system

Abstract

The system according to this embodiment aims to suggest the optimal way for users to spend their time based on their lifestyle and preferences. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, and a provision unit. The collection unit collects information about the user's lifestyle and preferences. The suggestion unit suggests the optimal way to spend time based on the information collected by the collection unit. The provision unit provides information that matches the individual's preferences 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to obtain an optimal proposal for spending free time on holidays meaningfully.

[0005] The system according to the embodiment aims to propose an optimal way of spending time based on the user's life and hobbies.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a proposal unit, and a provision unit. The collection unit collects information on the user's life and hobbies. The proposal unit proposes an optimal way of spending time based on the information collected by the collection unit. The provision unit provides information that matches the individual's hobbies based on the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal way for users to spend their time based on their lifestyle and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses an AI agent to suggest the optimal way for people to spend their free time on holidays who have nothing to do or are just sleeping. This system collects information about the user's lifestyle and preferences, and based on the collected information, the AI ​​agent traverses maps and calendars to suggest the optimal way for the user to spend their time. Furthermore, based on payment information, it provides information that matches the individual's preferences. For example, a user who spends a lot of money on curry would be suggested to go on a curry gourmet tour. This system allows users to spend their holidays meaningfully. For example, it collects information about the user's lifestyle and preferences. In this process, it collects the user's past behavior history, payment information, calendar information, etc. For example, it collects information such as what places the user has visited in the past, what kind of payments they have made, and what kind of plans they have on their calendar. This makes it possible to understand the user's preferences and lifestyle patterns. Next, based on the collected information, the AI ​​agent traverses maps and calendars to suggest the optimal way for the user to spend their time. For example, if the user thinks "I want to do something," the AI ​​agent suggests options such as studying, exercising, or changing their mood based on the user's preferences. Furthermore, if a user wants to spend time with someone, the AI ​​agent will use their calendar information to coordinate schedules with family and friends and suggest the best plan. In addition, it will provide information that matches the user's preferences based on their payment information. For example, if a user spends a lot of money on curry, the AI ​​agent will suggest a curry gourmet tour. Also, if a user is interested in movies or games, the AI ​​agent will suggest related events and places. This allows users to find ways to spend their time that suit their preferences. This system allows users to spend their holidays meaningfully. For example, if a user thinks "I want to do something," the AI ​​agent will suggest the best plan based on the user's preferences, saving the user the trouble of planning and researching. Also, since the AI ​​agent handles scheduling with family and friends, users can enjoy their holidays without stress.Furthermore, by providing information tailored to individual preferences based on payment information, users can find activities that align with their interests. This allows the system to suggest optimal ways to spend time based on the user's lifestyle and preferences, providing information that matches their individual tastes.

[0029] The system according to this embodiment comprises a collection unit, a suggestion unit, and a provision unit. The collection unit collects information about the user's lifestyle and preferences. For example, the collection unit collects the user's past behavioral history, payment information, and calendar information. For example, the collection unit collects information such as what places the user has visited in the past, what payments they have made, and what appointments they have on their calendar. The suggestion unit suggests the best way to spend time based on the information collected by the collection unit. For example, if the user wants to do something, the suggestion unit suggests options such as studying, exercising, or changing their mood based on the user's preferences. Also, if the user wants to spend time with someone, the suggestion unit adjusts schedules with family and friends based on calendar information and suggests the best plan. The provision unit provides information that matches the individual's preferences based on the information suggested by the suggestion unit. For example, the provision unit provides information that matches the individual's preferences based on payment information. For example, if the user spends a lot of money on curry, the provision unit suggests a curry gourmet tour. Also, if the user is interested in movies or games, the provision unit suggests related events and places. As a result, the system according to this embodiment can suggest the optimal way for users to spend their time based on their lifestyle and preferences, and provide information that matches individual preferences.

[0030] The data collection unit collects information about users' lifestyles and preferences. Specifically, it collects users' past behavioral history, payment information, and calendar information. For example, information about places users have visited in the past is obtained through GPS data and location services. This allows the unit to understand patterns in places users frequently visit or prefer to go. Payment information shows what goods and services users have paid for, clarifying their purchasing trends and preferences. This includes credit card usage history and electronic money transaction history. Calendar information provides details of users' schedules and events, helping to understand what activities users are planning. This information is automatically collected from users' smartphones and computers and stored in a cloud-based database. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data can be made accessible to the proposal and provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The suggestion department proposes the best way to spend time based on the information collected by the data collection department. Specifically, it analyzes the user's past behavioral history and preferences, and when the user wants to "do something," it suggests options such as studying, exercising, or relaxing. For example, if the user has a history of going to a fitness gym, the suggestion department can suggest exercising. Also, if the user has enjoyed reading in the past, the suggestion department can suggest reading a new book. Furthermore, if the user wants to spend time with someone, the suggestion department uses calendar information to coordinate schedules with family and friends and proposes the best plan. For example, if the user has an appointment with a friend on their calendar, it can suggest the best way to spend time to match that appointment. The suggestion department uses AI to analyze the user's preferences and behavioral patterns and make optimal suggestions. By learning from past data and understanding the user's preferences and tendencies, the AI ​​can make more accurate suggestions. As a result, the suggestion department can propose the best way to spend time based on the user's lifestyle and preferences, improving user satisfaction.

