system

Abstract

The system according to this embodiment aims to propose and execute an optimal date plan based on the user's preferences and schedule. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, a reservation unit, an activity proposal unit, and a travel route optimization unit. The reception unit receives input from the user regarding their preferences and schedule. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes an optimal date plan based on the information analyzed by the analysis unit. The reservation unit makes restaurant reservations based on the date plan proposed by the proposal unit. The activity proposal unit proposes activities based on the date plan proposed by the proposal unit. The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to propose and execute an optimal date plan based on the user's preferences and schedule.

[0005] The system according to the embodiment aims to propose and execute an optimal date plan based on the user's preferences and schedule.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, a reservation unit, an activity proposal unit, and a travel route optimization unit. The reception unit receives input from the user regarding their preferences and schedule. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes an optimal date plan based on the information analyzed by the analysis unit. The reservation unit makes restaurant reservations based on the date plan proposed by the proposal unit. The activity proposal unit proposes activities based on the date plan proposed by the proposal unit. The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest and execute an optimal date plan based on the user's preferences and schedule. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The date plan suggestion system according to an embodiment of the present invention is an assistant that suggests the optimal date plan based on specific circumstances and preferences. This date plan suggestion system receives input from the user regarding their preferences and schedule, and the AI ​​analyzes this information to make restaurant reservations, suggest activities, and optimize travel routes. This mechanism allows the user to enjoy a stress-free and perfect date. For example, the user inputs detailed information about their preferences and schedule, such as their favorite type of cuisine, desired locations, and preferred time of day. This information is then input into the AI. Next, the AI ​​analyzes the input information. Based on the user's preferences and schedule, the AI ​​suggests the optimal date plan. For example, if the user likes Italian food and desires a night date, the AI ​​searches for and makes reservations at restaurants that meet those criteria. It also suggests activities suitable for the date, such as watching a movie or visiting an art museum, tailored to the user's preferences. Furthermore, the AI ​​optimizes travel routes. When the user travels between date locations, the AI ​​suggests the optimal route, reducing travel time. For example, it suggests the shortest route from the restaurant to the movie theater, optimizing travel time. This mechanism allows the user to enjoy a stress-free and perfect date. Users can save time and effort on planning dates, making reservations, and coordinating travel routes, allowing them to focus on the date itself. Furthermore, the AI-suggested date plans are perfectly tailored to the user's preferences and schedule, resulting in high satisfaction. For example, if a user wants to spend special time together despite a busy schedule, the AI ​​can suggest an optimal date plan, allowing the user to enjoy the date according to that plan. Even users who lack confidence in planning dates or whose dates tend to become repetitive can benefit from the AI's suggestions for new locations and activities, preventing monotony. Thus, an assistant that suggests the optimal date plan based on specific situations and preferences is extremely convenient for users, enabling stress-free and perfect dates. In short, a date plan suggestion system can suggest the optimal date plan based on the user's preferences and schedule, resulting in stress-free and perfect dates.

[0029] The date plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a reservation unit, an activity suggestion unit, and a travel route optimization unit. The reception unit receives input from the user regarding their preferences and schedule. The reception unit allows the user to input details such as their favorite type of food, places they want to go, and the time of day for the date. The reception unit transmits the information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit. The analysis unit extracts data for suggesting the optimal date plan based on the user's preferences and schedule. The analysis unit uses AI to analyze the user's preferences and schedule. The suggestion unit proposes the optimal date plan based on the information analyzed by the analysis unit. For example, if the user likes Italian food and wants a night date, the suggestion unit searches for a restaurant that meets those conditions and makes a reservation. The suggestion unit also suggests activities that are perfect for a date. For example, it suggests activities that match the user's preferences, such as watching a movie or visiting an art museum. The reservation unit makes a restaurant reservation based on the date plan suggested by the suggestion unit. The reservation unit searches for a restaurant the user desires and makes a reservation. The activity suggestion unit suggests activities based on the date plan proposed by the suggestion unit. For example, if the user wants to see a movie, the activity suggestion unit searches for a nearby movie theater and suggests the showtimes. The travel route optimization unit optimizes the travel route based on the date plan proposed by the suggestion unit. For example, the travel route optimization unit suggests the shortest route from the restaurant to the movie theater, making travel time more efficient. As a result, the date plan suggestion system according to this embodiment can suggest the optimal date plan based on the user's preferences and schedule, enabling a stress-free and perfect date.

