System

Abstract

An object of a system according to an embodiment is to enable an elderly person to receive necessary support in various aspects of daily life in an integrated manner.SOLUTION: A system according to an embodiment includes a support selection unit, a proposal unit, and an arrangement unit. The support selection part receives support selection from a user. The proposal unit proposes an appropriate service based on the support selection received by the support selection unit. The arrangement unit arranges the service proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not allow elderly people to receive the support they need in various aspects of their daily lives in a centralized manner, and there is room for improvement.

[0005] The system according to the embodiment aims to enable elderly people to receive the necessary support in various aspects of daily life in a centralized manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a support selection unit, a proposal unit, and an arrangement unit. The support selection unit accepts a support selection from a user. The proposal unit proposes an appropriate service based on the support selection accepted by the support selection unit. The arrangement unit arranges for the service proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to receive the support they need in various aspects of their daily lives in a centralized manner. [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) An integrated portal service according to an embodiment of the present invention is a system that supports elderly people in various aspects of their daily lives, such as alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and providing nursing care support. In the integrated portal service, a user accesses the portal service and selects the necessary support. Based on the selected support, AI then proposes the optimal service. For example, this may include community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support. This allows elderly people to receive various types of support from a single portal. For example, a user accesses the portal service and selects the necessary support. The user selects from options such as alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and providing nursing care support. For example, if the user selects alleviating loneliness, community activities and online interactions are suggested. Based on the selected support, AI then proposes the optimal service. For example, if the user selects securing transportation, AI proposes and arranges the optimal transportation. This saves the elderly the trouble of having to find transportation themselves. Furthermore, if the user selects meal provision, AI proposes the optimal meal plan and arranges the meals. For example, it may suggest nutritionally balanced meals or meal plans tailored to specific health conditions. Furthermore, if the elderly person selects daily necessities procurement, the AI ​​will suggest the most suitable daily necessities and carry out the purchasing process. This saves the elderly person the trouble of having to search for daily necessities themselves. Finally, if nursing care support is selected, the AI ​​will suggest the most suitable nursing care service and make the arrangements. For example, home nursing care or day care services may be suggested. In this way, the integrated portal service can improve the quality of life of the elderly. In this way, the integrated portal service can improve the quality of life of the elderly. For example, the elderly person can receive various types of support through a single portal, improving their quality of life. Furthermore, by having the AI ​​suggest the most suitable service and make the arrangements, the elderly person will be saved the trouble of having to make the arrangements themselves.

[0029] An integrated portal service according to an embodiment includes a support selection unit, a proposal unit, and an arrangement unit. The support selection unit accepts a support selection from a user. The support selection includes, but is not limited to, for example, alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and nursing care support. The support selection unit enables, for example, a user to access a portal service and select the necessary support. The proposal unit proposes an optimal service based on the support selection accepted by the support selection unit. The proposal unit proposes an optimal service based on the user's selection, for example, using AI. For example, community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support are proposed. Some or all of the above-described processing by the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may propose a service using an AI model that proposes an optimal service based on the user's selection. The arrangement unit arranges the service proposed by the proposal unit. The arrangement unit arranges the proposed service, for example, using AI. For example, transportation arrangements, meal arrangements, daily necessities purchasing procedures, and nursing care services are proposed. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit may arrange the service using an AI model that arranges the proposed service. This allows the integrated portal service according to the embodiment to propose and arrange the optimal service based on the user's assistance selection.

[0030] The suggestion unit may include a community activity suggestion unit that suggests community activities to alleviate the user's sense of loneliness. The community activity suggestion unit may use, for example, AI to suggest community activities to alleviate the user's sense of loneliness. For example, the community activity suggestion unit may suggest local events or online interactions. The community activity suggestion unit may also suggest optimal community activities based on the user's interests and concerns. For example, the community activity suggestion unit may suggest that the user join a group that shares hobbies or special skills. This allows the community activity suggestion unit to suggest community activities to alleviate the user's sense of loneliness. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit may suggest community activities using an AI model that inputs the user's interests and concerns and suggests optimal community activities.

[0031] The suggestion unit may include a transportation suggestion unit that suggests transportation. The transportation suggestion unit suggests transportation for the user using, for example, AI. For example, the transportation suggestion unit suggests public transportation, taxis, rental cars, etc. The transportation suggestion unit can also suggest the optimal transportation based on the user's travel needs. For example, it suggests the transportation required by the user for going to the hospital or shopping. This allows the transportation suggestion unit to suggest transportation for the user. Some or all of the above-mentioned processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can suggest transportation using an AI model that inputs the user's travel needs and suggests the optimal transportation.

[0032] The suggestion unit may include a meal plan suggestion unit that suggests a meal plan. The meal plan suggestion unit may use, for example, AI to suggest a meal plan for the user. For example, the meal plan suggestion unit may suggest a nutritionally balanced diet or a meal plan tailored to a specific health condition. The meal plan suggestion unit may also suggest an optimal meal plan based on the user's health condition and nutritional needs. For example, the user may suggest a meal plan tailored to a health condition such as diabetes or high blood pressure. This allows the meal plan suggestion unit to suggest a meal plan for the user. Some or all of the above-described processing in the meal plan suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal plan suggestion unit may suggest a meal plan using an AI model that inputs the user's health condition and nutritional needs and suggests an optimal meal plan.

[0033] The suggestion unit may include a daily necessities suggestion unit that suggests daily necessities. The daily necessities suggestion unit suggests daily necessities for the user using, for example, AI. For example, the daily necessities suggestion unit suggests consumables, household items, etc. The daily necessities suggestion unit can also suggest optimal daily necessities based on the user's living situation and needs. For example, it suggests daily necessities depending on whether the user lives alone or with family members. In this way, the daily necessities suggestion unit can suggest daily necessities for the user. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily necessities suggestion unit can suggest daily necessities using an AI model that inputs the user's living situation and needs and suggests optimal daily necessities.

[0034] The suggestion unit may include a care service suggestion unit that suggests care services. The care service suggestion unit suggests care services for the user using, for example, AI. For example, the care service suggestion unit suggests home care, day care, etc. The care service suggestion unit can also suggest optimal care services based on the user's health condition and care needs. For example, it suggests care services that correspond to cases where the user needs assistance with daily life or medical care. In this way, the care service suggestion unit can suggest care services for the user. Some or all of the above-mentioned processing in the care service suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the care service suggestion unit can suggest care services using an AI model that inputs the user's health condition and care needs and suggests optimal care services.

[0035] The support selection unit can analyze the user's past support selection history and propose an appropriate support selection method. The support selection unit can, for example, use AI to analyze the user's past support selection history. For example, the support selection unit can prioritize and display support that the user has frequently selected in the past. The support selection unit can also propose support that is likely to be selected during a specific time period based on the user's past selection history. Furthermore, the support selection unit can also propose related new support based on the user's past selection history. This allows the support selection unit to propose an optimal support selection method based on the user's past support selection history. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can propose a support selection method using an AI model that inputs the user's past support selection history and proposes an optimal support selection method.

[0036] When selecting support, the support selection unit can perform filtering based on the user's current living situation and health condition. The support selection unit, for example, uses AI to analyze the user's current living situation and health condition. For example, when the user inputs their current health condition, the support selection unit filters appropriate support based on that input. The support selection unit can also suggest optimal support based on the user's living situation (e.g., living alone, with family). Furthermore, the support selection unit can filter appropriate support based on the user's current activity level. This allows the support selection unit to filter appropriate support based on the user's living situation and health condition. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can filter support using an AI model that inputs the user's living situation and health condition and filters optimal support.

[0037] When selecting assistance, the assistance selection unit can provide an appropriate selection means according to the user's input method. The assistance selection unit can analyze the user's input method using, for example, AI. For example, if the user selects voice input, the assistance selection unit can select assistance using voice recognition technology. Furthermore, if the user selects text input, the assistance selection unit can also provide a simple text input interface. Furthermore, if the user selects image input, the assistance selection unit can also select assistance using image recognition technology. This allows the assistance selection unit to provide the optimal selection means according to the user's input method. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can provide the selection means using an AI model that uses the user's input method as input and provides the optimal selection means.

[0038] When selecting assistance, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. The assistance selection unit can, for example, use AI to analyze the user's geographical location information. For example, the assistance selection unit can prioritize displaying nearby community activities based on the user's current location. The assistance selection unit can also suggest the nearest means of transportation based on the user's geographical location information. Furthermore, the assistance selection unit can prioritize displaying nearby meal provision services by taking into account the user's location information. In this way, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can display assistance using an AI model that inputs the user's geographical location information and prioritizes displaying highly relevant assistance.

[0039] When selecting assistance, the assistance selection unit can analyze the user's social media activity and suggest relevant assistance. The assistance selection unit can, for example, use AI to analyze the user's social media activity. For example, the assistance selection unit can suggest relevant assistance based on activities in which the user has shown interest on social media. The assistance selection unit can also analyze the content of the user's social media posts and suggest relevant assistance. Furthermore, the assistance selection unit can also suggest relevant assistance by referring to the activities of the user's friends on social media. In this way, the assistance selection unit can analyze the user's social media activity and suggest relevant assistance. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can suggest assistance using an AI model that inputs the user's social media activity and suggests relevant assistance.

[0040] The support selection unit can customize the selection method by reflecting the user's past feedback when selecting support. The support selection unit, for example, uses AI to analyze the user's past feedback. For example, the support selection unit customizes the support selection interface based on feedback provided by the user in the past. The support selection unit can also propose an optimal support selection method by reflecting the user's past feedback. Furthermore, the support selection unit can improve the display method of the support selection based on the user's feedback. This allows the support selection unit to customize the selection method by reflecting the user's past feedback. Some or all of the above-described processing in the support selection unit may be performed using AI, for example, or may be performed without using AI. For example, the support selection unit can customize the selection method by using an AI model that uses the user's past feedback as input and customizes the selection method.

[0041] The proposal unit can adjust the level of detail of the proposal based on the importance of the support when making a proposal. The proposal unit, for example, uses AI to evaluate the importance of the support. For example, the proposal unit adjusts the level of detail of the proposal based on the urgency and impact of the support. The proposal unit can also provide detailed information for support with a high level of importance. Furthermore, the proposal unit can also provide concise information for support with a low level of importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the support. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the level of detail using an AI model that inputs the importance of the support and adjusts the level of detail of the proposal.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of assistance when making a suggestion. The suggestion unit, for example, uses AI to classify the category of assistance. For example, the suggestion unit applies different suggestion algorithms depending on categories such as community activities, transportation, meal plans, daily necessities, and nursing care services. The suggestion unit can also apply an optimal suggestion algorithm based on the user's interests and concerns. For example, an algorithm that emphasizes the user's interests and concerns is applied to suggest community activities. This allows the suggestion unit to apply different suggestion algorithms depending on the category of assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the category of assistance and applies an optimal suggestion algorithm.

[0043] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, uses AI to analyze the user's past proposal results. For example, the suggestion unit makes an optimal proposal based on proposals that the user has previously accepted. The suggestion unit can also make a new proposal by avoiding proposals that the user has previously rejected. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. In this way, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's past proposal results as input and improves the accuracy of the proposal.