[0032] The service provider provides information tailored to individual preferences based on the information proposed by the suggestion provider. Specifically, it provides information tailored to individual preferences based on payment information. For example, if a user frequently spends money on curry, the service provider can suggest a curry gourmet tour. This includes information on curry specialty restaurants and curry events. Also, if a user is interested in movies or games, it will suggest related events and places. For example, if a user frequently spends money at movie theaters, the service provider can provide information on new movie screenings and film festivals. Furthermore, the service provider can also provide discount and special offer information for specific events and places based on the user's preferences. This allows users to obtain information that matches their interests and preferences, enabling them to spend their time more fulfilling. The service provider uses AI to analyze user preferences and behavioral patterns and provide optimal information. By learning from past data and understanding user preferences and trends, the AI ​​can provide more accurate information. This allows the service provider to provide optimal information based on the user's lifestyle and preferences, improving user satisfaction.

[0033] The data collection unit can collect the user's past behavioral history, payment information, and calendar information. For example, the data collection unit can collect the user's past behavioral history. For example, the data collection unit can collect information on places the user has visited and services the user has used in the past. The data collection unit can also collect the user's payment information. For example, the data collection unit can collect information on the user's purchase history and payment methods. The data collection unit can also collect the user's calendar information. For example, the data collection unit can collect information on the type and date of the user's appointments. This allows the data collection unit to understand the user's preferences and lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history, payment information, and calendar information into AI, which can then analyze this information to understand the user's preferences and lifestyle patterns.

[0034] The suggestion unit can propose options such as studying, exercising, and relaxation based on the collected information. For example, the suggestion unit can propose study options to the user based on the collected information. For example, the suggestion unit can propose study methods and materials in areas of interest to the user. The suggestion unit can also propose exercise options to the user based on the collected information. For example, the suggestion unit can propose an exercise plan tailored to the user's physical fitness and health condition. The suggestion unit can also propose relaxation options to the user based on the collected information. For example, the suggestion unit can propose places and activities where the user can relax. In this way, the suggestion unit can propose the best way for the user to spend their time based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the collected information into AI, and the AI ​​can propose the best way for the user to spend their time.

[0035] The suggestion unit can coordinate schedules with family and friends based on calendar information and propose the optimal plan. For example, the suggestion unit can coordinate schedules with family based on calendar information. For example, the suggestion unit can check the schedules of all family members and propose activities that everyone can participate in. The suggestion unit can also coordinate schedules with friends based on calendar information. For example, the suggestion unit can check the schedules of all friends and propose events that everyone can participate in. The suggestion unit can also propose the optimal plan based on calendar information. For example, the suggestion unit can propose an efficient schedule tailored to the user's schedule. In this way, the suggestion unit can coordinate schedules with family and friends based on calendar information and propose the optimal plan. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input calendar information into AI, and the AI ​​can coordinate schedules with family and friends and propose the optimal plan.

[0036] The service provider can provide information that matches an individual's preferences based on payment information. For example, the service provider can provide information on restaurants that match a user's preferences based on payment information. For example, the service provider can suggest similar restaurants based on information about restaurants the user has visited in the past. The service provider can also provide event information that matches a user's preferences based on payment information. For example, the service provider can suggest similar events based on information about events the user has participated in in the past. The service provider can also provide product information that matches a user's preferences based on payment information. For example, the service provider can suggest similar products based on information about products the user has purchased in the past. In this way, the service provider can provide information that matches an individual's preferences based on payment information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input payment information into AI, and the AI ​​can provide information that matches the user's preferences.

[0037] The service provider can suggest a curry gourmet tour if the user spends a lot of money on curry. For example, the service provider can analyze the user's payment information and suggest a curry gourmet tour if the user spends a lot on curry. For example, the service provider can provide a list of curry specialty restaurants and suggest restaurants the user should visit. The service provider can also provide route guidance for the curry gourmet tour. For example, the service provider can suggest the optimal route from the user's current location to a curry specialty restaurant. The service provider can also provide information on events related to the curry gourmet tour. For example, the service provider can provide information on events and festivals related to curry. This allows the service provider to make suggestions that match the user's personal preferences based on their payment information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input payment information into AI, and the AI ​​can suggest a curry gourmet tour that matches the user's preferences.

