This allows for efficient management of the life events of the entire user's family and improves the quality of life of the entire family.

[0063] The life event management system may further include a region information unit that provides region-specific information taking into account the user's geographical location information. The region information unit acquires the user's geographical location information (e.g., GPS data or address information) and provides information related to that region. For example, if the user lives in a specific region, it can provide information about pension procedures and insurance products in that region. Also, if the user is traveling, it can provide information about medical institutions and insurance in the user's travel destination. This makes it possible to provide personalized information taking into account the user's geographical location information, thereby improving user convenience.

[0064] The life event management system may further include a behavior analysis unit that analyzes the user's past behavioral history and makes optimal suggestions for life events. The behavior analysis unit analyzes the user's past behavioral history (e.g., past life events and selected options) and makes optimal suggestions for future life events. For example, it can suggest optimal future plans based on insurance products and pension plans selected by the user in the past. It can also make notifications and suggestions at specific times based on the user's past behavioral history. This enables personalized suggestions that take the user's past behavioral history into consideration, improving user convenience.

[0065] The life event management system may further include a social media analysis unit that analyzes the user's social media activity and provides related information. The social media analysis unit analyzes the user's social media activity (e.g., the content of posts and the number of likes) and provides related information. For example, related information can be provided based on life events shared by the user on social media. Related information can also be provided by taking into account the activities of the user's friends on social media. This makes it possible to provide personalized information that takes the user's social media activity into consideration, thereby improving user convenience.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The input unit inputs information about the user's life events. For example, the user can input information such as the age at which they can start receiving pension benefits or the maturity date of their insurance. The input unit receives the information entered by the user in digital form and sends it to the system. It also supports multiple input methods, such as voice input and text input. Step 2: The recording unit records the information entered by the input unit. For example, the recording unit stores information entered by the user, such as the pension start age and insurance maturity date, in a database. The recording unit also has the function of periodically backing up the recorded information. Step 3: The notification department notifies the insured at the appropriate time based on the information recorded by the recording department. For example, the notification department can notify the insured about the necessary procedures and document preparations one year before the start of pension benefits. It can also notify the insured about post-expiration procedures and new insurance proposals when the insurance expiration date approaches. Step 4: The provision unit provides additional information based on the information notified by the notification unit. For example, the provision unit may provide information on tax matters associated with the start of pension benefits. It may also provide advice on asset management after the insurance expires.

[0068] (Example 2) A life event management system according to an embodiment of the present invention records information about a user's life events and provides them with the information they need at that time (personal information) and additional information. The life event management system records the life event information entered by the user and notifies them at the appropriate time, providing additional information. For example, it notifies them of to-do items (TODOs) that they need to complete when they reach a certain age, such as when their pension or insurance maturity date arrives. Users pay a subscription fee to use the service, and monetization is achieved by providing them with information. For example, the user enters information such as the age at which they will start receiving their pension and the maturity date of their insurance, which the system records. Based on the recorded information, the system then notifies them of the necessary procedures and paperwork one year before their pension starts. Furthermore, when their insurance maturity date approaches, it notifies them of post-maturity procedures and new insurance proposals. Furthermore, based on the user's personal information, the system also provides additional information, such as tax considerations associated with the start of their pension and advice on asset management after their insurance maturity. This allows the life event management system to provide users with the information they need at the appropriate time, helping them hedge against future risks. This allows the life event management system to provide users with the necessary information for their life events at the right time, helping them hedge against future risks. For example, the system can quickly and accurately record information written by users and notify them at the right time, reducing the burden on users. In addition, users can learn specific information about their own life events, enabling them to respond in a planned manner.

