system

Abstract

The system according to this embodiment aims to support senior citizens in finding new encounters and communities. [Solution] The system according to the embodiment comprises a reception unit, a matching unit, a suggestion unit, and a support unit. The reception unit receives voice input. The matching unit performs matching based on the information received by the reception unit. The suggestion unit investigates and suggests potential destinations based on the information obtained by the matching unit. The support unit supports community building based on the information suggested by the suggestion 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 prior art, there was a problem that it was difficult for the senior layer to find new encounters and communities.

[0005] The system according to the embodiment aims to support the senior layer in finding new encounters and communities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a matching unit, a proposal unit, and a support unit. The reception unit receives voice input. The matching unit performs matching based on the information received by the reception unit. The proposal unit investigates and proposes potential destinations based on the information obtained by the matching unit. The support unit supports community building based on the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can support senior citizens in finding new encounters and communities. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 Ageless Friend System according to an embodiment of the present invention is a matching service for seniors. This Ageless Friend System is designed to be easy to use even for those who are not comfortable with smartphones, by providing voice control and clear guidance. Specifically, the user communicates their profile and desired friend criteria to an AI agent via voice input. Next, the AI ​​agent considers the best match based on data from other members and information found online. Furthermore, the AI ​​agent researches potential destinations and suggests them to the user. For example, it might ask, "I'd like to reserve a table at Cafe Poppo for November 18th at 11:00. Is that alright?" and the reservation is confirmed when the user responds, "Yes, please!" The Ageless Friend System also supports community building, allowing users to reserve and invite others to participate in events via voice input. This provides an environment where seniors can enjoy digital encounters with peace of mind. Thus, the Ageless Friend System can support optimal matching, suggestions, and community building through voice input in a matching service for seniors.

[0029] The Ageless Friend System according to this embodiment comprises a reception unit, a matching unit, a suggestion unit, and a community unit. The reception unit receives voice input. For example, the reception unit allows the user to communicate their profile and desired friend criteria to an AI agent via voice input. The reception unit converts the user's voice into text data using voice recognition technology. For example, the reception unit collects the user's voice using a microphone and converts it into text data using voice recognition software. The matching unit performs optimal matching based on the information received by the reception unit. For example, the matching unit considers the optimal matching based on data from other members and information on the internet. The matching unit uses AI to compare the user's profile information with data from other members and performs optimal matching. For example, the matching unit uses an AI model to suggest the optimal matching based on the user's hobbies and interests. The suggestion unit investigates and suggests potential destinations based on the information obtained by the matching unit. For example, the suggestion unit investigates information about potential destinations before suggesting them to the user. The suggestion unit uses AI to suggest the optimal destination based on the user's preferences. For example, the suggestion department uses an AI model to propose the most suitable outing destination based on the user's desired date, time, and location. The community department supports community building based on the information proposed by the suggestion department. The community department allows users to, for example, make reservations for events or invite others to participate through voice input. The community department uses AI to support the creation of the most suitable community based on the user's preferences. For example, the community department uses an AI model to propose the most suitable community based on the user's desired events and activities. As a result, the Ageless Friend System according to this embodiment can support optimal matching, suggestions, and community building through voice input in a matching service for seniors.

[0030] The reception desk accepts voice input. For example, users can communicate their profile and desired friend criteria to an AI agent via voice input. The reception desk uses speech recognition technology to convert the user's voice into text data. Specifically, the reception desk uses a high-sensitivity microphone to collect the user's voice and applies noise cancellation technology to obtain clear audio data. Then, speech recognition software is used to convert the audio data into text data. This speech recognition software employs a deep learning model and can recognize differences in the user's pronunciation and accent with high accuracy. For example, if a user voice-inputs, "I like reading and I'm looking for friends with the same hobby," the reception desk accurately converts this into text data and sends it to the system. Furthermore, the reception desk can provide real-time feedback on the user's voice input. For example, once voice input is complete, the system provides a voice guide such as, "Your profile information has been received. Next, please tell us your desired friend criteria," supporting the user in smoothly entering information. This allows the reception desk to process user voice input efficiently and accurately, improving the overall usability of the system.

[0031] The matching department performs optimal matching based on the information received by the reception department. For example, the matching department considers the best match based on data from other members and information found online. Specifically, the matching department uses AI to compare the user's profile information with data from other members to perform optimal matching. The AI ​​model considers the user's hobbies and interests, past behavioral history, and even psychological characteristics to select the most suitable friend candidates. For example, if a user says, "I like reading and would like to hold a book club at a cafe on the weekend," the matching department will prioritize selecting people with the same hobby or those who have previously participated in book clubs from among other members. The matching department can also utilize publicly available information online to suggest events and groups related to the user's interests. Furthermore, the matching department continuously improves its matching algorithm based on user feedback. For example, if a user provides feedback that "this person's hobbies didn't match mine" regarding a suggested friend candidate, the AI ​​learns from that information and reflects it in the next matching. In this way, the matching department can provide highly accurate matching that meets the user's needs and improve overall system satisfaction.

[0032] The Proposal Department investigates and proposes potential destinations based on information obtained by the Matching Department. For example, before proposing a destination to a user, the Proposal Department investigates information about the potential locations. Specifically, the Proposal Department uses AI to propose the most suitable destination based on the user's preferences. The AI ​​model selects the best destination by considering the user's desired date and time, location, budget, and activities of interest. For example, if a user wishes to "relax in nature on the weekend," the Proposal Department will suggest nearby parks, nature reserves, and relaxation facilities. The Proposal Department also collects and provides the latest information on potential locations to the user. For example, the Proposal Department collects ratings and event information for potential locations from online review sites and official websites and provides this information to the user. Furthermore, the Proposal Department continuously improves its suggestions based on user feedback. For example, if a user provides feedback that they "had a great time" on a suggested destination, the AI ​​learns from that information and incorporates it into future suggestions. This allows the Proposal Department to provide highly accurate suggestions that meet user needs and improve overall system satisfaction.

[0033] The Community Department supports community building based on information proposed by the Proposal Department. For example, the Community Department allows users to book or invite others to events via voice input. Specifically, the Community Department uses AI to support the creation of optimal communities based on user preferences. The AI ​​model considers the user's desired events and activities, as well as participant profile information, to propose the most suitable community. For example, if a user wants to hold a book club on the weekend, the Community Department will invite other members with similar interests to participate and coordinate the event details. The Community Department also manages event schedules and sends reminders. For example, as the event date approaches, it sends reminders to participants to encourage their attendance. Furthermore, the Community Department continuously improves the community building process based on user feedback. For example, if a user provides feedback that the event time didn't work for them, the AI ​​learns from that information and incorporates it into the next event schedule. This allows the Community Department to support highly accurate community building tailored to user needs and improve overall system satisfaction.

