system

Abstract

The system according to this embodiment aims to propose the most suitable experiences and events to users and to support their arrangement and preparation. [Solution] The system according to the embodiment comprises a collection unit, a suggestion unit, an arrangement unit, and a reminder unit. The collection unit collects the user's interests, behavioral patterns, and schedule. The suggestion unit analyzes the information collected by the collection unit and suggests the most suitable experiences and events to the user. The arrangement unit makes the necessary arrangements and preparations for the experiences and events suggested by the suggestion unit. The reminder unit sends a reminder immediately before the experiences and events arranged by the arrangement unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 find a new experience or event, and it takes time to prepare and arrange for it.

[0005] The system according to the embodiment aims to propose an optimal experience or event for the user and support the arrangement and preparation thereof.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a suggestion unit, an arrangement unit, and a reminder unit. The collection unit collects the user's interests, behavioral patterns, and schedule. The suggestion unit analyzes the information collected by the collection unit and suggests the most suitable experiences and events for the user. The arrangement unit makes the necessary arrangements and preparations for the experiences and events suggested by the suggestion unit. The reminder unit sends a reminder immediately before the experiences and events arranged by the arrangement unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose the most suitable experiences and events to users and support their arrangement and preparation. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Hobify system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problems of many people who "want to try something new but don't know what to do" or "want to use their free time meaningfully but can't take action." This system uses generative AI to automatically suggest experiences and events that can be easily enjoyed in the user's free time, taking into account the user's interests, behavioral patterns, and schedule. Furthermore, it goes beyond suggestions, providing full support such as arrangements and preparations necessary for participation, presenting packing lists, and reminders just before the event. By using the Hobify system, users can easily enjoy new experiences and hobbies without spending time on complicated preparations or searching for information. The optimal suggestions and comprehensive support made possible by generative AI make the user's daily life more colorful and fulfilling. For example, a user accesses Hobify and inputs their interests, behavioral patterns, and schedule. The generative AI analyzes this information and suggests the most suitable experiences and events for the user. For example, if a user inputs "I want to refresh myself on the weekend," the generative AI considers the user's past behavioral patterns and interests and suggests things like a music concert or pottery class held nearby. Next, the generative AI supports the arrangements and preparations necessary for the suggested experience or event. For example, it can handle tasks such as arranging tickets for a music concert or preparing materials for a pottery class. It also sends reminders shortly before events to ensure users don't forget to participate. Furthermore, the generative AI integrates with the user's calendar and automatically suggests the most suitable experiences based on their free time. For instance, if a user enters "I'm free in the afternoon of November 23, 2024," the generative AI will suggest watching a movie or other activities for that time, and assist with ticket reservations and preparations. In this way, the Hobify system utilizes generative AI to enrich and enhance users' free time. Users can easily enjoy new experiences and hobbies without having to spend time on tedious preparations or searching for information. As a result, the Hobify system can enrich users' free time.

[0029] The Hobify system according to this embodiment comprises a collection unit, a suggestion unit, an arrangement unit, and a reminder unit. The collection unit collects the user's interests, behavioral patterns, and schedule. For example, the collection unit stores the user's entered interests, behavioral patterns, and schedule in a database. The collection unit can also collect information from the user's past behavioral history and social media activity. For example, the collection unit analyzes events the user has participated in and topics they have shown interest in to understand the user's interests and behavioral patterns. Furthermore, the collection unit can also analyze the contents of the user's calendar app or planner to collect schedules. The suggestion unit uses generative AI to analyze the information collected by the collection unit and proposes the most suitable experiences and events to the user. For example, the suggestion unit uses generative AI to select the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. The suggestion unit can also use generative AI to analyze the user's past behavioral history and social media activity to make suggestions tailored to the user. For example, the suggestion unit uses generative AI to propose the most suitable experiences and events based on events the user has participated in and topics they have shown interest in. The Arrangement Department handles the necessary arrangements and preparations for experiences and events proposed by the Proposal Department. For example, the Arrangement Department arranges tickets and prepares equipment for proposed experiences and events. The Arrangement Department can also provide a list of items that users need to bring to participate. For example, the Arrangement Department arranges tickets for a user to attend a music concert and provides a list of items to bring. The Reminder Department sends a reminder shortly before the experience or event arranged by the Arrangement Department. For example, the Reminder Department sends a reminder the day before the event to ensure that users do not forget to participate. The Reminder Department can also send a reminder on the day of the event. For example, the Reminder Department sends a reminder one hour before the start time of the event to allow users to prepare. In this way, the Hobify system according to the embodiment can enrich the user's free time.

[0030] The data collection department collects user interests, behavioral patterns, and schedules. Specifically, it stores user-entered interests, behavioral patterns, and schedules in a database. For example, it collects information on hobbies such as sports, music, and travel, and uses this data to understand user preferences. The data collection department can also collect information from users' past behavioral history and social media activity. For example, it analyzes events users have participated in and topics they have shown interest in to understand their interests and behavioral patterns. This allows for a detailed understanding of what kinds of events and experiences users are interested in. Furthermore, the data collection department can analyze the contents of users' calendar apps and planners to collect schedules. For example, it analyzes appointments and reminders registered in users' calendars to identify free time and available time. This enables the provision of optimal suggestions tailored to the user's schedule. The data collection department centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the proposal and arrangement departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The suggestion department uses generative AI to analyze information collected by the data collection department and proposes the most suitable experiences and events to the user. Specifically, the generative AI selects the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. For example, if a user is interested in music, the generative AI will suggest nearby music live performances and concerts. The generative AI can also suggest the most suitable experiences and events based on events the user has previously attended and topics they have shown interest in. For example, based on art exhibitions and workshops the user has previously attended, the generative AI will suggest a new art event. Furthermore, the suggestion department can use generative AI to analyze the user's social media activity and make personalized suggestions. For example, it can analyze articles and posts the user has shared on social media and suggest related events and experiences. This allows the suggestion department to make personalized suggestions based on the user's interests and behavioral patterns. The suggestion department can utilize the learning function of the generative AI to respond to changes in the user's preferences and behavioral patterns. For example, if a user starts a new hobby, the generative AI will learn that information and make relevant suggestions. The suggestion department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the best possible experience and events, thereby improving user satisfaction.

