system

Abstract

The system according to this embodiment aims to make it easy for elderly people to find friends who share common hobbies. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives input from the user regarding their hobbies and interests. The analysis unit analyzes the information received from the reception unit and searches for other elderly people who share the same hobbies. The provision unit provides information about the other person based on the results found by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for the elderly to find friends with common hobbies and there is a risk of isolation.

[0005] The system according to the embodiment aims to enable the elderly to easily find friends with common hobbies.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the hobbies and interests of the user. The analysis unit analyzes the information input by the reception unit and searches for other elderly people with common hobbies. The provision unit provides the information of the counterpart based on the result searched by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment allows elderly people to easily find friends who share common hobbies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The elderly playmate matching system according to an embodiment of the present invention is a system that utilizes generative AI to enable elderly people to easily find friends with common hobbies. In this system, the user inputs their hobbies and interests, the generative AI analyzes the input information, and searches for other elderly people with similar hobbies. Furthermore, the generative AI provides information about the other person based on the search results. This system allows elderly people to easily find friends with common hobbies and can reduce feelings of loneliness. In addition, knowing information about the other person in advance allows them to make friends with peace of mind. For example, if an elderly person who likes Go uses this system, they can easily find other elderly people who like Go. Furthermore, because they can know information such as the other person's age and place of residence in advance, they can make friends with peace of mind. This system also addresses situations where schedules do not match with playmates or when there are few playmates available on weekends. The generative AI analyzes the user's schedule and suggests the most suitable playmate. For example, if the user inputs "I want to play Go on Saturday afternoon," the system will suggest other Go-loving users who are free on Saturday afternoons. Thus, this AI-powered matching system for seniors to play with is a groundbreaking system that allows seniors to easily find friends with shared hobbies, reducing feelings of loneliness and enabling them to make friends with peace of mind.

[0029] The elderly person playmate matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit inputs the user's hobbies and interests. The user's hobbies and interests include, but are not limited to, sports, music, and reading. For example, the user can input their hobbies and interests in text format into the reception unit. The reception unit can also input hobbies and interests using voice input. For example, if the user inputs "I like Go" by voice, the reception unit can receive that information. Furthermore, the reception unit can also refer to the user's past history of hobbies and interests and suggest the most suitable input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as candidates. The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for other elderly people with common hobbies. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generation AI to calculate the degree of match between the user's hobbies and interests. For example, the generation AI searches for other elderly people with common hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also analyze the user's hobbies and interests in detail using a generation AI. For example, the generation AI analyzes the user's hobbies and interests in detail and searches for other elderly people who share the same hobbies. The provision unit provides information about the other person based on the results searched by the analysis unit. The provision unit provides information such as the other person's age, place of residence, and details of their hobbies. For example, the provision unit displays the other person's age and place of residence. The provision unit can also display details of the other person's hobbies. For example, the provision unit provides information such as the type and frequency of the other person's hobbies and related activities. As a result, the elderly person playmate matching system according to the embodiment can easily find friends by searching for other elderly people who share the same hobbies based on the user's hobbies and interests and providing information about them. Some or all of the above processing in the provision unit may be performed using AI, for example, or without using AI. For example, the provision unit can provide information about the other person using an AI model that takes the results searched by the analysis unit as input and outputs the other person's information.

[0030] The reception desk inputs the user's hobbies and interests. These hobbies and interests include, but are not limited to, sports, music, and reading. For example, the reception desk can accept user input in text format. It can also accept input via voice. For instance, if a user voice-inputs "I like Go," the reception desk can receive this information. Furthermore, the reception desk can refer to the user's past history of hobbies and interests to suggest the most suitable input method. For example, it can automatically display hobbies and interests the user has frequently entered in the past as suggestions. The reception desk can utilize natural language processing technology to efficiently process the information entered by the user. For example, in the case of voice input, it uses speech recognition technology to convert the voice data into text data, and then uses natural language processing technology to analyze the hobbies and interests. Additionally, the reception desk can analyze the user's input in real time and present appropriate suggestions during the input process. For example, if a user starts typing "music," the system can display specific genres such as "classical music," "jazz," and "rock" as suggestions. This allows users to input their hobbies and interests in more detail, improving the system's accuracy. Furthermore, the reception desk can save user input and reuse it in the future. For example, if a user adds a new hobby, that information can be saved in the database and considered during future matching. This allows for flexible adaptation to changes in users' hobbies and interests.

[0031] The analysis unit uses a generative AI to analyze the information entered by the reception unit and search for other elderly people with similar hobbies. The generative AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generative AI to calculate the degree of similarity between the user's hobbies and interests. For example, the generative AI searches for other elderly people with similar hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also use the generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI analyzes the details of the user's hobbies and interests and searches for other elderly people with similar hobbies. The analysis unit uses the generative AI to analyze the text data of the user's hobbies and interests and search for other elderly people with similar hobbies. The generative AI, for example, uses natural language processing technology to analyze the text data of the user's hobbies and interests and search for other elderly people with similar hobbies. The analysis unit can also use the generative AI to calculate the degree of similarity between the user's hobbies and interests. For example, the generative AI analyzes the text data of the user's hobbies and interests and searches for other elderly people with similar hobbies. Furthermore, the analysis unit can also use generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI can analyze the details of the user's hobbies and interests and search for other elderly people who share the same hobbies. The analysis unit can use generative AI to analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. The generative AI can, for example, use natural language processing technology to analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. The analysis unit can also use generative AI to calculate the degree of match between the user's hobbies and interests. For example, the generative AI can analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. Furthermore, the analysis unit can also use generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI can analyze the details of the user's hobbies and interests and search for other elderly people who share the same hobbies.

[0032] The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider unit provides information about the other party based on the results retrieved by the analysis unit. For example, the information provider unit provides information such as the other party's age, place of residence, and details of their hobbies. For instance, the information provider unit displays the other party's age and place of residence. The information provider unit can also display details of the other party's hobbies. For example, the information provider unit provides information such as the type and frequency of the other party's hobbies and related activities.

[0033] The analysis unit can analyze a user's hobbies and interests using a generative AI and search for other elderly people who share the same hobbies. For example, the analysis unit uses a generative AI to analyze a user's hobbies and interests. For example, the generative AI searches for other elderly people who share the same hobbies based on the user's hobbies and interests. The analysis unit can also use the generative AI to calculate the degree of match between the user's hobbies and interests. For example, the generative AI analyzes the details of the user's hobbies and interests and searches for other elderly people who share the same hobbies. As a result, using a generative AI improves the accuracy of the analysis of hobbies and interests and allows for efficient searching of other elderly people who share the same hobbies. The generative AI is, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's hobbies and interests as input and outputs other elderly people who share the same hobbies.