[0030] The reception desk accepts user input of preferences and schedules. For example, users can input detailed information such as their favorite types of cuisine, places they want to visit, and preferred time slots for dates. Specifically, users access a dedicated application or website using their smartphone or computer and input information through the interface. Information entered by the user includes types of cuisine (e.g., Italian, Japanese, French, etc.), places they want to visit (e.g., parks, movie theaters, museums, etc.), and preferred time slots for dates (e.g., daytime, evening, night, etc.). Furthermore, users can also input specific events or special requests (e.g., birthdays, anniversaries, surprises, etc.). The reception desk collects this information and stores it in a database. The collected information is sent to the AI ​​and used for analysis in the analysis department. The reception desk also has a function to display a confirmation screen of the input content to verify the accuracy of the information entered by the user, prompting the user to reconfirm. This allows users to accurately input their preferences and schedules, enabling the system to suggest more accurate date plans.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit extracts data to propose the optimal date plan based on the user's preferences and schedule. Specifically, it uses AI to analyze the user's input information and generate a date plan that is best suited to the user's preferences and schedule. The AI ​​uses natural language processing technology to understand the user's input and derives the optimal plan based on past data and trend information. For example, if a user inputs "I like Italian food and would like a night date," the AI ​​analyzes this information and lists restaurants and activities that have received high ratings under similar conditions in the past. The AI ​​can also consider the user's past usage history and ratings to provide more personalized suggestions. Based on this information, the analysis unit extracts data to propose the optimal date plan to the user and sends it to the suggestion unit. This allows the analysis unit to generate the optimal date plan based on the user's preferences and schedule, and to provide the user with a highly satisfying suggestion.

[0032] The suggestion department proposes the optimal date plan based on the information analyzed by the analysis department. For example, if a user likes Italian food and wants a night date, the suggestion department will search for a restaurant that meets those criteria and make a reservation. Specifically, based on the data received from the analysis department, the suggestion department lists restaurants and activities that match the user's preferences and proposes them to the user. The suggestion department provides multiple options based on the user's preferences and schedule, allowing the user to choose. For example, if the user wants Italian food, the suggestion department will list nearby Italian restaurants and provide information such as the restaurant's rating, menu, and price range. The suggestion department also suggests activities that are perfect for a date. For example, it will suggest activities that match the user's preferences, such as watching a movie or visiting an art museum. The suggestion department makes reservations for the restaurants and activities selected by the user and sends a confirmation notification to the user. In this way, the suggestion department can propose the optimal date plan based on the user's preferences and schedule, providing the user with a highly satisfying date experience.

[0033] The reservation department makes restaurant reservations based on the date plan proposed by the suggestion department. For example, the reservation department searches for restaurants that the user wants and makes reservations. Specifically, the reservation department makes reservations for restaurants and activities selected by the user based on the information received from the suggestion department. The reservation department uses an online reservation system to check availability in real time and confirm the reservation. After the reservation is completed, the reservation department sends a confirmation notice to the user so that they can check the reservation details. The reservation department also handles changes and cancellations of reservations, and can respond quickly if the user wishes to change or cancel a reservation. In this way, the reservation department can provide a smooth reservation process based on the user's wishes and provide the user with a stress-free date experience.

[0034] The Activity Suggestion Department proposes activities based on the date plan suggested by the Proposal Department. For example, if a user wants to see a movie, the Activity Suggestion Department will search for nearby movie theaters and suggest showtimes. Specifically, the Activity Suggestion Department lists and suggests the most suitable activities based on the user's preferences and schedule. The Activity Suggestion Department collects and provides information on various activities such as movie theaters, museums, and concert halls. For example, if a user wants to see a movie, the Activity Suggestion Department will search for nearby movie theaters and provide information such as movies currently showing, showtimes, and ticket prices. The Activity Suggestion Department can also suggest special events and limited-time activities that match the user's preferences. In this way, the Activity Suggestion Department can suggest the most suitable activities based on the user's preferences and schedule, providing the user with a highly satisfying date experience.