[0069] A life event management system according to an embodiment includes an input unit, a recording unit, a notification unit, and a providing unit. The input unit inputs information related to a user's life events. For example, the user can input information such as the age at which pension benefits begin and the maturity date of an insurance policy. The input unit receives the information input by the user in digital form and transmits it to the system. The input unit can also support multiple input methods, such as voice input and text input. The recording unit records the information input by the input unit. For example, the recording unit stores the information input by the user, such as the age at which pension benefits begin and the maturity date of an insurance policy, in a database. The recording unit also has a function of periodically backing up the recorded information. The notification unit provides notifications at appropriate times based on the information recorded by the recording unit. For example, the notification unit notifies the user about the necessary procedures and document preparations one year before the start of pension benefits. The notification unit can also notify the user of post-maturity procedures and new insurance proposals when the maturity date of an insurance policy approaches. The providing unit provides additional information based on the information notified by the notification unit. For example, the providing unit may provide information on tax matters that accompany the start of pension benefits. The providing unit may also provide advice on asset management after insurance maturity. As a result, the life event management system according to the embodiment notifies the user of information about life events at the appropriate time and provides additional information, which is useful for hedging future risks.

[0070] The input unit can input the user's pension start age and insurance maturity date. Examples of pension start age include, but are not limited to, the statutory age and a personally set age. The input unit, for example, provides an interface for the user to input the pension start age. The input unit can also provide an interface for the user to input the insurance maturity date. For example, the input unit provides an interface that allows the user to select the pension start age and the insurance maturity date in a calendar format. The input unit can also support multiple input methods, such as voice input and text input. This allows the user to input information such as the pension start age and the insurance maturity date, so that notifications can be sent at appropriate times.

[0071] The notification unit can notify the user about the preparation of necessary procedures and documents one year before the start of pension benefits. The necessary procedures and documents include, but are not limited to, application forms and certificates. For example, the notification unit notifies the user about the preparation of necessary procedures and documents one year before the start of pension benefits. The notification unit can also provide an interface through which the user receives the notification. For example, the notification unit provides an interface through which the user can receive notifications by email, SMS, or the like. The notification unit can also provide an interface through which the user can set the timing for receiving the notification. In this way, by notifying the user about the preparation of necessary procedures and documents one year before the start of pension benefits, the user can make appropriate preparations.

[0072] The notification unit can notify the user of post-maturity procedures and new insurance product proposals when the insurance maturity date approaches. Examples of new insurance products include, but are not limited to, life insurance and medical insurance. For example, the notification unit can notify the user of post-maturity procedures and new insurance product proposals when the insurance maturity date approaches. The notification unit can also provide an interface for the user to receive the notification. For example, the notification unit can provide an interface that allows the user to receive notifications by email, SMS, or the like. The notification unit can also provide an interface that allows the user to set the timing for receiving notifications. In this way, the user can take appropriate action by being notified of post-maturity procedures and new insurance proposals when the insurance maturity date approaches.

[0073] The providing unit can provide tax points to be aware of when pension benefits begin. Examples of tax points to be aware of include, but are not limited to, tax rates and deductions. The providing unit can also provide, for example, tax points to be aware of when pension benefits begin to begin to a user. The providing unit can also provide an interface for the user to check the tax points to be aware of. For example, the providing unit can provide an interface for the user to check the tax points to be aware of through a website or an application. The providing unit can also provide a link for the user to obtain detailed information about the tax points to be aware of. In this way, by providing tax points to be aware of when pension benefits begin to begin to begin to begin, the user can take appropriate tax measures.

[0074] The providing unit can advise the user on a specific method of asset management after the insurance expires. Examples of asset management methods include, but are not limited to, investment trusts and stock investments. The providing unit, for example, advises the user on a specific method of asset management after the insurance expires. The providing unit can also provide an interface for the user to check the asset management advice. For example, the providing unit provides an interface for the user to check the asset management advice through a website or an application. The providing unit can also provide a link for the user to obtain detailed information about asset management. In this way, by providing advice on asset management after the insurance expires, the user can manage their assets appropriately.