[0034] The information provider can provide information such as the other person's age, place of residence, and details of their hobbies. For example, the information provider can display the other person's age and place of residence. For example, the information provider can help users feel more secure making friends based on the other person's age and place of residence. The information provider can also display details of the other person's hobbies. For example, the information provider can provide information such as the type and frequency of the other person's hobbies and related activities. By providing detailed information about the other person, users can feel more secure making friends. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can provide information about the other person using an AI model that takes the results searched by the analysis unit as input and outputs the other person's information.

[0035] The schedule analysis unit can analyze the user's schedule and suggest the most suitable playmate. For example, if the user inputs their schedule, the schedule analysis unit will suggest the most suitable playmate based on that. For example, if the user inputs "I want to play Go on Saturday afternoon," the schedule analysis unit will suggest other Go-loving users who are free on Saturday afternoon. The schedule analysis unit can also analyze the user's schedule and suggest the most suitable playmate. For example, the schedule analysis unit will suggest the most suitable playmate based on the user's schedule. This allows the system to handle situations where schedules do not match by suggesting the most suitable playmate based on the user's schedule. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can perform analysis using an AI model that takes the user's schedule as input and outputs the most suitable playmate.

[0036] The communication unit can provide means for the user to contact the suggested person. The communication unit can provide means such as telephone, email, or messaging apps. For example, the communication unit can enable the user to contact the suggested person by telephone. It can also enable the user to contact the suggested person by email. For example, the communication unit can enable the user to contact the suggested person by messaging app. This makes it easy for the user to contact the suggested person. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can provide means of communication using an AI model that provides means for the user to contact the suggested person.

[0037] The Privacy Protection Unit can provide functions that take user privacy into consideration. The Privacy Protection Unit protects user privacy using methods such as data encryption and access control. For example, the Privacy Protection Unit can encrypt user data to prevent third parties from accessing it. The Privacy Protection Unit can also restrict the permissions to which users can access user data. For example, the Privacy Protection Unit can ensure that only specific users can access the data. This protects user privacy and allows them to use the service with peace of mind. Some or all of the above processing in the Privacy Protection Unit may be performed using AI, for example, or not using AI. For example, the Privacy Protection Unit can perform privacy protection using an AI model that encrypts user data.

[0038] The interface unit can provide methods for using the service and the interface itself. For example, the interface unit can provide a user interface and an operation guide. For instance, the interface unit can provide an intuitive user interface to enable users to easily use the service. The interface unit can also provide an operation guide explaining how to use the service. For example, the interface unit can provide a step-by-step guide explaining the steps a user takes to use the service, thereby enabling users to easily use the service. Some or all of the above-described processes in the interface unit may be performed using AI, or not. For example, the interface unit can provide an interface using an AI model that provides the optimal interface based on the user's operation history.

[0039] The reception desk can analyze the user's past hobbies and interests and suggest the optimal input method. For example, the reception desk can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest hobbies and interests that the user will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past hobbies and interests as input and outputs the optimal input method.

[0040] The reception unit can filter the input of hobbies and interests based on the user's current lifestyle and areas of interest. For example, when the user inputs their current lifestyle, the reception unit can suggest relevant hobbies and interests based on that information. The reception unit can also analyze the user's areas of interest and prioritize the display of relevant hobbies and interests. Furthermore, the reception unit can filter and display appropriate hobbies and interests according to the user's lifestyle. This allows for the provision of more relevant information by suggesting appropriate hobbies and interests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the user's lifestyle and areas of interest as input and outputs appropriate hobbies and interests.

[0041] The reception desk can prioritize inputting highly relevant information when users input their hobbies and interests, taking into account their geographical location. For example, the reception desk can suggest hobbies and interests that can be enjoyed nearby based on the user's current location. Furthermore, the reception desk can prioritize displaying region-specific hobbies and interests based on the user's geographical location. In addition, the reception desk can suggest hobbies and interests that can be enjoyed in easily accessible locations, taking into account the user's location. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0042] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their hobbies and interests. For example, the reception desk can analyze the user's social media posts and suggest related hobbies and interests. It can also consider the user's social media friendships and suggest common hobbies and interests. Furthermore, the reception desk can suggest hobbies and interests that the user might be interested in based on their social media activity history. In this way, by analyzing social media activity, it is possible to suggest more relevant hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information using an AI model that takes the user's social media activity as input and outputs relevant information.

[0043] The analysis unit can adjust the level of detail in the analysis based on the importance of hobbies and interests. For example, the analysis unit can provide detailed analysis results for hobbies that the user is particularly interested in. It can also provide concise analysis results for hobbies that the user is not very interested in. Furthermore, if the user has multiple hobbies, the analysis unit can adjust the level of detail based on their importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of hobbies and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the user's hobbies and interests as input and adjust the level of detail using an AI model.

[0044] The analysis unit can apply different analysis algorithms depending on the category of hobbies and interests during the analysis. For example, in the case of a sports-related hobby, the analysis unit can apply an algorithm that analyzes exercise volume and calorie consumption. Furthermore, in the case of a cultural activity-related hobby, the analysis unit can apply an algorithm that analyzes related events and facility information. In addition, in the case of a crafts or arts hobby, the analysis unit can apply an algorithm that analyzes materials and methods. By applying the appropriate analysis algorithm according to the category of hobbies and interests, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category of hobbies and interests as input and applies an appropriate analysis algorithm.

[0045] The information provider can adjust the level of detail provided based on the importance of the recipient's information. For example, the provider may prioritize providing basic information such as the recipient's age and place of residence. It can also provide detailed information about the recipient's hobbies and interests. Furthermore, it can provide the recipient's past activity history and evaluations. By adjusting the level of detail based on the importance of the recipient's information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can use an AI model that takes the importance of the recipient's information as input and adjusts the level of detail provided.

[0046] The information provider can apply different information provision algorithms depending on the category of the recipient's information at the time of provision. For example, the provider can apply an algorithm to provide the recipient's basic information (age, place of residence, etc.). It can also apply an algorithm to provide detailed information about the recipient's hobbies and interests. Furthermore, it can apply an algorithm to provide the recipient's past activity history and evaluations. By applying the appropriate information provision algorithm according to the category of the recipient's information, more accurate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the recipient's information category as input and applies an appropriate information provision algorithm.

[0047] The schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history during schedule analysis. For example, the schedule analysis unit can select the optimal analysis method based on schedules that the user has frequently used in the past. The schedule analysis unit can also select an analysis method suitable for a specific time period from the user's past schedule history. Furthermore, the schedule analysis unit can analyze the user's past schedule history and select the most efficient analysis method. This makes it possible to perform more efficient analysis by selecting the optimal analysis method based on past schedule history. Some or all of the above processes in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can select an analysis method using an AI model that takes the user's past schedule history as input and outputs the optimal analysis method.