[0032] The Arrangement Department handles the necessary arrangements and preparations for experiences and events proposed by the Proposal Department. Specifically, this includes arranging tickets and preparing equipment for proposed experiences and events. For example, the Arrangement Department can arrange tickets for a user to attend a music concert and provide a packing list. The Arrangement Department can also provide a packing list for the user's participation. For example, if a user is going camping, the Arrangement Department will provide a packing list including tents, sleeping bags, and cooking equipment. Furthermore, the Arrangement Department can also arrange transportation and accommodation for the user's participation. For example, if a user is attending an event far away, the Arrangement Department will arrange flight or train tickets and hotel reservations. This allows users to participate in events and experiences without hassle. The Arrangement Department works in conjunction with the Proposal Department to make optimal arrangements tailored to the user's schedule. For example, they can check the user's calendar and arrange events to fit their free time. The Arrangement Department can also collect user feedback and continuously improve the accuracy and effectiveness of the arrangements. This allows the Arrangement Department to provide users with efficient and effective arrangements, thereby improving user satisfaction.

[0033] The Reminders Department sends reminders shortly before experiences and events arranged by the Arrangement Department. Specifically, it sends reminders the day before the event to ensure users don't forget to participate. For example, the Reminders Department sends emails or push notifications the day before the event to remind users to review event details and packing lists. The Reminders Department can also send reminders on the day of the event. For example, it can send a reminder one hour before the event starts to allow users time to prepare. Furthermore, the Reminders Department can adjust the timing of reminders to suit the user's schedule. For example, if a user is busy, the reminder timing can be set earlier to allow them ample time to prepare. The Reminders Department can collect user feedback and continuously improve the accuracy and effectiveness of its reminders. For example, users can provide feedback on the timing and content of reminders, allowing the Reminders Department to optimize its reminder methods based on that information. The Reminders Department can also reliably transmit information using multiple communication methods. For example, it can use SMS and voice calls in addition to email and push notifications to ensure important information is delivered reliably. This allows the reminder unit to provide users with quick and reliable reminders, thereby improving user satisfaction.

[0034] The data collection unit can analyze a user's past behavior history and select the optimal timing for data collection. For example, if a user was active on a specific day of the week or time of day in the past, the data collection unit can collect information according to that time. The data collection unit can also collect information when similar events are held, based on a user's past participation in specific events. Furthermore, if a user was active during a specific season in the past, the data collection unit can collect information according to that season. This allows the system to provide users with useful information by collecting data at the optimal time based on their past behavior 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 behavior history data into a generating AI and have the generating AI select the optimal timing for data collection.

[0035] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the collection process. For example, if the user is busy, the unit can prioritize collecting information on experiences and events that can be enjoyed in a short amount of time. Similarly, if the user wants to relax, the unit can prioritize collecting information on relaxing experiences and events. Furthermore, if the user wants to find a new hobby, the unit can prioritize collecting information on new hobbies. By filtering information based on the user's current lifestyle and areas of interest, the unit can provide the user with highly relevant information. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0036] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting information about events held near the user's current location. Furthermore, if the user is interested in a particular region, the data collection unit can prioritize collecting information about that region. Additionally, if the user is traveling, the data collection unit can prioritize collecting information about experiences and events available at their travel destination. This allows the data collection unit to provide highly relevant information to the user by considering their 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 instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0037] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can prioritize collecting information about events that the user has shown interest in on social media. It can also prioritize collecting information about events that accounts the user follows on social media are interested in. Furthermore, the data collection unit can prioritize collecting information about events that the user has expressed interest in on social media. This allows the data collection unit to provide users with highly relevant information by analyzing their social media activity. Some or all of the processing described above 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0038] The proposal function can adjust the level of detail in a proposal based on the importance of the experience or event. For example, the proposal function can provide detailed information for high-importance experiences and events. It can also provide concise information for less important experiences and events. Furthermore, it can provide detailed information for experiences and events that the user is particularly interested in. By adjusting the level of detail in a proposal based on the importance of the experience or event, the proposal function can provide information that is useful to the user. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input experience and event importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0039] The suggestion unit can apply different suggestion algorithms depending on the category of the experience or event. For example, in the case of a sports event, the suggestion unit makes suggestions based on the user's past sports activity history. Similarly, in the case of a cultural event, the suggestion unit can make suggestions based on the user's past cultural activity history. Furthermore, in the case of a leisure event, the suggestion unit can make suggestions based on the user's past leisure activity history. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the experience or event. 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 experience or event category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0040] The suggestion department can prioritize suggestions based on the timing of experiences and events. For example, it might prioritize suggesting experiences and events that are happening soon. It can also suggest experiences and events that are held at the optimal time to fit the user's schedule. Furthermore, it can consider seasonal events and make suggestions at the appropriate time. By prioritizing suggestions based on the timing of experiences and events, it can provide users with useful information. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input experience and event timing data into a generating AI and have the generating AI determine the priority of suggestions.

[0041] The suggestion unit can adjust the order of suggestions based on the relevance of experiences and events. For example, it might suggest experiences and events most relevant to the user's interests first. It can also prioritize suggesting relevant experiences and events based on the user's past behavior patterns. Furthermore, it can prioritize suggesting relevant experiences and events based on the user's current areas of interest. By adjusting the order of suggestions based on the relevance of experiences and events, it can provide users with useful information. 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 relevance data of experiences and events into a generating AI and have the generating AI adjust the order of suggestions.