[0075] The input unit can estimate the user's emotion and adjust the timing of input based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is feeling stressed, the input unit can send a notification prompting the user to input during a time when the user is able to relax. Furthermore, if the user is relaxed, the input unit can also send a notification prompting the user to input immediately. Furthermore, if the user is busy, the input unit can also send a notification prompting the user to input during a time when the user has more free time in their schedule. In this way, by adjusting the timing of input based on the user's emotion, the user can input information without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The input unit can analyze the user's past input history and select the optimal input method. Optimal input methods include, but are not limited to, voice input and text input. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The input unit can also simplify the input procedure based on information the user has previously input. The input unit can also send a notification prompting input at a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The input unit can filter the information based on the user's current living situation and areas of interest during input. Examples of living situations include, but are not limited to, family structure and income status. Examples of areas of interest include, but are not limited to, hobbies and occupations. The input unit, for example, preferentially suggests input of related life events based on the user's current living situation. The input unit can also prompt the user to input related information based on the user's areas of interest. The input unit can also customize the input procedure based on the user's living situation and areas of interest. This allows highly relevant information to be preferentially input by filtering based on the user's current living situation and areas of interest. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The input unit can select the optimal input means depending on the user's input method at the time of input. Input methods include, but are not limited to, voice input, text input, and image input, for example. For example, if the user prefers voice input, the input unit can preferentially suggest voice input. Furthermore, if the user prefers text input, the input unit can also preferentially suggest text input. Furthermore, if the user prefers image input, the input unit can also preferentially suggest image input. In this way, by selecting the optimal input means depending on the user's input method, it is possible to provide an input method that is easy for the user to use. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI or without using AI.

[0079] The input unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, when the user is stressed, the input unit postpones input of less important information. Furthermore, when the user is relaxed, the input unit can prioritize input of more important information. Furthermore, when the user is busy, the input unit can prioritize input of more important information and postpone information that can be input later. In this way, by determining the priority of information to be input based on the user's emotions, important information can be input preferentially. Emotion estimation is realized using an emotion estimation function using, 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.

[0080] The input unit can prioritize input of highly relevant information by taking into account the user's geographical location information at the time of input. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if the user lives in a specific area, the input unit can prioritize input of information related to that area. Furthermore, if the user is traveling, the input unit can prioritize input of information related to the user's travel destination. Furthermore, if the user is planning to move, the input unit can prioritize input of information related to the user's new residence. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The input unit can analyze the user's social media activity at the time of input and input relevant information. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The input unit can prompt the user to input relevant information based on, for example, life events shared by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest the input of relevant information. The input unit can also prompt the user to input relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant information can be input preferentially. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The input unit can customize the input method by reflecting the user's past feedback when inputting data. Past feedback includes, but is not limited to, for example, survey results and user comments. The input unit can improve the input procedure, for example, based on feedback provided by the user in the past. The input unit can also customize the input interface by referring to the user's past feedback. The input unit can also optimize the input method based on the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed, for example, using AI or without using AI.

[0083] The recording unit can estimate the user's emotion and adjust the recording method based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the recording unit can provide a simple recording method when the user is stressed. The recording unit can also provide a detailed recording method when the user is relaxed. The recording unit can also provide a simplified recording method when the user is busy. This allows the user to record information without feeling stressed by adjusting the recording method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] During recording, the recording unit can analyze the user's past recording history and select the optimal recording method. Optimal recording methods include, but are not limited to, text recording and audio recording. The recording unit can, for example, suggest the optimal recording method based on recording methods used by the user in the past. The recording unit can also send a notification prompting the user to record at a specific time period based on the user's past recording history. The recording unit can also analyze the user's past recording history and simplify the recording procedure. In this way, the optimal recording method can be suggested by analyzing the user's past recording history. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0085] The recording unit can customize the recording method based on the user's current living situation when recording. Examples of living situations include, but are not limited to, family structure and income status. The recording unit can, for example, suggest a relevant recording method based on the user's current living situation. The recording unit can also customize the recording procedure according to the user's living situation. The recording unit can also adjust the recording frequency based on the user's living situation. In this way, by customizing the recording method based on the user's current living situation, highly relevant information can be preferentially recorded. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0086] The recording unit can improve the recording method by reflecting user feedback during recording. Feedback includes, but is not limited to, for example, survey results and user comments. The recording unit improves the recording method, for example, based on feedback provided by the user. The recording unit can also customize the recording interface by referring to the user feedback. The recording unit can also optimize the recording procedure based on the user feedback. In this way, the recording method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or without using AI.

