system

Abstract

The system according to this embodiment aims to propose the optimal plan based on what you want to do and your current situation. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives input about what the user wants to do and the current situation. The analysis unit analyzes the information entered by the reception unit. The proposal unit proposes the optimal plan based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to find an optimal plan based on what one wants to do and the current situation, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal plan based on what one wants to do and the current situation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit inputs what one wants to do and the current situation. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes an optimal plan based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can propose an optimal plan based on what you want to do and your current situation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The leisure-killing plan suggestion system according to an embodiment of the present invention is a tool in which an AI agent suggests the optimal leisure-killing plan simply by inputting what the user wants to do and their current situation (location, time, number of people, etc.). This system allows the user to input what they want to do and their current situation, and the AI ​​agent analyzes this information to suggest the optimal plan. For example, it can be used when you don't know what to do during free time on holidays or weekdays, due to sudden weather changes, or schedule changes. It is also useful when you have something you want to do but are unsure whether a shop is open or crowded. The AI ​​agent suggests the optimal plan simply by the user inputting conditions such as the number of people, location, time, genre (eating, playing, studying), indoor / outdoor, and budget. Furthermore, by allowing facilities to input information, the accuracy of search results can be improved, contributing to increased customer traffic. For example, by inputting information such as available time slots and recommended points, facilities can suggest more appropriate plans for users. This mechanism allows users to effectively utilize their free time, and facilities can increase their customer traffic. For example, even if plans change due to sudden rain, the AI ​​agent can suggest the optimal indoor leisure-killing plan. Furthermore, the system can propose plans that take into account sudden schedule changes during business trips or crowd conditions when going out with family. This allows the leisure-killing plan suggestion system to propose the optimal plan based on what the user wants to do and their current situation.

[0029] The leisure-killing plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives input about what the user wants to do and their current situation. What the user wants to do includes, but is not limited to, travel, events, hobbies, etc. Current situation includes, but is not limited to, current location, time, budget, etc. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data analysis methods and algorithms. The suggestion unit proposes the optimal plan based on the information analyzed by the analysis unit. The suggestion unit proposes, for example, a plan that best matches the user's wishes or a plan with high cost performance. Thus, the leisure-killing plan suggestion system according to this embodiment can propose the optimal plan based on what the user wants to do and their current situation.

[0030] The reception desk provides an interface for users to input what they want to do and their current situation. What they want to do includes, but is not limited to, travel, events, and hobbies; it encompasses a wide range of options such as watching sports, watching movies, shopping, and outdoor activities. Users can select options that match their interests and preferences. Their current situation includes, but is not limited to, their current location, time, and budget; they can also input information such as weather, traffic conditions, whether they are accompanied by someone, and their physical condition. This allows the reception desk to accurately understand the user's detailed wishes and situation, providing the necessary information to the subsequent analysis and proposal departments. Furthermore, by saving the information entered by users and referencing past data, the reception desk can learn user preferences and behavioral patterns, laying the foundation for more personalized suggestions. For example, based on previously entered information, it can analyze the user's preferred activities and visited locations and reflect this in future suggestions. This enables the reception desk to collect flexible and highly accurate information tailored to user needs, enhancing the overall effectiveness of the system.

[0031] The Analysis Department plays a crucial role in analyzing information entered by the Reception Department and deriving the optimal plan based on the user's wishes and circumstances. The Analysis Department utilizes various data analysis methods and algorithms to comprehensively analyze user input. For example, it uses natural language processing technology to analyze text data about what the user wants to do, extracting their intentions and specific desires. It also uses machine learning algorithms to analyze the user's current situation data, identifying patterns and trends to derive the optimal plan. Furthermore, the Analysis Department can integrate information obtained from external data sources to perform more accurate analysis. For instance, it can incorporate weather forecast data and traffic information data to perform analysis to suggest the optimal activity based on the user's current location and time. The Analysis Department processes this data in real time, providing the latest information tailored to the user's situation. This allows the Analysis Department to quickly and accurately derive the optimal plan based on the user's wishes and circumstances and provide it to the Proposal Department. Additionally, the Analysis Department can continuously improve the accuracy of its analysis algorithms by utilizing past data and user feedback. This allows the analysis unit to always provide the latest information and highly accurate analysis results, improving the overall reliability of the system and user satisfaction.