[0048] The planned analysis unit can customize the analysis methods based on the user's current living situation during the planned analysis. For example, when the user inputs their current living situation, the planned analysis unit proposes the most suitable analysis method based on that information. The planned analysis unit can also customize and provide appropriate analysis methods according to the user's living situation. Furthermore, the planned analysis unit can adjust the analysis methods considering the user's current living situation. By customizing the analysis methods based on the current living situation, it is possible to provide more appropriate analysis results. Some or all of the above-described processes in the planned analysis unit may be performed using AI, for example, or without AI. For example, the planned analysis unit can customize the methods using an AI model that takes the user's living situation as input and outputs appropriate analysis methods.

[0049] The schedule analysis unit can select the optimal analysis method by considering the user's geographical location information during schedule analysis. For example, the schedule analysis unit can prioritize analyzing nearby schedules based on the user's current location. It can also prioritize analyzing region-specific schedules based on the user's geographical location information. Furthermore, the schedule analysis unit can prioritize analyzing schedules held in easily accessible locations by considering the user's location information. This allows for the provision of more appropriate analysis results by considering geographical location information. Some or all of the above-described processes in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can select an analysis method using an AI model that takes the user's geographical location information as input and outputs the optimal analysis method.

[0050] The schedule analysis unit can analyze a user's social media activity and propose analysis methods during schedule analysis. For example, the schedule analysis unit can analyze the content of a user's social media posts and propose relevant schedules. It can also consider the user's social media friendships and propose common schedules. Furthermore, based on the user's social media activity history, the schedule analysis unit can propose schedules that the user might be interested in. In this way, by analyzing social media activity, it is possible to propose more appropriate analysis methods. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can take the user's social media activity as input and propose analysis methods using an AI model that proposes analysis methods.

[0051] The communication unit can adjust the level of detail in a communication based on the importance of the recipient's contact information. For example, if the recipient's contact information is important, the communication unit will provide detailed communication. Conversely, if the recipient's contact information is not very important, the communication unit can provide concise communication. Furthermore, the communication unit can adjust the level of detail in a communication based on the importance of the recipient's contact information. This allows for more appropriate communication by adjusting the level of detail in a communication based on the importance of the recipient's contact information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can use an AI model that takes the importance of the recipient's contact information as input and adjusts the level of detail in the communication.

[0052] The communication unit can determine the priority of communications based on when the recipient's contact information was obtained. For example, the communication unit may prioritize communications using recently acquired contact information. It can also prioritize communications using contact information acquired during a specific time period. Furthermore, it can prioritize communications using contact information acquired frequently. By determining the priority of communications based on when the recipient's contact information was obtained, more appropriate communications become possible. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit may use an AI model that takes the acquisition date of the recipient's contact information as input to determine the priority of communications.

[0053] The privacy protection unit can select the optimal protection method by referring to the user's past privacy settings history when protecting privacy. For example, the privacy protection unit can select the optimal protection method based on the privacy protection level previously set by the user. The privacy protection unit can also select a protection method suitable for a specific situation from the user's past privacy settings history. Furthermore, the privacy protection unit can analyze the user's past privacy settings history and select the most efficient protection method. This enables more appropriate privacy protection by selecting the optimal protection method based on past privacy settings history. Some or all of the above processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can select a protection method using an AI model that takes the user's past privacy settings history as input and outputs the optimal protection method.

[0054] The privacy protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. For example, the privacy protection unit can prioritize privacy protection that is performed nearby based on the user's current location. It can also prioritize region-specific privacy protection based on the user's geographical location information. Furthermore, the privacy protection unit can prioritize privacy protection that is performed in easily accessible locations, taking into account the user's location information. This makes it possible to provide more appropriate privacy protection by considering geographical location information. Some or all of the above processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can select a protection method using an AI model that takes the user's geographical location information as input and outputs the optimal protection method.

[0055] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize providing display methods that the user has frequently used in the past. The interface unit can also select a display method suitable for a specific time period based on the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the most efficient display method. This allows for a more appropriate display by selecting the optimal display method based on past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0056] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows for more appropriate display by considering device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can analyze the user's past hobbies and interests and suggest the optimal input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest hobbies and interests that the user will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past hobbies and interests as input and outputs the optimal input method.

[0059] The reception unit can filter the input of hobbies and interests based on the user's current lifestyle and areas of interest. For example, when a user inputs their current lifestyle, the reception unit can suggest relevant hobbies and interests based on that information. The reception unit can also analyze the user's areas of interest and prioritize the display of relevant hobbies and interests. Furthermore, the reception unit can filter and display appropriate hobbies and interests according to the user's lifestyle. This allows for the provision of more relevant information by suggesting appropriate hobbies and interests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the user's lifestyle and areas of interest as input and outputs appropriate hobbies and interests.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's hobbies and interests. For example, it can provide detailed analysis results for hobbies that the user is particularly interested in, and concise analysis results for hobbies that the user is not very interested in. Furthermore, if the user has multiple hobbies, the analysis unit can adjust the level of detail based on their importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of hobbies and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the user's hobbies and interests as input and adjust the level of detail using an AI model.

[0061] The information provider can adjust the level of detail provided based on the importance of the recipient's information. For example, it may prioritize providing basic information such as the recipient's age and place of residence. The information provider can also provide detailed information about the recipient's hobbies and interests. Furthermore, it can provide the recipient's past activity history and evaluations. By adjusting the level of detail based on the importance of the recipient's information, it is possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can use an AI model that takes the importance of the recipient's information as input and adjusts the level of detail provided.

[0062] The communication unit can determine the priority of communications based on when the recipient's contact information was obtained. For example, it can prioritize communications using recently acquired contact information. It can also prioritize communications using contact information acquired during a specific time period. Furthermore, it can prioritize communications using contact information acquired frequently. By determining the priority of communications based on when the recipient's contact information was obtained, more appropriate communications become possible. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can use an AI model that takes the acquisition date of the recipient's contact information as input to determine the priority of communications.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs the user's hobbies and interests. These hobbies and interests may include, but are not limited to, sports, music, and reading. For example, the reception desk can accept the user's hobbies and interests in text format. It can also accept input via voice. For instance, if the user voice-inputs "I like Go," the reception desk can receive this information. Furthermore, the reception desk can refer to the user's past history of hobbies and interests to suggest the most suitable input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for other elderly people with similar hobbies. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generation AI to calculate the degree of match between the user's hobbies and interests. For example, the generation AI searches for other elderly people with similar hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also use the generation AI to analyze the details of the user's hobbies and interests. For example, the generation AI analyzes the details of the user's hobbies and interests and searches for other elderly people with similar hobbies. Step 3: The provisioning unit provides information about the other party based on the results retrieved by the analysis unit. The provisioning unit provides information such as the other party's age, place of residence, and details of their hobbies. For example, the provisioning unit displays the other party's age and place of residence. The provisioning unit can also display details of the other party's hobbies. For example, the provisioning unit provides information such as the type and frequency of the other party's hobbies and related activities. Thus, the elderly playmate matching system according to the embodiment can easily find friends by searching for other elderly people with common hobbies based on the user's hobbies and interests and providing information about them. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can provide information about the other party using an AI model that takes the results retrieved by the analysis unit as input and outputs information about the other party.