[0038] The data collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the data collection unit can collect relevant information based on places the user has visited or events they have attended in the past. For example, the data collection unit can analyze the user's past behavioral patterns and collect information to suggest new activities that might interest them. The data collection unit can also collect relevant information based on the user's past usage history of services and applications. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can collect information to suggest appropriate activities based on the user's current living situation. For example, the data collection unit can prioritize the collection of relevant information based on the user's areas of interest. The data collection unit can also filter out unnecessary information based on the user's current living situation and areas of interest. In this way, the data collection unit can filter information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current living situation and areas of interest into the AI, which can then filter the information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information on nearby events and activities based on the user's current location. For example, the data collection unit can prioritize the collection of information on easily accessible locations based on the user's geographical location. The data collection unit can also collect information to minimize travel time by considering the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity history and collect information to suggest new activities that the user might be interested in. The data collection unit can also collect relevant information based on information about accounts the user follows on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, and the AI ​​can collect relevant information.

[0042] The suggestion function can adjust the level of detail of a suggestion based on the user's importance to that suggestion. For example, the suggestion function can provide detailed information for activities that the user is very interested in, and concise information for activities that the user is not very interested in. It can also provide detailed schedules and procedures for activities that the user considers important. In this way, the suggestion function can adjust the level of detail of a suggestion based on the user's importance to that suggestion. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the user's importance to the AI, and the AI ​​can adjust the level of detail of the suggestion.

[0043] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user likes sports, the suggestion unit can apply a suggestion algorithm related to exercise. If the user likes reading, the suggestion unit can apply a suggestion algorithm related to reading. Also, if the user likes traveling, the suggestion unit can apply a suggestion algorithm related to travel. In this way, the suggestion unit can apply different suggestion algorithms depending on the user's category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's category information into the AI, and the AI ​​can apply different suggestion algorithms.

[0044] The proposal department can determine the priority of proposals based on the user's submission timing. For example, the proposal department may prioritize proposals based on the information the user has most recently submitted. The proposal department may also adjust the priority of proposals based on information the user has previously submitted. Furthermore, the proposal department can determine the priority of proposals based on information the user submitted at a specific time. This allows the proposal department to determine the priority of proposals based on the user's submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department may input user submission timing information into the AI, which can then determine the priority of proposals.

[0045] The suggestion unit can adjust the order of suggestions based on user relevance. For example, it might suggest activities that the user is very interested in first. It might also postpone suggesting activities that the user is not very interested in. Furthermore, it can prioritize suggesting activities that are highly relevant to the user. In this way, the suggestion unit can adjust the order of suggestions based on user relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user relevance information into the AI, which can then adjust the order of suggestions.

[0046] The information delivery unit can analyze the user's past purchasing behavior and select the optimal information delivery method at the time of delivery. For example, the information delivery unit can provide relevant information based on the products and services the user has purchased in the past. For example, the information delivery unit can analyze the user's past purchasing behavior and provide information to suggest new products and services that the user might be interested in. Furthermore, the information delivery unit can select the optimal information delivery method based on the payment methods the user has used in the past. In this way, the information delivery unit can select the optimal information delivery method by analyzing the user's past purchasing behavior. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input data on the user's past purchasing behavior into AI, and the AI ​​can select the optimal information delivery method.

[0047] The information provider can customize the means of providing information based on the user's current living situation at the time of provision. For example, the information provider can provide information to suggest appropriate activities according to the user's current living situation. For example, the information provider can prioritize providing relevant information based on the user's living situation. The information provider can also filter out unnecessary information based on the user's current living situation. In this way, the information provider can customize the means of providing information based on the user's current living situation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information about the user's current living situation into the AI, and the AI ​​can customize the means of providing information.

[0048] The information provider can select the optimal method of information delivery by considering the user's geographical location information at the time of delivery. For example, the information provider can provide information about nearby events and activities based on the user's current location. For example, the information provider can prioritize providing information about easily accessible locations based on the user's geographical location information. Furthermore, the information provider can provide information to minimize travel time by considering the user's geographical location information. In this way, the information provider can select the optimal method of information delivery by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into AI, and the AI ​​can select the optimal method of information delivery.

[0049] The service provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the service provider can provide relevant information based on topics the user has shown interest in on social media. For example, the service provider can analyze the user's social media activity history and provide information to suggest new activities that the user might be interested in. The service provider can also provide relevant information based on information about accounts the user follows on social media. In this way, the service provider can propose means of providing information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into AI, and the AI ​​can propose means of providing information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can prioritize collecting information based on the user's current location, taking into account the user's geographical location. For example, it can collect information about events and activities happening near the user's current location. It can also prioritize collecting information about easily accessible locations based on the user's geographical location. Furthermore, it can collect information to minimize travel time, taking into account the user's geographical location. As a result, the data collection unit can prioritize collecting highly relevant information, taking into account the user's geographical location.