[0032] The Proposal Department proposes the most suitable plan to the user based on the information analyzed by the Analysis Department. To propose the plan that best matches the user's wishes and offers the best value for money, the Proposal Department generates multiple options and selects the optimal one. For example, if a user wants to travel, the Proposal Department suggests destinations, accommodations, and sightseeing spots based on the user's budget, current location, and available time. If a user wants to attend an event, the Proposal Department gathers event information that matches the user's interests and provides detailed information such as location, date, time, and participation fee. Furthermore, the Proposal Department can provide more personalized suggestions by considering the user's past activity history and feedback. For example, by suggesting similar plans based on data of activities and places the user has previously enjoyed, user satisfaction can be increased. The Proposal Department also considers interface design and user experience to present these suggestions visually and clearly to the user. For example, it displays the locations of suggested plans on a map or presents detailed plan information in a card format, providing information in an intuitively understandable format. This allows the Proposal Department to quickly and effectively propose the most suitable plan to the user, enriching their leisure time. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to consistently provide optimal plans tailored to user needs, thereby improving the overall reliability of the system and user satisfaction.

[0033] The suggestion unit can generate plans while taking into account the latest information, such as weather and facility availability. For example, the suggestion unit can suggest indoor and outdoor activities based on weather information. It can also suggest plans that avoid congestion by considering facility availability. Furthermore, it can suggest plans that optimize travel time based on traffic information. In this way, by considering the latest information, it is possible to propose more appropriate plans. The latest information includes, but is not limited to, weather information, facility availability, and traffic information. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can generate plans using an AI model that takes weather information and facility availability as input and outputs the optimal plan.

[0034] The reception desk allows users to input conditions such as the number of users, location, time, genre, indoor / outdoor, and budget. For example, the reception desk can suggest an appropriate plan based on the number of users entered. It can also suggest nearby facilities based on the location entered by the user. Furthermore, it can suggest a plan suitable for the time of day based on the time entered by the user. This allows the system to suggest plans based on the user's detailed conditions. These conditions include, but are not limited to, the number of people, location, time, genre, indoor / outdoor, and budget. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest a plan using an AI model that takes the conditions entered by the user as input and outputs the optimal plan.

[0035] The reception desk allows the facility to input information such as available time slots and recommended features. For example, the reception desk can propose an appropriate plan to the user based on the available time slot information entered by the facility. The reception desk can also propose an attractive plan to the user based on the recommended features entered by the facility. Furthermore, the reception desk can propose a plan that enhances the facility's ability to attract customers based on the information entered by the facility. In this way, by considering the facility's information, a more accurate plan can be proposed. Available time slot information includes, but is not limited to, the facility's reservation status and available time slots. Recommended features include, but is not limited to, the facility's features, benefits, and reasons for its popularity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can propose a plan using an AI model that takes the information entered by the facility as input and outputs the optimal plan.

[0036] The analysis unit can analyze user information and facility information. For example, the analysis unit can combine and analyze information entered by the user and information entered by the facility. The analysis unit can also analyze considering the user's wishes and the facility's availability. Furthermore, the analysis unit can analyze based on the user's current situation and the facility's characteristics. By analyzing user and facility information, it can propose the optimal plan. The analysis includes, but is not limited to, data analysis methods and algorithms used. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that takes user and facility information as input and outputs analysis results.

[0037] The proposal unit can propose the optimal plan based on the user's preferences. For example, the proposal unit can propose a plan based on the genre the user desires. It can also propose a plan based on the budget the user desires. Furthermore, it can propose a plan based on the time of day the user desires. This allows the proposal to suggest the optimal plan based on the user's preferences. The optimal plan includes, but is not limited to, a plan that best matches the user's preferences or a plan with high cost performance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can propose a plan using an AI model that takes the user's preferences as input and outputs the optimal plan.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions what the user wants to do and their current situation that they have frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest what the user wants to do and their current situation for a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing past input history. Past input history includes, but is not limited to, past input data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0039] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do and their current situation. For example, when a user opens the app, the reception desk automatically acquires their current location and sets it as the location. The reception desk can also suggest optimal candidate locations by considering the distance from the current location when the user inputs what they want to do. Furthermore, if the user uses the app while on the move, the reception desk can update their current location in real time and reflect it as the location. This simplifies input by automatically acquiring the current location information. Current location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can simplify input by using an AI model that takes the user's current location information as input.