[0042] The booking unit can analyze the user's past participation history to select the optimal booking method when making arrangements. For example, the booking unit can select the optimal booking method by referring to the booking methods used for events the user has previously participated in. The booking unit can also optimize the booking procedure based on the user's past participation history. Furthermore, the booking unit can select the optimal booking method based on the booking methods the user has used in the past. This allows for bookings that are beneficial to the user by selecting the optimal booking method based on past participation history. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input the user's past participation history data into a generating AI and have the generating AI select the optimal booking method.

[0043] The booking unit can customize the booking process based on the user's current living situation. For example, if the user is busy, the booking unit can simplify the booking procedure. If the user is relaxed, the booking unit can also provide a detailed booking procedure. Furthermore, if the user is in a hurry, the booking unit can expedite the booking process. In this way, by customizing the booking process based on the user's current living situation, the system can make arrangements that are beneficial to the user. Some or all of the above-described processes in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the booking process.

[0044] The booking unit can select the optimal booking method by considering the user's geographical location information during the booking process. For example, the booking unit can prioritize booking events held near the user's current location. Furthermore, if the user is interested in a particular region, the booking unit can prioritize booking events held in that region. Additionally, if the user is traveling, the booking unit can prioritize booking events held at their travel destination. By selecting a booking method that considers geographical location information, the booking unit can provide the user with beneficial bookings. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal booking method.

[0045] The arrangement unit can analyze the user's social media activity and propose arrangement methods when making arrangements. For example, the arrangement unit can prioritize arranging events that the user has shown interest in on social media. It can also prioritize arranging events that accounts the user follows on social media are interested in. Furthermore, the arrangement unit can prioritize arranging events that the user has expressed their intention to attend on social media. In this way, by analyzing social media activity, it is possible to make arrangements that are highly relevant to the user. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or not using AI. For example, the arrangement unit can input the user's social media activity data into a generating AI and have the generating AI propose arrangement methods.

[0046] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the user's past effective reminder methods. The reminder unit can also send a reminder at the optimal timing based on the user's past reminder history. Furthermore, the reminder unit can select the optimal reminder method based on the user's preferred reminder methods used in the past. This allows for the provision of useful reminders to the user by selecting the optimal reminder method based on past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past reminder history data into a generating AI and have the generating AI select the optimal reminder method.

[0047] The reminder unit can customize the reminder method based on the user's current life situation when a reminder is sent. For example, if the user is busy, the reminder unit can send a concise and quick reminder. If the user is relaxed, the reminder unit can also send a detailed reminder. Furthermore, if the user is in a hurry, the reminder unit can also send a concise and quick reminder. In this way, by customizing the reminder method based on the user's current life situation, the system can provide reminders that are beneficial to the user. Some or all of the above processing in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can input the user's current life situation data into a generating AI and have the generating AI perform the customization of the reminder method.

[0048] The reminder unit can select the optimal reminder method by considering the user's geographical location. For example, the reminder unit can prioritize sending reminders for events held near the user's current location. Furthermore, if the user is interested in a particular region, the reminder unit can prioritize sending reminders for events held in that region. Additionally, if the user is traveling, the reminder unit can prioritize sending reminders for events held at their travel destination. By selecting a reminder method that considers geographical location, the system can provide more useful reminders to the user. Some or all of the above processing in the reminder unit may be performed using AI, or without AI. For example, the reminder unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal reminder method.

[0049] The reminder unit can analyze the user's social media activity and suggest reminder methods when sending reminders. For example, the reminder unit can prioritize sending reminders for events the user has shown interest in on social media. It can also prioritize sending reminders for events that accounts the user follows on social media are interested in. Furthermore, the reminder unit can prioritize sending reminders for events the user has indicated they will attend on social media. This allows the reminder unit to provide highly relevant reminders to the user by analyzing their social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's social media activity data into a generating AI and have the generating AI suggest reminder methods.

[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 collects user health data, and the suggestion unit can then suggest experiences and events tailored to the user's health condition based on the collected data. For example, the data collection unit can collect heart rate and sleep data from the user's fitness tracker or smartwatch. The suggestion unit can analyze this data and suggest relaxing experiences if the user is tired, or active events if they are energetic. The data collection unit can also collect the user's food diary and calorie intake, and the suggestion unit can then suggest healthy cooking classes or nutrition seminars based on this data. Furthermore, the data collection unit can measure the user's stress level, and the suggestion unit can suggest yoga classes or meditation sessions that help reduce stress.

[0052] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, it can prioritize collecting information about events held near the user's current location. It can also prioritize collecting information about a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize collecting information about experiences and events available at their travel destination. By collecting information while considering geographical location, the system can provide users with highly relevant information.

[0053] The proposal team can adjust the level of detail in their proposals based on the importance of the experiences and events. For example, they can provide detailed information for high-priority experiences and events, and concise information for lower-priority experiences and events. Furthermore, they can provide detailed information for experiences and events that users are particularly interested in. By adjusting the level of detail in proposals based on the importance of the experiences and events, they can provide users with useful information.

[0054] The booking department can analyze a user's past participation history to select the optimal booking method. For example, it can select the best booking method by referring to the booking methods used for events the user has previously attended. Furthermore, the booking department can optimize the booking procedure based on the user's past participation history. In addition, the booking department can select the optimal booking method based on the booking methods the user has used in the past. This allows for bookings that are beneficial to the user by selecting the optimal booking method based on past participation history.