[0052] The suggestion department can analyze a user's past behavioral history and propose new activities based on their preferences. For example, it can suggest relevant new activities based on places the user has visited and events they have attended in the past. It can also analyze a user's past behavioral patterns and suggest new activities that they might be interested in. Furthermore, it can suggest relevant new activities based on the user's past usage history of services and apps. In this way, the suggestion department can propose optimal new activities by analyzing a user's past behavioral history.

[0053] The service provider can analyze users' social media activity and provide information based on topics they have shown interest in. For example, it can provide information related to topics users have shown interest in on social media. It can also analyze users' social media activity history and provide information to suggest new activities they might be interested in. Furthermore, it can provide relevant information based on the accounts users follow on social media. In this way, the service provider can provide relevant information by analyzing users' social media activity.

[0054] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, it can collect information to suggest appropriate activities based on the user's current lifestyle. It can also prioritize the collection of relevant information based on the user's areas of interest. Furthermore, it can filter out unnecessary information based on the user's current lifestyle and areas of interest. In this way, the data collection unit can filter information based on the user's current lifestyle and areas of interest.

[0055] The proposal department can prioritize proposals based on when they are submitted by users. For example, it can prioritize proposals based on the information a user has most recently submitted. It can also adjust the priority of proposals based on information a user has submitted in the past. Furthermore, it can determine the priority of proposals based on information a user has submitted at a specific time. In this way, the proposal department can determine the priority of proposals based on when they are submitted by users.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects information about the user's lifestyle and preferences. Specifically, it collects the user's past activity history, payment information, and calendar information. For example, it collects information such as what places the user has visited in the past, what payments they have made, and what appointments they have on their calendar. Step 2: The suggestion unit proposes the best way to spend time based on the information collected by the collection unit. For example, if the user wants to "do something," it suggests options such as studying, exercising, or relaxing, based on the user's preferences. Also, if the user wants to spend time with someone, it coordinates schedules with family and friends based on calendar information and proposes the best plan. Step 3: The provision department provides information that matches the individual's preferences based on the information proposed by the suggestion department. For example, based on payment information, it provides information that matches the individual's preferences. If the user spends a lot of money on curry, it suggests a curry gourmet tour. Also, if the user is interested in movies or games, it suggests events and places related to those.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to suggest the optimal way for people to spend their free time on holidays who have nothing to do or are just sleeping. This system collects information about the user's lifestyle and preferences, and based on the collected information, the AI ​​agent traverses maps and calendars to suggest the optimal way for the user to spend their time. Furthermore, based on payment information, it provides information that matches the individual's preferences. For example, a user who spends a lot of money on curry would be suggested to go on a curry gourmet tour. This system allows users to spend their holidays meaningfully. For example, it collects information about the user's lifestyle and preferences. In this process, it collects the user's past behavior history, payment information, calendar information, etc. For example, it collects information such as what places the user has visited in the past, what kind of payments they have made, and what kind of plans they have on their calendar. This makes it possible to understand the user's preferences and lifestyle patterns. Next, based on the collected information, the AI ​​agent traverses maps and calendars to suggest the optimal way for the user to spend their time. For example, if the user thinks "I want to do something," the AI ​​agent suggests options such as studying, exercising, or changing their mood based on the user's preferences. Furthermore, if a user wants to spend time with someone, the AI ​​agent will use their calendar information to coordinate schedules with family and friends and suggest the best plan. In addition, it will provide information that matches the user's preferences based on their payment information. For example, if a user spends a lot of money on curry, the AI ​​agent will suggest a curry gourmet tour. Also, if a user is interested in movies or games, the AI ​​agent will suggest related events and places. This allows users to find ways to spend their time that suit their preferences. This system allows users to spend their holidays meaningfully. For example, if a user thinks "I want to do something," the AI ​​agent will suggest the best plan based on the user's preferences, saving the user the trouble of planning and researching. Also, since the AI ​​agent handles scheduling with family and friends, users can enjoy their holidays without stress.Furthermore, by providing information tailored to individual preferences based on payment information, users can find activities that align with their interests. This allows the system to suggest optimal ways to spend time based on the user's lifestyle and preferences, providing information that matches their individual tastes.

[0059] The system according to this embodiment comprises a collection unit, a suggestion unit, and a provision unit. The collection unit collects information about the user's lifestyle and preferences. For example, the collection unit collects the user's past behavioral history, payment information, and calendar information. For example, the collection unit collects information such as what places the user has visited in the past, what payments they have made, and what appointments they have on their calendar. The suggestion unit suggests the best way to spend time based on the information collected by the collection unit. For example, if the user wants to do something, the suggestion unit suggests options such as studying, exercising, or changing their mood based on the user's preferences. Also, if the user wants to spend time with someone, the suggestion unit adjusts schedules with family and friends based on calendar information and suggests the best plan. The provision unit provides information that matches the individual's preferences based on the information suggested by the suggestion unit. For example, the provision unit provides information that matches the individual's preferences based on payment information. For example, if the user spends a lot of money on curry, the provision unit suggests a curry gourmet tour. Also, if the user is interested in movies or games, the provision unit suggests related events and places. As a result, the system according to this embodiment can suggest the optimal way for users to spend their time based on their lifestyle and preferences, and provide information that matches individual preferences.