[0040] The reception desk can automatically suggest options based on the user's past activity history when the user inputs what they want to do and their current situation. For example, the reception desk can automatically display activities that the user has frequently performed in the past as options. The reception desk can also predict activities that the user will perform on specific days of the week or time slots and suggest them as options. Furthermore, the reception desk can analyze the user's past behavior patterns and suggest the most suitable options. This allows the system to automatically suggest the best options by referring to past activity history. Past activity history includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest options using an AI model that takes the user's past activity history as input and outputs the best options.

[0041] The reception desk can refer to the user's calendar information and make schedule-based suggestions when the user inputs what they want to do and their current situation. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set what they want to do and their current situation. The reception desk can also suggest activities related to a specific event as candidates based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable plan based on the user's calendar information. This makes it possible to make schedule-based suggestions by referring to calendar information. Calendar information includes, but is not limited to, the type of appointment, time, and location. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can make suggestions using an AI model that takes the user's calendar information as input and outputs schedule-based suggestions.

[0042] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral data during the analysis process. For example, the analysis unit can propose an optimal plan based on the user's past activities. It can also propose a plan that avoids congestion based on the user's past behavioral data. Furthermore, the analysis unit can analyze the user's past behavioral data and propose the most efficient plan. This improves the accuracy of the analysis by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavioral data, frequency, and trends. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can take the user's past behavioral data as input and perform analysis using an AI model that improves the accuracy of the analysis.

[0043] The analysis unit can customize its analysis method based on the user's current situation during analysis. For example, the analysis unit can propose an optimal plan based on information about the user's current location. It can also propose an appropriate plan based on the user's current time of day. Furthermore, it can propose an optimal plan based on the current number of users. By customizing the analysis method based on the current situation, more appropriate analysis becomes possible. The current situation includes, but is not limited to, current location, time, and budget. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the user's current situation as input and perform analysis using an AI model that customizes the analysis method.

[0044] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis. For example, the analysis unit can propose an optimal plan based on information about the user's current location. It can also propose a plan that avoids congestion based on the user's geographical location information. Furthermore, the analysis unit can analyze the user's geographical location information and propose the most efficient plan. In this way, the accuracy of the analysis is improved by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can take the user's geographical location information as input and perform the analysis using an AI model that improves the accuracy of the analysis.

[0045] The analysis unit can analyze a user's social media activity and utilize relevant data during the analysis. For example, the analysis unit can propose an optimal plan based on the user's activities shared on social media. It can also identify genres of interest from the user's social media activity and propose a plan based on that. Furthermore, the analysis unit can analyze the user's social media activity and propose a plan based on trends. This allows for more appropriate analysis by analyzing social media activity. Social media activity includes, but is not limited to, posts, content, frequency, and reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's social media activity as input and utilizes relevant data for analysis.

[0046] The suggestion unit can make optimal suggestions by referring to the user's past behavioral data when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's past activities. The suggestion unit can also make suggestions to avoid congestion based on the user's past behavioral data. Furthermore, the suggestion unit can analyze the user's past behavioral data and make the most efficient suggestions. This makes it possible to make optimal suggestions by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's past behavioral data as input and outputs optimal suggestions.

[0047] The suggestion unit can customize its suggestions based on the user's current situation. For example, it can make optimal suggestions based on the user's current location. It can also make appropriate suggestions based on the user's current time of day. Furthermore, it can make optimal suggestions based on the number of users currently present. By customizing the suggestions based on the current situation, more appropriate suggestions become possible. Current situation includes, but is not limited to, current location, time, and budget. 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 use an AI model that takes the user's current situation as input and customizes the suggestions to make them.

[0048] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions based on information about the user's current location. The suggestion unit can also make suggestions to avoid congestion based on the user's geographical location information. Furthermore, the suggestion unit can analyze the user's geographical location information and make the most efficient suggestions. In this way, optimal suggestions become possible by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's geographical location information as input and outputs optimal suggestions.

[0049] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's activities shared on social media. The suggestion unit can also identify genres of interest from the user's social media activity and make suggestions based on those genres. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions based on trends. This allows for more appropriate suggestions by analyzing social media activity. Social media activity includes, but is not limited to, the content, frequency, and reactions of posts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's social media activity as input and outputs relevant suggestions.

[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 analysis unit can improve the accuracy of its analysis by referring to the user's past behavior data. For example, it can suggest the optimal plan based on the user's past activities. It can also suggest a plan that avoids congestion based on the user's past behavior data. Furthermore, it can analyze the user's past behavior data and suggest the most efficient plan. In this way, the accuracy of the analysis is improved by referring to past behavior data. Past behavior data includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can take the user's past behavior data as input and perform analysis using an AI model that improves the accuracy of the analysis.