[0055] The reminder function can select the most effective reminder method by referring to the user's past reminder history. For example, the reminder function can select the most effective reminder method based on the user's past experiences. It can also send reminders at the optimal time based on the user's past reminder history. Furthermore, the reminder function can select the most effective reminder method based on the user's preferred past experiences. This allows for more effective reminders by selecting the most appropriate method based on past reminder history.

[0056] The proposal team can prioritize proposals based on the timing of experiences and events. For example, they can prioritize proposals for experiences and events that are happening soon. The proposal team can also propose experiences and events that are held at the optimal time to fit the user's schedule. Furthermore, they can consider seasonal events and make proposals at the appropriate time. By prioritizing proposals based on the timing of experiences and events, they can provide users with useful information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects user interests, behavioral patterns, and schedules. For example, the data collection unit stores user-entered interests, behavioral patterns, and schedules in a database. The data collection unit can also collect information from the user's past activity history and social media activity. For example, the data collection unit analyzes events the user has participated in and topics they have shown interest in to understand the user's interests and behavioral patterns. Furthermore, the data collection unit can also analyze the contents of the user's calendar app or planner to collect schedules. Step 2: The suggestion department uses generative AI to analyze the information collected by the collection department and proposes the most suitable experiences and events for the user. For example, the suggestion department uses generative AI to select the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. The suggestion department can also use generative AI to analyze the user's past behavioral history and social media activity to make personalized suggestions. For example, the suggestion department uses generative AI to propose the most suitable experiences and events based on events the user has previously participated in and topics they have shown interest in. Step 3: The arrangements department makes the necessary arrangements and preparations for the experience or event proposed by the proposal department. For example, the arrangements department may arrange tickets and prepare equipment for the proposed experience or event. The arrangements department may also provide a list of items that the user needs to bring to participate. For example, the arrangements department may arrange tickets for a user to attend a music concert and provide a list of items to bring. Step 4: The Reminders team sends reminders shortly before the experiences or events arranged by the Arrangements team. For example, the Reminders team might send a reminder the day before the event to ensure users don't forget to attend. The Reminders team can also send reminders on the day of the event. For example, they might send a reminder one hour before the event starts to allow users time to prepare.

[0059] (Example of form 2) The Hobify system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problems of many people who "want to try something new but don't know what to do" or "want to use their free time meaningfully but can't take action." This system uses generative AI to automatically suggest experiences and events that can be easily enjoyed in the user's free time, taking into account the user's interests, behavioral patterns, and schedule. Furthermore, it goes beyond suggestions, providing full support such as arrangements and preparations necessary for participation, presenting packing lists, and reminders just before the event. By using the Hobify system, users can easily enjoy new experiences and hobbies without spending time on complicated preparations or searching for information. The optimal suggestions and comprehensive support made possible by generative AI make the user's daily life more colorful and fulfilling. For example, a user accesses Hobify and inputs their interests, behavioral patterns, and schedule. The generative AI analyzes this information and suggests the most suitable experiences and events for the user. For example, if a user inputs "I want to refresh myself on the weekend," the generative AI considers the user's past behavioral patterns and interests and suggests things like a music concert or pottery class held nearby. Next, the generative AI supports the arrangements and preparations necessary for the suggested experience or event. For example, it can handle tasks such as arranging tickets for a music concert or preparing materials for a pottery class. It also sends reminders shortly before events to ensure users don't forget to participate. Furthermore, the generative AI integrates with the user's calendar and automatically suggests the most suitable experiences based on their free time. For instance, if a user enters "I'm free in the afternoon of November 23, 2024," the generative AI will suggest watching a movie or other activities for that time, and assist with ticket reservations and preparations. In this way, the Hobify system utilizes generative AI to enrich and enhance users' free time. Users can easily enjoy new experiences and hobbies without having to spend time on tedious preparations or searching for information. As a result, the Hobify system can enrich users' free time.

[0060] The Hobify system according to this embodiment comprises a collection unit, a suggestion unit, an arrangement unit, and a reminder unit. The collection unit collects the user's interests, behavioral patterns, and schedule. For example, the collection unit stores the user's entered interests, behavioral patterns, and schedule in a database. The collection unit can also collect information from the user's past behavioral history and social media activity. For example, the collection unit analyzes events the user has participated in and topics they have shown interest in to understand the user's interests and behavioral patterns. Furthermore, the collection unit can also analyze the contents of the user's calendar app or planner to collect schedules. The suggestion unit uses generative AI to analyze the information collected by the collection unit and proposes the most suitable experiences and events to the user. For example, the suggestion unit uses generative AI to select the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. The suggestion unit can also use generative AI to analyze the user's past behavioral history and social media activity to make suggestions tailored to the user. For example, the suggestion unit uses generative AI to propose the most suitable experiences and events based on events the user has participated in and topics they have shown interest in. The Arrangement Department handles the necessary arrangements and preparations for experiences and events proposed by the Proposal Department. For example, the Arrangement Department arranges tickets and prepares equipment for proposed experiences and events. The Arrangement Department can also provide a list of items that users need to bring to participate. For example, the Arrangement Department arranges tickets for a user to attend a music concert and provides a list of items to bring. The Reminder Department sends a reminder shortly before the experience or event arranged by the Arrangement Department. For example, the Reminder Department sends a reminder the day before the event to ensure that users do not forget to participate. The Reminder Department can also send a reminder on the day of the event. For example, the Reminder Department sends a reminder one hour before the start time of the event to allow users to prepare. In this way, the Hobify system according to the embodiment can enrich the user's free time.