[0060] The data collection unit collects information about users' lifestyles and preferences. Specifically, it collects users' past behavioral history, payment information, and calendar information. For example, information about places users have visited in the past is obtained through GPS data and location services. This allows the unit to understand patterns in places users frequently visit or prefer to go. Payment information shows what goods and services users have paid for, clarifying their purchasing trends and preferences. This includes credit card usage history and electronic money transaction history. Calendar information provides details of users' schedules and events, helping to understand what activities users are planning. This information is automatically collected from users' smartphones and computers and stored in a cloud-based database. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data can be made accessible to the proposal and provision departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The suggestion department proposes the best way to spend time based on the information collected by the data collection department. Specifically, it analyzes the user's past behavioral history and preferences, and when the user wants to "do something," it suggests options such as studying, exercising, or relaxing. For example, if the user has a history of going to a fitness gym, the suggestion department can suggest exercising. Also, if the user has enjoyed reading in the past, the suggestion department can suggest reading a new book. Furthermore, if the user wants to spend time with someone, the suggestion department uses calendar information to coordinate schedules with family and friends and proposes the best plan. For example, if the user has an appointment with a friend on their calendar, it can suggest the best way to spend time to match that appointment. The suggestion department uses AI to analyze the user's preferences and behavioral patterns and make optimal suggestions. By learning from past data and understanding the user's preferences and tendencies, the AI ​​can make more accurate suggestions. As a result, the suggestion department can propose the best way to spend time based on the user's lifestyle and preferences, improving user satisfaction.

[0062] The service provider provides information tailored to individual preferences based on the information proposed by the suggestion provider. Specifically, it provides information tailored to individual preferences based on payment information. For example, if a user frequently spends money on curry, the service provider can suggest a curry gourmet tour. This includes information on curry specialty restaurants and curry events. Also, if a user is interested in movies or games, it will suggest related events and places. For example, if a user frequently spends money at movie theaters, the service provider can provide information on new movie screenings and film festivals. Furthermore, the service provider can also provide discount and special offer information for specific events and places based on the user's preferences. This allows users to obtain information that matches their interests and preferences, enabling them to spend their time more fulfilling. The service provider uses AI to analyze user preferences and behavioral patterns and provide optimal information. By learning from past data and understanding user preferences and trends, the AI ​​can provide more accurate information. This allows the service provider to provide optimal information based on the user's lifestyle and preferences, improving user satisfaction.

[0063] The data collection unit can collect the user's past behavioral history, payment information, and calendar information. For example, the data collection unit can collect the user's past behavioral history. For example, the data collection unit can collect information on places the user has visited and services the user has used in the past. The data collection unit can also collect the user's payment information. For example, the data collection unit can collect information on the user's purchase history and payment methods. The data collection unit can also collect the user's calendar information. For example, the data collection unit can collect information on the type and date of the user's appointments. This allows the data collection unit to understand the user's preferences and lifestyle patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history, payment information, and calendar information into AI, which can then analyze this information to understand the user's preferences and lifestyle patterns.

[0064] The suggestion unit can propose options such as studying, exercising, and relaxation based on the collected information. For example, the suggestion unit can propose study options to the user based on the collected information. For example, the suggestion unit can propose study methods and materials in areas of interest to the user. The suggestion unit can also propose exercise options to the user based on the collected information. For example, the suggestion unit can propose an exercise plan tailored to the user's physical fitness and health condition. The suggestion unit can also propose relaxation options to the user based on the collected information. For example, the suggestion unit can propose places and activities where the user can relax. In this way, the suggestion unit can propose the best way for the user to spend their time based on the collected information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the collected information into AI, and the AI ​​can propose the best way for the user to spend their time.

[0065] The suggestion unit can coordinate schedules with family and friends based on calendar information and propose the optimal plan. For example, the suggestion unit can coordinate schedules with family based on calendar information. For example, the suggestion unit can check the schedules of all family members and propose activities that everyone can participate in. The suggestion unit can also coordinate schedules with friends based on calendar information. For example, the suggestion unit can check the schedules of all friends and propose events that everyone can participate in. The suggestion unit can also propose the optimal plan based on calendar information. For example, the suggestion unit can propose an efficient schedule tailored to the user's schedule. In this way, the suggestion unit can coordinate schedules with family and friends based on calendar information and propose the optimal plan. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input calendar information into AI, and the AI ​​can coordinate schedules with family and friends and propose the optimal plan.