[0034] The suggestion unit can propose potential destinations to the user. For example, the suggestion unit can suggest potential destinations to the user. The suggestion unit uses AI to suggest the best destination based on the user's preferences. For example, the suggestion unit uses an AI model to suggest the best destination based on the user's desired date, time, and location. The suggestion unit can suggest options such as tourist attractions, restaurants, and events. In this way, the suggestion unit can support senior citizens' outings by suggesting potential destinations to the user.

[0035] The Community Department allows users to call for event reservations and participation via voice input. For example, the Community Department allows users to call for event reservations and participation via voice input. The Community Department uses AI to support the creation of optimal communities based on user preferences. For example, the Community Department uses an AI model to suggest optimal community creation based on the events and activities desired by the user. The Community Department can call for reservations and participation in events such as sports events, cultural events, and reservation systems. This allows the Community Department to support community building by enabling users to call for event reservations and participation via voice input.

[0036] The matching department can consider matching based on data from other members and information found online. For example, the matching department considers the optimal match based on data from other members and information found online. The matching department uses AI to compare a user's profile information with data from other members to achieve the optimal match. For example, the matching department uses an AI model to suggest the optimal match based on the user's hobbies and interests. The matching department can consider matching based on information such as other members' profile information, past activity history, social media posts, and publicly available data. This allows the matching department to achieve highly accurate matching by considering the optimal match based on data from other members and information found online.

[0037] The suggestion unit can research potential destinations before suggesting them to the user. For example, the suggestion unit researches potential destinations before suggesting them to the user. The suggestion unit uses AI to research information about potential destinations before suggesting the best destination based on the user's preferences. For example, the suggestion unit uses an AI model to research information about potential destinations such as tourist spots, restaurants, and events before suggesting the best destination based on the user's desired date, time, and location. This allows the suggestion unit to make more appropriate suggestions by researching potential destinations before suggesting them to the user.

[0038] The Community Department allows users to make reservations for gateball courts via voice input. The Community Department uses AI to support the creation of optimal communities based on user preferences. For example, the Community Department uses an AI model to suggest optimal community building based on the user's desired events and activities. The Community Department can make reservations for gateball courts at local sports facilities, online reservation systems, and other locations. This allows the Community Department to support sports activities for senior citizens by enabling them to make gateball court reservations via voice input.

[0039] The reception desk can analyze the user's past voice input history and select the optimal reception method. For example, the reception desk analyzes the user's past voice input history and selects the optimal reception method. The reception desk uses AI to analyze the user's past voice input history and select the optimal reception method. For example, the reception desk uses an AI model to analyze the user's past voice input history, including the content, frequency, and time of day. For example, the reception desk prioritizes receiving voice commands that the user has frequently used in the past. For example, the reception desk selects the optimal reception method for a specific time period based on the user's past voice input history. For example, the reception desk analyzes the user's past voice input history and proposes the most efficient reception method. In this way, the reception desk can select the optimal reception method by analyzing the user's past voice input history.

[0040] The reception unit can filter voice input based on the user's current lifestyle and areas of interest. For example, the reception unit can filter voice input based on the user's current lifestyle and areas of interest. The reception unit uses AI to filter based on the user's current lifestyle and areas of interest. For example, the reception unit uses an AI model to analyze the user's health status and daily activity patterns and perform filtering. For example, the reception unit prioritizes receiving voice input related to topics of interest based on the user's current lifestyle. For example, the reception unit filters relevant voice input based on the user's areas of interest and provides optimal information. For example, the reception unit adjusts the reception of voice input considering the user's current lifestyle and provides the information the user needs. In this way, the reception unit can provide optimal information by filtering based on the user's current lifestyle and areas of interest.

[0041] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving voice input. For example, the reception unit prioritizes receiving highly relevant information by considering the user's geographical location when receiving voice input. The reception unit uses AI to prioritize receiving highly relevant information by considering the user's geographical location. For example, the reception unit uses an AI model to analyze the user's GPS data and location services and filter out highly relevant information. For example, if the user is in a specific area, the reception unit prioritizes receiving voice input related to that area. For example, the reception unit filters out highly relevant information based on the user's geographical location and receives the most relevant voice input. For example, if the user is on the move, the reception unit accepts voice input while considering the user's current geographical location. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location.

[0042] The reception unit can analyze the user's social media activity and receive relevant information when receiving voice input. For example, the reception unit analyzes the user's social media activity and receives relevant information when receiving voice input. The reception unit uses AI to analyze the user's social media activity and receive relevant information. For example, the reception unit uses an AI model to analyze the user's posts and follower reactions and filters relevant information. For example, the reception unit analyzes the user's social media activity and prioritizes receiving relevant voice input. For example, the reception unit receives voice input related to topics the user has shown interest in on social media. For example, the reception unit suggests the most suitable voice input based on the user's social media activity. In this way, the reception unit can prioritize receiving relevant information by analyzing the user's social media activity.

[0043] The matching unit can improve the accuracy of matching by considering the relationships between other members during the matching process. For example, the matching unit can improve the accuracy of matching by considering the relationships between other members during the matching process. The matching unit uses AI to improve the accuracy of matching by considering the relationships between other members. For example, the matching unit uses an AI model to analyze the friendships and past interaction history of other members to improve the accuracy of matching. For example, the matching unit considers the friendships of other members and prioritizes suggesting candidates with mutual friends. For example, the matching unit analyzes the past matching history of other members and suggests candidates with a high success rate. For example, the matching unit suggests the optimal match based on the relationships between other members. In this way, the matching unit can improve the accuracy of matching by considering the relationships between other members.

[0044] The matching unit can perform matching by considering the attribute information of other members. For example, the matching unit can perform matching by considering the attribute information of other members. The matching unit uses AI to perform matching by considering the attribute information of other members. For example, the matching unit uses an AI model to analyze the attribute information of other members, such as age, gender, hobbies and interests, occupation and lifestyle, and perform matching. For example, the matching unit proposes the optimal match based on the attribute information of other members, such as age and gender. For example, the matching unit considers the hobbies and interests of other members and proposes candidates with many commonalities. For example, the matching unit proposes compatible candidates based on the occupation and lifestyle of other members. In this way, the matching unit can perform more appropriate matching by considering the attribute information of other members.

[0045] The matching unit can perform matching while considering the geographical distribution of other members. For example, the matching unit can perform matching while considering the geographical distribution of other members. The matching unit uses AI to perform matching while considering the geographical distribution of other members. For example, the matching unit uses an AI model to analyze the residences and activity ranges of other members and perform matching. For example, the matching unit prioritizes suggesting nearby candidates based on the residences of other members. For example, the matching unit considers the geographical distribution of other members and suggests candidates that are easily accessible. For example, the matching unit suggests the optimal match based on the geographical distribution of other members. As a result, the matching unit can prioritize suggesting nearby candidates by considering the geographical distribution of other members.