[0052] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as suggestions what the user frequently wants to do and their current situation in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest what the user wants to do and their current situation at a specific time period based on their past input history. In this way, by analyzing past input history, the optimal input method can be suggested. Past input history includes, but is not limited to, past input data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0053] The suggestion unit can make optimal suggestions by referring to the user's past behavioral data. For example, it can make optimal suggestions based on the user's past activities. It can also make suggestions to avoid crowded places based on the user's past behavioral data. Furthermore, it can analyze the user's past behavioral data to make the most efficient suggestions. In this way, optimal suggestions become possible by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavior data, frequency, and trends. 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 make suggestions using an AI model that takes the user's past behavioral data as input and outputs optimal suggestions.

[0054] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do and their current situation. For example, when a user opens the app, it can automatically acquire their current location and set it as the location. It can also suggest the most suitable candidate location by considering the distance from the current location when the user inputs what they want to do. Furthermore, if the user uses the app while on the move, it can update their current location in real time and reflect it as the location. This simplifies input by automatically acquiring the current location information. Current location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can simplify input by using an AI model that takes the user's current location information as input.

[0055] The analysis unit can customize its analysis method based on the user's current situation during the analysis. For example, it can suggest an optimal plan based on the user's current location. It can also suggest an appropriate plan based on the user's current time of day. Furthermore, it can suggest an optimal plan based on the number of users currently present. By customizing the analysis method based on the current situation, a more appropriate analysis becomes possible. The current situation includes, but is not limited to, current location, time, and budget. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can take the user's current situation as input and perform the analysis using an AI model that customizes the analysis method.

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

[0057] Step 1: The reception desk enters what you want to do and your current situation. What you want to do may include, but is not limited to, travel, events, hobbies, etc. Your current situation may include, but is not limited to, your current location, time, budget, etc. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data analysis methods and algorithms. Step 3: The proposal department proposes the optimal plan based on the information analyzed by the analysis department. For example, the proposal department will propose a plan that best matches the user's wishes or a plan that offers high cost performance.

[0058] (Example of form 2) The leisure-killing plan suggestion system according to an embodiment of the present invention is a tool in which an AI agent suggests the optimal leisure-killing plan simply by inputting what the user wants to do and their current situation (location, time, number of people, etc.). This system allows the user to input what they want to do and their current situation, and the AI ​​agent analyzes this information to suggest the optimal plan. For example, it can be used when you don't know what to do during free time on holidays or weekdays, due to sudden weather changes, or schedule changes. It is also useful when you have something you want to do but are unsure whether a shop is open or crowded. The AI ​​agent suggests the optimal plan simply by the user inputting conditions such as the number of people, location, time, genre (eating, playing, studying), indoor / outdoor, and budget. Furthermore, by allowing facilities to input information, the accuracy of search results can be improved, contributing to increased customer traffic. For example, by inputting information such as available time slots and recommended points, facilities can suggest more appropriate plans for users. This mechanism allows users to effectively utilize their free time, and facilities can increase their customer traffic. For example, even if plans change due to sudden rain, the AI ​​agent can suggest the optimal indoor leisure-killing plan. Furthermore, the system can propose plans that take into account sudden schedule changes during business trips or crowd conditions when going out with family. This allows the leisure-killing plan suggestion system to propose the optimal plan based on what the user wants to do and their current situation.

[0059] The leisure-killing plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, and a suggestion unit. The reception unit receives input about what the user wants to do and their current situation. What the user wants to do includes, but is not limited to, travel, events, hobbies, etc. Current situation includes, but is not limited to, current location, time, budget, etc. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data analysis methods and algorithms. The suggestion unit proposes the optimal plan based on the information analyzed by the analysis unit. The suggestion unit proposes, for example, a plan that best matches the user's wishes or a plan with high cost performance. Thus, the leisure-killing plan suggestion system according to this embodiment can propose the optimal plan based on what the user wants to do and their current situation.

[0060] The reception desk provides an interface for users to input what they want to do and their current situation. What they want to do includes, but is not limited to, travel, events, and hobbies; it encompasses a wide range of options such as watching sports, watching movies, shopping, and outdoor activities. Users can select options that match their interests and preferences. Their current situation includes, but is not limited to, their current location, time, and budget; they can also input information such as weather, traffic conditions, whether they are accompanied by someone, and their physical condition. This allows the reception desk to accurately understand the user's detailed wishes and situation, providing the necessary information to the subsequent analysis and proposal departments. Furthermore, by saving the information entered by users and referencing past data, the reception desk can learn user preferences and behavioral patterns, laying the foundation for more personalized suggestions. For example, based on previously entered information, it can analyze the user's preferred activities and visited locations and reflect this in future suggestions. This enables the reception desk to collect flexible and highly accurate information tailored to user needs, enhancing the overall effectiveness of the system.