[0087] The recording unit can estimate the user's emotions and determine the priority of information to be recorded based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition and voice analysis. For example, when the user is stressed, the recording unit postpones recording of less important information. Furthermore, when the user is relaxed, the recording unit can prioritize recording of more important information. Furthermore, when the user is busy, the recording unit can prioritize recording of more important information and postpone information that can be recorded later. In this way, by determining the priority of information to be recorded based on the user's emotions, important information can be recorded preferentially. Emotion estimation is realized using an emotion estimation function using, 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.

[0088] When recording, the recording unit can select the optimal recording method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if the user lives in a specific area, the recording unit can prioritize recording information related to that area. Furthermore, if the user is traveling, the recording unit can prioritize recording information related to the travel destination. Furthermore, if the user is planning to move, the recording unit can prioritize recording information related to the user's new residence. In this way, by taking the user's geographical location information into account, highly relevant information can be preferentially recorded. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or without using AI.

[0089] The recording unit can analyze the user's social media activity during recording and suggest a means of recording. Social media activity includes, for example, but is not limited to, the content of posts and the number of likes. The recording unit can, for example, prompt the user to record related information based on life events shared by the user on social media. The recording unit can also analyze the content of the user's social media posts and suggest recording related information. The recording unit can also prompt the user to record related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be recorded preferentially. Some or all of the above-described processing in the recording unit can be performed, for example, using AI or without using AI.

[0090] The recording unit can customize the recording method by reflecting the user's past feedback during recording. Past feedback includes, but is not limited to, for example, survey results and user comments. The recording unit can improve the recording procedure, for example, based on feedback provided by the user in the past. The recording unit can also customize the recording interface by referring to the user's past feedback. The recording unit can also optimize the recording method based on the user's past feedback. In this way, the recording method can be optimized by reflecting the user's past feedback. Some or all of the above-described processing in the recording unit may be performed, for example, using AI or without using AI.

[0091] The notification unit can estimate the user's emotion and adjust the notification presentation method based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the notification unit can send a simple, highly visible notification. If the user is relaxed, the notification unit can also send a notification containing detailed information. If the user is in a hurry, the notification unit can also send a notification that focuses on the main points. In this way, by adjusting the notification presentation method based on the user's emotion, the user can receive notifications without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The notification unit can adjust the level of detail of the notification based on the importance of the information when sending a notification. The importance of the information includes, but is not limited to, for example, urgency and impact. For example, the notification unit can send a detailed notification for information of high importance. The notification unit can also send a simplified notification for information of low importance. The notification unit can also adjust the level of detail of the notification depending on the importance of the information. In this way, important information can be notified in detail by adjusting the level of detail of the notification based on the importance of the information. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0093] The notification unit can apply different notification algorithms depending on the category of information when sending a notification. Information categories include, but are not limited to, financial information and health information, for example. For example, in the case of pension information, the notification unit can send a notification including detailed procedures. In addition, in the case of insurance information, the notification unit can also send a notification including new insurance proposals. In addition, in the case of tax information, the notification unit can also send a notification including important points. In this way, by applying different notification algorithms depending on the category of information, appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The notification unit can improve the accuracy of notifications by referring to the user's past notification results when sending notifications. Past notification results include, but are not limited to, for example, the notification open rate and the user's response. The notification unit can, for example, analyze the user's response to notifications received in the past and improve the content of the notification. The notification unit can also optimize the timing of notifications based on the user's past notification results. The notification unit can also customize the notification method by referring to the user's past notification results. In this way, the accuracy of notifications can be improved by referring to the user's past notification results. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0095] The notification unit can estimate the user's emotion and adjust the length of the notification based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the notification unit can send a short, to-the-point notification. Alternatively, if the user is relaxed, the notification unit can send a longer notification with detailed information. Alternatively, if the user is in a hurry, the notification unit can send a concise, quick notification. In this way, by adjusting the length of the notification based on the user's emotion, the user can receive the notification without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0096] The notification unit can determine the priority of notifications based on the time of submission of information at the time of notification. The time of submission of information includes, but is not limited to, a submission deadline or the occurrence of an event. For example, the notification unit can prioritize sending notifications for information with an approaching submission deadline. Furthermore, the notification unit can also postpone sending notifications for information with a distant submission deadline. Furthermore, the notification unit can adjust the priority of notifications depending on the time of submission. In this way, by determining the priority of notifications based on the time of submission of information, important information can be prioritized. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0097] The notification unit can adjust the order of notifications based on the relevance of the information when notifying the user. The relevance of the information includes, but is not limited to, similarity of content and related topics, for example. The notification unit, for example, prioritizes notifying information related to the user's current situation. The notification unit can also prioritize notifying highly relevant information based on the user's past behavior. The notification unit can also adjust the order of notifications according to the relevance of the information. In this way, by adjusting the order of notifications based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI.