[0044] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of providing support. The proposal unit evaluates the timing of providing support using, for example, AI. For example, the proposal unit determines the priority of the proposal based on the urgency of the support and the scheduled date. The proposal unit can also prioritize proposals for support with high urgency. Furthermore, the proposal unit can also prioritize proposals for support whose provision time is approaching. This allows the proposal unit to determine the priority of the proposal based on the timing of providing support. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can determine the priority using an AI model that inputs the timing of providing support and determines the priority of proposals.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of the assistance when making suggestions. The suggestion unit, for example, uses AI to evaluate the relevance of the assistance. For example, the suggestion unit preferentially suggests highly relevant assistance based on the user's current needs and past selection history. The suggestion unit can also gradually adjust the order of suggestions according to the relevance of the assistance. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the assistance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that inputs the relevance of the assistance and adjusts the order of suggestions.

[0046] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. The suggestion unit can, for example, use AI to evaluate the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Also, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler terms. Furthermore, the suggestion unit can gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the use of technical terms using an AI model that uses the user's level of expertise as an input and adjusts the use of technical terms in the proposal.

[0047] When making arrangements, the arrangement unit can analyze the user's past arrangement history and select the optimal arrangement method. The arrangement unit, for example, uses AI to analyze the user's past arrangement history. For example, the arrangement unit proposes the optimal arrangement method based on arrangement methods used by the user in the past. The arrangement unit can also propose an arrangement method that avoids congestion based on the user's past arrangement history. Furthermore, the arrangement unit can analyze the user's past arrangement history and propose the most efficient arrangement method. This allows the arrangement unit to select the optimal arrangement method based on the user's past arrangement history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method using an AI model that inputs the user's past arrangement history and selects the optimal arrangement method.

[0048] The arrangement unit can customize the arrangement means based on the user's current living situation when making arrangements. The arrangement unit, for example, uses AI to analyze the user's current living situation. For example, if the user lives alone, the arrangement unit can suggest a home visit arrangement method. Furthermore, if the user has family members living with them, the arrangement unit can also suggest an arrangement method in cooperation with the family. Furthermore, the arrangement unit can customize the arrangement means according to the user's living situation. This allows the arrangement unit to customize the arrangement means according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can customize the means by inputting the user's living situation and using an AI model that customizes the arrangement means.

[0049] The dispatch unit can improve the dispatch method by reflecting user feedback during dispatch. The dispatch unit, for example, uses AI to analyze user feedback. For example, the dispatch unit customizes the dispatch interface based on feedback provided by the user in the past. The dispatch unit can also propose an optimal dispatch method by reflecting the user's past feedback. Furthermore, the dispatch unit can improve the method of displaying dispatches based on user feedback. This allows the dispatch unit to improve the dispatch method based on user feedback. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can improve the method by using an AI model that uses user feedback as input and improves the dispatch method.

[0050] When making arrangements, the arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. The arrangement unit, for example, uses AI to analyze the user's geographical location information. For example, the arrangement unit prioritizes arranging nearby services based on the user's current location. The arrangement unit can also arrange the nearest means of transportation based on the user's geographical location information. Furthermore, the arrangement unit can arrange a nearby meal delivery service by taking into account the user's location information. This allows the arrangement unit to select the optimal arrangement method by taking into account the user's geographical location information. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method by using an AI model that inputs the user's geographical location information and selects the optimal arrangement method.

[0051] The dispatch unit can analyze the user's social media activity and suggest related arrangements when making arrangements. The dispatch unit can, for example, use AI to analyze the user's social media activity. For example, the dispatch unit can suggest related arrangements based on activities in which the user has shown interest on social media. The dispatch unit can also analyze the content of the user's social media posts and suggest related arrangements. Furthermore, the dispatch unit can also suggest related arrangements based on the activities of the user's friends on social media. In this way, the dispatch unit can analyze the user's social media activity and suggest related arrangements. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can suggest arrangements using an AI model that inputs the user's social media activity and suggests related arrangements.

[0052] The dispatch unit can customize the dispatch method by reflecting the user's past feedback when dispatching. The dispatch unit, for example, uses AI to analyze the user's past feedback. For example, the dispatch unit customizes the dispatch interface based on feedback provided by the user in the past. The dispatch unit can also propose an optimal dispatch method by reflecting the user's past feedback. Furthermore, the dispatch unit can improve the method of displaying dispatches based on the user's feedback. This allows the dispatch unit to customize the dispatch method by reflecting the user's past feedback. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can customize the method by using an AI model that uses the user's past feedback as input and customizes the dispatch method.

[0053] When proposing a community activity, the community activity suggestion unit can provide optimal suggestions by referring to the user's past participation history. The community activity suggestion unit can, for example, use AI to analyze the user's past participation history. For example, the community activity suggestion unit makes optimal suggestions based on community activities in which the user has participated in the past. The community activity suggestion unit can also suggest related new community activities based on the user's past participation history. Furthermore, the community activity suggestion unit can analyze the user's past participation history and suggest the most suitable community activity. This allows the community activity suggestion unit to suggest optimal community activities based on the user's past participation history. Some or all of the above-described processing in the community activity suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's past participation history and suggests optimal community activities.

[0054] When proposing a community activity, the community activity suggestion unit can analyze the user's current living situation and interests and suggest optimal activities. The community activity suggestion unit can, for example, use AI to analyze the user's current living situation and interests. For example, the community activity suggestion unit can suggest optimal community activities based on the user's current living situation (e.g., living alone, living with family). The community activity suggestion unit can also suggest related community activities based on the user's interests and concerns. Furthermore, the community activity suggestion unit can also suggest appropriate community activities based on the user's current activity level. This allows the community activity suggestion unit to suggest optimal community activities based on the user's living situation and interests. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's living situation and interests and suggests optimal community activities.

[0055] The community activity suggestion unit can improve the suggestion method by reflecting user feedback when suggesting a community activity. The community activity suggestion unit can analyze the user's feedback, for example, using AI. For example, the community activity suggestion unit can customize the suggestion interface based on feedback previously provided by the user. The community activity suggestion unit can also propose an optimal suggestion method by reflecting the user's past feedback. Furthermore, the community activity suggestion unit can improve the proposal display method based on the user's feedback. In this way, the community activity suggestion unit can improve the community activity suggestion method based on the user's feedback. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0056] When suggesting a community activity, the community activity suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The community activity suggestion unit can, for example, use AI to analyze the user's geographical location information. For example, the community activity suggestion unit can preferentially suggest nearby community activities based on the user's current location. The community activity suggestion unit can also suggest the nearest community activity based on the user's geographical location information. Furthermore, the community activity suggestion unit can also suggest nearby community activities by taking into account the user's location information. This allows the community activity suggestion unit to suggest optimal community activities by taking into account the user's geographical location information. Some or all of the above-described processing in the community activity suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal community activities.

[0057] When suggesting a community activity, the community activity suggestion unit can analyze the user's social media activities and suggest related activities. The community activity suggestion unit can, for example, use AI to analyze the user's social media activities. For example, the community activity suggestion unit can suggest related community activities based on activities in which the user has shown interest on social media. The community activity suggestion unit can also analyze the content of the user's social media posts and suggest related community activities. Furthermore, the community activity suggestion unit can suggest related community activities based on the activities of the user's friends on social media. In this way, the community activity suggestion unit can analyze the user's social media activities and suggest related community activities. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that uses the user's social media activities as input and suggests related community activities.

[0058] When suggesting a transportation means, the transportation suggestion unit can provide the optimal suggestion by referring to the user's past usage history. The transportation suggestion unit can, for example, use AI to analyze the user's past usage history. For example, the transportation suggestion unit makes the optimal suggestion based on the transportation means used by the user in the past. The transportation suggestion unit can also suggest a new related transportation means based on the user's past usage history. Furthermore, the transportation suggestion unit can analyze the user's past usage history and suggest the most suitable transportation means. In this way, the transportation suggestion unit can suggest the optimal transportation means based on the user's past usage history. Some or all of the above-mentioned processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's past usage history and suggests the optimal transportation means.

[0059] When proposing a transportation means, the transportation means suggestion unit can analyze the user's current living situation and travel needs and suggest the optimal means. The transportation means suggestion unit can, for example, use AI to analyze the user's current living situation and travel needs. For example, the transportation means suggestion unit can suggest the optimal transportation means based on the user's current living situation (e.g., living alone, living with family). The transportation means suggestion unit can also suggest related transportation means based on the user's travel needs. Furthermore, the transportation means suggestion unit can also suggest appropriate transportation means based on the user's current activity level. This allows the transportation means suggestion unit to suggest the optimal transportation means based on the user's living situation and travel needs. Some or all of the above-mentioned processing in the transportation means suggestion unit can be performed, for example, using AI or without AI. For example, the transportation means suggestion unit can make suggestions using an AI model that inputs the user's living situation and travel needs and suggests the optimal transportation means.

[0060] The transportation suggestion unit can improve the suggestion method by reflecting user feedback when suggesting transportation means. The transportation suggestion unit can, for example, use AI to analyze user feedback. For example, the transportation suggestion unit can customize the suggestion interface based on feedback previously provided by the user. The transportation suggestion unit can also propose an optimal suggestion method by reflecting the user's past feedback. Furthermore, the transportation suggestion unit can improve the display method of suggestions based on user feedback. In this way, the transportation suggestion unit can improve the transportation suggestion method based on user feedback. Some or all of the above-mentioned processing in the transportation suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the transportation suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0061] When suggesting transportation means, the transportation suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The transportation suggestion unit can, for example, use AI to analyze the user's geographical location information. For example, the transportation suggestion unit can preferentially suggest nearby transportation means based on the user's current location. The transportation suggestion unit can also suggest the nearest transportation means based on the user's geographical location information. Furthermore, the transportation suggestion unit can also suggest nearby transportation means by taking into account the user's location information. In this way, the transportation suggestion unit can suggest optimal transportation means by taking into account the user's geographical location information. Some or all of the above-described processing in the transportation suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal transportation means.

[0062] When suggesting transportation means, the transportation suggestion unit can analyze the user's social media activity and suggest relevant transportation means. The transportation suggestion unit can, for example, use AI to analyze the user's social media activity. For example, the transportation suggestion unit can suggest relevant transportation means based on transportation means in which the user has shown interest on social media. The transportation suggestion unit can also analyze the content of the user's social media posts and suggest relevant transportation means. Furthermore, the transportation suggestion unit can suggest relevant transportation means based on the activity of the user's friends on social media. In this way, the transportation suggestion unit can analyze the user's social media activity and suggest relevant transportation means. Some or all of the above-described processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's social media activity and suggests relevant transportation means.

[0063] When proposing a meal plan, the meal plan proposal unit can provide an optimal proposal by referring to the user's past meal history. The meal plan proposal unit can, for example, use AI to analyze the user's past meal history. For example, the meal plan proposal unit makes an optimal proposal based on meal plans previously selected by the user. The meal plan proposal unit can also propose a new related meal plan based on the user's past meal history. Furthermore, the meal plan proposal unit can analyze the user's past meal history and propose the most suitable meal plan. This allows the meal plan proposal unit to propose an optimal meal plan based on the user's past meal history. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's past meal history and proposes an optimal meal plan.

[0064] When proposing a meal plan, the meal plan proposal unit can analyze the user's current health condition and nutritional needs and propose an optimal plan. The meal plan proposal unit can, for example, use AI to analyze the user's current health condition and nutritional needs. For example, the meal plan proposal unit can propose an optimal meal plan based on the user's current health condition (e.g., diabetes, high blood pressure). The meal plan proposal unit can also propose a related meal plan based on the user's nutritional needs. Furthermore, the meal plan proposal unit can also propose an appropriate meal plan based on the user's current activity level. This allows the meal plan proposal unit to propose an optimal meal plan based on the user's health condition and nutritional needs. Some or all of the above-mentioned processing in the meal plan proposal unit may be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's health condition and nutritional needs and proposes an optimal meal plan.