[0066] The service provider can provide information that matches an individual's preferences based on payment information. For example, the service provider can provide information on restaurants that match a user's preferences based on payment information. For example, the service provider can suggest similar restaurants based on information about restaurants the user has visited in the past. The service provider can also provide event information that matches a user's preferences based on payment information. For example, the service provider can suggest similar events based on information about events the user has participated in in the past. The service provider can also provide product information that matches a user's preferences based on payment information. For example, the service provider can suggest similar products based on information about products the user has purchased in the past. In this way, the service provider can provide information that matches an individual's preferences based on payment information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input payment information into AI, and the AI ​​can provide information that matches the user's preferences.

[0067] The service provider can suggest a curry gourmet tour if the user spends a lot of money on curry. For example, the service provider can analyze the user's payment information and suggest a curry gourmet tour if the user spends a lot on curry. For example, the service provider can provide a list of curry specialty restaurants and suggest restaurants the user should visit. The service provider can also provide route guidance for the curry gourmet tour. For example, the service provider can suggest the optimal route from the user's current location to a curry specialty restaurant. The service provider can also provide information on events related to the curry gourmet tour. For example, the service provider can provide information on events and festivals related to curry. This allows the service provider to make suggestions that match the user's personal preferences based on their payment information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input payment information into AI, and the AI ​​can suggest a curry gourmet tour that matches the user's preferences.

[0068] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information about relaxing activities. For example, if the user is having fun, the data collection unit can collect information about entertainment. Also, if the user is tired, the data collection unit can collect information about rest and refreshment. In this way, the data collection unit can adjust the type of information it collects based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and adjust the type of information the AI ​​collects.

[0069] The data collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the data collection unit can collect relevant information based on places the user has visited or events they have attended in the past. For example, the data collection unit can analyze the user's past behavioral patterns and collect information to suggest new activities that might interest them. The data collection unit can also collect relevant information based on the user's past usage history of services and applications. In this way, the data collection unit can select the optimal information collection method by analyzing the user's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral history into AI, which can then select the optimal information collection method.

[0070] The data collection unit can filter information based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can collect information to suggest appropriate activities based on the user's current living situation. For example, the data collection unit can prioritize the collection of relevant information based on the user's areas of interest. The data collection unit can also filter out unnecessary information based on the user's current living situation and areas of interest. In this way, the data collection unit can filter information based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's current living situation and areas of interest into the AI, which can then filter the information.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information about relaxing activities. For example, if the user is having fun, the data collection unit may prioritize collecting information about entertainment. Also, if the user is tired, the data collection unit may prioritize collecting information about rest and refreshment. In this way, the data collection unit can determine the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and determine the priority of information to collect for the AI.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can collect information on nearby events and activities based on the user's current location. For example, the data collection unit can prioritize the collection of information on easily accessible locations based on the user's geographical location. The data collection unit can also collect information to minimize travel time by considering the user's geographical location. As a result, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then prioritize the collection of highly relevant information.

[0073] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity history and collect information to suggest new activities that the user might be interested in. The data collection unit can also collect relevant information based on information about accounts the user follows on social media. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, and the AI ​​can collect relevant information.

[0074] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will use gentle language when suggesting relaxing activities. If the user is having fun, the suggestion unit can make entertainment suggestions in a bright and cheerful manner. If the user is tired, the suggestion unit can make suggestions about rest and refreshment in a simple and easy-to-understand manner. In this way, the suggestion unit can adjust the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the way it presents suggestions.

[0075] The suggestion function can adjust the level of detail of a suggestion based on the user's importance to that suggestion. For example, the suggestion function can provide detailed information for activities that the user is very interested in, and concise information for activities that the user is not very interested in. It can also provide detailed schedules and procedures for activities that the user considers important. In this way, the suggestion function can adjust the level of detail of a suggestion based on the user's importance to that suggestion. Some or all of the above processing in the suggestion function may be performed using AI, for example, or not. For example, the suggestion function can input information about the user's importance to the AI, and the AI ​​can adjust the level of detail of the suggestion.

[0076] The suggestion unit can apply different suggestion algorithms depending on the user's category when making suggestions. For example, if the user likes sports, the suggestion unit can apply a suggestion algorithm related to exercise. If the user likes reading, the suggestion unit can apply a suggestion algorithm related to reading. Also, if the user likes traveling, the suggestion unit can apply a suggestion algorithm related to travel. In this way, the suggestion unit can apply different suggestion algorithms depending on the user's category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's category information into the AI, and the AI ​​can apply different suggestion algorithms.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. In this way, the suggestion unit can adjust the length of the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can then adjust the length of the suggestions.

[0078] The proposal department can determine the priority of proposals based on the user's submission timing. For example, the proposal department may prioritize proposals based on the information the user has most recently submitted. The proposal department may also adjust the priority of proposals based on information the user has previously submitted. Furthermore, the proposal department can determine the priority of proposals based on information the user submitted at a specific time. This allows the proposal department to determine the priority of proposals based on the user's submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department may input user submission timing information into the AI, which can then determine the priority of proposals.