[0046] The matching unit can improve the accuracy of matching by referring to the relevant literature of other members during the matching process. For example, the matching unit can improve the accuracy of matching by referring to the relevant literature of other members during the matching process. The matching unit uses AI to improve the accuracy of matching by referring to the relevant literature of other members. For example, the matching unit uses an AI model to analyze literature related to the profiles of other members and improve the accuracy of matching. For example, the matching unit refers to literature related to the profiles of other members and proposes the best match. For example, the matching unit suggests candidates with many commonalities based on literature related to the hobbies and interests of other members. For example, the matching unit suggests compatible candidates by referring to literature related to the occupations and lifestyles of other members. In this way, the matching unit can improve the accuracy of matching by referring to the relevant literature of other members.

[0047] The suggestion function can adjust the level of detail in suggestions based on the importance of the destination. For example, the suggestion function adjusts the level of detail in suggestions based on the importance of the destination. The suggestion function uses AI to adjust the level of detail in suggestions based on the importance of the destination. For example, the suggestion function uses an AI model to analyze the popularity and safety of a destination and adjust the level of detail in suggestions. For example, in the case of an important destination, the suggestion function provides detailed information to ensure the user can use it with peace of mind. For example, in the case of a general destination, the suggestion function provides simple information to ensure the user can easily understand it. For example, the suggestion function adjusts the level of detail in suggestions based on the user's level of interest to make the best suggestions. In this way, the suggestion function can ensure the user can use it with peace of mind by adjusting the level of detail in suggestions based on the importance of the destination.

[0048] The suggestion function can apply different suggestion algorithms depending on the category of destination when making suggestions. For example, the suggestion function applies different suggestion algorithms depending on the category of destination when making suggestions. The suggestion function uses AI to apply different suggestion algorithms depending on the category of destination. For example, the suggestion function uses an AI model to analyze the category of destination and apply the optimal suggestion algorithm. For example, in the case of a restaurant, the suggestion function applies a suggestion algorithm based on the user's preferences. For example, in the case of a park, the suggestion function applies a suggestion algorithm based on the user's activity style. For example, in the case of a shopping mall, the suggestion function applies a suggestion algorithm based on the user's purchase history. In this way, the suggestion function can make suggestions that match the user's preferences by applying different suggestion algorithms depending on the category of destination.

[0049] The proposal department can determine the priority of proposals based on the timing of submission of potential destinations. For example, the proposal department can determine the priority of proposals based on the timing of submission of potential destinations. The proposal department uses AI to determine the priority of proposals based on the timing of submission of potential destinations. For example, the proposal department uses an AI model to analyze event dates and reservation deadlines to determine the priority of proposals. For example, the proposal department will prioritize proposals for destinations that are coming up soon. For example, the proposal department will make proposals at the optimal time based on the user's schedule. For example, the proposal department will adjust the priority based on the timing of proposal submissions to make the best proposals. In this way, the proposal department can make proposals at the optimal time for the user by determining the priority of proposals based on the timing of submission of potential destinations.

[0050] The suggestion function can adjust the order of suggestions based on the relevance of destinations. For example, the suggestion function adjusts the order of suggestions based on the relevance of destinations. The suggestion function uses AI to adjust the order of suggestions based on the relevance of destinations. For example, the suggestion function uses an AI model to analyze the user's interests and past behavior history and adjust the order of suggestions. For example, the suggestion function prioritizes suggesting highly relevant destinations based on the user's level of interest. For example, the suggestion function suggests highly relevant destinations based on the user's past history. For example, the suggestion function adjusts the order of suggestions based on the category of the destination. In this way, the suggestion function can provide the best possible suggestions to the user by adjusting the order of suggestions based on the relevance of destinations.

[0051] The Community Department can select the optimal community building method by analyzing users' past participation history when creating a community. For example, the Community Department analyzes users' past participation history to select the optimal community building method. The Community Department uses AI to analyze users' past participation history and select the optimal community building method. For example, the Community Department uses an AI model to analyze users' past event participation records and frequency to select the optimal community building method. For example, the Community Department proposes the optimal community building method based on the user's past event participation history. For example, the Community Department selects a community building method with a high success rate based on the user's past participation history. For example, the Community Department analyzes users' past participation history and proposes the most effective community building method. In this way, the Community Department can select the optimal community building method by analyzing users' past participation history.

[0052] The Community Department can customize the means of community building based on the user's current living situation. For example, the Community Department customizes the means of community building based on the user's current living situation. The Community Department uses AI to customize the means of community building based on the user's current living situation. For example, the Community Department uses an AI model to analyze the user's health status and daily activity patterns and customize the means of community building. For example, the Community Department considers the user's current living situation and proposes the optimal means of community building. For example, the Community Department provides customized means of community building based on the user's lifestyle. For example, the Community Department proposes the most effective means of community building based on the user's current living situation. This allows the Community Department to create more appropriate communities by customizing the means of community building based on the user's current living situation.

[0053] The Community Department can select the optimal community building method when creating a community, taking into account the user's geographical location information. For example, the Community Department selects the optimal community building method when creating a community, taking into account the user's geographical location information. The Community Department uses AI to select the optimal community building method when considering the user's geographical location information. For example, the Community Department uses an AI model to analyze the user's GPS data and location information services to select the optimal community building method. For example, the Community Department proposes nearby community building methods based on the user's geographical location information. For example, the Community Department selects easily accessible community building methods, taking into account the user's geographical location information. For example, the Community Department proposes the optimal community building method based on the user's geographical location information. This allows the Community Department to select the optimal community building method by considering the user's geographical location information.

[0054] The Community Department can analyze users' social media activity and propose methods for community building. For example, the Community Department can analyze users' social media activity and propose methods for community building. The Community Department uses AI to analyze users' social media activity and propose methods for community building. For example, the Community Department uses an AI model to analyze users' posts and follower reactions and propose methods for community building. For example, the Community Department analyzes users' social media activity and proposes relevant community building methods. For example, the Community Department proposes community building methods based on topics users have shown interest in on social media. For example, the Community Department proposes the optimal community building method based on users' social media activity. In this way, the Community Department can propose relevant community building methods by analyzing users' social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The Ageless Friend System can also be equipped with a Health Management Department. This department can monitor users' health status and provide health-conscious matching and suggestions. For example, it can collect data such as users' steps and heart rate to assess their health. Based on the user's health status, the Health Management Department can suggest appropriate exercise and dietary recommendations. Furthermore, it can suggest suitable outings and events according to the user's health condition. This allows the Ageless Friend System to provide a safe and secure environment while supporting users' health.

[0057] The Community Department can suggest communities aligned with specific themes based on users' hobbies and interests. For example, if a user is interested in music, the Community Department can suggest communities for music events and concerts. If a user is interested in cooking, it can suggest communities for cooking classes and recipe exchange. If a user is interested in travel, it can suggest communities for travel planning and sharing travel experiences. In this way, the Community Department can support the creation of communities that users can enjoy more by suggesting communities aligned with specific themes based on users' hobbies and interests.