[0065] (Example of form 2) The elderly playmate matching system according to an embodiment of the present invention is a system that utilizes generative AI to enable elderly people to easily find friends with common hobbies. In this system, the user inputs their hobbies and interests, the generative AI analyzes the input information, and searches for other elderly people with similar hobbies. Furthermore, the generative AI provides information about the other person based on the search results. This system allows elderly people to easily find friends with common hobbies and can reduce feelings of loneliness. In addition, knowing information about the other person in advance allows them to make friends with peace of mind. For example, if an elderly person who likes Go uses this system, they can easily find other elderly people who like Go. Furthermore, because they can know information such as the other person's age and place of residence in advance, they can make friends with peace of mind. This system also addresses situations where schedules do not match with playmates or when there are few playmates available on weekends. The generative AI analyzes the user's schedule and suggests the most suitable playmate. For example, if the user inputs "I want to play Go on Saturday afternoon," the system will suggest other Go-loving users who are free on Saturday afternoons. Thus, this AI-powered matching system for seniors to play with is a groundbreaking system that allows seniors to easily find friends with shared hobbies, reducing feelings of loneliness and enabling them to make friends with peace of mind.

[0066] The elderly person playmate matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit inputs the user's hobbies and interests. The user's hobbies and interests include, but are not limited to, sports, music, and reading. For example, the user can input their hobbies and interests in text format into the reception unit. The reception unit can also input hobbies and interests using voice input. For example, if the user inputs "I like Go" by voice, the reception unit can receive that information. Furthermore, the reception unit can also refer to the user's past history of hobbies and interests and suggest the most suitable input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as candidates. The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for other elderly people with common hobbies. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generation AI to calculate the degree of match between the user's hobbies and interests. For example, the generation AI searches for other elderly people with common hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also analyze the user's hobbies and interests in detail using a generation AI. For example, the generation AI analyzes the user's hobbies and interests in detail and searches for other elderly people who share the same hobbies. The provision unit provides information about the other person based on the results searched by the analysis unit. The provision unit provides information such as the other person's age, place of residence, and details of their hobbies. For example, the provision unit displays the other person's age and place of residence. The provision unit can also display details of the other person's hobbies. For example, the provision unit provides information such as the type and frequency of the other person's hobbies and related activities. As a result, the elderly person playmate matching system according to the embodiment can easily find friends by searching for other elderly people who share the same hobbies based on the user's hobbies and interests and providing information about them. Some or all of the above processing in the provision unit may be performed using AI, for example, or without using AI. For example, the provision unit can provide information about the other person using an AI model that takes the results searched by the analysis unit as input and outputs the other person's information.

[0067] The reception desk inputs the user's hobbies and interests. These hobbies and interests include, but are not limited to, sports, music, and reading. For example, the reception desk can accept user input in text format. It can also accept input via voice. For instance, if a user voice-inputs "I like Go," the reception desk can receive this information. Furthermore, the reception desk can refer to the user's past history of hobbies and interests to suggest the most suitable input method. For example, it can automatically display hobbies and interests the user has frequently entered in the past as suggestions. The reception desk can utilize natural language processing technology to efficiently process the information entered by the user. For example, in the case of voice input, it uses speech recognition technology to convert the voice data into text data, and then uses natural language processing technology to analyze the hobbies and interests. Additionally, the reception desk can analyze the user's input in real time and present appropriate suggestions during the input process. For example, if a user starts typing "music," the system can display specific genres such as "classical music," "jazz," and "rock" as suggestions. This allows users to input their hobbies and interests in more detail, improving the system's accuracy. Furthermore, the reception desk can save user input and reuse it in the future. For example, if a user adds a new hobby, that information can be saved in the database and considered during future matching. This allows for flexible adaptation to changes in users' hobbies and interests.

[0068] The analysis unit uses a generative AI to analyze the information entered by the reception unit and search for other elderly people with similar hobbies. The generative AI, for example, uses a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generative AI to calculate the degree of similarity between the user's hobbies and interests. For example, the generative AI searches for other elderly people with similar hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also use the generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI analyzes the details of the user's hobbies and interests and searches for other elderly people with similar hobbies. The analysis unit uses the generative AI to analyze the text data of the user's hobbies and interests and search for other elderly people with similar hobbies. The generative AI, for example, uses natural language processing technology to analyze the text data of the user's hobbies and interests and search for other elderly people with similar hobbies. The analysis unit can also use the generative AI to calculate the degree of similarity between the user's hobbies and interests. For example, the generative AI analyzes the text data of the user's hobbies and interests and searches for other elderly people with similar hobbies. Furthermore, the analysis unit can also use generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI can analyze the details of the user's hobbies and interests and search for other elderly people who share the same hobbies. The analysis unit can use generative AI to analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. The generative AI can, for example, use natural language processing technology to analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. The analysis unit can also use generative AI to calculate the degree of match between the user's hobbies and interests. For example, the generative AI can analyze the text data of the user's hobbies and interests and search for other elderly people who share the same hobbies. Furthermore, the analysis unit can also use generative AI to analyze the details of the user's hobbies and interests. For example, the generative AI can analyze the details of the user's hobbies and interests and search for other elderly people who share the same hobbies.

[0069] The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider provides information about the other party based on the results retrieved by the analysis unit. The information provider provides information such as the other party's age, place of residence, and details of their hobbies. For example, the information provider displays the other party's age and place of residence. The information provider can also display details of the other party's hobbies. For example, the information provider provides information such as the type and frequency of the other party's hobbies and related activities. The information provider unit provides information about the other party based on the results retrieved by the analysis unit. For example, the information provider unit provides information such as the other party's age, place of residence, and details of their hobbies. For instance, the information provider unit displays the other party's age and place of residence. The information provider unit can also display details of the other party's hobbies. For example, the information provider unit provides information such as the type and frequency of the other party's hobbies and related activities.