[0098] When sending a notification, the notification unit can adjust the use of technical terms in the notification depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, whether or not the user has a qualification or past experience. For example, if the user has technical expertise, the notification unit can send a notification using technical terms. Furthermore, if the user does not have technical expertise, the notification unit can send a notification that explains the information in simple terms. Furthermore, the notification unit can adjust the content of the notification depending on the user's level of expertise. By adjusting the use of technical terms in the notification depending on the user's level of expertise, the notification can be made easier for the user to understand. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI.

[0099] The providing unit can estimate the user's emotion and adjust the way in which information is presented based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the providing unit can provide simple, highly visible information. If the user is relaxed, the providing unit can also provide detailed information. If the user is in a hurry, the providing unit can also provide information that focuses on the main points. By adjusting the way in which information is presented based on the user's emotion, the user can receive information without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0100] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The importance of the information includes, for example, urgency and impact, but is not limited to these examples. For example, the providing unit provides detailed information when the information is highly important. Furthermore, the providing unit can also provide simplified information when the information is low important. Furthermore, the providing unit can adjust the level of detail of the information provided depending on the importance of the information. In this way, important information can be provided in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0101] The providing unit can apply different providing algorithms depending on the category of information when providing the information. Information categories include, but are not limited to, financial information and health information, for example. For example, in the case of pension information, the providing unit can provide information including detailed procedures. Furthermore, in the case of insurance information, the providing unit can also provide information including suggestions for new insurance. Furthermore, in the case of tax information, the providing unit can also provide information including important points to note. In this way, by applying different providing algorithms depending on the category of information, appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0102] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results when providing the information. Past provision results include, but are not limited to, for example, the acceptance rate of the information provided and the user's reactions. For example, the providing unit analyzes the user's reactions to information received in the past and improves the content of the information provided. The providing unit can also optimize the timing of the information provided based on the user's past provision results. The providing unit can also customize the method of providing the information by referring to the user's past provision results. In this way, the accuracy of the information provided can be improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0103] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition and voice analysis. For example, if the user is nervous, the providing unit can provide short, to-the-point information. If the user is relaxed, the providing unit can also provide detailed information. If the user is in a hurry, the providing unit can also provide concise, quick information. By adjusting the length of the information to be provided based on the user's emotion, the user can receive information without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0104] The providing unit can determine the priority of provision based on the time of submission of information at the time of provision. The time of submission of information includes, for example, a submission deadline or when an event occurs, but is not limited to such examples. For example, the providing unit provides information with a close submission deadline preferentially. Furthermore, the providing unit can also postpone the provision of information with a distant submission deadline. Furthermore, the providing unit can adjust the priority of provision according to the time of submission. In this way, by determining the priority of provision based on the time of submission of information, important information can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0105] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. The relevance of the information includes, but is not limited to, for example, similarity of content and related topics. For example, the providing unit can preferentially provide information related to the user's current situation. The providing unit can also preferentially provide highly relevant information based on the user's past behavior. The providing unit can also adjust the order of providing the information according to the relevance of the information. In this way, by adjusting the order of providing the information based on the relevance of the information, highly relevant information can be preferentially provided. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0106] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the presence or absence of qualifications and past experience. For example, if the user has technical expertise, the providing unit can provide information using technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information explained in simple language. Furthermore, the providing unit can adjust the content of the provided information depending on the user's level of expertise. By adjusting the use of technical terminology in the provided information depending on the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, recording unit, notification unit, and providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, allowing the user to input information such as the pension start age and the insurance maturity date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the input information in the database 24. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of the procedures and document preparations required one year before the start of pension benefits. The providing unit is realized, for example, by the output device 40 of the smart device 14, and provides additional information such as tax precautions associated with the start of pension benefits and advice on asset management after the insurance maturity date. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, recording unit, notification unit, and providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, allowing the user to vocally input information such as the pension start age and the insurance maturity date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the input information in the database 24. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of the procedures and document preparations required one year before the start of pension benefits. The providing unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides additional information such as tax precautions associated with the start of pension benefits and advice on asset management after the insurance maturity date. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, recording unit, notification unit, and providing unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset terminal 314, allowing the user to vocally input information such as the pension start age and the insurance maturity date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the input information in the database 24. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of the procedures and paperwork required one year before the start of pension benefits. The providing unit is realized, for example, by the speaker 240 of the headset terminal 314, and provides additional information such as tax considerations associated with the start of pension benefits and advice on asset management after the insurance maturity date. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, recording unit, notification unit, and providing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, allowing the user to vocally input information such as the pension start age and the insurance maturity date. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the input information in the database 24. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and notifies the user of the procedures and document preparations required one year before the start of pension benefits. The providing unit is realized, for example, by the speaker 240 of the robot 414, and provides additional information such as tax considerations associated with the start of pension benefits and advice on asset management after the insurance maturity date.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The life event management system may further include a health management unit that monitors the user's health condition. The health management unit acquires the user's health data (e.g., heart rate, blood pressure, body temperature, etc.) and predicts health risks associated with life events. For example, the health management unit may send a notification recommending a health check before the start of pension benefits. In addition, when proposing a new insurance product after an insurance policy expires, the system may suggest the most suitable insurance product based on the user's health condition. This enables life event management that takes the user's health condition into consideration, enabling more personalized services to be provided.

[0109] The life event management system can further include a hobby suggestion unit that suggests events based on the user's hobbies and interests. The hobby suggestion unit analyzes information about the hobbies and interests entered by the user and suggests related events and activities. For example, if the user's hobby is traveling, the unit can suggest recommended travel destinations and travel plans after the user begins receiving pension benefits. Also, if the user is interested in sports, the unit can suggest sporting events and clubs that the user can participate in after the insurance matures. This makes it possible to make suggestions that take into account the hobbies and interests related to the user's life events, thereby improving the user's quality of life.

[0110] The life event management system can further include an emotion care unit that estimates the user's emotions and provides advice for reducing stress based on the estimated emotions. The emotion care unit estimates the user's emotions using facial expression recognition, voice analysis, etc., and suggests relaxation methods and activities for reducing stress if the user is feeling stressed. For example, if the user is feeling stressed before the start of pension benefits, it can suggest relaxing yoga or meditation classes. Also, if the user is feeling anxious after the insurance maturity date, it can suggest counseling services or relaxation methods. This makes it possible to provide support based on the user's emotions and support the user's mental health.

[0111] The life event management system may further include a family management unit that manages the life events of the entire family based on the user's family structure. The family management unit inputs the user's family structure (e.g., spouse, children, parents, etc.) and centrally manages the life events of the entire family. For example, it can record events such as a child's entry into higher education or a spouse's retirement and send notifications at the appropriate time. It can also integrate information on insurance and pensions for the entire family and propose optimal plans. This allows for efficient management of the life events of the entire user's family and improves the quality of life of the entire family.