[0058] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, by analyzing a user's past proposal history, the proposal department can understand the user's preferred proposal trends. Based on the user's past selections of destinations and events, it can make similar suggestions. Based on the user's past rejected suggestions, it can identify suggestions to avoid. In this way, the proposal department can select a more appropriate proposal method by analyzing a user's past proposal history.

[0059] The reception desk can select the optimal voice input method by considering the user's geographical location. For example, if the user is at home, the reception desk can prioritize voice input in a relaxed environment. If the user is out, it can prioritize voice input utilizing noise cancellation, taking into account the surrounding noise. If the user is at a specific facility, it can prioritize receiving information related to that facility. In this way, the reception desk can select the optimal voice input method by considering the user's geographical location.

[0060] The matching unit can analyze users' social media activity and make optimal matches. For example, the matching unit can analyze the content of users' social media posts and follower reactions to understand users' interests and preferences. Based on the topics users have shown interest in on social media, it can suggest candidates with similar interests. Based on the user's social media activity history, it can suggest compatible candidates. In this way, the matching unit can make more appropriate matches by analyzing users' social media activity.

[0061] The suggestion function can apply different suggestion algorithms depending on the category of destination. For example, for restaurants, it can apply a suggestion algorithm based on the user's preferences. For parks, it can apply a suggestion algorithm based on the user's activity style. For shopping malls, it can apply a suggestion algorithm based on the user's purchase history. This allows the suggestion function to provide suggestions that match the user's preferences by applying different suggestion algorithms depending on the category of destination.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts voice input. For example, a user can communicate their profile and desired friend criteria to an AI agent via voice input. The reception desk uses speech recognition technology to convert the user's voice into text data. For example, it collects the user's voice using a microphone and converts it into text data using speech recognition software. Step 2: The matching department performs optimal matching based on the information received by the reception department. For example, it considers the best match based on data from other members and information found online. The matching department uses AI to compare the user's profile information with data from other members to perform optimal matching. For example, it uses an AI model to suggest the best match based on the user's hobbies and interests. Step 3: The suggestion department investigates and proposes potential destinations based on the information obtained by the matching department. For example, before proposing potential destinations to the user, it investigates information about the candidate locations. The suggestion department uses AI to propose the best destination based on the user's preferences. For example, it uses an AI model to propose the best destination based on the user's desired date, time, and location. Step 4: The Community Department supports community building based on the information proposed by the Proposal Department. For example, users can make reservations or invite others to participate in events via voice input. The Community Department uses AI to support optimal community building based on user preferences. For example, it uses an AI model to propose optimal community building based on the events and activities desired by users.

[0064] (Example of form 2) The Ageless Friend System according to an embodiment of the present invention is a matching service for seniors. This Ageless Friend System is designed to be easy to use even for those who are not comfortable with smartphones, by providing voice control and clear guidance. Specifically, the user communicates their profile and desired friend criteria to an AI agent via voice input. Next, the AI ​​agent considers the best match based on data from other members and information found online. Furthermore, the AI ​​agent researches potential destinations and suggests them to the user. For example, it might ask, "I'd like to reserve a table at Cafe Poppo for November 18th at 11:00. Is that alright?" and the reservation is confirmed when the user responds, "Yes, please!" The Ageless Friend System also supports community building, allowing users to reserve and invite others to participate in events via voice input. This provides an environment where seniors can enjoy digital encounters with peace of mind. Thus, the Ageless Friend System can support optimal matching, suggestions, and community building through voice input in a matching service for seniors.

[0065] The Ageless Friend System according to this embodiment comprises a reception unit, a matching unit, a suggestion unit, and a community unit. The reception unit receives voice input. For example, the reception unit allows the user to communicate their profile and desired friend criteria to an AI agent via voice input. The reception unit converts the user's voice into text data using voice recognition technology. For example, the reception unit collects the user's voice using a microphone and converts it into text data using voice recognition software. The matching unit performs optimal matching based on the information received by the reception unit. For example, the matching unit considers the optimal matching based on data from other members and information on the internet. The matching unit uses AI to compare the user's profile information with data from other members and performs optimal matching. For example, the matching unit uses an AI model to suggest the optimal matching based on the user's hobbies and interests. The suggestion unit investigates and suggests potential destinations based on the information obtained by the matching unit. For example, the suggestion unit investigates information about potential destinations before suggesting them to the user. The suggestion unit uses AI to suggest the optimal destination based on the user's preferences. For example, the suggestion department uses an AI model to propose the most suitable outing destination based on the user's desired date, time, and location. The community department supports community building based on the information proposed by the suggestion department. The community department allows users to, for example, make reservations for events or invite others to participate through voice input. The community department uses AI to support the creation of the most suitable community based on the user's preferences. For example, the community department uses an AI model to propose the most suitable community based on the user's desired events and activities. As a result, the Ageless Friend System according to this embodiment can support optimal matching, suggestions, and community building through voice input in a matching service for seniors.

[0066] The reception desk accepts voice input. For example, users can communicate their profile and desired friend criteria to an AI agent via voice input. The reception desk uses speech recognition technology to convert the user's voice into text data. Specifically, the reception desk uses a high-sensitivity microphone to collect the user's voice and applies noise cancellation technology to obtain clear audio data. Then, speech recognition software is used to convert the audio data into text data. This speech recognition software employs a deep learning model and can recognize differences in the user's pronunciation and accent with high accuracy. For example, if a user voice-inputs, "I like reading and I'm looking for friends with the same hobby," the reception desk accurately converts this into text data and sends it to the system. Furthermore, the reception desk can provide real-time feedback on the user's voice input. For example, once voice input is complete, the system provides a voice guide such as, "Your profile information has been received. Next, please tell us your desired friend criteria," supporting the user in smoothly entering information. This allows the reception desk to process user voice input efficiently and accurately, improving the overall usability of the system.

[0067] The matching department performs optimal matching based on the information received by the reception department. For example, the matching department considers the best match based on data from other members and information found online. Specifically, the matching department uses AI to compare the user's profile information with data from other members to perform optimal matching. The AI ​​model considers the user's hobbies and interests, past behavioral history, and even psychological characteristics to select the most suitable friend candidates. For example, if a user says, "I like reading and would like to hold a book club at a cafe on the weekend," the matching department will prioritize selecting people with the same hobby or those who have previously participated in book clubs from among other members. The matching department can also utilize publicly available information online to suggest events and groups related to the user's interests. Furthermore, the matching department continuously improves its matching algorithm based on user feedback. For example, if a user provides feedback that "this person's hobbies didn't match mine" regarding a suggested friend candidate, the AI ​​learns from that information and reflects it in the next matching. In this way, the matching department can provide highly accurate matching that meets the user's needs and improve overall system satisfaction.