[0061] The Analysis Department plays a crucial role in analyzing information entered by the Reception Department and deriving the optimal plan based on the user's wishes and circumstances. The Analysis Department utilizes various data analysis methods and algorithms to comprehensively analyze user input. For example, it uses natural language processing technology to analyze text data about what the user wants to do, extracting their intentions and specific desires. It also uses machine learning algorithms to analyze the user's current situation data, identifying patterns and trends to derive the optimal plan. Furthermore, the Analysis Department can integrate information obtained from external data sources to perform more accurate analysis. For instance, it can incorporate weather forecast data and traffic information data to perform analysis to suggest the optimal activity based on the user's current location and time. The Analysis Department processes this data in real time, providing the latest information tailored to the user's situation. This allows the Analysis Department to quickly and accurately derive the optimal plan based on the user's wishes and circumstances and provide it to the Proposal Department. Additionally, the Analysis Department can continuously improve the accuracy of its analysis algorithms by utilizing past data and user feedback. This allows the analysis unit to always provide the latest information and highly accurate analysis results, improving the overall reliability of the system and user satisfaction.

[0062] The Proposal Department proposes the most suitable plan to the user based on the information analyzed by the Analysis Department. To propose the plan that best matches the user's wishes and offers the best value for money, the Proposal Department generates multiple options and selects the optimal one. For example, if a user wants to travel, the Proposal Department suggests destinations, accommodations, and sightseeing spots based on the user's budget, current location, and available time. If a user wants to attend an event, the Proposal Department gathers event information that matches the user's interests and provides detailed information such as location, date, time, and participation fee. Furthermore, the Proposal Department can provide more personalized suggestions by considering the user's past activity history and feedback. For example, by suggesting similar plans based on data of activities and places the user has previously enjoyed, user satisfaction can be increased. The Proposal Department also considers interface design and user experience to present these suggestions visually and clearly to the user. For example, it displays the locations of suggested plans on a map or presents detailed plan information in a card format, providing information in an intuitively understandable format. This allows the Proposal Department to quickly and effectively propose the most suitable plan to the user, enriching their leisure time. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to consistently provide optimal plans tailored to user needs, thereby improving the overall reliability of the system and user satisfaction.

[0063] The suggestion unit can generate plans while taking into account the latest information, such as weather and facility availability. For example, the suggestion unit can suggest indoor and outdoor activities based on weather information. It can also suggest plans that avoid congestion by considering facility availability. Furthermore, it can suggest plans that optimize travel time based on traffic information. In this way, by considering the latest information, it is possible to propose more appropriate plans. The latest information includes, but is not limited to, weather information, facility availability, and traffic information. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can generate plans using an AI model that takes weather information and facility availability as input and outputs the optimal plan.

[0064] The reception desk allows users to input conditions such as the number of users, location, time, genre, indoor / outdoor, and budget. For example, the reception desk can suggest an appropriate plan based on the number of users entered. It can also suggest nearby facilities based on the location entered by the user. Furthermore, it can suggest a plan suitable for the time of day based on the time entered by the user. This allows the system to suggest plans based on the user's detailed conditions. These conditions include, but are not limited to, the number of people, location, time, genre, indoor / outdoor, and budget. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can suggest a plan using an AI model that takes the conditions entered by the user as input and outputs the optimal plan.

[0065] The reception desk allows the facility to input information such as available time slots and recommended features. For example, the reception desk can propose an appropriate plan to the user based on the available time slot information entered by the facility. The reception desk can also propose an attractive plan to the user based on the recommended features entered by the facility. Furthermore, the reception desk can propose a plan that enhances the facility's ability to attract customers based on the information entered by the facility. In this way, by considering the facility's information, a more accurate plan can be proposed. Available time slot information includes, but is not limited to, the facility's reservation status and available time slots. Recommended features include, but is not limited to, the facility's features, benefits, and reasons for its popularity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can propose a plan using an AI model that takes the information entered by the facility as input and outputs the optimal plan.