[0061] The data collection department collects user interests, behavioral patterns, and schedules. Specifically, it stores user-entered interests, behavioral patterns, and schedules in a database. For example, it collects information on hobbies such as sports, music, and travel, and uses this data to understand user preferences. The data collection department can also collect information from users' past behavioral history and social media activity. For example, it analyzes events users have participated in and topics they have shown interest in to understand their interests and behavioral patterns. This allows for a detailed understanding of what kinds of events and experiences users are interested in. Furthermore, the data collection department can analyze the contents of users' calendar apps and planners to collect schedules. For example, it analyzes appointments and reminders registered in users' calendars to identify free time and available time. This enables the provision of optimal suggestions tailored to the user's schedule. The data collection department centrally manages this information and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the proposal and arrangement departments. By adjusting the frequency and accuracy of data collection, flexible responses can be made according to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The suggestion department uses generative AI to analyze information collected by the data collection department and proposes the most suitable experiences and events to the user. Specifically, the generative AI selects the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. For example, if a user is interested in music, the generative AI will suggest nearby music live performances and concerts. The generative AI can also suggest the most suitable experiences and events based on events the user has previously attended and topics they have shown interest in. For example, based on art exhibitions and workshops the user has previously attended, the generative AI will suggest a new art event. Furthermore, the suggestion department can use generative AI to analyze the user's social media activity and make personalized suggestions. For example, it can analyze articles and posts the user has shared on social media and suggest related events and experiences. This allows the suggestion department to make personalized suggestions based on the user's interests and behavioral patterns. The suggestion department can utilize the learning function of the generative AI to respond to changes in the user's preferences and behavioral patterns. For example, if a user starts a new hobby, the generative AI will learn that information and make relevant suggestions. The suggestion department can also collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the best possible experience and events, thereby improving user satisfaction.

[0063] The Arrangement Department handles the necessary arrangements and preparations for experiences and events proposed by the Proposal Department. Specifically, this includes arranging tickets and preparing equipment for proposed experiences and events. For example, the Arrangement Department can arrange tickets for a user to attend a music concert and provide a packing list. The Arrangement Department can also provide a packing list for the user's participation. For example, if a user is going camping, the Arrangement Department will provide a packing list including tents, sleeping bags, and cooking equipment. Furthermore, the Arrangement Department can also arrange transportation and accommodation for the user's participation. For example, if a user is attending an event far away, the Arrangement Department will arrange flight or train tickets and hotel reservations. This allows users to participate in events and experiences without hassle. The Arrangement Department works in conjunction with the Proposal Department to make optimal arrangements tailored to the user's schedule. For example, they can check the user's calendar and arrange events to fit their free time. The Arrangement Department can also collect user feedback and continuously improve the accuracy and effectiveness of the arrangements. This allows the Arrangement Department to provide users with efficient and effective arrangements, thereby improving user satisfaction.

[0064] The Reminders Department sends reminders shortly before experiences and events arranged by the Arrangement Department. Specifically, it sends reminders the day before the event to ensure users don't forget to participate. For example, the Reminders Department sends emails or push notifications the day before the event to remind users to review event details and packing lists. The Reminders Department can also send reminders on the day of the event. For example, it can send a reminder one hour before the event starts to allow users time to prepare. Furthermore, the Reminders Department can adjust the timing of reminders to suit the user's schedule. For example, if a user is busy, the reminder timing can be set earlier to allow them ample time to prepare. The Reminders Department can collect user feedback and continuously improve the accuracy and effectiveness of its reminders. For example, users can provide feedback on the timing and content of reminders, allowing the Reminders Department to optimize its reminder methods based on that information. The Reminders Department can also reliably transmit information using multiple communication methods. For example, it can use SMS and voice calls in addition to email and push notifications to ensure important information is delivered reliably. This allows the reminder unit to provide users with quick and reliable reminders, thereby improving user satisfaction.

[0065] The data collection unit can estimate the user's emotions and adjust the method of collecting information on interests and behavioral patterns based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information on relaxing experiences and events. It can also prioritize collecting information on active experiences and events if the user is excited. Furthermore, if the user is tired, it can prioritize collecting information on relaxing experiences and events. This allows for the collection of more relevant information by adjusting the collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0066] The data collection unit can analyze a user's past behavior history and select the optimal timing for data collection. For example, if a user was active on a specific day of the week or time of day in the past, the data collection unit can collect information according to that time. The data collection unit can also collect information when similar events are held, based on a user's past participation in specific events. Furthermore, if a user was active during a specific season in the past, the data collection unit can collect information according to that season. This allows the system to provide users with useful information by collecting data at the optimal time based on their past behavior 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 behavior history data into a generating AI and have the generating AI select the optimal timing for data collection.

[0067] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the collection process. For example, if the user is busy, the unit can prioritize collecting information on experiences and events that can be enjoyed in a short amount of time. Similarly, if the user wants to relax, the unit can prioritize collecting information on relaxing experiences and events. Furthermore, if the user wants to find a new hobby, the unit can prioritize collecting information on new hobbies. By filtering information based on the user's current lifestyle and areas of interest, the unit can provide the user with highly relevant information. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0068] 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 will prioritize collecting information about relaxing experiences and events. Similarly, if the user is excited, the data collection unit can prioritize collecting information about active experiences and events. Furthermore, if the user is tired, the data collection unit can prioritize collecting information about relaxing experiences and events. This allows for the provision of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0069] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can prioritize collecting information about events held near the user's current location. Furthermore, if the user is interested in a particular region, the data collection unit can prioritize collecting information about that region. Additionally, if the user is traveling, the data collection unit can prioritize collecting information about experiences and events available at their travel destination. This allows the data collection unit to provide highly relevant information to the user by considering their 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 instance, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0070] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can prioritize collecting information about events that the user has shown interest in on social media. It can also prioritize collecting information about events that accounts the user follows on social media are interested in. Furthermore, the data collection unit can prioritize collecting information about events that the user has expressed interest in on social media. This allows the data collection unit to provide users with highly relevant information by analyzing their social media activity. Some or all of the processing described above 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0071] 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 relaxed, the suggestion unit can present suggestions in a calm manner. If the user is excited, the suggestion unit can present suggestions in an energetic manner. Furthermore, if the user is stressed, the suggestion unit can present suggestions in a calm manner. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 a generative AI and have the generative AI adjust the way suggestions are presented.