[0070] The analysis unit can analyze a user's hobbies and interests using a generative AI and search for other elderly people who share the same hobbies. For example, the analysis unit uses a generative AI to analyze a user's hobbies and interests. For example, the generative AI searches for other elderly people who share the same hobbies based on the user's hobbies and interests. The analysis unit can also use the generative AI to calculate the degree of match between the user's hobbies and interests. For example, the generative AI analyzes the details of the user's hobbies and interests and searches for other elderly people who share the same hobbies. As a result, using a generative AI improves the accuracy of the analysis of hobbies and interests and allows for efficient searching of other elderly people who share the same hobbies. The generative AI is, 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's hobbies and interests as input and outputs other elderly people who share the same hobbies.

[0071] The information provider can provide information such as the other person's age, place of residence, and details of their hobbies. For example, the information provider can display the other person's age and place of residence. For example, the information provider can help users feel more secure making friends based on the other person's age and place of residence. The information provider can also display details of the other person's hobbies. For example, the information provider can provide information such as the type and frequency of the other person's hobbies and related activities. By providing detailed information about the other person, users can feel more secure making friends. Some or all of the processing described above in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can provide information about the other person using an AI model that takes the results searched by the analysis unit as input and outputs the other person's information.

[0072] The schedule analysis unit can analyze the user's schedule and suggest the most suitable playmate. For example, if the user inputs their schedule, the schedule analysis unit will suggest the most suitable playmate based on that. For example, if the user inputs "I want to play Go on Saturday afternoon," the schedule analysis unit will suggest other Go-loving users who are free on Saturday afternoon. The schedule analysis unit can also analyze the user's schedule and suggest the most suitable playmate. For example, the schedule analysis unit will suggest the most suitable playmate based on the user's schedule. This allows the system to handle situations where schedules do not match by suggesting the most suitable playmate based on the user's schedule. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can perform analysis using an AI model that takes the user's schedule as input and outputs the most suitable playmate.

[0073] The communication unit can provide means for the user to contact the suggested person. The communication unit can provide means such as telephone, email, or messaging apps. For example, the communication unit can enable the user to contact the suggested person by telephone. It can also enable the user to contact the suggested person by email. For example, the communication unit can enable the user to contact the suggested person by messaging app. This makes it easy for the user to contact the suggested person. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can provide means of communication using an AI model that provides means for the user to contact the suggested person.

[0074] The Privacy Protection Unit can provide functions that take user privacy into consideration. The Privacy Protection Unit protects user privacy using methods such as data encryption and access control. For example, the Privacy Protection Unit can encrypt user data to prevent third parties from accessing it. The Privacy Protection Unit can also restrict the permissions to which users can access user data. For example, the Privacy Protection Unit can ensure that only specific users can access the data. This protects user privacy and allows them to use the service with peace of mind. Some or all of the above processing in the Privacy Protection Unit may be performed using AI, for example, or not using AI. For example, the Privacy Protection Unit can perform privacy protection using an AI model that encrypts user data.

[0075] The interface unit can provide methods for using the service and the interface itself. For example, the interface unit can provide a user interface and an operation guide. For instance, the interface unit can provide an intuitive user interface to enable users to easily use the service. The interface unit can also provide an operation guide explaining how to use the service. For example, the interface unit can provide a step-by-step guide explaining the steps a user takes to use the service, thereby enabling users to easily use the service. Some or all of the above-described processes in the interface unit may be performed using AI, or not. For example, the interface unit can provide an interface using an AI model that provides the optimal interface based on the user's operation history.

[0076] The reception desk can estimate the user's emotions and adjust the input method for hobbies and interests based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of hobbies and interests. This allows for more appropriate input by adjusting the input 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. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can take user emotion data as input and adjust the input method using an AI model that adjusts the input method based on the emotions.

[0077] The reception desk can analyze the user's past hobbies and interests and suggest the optimal input method. For example, the reception desk can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest hobbies and interests that the user will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past hobbies and interests as input and outputs the optimal input method.

[0078] The reception unit can filter the input of hobbies and interests based on the user's current lifestyle and areas of interest. For example, when the user inputs their current lifestyle, the reception unit can suggest relevant hobbies and interests based on that information. The reception unit can also analyze the user's areas of interest and prioritize the display of relevant hobbies and interests. Furthermore, the reception unit can filter and display appropriate hobbies and interests according to the user's lifestyle. This allows for the provision of more relevant information by suggesting appropriate hobbies and interests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the user's lifestyle and areas of interest as input and outputs appropriate hobbies and interests.

[0079] The reception unit can estimate the user's emotions and determine the priority of hobbies and interests to be entered based on the estimated emotions. For example, if the user is excited, the reception unit will prioritize displaying stimulating hobbies and interests. It can also prioritize displaying relaxing hobbies and interests if the user is relaxed. Furthermore, if the user is stressed, it can prioritize displaying hobbies and interests that help relieve stress. This allows for the provision of more appropriate information by prioritizing hobbies and interests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can take user emotion data as input and determine priorities using an AI model that prioritizes hobbies and interests based on emotions.

[0080] The reception desk can prioritize inputting highly relevant information when users input their hobbies and interests, taking into account their geographical location. For example, the reception desk can suggest hobbies and interests that can be enjoyed nearby based on the user's current location. Furthermore, the reception desk can prioritize displaying region-specific hobbies and interests based on the user's geographical location. In addition, the reception desk can suggest hobbies and interests that can be enjoyed in easily accessible locations, taking into account the user's location. This allows for the provision of more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input information using an AI model that takes the user's geographical location as input and outputs highly relevant information.

[0081] The reception desk can analyze the user's social media activity and input relevant information when the user inputs their hobbies and interests. For example, the reception desk can analyze the user's social media posts and suggest related hobbies and interests. It can also consider the user's social media friendships and suggest common hobbies and interests. Furthermore, the reception desk can suggest hobbies and interests that the user might be interested in based on their social media activity history. In this way, by analyzing social media activity, it is possible to suggest more relevant hobbies and interests. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information using an AI model that takes the user's social media activity as input and outputs relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and adjust the presentation using an AI model that adjusts the presentation of the analysis based on the emotions.

[0083] The analysis unit can adjust the level of detail in the analysis based on the importance of hobbies and interests. For example, the analysis unit can provide detailed analysis results for hobbies that the user is particularly interested in. It can also provide concise analysis results for hobbies that the user is not very interested in. Furthermore, if the user has multiple hobbies, the analysis unit can adjust the level of detail based on their importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of hobbies and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the user's hobbies and interests as input and adjust the level of detail using an AI model.

[0084] The analysis unit can apply different analysis algorithms depending on the category of hobbies and interests during the analysis. For example, in the case of a sports-related hobby, the analysis unit can apply an algorithm that analyzes exercise volume and calorie consumption. Furthermore, in the case of a cultural activity-related hobby, the analysis unit can apply an algorithm that analyzes related events and facility information. In addition, in the case of a crafts or arts hobby, the analysis unit can apply an algorithm that analyzes materials and methods. By applying the appropriate analysis algorithm according to the category of hobbies and interests, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category of hobbies and interests as input and applies an appropriate analysis algorithm.