[0035] The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit. For example, the travel route optimization unit proposes the shortest route from a restaurant to a movie theater, making travel time more efficient. Specifically, the travel route optimization unit calculates the optimal travel route between each destination based on the user's date plan. The travel route optimization unit proposes the optimal route considering real-time traffic information and the operating status of public transportation. For example, if a user wants to have dinner at a restaurant and then watch a movie at a movie theater, the travel route optimization unit calculates the shortest route from the restaurant to the movie theater, minimizing travel time. The travel route optimization unit also allows the user to choose from various modes of transportation, such as walking, cycling, driving, and public transportation, and proposes the optimal route for each mode of transportation. In this way, the travel route optimization unit can provide an efficient travel route based on the user's date plan, providing the user with a stress-free date experience.

[0036] The reception desk can analyze the user's past dating history and suggest the optimal input format. For example, the reception desk can automatically display preferences and appointments that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest preferences and appointments to be used during specific time slots based on the user's past dating history. This streamlines the user's input process by suggesting the optimal input format based on past dating history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past dating history data into a generating AI and have the generating AI suggest the optimal input format.

[0037] The reception desk can filter input content based on the user's current mood and physical condition. For example, if the user is tired, the reception desk can prioritize inputting relaxing date plans. If the user is energetic, the reception desk can also prioritize inputting active date plans. Furthermore, if the user is stressed, the reception desk can prioritize inputting date plans that help relieve stress. In this way, by providing input content that matches the user's mood and physical condition, it can suggest a more appropriate date plan. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's mood and physical condition data into a generating AI and have the generating AI perform the filtering of the input content.

[0038] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, the reception desk can prioritize inputting restaurants and activities close to the user's current location. Furthermore, if the user is in a specific region, the reception desk can prioritize inputting popular spots in that region. Additionally, if the user is traveling, the reception desk can prioritize inputting tourist attractions and restaurants in their travel destination. This allows the system to provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​suggest highly relevant information.

[0039] The reception desk can analyze a user's social media activity and automatically input relevant preferences and plans. For example, the reception desk can automatically input places that the user has mentioned as places they "want to go" on social media. It can also automatically input restaurants and activities that the user has "liked." Furthermore, the reception desk can predict preferences and plans from the content of the user's social media posts and automatically input them. This streamlines the user's input process by automating input based on social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the automatic input of relevant preferences and plans.

[0040] The analysis unit can improve the accuracy of its analysis by referring to the user's past dating history. For example, the analysis unit can suggest similar plans based on dating plans the user has preferred in the past. It can also analyze successful plans from the user's past dating history and suggest the optimal plan. Furthermore, the analysis unit can refer to the user's past dating history and analyze it to avoid unsuccessful plans. In this way, by improving the accuracy of the analysis based on past dating history, it can suggest more appropriate dating plans. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past dating history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0041] The analysis unit can customize the analysis results based on the user's current lifestyle. For example, if the user is busy, the analysis unit can suggest a date plan that can be enjoyed in a short amount of time. It can also suggest a date plan that allows for a more relaxed experience if the user is on holiday. Furthermore, if the user has plans to attend a specific event, the analysis unit can suggest a date plan tailored to that event. This allows the analysis unit to provide more appropriate date plans by offering analysis results that match the user's current lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For instance, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI customize the analysis results.

[0042] The analysis unit can classify the analysis results by region, taking into account the user's geographical location information. For example, the analysis unit can prioritize displaying restaurants and activities close to the user's current location. Furthermore, if the user is in a specific region, the analysis unit can prioritize displaying popular spots in that region. Additionally, if the user is traveling, the analysis unit can prioritize displaying tourist attractions and restaurants in their travel destination. This allows for the suggestion of more relevant date plans by classifying the analysis results based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the regional classification.

[0043] The analysis unit can analyze a user's social media activity and reflect relevant date plans in the analysis. For example, the analysis unit can reflect places that the user has mentioned as places they "want to go" on social media. It can also reflect restaurants and activities that the user has "liked" in the analysis. Furthermore, the analysis unit can predict preferences and plans from the content of the user's social media posts and reflect them in the analysis. In this way, by analyzing based on social media activity, it can suggest more appropriate date plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant date plans.