[0079] The suggestion unit can adjust the order of suggestions based on user relevance. For example, it might suggest activities that the user is very interested in first. It might also postpone suggesting activities that the user is not very interested in. Furthermore, it can prioritize suggesting activities that are highly relevant to the user. In this way, the suggestion unit can adjust the order of suggestions based on user relevance. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user relevance information into the AI, which can then adjust the order of suggestions.

[0080] The service provider can estimate the user's emotions and determine the priority of the information to provide based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing information about relaxing activities. For example, if the user is having fun, the service provider can prioritize providing information about entertainment. Also, if the user is tired, the service provider can prioritize providing information about rest and refreshment. In this way, the service provider can determine the priority of the information to provide based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and determine the priority of the information that AI provides.

[0081] The information delivery unit can analyze the user's past purchasing behavior and select the optimal information delivery method at the time of delivery. For example, the information delivery unit can provide relevant information based on the products and services the user has purchased in the past. For example, the information delivery unit can analyze the user's past purchasing behavior and provide information to suggest new products and services that the user might be interested in. Furthermore, the information delivery unit can select the optimal information delivery method based on the payment methods the user has used in the past. In this way, the information delivery unit can select the optimal information delivery method by analyzing the user's past purchasing behavior. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input data on the user's past purchasing behavior into AI, and the AI ​​can select the optimal information delivery method.

[0082] The information provider can customize the means of providing information based on the user's current living situation at the time of provision. For example, the information provider can provide information to suggest appropriate activities according to the user's current living situation. For example, the information provider can prioritize providing relevant information based on the user's living situation. The information provider can also filter out unnecessary information based on the user's current living situation. In this way, the information provider can customize the means of providing information based on the user's current living situation. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input information about the user's current living situation into the AI, and the AI ​​can customize the means of providing information.

[0083] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is having fun, the service provider can provide a visually pleasing display method. Furthermore, if the user is tired, the service provider can provide a simple and easy-to-understand display method. In this way, the service provider can adjust how the information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into AI and adjust how the information provided by AI is displayed.

[0084] The information provider can select the optimal method of information delivery by considering the user's geographical location information at the time of delivery. For example, the information provider can provide information about nearby events and activities based on the user's current location. For example, the information provider can prioritize providing information about easily accessible locations based on the user's geographical location information. Furthermore, the information provider can provide information to minimize travel time by considering the user's geographical location information. In this way, the information provider can select the optimal method of information delivery by considering the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into AI, and the AI ​​can select the optimal method of information delivery.

[0085] The service provider can analyze the user's social media activity and propose means of providing information at the time of provision. For example, the service provider can provide relevant information based on topics the user has shown interest in on social media. For example, the service provider can analyze the user's social media activity history and provide information to suggest new activities that the user might be interested in. The service provider can also provide relevant information based on information about accounts the user follows on social media. In this way, the service provider can propose means of providing information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into AI, and the AI ​​can propose means of providing information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is feeling stressed, it can immediately suggest relaxing activities. If the user is enjoying themselves, it can offer timely suggestions related to entertainment. Furthermore, if the user is tired, it can offer suggestions related to rest and refreshment at the appropriate time. In this way, the suggestion function can adjust the timing of suggestions based on the user's emotions.

[0088] The information provider can estimate the user's emotions and adjust the content of the information provided based on those emotions. For example, if the user is stressed, it can provide information on relaxing music or videos. If the user is enjoying themselves, it can provide information on entertainment. Furthermore, if the user is tired, it can provide information on rest and refreshment. In this way, the information provider can adjust the content of the information provided based on the user's emotions.

[0089] The data collection unit can estimate the user's emotions and adjust the accuracy of the information it collects based on those emotions. For example, if the user is stressed, it can collect detailed information about relaxing activities. If the user is having fun, it can collect detailed information about entertainment. Furthermore, if the user is tired, it can collect detailed information about rest and refreshment. In this way, the data collection unit can adjust the accuracy of the information it collects based on the user's emotions.

[0090] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on those emotions. For example, if the user is stressed, it can frequently suggest relaxing activities. If the user is having fun, it can suggest entertainment at a moderate frequency. Furthermore, if the user is tired, it can suggest rest and refreshment at an appropriate frequency. In this way, the suggestion unit can adjust the frequency of suggestions based on the user's emotions.

[0091] The information provider can estimate the user's emotions and adjust the format of the information provided based on those emotions. For example, if the user is stressed, the information can be provided in a simple, visually calming format. If the user is enjoying themselves, the information can be provided in a visually pleasing format. Furthermore, if the user is tired, the information can be provided in a simple, easy-to-understand format. In this way, the information provider can adjust the format of the information provided based on the user's emotions.

[0092] The data collection unit can prioritize collecting information based on the user's current location, taking into account the user's geographical location. For example, it can collect information about events and activities happening near the user's current location. It can also prioritize collecting information about easily accessible locations based on the user's geographical location. Furthermore, it can collect information to minimize travel time, taking into account the user's geographical location. As a result, the data collection unit can prioritize collecting highly relevant information, taking into account the user's geographical location.