[0065] The meal plan proposal unit can improve the proposal method by reflecting user feedback when proposing a meal plan. The meal plan proposal unit can, for example, use AI to analyze the user's feedback. For example, the meal plan proposal unit can customize the proposal interface based on feedback previously provided by the user. The meal plan proposal unit can also propose an optimal proposal method by reflecting the user's past feedback. Furthermore, the meal plan proposal unit can improve the proposal display method based on the user's feedback. This allows the meal plan proposal unit to improve the meal plan proposal method based on the user's feedback. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can improve the proposal method by using an AI model that uses user feedback as input and improves the proposal method.

[0066] When proposing a meal plan, the meal plan proposal unit can provide an optimal proposal taking into account the user's geographical location information. The meal plan proposal unit can, for example, use AI to analyze the user's geographical location information. For example, the meal plan proposal unit can prioritize proposing nearby meal provision services based on the user's current location. The meal plan proposal unit can also propose the nearest meal provision service based on the user's geographical location information. Furthermore, the meal plan proposal unit can also propose nearby meal provision services taking into account the user's location information. This allows the meal plan proposal unit to propose an optimal meal plan taking into account the user's geographical location information. Some or all of the above-described processing in the meal plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's geographical location information and proposes an optimal meal plan.

[0067] The meal plan proposal unit can analyze the user's social media activity and propose related plans when proposing a meal plan. The meal plan proposal unit can, for example, use AI to analyze the user's social media activity. For example, the meal plan proposal unit can propose related plans based on meal plans in which the user has shown interest on social media. The meal plan proposal unit can also analyze the user's social media posts and propose related meal plans. Furthermore, the meal plan proposal unit can also propose related meal plans based on the activity of the user's friends on social media. In this way, the meal plan proposal unit can analyze the user's social media activity and propose related meal plans. Some or all of the above-described processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make proposals using an AI model that inputs the user's social media activity and proposes related meal plans.

[0068] When suggesting daily necessities, the daily necessities suggestion unit can provide optimal suggestions by referring to the user's past purchase history. The daily necessities suggestion unit, for example, uses AI to analyze the user's past purchase history. For example, the daily necessities suggestion unit makes optimal suggestions based on daily necessities purchased by the user in the past. The daily necessities suggestion unit can also suggest new related daily necessities based on the user's past purchase history. Furthermore, the daily necessities suggestion unit can analyze the user's past purchase history and suggest the most suitable daily necessities. In this way, the daily necessities suggestion unit can suggest optimal daily necessities based on the user's past purchase history. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's past purchase history and suggests optimal daily necessities.

[0069] When suggesting daily necessities, the daily necessities suggestion unit can analyze the user's current living situation and needs and suggest optimal products. The daily necessities suggestion unit can, for example, use AI to analyze the user's current living situation and needs. For example, the daily necessities suggestion unit can suggest optimal daily necessities based on the user's current living situation (e.g., living alone, living with family). The daily necessities suggestion unit can also suggest related daily necessities based on the user's needs. Furthermore, the daily necessities suggestion unit can also suggest appropriate daily necessities based on the user's current activity level. This allows the daily necessities suggestion unit to suggest optimal daily necessities based on the user's living situation and needs. Some or all of the above-mentioned processing in the daily necessities suggestion unit can be performed, for example, using AI or without AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's living situation and needs and suggests optimal daily necessities.

[0070] The daily necessities suggestion unit can improve the suggestion method by reflecting user feedback when suggesting daily necessities. The daily necessities suggestion unit, for example, uses AI to analyze user feedback. For example, the daily necessities suggestion unit customizes the suggestion interface based on feedback provided by the user in the past. The daily necessities suggestion unit can also suggest an optimal suggestion method by reflecting the user's past feedback. Furthermore, the daily necessities suggestion unit can improve the suggestion display method based on user feedback. This allows the daily necessities suggestion unit to improve the daily necessities suggestion method based on user feedback. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the daily necessities suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0071] When suggesting daily necessities, the daily necessities suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The daily necessities suggestion unit, for example, uses AI to analyze the user's geographical location information. For example, the daily necessities suggestion unit preferentially suggests daily necessities that can be purchased at nearby stores based on the user's current location. The daily necessities suggestion unit can also suggest daily necessities that can be purchased at the nearest store based on the user's geographical location information. Furthermore, the daily necessities suggestion unit can also suggest daily necessities that can be purchased at nearby stores by taking into account the user's location information. In this way, the daily necessities suggestion unit can suggest optimal daily necessities by taking into account the user's geographical location information. Some or all of the above-described processing in the daily necessities suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal daily necessities.

[0072] When suggesting a daily item, the daily item suggestion unit can analyze the user's social media activity and suggest related products. The daily item suggestion unit can analyze the user's social media activity using, for example, AI. For example, the daily item suggestion unit can suggest related products based on daily items in which the user has shown interest on social media. The daily item suggestion unit can also analyze the content of the user's social media posts and suggest related daily items. Furthermore, the daily item suggestion unit can suggest related daily items based on the activity of the user's friends on social media. In this way, the daily item suggestion unit can analyze the user's social media activity and suggest related daily items. Some or all of the above-mentioned processing in the daily item suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily item suggestion unit can make suggestions using an AI model that uses the user's social media activity as input and suggests related daily items.

[0073] When proposing a care service, the care service proposal unit can provide an optimal proposal by referring to the user's past care history. The care service proposal unit, for example, uses AI to analyze the user's past care history. For example, the care service proposal unit makes an optimal proposal based on care services used by the user in the past. The care service proposal unit can also propose new related care services based on the user's past care history. Furthermore, the care service proposal unit can analyze the user's past care history and propose the most suitable care service. This allows the care service proposal unit to propose the optimal care service based on the user's past care history. Some or all of the above-mentioned processing in the care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the care service proposal unit can make a proposal using an AI model that inputs the user's past care history and proposes the optimal care service.

[0074] When proposing a nursing care service, the nursing care service proposal unit can analyze the user's current health condition and nursing care needs and propose the optimal service. The nursing care service proposal unit can, for example, use AI to analyze the user's current health condition and nursing care needs. For example, the nursing care service proposal unit can propose the optimal nursing care service based on the user's current health condition (e.g., diabetes, high blood pressure). The nursing care service proposal unit can also propose related nursing care services based on the user's nursing care needs. Furthermore, the nursing care service proposal unit can also propose appropriate nursing care services based on the user's current activity level. This allows the nursing care service proposal unit to propose the optimal nursing care service based on the user's health condition and nursing care needs. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the nursing care service proposal unit can make a proposal using an AI model that inputs the user's health condition and nursing care needs and proposes the optimal nursing care service.

[0075] The nursing care service proposal unit can improve the proposal method by reflecting user feedback when proposing nursing care services. The nursing care service proposal unit, for example, uses AI to analyze the user's feedback. For example, the nursing care service proposal unit customizes the proposal interface based on feedback provided by the user in the past. The nursing care service proposal unit can also propose an optimal proposal method by reflecting the user's past feedback. Furthermore, the nursing care service proposal unit can improve the proposal display method based on the user's feedback. This allows the nursing care service proposal unit to improve the nursing care service proposal method based on the user's feedback. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the nursing care service proposal unit can improve the proposal method by using an AI model that uses user feedback as input and improves the proposal method.

[0076] When proposing a nursing care service, the nursing care service proposal unit can provide an optimal proposal taking into account the user's geographical location information. The nursing care service proposal unit, for example, uses AI to analyze the user's geographical location information. For example, the nursing care service proposal unit preferentially proposes nearby nursing care services based on the user's current location. The nursing care service proposal unit can also propose the nearest nursing care service based on the user's geographical location information. Furthermore, the nursing care service proposal unit can also propose nearby nursing care services taking into account the user's location information. This allows the nursing care service proposal unit to propose the optimal nursing care service taking into account the user's geographical location information. Some or all of the above-described processing in the nursing care service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the nursing care service proposal unit can make a proposal using an AI model that inputs the user's geographical location information and proposes the optimal nursing care service.

[0077] The nursing care service proposal unit can analyze the user's social media activity and propose related services when proposing nursing care services. The nursing care service proposal unit can analyze the user's social media activity using, for example, AI. For example, the nursing care service proposal unit can propose related services based on nursing care services in which the user has shown interest on social media. The nursing care service proposal unit can also analyze the content of the user's social media posts and propose related nursing care services. Furthermore, the nursing care service proposal unit can propose related nursing care services based on the activities of the user's friends on social media. In this way, the nursing care service proposal unit can analyze the user's social media activity and propose related nursing care services. Some or all of the above-described processing in the nursing care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the nursing care service proposal unit can make proposals using an AI model that uses the user's social media activity as input and proposes related nursing care services.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The support selection unit can analyze the user's past support selection history and propose an appropriate support selection method. For example, the support selection unit can prioritize and display support that the user has frequently selected in the past. The support selection unit can also propose support that is likely to be selected during a specific time period based on the user's past selection history. Furthermore, the support selection unit can also propose related new support based on the user's past selection history. This allows the support selection unit to propose an optimal support selection method based on the user's past support selection history. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can propose a support selection method using an AI model that inputs the user's past support selection history and proposes an optimal support selection method.

[0080] When making arrangements, the arrangement unit can analyze the user's past arrangement history and select the optimal arrangement method. For example, the arrangement unit can suggest the optimal arrangement method based on arrangement methods used by the user in the past. The arrangement unit can also suggest an arrangement method that avoids congestion based on the user's past arrangement history. Furthermore, the arrangement unit can analyze the user's past arrangement history and suggest the most efficient arrangement method. This allows the arrangement unit to select the optimal arrangement method based on the user's past arrangement history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method using an AI model that inputs the user's past arrangement history and selects the optimal arrangement method.

[0081] The suggestion unit can adjust the level of detail of the proposal based on the importance of the support when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the urgency or impact of the support. The suggestion unit can also provide detailed information for support with a high level of importance. Furthermore, the suggestion unit can also provide concise information for support with a low level of importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the support. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs the importance of the support and adjusts the level of detail of the proposal.

[0082] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of assistance. For example, the suggestion unit applies different suggestion algorithms depending on categories such as community activities, transportation, meal plans, daily necessities, and nursing care services. The suggestion unit can also apply an optimal suggestion algorithm based on the user's interests and concerns. For example, an algorithm that emphasizes the user's interests and concerns is applied to suggest community activities. This allows the suggestion unit to apply different suggestion algorithms depending on the category of assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the category of assistance and applies an optimal suggestion algorithm.

[0083] When selecting support, the support selection unit can perform filtering based on the user's current living situation and health condition. For example, when the user inputs their current health condition, the support selection unit filters appropriate support based on that input. The support selection unit can also propose optimal support based on the user's living situation (e.g., living alone, with family). Furthermore, the support selection unit can filter appropriate support based on the user's current activity level. This allows the support selection unit to filter appropriate support based on the user's living situation and health condition. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can filter support using an AI model that inputs the user's living situation and health condition and filters optimal support.