[0044] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the date. For example, it can provide detailed suggestions for special anniversary dates, while offering concise suggestions for casual dates. Furthermore, it can provide carefully selected suggestions for first dates. This allows the suggestion unit to propose more appropriate date plans by offering suggestions tailored to the importance of each date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input date importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0045] The suggestion unit can apply different suggestion algorithms depending on the date category when making suggestions. For example, for a romantic date, the suggestion unit can suggest restaurants with a good atmosphere and activities. For an active date, the suggestion unit can also suggest sports and outdoor activities. Furthermore, for a cultural date, the suggestion unit can suggest art museums and museums. By providing suggestions tailored to the date category, it can suggest more appropriate date plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input date category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0046] The suggestion unit can prioritize suggestions based on the planned timing of the date. For example, it will prioritize suggestions for upcoming dates. It can also postpone suggestions for dates far in the future. Furthermore, if the date is tied to a specific event, it can tailor the suggestion unit's suggestions to that event. This allows the suggestion unit to propose a more appropriate date plan by providing suggestions that are appropriate for the planned timing of the date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input date timing data into a generating AI and have the generating AI determine the priority of the suggestions.

[0047] The suggestion unit can adjust the order of suggestions based on the relevance of the date. For example, the suggestion unit can first suggest the one that best suits the user's preferences. It can also first suggest the one that best suits the user's schedule. Furthermore, it can first suggest the one that is most likely to be successful based on the user's past dating history. This allows the suggestion unit to propose a more appropriate date plan by providing suggestions according to the relevance of the date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input date relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0048] The reservation department can select the optimal reservation method by referring to the user's past reservation history. For example, the reservation department may prioritize reservations at restaurants the user has previously enjoyed. The reservation department can also select successful reservation methods from the user's past reservation history. Furthermore, the reservation department can refer to the user's past reservation history and avoid unsuccessful reservation methods. By providing the optimal reservation method based on past reservation history, it can suggest a more appropriate date plan. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's past reservation history data into a generating AI and have the generating AI select the optimal reservation method.

[0049] The reservation department can customize reservation details based on the user's current lifestyle. For example, if the user is busy, the reservation department can reserve a restaurant that can be enjoyed in a short amount of time. If the user is on holiday, the reservation department can reserve a restaurant where they can relax. Furthermore, if the user has plans to attend a specific event, the reservation department can reserve a restaurant that suits that event. By providing reservation details that match the user's current lifestyle, the department can suggest a more appropriate date plan. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the reservation details.

[0050] The reservation system can prioritize booking restaurants that are highly relevant to the user, taking into account the user's geographical location. For example, the reservation system can prioritize booking restaurants close to the user's current location. Furthermore, if the user is in a specific region, the reservation system can prioritize booking popular restaurants in that region. Additionally, if the user is traveling, the reservation system can prioritize booking restaurants in their travel destination. This allows for the suggestion of more appropriate date plans by providing highly relevant restaurants based on geographical location information. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system can input the user's geographical location data into a generating AI and have the AI ​​generate suggestions for highly relevant restaurants.

[0051] The reservation department can analyze a user's social media activity and automatically make reservations at relevant restaurants. For example, the reservation department can automatically reserve restaurants that a user has mentioned as places they "want to go" to on social media. It can also automatically reserve restaurants that a user has "liked." Furthermore, the reservation department can predict a user's preferences and plans from their social media posts and automatically reserve relevant restaurants. This allows for more appropriate date plans to be suggested by making reservations based on social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input a user's social media data into a generating AI and have the generating AI perform the automatic reservation of relevant restaurants.

[0052] The activity suggestion unit can provide optimal suggestions by referring to the user's past activity history. For example, the activity suggestion unit can suggest similar activities based on activities the user has enjoyed in the past. The activity suggestion unit can also analyze successful activities from the user's past activity history and suggest optimal activities. Furthermore, the activity suggestion unit can refer to the user's past activity history and suggest activities that have failed. In this way, by providing optimal suggestions based on past activity history, it can suggest more appropriate date plans. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or not using AI. For example, the activity suggestion unit can input the user's past activity history data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0053] The activity suggestion unit can customize activity suggestions based on the user's current lifestyle. For example, if the user is busy, the activity suggestion unit can suggest activities that can be enjoyed in a short amount of time. It can also suggest activities that allow the user to relax if they have a day off. Furthermore, if the user has plans to attend a specific event, the activity suggestion unit can suggest activities tailored to that event. This allows the unit to provide activity suggestions that are appropriate to the user's current lifestyle, thereby suggesting a more suitable date plan. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or without AI. For example, the activity suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI customize the suggestions.