[0068] The Proposal Department investigates and proposes potential destinations based on information obtained by the Matching Department. For example, before proposing a destination to a user, the Proposal Department investigates information about the potential locations. Specifically, the Proposal Department uses AI to propose the most suitable destination based on the user's preferences. The AI ​​model selects the best destination by considering the user's desired date and time, location, budget, and activities of interest. For example, if a user wishes to "relax in nature on the weekend," the Proposal Department will suggest nearby parks, nature reserves, and relaxation facilities. The Proposal Department also collects and provides the latest information on potential locations to the user. For example, the Proposal Department collects ratings and event information for potential locations from online review sites and official websites and provides this information to the user. Furthermore, the Proposal Department continuously improves its suggestions based on user feedback. For example, if a user provides feedback that they "had a great time" on a suggested destination, the AI ​​learns from that information and incorporates it into future suggestions. This allows the Proposal Department to provide highly accurate suggestions that meet user needs and improve overall system satisfaction.

[0069] The Community Department supports community building based on information proposed by the Proposal Department. For example, the Community Department allows users to book or invite others to events via voice input. Specifically, the Community Department uses AI to support the creation of optimal communities based on user preferences. The AI ​​model considers the user's desired events and activities, as well as participant profile information, to propose the most suitable community. For example, if a user wants to hold a book club on the weekend, the Community Department will invite other members with similar interests to participate and coordinate the event details. The Community Department also manages event schedules and sends reminders. For example, as the event date approaches, it sends reminders to participants to encourage their attendance. Furthermore, the Community Department continuously improves the community building process based on user feedback. For example, if a user provides feedback that the event time didn't work for them, the AI ​​learns from that information and incorporates it into the next event schedule. This allows the Community Department to support highly accurate community building tailored to user needs and improve overall system satisfaction.

[0070] The suggestion unit can propose potential destinations to the user. For example, the suggestion unit can suggest potential destinations to the user. The suggestion unit uses AI to suggest the best destination based on the user's preferences. For example, the suggestion unit uses an AI model to suggest the best destination based on the user's desired date, time, and location. The suggestion unit can suggest options such as tourist attractions, restaurants, and events. In this way, the suggestion unit can support senior citizens' outings by suggesting potential destinations to the user.

[0071] The Community Department allows users to call for event reservations and participation via voice input. For example, the Community Department allows users to call for event reservations and participation via voice input. The Community Department uses AI to support the creation of optimal communities based on user preferences. For example, the Community Department uses an AI model to suggest optimal community creation based on the events and activities desired by the user. The Community Department can call for reservations and participation in events such as sports events, cultural events, and reservation systems. This allows the Community Department to support community building by enabling users to call for event reservations and participation via voice input.

[0072] The matching department can consider matching based on data from other members and information found online. For example, the matching department considers the optimal match based on data from other members and information found online. The matching department uses AI to compare a user's profile information with data from other members to achieve the optimal match. For example, the matching department uses an AI model to suggest the optimal match based on the user's hobbies and interests. The matching department can consider matching based on information such as other members' profile information, past activity history, social media posts, and publicly available data. This allows the matching department to achieve highly accurate matching by considering the optimal match based on data from other members and information found online.

[0073] The suggestion unit can research potential destinations before suggesting them to the user. For example, the suggestion unit researches potential destinations before suggesting them to the user. The suggestion unit uses AI to research information about potential destinations before suggesting the best destination based on the user's preferences. For example, the suggestion unit uses an AI model to research information about potential destinations such as tourist spots, restaurants, and events before suggesting the best destination based on the user's desired date, time, and location. This allows the suggestion unit to make more appropriate suggestions by researching potential destinations before suggesting them to the user.

[0074] The Community Department allows users to make reservations for gateball courts via voice input. The Community Department uses AI to support the creation of optimal communities based on user preferences. For example, the Community Department uses an AI model to suggest optimal community building based on the user's desired events and activities. The Community Department can make reservations for gateball courts at local sports facilities, online reservation systems, and other locations. This allows the Community Department to support sports activities for senior citizens by enabling them to make gateball court reservations via voice input.

[0075] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of voice input reception based on the estimated emotions. The reception unit uses AI to estimate the user's emotions and adjust the timing of voice input reception. For example, the reception unit uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the reception unit adjusts the timing of voice input reception slowly to provide a comfortable environment for the user to speak. For example, if the user is nervous, the reception unit speeds up the timing of voice input reception so the user can input immediately. For example, if the user is excited, the reception unit adjusts the timing of voice input reception so the user can input calmly. In this way, the reception unit can provide a comfortable environment for the user to speak by adjusting the timing of voice input reception based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0076] The reception desk can analyze the user's past voice input history and select the optimal reception method. For example, the reception desk analyzes the user's past voice input history and selects the optimal reception method. The reception desk uses AI to analyze the user's past voice input history and select the optimal reception method. For example, the reception desk uses an AI model to analyze the user's past voice input history, including the content, frequency, and time of day. For example, the reception desk prioritizes receiving voice commands that the user has frequently used in the past. For example, the reception desk selects the optimal reception method for a specific time period based on the user's past voice input history. For example, the reception desk analyzes the user's past voice input history and proposes the most efficient reception method. In this way, the reception desk can select the optimal reception method by analyzing the user's past voice input history.

[0077] The reception unit can filter voice input based on the user's current lifestyle and areas of interest. For example, the reception unit can filter voice input based on the user's current lifestyle and areas of interest. The reception unit uses AI to filter based on the user's current lifestyle and areas of interest. For example, the reception unit uses an AI model to analyze the user's health status and daily activity patterns and perform filtering. For example, the reception unit prioritizes receiving voice input related to topics of interest based on the user's current lifestyle. For example, the reception unit filters relevant voice input based on the user's areas of interest and provides optimal information. For example, the reception unit adjusts the reception of voice input considering the user's current lifestyle and provides the information the user needs. In this way, the reception unit can provide optimal information by filtering based on the user's current lifestyle and areas of interest.

[0078] The reception unit can estimate the user's emotions and determine the priority of voice input to be received based on the estimated emotions. For example, the reception unit can estimate the user's emotions and determine the priority of voice input to be received based on the estimated emotions. The reception unit uses AI to estimate the user's emotions and determine the priority of voice input. For example, the reception unit uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is stressed, the reception unit prioritizes important voice input. For example, if the user is relaxed, the reception unit prioritizes normal voice input. For example, if the user is in a hurry, the reception unit prioritizes urgent voice input. This allows the reception unit to prioritize important voice input by determining the priority of voice input based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving voice input. For example, the reception unit prioritizes receiving highly relevant information by considering the user's geographical location when receiving voice input. The reception unit uses AI to prioritize receiving highly relevant information by considering the user's geographical location. For example, the reception unit uses an AI model to analyze the user's GPS data and location services and filter out highly relevant information. For example, if the user is in a specific area, the reception unit prioritizes receiving voice input related to that area. For example, the reception unit filters out highly relevant information based on the user's geographical location and receives the most relevant voice input. For example, if the user is on the move, the reception unit accepts voice input while considering the user's current geographical location. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location.