[0112] The life event management system can further estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, notifications can be withheld or sent at a time when the user is relaxed. Also, if the user is relaxed, notifications can be sent immediately. In this way, by adjusting the timing of notifications based on the user's emotions, the user can receive notifications without feeling stressed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI.

[0113] The life event management system may further include a region information unit that provides region-specific information taking into account the user's geographical location information. The region information unit acquires the user's geographical location information (e.g., GPS data or address information) and provides information related to that region. For example, if the user lives in a specific region, it can provide information about pension procedures and insurance products in that region. Also, if the user is traveling, it can provide information about medical institutions and insurance in the user's travel destination. This makes it possible to provide personalized information taking into account the user's geographical location information, thereby improving user convenience.

[0114] The life event management system can further estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is nervous, simple, highly visible information can be provided. On the other hand, if the user is relaxed, detailed information can be provided. By adjusting the way information is presented based on the user's emotions, the user can receive information without feeling stressed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI.

[0115] The life event management system may further include a behavior analysis unit that analyzes the user's past behavioral history and makes optimal suggestions for life events. The behavior analysis unit analyzes the user's past behavioral history (e.g., past life events and selected options) and makes optimal suggestions for future life events. For example, it can suggest optimal future plans based on insurance products and pension plans selected by the user in the past. It can also make notifications and suggestions at specific times based on the user's past behavioral history. This enables personalized suggestions that take the user's past behavioral history into consideration, improving user convenience.

[0116] The life event management system can further estimate the user's emotions and determine the priority of information to be provided based on the estimated emotions. For example, if the user is feeling stressed, the system can postpone the provision of less important information. Also, if the user is relaxed, the system can prioritize the provision of more important information. In this way, by determining the priority of information to be provided based on the user's emotions, important information can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI.

[0117] The life event management system may further include a social media analysis unit that analyzes the user's social media activity and provides related information. The social media analysis unit analyzes the user's social media activity (e.g., the content of posts and the number of likes) and provides related information. For example, related information can be provided based on life events shared by the user on social media. Related information can also be provided by taking into account the activities of the user's friends on social media. This makes it possible to provide personalized information that takes the user's social media activity into consideration, thereby improving user convenience.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The input unit inputs information about the user's life events. For example, the user can input information such as the age at which they can start receiving pension benefits or the maturity date of their insurance. The input unit receives the information entered by the user in digital form and sends it to the system. It also supports multiple input methods, such as voice input and text input. Step 2: The recording unit records the information entered by the input unit. For example, the recording unit stores information entered by the user, such as the pension start age and insurance maturity date, in a database. The recording unit also has the function of periodically backing up the recorded information. Step 3: The notification department notifies the insured at the appropriate time based on the information recorded by the recording department. For example, the notification department can notify the insured about the necessary procedures and document preparations one year before the start of pension benefits. It can also notify the insured about post-expiration procedures and new insurance proposals when the insurance expiration date approaches. Step 4: The provision unit provides additional information based on the information notified by the notification unit. For example, the provision unit may provide information on tax matters associated with the start of pension benefits. It may also provide advice on asset management after the insurance expires.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit for inputting information about a user's life events; a recording unit that records the information input by the input unit; a notification unit that issues a notification at a preset timing based on the information recorded by the recording unit; a providing unit that provides additional information based on the information notified by the notifying unit. A system characterized by:

2. The input unit Enter the user's pension age and insurance expiration date 2. The system of claim 1.

3. The notification unit You will be notified of the necessary procedures and paperwork to be prepared one year before you start receiving your pension.

2. The system of claim 1.

4. The notification unit When the expiration date of the insurance approaches, we will notify you about post-expiration procedures and propose new insurance products.

2. The system of claim 1.

5. The providing unit Providing information on tax matters related to starting pension benefits 2. The system of claim 1.

6. The providing unit Advice on specific asset management methods after the insurance expires 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of input based on the estimated user emotions.

2. The system of claim 1.

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

Images