[0054] The activity suggestion unit can prioritize suggesting highly relevant activities by taking into account the user's geographical location. For example, the activity suggestion unit can prioritize suggesting activities close to the user's current location. Furthermore, if the user is in a specific region, the activity suggestion unit can prioritize suggesting popular activities in that region. Additionally, if the user is traveling, the activity suggestion unit can prioritize suggesting activities in their travel destination. This allows for the suggestion of more appropriate date plans by providing highly relevant activities based on geographical location information. Some or all of the above processing in the activity suggestion unit may be performed using AI, or not. For example, the activity suggestion unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant activities.

[0055] The activity suggestion unit can analyze a user's social media activity and automatically suggest relevant activities. For example, it can automatically suggest activities that a user has mentioned as "wanting to go" on social media. It can also automatically suggest activities that a user has "liked." Furthermore, it can predict a user's preferences and plans from their social media posts and automatically suggest relevant activities. This allows for more appropriate date plans to be suggested by automatically making suggestions based on social media activity. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or without AI. For example, the activity suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the automatic suggestion of relevant activities.

[0056] The travel route optimization unit can propose the optimal travel route by referring to the user's past travel history. For example, the travel route optimization unit proposes the optimal route based on routes the user has used in the past. The travel route optimization unit can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the travel route optimization unit can analyze the user's past travel history and propose the most efficient route. In this way, by providing the optimal travel route based on past travel history, it proposes a more appropriate date plan. Some or all of the above processing in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel route.

[0057] The travel route optimization unit can customize travel routes based on the user's current lifestyle. For example, if the user is busy, the travel route optimization unit can suggest the shortest route. It can also suggest a scenic route if the user is on holiday. Furthermore, if the user has plans to attend a specific event, the travel route optimization unit can suggest a route tailored to that event. This allows for the provision of more appropriate date plans by offering travel routes that match the user's current lifestyle. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, for example, or without AI. For instance, the travel route optimization unit can input user lifestyle data into a generating AI and have the generating AI perform the travel route customization.

[0058] The travel route optimization unit can propose the optimal travel route by taking into account the user's geographical location information. For example, the travel route optimization unit can prioritize proposing routes that are close to the user's current location. Furthermore, if the user is in a specific region, the travel route optimization unit can also propose the optimal route for that region. In addition, if the user is traveling, the travel route optimization unit can propose the optimal route for their destination. By providing the optimal travel route based on geographical location information, it can propose a more appropriate date plan. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's geographical location data into a generating AI and have the generating AI propose the optimal travel route.

[0059] The travel route optimization unit can analyze a user's social media activity and automatically suggest relevant travel routes. For example, the travel route optimization unit can automatically suggest routes to places the user has mentioned as places they "want to go" on social media. It can also automatically suggest routes to places the user has "liked." Furthermore, the travel route optimization unit can predict the user's preferences and plans from the content of their social media posts and automatically suggest relevant travel routes. This allows for the suggestion of more appropriate date plans by automatically making suggestions based on social media activity. Some or all of the above processing in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's social media data into a generating AI and have the generating AI perform the automatic suggestion of relevant travel routes.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The date plan suggestion system can further acquire user health data and customize its suggestions. For example, it can suggest a date plan with an appropriate walking distance based on the user's step count data. It can also suggest relaxing activities based on the user's heart rate data. Furthermore, it can suggest a date plan tailored to the user's fatigue level based on their sleep data. By providing date plans that match the user's health condition, it can help create more appropriate dates.

[0062] The date plan suggestion system can further customize its suggestions by taking into account the user's hobbies and interests. For example, if the user likes music, it can suggest live concerts or music events. If the user likes sports, it can suggest watching sports or other active activities. Furthermore, if the user likes art, it can suggest visiting museums or galleries. By providing date plans tailored to the user's hobbies and interests, it can lead to more satisfying dates.

[0063] The date plan suggestion system can further improve its suggestions by collecting feedback on users' past date plans. For example, if a user gives a high rating to a past date plan, it will suggest a similar plan. Conversely, if a user gives a low rating, the system can adjust the suggestions to avoid those elements. Furthermore, it can generate new date plan ideas based on user feedback. This allows for more satisfying dates by providing date plans that reflect user feedback.

[0064] The date plan suggestion system can further customize its suggestions by incorporating the opinions of the user's friends and family. For example, it can suggest restaurants and activities recommended by the user's friends. It can also suggest places that the user's family likes. Furthermore, it can generate new date plan ideas based on the opinions of the user's friends and family. This allows for more satisfying dates by providing date plans that reflect the opinions of those around the user.