[0084] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results. For example, the suggestion unit makes an optimal proposal based on proposals that the user has accepted in the past. The suggestion unit can also make a new proposal by avoiding proposals that the user has rejected in the past. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. In this way, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's past proposal results as input and improves the accuracy of the proposal.

[0085] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of providing support. For example, the proposal unit determines the priority of the proposal based on the urgency of the support or the scheduled date. The proposal unit can also prioritize proposals for support with high urgency. Furthermore, the proposal unit can also prioritize proposals for support that will soon be provided. This allows the proposal unit to determine the priority of the proposals based on the timing of providing support. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can determine the priority using an AI model that inputs the timing of providing support and determines the priority of proposals.

[0086] When selecting assistance, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. For example, the assistance selection unit can prioritize displaying nearby community activities based on the user's current location. The assistance selection unit can also suggest the nearest means of transportation based on the user's geographical location information. Furthermore, the assistance selection unit can prioritize displaying nearby meal delivery services by taking into account the user's location information. This allows the assistance selection unit to prioritize displaying highly relevant assistance by taking into account the user's geographical location information. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can display assistance using an AI model that inputs the user's geographical location information and prioritizes displaying highly relevant assistance.

[0087] The arrangement unit can customize the arrangement means based on the user's current living situation when making arrangements. For example, if the user lives alone, the arrangement unit can suggest a home visit arrangement method. Furthermore, if the user has family members living with them, the arrangement unit can also suggest an arrangement method in cooperation with the family. Furthermore, the arrangement unit can customize the arrangement means according to the user's living situation. This allows the arrangement unit to customize the arrangement means according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can customize the means by inputting the user's living situation and using an AI model that customizes the arrangement means.

[0088] The suggestion unit can adjust the order of suggestions based on the relevance of the assistance when making suggestions. For example, the suggestion unit preferentially suggests highly relevant assistance based on the user's current needs and past selection history. The suggestion unit can also adjust the order of suggestions in stages according to the relevance of the assistance. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that inputs the relevance of the assistance and adjusts the order of suggestions.

[0089] The processing flow of the first embodiment will be briefly explained below.

[0090] Step 1: The support selection unit accepts support selection from the user. Support selection includes, for example, alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and nursing care support. The user can access the portal service and select the support they need. Step 2: The suggestion unit proposes the optimal service based on the support selection accepted by the support selection unit. For example, the suggestion unit uses AI to propose the optimal service based on the user's selection. Specifically, the proposals include community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support. Step 3: The arrangement unit arranges the services proposed by the proposal unit. The arrangement unit arranges the proposed services using, for example, AI. Specifically, it arranges transportation, meals, purchasing procedures for daily necessities, nursing care services, etc.

[0091] (Example 2) An integrated portal service according to an embodiment of the present invention is a system that supports elderly people in various aspects of their daily lives, such as alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and providing nursing care support. In the integrated portal service, a user accesses the portal service and selects the necessary support. Based on the selected support, AI then proposes the optimal service. For example, this may include community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support. This allows elderly people to receive various types of support from a single portal. For example, a user accesses the portal service and selects the necessary support. The user selects from options such as alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and providing nursing care support. For example, if the user selects alleviating loneliness, community activities and online interactions are suggested. Based on the selected support, AI then proposes the optimal service. For example, if the user selects securing transportation, AI proposes and arranges the optimal transportation. This saves the elderly the trouble of having to find transportation themselves. Furthermore, if the user selects meal provision, AI proposes the optimal meal plan and arranges the meals. For example, it may suggest nutritionally balanced meals or meal plans tailored to specific health conditions. Furthermore, if the elderly person selects daily necessities procurement, the AI ​​will suggest the most suitable daily necessities and carry out the purchasing process. This saves the elderly person the trouble of having to search for daily necessities themselves. Finally, if nursing care support is selected, the AI ​​will suggest the most suitable nursing care service and make the arrangements. For example, home nursing care or day care services may be suggested. In this way, the integrated portal service can improve the quality of life of the elderly. In this way, the integrated portal service can improve the quality of life of the elderly. For example, the elderly person can receive various types of support through a single portal, improving their quality of life. Furthermore, by having the AI ​​suggest the most suitable service and make the arrangements, the elderly person will be saved the trouble of having to make the arrangements themselves.

[0092] An integrated portal service according to an embodiment includes a support selection unit, a proposal unit, and an arrangement unit. The support selection unit accepts a support selection from a user. The support selection includes, but is not limited to, for example, alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and nursing care support. The support selection unit enables, for example, a user to access a portal service and select the necessary support. The proposal unit proposes an optimal service based on the support selection accepted by the support selection unit. The proposal unit proposes an optimal service based on the user's selection, for example, using AI. For example, community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support are proposed. Some or all of the above-described processing by the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit may propose a service using an AI model that proposes an optimal service based on the user's selection. The arrangement unit arranges the service proposed by the proposal unit. The arrangement unit arranges the proposed service, for example, using AI. For example, transportation arrangements, meal arrangements, daily necessities purchasing procedures, and nursing care services are proposed. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit may arrange the service using an AI model that arranges the proposed service. This allows the integrated portal service according to the embodiment to propose and arrange the optimal service based on the user's assistance selection.

[0093] The suggestion unit may include a community activity suggestion unit that suggests community activities to alleviate the user's sense of loneliness. The community activity suggestion unit may use, for example, AI to suggest community activities to alleviate the user's sense of loneliness. For example, the community activity suggestion unit may suggest local events or online interactions. The community activity suggestion unit may also suggest optimal community activities based on the user's interests and concerns. For example, the community activity suggestion unit may suggest that the user join a group that shares hobbies or special skills. This allows the community activity suggestion unit to suggest community activities to alleviate the user's sense of loneliness. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit may suggest community activities using an AI model that inputs the user's interests and concerns and suggests optimal community activities.

[0094] The suggestion unit may include a transportation suggestion unit that suggests transportation. The transportation suggestion unit suggests transportation for the user using, for example, AI. For example, the transportation suggestion unit suggests public transportation, taxis, rental cars, etc. The transportation suggestion unit can also suggest the optimal transportation based on the user's travel needs. For example, it suggests the transportation required by the user for going to the hospital or shopping. This allows the transportation suggestion unit to suggest transportation for the user. Some or all of the above-mentioned processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can suggest transportation using an AI model that inputs the user's travel needs and suggests the optimal transportation.

[0095] The suggestion unit may include a meal plan suggestion unit that suggests a meal plan. The meal plan suggestion unit may use, for example, AI to suggest a meal plan for the user. For example, the meal plan suggestion unit may suggest a nutritionally balanced diet or a meal plan tailored to a specific health condition. The meal plan suggestion unit may also suggest an optimal meal plan based on the user's health condition and nutritional needs. For example, the user may suggest a meal plan tailored to a health condition such as diabetes or high blood pressure. This allows the meal plan suggestion unit to suggest a meal plan for the user. Some or all of the above-described processing in the meal plan suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal plan suggestion unit may suggest a meal plan using an AI model that inputs the user's health condition and nutritional needs and suggests an optimal meal plan.

[0096] The suggestion unit may include a daily necessities suggestion unit that suggests daily necessities. The daily necessities suggestion unit suggests daily necessities for the user using, for example, AI. For example, the daily necessities suggestion unit suggests consumables, household items, etc. The daily necessities suggestion unit can also suggest optimal daily necessities based on the user's living situation and needs. For example, it suggests daily necessities depending on whether the user lives alone or with family members. In this way, the daily necessities suggestion unit can suggest daily necessities for the user. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily necessities suggestion unit can suggest daily necessities using an AI model that inputs the user's living situation and needs and suggests optimal daily necessities.

[0097] The suggestion unit may include a care service suggestion unit that suggests care services. The care service suggestion unit suggests care services for the user using, for example, AI. For example, the care service suggestion unit suggests home care, day care, etc. The care service suggestion unit can also suggest optimal care services based on the user's health condition and care needs. For example, it suggests care services that correspond to cases where the user needs assistance with daily life or medical care. In this way, the care service suggestion unit can suggest care services for the user. Some or all of the above-mentioned processing in the care service suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the care service suggestion unit can suggest care services using an AI model that inputs the user's health condition and care needs and suggests optimal care services.

[0098] The support selection unit can estimate the user's emotion and adjust the display method of the support selection based on the estimated user's emotion. The support selection unit estimates the user's emotion using, for example, AI. For example, the support selection unit estimates the user's emotion using facial expression recognition or voice analysis. The support selection unit can also adjust the display method of the support selection based on the estimated user's emotion. For example, if the user feels lonely, an interface with warm colors and a friendly design can be provided. If the user feels stressed, a simple and visually calming interface can be provided. Furthermore, if the user is relaxed, an interface including detailed information can be provided to increase the number of options. This allows the support selection unit to adjust the display method of the support selection according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support selection unit can be performed using, for example, AI, or without AI. For example, the support selection unit can adjust the display method using an AI model that takes the user's emotional data as input and adjusts the display method of the support selection.

[0099] The support selection unit can analyze the user's past support selection history and propose an appropriate support selection method. The support selection unit can, for example, use AI to analyze the user's past support selection history. For example, the support selection unit can prioritize and display support that the user has frequently selected in the past. The support selection unit can also propose support that is likely to be selected during a specific time period based on the user's past selection history. Furthermore, the support selection unit can also propose related new support based on the user's past selection history. This allows the support selection unit to propose an optimal support selection method based on the user's past support selection history. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can propose a support selection method using an AI model that inputs the user's past support selection history and proposes an optimal support selection method.

[0100] When selecting support, the support selection unit can perform filtering based on the user's current living situation and health condition. The support selection unit, for example, uses AI to analyze the user's current living situation and health condition. For example, when the user inputs their current health condition, the support selection unit filters appropriate support based on that input. The support selection unit can also suggest optimal support based on the user's living situation (e.g., living alone, with family). Furthermore, the support selection unit can filter appropriate support based on the user's current activity level. This allows the support selection unit to filter appropriate support based on the user's living situation and health condition. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can filter support using an AI model that inputs the user's living situation and health condition and filters optimal support.

[0101] When selecting assistance, the assistance selection unit can provide an appropriate selection means according to the user's input method. The assistance selection unit can analyze the user's input method using, for example, AI. For example, if the user selects voice input, the assistance selection unit can select assistance using voice recognition technology. Furthermore, if the user selects text input, the assistance selection unit can also provide a simple text input interface. Furthermore, if the user selects image input, the assistance selection unit can also select assistance using image recognition technology. This allows the assistance selection unit to provide the optimal selection means according to the user's input method. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can provide the selection means using an AI model that uses the user's input method as input and provides the optimal selection means.

[0102] The support selection unit can estimate the user's emotions and determine the priority of support selection based on the estimated user's emotions. The support selection unit, for example, uses AI to estimate the user's emotions. For example, the support selection unit can estimate the user's emotions using facial expression recognition or voice analysis. The support selection unit can also determine the priority of support selection based on the estimated user's emotions. For example, if the user is feeling lonely, community activities can be displayed preferentially. Furthermore, if the user is feeling stressed, support that helps the user relax can be displayed preferentially. Furthermore, if the user is relaxed, practical support such as procuring daily necessities or providing meals can be displayed preferentially. This allows the support selection unit to determine the priority of support selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the support selection unit may be performed using, for example, AI, or without AI. For example, the support selection unit can determine the priority using an AI model that receives user emotion data as input and determines the priority of support selection.