[0066] The analysis unit can analyze user information and facility information. For example, the analysis unit can combine and analyze information entered by the user and information entered by the facility. The analysis unit can also analyze considering the user's wishes and the facility's availability. Furthermore, the analysis unit can analyze based on the user's current situation and the facility's characteristics. By analyzing user and facility information, it can propose the optimal plan. The analysis includes, but is not limited to, data analysis methods and algorithms used. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that takes user and facility information as input and outputs analysis results.

[0067] The proposal unit can propose the optimal plan based on the user's preferences. For example, the proposal unit can propose a plan based on the genre the user desires. It can also propose a plan based on the budget the user desires. Furthermore, it can propose a plan based on the time of day the user desires. This allows the proposal to suggest the optimal plan based on the user's preferences. The optimal plan includes, but is not limited to, a plan that best matches the user's preferences or a plan with high cost performance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can propose a plan using an AI model that takes the user's preferences as input and outputs the optimal plan.

[0068] The reception desk can estimate the user's emotions and adjust the input method for what the user wants to do and their current situation based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of what the user wants to do and their current situation. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can take user emotion data as input and adjust the input method using an AI model that adjusts the input method based on the emotions.

[0069] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions what the user wants to do and their current situation that they have frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest what the user wants to do and their current situation for a specific time period based on the user's past input history. In this way, the optimal input method can be suggested by analyzing past input history. Past input history includes, but is not limited to, past input data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0070] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do and their current situation. For example, when a user opens the app, the reception desk automatically acquires their current location and sets it as the location. The reception desk can also suggest optimal candidate locations by considering the distance from the current location when the user inputs what they want to do. Furthermore, if the user uses the app while on the move, the reception desk can update their current location in real time and reflect it as the location. This simplifies input by automatically acquiring the current location information. Current location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can simplify input by using an AI model that takes the user's current location information as input.

[0071] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for more appropriate input by adjusting the input interface design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can take user emotion data as input and adjust the design using an AI model that adjusts the input interface design based on the emotions.

[0072] The reception desk can automatically suggest options based on the user's past activity history when the user inputs what they want to do and their current situation. For example, the reception desk can automatically display activities that the user has frequently performed in the past as options. The reception desk can also predict activities that the user will perform on specific days of the week or time slots and suggest them as options. Furthermore, the reception desk can analyze the user's past behavior patterns and suggest the most suitable options. This allows the system to automatically suggest the best options by referring to past activity history. Past activity history includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can suggest options using an AI model that takes the user's past activity history as input and outputs the best options.

[0073] The reception desk can refer to the user's calendar information and make schedule-based suggestions when the user inputs what they want to do and their current situation. For example, the reception desk can refer to the appointments registered in the user's calendar and automatically set what they want to do and their current situation. The reception desk can also suggest activities related to a specific event as candidates based on the user's calendar information. Furthermore, the reception desk can suggest the most suitable plan based on the user's calendar information. This makes it possible to make schedule-based suggestions by referring to calendar information. Calendar information includes, but is not limited to, the type of appointment, time, and location. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can make suggestions using an AI model that takes the user's calendar information as input and outputs schedule-based suggestions.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and propose multiple plans. If the user is in a hurry, the analysis unit can perform a rapid analysis and propose the most suitable plan. Furthermore, if the user is excited, the analysis unit can adjust the analysis algorithm to propose a visually stimulating plan. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the algorithm using an AI model that takes user emotion data as input and adjusts the analysis algorithm based on the emotions.

[0075] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral data during the analysis process. For example, the analysis unit can propose an optimal plan based on the user's past activities. It can also propose a plan that avoids congestion based on the user's past behavioral data. Furthermore, the analysis unit can analyze the user's past behavioral data and propose the most efficient plan. This improves the accuracy of the analysis by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavioral data, frequency, and trends. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can take the user's past behavioral data as input and perform analysis using an AI model that improves the accuracy of the analysis.

[0076] The analysis unit can customize its analysis method based on the user's current situation during analysis. For example, the analysis unit can propose an optimal plan based on information about the user's current location. It can also propose an appropriate plan based on the user's current time of day. Furthermore, it can propose an optimal plan based on the current number of users. By customizing the analysis method based on the current situation, more appropriate analysis becomes possible. The current situation includes, but is not limited to, current location, time, and budget. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the user's current situation as input and perform analysis using an AI model that customizes the analysis method.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and adjust the display method using an AI model that adjusts the display method of the analysis results based on the emotions.