[0065] The date plan suggestion system can further customize its suggestions by taking into account the user's travel history. For example, it can suggest similar date plans based on places the user has visited in the past. It can also suggest places the user wants to visit. Furthermore, it can generate new date plan ideas based on the user's travel history. By providing date plans that reflect the user's travel history, it can lead to more satisfying dates.

[0066] The date plan suggestion system can further customize its suggestions by taking into account the user's cultural background. For example, it can suggest restaurants and activities based on the user's culture. It can also suggest cultural events and festivals related to the user. Furthermore, it can generate new date plan ideas based on the user's cultural background. By providing date plans that reflect the user's cultural background, it can lead to more satisfying dates.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk accepts user input about their preferences and schedule. For example, users can enter details such as their favorite type of food, places they want to go, and preferred time for dates. The reception desk then sends the information entered by the user to the AI. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it extracts data to suggest the optimal date plan based on the user's preferences and schedule. The analysis unit uses AI to analyze the user's preferences and schedule. Step 3: The suggestion unit proposes the optimal date plan based on the information analyzed by the analysis unit. For example, if the user likes Italian food and wants a night date, the unit will search for a restaurant that meets those criteria and make a reservation. It will also suggest activities that are perfect for a date. For example, it will suggest activities that suit the user's preferences, such as watching a movie or visiting an art museum. Step 4: The reservation department makes restaurant reservations based on the date plan proposed by the suggestion department. For example, it searches for a restaurant the user wants and makes a reservation. Step 5: The activity suggestion unit proposes activities based on the date plan suggested by the suggestion unit. For example, if the user wants to see a movie, it searches for nearby movie theaters and suggests showtimes. Step 6: The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit. For example, it proposes the shortest route from the restaurant to the movie theater, making travel time more efficient.

[0069] (Example of form 2) The date plan suggestion system according to an embodiment of the present invention is an assistant that suggests the optimal date plan based on specific circumstances and preferences. This date plan suggestion system receives input from the user regarding their preferences and schedule, and the AI ​​analyzes this information to make restaurant reservations, suggest activities, and optimize travel routes. This mechanism allows the user to enjoy a stress-free and perfect date. For example, the user inputs detailed information about their preferences and schedule, such as their favorite type of cuisine, desired locations, and preferred time of day. This information is then input into the AI. Next, the AI ​​analyzes the input information. Based on the user's preferences and schedule, the AI ​​suggests the optimal date plan. For example, if the user likes Italian food and desires a night date, the AI ​​searches for and makes reservations at restaurants that meet those criteria. It also suggests activities suitable for the date, such as watching a movie or visiting an art museum, tailored to the user's preferences. Furthermore, the AI ​​optimizes travel routes. When the user travels between date locations, the AI ​​suggests the optimal route, reducing travel time. For example, it suggests the shortest route from the restaurant to the movie theater, optimizing travel time. This mechanism allows the user to enjoy a stress-free and perfect date. Users can save time and effort on planning dates, making reservations, and coordinating travel routes, allowing them to focus on the date itself. Furthermore, the AI-suggested date plans are perfectly tailored to the user's preferences and schedule, resulting in high satisfaction. For example, if a user wants to spend special time together despite a busy schedule, the AI ​​can suggest an optimal date plan, allowing the user to enjoy the date according to that plan. Even users who lack confidence in planning dates or whose dates tend to become repetitive can benefit from the AI's suggestions for new locations and activities, preventing monotony. Thus, an assistant that suggests the optimal date plan based on specific situations and preferences is extremely convenient for users, enabling stress-free and perfect dates. In short, a date plan suggestion system can suggest the optimal date plan based on the user's preferences and schedule, resulting in stress-free and perfect dates.