[0072] The proposal function can adjust the level of detail in a proposal based on the importance of the experience or event. For example, the proposal function can provide detailed information for high-importance experiences and events. It can also provide concise information for less important experiences and events. Furthermore, it can provide detailed information for experiences and events that the user is particularly interested in. By adjusting the level of detail in a proposal based on the importance of the experience or event, the proposal function can provide information that is useful to the user. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input experience and event importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0073] The suggestion unit can apply different suggestion algorithms depending on the category of the experience or event. For example, in the case of a sports event, the suggestion unit makes suggestions based on the user's past sports activity history. Similarly, in the case of a cultural event, the suggestion unit can make suggestions based on the user's past cultural activity history. Furthermore, in the case of a leisure event, the suggestion unit can make suggestions based on the user's past leisure activity history. This allows for more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of the experience or event. 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 experience or event category data into a generating AI and have the generating AI execute the application of different suggestion algorithms.

[0074] 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 more detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0075] The suggestion department can prioritize suggestions based on the timing of experiences and events. For example, it might prioritize suggesting experiences and events that are happening soon. It can also suggest experiences and events that are held at the optimal time to fit the user's schedule. Furthermore, it can consider seasonal events and make suggestions at the appropriate time. By prioritizing suggestions based on the timing of experiences and events, it can provide users with useful information. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input experience and event timing data into a generating AI and have the generating AI determine the priority of suggestions.

[0076] The suggestion unit can adjust the order of suggestions based on the relevance of experiences and events. For example, it might suggest experiences and events most relevant to the user's interests first. It can also prioritize suggesting relevant experiences and events based on the user's past behavior patterns. Furthermore, it can prioritize suggesting relevant experiences and events based on the user's current areas of interest. By adjusting the order of suggestions based on the relevance of experiences and events, it can provide users with useful information. 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 relevance data of experiences and events into a generating AI and have the generating AI adjust the order of suggestions.

[0077] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is stressed, the ordering unit can simplify the ordering procedure. If the user is relaxed, the ordering unit can also provide a detailed ordering procedure. Furthermore, if the user is in a hurry, the ordering unit can make the order quickly. In this way, by adjusting the ordering method according to the user's emotions, more appropriate ordering can be made. 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 ordering unit may be performed using AI or not using AI. For example, the ordering unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the ordering method.

[0078] The booking unit can analyze the user's past participation history to select the optimal booking method when making arrangements. For example, the booking unit can select the optimal booking method by referring to the booking methods used for events the user has previously participated in. The booking unit can also optimize the booking procedure based on the user's past participation history. Furthermore, the booking unit can select the optimal booking method based on the booking methods the user has used in the past. This allows for bookings that are beneficial to the user by selecting the optimal booking method based on past participation history. Some or all of the above processing in the booking unit may be performed using AI, for example, or without AI. For example, the booking unit can input the user's past participation history data into a generating AI and have the generating AI select the optimal booking method.

[0079] The booking unit can customize the booking process based on the user's current living situation. For example, if the user is busy, the booking unit can simplify the booking procedure. If the user is relaxed, the booking unit can also provide a detailed booking procedure. Furthermore, if the user is in a hurry, the booking unit can expedite the booking process. In this way, by customizing the booking process based on the user's current living situation, the system can make arrangements that are beneficial to the user. Some or all of the above-described processes in the booking unit may be performed using AI, for example, or not. For example, the booking unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the booking process.

[0080] The booking unit can estimate the user's emotions and determine booking priorities based on those emotions. For example, if the user is stressed, the booking unit can simplify the booking process. Conversely, if the user is relaxed, the booking unit can provide detailed booking instructions. Furthermore, if the user is in a hurry, the booking unit can expedite the booking process. This allows for more appropriate bookings by prioritizing bookings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the booking unit may be performed using AI or not. For example, the booking unit can input user emotion data into a generative AI and have the generative AI determine the booking priorities.

[0081] The booking unit can select the optimal booking method by considering the user's geographical location information during the booking process. For example, the booking unit can prioritize booking events held near the user's current location. Furthermore, if the user is interested in a particular region, the booking unit can prioritize booking events held in that region. Additionally, if the user is traveling, the booking unit can prioritize booking events held at their travel destination. By selecting a booking method that considers geographical location information, the booking unit can provide the user with beneficial bookings. Some or all of the above processing in the booking unit may be performed using AI, or not. For example, the booking unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal booking method.

[0082] The arrangement unit can analyze the user's social media activity and propose arrangement methods when making arrangements. For example, the arrangement unit can prioritize arranging events that the user has shown interest in on social media. It can also prioritize arranging events that accounts the user follows on social media are interested in. Furthermore, the arrangement unit can prioritize arranging events that the user has expressed their intention to attend on social media. In this way, by analyzing social media activity, it is possible to make arrangements that are highly relevant to the user. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or not using AI. For example, the arrangement unit can input the user's social media activity data into a generating AI and have the generating AI propose arrangement methods.

[0083] The reminder unit can estimate the user's emotions and adjust the reminder method based on the estimated emotions. For example, if the user is stressed, the reminder unit can send a reminder in a calm tone. If the user is relaxed, the reminder unit can send a reminder in a cheerful tone. Furthermore, if the user is in a hurry, the reminder unit can send a concise and quick reminder. This allows for more appropriate reminders by adjusting the reminder method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI 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 reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI adjust the reminder method.