[0085] The information provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is relaxed, the provider can provide detailed information. If the user is in a hurry, the provider can also provide concise information. Furthermore, if the user is excited, the provider can provide visually stimulating information. In this way, by adjusting the way the information is presented according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can take user emotion data as input and adjust the presentation using an AI model that adjusts the way the information is presented based on the emotions.

[0086] The information provider can adjust the level of detail provided based on the importance of the recipient's information. For example, the provider may prioritize providing basic information such as the recipient's age and place of residence. It can also provide detailed information about the recipient's hobbies and interests. Furthermore, it can provide the recipient's past activity history and evaluations. By adjusting the level of detail based on the importance of the recipient's information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can use an AI model that takes the importance of the recipient's information as input and adjusts the level of detail provided.

[0087] The information provider can apply different information provision algorithms depending on the category of the recipient's information at the time of provision. For example, the provider can apply an algorithm to provide the recipient's basic information (age, place of residence, etc.). It can also apply an algorithm to provide detailed information about the recipient's hobbies and interests. Furthermore, it can apply an algorithm to provide the recipient's past activity history and evaluations. By applying the appropriate information provision algorithm according to the category of the recipient's information, more accurate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can provide information using an AI model that takes the recipient's information category as input and applies an appropriate information provision algorithm.

[0088] The schedule analysis unit can estimate the user's emotions and adjust the schedule analysis method based on the estimated emotions. For example, if the user is relaxed, the schedule analysis unit can perform a detailed schedule analysis. If the user is in a hurry, the schedule analysis unit can perform a concise schedule analysis. Furthermore, if the user is excited, the schedule analysis unit can perform a visually stimulating schedule analysis. By adjusting the schedule analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can take user emotion data as input and adjust the method using an AI model that adjusts the schedule analysis method based on emotions.

[0089] The schedule analysis unit can select the optimal analysis method by referring to the user's past schedule history during schedule analysis. For example, the schedule analysis unit can select the optimal analysis method based on schedules that the user has frequently used in the past. The schedule analysis unit can also select an analysis method suitable for a specific time period from the user's past schedule history. Furthermore, the schedule analysis unit can analyze the user's past schedule history and select the most efficient analysis method. This makes it possible to perform more efficient analysis by selecting the optimal analysis method based on past schedule history. Some or all of the above processes in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can select an analysis method using an AI model that takes the user's past schedule history as input and outputs the optimal analysis method.

[0090] The planned analysis unit can customize the analysis methods based on the user's current living situation during the planned analysis. For example, when the user inputs their current living situation, the planned analysis unit proposes the most suitable analysis method based on that information. The planned analysis unit can also customize and provide appropriate analysis methods according to the user's living situation. Furthermore, the planned analysis unit can adjust the analysis methods considering the user's current living situation. By customizing the analysis methods based on the current living situation, it is possible to provide more appropriate analysis results. Some or all of the above-described processes in the planned analysis unit may be performed using AI, for example, or without AI. For example, the planned analysis unit can customize the methods using an AI model that takes the user's living situation as input and outputs appropriate analysis methods.

[0091] The schedule analysis unit can estimate the user's emotions and determine the priority of schedule analysis based on the estimated emotions. For example, if the user is in a hurry, the schedule analysis unit will prioritize analyzing important appointments. It can also perform a detailed schedule analysis if the user is relaxed. Furthermore, if the user is excited, the schedule analysis unit can perform a visually stimulating schedule analysis. This allows for more appropriate analysis results by prioritizing schedule analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the schedule analysis unit may be performed using AI, or not. For example, the schedule analysis unit can take user emotion data as input and determine priorities using an AI model that prioritizes schedule analysis based on emotions.

[0092] The schedule analysis unit can select the optimal analysis method by considering the user's geographical location information during schedule analysis. For example, the schedule analysis unit can prioritize analyzing nearby schedules based on the user's current location. It can also prioritize analyzing region-specific schedules based on the user's geographical location information. Furthermore, the schedule analysis unit can prioritize analyzing schedules held in easily accessible locations by considering the user's location information. This allows for the provision of more appropriate analysis results by considering geographical location information. Some or all of the above-described processes in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can select an analysis method using an AI model that takes the user's geographical location information as input and outputs the optimal analysis method.

[0093] The schedule analysis unit can analyze a user's social media activity and propose analysis methods during schedule analysis. For example, the schedule analysis unit can analyze the content of a user's social media posts and propose relevant schedules. It can also consider the user's social media friendships and propose common schedules. Furthermore, based on the user's social media activity history, the schedule analysis unit can propose schedules that the user might be interested in. In this way, by analyzing social media activity, it is possible to propose more appropriate analysis methods. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can take the user's social media activity as input and propose analysis methods using an AI model that proposes analysis methods.

[0094] The communication unit can estimate the user's emotions and adjust its communication method based on those emotions. For example, if the user is nervous, the communication unit will communicate in a calm voice. If the user is relaxed, it can communicate in a cheerful voice. Furthermore, if the user is in a hurry, the communication unit can communicate quickly and concisely. By adjusting the communication method according to the user's emotions, more appropriate communication becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can take user emotion data as input and adjust its method using an AI model that adjusts the communication method based on the emotion.

[0095] The communication unit can adjust the level of detail in a communication based on the importance of the recipient's contact information. For example, if the recipient's contact information is important, the communication unit will provide detailed communication. Conversely, if the recipient's contact information is not very important, the communication unit can provide concise communication. Furthermore, the communication unit can adjust the level of detail in a communication based on the importance of the recipient's contact information. This allows for more appropriate communication by adjusting the level of detail in a communication based on the importance of the recipient's contact information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can use an AI model that takes the importance of the recipient's contact information as input and adjusts the level of detail in the communication.

[0096] The communication unit can estimate the user's emotions and determine the priority of communications based on the estimated emotions. For example, if the user is in a hurry, the communication unit will prioritize important communications. If the user is relaxed, the communication unit can also send detailed communications. Furthermore, if the user is excited, the communication unit can send visually stimulating communications. This allows for more appropriate communications by prioritizing communications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI or not. For example, the communication unit can take user emotion data as input and determine priorities using an AI model that determines the priority of communications based on emotions.

[0097] The communication unit can determine the priority of communications based on when the recipient's contact information was obtained. For example, the communication unit may prioritize communications using recently acquired contact information. It can also prioritize communications using contact information acquired during a specific time period. Furthermore, it can prioritize communications using contact information acquired frequently. By determining the priority of communications based on when the recipient's contact information was obtained, more appropriate communications become possible. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit may use an AI model that takes the acquisition date of the recipient's contact information as input to determine the priority of communications.