[0070] The date plan suggestion system according to this embodiment comprises a reception unit, an analysis unit, a suggestion unit, a reservation unit, an activity suggestion unit, and a travel route optimization unit. The reception unit receives input from the user regarding their preferences and schedule. The reception unit allows the user to input details such as their favorite type of food, places they want to go, and the time of day for the date. The reception unit transmits the information entered by the user to the AI. The analysis unit analyzes the information received by the reception unit. The analysis unit extracts data for suggesting the optimal date plan based on the user's preferences and schedule. The analysis unit uses AI to analyze the user's preferences and schedule. The suggestion unit proposes the optimal date plan based on the information analyzed by the analysis unit. For example, if the user likes Italian food and wants a night date, the suggestion unit searches for a restaurant that meets those conditions and makes a reservation. The suggestion unit also suggests activities that are perfect for a date. For example, it suggests activities that match the user's preferences, such as watching a movie or visiting an art museum. The reservation unit makes a restaurant reservation based on the date plan suggested by the suggestion unit. The reservation unit searches for a restaurant the user desires and makes a reservation. The activity suggestion unit suggests activities based on the date plan proposed by the suggestion unit. For example, if the user wants to see a movie, the activity suggestion unit searches for a nearby movie theater and suggests the showtimes. The travel route optimization unit optimizes the travel route based on the date plan proposed by the suggestion unit. For example, the travel route optimization unit suggests the shortest route from the restaurant to the movie theater, making travel time more efficient. As a result, the date plan suggestion system according to this embodiment can suggest the optimal date plan based on the user's preferences and schedule, enabling a stress-free and perfect date.

[0071] The reception desk accepts user input of preferences and schedules. For example, users can input detailed information such as their favorite types of cuisine, places they want to visit, and preferred time slots for dates. Specifically, users access a dedicated application or website using their smartphone or computer and input information through the interface. Information entered by the user includes types of cuisine (e.g., Italian, Japanese, French, etc.), places they want to visit (e.g., parks, movie theaters, museums, etc.), and preferred time slots for dates (e.g., daytime, evening, night, etc.). Furthermore, users can also input specific events or special requests (e.g., birthdays, anniversaries, surprises, etc.). The reception desk collects this information and stores it in a database. The collected information is sent to the AI ​​and used for analysis in the analysis department. The reception desk also has a function to display a confirmation screen of the input content to verify the accuracy of the information entered by the user, prompting the user to reconfirm. This allows users to accurately input their preferences and schedules, enabling the system to suggest more accurate date plans.

[0072] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit extracts data to propose the optimal date plan based on the user's preferences and schedule. Specifically, it uses AI to analyze the user's input information and generate a date plan that is best suited to the user's preferences and schedule. The AI ​​uses natural language processing technology to understand the user's input and derives the optimal plan based on past data and trend information. For example, if a user inputs "I like Italian food and would like a night date," the AI ​​analyzes this information and lists restaurants and activities that have received high ratings under similar conditions in the past. The AI ​​can also consider the user's past usage history and ratings to provide more personalized suggestions. Based on this information, the analysis unit extracts data to propose the optimal date plan to the user and sends it to the suggestion unit. This allows the analysis unit to generate the optimal date plan based on the user's preferences and schedule, and to provide the user with a highly satisfying suggestion.

[0073] The suggestion department proposes the optimal date plan based on the information analyzed by the analysis department. For example, if a user likes Italian food and wants a night date, the suggestion department will search for a restaurant that meets those criteria and make a reservation. Specifically, based on the data received from the analysis department, the suggestion department lists restaurants and activities that match the user's preferences and proposes them to the user. The suggestion department provides multiple options based on the user's preferences and schedule, allowing the user to choose. For example, if the user wants Italian food, the suggestion department will list nearby Italian restaurants and provide information such as the restaurant's rating, menu, and price range. The suggestion department also suggests activities that are perfect for a date. For example, it will suggest activities that match the user's preferences, such as watching a movie or visiting an art museum. The suggestion department makes reservations for the restaurants and activities selected by the user and sends a confirmation notification to the user. In this way, the suggestion department can propose the optimal date plan based on the user's preferences and schedule, providing the user with a highly satisfying date experience.

[0074] The reservation department makes restaurant reservations based on the date plan proposed by the suggestion department. For example, the reservation department searches for restaurants that the user wants and makes reservations. Specifically, the reservation department makes reservations for restaurants and activities selected by the user based on the information received from the suggestion department. The reservation department uses an online reservation system to check availability in real time and confirm the reservation. After the reservation is completed, the reservation department sends a confirmation notice to the user so that they can check the reservation details. The reservation department also handles changes and cancellations of reservations, and can respond quickly if the user wishes to change or cancel a reservation. In this way, the reservation department can provide a smooth reservation process based on the user's wishes and provide the user with a stress-free date experience.