[0084] The reminder unit can select the optimal reminder method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the user's past effective reminder methods. The reminder unit can also send a reminder at the optimal timing based on the user's past reminder history. Furthermore, the reminder unit can select the optimal reminder method based on the user's preferred reminder methods used in the past. This allows for the provision of useful reminders to the user by selecting the optimal reminder method based on past reminder history. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past reminder history data into a generating AI and have the generating AI select the optimal reminder method.

[0085] The reminder unit can customize the reminder method based on the user's current life situation when a reminder is sent. For example, if the user is busy, the reminder unit can send a concise and quick reminder. If the user is relaxed, the reminder unit can also send a detailed reminder. Furthermore, if the user is in a hurry, the reminder unit can also send a concise and quick reminder. In this way, by customizing the reminder method based on the user's current life situation, the system can provide reminders that are beneficial to the user. Some or all of the above processing in the reminder unit may be performed using AI, for example, or not using AI. For example, the reminder unit can input the user's current life situation data into a generating AI and have the generating AI perform the customization of the reminder method.

[0086] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is feeling stressed, the reminder unit will prioritize sending important reminders. It can also send detailed reminders if the user is relaxed. Furthermore, if the user is in a hurry, the reminder unit can prioritize sending important reminders. This allows for more appropriate reminders by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into a generative AI and have the generative AI determine the priority of reminders.

[0087] The reminder unit can select the optimal reminder method by considering the user's geographical location. For example, the reminder unit can prioritize sending reminders for events held near the user's current location. Furthermore, if the user is interested in a particular region, the reminder unit can prioritize sending reminders for events held in that region. Additionally, if the user is traveling, the reminder unit can prioritize sending reminders for events held at their travel destination. By selecting a reminder method that considers geographical location, the system can provide more useful reminders to the user. Some or all of the above processing in the reminder unit may be performed using AI, or without AI. For example, the reminder unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal reminder method.

[0088] The reminder unit can analyze the user's social media activity and suggest reminder methods when sending reminders. For example, the reminder unit can prioritize sending reminders for events the user has shown interest in on social media. It can also prioritize sending reminders for events that accounts the user follows on social media are interested in. Furthermore, the reminder unit can prioritize sending reminders for events the user has indicated they will attend on social media. This allows the reminder unit to provide highly relevant reminders to the user by analyzing their social media activity. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's social media activity data into a generating AI and have the generating AI suggest reminder methods.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The data collection unit collects user health data, and the suggestion unit can then suggest experiences and events tailored to the user's health condition based on the collected data. For example, the data collection unit can collect heart rate and sleep data from the user's fitness tracker or smartwatch. The suggestion unit can analyze this data and suggest relaxing experiences if the user is tired, or active events if they are energetic. The data collection unit can also collect the user's food diary and calorie intake, and the suggestion unit can then suggest healthy cooking classes or nutrition seminars based on this data. Furthermore, the data collection unit can measure the user's stress level, and the suggestion unit can suggest yoga classes or meditation sessions that help reduce stress.

[0091] The data collection unit can estimate the user's emotions and adjust how it collects information about the user's interests and behavioral patterns based on those estimated emotions. For example, if the user is feeling stressed, the data collection unit will prioritize collecting information about relaxing experiences and events. Similarly, if the user is excited, it can prioritize collecting information about active experiences and events. Furthermore, if the user is tired, it can prioritize collecting information about relaxing experiences and events. This allows for the collection of more relevant information by adjusting the collection method according to the user's emotions.

[0092] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion function will present suggestions in a calm manner. If the user is excited, it can present suggestions in an energetic manner. Furthermore, if the user is stressed, it can present suggestions in a calm manner. By adjusting the way suggestions are presented according to the user's emotions, the function can provide more appropriate suggestions.

[0093] The arrangement unit can estimate the user's emotions and adjust the arrangement method based on those emotions. For example, if the arrangement unit is stressed, it can simplify the arrangement procedure. Conversely, if the user is relaxed, it can provide a detailed arrangement procedure. Furthermore, if the user is in a hurry, the arrangement unit can expedite the process. In this way, by adjusting the arrangement method according to the user's emotions, more appropriate arrangements can be made.

[0094] The reminder function can estimate the user's emotions and adjust the reminder method based on those emotions. For example, if the user is stressed, the reminder function can send a reminder in a calm tone. It can also send a reminder in a cheerful tone if the user is relaxed. Furthermore, if the user is in a hurry, the reminder function can send a concise and quick reminder. This allows for more appropriate reminders by adjusting the reminder method according to the user's emotions.

[0095] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, it can prioritize collecting information about events held near the user's current location. It can also prioritize collecting information about a specific region if the user is interested in that region. Furthermore, if the user is traveling, it can prioritize collecting information about experiences and events available at their travel destination. By collecting information while considering geographical location, the system can provide users with highly relevant information.

[0096] The proposal team can adjust the level of detail in their proposals based on the importance of the experiences and events. For example, they can provide detailed information for high-priority experiences and events, and concise information for lower-priority experiences and events. Furthermore, they can provide detailed information for experiences and events that users are particularly interested in. By adjusting the level of detail in proposals based on the importance of the experiences and events, they can provide users with useful information.

[0097] The booking department can analyze a user's past participation history to select the optimal booking method. For example, it can select the best booking method by referring to the booking methods used for events the user has previously attended. Furthermore, the booking department can optimize the booking procedure based on the user's past participation history. In addition, the booking department can select the optimal booking method based on the booking methods the user has used in the past. This allows for bookings that are beneficial to the user by selecting the optimal booking method based on past participation history.