[0103] When selecting assistance, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. The assistance selection unit can, for example, use AI to analyze the user's geographical location information. For example, the assistance selection unit can prioritize displaying nearby community activities based on the user's current location. The assistance selection unit can also suggest the nearest means of transportation based on the user's geographical location information. Furthermore, the assistance selection unit can prioritize displaying nearby meal provision services by taking into account the user's location information. In this way, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can display assistance using an AI model that inputs the user's geographical location information and prioritizes displaying highly relevant assistance.

[0104] When selecting assistance, the assistance selection unit can analyze the user's social media activity and suggest relevant assistance. The assistance selection unit can, for example, use AI to analyze the user's social media activity. For example, the assistance selection unit can suggest relevant assistance based on activities in which the user has shown interest on social media. The assistance selection unit can also analyze the content of the user's social media posts and suggest relevant assistance. Furthermore, the assistance selection unit can also suggest relevant assistance by referring to the activities of the user's friends on social media. In this way, the assistance selection unit can analyze the user's social media activity and suggest relevant assistance. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can suggest assistance using an AI model that inputs the user's social media activity and suggests relevant assistance.

[0105] The support selection unit can customize the selection method by reflecting the user's past feedback when selecting support. The support selection unit, for example, uses AI to analyze the user's past feedback. For example, the support selection unit customizes the support selection interface based on feedback provided by the user in the past. The support selection unit can also propose an optimal support selection method by reflecting the user's past feedback. Furthermore, the support selection unit can improve the display method of the support selection based on the user's feedback. This allows the support selection unit to customize the selection method by reflecting the user's past feedback. Some or all of the above-described processing in the support selection unit may be performed using AI, for example, or may be performed without using AI. For example, the support selection unit can customize the selection method by using an AI model that uses the user's past feedback as input and customizes the selection method.

[0106] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit estimates the user's emotion using, for example, AI. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user feels lonely, the suggestion unit can make the suggestion using warm words. If the user feels stressed, the suggestion unit can make the suggestion using simple, easy-to-understand words. Furthermore, if the user is relaxed, the suggestion unit can make the suggestion including detailed information. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can adjust the expression method using an AI model that takes the user's emotional data as input and adjusts the expression method of the suggestion.

[0107] The proposal unit can adjust the level of detail of the proposal based on the importance of the support when making a proposal. The proposal unit, for example, uses AI to evaluate the importance of the support. For example, the proposal unit adjusts the level of detail of the proposal based on the urgency and impact of the support. The proposal unit can also provide detailed information for support with a high level of importance. Furthermore, the proposal unit can also provide concise information for support with a low level of importance. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the support. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can adjust the level of detail using an AI model that inputs the importance of the support and adjusts the level of detail of the proposal.

[0108] The suggestion unit can apply different suggestion algorithms depending on the category of assistance when making a suggestion. The suggestion unit, for example, uses AI to classify the category of assistance. For example, the suggestion unit applies different suggestion algorithms depending on categories such as community activities, transportation, meal plans, daily necessities, and nursing care services. The suggestion unit can also apply an optimal suggestion algorithm based on the user's interests and concerns. For example, an algorithm that emphasizes the user's interests and concerns is applied to suggest community activities. This allows the suggestion unit to apply different suggestion algorithms depending on the category of assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the category of assistance and applies an optimal suggestion algorithm.

[0109] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, uses AI to analyze the user's past proposal results. For example, the suggestion unit makes an optimal proposal based on proposals that the user has previously accepted. The suggestion unit can also make a new proposal by avoiding proposals that the user has previously rejected. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. In this way, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's past proposal results as input and improves the accuracy of the proposal.

[0110] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, AI. For example, the suggestion unit can estimate the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can adjust the length of the suggestion using an AI model that inputs user emotion data and adjusts the length of the suggestion.

[0111] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of providing support. The proposal unit evaluates the timing of providing support using, for example, AI. For example, the proposal unit determines the priority of the proposal based on the urgency of the support and the scheduled date. The proposal unit can also prioritize proposals for support with high urgency. Furthermore, the proposal unit can also prioritize proposals for support whose provision time is approaching. This allows the proposal unit to determine the priority of the proposal based on the timing of providing support. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can determine the priority using an AI model that inputs the timing of providing support and determines the priority of proposals.

[0112] The suggestion unit can adjust the order of suggestions based on the relevance of the assistance when making suggestions. The suggestion unit, for example, uses AI to evaluate the relevance of the assistance. For example, the suggestion unit preferentially suggests highly relevant assistance based on the user's current needs and past selection history. The suggestion unit can also gradually adjust the order of suggestions according to the relevance of the assistance. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the assistance. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that inputs the relevance of the assistance and adjusts the order of suggestions.

[0113] When making a proposal, the suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise. The suggestion unit can, for example, use AI to evaluate the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a proposal using technical terms. Also, if the user does not have technical expertise, the suggestion unit can make a proposal in simpler terms. Furthermore, the suggestion unit can gradually adjust the use of technical terms in the proposal according to the user's level of expertise. This allows the suggestion unit to adjust the use of technical terms in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit can be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the use of technical terms using an AI model that uses the user's level of expertise as an input and adjusts the use of technical terms in the proposal.

[0114] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user's emotions. The dispatch unit, for example, uses AI to estimate the user's emotions. For example, the dispatch unit can estimate the user's emotions using facial expression recognition or voice analysis. The dispatch unit can also adjust the dispatch method based on the estimated user's emotions. For example, if the user feels lonely, the dispatch unit can make the dispatch using friendly language. If the user feels stressed, the dispatch unit can provide a simple and easy-to-understand dispatch method. Furthermore, if the user is relaxed, the dispatch unit can provide a dispatch method that includes detailed information. This allows the dispatch unit to adjust the dispatch method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit may be performed using AI, for example, or without AI. For example, the dispatch unit can adjust the dispatch method using an AI model that inputs the user's emotion data and adjusts the dispatch method.

[0115] When making arrangements, the arrangement unit can analyze the user's past arrangement history and select the optimal arrangement method. The arrangement unit, for example, uses AI to analyze the user's past arrangement history. For example, the arrangement unit proposes the optimal arrangement method based on arrangement methods used by the user in the past. The arrangement unit can also propose an arrangement method that avoids congestion based on the user's past arrangement history. Furthermore, the arrangement unit can analyze the user's past arrangement history and propose the most efficient arrangement method. This allows the arrangement unit to select the optimal arrangement method based on the user's past arrangement history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method using an AI model that inputs the user's past arrangement history and selects the optimal arrangement method.

[0116] The arrangement unit can customize the arrangement means based on the user's current living situation when making arrangements. The arrangement unit, for example, uses AI to analyze the user's current living situation. For example, if the user lives alone, the arrangement unit can suggest a home visit arrangement method. Furthermore, if the user has family members living with them, the arrangement unit can also suggest an arrangement method in cooperation with the family. Furthermore, the arrangement unit can customize the arrangement means according to the user's living situation. This allows the arrangement unit to customize the arrangement means according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can customize the means by inputting the user's living situation and using an AI model that customizes the arrangement means.

[0117] The dispatch unit can improve the dispatch method by reflecting user feedback during dispatch. The dispatch unit, for example, uses AI to analyze user feedback. For example, the dispatch unit customizes the dispatch interface based on feedback provided by the user in the past. The dispatch unit can also propose an optimal dispatch method by reflecting the user's past feedback. Furthermore, the dispatch unit can improve the method of displaying dispatches based on user feedback. This allows the dispatch unit to improve the dispatch method based on user feedback. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can improve the method by using an AI model that uses user feedback as input and improves the dispatch method.

[0118] The dispatch unit can estimate the user's emotions and determine the priority of arrangements based on the estimated user emotions. The dispatch unit, for example, uses AI to estimate the user's emotions. For example, the dispatch unit can estimate the user's emotions using facial expression recognition or voice analysis. The dispatch unit can also determine the priority of arrangements based on the estimated user emotions. For example, if the user is feeling lonely, the dispatch unit can prioritize arrangements for community activities. If the user is feeling stressed, the dispatch unit can prioritize arrangements that will help the user relax. Furthermore, if the user is relaxed, the dispatch unit can prioritize practical arrangements such as procuring daily necessities and providing meals. This allows the dispatch unit to determine the priority of arrangements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit may be performed using, for example, AI, or without AI. For example, the dispatch unit can determine priorities using an AI model that uses user emotion data as input and determines dispatch priorities.

[0119] When making arrangements, the arrangement unit can select the optimal arrangement method by taking into account the user's geographical location information. The arrangement unit, for example, uses AI to analyze the user's geographical location information. For example, the arrangement unit prioritizes arranging nearby services based on the user's current location. The arrangement unit can also arrange the nearest means of transportation based on the user's geographical location information. Furthermore, the arrangement unit can arrange a nearby meal delivery service by taking into account the user's location information. This allows the arrangement unit to select the optimal arrangement method by taking into account the user's geographical location information. Some or all of the above-described processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method by using an AI model that inputs the user's geographical location information and selects the optimal arrangement method.

[0120] The dispatch unit can analyze the user's social media activity and suggest related arrangements when making arrangements. The dispatch unit can, for example, use AI to analyze the user's social media activity. For example, the dispatch unit can suggest related arrangements based on activities in which the user has shown interest on social media. The dispatch unit can also analyze the content of the user's social media posts and suggest related arrangements. Furthermore, the dispatch unit can also suggest related arrangements based on the activities of the user's friends on social media. In this way, the dispatch unit can analyze the user's social media activity and suggest related arrangements. Some or all of the above-mentioned processing in the dispatch unit may be performed, for example, using AI, or may be performed without using AI. For example, the dispatch unit can suggest arrangements using an AI model that inputs the user's social media activity and suggests related arrangements.

[0121] The dispatch unit can customize the dispatch method by reflecting the user's past feedback when dispatching. The dispatch unit, for example, uses AI to analyze the user's past feedback. For example, the dispatch unit customizes the dispatch interface based on feedback provided by the user in the past. The dispatch unit can also propose an optimal dispatch method by reflecting the user's past feedback. Furthermore, the dispatch unit can improve the method of displaying dispatches based on the user's feedback. This allows the dispatch unit to customize the dispatch method by reflecting the user's past feedback. Some or all of the above-described processing in the dispatch unit may be performed using AI, for example, or may be performed without using AI. For example, the dispatch unit can customize the method by using an AI model that uses the user's past feedback as input and customizes the dispatch method.

[0122] The community activity suggestion unit can estimate the user's emotions and adjust the method for suggesting community activities based on the estimated user's emotions. The community activity suggestion unit estimates the user's emotions using, for example, AI. For example, the community activity suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. The community activity suggestion unit can also adjust the method for suggesting community activities based on the estimated user's emotions. For example, if the user feels lonely, the community activity suggestion unit can suggest community activities using warm words. If the user feels stressed, the community activity suggestion unit can also suggest relaxing community activities. Furthermore, if the user is relaxed, the community activity suggestion unit can also suggest community activities that include detailed information. This allows the community activity suggestion unit to adjust the method for suggesting community activities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the community activity suggestion unit can be performed using, for example, AI, or without AI. For example, the community activity suggestion unit can adjust the suggestion method using an AI model that takes user emotion data as input and adjusts the community activity suggestion method.