[0080] The reception unit can analyze the user's social media activity and receive relevant information when receiving voice input. For example, the reception unit analyzes the user's social media activity and receives relevant information when receiving voice input. The reception unit uses AI to analyze the user's social media activity and receive relevant information. For example, the reception unit uses an AI model to analyze the user's posts and follower reactions and filters relevant information. For example, the reception unit analyzes the user's social media activity and prioritizes receiving relevant voice input. For example, the reception unit receives voice input related to topics the user has shown interest in on social media. For example, the reception unit suggests the most suitable voice input based on the user's social media activity. In this way, the reception unit can prioritize receiving relevant information by analyzing the user's social media activity.

[0081] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, the matching unit estimates the user's emotions and adjusts the matching criteria based on the estimated emotions. The matching unit uses AI to estimate the user's emotions and adjust the matching criteria. For example, the matching unit uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the matching unit applies broad matching criteria and suggests a variety of candidates. For example, if the user is tense, the matching unit applies strict matching criteria and suggests reliable candidates. For example, if the user is excited, the matching unit applies emotion-based matching criteria and suggests interesting candidates. This allows the matching unit to achieve more appropriate matching by adjusting the matching criteria based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0082] The matching unit can improve the accuracy of matching by considering the relationships between other members during the matching process. For example, the matching unit can improve the accuracy of matching by considering the relationships between other members during the matching process. The matching unit uses AI to improve the accuracy of matching by considering the relationships between other members. For example, the matching unit uses an AI model to analyze the friendships and past interaction history of other members to improve the accuracy of matching. For example, the matching unit considers the friendships of other members and prioritizes suggesting candidates with mutual friends. For example, the matching unit analyzes the past matching history of other members and suggests candidates with a high success rate. For example, the matching unit suggests the optimal match based on the relationships between other members. In this way, the matching unit can improve the accuracy of matching by considering the relationships between other members.

[0083] The matching unit can perform matching by considering the attribute information of other members. For example, the matching unit can perform matching by considering the attribute information of other members. The matching unit uses AI to perform matching by considering the attribute information of other members. For example, the matching unit uses an AI model to analyze the attribute information of other members, such as age, gender, hobbies and interests, occupation and lifestyle, and perform matching. For example, the matching unit proposes the optimal match based on the attribute information of other members, such as age and gender. For example, the matching unit considers the hobbies and interests of other members and proposes candidates with many commonalities. For example, the matching unit proposes compatible candidates based on the occupation and lifestyle of other members. In this way, the matching unit can perform more appropriate matching by considering the attribute information of other members.

[0084] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, the matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. The matching unit uses AI to estimate the user's emotions and adjust the order in which matching results are displayed. For example, the matching unit uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the matching unit displays a wide range of candidates, increasing the options. For example, if the user is tense, the matching unit prioritizes displaying reliable candidates. For example, if the user is excited, the matching unit prioritizes displaying emotion-based candidates. This allows the matching unit to prioritize displaying the most suitable candidates for the user by adjusting the order in which matching results are displayed based on 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.

[0085] The matching unit can perform matching while considering the geographical distribution of other members. For example, the matching unit can perform matching while considering the geographical distribution of other members. The matching unit uses AI to perform matching while considering the geographical distribution of other members. For example, the matching unit uses an AI model to analyze the residences and activity ranges of other members and perform matching. For example, the matching unit prioritizes suggesting nearby candidates based on the residences of other members. For example, the matching unit considers the geographical distribution of other members and suggests candidates that are easily accessible. For example, the matching unit suggests the optimal match based on the geographical distribution of other members. As a result, the matching unit can prioritize suggesting nearby candidates by considering the geographical distribution of other members.

[0086] The matching unit can improve the accuracy of matching by referring to the relevant literature of other members during the matching process. For example, the matching unit can improve the accuracy of matching by referring to the relevant literature of other members during the matching process. The matching unit uses AI to improve the accuracy of matching by referring to the relevant literature of other members. For example, the matching unit uses an AI model to analyze literature related to the profiles of other members and improve the accuracy of matching. For example, the matching unit refers to literature related to the profiles of other members and proposes the best match. For example, the matching unit suggests candidates with many commonalities based on literature related to the hobbies and interests of other members. For example, the matching unit suggests compatible candidates by referring to literature related to the occupations and lifestyles of other members. In this way, the matching unit can improve the accuracy of matching by referring to the relevant literature of other members.

[0087] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. The suggestion unit uses AI to estimate the user's emotions and adjust the way it presents its suggestions. For example, the suggestion unit uses an AI model to analyze the user's voice tone and facial expressions to estimate their emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions and increases the options. For example, if the user is tense, the suggestion unit provides simple and easy-to-understand suggestions. For example, if the user is excited, the suggestion unit provides emotion-based suggestions to pique their interest. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The suggestion function can adjust the level of detail in suggestions based on the importance of the destination. For example, the suggestion function adjusts the level of detail in suggestions based on the importance of the destination. The suggestion function uses AI to adjust the level of detail in suggestions based on the importance of the destination. For example, the suggestion function uses an AI model to analyze the popularity and safety of a destination and adjust the level of detail in suggestions. For example, in the case of an important destination, the suggestion function provides detailed information to ensure the user can use it with peace of mind. For example, in the case of a general destination, the suggestion function provides simple information to ensure the user can easily understand it. For example, the suggestion function adjusts the level of detail in suggestions based on the user's level of interest to make the best suggestions. In this way, the suggestion function can ensure the user can use it with peace of mind by adjusting the level of detail in suggestions based on the importance of the destination.