[0098] The reminder function can select the most effective reminder method by referring to the user's past reminder history. For example, the reminder function can select the most effective reminder method based on the user's past experiences. It can also send reminders at the optimal time based on the user's past reminder history. Furthermore, the reminder function can select the most effective reminder method based on the user's preferred past experiences. This allows for more effective reminders by selecting the most appropriate method based on past reminder history.

[0099] The proposal team can prioritize proposals based on the timing of experiences and events. For example, they can prioritize proposals for experiences and events that are happening soon. The proposal team can also propose experiences and events that are held at the optimal time to fit the user's schedule. Furthermore, they can consider seasonal events and make proposals at the appropriate time. By prioritizing proposals based on the timing of experiences and events, they can provide users with useful information.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit collects user interests, behavioral patterns, and schedules. For example, the data collection unit stores user-entered interests, behavioral patterns, and schedules in a database. The data collection unit can also collect information from the user's past activity history and social media activity. For example, the data collection unit analyzes events the user has participated in and topics they have shown interest in to understand the user's interests and behavioral patterns. Furthermore, the data collection unit can also analyze the contents of the user's calendar app or planner to collect schedules. Step 2: The suggestion department uses generative AI to analyze the information collected by the collection department and proposes the most suitable experiences and events for the user. For example, the suggestion department uses generative AI to select the most suitable experiences and events based on the user's interests, behavioral patterns, and schedule. The suggestion department can also use generative AI to analyze the user's past behavioral history and social media activity to make personalized suggestions. For example, the suggestion department uses generative AI to propose the most suitable experiences and events based on events the user has previously participated in and topics they have shown interest in. Step 3: The arrangements department makes the necessary arrangements and preparations for the experience or event proposed by the proposal department. For example, the arrangements department may arrange tickets and prepare equipment for the proposed experience or event. The arrangements department may also provide a list of items that the user needs to bring to participate. For example, the arrangements department may arrange tickets for a user to attend a music concert and provide a list of items to bring. Step 4: The Reminders team sends reminders shortly before the experiences or events arranged by the Arrangements team. For example, the Reminders team might send a reminder the day before the event to ensure users don't forget to attend. The Reminders team can also send reminders on the day of the event. For example, they might send a reminder one hour before the event starts to allow users time to prepare.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the collection unit, proposal unit, arrangement unit, and reminder unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's interests, behavioral patterns, and schedule by the control unit 46A of the smart device 14 and stores them in the database 24 by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal experience or event to the user using generated AI by the identification processing unit 290 of the data processing unit 12. The arrangement unit arranges and prepares the proposed experience or event by the control unit 46A of the smart device 14. The reminder unit sends a reminder immediately before the event by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the collection unit, suggestion unit, arrangement unit, and reminder unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's interests, behavioral patterns, and schedule by the control unit 46A of the smart glasses 214 and stores them in the database 24 by the identification processing unit 290 of the data processing unit 12. The suggestion unit proposes the optimal experience or event to the user using generated AI by the identification processing unit 290 of the data processing unit 12. The arrangement unit arranges and prepares the suggested experience or event by the control unit 46A of the smart glasses 214. The reminder unit sends a reminder immediately before the event by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the collection unit, proposal unit, arrangement unit, and reminder unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's interests, behavioral patterns, and schedule by the control unit 46A of the headset terminal 314 and stores them in the database 24 by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal experience or event to the user using generated AI by the identification processing unit 290 of the data processing unit 12. The arrangement unit arranges and prepares the proposed experience or event by the control unit 46A of the headset terminal 314. The reminder unit sends a reminder immediately before the event by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0149] In 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.

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

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0153] The data processing system 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.

[0154] Each of the multiple elements described above, including the collection unit, proposal unit, arrangement unit, and reminder unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's interests, behavior patterns, and schedule by the control unit 46A of the robot 414 and stores them in the database 24 by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal experience or event to the user using generated AI by the identification processing unit 290 of the data processing unit 12. The arrangement unit arranges and prepares the proposed experience or event by the control unit 46A of the robot 414. The reminder unit sends a reminder immediately before the event by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A data collection unit that collects user interests, behavioral patterns, and schedules, The information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the most suitable experience or event for the user. The Arrangement Department is responsible for making the necessary arrangements and preparations for the experiences and events proposed by the aforementioned Proposal Department, The system includes a reminder unit that sends a reminder immediately before an experience or event arranged by the arrangement unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust how we collect interest and behavioral patterns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, 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 5) 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 6) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the experience or event. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the experience or event. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the experiences and events. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of experiences and events. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned ordering unit, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned ordering unit, When making arrangements, the system analyzes the user's past participation history to select the most suitable arrangement method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned ordering unit, When making arrangements, the method of arrangement is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned ordering unit, It estimates the user's emotions and determines the priority of arrangements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned ordering unit, When making arrangements, the optimal arrangement method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned ordering unit, When making arrangements, we analyze the user's social media activity and suggest arrangement methods. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reminder unit, It estimates the user's emotions and adjusts the reminder method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reminder unit, When sending a reminder, the system will refer to the user's past reminder history to select the most suitable reminder method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reminder unit, When sending reminders, customize the reminder method based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reminder unit, When sending a reminder, the system will select the most appropriate reminder method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reminder unit, When sending reminders, we analyze the user's social media activity and suggest effective reminder methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 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 data collection unit that collects user interests, behavioral patterns, and schedules, The information collected by the aforementioned collection unit is analyzed, and the proposal unit proposes the most suitable experience or event for the user. The Arrangement Department is responsible for making the necessary arrangements and preparations for the experiences and events proposed by the aforementioned Proposal Department, The system includes a reminder unit that sends a reminder immediately before an experience or event arranged by the arrangement unit. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust how we collect interest and behavioral patterns based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system according to feature 1.

4. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. 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.

6. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system according to feature 1.

8. 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 according to feature 1.

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