[0098] The privacy protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated emotions. For example, if the user is stressed, the privacy protection unit can provide strict privacy protection. It can also provide flexible privacy protection if the user is relaxed. Furthermore, if the user is in a hurry, the privacy protection unit can provide rapid privacy protection. This allows for more appropriate privacy protection by adjusting the privacy protection 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. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the privacy protection unit may be performed using AI, or not. For example, the privacy protection unit can take user emotion data as input and adjust the method using an AI model that adjusts the privacy protection method based on emotions.

[0099] The privacy protection unit can select the optimal protection method by referring to the user's past privacy settings history when protecting privacy. For example, the privacy protection unit can select the optimal protection method based on the privacy protection level previously set by the user. The privacy protection unit can also select a protection method suitable for a specific situation from the user's past privacy settings history. Furthermore, the privacy protection unit can analyze the user's past privacy settings history and select the most efficient protection method. This enables more appropriate privacy protection by selecting the optimal protection method based on past privacy settings history. Some or all of the above processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can select a protection method using an AI model that takes the user's past privacy settings history as input and outputs the optimal protection method.

[0100] The privacy protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated emotions. For example, if the user is in a hurry, the privacy protection unit will prioritize important privacy protection. It can also perform detailed privacy protection if the user is relaxed. Furthermore, if the user is excited, the privacy protection unit can perform visually stimulating privacy protection. This allows for more appropriate privacy protection by prioritizing it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the privacy protection unit may be performed using AI, or not. For example, the privacy protection unit can take user emotion data as input and determine priorities using an AI model that determines privacy protection priorities based on emotions.

[0101] The privacy protection unit can select the optimal protection method when protecting privacy, taking into account the user's geographical location information. For example, the privacy protection unit can prioritize privacy protection that is performed nearby based on the user's current location. It can also prioritize region-specific privacy protection based on the user's geographical location information. Furthermore, the privacy protection unit can prioritize privacy protection that is performed in easily accessible locations, taking into account the user's location information. This makes it possible to provide more appropriate privacy protection by considering geographical location information. Some or all of the above processing in the privacy protection unit may be performed using AI, for example, or without AI. For example, the privacy protection unit can select a protection method using an AI model that takes the user's geographical location information as input and outputs the optimal protection method.

[0102] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the interface unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the interface unit can provide a simple and highly visible interface to facilitate the input process. This allows for a more appropriate display by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, or not. For example, the interface unit can take user emotion data as input and adjust the display method using an AI model that adjusts the interface display method based on the emotions.

[0103] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize providing display methods that the user has frequently used in the past. The interface unit can also select a display method suitable for a specific time period based on the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the most efficient display method. This allows for a more appropriate display by selecting the optimal display method based on past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0104] The interface unit can estimate the user's emotions and adjust the interface's operating procedures based on the estimated emotions. For example, if the user is nervous, the interface unit can provide simple and highly visible operating procedures. If the user is enjoying themselves, the interface unit can also provide detailed operating procedures. Furthermore, if the user is tired, the interface unit can provide concise and highly visible operating procedures. This allows for more appropriate operation by adjusting the operating procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using AI, or not. For example, the interface unit can take user emotion data as input and adjust the procedures using an AI model that adjusts the operating procedures based on the emotions.

[0105] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the interface unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. This allows for more appropriate display by considering device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

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

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is in a hurry, it can prioritize analyzing important hobbies and interests. If the user is relaxed, it can perform a more detailed analysis. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can take user emotion data as input and determine the priority of analysis using an AI model that determines the priority of analysis based on emotions.

[0108] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, concise information can be provided. Furthermore, if the user is excited, visually stimulating information can be provided. In this way, more appropriate information can be provided by adjusting the level of detail according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can take user emotion data as input and adjust the level of detail of information using an AI model that adjusts the level of detail of information based on emotions.

[0109] The reception desk can estimate the user's emotions and adjust the input method for hobbies and interests based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of hobbies and interests. This allows for more appropriate input by adjusting the input 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. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can take the user's emotion data as input and adjust the input method using an AI model that adjusts the input method based on the emotion.

[0110] The schedule analysis unit can estimate the user's emotions and adjust the schedule analysis method based on the estimated emotions. For example, if the user is relaxed, a detailed schedule analysis can be performed. If the user is in a hurry, a concise schedule analysis can be performed. Furthermore, if the user is excited, a visually stimulating schedule analysis can be performed. By adjusting the schedule analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule analysis unit may be performed using AI, for example, or without AI. For example, the schedule analysis unit can take user emotion data as input and adjust the method using an AI model that adjusts the schedule analysis method based on emotions.

[0111] The communication unit can estimate the user's emotions and adjust its communication method based on those emotions. For example, if the user is nervous, it can communicate in a calm voice. If the user is relaxed, it can communicate in a cheerful voice. Furthermore, if the user is in a hurry, it can communicate quickly and concisely. By adjusting the communication method according to the user's emotions, more appropriate communication becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can take user emotion data as input and adjust its communication method using an AI model that adjusts the communication method based on the emotion.

[0112] The reception desk can analyze the user's past hobbies and interests and suggest the optimal input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest hobbies and interests that the user will use at a specific time of day based on their past input history. This improves input efficiency by suggesting the optimal input method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past hobbies and interests as input and outputs the optimal input method.

[0113] The reception unit can filter the input of hobbies and interests based on the user's current lifestyle and areas of interest. For example, when a user inputs their current lifestyle, the reception unit can suggest relevant hobbies and interests based on that information. The reception unit can also analyze the user's areas of interest and prioritize the display of relevant hobbies and interests. Furthermore, the reception unit can filter and display appropriate hobbies and interests according to the user's lifestyle. This allows for the provision of more relevant information by suggesting appropriate hobbies and interests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can perform filtering using an AI model that takes the user's lifestyle and areas of interest as input and outputs appropriate hobbies and interests.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's hobbies and interests. For example, it can provide detailed analysis results for hobbies that the user is particularly interested in, and concise analysis results for hobbies that the user is not very interested in. Furthermore, if the user has multiple hobbies, the analysis unit can adjust the level of detail based on their importance. This allows for the provision of more appropriate analysis results by adjusting the level of detail based on the importance of hobbies and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the importance of the user's hobbies and interests as input and adjust the level of detail using an AI model.

[0115] The information provider can adjust the level of detail provided based on the importance of the recipient's information. For example, it may prioritize providing basic information such as the recipient's age and place of residence. The information provider can also provide detailed information about the recipient's hobbies and interests. Furthermore, it can provide the recipient's past activity history and evaluations. By adjusting the level of detail based on the importance of the recipient's information, it is possible to provide more appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can use an AI model that takes the importance of the recipient's information as input and adjusts the level of detail provided.