[0089] The suggestion function can apply different suggestion algorithms depending on the category of destination when making suggestions. For example, the suggestion function applies different suggestion algorithms depending on the category of destination when making suggestions. The suggestion function uses AI to apply different suggestion algorithms depending on the category of destination. For example, the suggestion function uses an AI model to analyze the category of destination and apply the optimal suggestion algorithm. For example, in the case of a restaurant, the suggestion function applies a suggestion algorithm based on the user's preferences. For example, in the case of a park, the suggestion function applies a suggestion algorithm based on the user's activity style. For example, in the case of a shopping mall, the suggestion function applies a suggestion algorithm based on the user's purchase history. In this way, the suggestion function can make suggestions that match the user's preferences by applying different suggestion algorithms depending on the category of destination.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, the suggestion unit estimates the user's emotions and adjusts the length of the suggestions based on the estimated emotions. The suggestion unit uses AI to estimate the user's emotions and adjust the length of the suggestions. For example, the suggestion unit uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the suggestion unit provides detailed suggestions and increases the options. For example, if the user is tense, the suggestion unit provides simple and easy-to-understand suggestions. For example, if the user is excited, the suggestion unit provides emotion-based suggestions to pique their interest. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The proposal department can determine the priority of proposals based on the timing of submission of potential destinations. For example, the proposal department can determine the priority of proposals based on the timing of submission of potential destinations. The proposal department uses AI to determine the priority of proposals based on the timing of submission of potential destinations. For example, the proposal department uses an AI model to analyze event dates and reservation deadlines to determine the priority of proposals. For example, the proposal department will prioritize proposals for destinations that are coming up soon. For example, the proposal department will make proposals at the optimal time based on the user's schedule. For example, the proposal department will adjust the priority based on the timing of proposal submissions to make the best proposals. In this way, the proposal department can make proposals at the optimal time for the user by determining the priority of proposals based on the timing of submission of potential destinations.

[0092] The suggestion function can adjust the order of suggestions based on the relevance of destinations. For example, the suggestion function adjusts the order of suggestions based on the relevance of destinations. The suggestion function uses AI to adjust the order of suggestions based on the relevance of destinations. For example, the suggestion function uses an AI model to analyze the user's interests and past behavior history and adjust the order of suggestions. For example, the suggestion function prioritizes suggesting highly relevant destinations based on the user's level of interest. For example, the suggestion function suggests highly relevant destinations based on the user's past history. For example, the suggestion function adjusts the order of suggestions based on the category of the destination. In this way, the suggestion function can provide the best possible suggestions to the user by adjusting the order of suggestions based on the relevance of destinations.

[0093] The community department can estimate user emotions and adjust community building methods based on the estimated user emotions. For example, the community department estimates user emotions and adjusts community building methods based on the estimated user emotions. The community department uses AI to estimate user emotions and adjust community building methods. For example, the community department uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the community department suggests community building at a relaxed pace. For example, if the user is tense, the community department suggests simple and easy-to-understand community building. For example, if the user is excited, the community department suggests emotion-based community building. This allows the community department to create more appropriate communities by adjusting community building methods based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The Community Department can select the optimal community building method by analyzing users' past participation history when creating a community. For example, the Community Department analyzes users' past participation history to select the optimal community building method. The Community Department uses AI to analyze users' past participation history and select the optimal community building method. For example, the Community Department uses an AI model to analyze users' past event participation records and frequency to select the optimal community building method. For example, the Community Department proposes the optimal community building method based on the user's past event participation history. For example, the Community Department selects a community building method with a high success rate based on the user's past participation history. For example, the Community Department analyzes users' past participation history and proposes the most effective community building method. In this way, the Community Department can select the optimal community building method by analyzing users' past participation history.

[0095] The Community Department can customize the means of community building based on the user's current living situation. For example, the Community Department customizes the means of community building based on the user's current living situation. The Community Department uses AI to customize the means of community building based on the user's current living situation. For example, the Community Department uses an AI model to analyze the user's health status and daily activity patterns and customize the means of community building. For example, the Community Department considers the user's current living situation and proposes the optimal means of community building. For example, the Community Department provides customized means of community building based on the user's lifestyle. For example, the Community Department proposes the most effective means of community building based on the user's current living situation. This allows the Community Department to create more appropriate communities by customizing the means of community building based on the user's current living situation.

[0096] The community department can estimate user emotions and determine community building priorities based on those estimated emotions. For example, the community department can estimate user emotions and determine community building priorities based on those estimated emotions. The community department uses AI to estimate user emotions and determine community building priorities. For example, the community department uses an AI model to analyze the user's voice tone and facial expressions to estimate emotions. For example, if the user is relaxed, the community department prioritizes community building that proceeds at a relaxed pace. For example, if the user is tense, the community department prioritizes community building that is simple and easy to understand. For example, if the user is excited, the community department prioritizes emotion-based community building. This allows the community department to create more appropriate communities by determining priorities based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The Community Department can select the optimal community building method when creating a community, taking into account the user's geographical location information. For example, the Community Department selects the optimal community building method when creating a community, taking into account the user's geographical location information. The Community Department uses AI to select the optimal community building method when considering the user's geographical location information. For example, the Community Department uses an AI model to analyze the user's GPS data and location information services to select the optimal community building method. For example, the Community Department proposes nearby community building methods based on the user's geographical location information. For example, the Community Department selects easily accessible community building methods, taking into account the user's geographical location information. For example, the Community Department proposes the optimal community building method based on the user's geographical location information. This allows the Community Department to select the optimal community building method by considering the user's geographical location information.

[0098] The Community Department can analyze users' social media activity and propose methods for community building. For example, the Community Department can analyze users' social media activity and propose methods for community building. The Community Department uses AI to analyze users' social media activity and propose methods for community building. For example, the Community Department uses an AI model to analyze users' posts and follower reactions and propose methods for community building. For example, the Community Department analyzes users' social media activity and proposes relevant community building methods. For example, the Community Department proposes community building methods based on topics users have shown interest in on social media. For example, the Community Department proposes the optimal community building method based on users' social media activity. In this way, the Community Department can propose relevant community building methods by analyzing users' social media activity.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The Ageless Friend System can also be equipped with a Health Management Department. This department can monitor users' health status and provide health-conscious matching and suggestions. For example, it can collect data such as users' steps and heart rate to assess their health. Based on the user's health status, the Health Management Department can suggest appropriate exercise and dietary recommendations. Furthermore, it can suggest suitable outings and events according to the user's health condition. This allows the Ageless Friend System to provide a safe and secure environment while supporting users' health.

[0101] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions and increase the number of options. If the user is tense, it can provide simple and easy-to-understand suggestions. If the user is excited, it can provide emotion-based suggestions to pique their interest. In this way, the suggestion function can provide more appropriate suggestions by adjusting the content of its suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The Community Department can suggest communities aligned with specific themes based on users' hobbies and interests. For example, if a user is interested in music, the Community Department can suggest communities for music events and concerts. If a user is interested in cooking, it can suggest communities for cooking classes and recipe exchange. If a user is interested in travel, it can suggest communities for travel planning and sharing travel experiences. In this way, the Community Department can support the creation of communities that users can enjoy more by suggesting communities aligned with specific themes based on users' hobbies and interests.

[0103] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit can apply broad matching criteria and suggest a variety of candidates. If the user is tense, it can apply strict matching criteria and suggest reliable candidates. If the user is excited, it can apply emotion-based matching criteria and suggest interesting candidates. In this way, the matching unit can achieve more appropriate matching by adjusting the matching criteria based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The proposal department can analyze a user's past proposal history and select the most suitable proposal method. For example, by analyzing a user's past proposal history, the proposal department can understand the user's preferred proposal trends. Based on the user's past selections of destinations and events, it can make similar suggestions. Based on the user's past rejected suggestions, it can identify suggestions to avoid. In this way, the proposal department can select a more appropriate proposal method by analyzing a user's past proposal history.