[0123] When proposing a community activity, the community activity suggestion unit can provide optimal suggestions by referring to the user's past participation history. The community activity suggestion unit can, for example, use AI to analyze the user's past participation history. For example, the community activity suggestion unit makes optimal suggestions based on community activities in which the user has participated in the past. The community activity suggestion unit can also suggest related new community activities based on the user's past participation history. Furthermore, the community activity suggestion unit can analyze the user's past participation history and suggest the most suitable community activity. This allows the community activity suggestion unit to suggest optimal community activities based on the user's past participation history. Some or all of the above-described processing in the community activity suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's past participation history and suggests optimal community activities.

[0124] When proposing a community activity, the community activity suggestion unit can analyze the user's current living situation and interests and suggest optimal activities. The community activity suggestion unit can, for example, use AI to analyze the user's current living situation and interests. For example, the community activity suggestion unit can suggest optimal community activities based on the user's current living situation (e.g., living alone, living with family). The community activity suggestion unit can also suggest related community activities based on the user's interests and concerns. Furthermore, the community activity suggestion unit can also suggest appropriate community activities based on the user's current activity level. This allows the community activity suggestion unit to suggest optimal community activities based on the user's living situation and interests. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's living situation and interests and suggests optimal community activities.

[0125] The community activity suggestion unit can improve the suggestion method by reflecting user feedback when suggesting a community activity. The community activity suggestion unit can analyze the user's feedback, for example, using AI. For example, the community activity suggestion unit can customize the suggestion interface based on feedback previously provided by the user. The community activity suggestion unit can also propose an optimal suggestion method by reflecting the user's past feedback. Furthermore, the community activity suggestion unit can improve the proposal display method based on the user's feedback. In this way, the community activity suggestion unit can improve the community activity suggestion method based on the user's feedback. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0126] The community activity suggestion unit can estimate the user's emotions and prioritize community activities based on the estimated user's emotions. The community activity suggestion unit estimates the user's emotions using, for example, AI. For example, the community activity suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. The community activity suggestion unit can also prioritize community activities based on the estimated user's emotions. For example, if the user is feeling lonely, the community activity suggestion unit can prioritize community activities. If the user is feeling stressed, the community activity suggestion unit can prioritize relaxing community activities. If the user is feeling relaxed, the community activity suggestion unit can prioritize practical support such as procuring daily necessities or providing meals. This allows the community activity suggestion unit to prioritize community activities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or without AI. For example, the community activity suggestion unit can determine priorities using an AI model that uses user emotion data as input and determines priorities for community activities.

[0127] When suggesting a community activity, the community activity suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The community activity suggestion unit can, for example, use AI to analyze the user's geographical location information. For example, the community activity suggestion unit can preferentially suggest nearby community activities based on the user's current location. The community activity suggestion unit can also suggest the nearest community activity based on the user's geographical location information. Furthermore, the community activity suggestion unit can also suggest nearby community activities by taking into account the user's location information. This allows the community activity suggestion unit to suggest optimal community activities by taking into account the user's geographical location information. Some or all of the above-described processing in the community activity suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal community activities.

[0128] When suggesting a community activity, the community activity suggestion unit can analyze the user's social media activities and suggest related activities. The community activity suggestion unit can, for example, use AI to analyze the user's social media activities. For example, the community activity suggestion unit can suggest related community activities based on activities in which the user has shown interest on social media. The community activity suggestion unit can also analyze the content of the user's social media posts and suggest related community activities. Furthermore, the community activity suggestion unit can suggest related community activities based on the activities of the user's friends on social media. In this way, the community activity suggestion unit can analyze the user's social media activities and suggest related community activities. Some or all of the above-described processing in the community activity suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the community activity suggestion unit can make suggestions using an AI model that uses the user's social media activities as input and suggests related community activities.

[0129] The transportation suggestion unit can estimate the user's emotions and adjust the method of suggesting transportation methods based on the estimated user's emotions. The transportation suggestion unit can estimate the user's emotions using, for example, AI. For example, the transportation suggestion unit can estimate the user's emotions using facial expression recognition or voice analysis. The transportation suggestion unit can also adjust the method of suggesting transportation methods based on the estimated user's emotions. For example, if the user feels lonely, the transportation suggestion unit can suggest transportation methods using friendly language. If the user feels stressed, the transportation suggestion unit can suggest simple and easy-to-understand transportation methods. Furthermore, if the user is relaxed, the transportation suggestion unit can suggest transportation methods including detailed information. This allows the transportation suggestion unit to adjust the method of suggesting transportation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transportation suggestion unit can be performed using, for example, AI, or without AI. For example, the transportation suggestion unit can adjust the suggestion method using an AI model that takes user emotion data as input and adjusts the transportation suggestion method.

[0130] When suggesting a transportation means, the transportation suggestion unit can provide the optimal suggestion by referring to the user's past usage history. The transportation suggestion unit can, for example, use AI to analyze the user's past usage history. For example, the transportation suggestion unit makes the optimal suggestion based on the transportation means used by the user in the past. The transportation suggestion unit can also suggest a new related transportation means based on the user's past usage history. Furthermore, the transportation suggestion unit can analyze the user's past usage history and suggest the most suitable transportation means. In this way, the transportation suggestion unit can suggest the optimal transportation means based on the user's past usage history. Some or all of the above-mentioned processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's past usage history and suggests the optimal transportation means.

[0131] When proposing a transportation means, the transportation means suggestion unit can analyze the user's current living situation and travel needs and suggest the optimal means. The transportation means suggestion unit can, for example, use AI to analyze the user's current living situation and travel needs. For example, the transportation means suggestion unit can suggest the optimal transportation means based on the user's current living situation (e.g., living alone, living with family). The transportation means suggestion unit can also suggest related transportation means based on the user's travel needs. Furthermore, the transportation means suggestion unit can also suggest appropriate transportation means based on the user's current activity level. This allows the transportation means suggestion unit to suggest the optimal transportation means based on the user's living situation and travel needs. Some or all of the above-mentioned processing in the transportation means suggestion unit can be performed, for example, using AI or without AI. For example, the transportation means suggestion unit can make suggestions using an AI model that inputs the user's living situation and travel needs and suggests the optimal transportation means.

[0132] The transportation suggestion unit can improve the suggestion method by reflecting user feedback when suggesting transportation means. The transportation suggestion unit can, for example, use AI to analyze user feedback. For example, the transportation suggestion unit can customize the suggestion interface based on feedback previously provided by the user. The transportation suggestion unit can also propose an optimal suggestion method by reflecting the user's past feedback. Furthermore, the transportation suggestion unit can improve the display method of suggestions based on user feedback. In this way, the transportation suggestion unit can improve the transportation suggestion method based on user feedback. Some or all of the above-mentioned processing in the transportation suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the transportation suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0133] The transportation suggestion unit can estimate the user's emotions and prioritize transportation means based on the estimated user emotions. The transportation suggestion unit, for example, uses AI to estimate the user's emotions. For example, the transportation suggestion unit can estimate the user's emotions using facial expression recognition or voice analysis. The transportation suggestion unit can also prioritize transportation means based on the estimated user emotions. For example, if the user feels lonely, familiar transportation means can be preferentially suggested. Furthermore, if the user feels stressed, relaxing transportation means can be preferentially suggested. Furthermore, if the user is relaxed, practical transportation means can be preferentially suggested. In this way, the transportation suggestion unit can prioritize transportation means according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the transportation suggestion unit may be performed using, for example, AI, or without AI. For example, the transportation suggestion unit can input the user's emotional data and determine the priority of transportation methods using an AI model that determines the priority of transportation methods.

[0134] When suggesting transportation means, the transportation suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The transportation suggestion unit can, for example, use AI to analyze the user's geographical location information. For example, the transportation suggestion unit can preferentially suggest nearby transportation means based on the user's current location. The transportation suggestion unit can also suggest the nearest transportation means based on the user's geographical location information. Furthermore, the transportation suggestion unit can also suggest nearby transportation means by taking into account the user's location information. In this way, the transportation suggestion unit can suggest optimal transportation means by taking into account the user's geographical location information. Some or all of the above-described processing in the transportation suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal transportation means.

[0135] When suggesting transportation means, the transportation suggestion unit can analyze the user's social media activity and suggest relevant transportation means. The transportation suggestion unit can, for example, use AI to analyze the user's social media activity. For example, the transportation suggestion unit can suggest relevant transportation means based on transportation means in which the user has shown interest on social media. The transportation suggestion unit can also analyze the content of the user's social media posts and suggest relevant transportation means. Furthermore, the transportation suggestion unit can suggest relevant transportation means based on the activity of the user's friends on social media. In this way, the transportation suggestion unit can analyze the user's social media activity and suggest relevant transportation means. Some or all of the above-described processing in the transportation suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the transportation suggestion unit can make suggestions using an AI model that inputs the user's social media activity and suggests relevant transportation means.

[0136] The meal plan proposal unit can estimate the user's emotions and adjust the meal plan proposal method based on the estimated user's emotions. The meal plan proposal unit, for example, uses AI to estimate the user's emotions. For example, the meal plan proposal unit can estimate the user's emotions using facial expression recognition or voice analysis. The meal plan proposal unit can also adjust the meal plan proposal method based on the estimated user's emotions. For example, if the user feels lonely, the meal plan proposal unit can propose a meal plan using warm words. If the user feels stressed, the meal plan proposal unit can also propose a simple and easy-to-understand meal plan. Furthermore, if the user is relaxed, the meal plan proposal unit can also propose a meal plan including detailed information. This allows the meal plan proposal unit to adjust the meal plan proposal method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan suggestion unit can adjust the suggestion method using an AI model that takes user emotion data as input and adjusts the meal plan suggestion method.

[0137] When proposing a meal plan, the meal plan proposal unit can provide an optimal proposal by referring to the user's past meal history. The meal plan proposal unit can, for example, use AI to analyze the user's past meal history. For example, the meal plan proposal unit makes an optimal proposal based on meal plans previously selected by the user. The meal plan proposal unit can also propose a new related meal plan based on the user's past meal history. Furthermore, the meal plan proposal unit can analyze the user's past meal history and propose the most suitable meal plan. This allows the meal plan proposal unit to propose an optimal meal plan based on the user's past meal history. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's past meal history and proposes an optimal meal plan.

[0138] When proposing a meal plan, the meal plan proposal unit can analyze the user's current health condition and nutritional needs and propose an optimal plan. The meal plan proposal unit can, for example, use AI to analyze the user's current health condition and nutritional needs. For example, the meal plan proposal unit can propose an optimal meal plan based on the user's current health condition (e.g., diabetes, high blood pressure). The meal plan proposal unit can also propose a related meal plan based on the user's nutritional needs. Furthermore, the meal plan proposal unit can also propose an appropriate meal plan based on the user's current activity level. This allows the meal plan proposal unit to propose an optimal meal plan based on the user's health condition and nutritional needs. Some or all of the above-mentioned processing in the meal plan proposal unit may be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's health condition and nutritional needs and proposes an optimal meal plan.

[0139] The meal plan proposal unit can improve the proposal method by reflecting user feedback when proposing a meal plan. The meal plan proposal unit can, for example, use AI to analyze the user's feedback. For example, the meal plan proposal unit can customize the proposal interface based on feedback previously provided by the user. The meal plan proposal unit can also propose an optimal proposal method by reflecting the user's past feedback. Furthermore, the meal plan proposal unit can improve the proposal display method based on the user's feedback. This allows the meal plan proposal unit to improve the meal plan proposal method based on the user's feedback. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can improve the proposal method by using an AI model that uses user feedback as input and improves the proposal method.