[0116] The communication unit can determine the priority of communications based on when the recipient's contact information was obtained. For example, it can prioritize communications using recently acquired contact information. It can also prioritize communications using contact information acquired during a specific time period. Furthermore, it can prioritize communications using contact information acquired frequently. By determining the priority of communications based on when the recipient's contact information was obtained, more appropriate communications become possible. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can use an AI model that takes the acquisition date of the recipient's contact information as input to determine the priority of communications.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk inputs the user's hobbies and interests. These hobbies and interests may include, but are not limited to, sports, music, and reading. For example, the reception desk can accept the user's hobbies and interests in text format. It can also accept input via voice. For instance, if the user voice-inputs "I like Go," the reception desk can receive this information. Furthermore, the reception desk can refer to the user's past history of hobbies and interests to suggest the most suitable input method. For example, it can automatically display hobbies and interests that the user has frequently entered in the past as suggestions. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and search for other elderly people with similar hobbies. The generation AI uses, for example, a text generation AI (e.g., LLM) to analyze the user's hobbies and interests. The analysis unit can also use the generation AI to calculate the degree of match between the user's hobbies and interests. For example, the generation AI searches for other elderly people with similar hobbies based on the user's hobbies and interests. Furthermore, the analysis unit can also use the generation AI to analyze the details of the user's hobbies and interests. For example, the generation AI analyzes the details of the user's hobbies and interests and searches for other elderly people with similar hobbies. Step 3: The provisioning unit provides information about the other party based on the results retrieved by the analysis unit. The provisioning unit provides information such as the other party's age, place of residence, and details of their hobbies. For example, the provisioning unit displays the other party's age and place of residence. The provisioning unit can also display details of the other party's hobbies. For example, the provisioning unit provides information such as the type and frequency of the other party's hobbies and related activities. Thus, the elderly playmate matching system according to the embodiment can easily find friends by searching for other elderly people with common hobbies based on the user's hobbies and interests and providing information about them. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without using AI. For example, the provisioning unit can provide information about the other party using an AI model that takes the results retrieved by the analysis unit as input and outputs information about the other party.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, schedule analysis unit, communication unit, privacy protection unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's hobbies and interests. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information about the other party based on the analysis results. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule and suggests the most suitable playmate. The communication unit is implemented by the control unit 46A of the smart device 14 and provides a means for the user to contact the suggested partner. The privacy protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's privacy. The interface unit is implemented by the control unit 46A of the smart device 14 and provides the service usage method and interface. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, schedule analysis unit, communication unit, privacy protection unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and takes the user's hobbies and interests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information about the other party based on the analysis results. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule and suggests the most suitable playmate. The communication unit is implemented by the control unit 46A of the smart glasses 214 and provides a means for the user to contact the suggested partner. The privacy protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's privacy. The interface unit is implemented by the control unit 46A of the smart glasses 214 and provides the service usage method and interface. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, schedule analysis unit, communication unit, privacy protection unit, and interface unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and takes the user's hobbies and interests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information about the other party based on the analysis results. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule and suggests the most suitable playmate. The communication unit is implemented by the control unit 46A of the headset terminal 314 and provides a means for the user to contact the suggested partner. The privacy protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's privacy. The interface unit is implemented by the control unit 46A of the headset terminal 314 and provides the service usage method and interface. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, schedule analysis unit, communication unit, privacy protection unit, and interface unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and takes the user's hobbies and interests as input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information about the other party based on the analysis results. The schedule analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's schedule and suggests the most suitable playmate. The communication unit is implemented by the control unit 46A of the robot 414 and provides a means for the user to contact the suggested partner. The privacy protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and protects the user's privacy. The interface unit is implemented by the control unit 46A of the robot 414 and provides the method of using the service and the interface. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A reception area where users input their hobbies and interests, The analysis unit analyzes the information entered by the reception unit and searches for other elderly people who share the same hobbies, The system includes a providing unit that provides information about the other party based on the results retrieved by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The AI ​​generates data to analyze the user's hobbies and interests, and then searches for other elderly people who share similar interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide information such as the other person's age, place of residence, and details of their hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a schedule analysis unit that analyzes the user's schedule and suggests the most suitable playmates. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a communication section for users to contact the suggested contact person. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a privacy protection section that takes user privacy into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes an interface section that provides instructions on how to use the service and an interface to it. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how hobbies and interests are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past hobbies and interests and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their hobbies and interests, the system filters the results based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of hobbies and interests to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their hobbies and interests, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter their hobbies and interests, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, adjust the level of detail based on the importance of the recipient's information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, different provision algorithms are applied depending on the category of the recipient's information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planned analysis unit, We estimate the user's emotions and adjust the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planned analysis unit, During schedule analysis, the system selects the optimal analysis method by referring to the user's past schedule history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planned analysis unit, During the planned analysis, the analysis method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planned analysis unit, It estimates the user's emotions and determines the priority of planned analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planned analysis unit, During the planned analysis, the optimal analysis method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned planned analysis unit, During the planned analysis, we will analyze users' social media activity and propose analysis methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned liaison department, It estimates the user's emotions and adjusts the communication method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned liaison department, When contacting someone, adjust the level of detail based on the importance of their contact information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned liaison department, It estimates the user's emotions and determines the priority of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned liaison department, When contacting someone, prioritize communication based on when you obtained their contact information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned privacy protection section is We estimate the user's emotions and adjust our privacy protection methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned privacy protection section is When protecting privacy, the system selects the most appropriate protection method by referring to the user's past privacy settings history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned privacy protection section is It estimates user sentiment and determines privacy protection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned privacy protection section is When protecting privacy, the optimal protection method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 34) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception area where users input their hobbies and interests, The analysis unit analyzes the information entered by the reception unit and searches for other elderly people who share the same hobbies, The system includes a providing unit that provides information about the other party based on the results retrieved by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, The system uses AI to analyze users' hobbies and interests and searches for other elderly people who share similar interests. The system according to feature 1.

3. The aforementioned supply unit is, Provide information such as the other person's age, place of residence, and details of their hobbies. The system according to feature 1.

4. It features a schedule analysis unit that analyzes the user's schedule and suggests the most suitable playmates. The system according to feature 1.

5. It includes a communication section for users to contact the suggested contact person. The system according to feature 1.

6. It includes a privacy protection section that takes user privacy into consideration. The system according to feature 1.

7. It includes an interface section that provides instructions on how to use the service and an interface to it. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how hobbies and interests are entered based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is It analyzes the user's past hobbies and interests and suggests the optimal input method. The system according to feature 1.

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