[0105] The community development team can estimate user emotions and adjust community building methods based on those estimated emotions. For example, if a user is relaxed, the community development team can suggest a relaxed pace of community building. If a user is stressed, it can suggest a simple and easy-to-understand approach. If a user is excited, it can suggest an emotion-based approach. This allows the community development team to create more appropriate communities by adjusting its methods based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The reception desk can select the optimal voice input method by considering the user's geographical location. For example, if the user is at home, the reception desk can prioritize voice input in a relaxed environment. If the user is out, it can prioritize voice input utilizing noise cancellation, taking into account the surrounding noise. If the user is at a specific facility, it can prioritize receiving information related to that facility. In this way, the reception desk can select the optimal voice input method by considering the user's geographical location.

[0107] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is relaxed, the suggestion function can provide detailed suggestions and increase the number of options. If the user is tense, it can provide simple and easy-to-understand suggestions. If the user is excited, it can provide emotion-based suggestions to pique their interest. This allows the suggestion function to provide more appropriate suggestions by adjusting the length of suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The matching unit can analyze users' social media activity and make optimal matches. For example, the matching unit can analyze the content of users' social media posts and follower reactions to understand users' interests and preferences. Based on the topics users have shown interest in on social media, it can suggest candidates with similar interests. Based on the user's social media activity history, it can suggest compatible candidates. In this way, the matching unit can make more appropriate matches by analyzing users' social media activity.

[0109] The suggestion function can apply different suggestion algorithms depending on the category of destination. For example, for restaurants, it can apply a suggestion algorithm based on the user's preferences. For parks, it can apply a suggestion algorithm based on the user's activity style. For shopping malls, it can apply a suggestion algorithm based on the user's purchase history. This allows the suggestion function to provide suggestions that match the user's preferences by applying different suggestion algorithms depending on the category of destination.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk accepts voice input. For example, a user can communicate their profile and desired friend criteria to an AI agent via voice input. The reception desk uses speech recognition technology to convert the user's voice into text data. For example, it collects the user's voice using a microphone and converts it into text data using speech recognition software. Step 2: The matching department performs optimal matching based on the information received by the reception department. For example, it considers the best match based on data from other members and information found online. The matching department uses AI to compare the user's profile information with data from other members to perform optimal matching. For example, it uses an AI model to suggest the best match based on the user's hobbies and interests. Step 3: The suggestion department investigates and proposes potential destinations based on the information obtained by the matching department. For example, before proposing potential destinations to the user, it investigates information about the candidate locations. The suggestion department uses AI to propose the best destination based on the user's preferences. For example, it uses an AI model to propose the best destination based on the user's desired date, time, and location. Step 4: The Community Department supports community building based on the information proposed by the Proposal Department. For example, users can make reservations or invite others to participate in events via voice input. The Community Department uses AI to support optimal community building based on user preferences. For example, it uses an AI model to propose optimal community building based on the events and activities desired by users.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, matching unit, proposal unit, and community unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit collects the user's voice using the microphone 38B of the smart device 14 and converts the voice into text data by the control unit 46A. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching based on data of other members and information on the internet. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and investigates and proposes potential destinations based on the user's preferences. The community unit is implemented by, for example, the control unit 46A of the smart device 14 and allows users to make reservations for events and invite others to participate through voice input. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, matching unit, proposal unit, and community unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit collects the user's voice using the microphone 238 of the smart glasses 214 and converts the voice into text data by the control unit 46A. The matching unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and performs optimal matching based on data of other members and information on the internet. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and investigates and proposes potential destinations based on the user's preferences. The community unit is implemented by, for example, the control unit 46A of the smart glasses 214 and allows users to make reservations for events and invite others to participate through voice input. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, matching unit, proposal unit, and community 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 collects the user's voice using the microphone 238 of the headset terminal 314 and converts the voice into text data by the control unit 46A. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching based on data of other members and information on the internet. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and investigates and proposes potential destinations based on the user's preferences. The community unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows users to make reservations for events and invite others to participate through voice input. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, matching unit, proposal unit, and community unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit collects the user's voice using the microphone 238 of the robot 414 and converts the voice into text data by the control unit 46A. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs optimal matching based on data of other members and information on the internet. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and investigates and proposes potential destinations based on the user's wishes. The community unit is implemented by, for example, the control unit 46A of the robot 414 and allows users to make reservations for events and invite others to participate through voice input. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that accepts voice input, A matching unit that performs matching based on the information received by the aforementioned reception unit, Based on the information obtained by the matching unit, the proposal unit investigates and proposes potential destinations, The community department provides support for community building based on the information proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Suggesting destinations to users The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned community section, Users can use voice input to book or invite others to events. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is We consider matching based on data from other members and information found online. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Before suggesting destinations to users, we research potential destinations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned community section, Users can make reservations for gateball courts via voice input. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past voice input history and selects the appropriate method of reception. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving voice input, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It can estimate the user's emotions and determine the priority of voice input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice input, the system can prioritize receiving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving voice input, the system can analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The matching unit is During the matching process, the accuracy of the matching can be improved by considering the relationships between other members. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is During the matching process, the system can take into account the attribute information of other members. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is During the matching process, the geographical distribution of other members can be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is During the matching process, you can improve the accuracy of the matching by referring to related literature from other members. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, you can adjust the level of detail in the proposal based on the importance of the destination. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms can be applied depending on the category of destination. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, you can determine the priority of the proposals based on when the potential destinations were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, you can adjust the order of suggestions based on the relevance of the destinations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned community section, We estimate user sentiment and adjust community building methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned community section, When creating a community, analyze users' past participation history to select the best community building method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned community section, When building a community, the means of community building can be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned community section, It estimates user sentiment and determines community building priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned community section, When creating a community, select a community-building method that takes into account the users' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned community section, When building a community, it is possible to analyze users' social media activity and propose methods for community building. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 desk that accepts voice input, A matching unit that performs matching based on the information received by the aforementioned reception unit, Based on the information obtained by the matching unit, the proposal unit investigates and proposes potential destinations, The community department provides support for community building based on the information proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned proposal section is, Suggesting destinations to users The system according to feature 1.

3. The aforementioned community department, Users can use voice input to book or invite others to events. The system according to feature 1.

4. The matching unit is We consider matching based on data from other members and information found online. The system according to feature 1.

5. The aforementioned proposal section is, Before suggesting destinations to users, we research potential destinations. The system according to feature 1.

6. The aforementioned community department, Users can make reservations for gateball courts via voice input. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past voice input history and selects the appropriate method of reception. The system according to feature 1.

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