[0078] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information during the analysis. For example, the analysis unit can propose an optimal plan based on information about the user's current location. It can also propose a plan that avoids congestion based on the user's geographical location information. Furthermore, the analysis unit can analyze the user's geographical location information and propose the most efficient plan. In this way, the accuracy of the analysis is improved by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can take the user's geographical location information as input and perform the analysis using an AI model that improves the accuracy of the analysis.

[0079] The analysis unit can analyze a user's social media activity and utilize relevant data during the analysis. For example, the analysis unit can propose an optimal plan based on the user's activities shared on social media. It can also identify genres of interest from the user's social media activity and propose a plan based on that. Furthermore, the analysis unit can analyze the user's social media activity and propose a plan based on trends. This allows for more appropriate analysis by analyzing social media activity. Social media activity includes, but is not limited to, posts, content, frequency, and reactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the user's social media activity as input and utilizes relevant data for analysis.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, it can also provide a suggestion that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise suggestion. 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 generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can take user emotion data as input and adjust the way suggestions are presented using an AI model that adjusts the presentation based on the emotion.

[0081] The suggestion unit can make optimal suggestions by referring to the user's past behavioral data when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's past activities. The suggestion unit can also make suggestions to avoid congestion based on the user's past behavioral data. Furthermore, the suggestion unit can analyze the user's past behavioral data and make the most efficient suggestions. This makes it possible to make optimal suggestions by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's past behavioral data as input and outputs optimal suggestions.

[0082] The suggestion unit can customize its suggestions based on the user's current situation. For example, it can make optimal suggestions based on the user's current location. It can also make appropriate suggestions based on the user's current time of day. Furthermore, it can make optimal suggestions based on the number of users currently present. By customizing the suggestions based on the current situation, more appropriate suggestions become possible. Current situation includes, but is not limited to, current location, time, and budget. 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 use an AI model that takes the user's current situation as input and customizes the suggestions to make them.

[0083] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is tense, the suggestion unit will prioritize suggestions that help them relax. If the user is relaxed, the suggestion unit may also prioritize suggestions that they can enjoy. Furthermore, if the user is in a hurry, the suggestion unit may prioritize suggestions that can be acted upon quickly. This allows for more appropriate suggestions by prioritizing suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can take user emotion data as input and determine priorities using an AI model that determines the priority of suggestions based on emotions.

[0084] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can make optimal suggestions based on information about the user's current location. The suggestion unit can also make suggestions to avoid congestion based on the user's geographical location information. Furthermore, the suggestion unit can analyze the user's geographical location information and make the most efficient suggestions. In this way, optimal suggestions become possible by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the processing described above in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's geographical location information as input and outputs optimal suggestions.

[0085] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can make optimal suggestions based on the user's activities shared on social media. The suggestion unit can also identify genres of interest from the user's social media activity and make suggestions based on those genres. Furthermore, the suggestion unit can analyze the user's social media activity and make suggestions based on trends. This allows for more appropriate suggestions by analyzing social media activity. Social media activity includes, but is not limited to, the content, frequency, and reactions of posts. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can make suggestions using an AI model that takes the user's social media activity as input and outputs relevant suggestions.

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

[0087] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing activities. If the user is enjoying themselves, it can suggest even more enjoyable activities. Furthermore, if the user is tired, it can suggest activities that allow them to rest. This enables optimal suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and 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 take user emotion data as input and make suggestions using an AI model that adjusts the suggestion content based on the emotions.

[0088] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior data. For example, it can suggest the optimal plan based on the user's past activities. It can also suggest a plan that avoids congestion based on the user's past behavior data. Furthermore, it can analyze the user's past behavior data and suggest the most efficient plan. In this way, the accuracy of the analysis is improved by referring to past behavior data. Past behavior data includes, but is not limited to, past behavior data, frequency, and trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can take the user's past behavior data as input and perform analysis using an AI model that improves the accuracy of the analysis.

[0089] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. Furthermore, if the user is in a hurry, it can provide a suggestion that gets straight to the point. 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 engine or generative AI. Generative AI includes, but is not limited to, text generation AI and 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 take user emotion data as input and adjust the way suggestions are presented using an AI model that adjusts the way suggestions are presented based on those emotions.