[0140] The meal plan proposal unit can estimate the user's emotions and prioritize meal plans based on the estimated user's emotions. The meal plan proposal unit, for example, uses AI to estimate the user's emotions. For example, the meal plan proposal unit can estimate the user's emotions using facial expression recognition or voice analysis. The meal plan proposal unit can also prioritize meal plans based on the estimated user's emotions. For example, if the user is feeling lonely, it can prioritize warm-hearted meal plans. If the user is feeling stressed, it can prioritize relaxing meal plans. If the user is relaxed, it can prioritize practical meal plans. This allows the meal plan proposal unit to prioritize meal plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan suggestion unit can determine priorities using an AI model that uses user emotional data as input and determines priorities for meal plans.

[0141] When proposing a meal plan, the meal plan proposal unit can provide an optimal proposal taking into account the user's geographical location information. The meal plan proposal unit can, for example, use AI to analyze the user's geographical location information. For example, the meal plan proposal unit can prioritize proposing nearby meal provision services based on the user's current location. The meal plan proposal unit can also propose the nearest meal provision service based on the user's geographical location information. Furthermore, the meal plan proposal unit can also propose nearby meal provision services taking into account the user's location information. This allows the meal plan proposal unit to propose an optimal meal plan taking into account the user's geographical location information. Some or all of the above-described processing in the meal plan proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the meal plan proposal unit can make a proposal using an AI model that inputs the user's geographical location information and proposes an optimal meal plan.

[0142] The meal plan proposal unit can analyze the user's social media activity and propose related plans when proposing a meal plan. The meal plan proposal unit can, for example, use AI to analyze the user's social media activity. For example, the meal plan proposal unit can propose related plans based on meal plans in which the user has shown interest on social media. The meal plan proposal unit can also analyze the user's social media posts and propose related meal plans. Furthermore, the meal plan proposal unit can also propose related meal plans based on the activity of the user's friends on social media. In this way, the meal plan proposal unit can analyze the user's social media activity and propose related meal plans. Some or all of the above-described processing in the meal plan proposal unit can be performed, for example, using AI or without AI. For example, the meal plan proposal unit can make proposals using an AI model that inputs the user's social media activity and proposes related meal plans.

[0143] The daily item suggestion unit can estimate the user's emotions and adjust the method of suggesting daily items based on the estimated user's emotions. The daily item suggestion unit estimates the user's emotions using, for example, AI. For example, the daily item suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. The daily item suggestion unit can also adjust the method of suggesting daily items based on the estimated user's emotions. For example, if the user is feeling lonely, the daily item suggestion unit can suggest daily items using warm words. If the user is feeling stressed, the daily item suggestion unit can also suggest simple and easy-to-understand daily items. Furthermore, if the user is relaxed, the daily item suggestion unit can also suggest daily items that include detailed information. This allows the daily item suggestion unit to adjust the method of suggesting daily items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the daily item suggestion unit may be performed using, for example, AI, or without AI. For example, the daily necessities suggestion unit can adjust the suggestion method using an AI model that takes user emotion data as input and adjusts the method for suggesting daily necessities.

[0144] When suggesting daily necessities, the daily necessities suggestion unit can provide optimal suggestions by referring to the user's past purchase history. The daily necessities suggestion unit, for example, uses AI to analyze the user's past purchase history. For example, the daily necessities suggestion unit makes optimal suggestions based on daily necessities purchased by the user in the past. The daily necessities suggestion unit can also suggest new related daily necessities based on the user's past purchase history. Furthermore, the daily necessities suggestion unit can analyze the user's past purchase history and suggest the most suitable daily necessities. In this way, the daily necessities suggestion unit can suggest optimal daily necessities based on the user's past purchase history. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's past purchase history and suggests optimal daily necessities.

[0145] When suggesting daily necessities, the daily necessities suggestion unit can analyze the user's current living situation and needs and suggest optimal products. The daily necessities suggestion unit can, for example, use AI to analyze the user's current living situation and needs. For example, the daily necessities suggestion unit can suggest optimal daily necessities based on the user's current living situation (e.g., living alone, living with family). The daily necessities suggestion unit can also suggest related daily necessities based on the user's needs. Furthermore, the daily necessities suggestion unit can also suggest appropriate daily necessities based on the user's current activity level. This allows the daily necessities suggestion unit to suggest optimal daily necessities based on the user's living situation and needs. Some or all of the above-mentioned processing in the daily necessities suggestion unit can be performed, for example, using AI or without AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's living situation and needs and suggests optimal daily necessities.

[0146] The daily necessities suggestion unit can improve the suggestion method by reflecting user feedback when suggesting daily necessities. The daily necessities suggestion unit, for example, uses AI to analyze user feedback. For example, the daily necessities suggestion unit customizes the suggestion interface based on feedback provided by the user in the past. The daily necessities suggestion unit can also suggest an optimal suggestion method by reflecting the user's past feedback. Furthermore, the daily necessities suggestion unit can improve the suggestion display method based on user feedback. This allows the daily necessities suggestion unit to improve the daily necessities suggestion method based on user feedback. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the daily necessities suggestion unit can improve the suggestion method by using an AI model that uses user feedback as input and improves the suggestion method.

[0147] The daily necessities suggestion unit can estimate the user's emotions and prioritize the daily necessities based on the estimated user's emotions. The daily necessities suggestion unit estimates the user's emotions using, for example, AI. For example, the daily necessities suggestion unit estimates the user's emotions using facial expression recognition or voice analysis. The daily necessities suggestion unit can also prioritize the daily necessities based on the estimated user's emotions. For example, if the user is feeling lonely, warm daily necessities can be preferentially suggested. If the user is feeling stressed, relaxing daily necessities can be preferentially suggested. Furthermore, if the user is relaxed, practical daily necessities can be preferentially suggested. In this way, the daily necessities suggestion unit can prioritize the daily necessities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the daily necessities suggestion unit may be performed using, for example, AI, or without AI. For example, the daily necessities suggestion unit can determine priorities using an AI model that uses user emotion data as input and determines priorities for daily necessities.

[0148] When suggesting daily necessities, the daily necessities suggestion unit can provide optimal suggestions by taking into account the user's geographical location information. The daily necessities suggestion unit, for example, uses AI to analyze the user's geographical location information. For example, the daily necessities suggestion unit preferentially suggests daily necessities that can be purchased at nearby stores based on the user's current location. The daily necessities suggestion unit can also suggest daily necessities that can be purchased at the nearest store based on the user's geographical location information. Furthermore, the daily necessities suggestion unit can also suggest daily necessities that can be purchased at nearby stores by taking into account the user's location information. In this way, the daily necessities suggestion unit can suggest optimal daily necessities by taking into account the user's geographical location information. Some or all of the above-described processing in the daily necessities suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the daily necessities suggestion unit can make suggestions using an AI model that inputs the user's geographical location information and suggests optimal daily necessities.

[0149] When suggesting a daily item, the daily item suggestion unit can analyze the user's social media activity and suggest related products. The daily item suggestion unit can analyze the user's social media activity using, for example, AI. For example, the daily item suggestion unit can suggest related products based on daily items in which the user has shown interest on social media. The daily item suggestion unit can also analyze the content of the user's social media posts and suggest related daily items. Furthermore, the daily item suggestion unit can suggest related daily items based on the activity of the user's friends on social media. In this way, the daily item suggestion unit can analyze the user's social media activity and suggest related daily items. Some or all of the above-mentioned processing in the daily item suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the daily item suggestion unit can make suggestions using an AI model that uses the user's social media activity as input and suggests related daily items.

[0150] The nursing care service proposal unit can estimate the user's emotions and adjust the method of proposing nursing care services based on the estimated user's emotions. The nursing care service proposal unit estimates the user's emotions using, for example, AI. For example, the nursing care service proposal unit estimates the user's emotions using facial expression recognition or voice analysis. The nursing care service proposal unit can also adjust the method of proposing nursing care services based on the estimated user's emotions. For example, if the user feels lonely, nursing care services can be proposed using warm words. If the user feels stressed, simple and easy-to-understand nursing care services can be proposed. Furthermore, if the user is relaxed, nursing care services including detailed information can be proposed. This allows the nursing care service proposal unit to adjust the method of proposing nursing care services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using, for example, AI, or without AI. For example, the care service suggestion unit can adjust the suggestion method using an AI model that takes the user's emotional data as input and adjusts the method of suggesting care services.

[0151] When proposing a care service, the care service proposal unit can provide an optimal proposal by referring to the user's past care history. The care service proposal unit, for example, uses AI to analyze the user's past care history. For example, the care service proposal unit makes an optimal proposal based on care services used by the user in the past. The care service proposal unit can also propose new related care services based on the user's past care history. Furthermore, the care service proposal unit can analyze the user's past care history and propose the most suitable care service. This allows the care service proposal unit to propose the optimal care service based on the user's past care history. Some or all of the above-mentioned processing in the care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the care service proposal unit can make a proposal using an AI model that inputs the user's past care history and proposes the optimal care service.

[0152] When proposing a nursing care service, the nursing care service proposal unit can analyze the user's current health condition and nursing care needs and propose the optimal service. The nursing care service proposal unit can, for example, use AI to analyze the user's current health condition and nursing care needs. For example, the nursing care service proposal unit can propose the optimal nursing care service based on the user's current health condition (e.g., diabetes, high blood pressure). The nursing care service proposal unit can also propose related nursing care services based on the user's nursing care needs. Furthermore, the nursing care service proposal unit can also propose appropriate nursing care services based on the user's current activity level. This allows the nursing care service proposal unit to propose the optimal nursing care service based on the user's health condition and nursing care needs. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the nursing care service proposal unit can make a proposal using an AI model that inputs the user's health condition and nursing care needs and proposes the optimal nursing care service.

[0153] The nursing care service proposal unit can improve the proposal method by reflecting user feedback when proposing nursing care services. The nursing care service proposal unit, for example, uses AI to analyze the user's feedback. For example, the nursing care service proposal unit customizes the proposal interface based on feedback provided by the user in the past. The nursing care service proposal unit can also propose an optimal proposal method by reflecting the user's past feedback. Furthermore, the nursing care service proposal unit can improve the proposal display method based on the user's feedback. This allows the nursing care service proposal unit to improve the nursing care service proposal method based on the user's feedback. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the nursing care service proposal unit can improve the proposal method by using an AI model that uses user feedback as input and improves the proposal method.

[0154] The nursing care service proposal unit can estimate the user's emotions and determine the priority of nursing care services based on the estimated user's emotions. The nursing care service proposal unit, for example, uses AI to estimate the user's emotions. For example, the nursing care service proposal unit can estimate the user's emotions using facial expression recognition or voice analysis. The nursing care service proposal unit can also determine the priority of nursing care services based on the estimated user's emotions. For example, if the user feels lonely, warm nursing care services can be preferentially proposed. Furthermore, if the user feels stressed, relaxing nursing care services can be preferentially proposed. Furthermore, if the user is relaxed, practical nursing care services can be preferentially proposed. In this way, the nursing care service proposal unit can determine the priority of nursing care services according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the nursing care service proposal unit may be performed using, for example, AI, or without AI. For example, the care service suggestion unit can input the user's emotional data and determine the priority order of care services using an AI model that determines the priority order of care services.