[0093] The suggestion department can analyze a user's past behavioral history and propose new activities based on their preferences. For example, it can suggest relevant new activities based on places the user has visited and events they have attended in the past. It can also analyze a user's past behavioral patterns and suggest new activities that they might be interested in. Furthermore, it can suggest relevant new activities based on the user's past usage history of services and apps. In this way, the suggestion department can propose optimal new activities by analyzing a user's past behavioral history.

[0094] The service provider can analyze users' social media activity and provide information based on topics they have shown interest in. For example, it can provide information related to topics users have shown interest in on social media. It can also analyze users' social media activity history and provide information to suggest new activities they might be interested in. Furthermore, it can provide relevant information based on the accounts users follow on social media. In this way, the service provider can provide relevant information by analyzing users' social media activity.

[0095] The data collection unit can filter information based on the user's current lifestyle and areas of interest. For example, it can collect information to suggest appropriate activities based on the user's current lifestyle. It can also prioritize the collection of relevant information based on the user's areas of interest. Furthermore, it can filter out unnecessary information based on the user's current lifestyle and areas of interest. In this way, the data collection unit can filter information based on the user's current lifestyle and areas of interest.

[0096] The proposal department can prioritize proposals based on when they are submitted by users. For example, it can prioritize proposals based on the information a user has most recently submitted. It can also adjust the priority of proposals based on information a user has submitted in the past. Furthermore, it can determine the priority of proposals based on information a user has submitted at a specific time. In this way, the proposal department can determine the priority of proposals based on when they are submitted by users.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects information about the user's lifestyle and preferences. Specifically, it collects the user's past activity history, payment information, and calendar information. For example, it collects information such as what places the user has visited in the past, what payments they have made, and what appointments they have on their calendar. Step 2: The suggestion unit proposes the best way to spend time based on the information collected by the collection unit. For example, if the user wants to "do something," it suggests options such as studying, exercising, or relaxing, based on the user's preferences. Also, if the user wants to spend time with someone, it coordinates schedules with family and friends based on calendar information and proposes the best plan. Step 3: The provision department provides information that matches the individual's preferences based on the information proposed by the suggestion department. For example, based on payment information, it provides information that matches the individual's preferences. If the user spends a lot of money on curry, it suggests a curry gourmet tour. Also, if the user is interested in movies or games, it suggests events and places related to those.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the collection unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information about the user's lifestyle and preferences using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to spend time based on the collected information. The provision unit is implemented in the control unit 46A of the smart device 14 and provides information that matches the individual's preferences based on the proposed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, suggestion unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information about the user's lifestyle and preferences using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A transmits the collected information to the data processing unit 12. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests the optimal way to spend time based on the collected information. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides information that matches the individual's preferences based on the suggested information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information about the user's lifestyle and preferences, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal way to spend time based on the collected information. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides information that matches the individual's preferences based on the proposed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, proposal unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information about the user's lifestyle and preferences, and the control unit 46A transmits the collected information to the data processing unit 12. The proposal unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and proposes the optimal way to spend time based on the collected information. The provision unit is implemented in, for example, the control unit 46A of the robot 414, and provides information that matches the individual's preferences based on the proposed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A collection unit that collects information about the user's lifestyle and preferences, A proposal unit that proposes the optimal way to spend time based on the information collected by the aforementioned collection unit, A provisioning unit provides information that matches individual preferences based on the information proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects users' past behavioral history, payment information, and calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the collected information, we suggest options such as studying, exercising, and relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on your calendar information, we'll coordinate schedules with family and friends and suggest the best plan for you. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on payment information, we provide information that matches individual preferences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If a user spends a lot on curry, we suggest a curry gourmet tour. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, 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 collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting 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 collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned proposal section is, When submitting a proposal, we prioritize the proposals based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, the system analyzes the user's past purchasing behavior to select the most suitable method of information delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and propose methods for providing information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 collection unit that collects information about the user's lifestyle and preferences, A proposal unit that proposes the optimal way to spend time based on the information collected by the aforementioned collection unit, A provisioning unit provides information that matches individual preferences based on the information proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collects users' past behavioral history, payment information, and calendar information. The system according to feature 1.

3. The aforementioned proposal section is, Based on the collected information, we suggest options such as studying, exercising, and relaxation. The system according to feature 1.

4. The aforementioned proposal section is, Based on your calendar information, we'll coordinate schedules with family and friends and suggest the best plan for you. The system according to feature 1.

5. The aforementioned supply unit is, Based on payment information, we provide information that matches individual preferences. The system according to feature 1.

6. The aforementioned supply unit is, If a user spends a lot on curry, we suggest a curry gourmet tour. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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