[0090] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as suggestions what the user frequently wants to do and their current situation in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest what the user wants to do and their current situation at a specific time period based on their past input history. In this way, by analyzing past input history, the optimal input method can be suggested. Past input history includes, but is not limited to, past input data, frequency, and trends. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest an input method using an AI model that takes the user's past input history as input and outputs the optimal input method.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis and propose multiple plans. If the user is in a hurry, it can perform a rapid analysis and propose the most suitable plan. Furthermore, if the user is excited, it can adjust the analysis algorithm to propose a visually stimulating plan. By adjusting the analysis algorithm according to the user's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the algorithm using an AI model that takes user emotion data as input and adjusts the analysis algorithm based on the emotions.

[0092] The suggestion unit can make optimal suggestions by referring to the user's past behavioral data. For example, it can make optimal suggestions based on the user's past activities. It can also make suggestions to avoid crowded places based on the user's past behavioral data. Furthermore, it can analyze the user's past behavioral data to make the most efficient suggestions. In this way, optimal suggestions become possible by referring to past behavioral data. Past behavioral data includes, but is not limited to, past behavior data, frequency, and trends. 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 make suggestions using an AI model that takes the user's past behavioral data as input and outputs optimal suggestions.

[0093] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is tense, it can prioritize suggestions that help them relax. If the user is relaxed, it can also prioritize suggestions that they will enjoy. Furthermore, if the user is in a hurry, it can prioritize suggestions that can be acted upon quickly. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and 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 take user emotion data as input and determine priorities using an AI model that determines the priority of suggestions based on emotions.

[0094] The reception desk can simplify input by automatically acquiring the user's current location information when they input what they want to do and their current situation. For example, when a user opens the app, it can automatically acquire their current location and set it as the location. It can also suggest the most suitable candidate location by considering the distance from the current location when the user inputs what they want to do. Furthermore, if the user uses the app while on the move, it can update their current location in real time and reflect it as the location. This simplifies input by automatically acquiring the current location information. Current location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can simplify input by using an AI model that takes the user's current location information as input.

[0095] The analysis unit can customize its analysis method based on the user's current situation during the analysis. For example, it can suggest an optimal plan based on the user's current location. It can also suggest an appropriate plan based on the user's current time of day. Furthermore, it can suggest an optimal plan based on the number of users currently present. By customizing the analysis method based on the current situation, a more appropriate analysis becomes possible. The current situation includes, but is not limited to, current location, time, and budget. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can take the user's current situation as input and perform the analysis using an AI model that customizes the analysis method.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can take user emotion data as input and adjust the display method using an AI model that adjusts the display method of the analysis results based on emotions.

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

[0098] Step 1: The reception desk enters what you want to do and your current situation. What you want to do may include, but is not limited to, travel, events, hobbies, etc. Your current situation may include, but is not limited to, your current location, time, budget, etc. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data analysis methods and algorithms. Step 3: The proposal department proposes the optimal plan based on the information analyzed by the analysis department. For example, the proposal department will propose a plan that best matches the user's wishes or a plan that offers high cost performance.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, where the user inputs what they want to do and their current situation. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, where it proposes the optimal plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice 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.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which allows the user to input what they want to do or their current situation by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice 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.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, where the user inputs what they want to do or their current situation by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it analyzes the input information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where it proposes the optimal plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice 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.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which allows the user to input what they want to do or their current situation by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the optimal plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The reception desk where you input what you want to do and your current situation, An analysis unit analyzes the information input by the reception unit, The system includes a proposal unit that proposes an optimal plan based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, The plan is generated taking into account the latest information such as weather and facility availability. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Enter the number of users, location, time, genre, indoor / outdoor, budget, and other conditions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The facility enters information such as available time slots and recommended features. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze user information and facility information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal plan based on the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for what the user wants to do and the current situation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users input what they want to do and their current situation, the system automatically retrieves their current location information to simplify the input process. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input what they want to do and their current situation, the system automatically suggests options based on their past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input what they want to do and their current situation, the system references their calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant data is used for the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, we refer to the user's past behavioral data to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you input what you want to do and your current situation, An analysis unit analyzes the information input by the reception unit, The system includes a proposal unit that proposes an optimal plan based on the information analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, The plan is generated taking into account the latest information such as weather and facility availability. The system according to feature 1.

3. The aforementioned reception unit is Enter the number of users, location, time, genre, indoor / outdoor, budget, and other conditions. The system according to feature 1.

4. The aforementioned reception unit is The facility enters information such as available time slots and recommended features. The system according to feature 1.

5. The aforementioned analysis unit, Analyze user information and facility information. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal plan based on the user's preferences. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input methods for what the user wants to do and the current situation based on the estimated emotions. The system according to feature 1.

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

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