[0155] When proposing a nursing care service, the nursing care service proposal unit can provide an optimal proposal taking into account the user's geographical location information. The nursing care service proposal unit, for example, uses AI to analyze the user's geographical location information. For example, the nursing care service proposal unit preferentially proposes nearby nursing care services based on the user's current location. The nursing care service proposal unit can also propose the nearest nursing care service based on the user's geographical location information. Furthermore, the nursing care service proposal unit can also propose nearby nursing care services taking into account the user's location information. This allows the nursing care service proposal unit to propose the optimal nursing care service taking into account the user's geographical location information. Some or all of the above-described processing in the nursing care service proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the nursing care service proposal unit can make a proposal using an AI model that inputs the user's geographical location information and proposes the optimal nursing care service.

[0156] The nursing care service proposal unit can analyze the user's social media activity and propose related services when proposing nursing care services. The nursing care service proposal unit can analyze the user's social media activity using, for example, AI. For example, the nursing care service proposal unit can propose related services based on nursing care services in which the user has shown interest on social media. The nursing care service proposal unit can also analyze the content of the user's social media posts and propose related nursing care services. Furthermore, the nursing care service proposal unit can propose related nursing care services based on the activities of the user's friends on social media. In this way, the nursing care service proposal unit can analyze the user's social media activity and propose related nursing care services. Some or all of the above-described processing in the nursing care service proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the nursing care service proposal unit can make proposals using an AI model that uses the user's social media activity as input and proposes related nursing care services. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned support selection unit, proposal unit, and arrangement unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the support selection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal service using an AI model. The arrangement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and arranges the proposed service. This allows the integrated portal service to propose and arrange an optimal service based on the user's support selection. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned support selection unit, proposal unit, and arrangement unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the support selection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal service using an AI model. The arrangement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and arranges the proposed service. This allows the integrated portal service to propose and arrange an optimal service based on the user's support selection. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned support selection unit, proposal unit, and arrangement unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the support selection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal service using an AI model. The arrangement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and arranges for the proposed service. This allows the integrated portal service to propose and arrange an optimal service based on the user's support selection. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned assistance selection unit, proposal unit, and arrangement unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the assistance selection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal service using an AI model. The arrangement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and arranges the proposed service. This allows the integrated portal service to propose and arrange an optimal service based on the assistance selection of the user.

[0157] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0158] The support selection unit can analyze the user's past support selection history and propose an appropriate support selection method. For example, the support selection unit can prioritize and display support that the user has frequently selected in the past. The support selection unit can also propose support that is likely to be selected during a specific time period based on the user's past selection history. Furthermore, the support selection unit can also propose related new support based on the user's past selection history. This allows the support selection unit to propose an optimal support selection method based on the user's past support selection history. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can propose a support selection method using an AI model that inputs the user's past support selection history and proposes an optimal support selection method.

[0159] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user feels lonely, the suggestion unit can make the suggestion in warm language. If the user feels stressed, the suggestion unit can make the suggestion in simple, easy-to-understand language. Furthermore, if the user is relaxed, the suggestion unit can make the suggestion including detailed information. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can adjust the way the suggestion is expressed using an AI model that receives user emotion data as input and adjusts the way the suggestion is expressed.

[0160] When making arrangements, the arrangement unit can analyze the user's past arrangement history and select the optimal arrangement method. For example, the arrangement unit can suggest the optimal arrangement method based on arrangement methods used by the user in the past. The arrangement unit can also suggest an arrangement method that avoids congestion based on the user's past arrangement history. Furthermore, the arrangement unit can analyze the user's past arrangement history and suggest the most efficient arrangement method. This allows the arrangement unit to select the optimal arrangement method based on the user's past arrangement history. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can select the arrangement method using an AI model that inputs the user's past arrangement history and selects the optimal arrangement method.

[0161] The support selection unit can estimate the user's emotions and adjust the display method of the support selection based on the estimated user's emotions. For example, the support selection unit estimates the user's emotions using facial expression recognition or voice analysis. The support selection unit can also adjust the display method of the support selection based on the estimated user's emotions. For example, if the user feels lonely, the support selection unit can provide an interface with warm colors and a friendly design. If the user feels stressed, the support selection unit can provide a simple, visually calming interface. Furthermore, if the user is relaxed, the support selection unit can provide an interface including detailed information and increase the number of options. This allows the support selection unit to adjust the display method of the support selection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or without AI. For example, the support selection unit can adjust the display method using an AI model that inputs user emotion data and adjusts the display method of the support selection.

[0162] The suggestion unit can adjust the level of detail of the proposal based on the importance of the support when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the urgency or impact of the support. The suggestion unit can also provide detailed information for support with a high level of importance. Furthermore, the suggestion unit can also provide concise information for support with a low level of importance. This allows the suggestion unit to adjust the level of detail of the proposal based on the importance of the support. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the level of detail using an AI model that inputs the importance of the support and adjusts the level of detail of the proposal.

[0163] The dispatch unit can estimate the user's emotions and adjust the dispatch method based on the estimated user's emotions. For example, the dispatch unit estimates the user's emotions using facial expression recognition or voice analysis. The dispatch unit can also adjust the dispatch method based on the estimated user's emotions. For example, if the user feels lonely, the dispatch unit can make the dispatch using friendly language. If the user feels stressed, the dispatch unit can provide a simple and easy-to-understand dispatch method. Furthermore, if the user is relaxed, the dispatch unit can provide a dispatch method that includes detailed information. This allows the dispatch unit to adjust the dispatch method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dispatch unit may be performed using AI, for example, or without AI. For example, the dispatch unit can adjust the dispatch method using an AI model that inputs the user's emotion data and adjusts the dispatch method.

[0164] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of assistance. For example, the suggestion unit applies different suggestion algorithms depending on categories such as community activities, transportation, meal plans, daily necessities, and nursing care services. The suggestion unit can also apply an optimal suggestion algorithm based on the user's interests and concerns. For example, an algorithm that emphasizes the user's interests and concerns is applied to suggest community activities. This allows the suggestion unit to apply different suggestion algorithms depending on the category of assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a suggestion using an AI model that inputs the category of assistance and applies an optimal suggestion algorithm.

[0165] When selecting support, the support selection unit can perform filtering based on the user's current living situation and health condition. For example, when the user inputs their current health condition, the support selection unit filters appropriate support based on that input. The support selection unit can also propose optimal support based on the user's living situation (e.g., living alone, with family). Furthermore, the support selection unit can filter appropriate support based on the user's current activity level. This allows the support selection unit to filter appropriate support based on the user's living situation and health condition. Some or all of the above-described processing in the support selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the support selection unit can filter support using an AI model that inputs the user's living situation and health condition and filters optimal support.

[0166] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results. For example, the suggestion unit makes an optimal proposal based on proposals that the user has accepted in the past. The suggestion unit can also make a new proposal by avoiding proposals that the user has rejected in the past. Furthermore, the suggestion unit can analyze the user's past proposal results and improve the accuracy of the proposal. In this way, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that uses the user's past proposal results as input and improves the accuracy of the proposal.

[0167] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of providing support. For example, the proposal unit determines the priority of the proposal based on the urgency of the support or the scheduled date. The proposal unit can also prioritize proposals for support with high urgency. Furthermore, the proposal unit can also prioritize proposals for support that will soon be provided. This allows the proposal unit to determine the priority of the proposals based on the timing of providing support. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can determine the priority using an AI model that inputs the timing of providing support and determines the priority of proposals.

[0168] When selecting assistance, the assistance selection unit can prioritize displaying highly relevant assistance by taking into account the user's geographical location information. For example, the assistance selection unit can prioritize displaying nearby community activities based on the user's current location. The assistance selection unit can also suggest the nearest means of transportation based on the user's geographical location information. Furthermore, the assistance selection unit can prioritize displaying nearby meal delivery services by taking into account the user's location information. This allows the assistance selection unit to prioritize displaying highly relevant assistance by taking into account the user's geographical location information. Some or all of the above-described processing in the assistance selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the assistance selection unit can display assistance using an AI model that inputs the user's geographical location information and prioritizes displaying highly relevant assistance.

[0169] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion using facial expression recognition or voice analysis. The suggestion unit can also adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows the suggestion unit to adjust the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can adjust the length of the suggestion using an AI model that inputs the user's emotion data and adjusts the length of the suggestion.

[0170] The arrangement unit can customize the arrangement means based on the user's current living situation when making arrangements. For example, if the user lives alone, the arrangement unit can suggest a home visit arrangement method. Furthermore, if the user has family members living with them, the arrangement unit can also suggest an arrangement method in cooperation with the family. Furthermore, the arrangement unit can customize the arrangement means according to the user's living situation. This allows the arrangement unit to customize the arrangement means according to the user's living situation. Some or all of the above-mentioned processing in the arrangement unit may be performed using, for example, AI, or may be performed without using AI. For example, the arrangement unit can customize the means by inputting the user's living situation and using an AI model that customizes the arrangement means.

[0171] The suggestion unit can adjust the order of suggestions based on the relevance of the assistance when making suggestions. For example, the suggestion unit preferentially suggests highly relevant assistance based on the user's current needs and past selection history. The suggestion unit can also adjust the order of suggestions in stages according to the relevance of the assistance. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can adjust the order using an AI model that inputs the relevance of the assistance and adjusts the order of suggestions.

[0172] The processing flow of the second embodiment will be briefly explained below.

[0173] Step 1: The support selection unit accepts support selection from the user. Support selection includes, for example, alleviating loneliness, securing transportation, providing meals, procuring daily necessities, and nursing care support. The user can access the portal service and select the support they need. Step 2: The suggestion unit proposes the optimal service based on the support selection accepted by the support selection unit. For example, the suggestion unit uses AI to propose the optimal service based on the user's selection. Specifically, the proposals include community activities to alleviate loneliness, arranging transportation, providing meals, procuring daily necessities, and nursing care support. Step 3: The arrangement unit arranges the services proposed by the proposal unit. The arrangement unit arranges the proposed services using, for example, AI. Specifically, it arranges transportation, meals, purchasing procedures for daily necessities, nursing care services, etc.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0178] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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 AI 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.

[0192] 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.

[0193] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0194] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 AI 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.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0210] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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 AI 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.

[0225] 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.

[0226] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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."

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0244] 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.

[0245] [Explanation of symbols]

[0246] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a support selection unit that accepts a support selection from a user; a suggestion unit that suggests an appropriate service based on the support selection accepted by the support selection unit; an arrangement unit that arranges for the service proposed by the proposal unit; A system characterized by:

2. The proposal unit Establish a community activity proposal department that proposes community activities to alleviate loneliness The system of claim 1 .

3. The proposal unit Equipped with a transportation suggestion department that suggests transportation options The system of claim 1 .

4. The proposal unit Equipped with a meal plan suggestion section that proposes meal plans The system of claim 1 .

5. The proposal unit Equipped with a daily necessities suggestion department that suggests daily necessities The system of claim 1 .

6. The proposal unit Equipped with a nursing care service proposal department that proposes nursing care services The system of claim 1 .

7. The support selection unit Estimate the user's emotions and adjust the display method of the assistance selection based on the estimated user emotions. The system of claim 1 .

8. The support selection unit Analyze the user's past support selection history and propose appropriate support selection methods The system of claim 1 .

9. The support selection unit When selecting assistance, filtering is performed based on the user's current living situation and health condition. The system of claim 1 .

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