[0075] The Activity Suggestion Department proposes activities based on the date plan suggested by the Proposal Department. For example, if a user wants to see a movie, the Activity Suggestion Department will search for nearby movie theaters and suggest showtimes. Specifically, the Activity Suggestion Department lists and suggests the most suitable activities based on the user's preferences and schedule. The Activity Suggestion Department collects and provides information on various activities such as movie theaters, museums, and concert halls. For example, if a user wants to see a movie, the Activity Suggestion Department will search for nearby movie theaters and provide information such as movies currently showing, showtimes, and ticket prices. The Activity Suggestion Department can also suggest special events and limited-time activities that match the user's preferences. In this way, the Activity Suggestion Department can suggest the most suitable activities based on the user's preferences and schedule, providing the user with a highly satisfying date experience.

[0076] The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit. For example, the travel route optimization unit proposes the shortest route from a restaurant to a movie theater, making travel time more efficient. Specifically, the travel route optimization unit calculates the optimal travel route between each destination based on the user's date plan. The travel route optimization unit proposes the optimal route considering real-time traffic information and the operating status of public transportation. For example, if a user wants to have dinner at a restaurant and then watch a movie at a movie theater, the travel route optimization unit calculates the shortest route from the restaurant to the movie theater, minimizing travel time. The travel route optimization unit also allows the user to choose from various modes of transportation, such as walking, cycling, driving, and public transportation, and proposes the optimal route for each mode of transportation. In this way, the travel route optimization unit can provide an efficient travel route based on the user's date plan, providing the user with a stress-free date experience.

[0077] The reception desk can estimate the user's emotions and adjust the input method for preferences and appointments based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of preferences and appointments. This reduces user stress and improves input efficiency by providing input methods that are tailored 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 input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can analyze the user's past dating history and suggest the optimal input format. For example, the reception desk can automatically display preferences and appointments that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest preferences and appointments to be used during specific time slots based on the user's past dating history. This streamlines the user's input process by suggesting the optimal input format based on past dating history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past dating history data into a generating AI and have the generating AI suggest the optimal input format.

[0079] The reception desk can filter input content based on the user's current mood and physical condition. For example, if the user is tired, the reception desk can prioritize inputting relaxing date plans. If the user is energetic, the reception desk can also prioritize inputting active date plans. Furthermore, if the user is stressed, the reception desk can prioritize inputting date plans that help relieve stress. In this way, by providing input content that matches the user's mood and physical condition, it can suggest a more appropriate date plan. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's mood and physical condition data into a generating AI and have the generating AI perform the filtering of the input content.

[0080] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize suggesting a relaxing date plan. If the user is having fun, the reception desk can also prioritize suggesting an active date plan. Furthermore, if the user is tired, the reception desk can prioritize suggesting a date plan that emphasizes rest. In this way, by prioritizing input content according to the user's emotions, a more appropriate date plan is suggested. 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 using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location. For example, the reception desk can prioritize inputting restaurants and activities close to the user's current location. Furthermore, if the user is in a specific region, the reception desk can prioritize inputting popular spots in that region. Additionally, if the user is traveling, the reception desk can prioritize inputting tourist attractions and restaurants in their travel destination. This allows the system to provide more relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the AI ​​suggest highly relevant information.

[0082] The reception desk can analyze a user's social media activity and automatically input relevant preferences and plans. For example, the reception desk can automatically input places that the user has mentioned as places they "want to go" on social media. It can also automatically input restaurants and activities that the user has "liked." Furthermore, the reception desk can predict preferences and plans from the content of the user's social media posts and automatically input them. This streamlines the user's input process by automating input based on social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI perform the automatic input of relevant preferences and plans.

[0083] 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 suggest multiple date plans. If the user is in a hurry, the analysis unit can perform a quick analysis and suggest only one optimal date plan. Furthermore, if the user is excited, the analysis unit can suggest a visually appealing date plan. By providing an analysis algorithm that responds to the user's emotions, it can suggest a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of its analysis by referring to the user's past dating history. For example, the analysis unit can suggest similar plans based on dating plans the user has preferred in the past. It can also analyze successful plans from the user's past dating history and suggest the optimal plan. Furthermore, the analysis unit can refer to the user's past dating history and analyze it to avoid unsuccessful plans. In this way, by improving the accuracy of the analysis based on past dating history, it can suggest more appropriate dating plans. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past dating history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The analysis unit can customize the analysis results based on the user's current lifestyle. For example, if the user is busy, the analysis unit can suggest a date plan that can be enjoyed in a short amount of time. It can also suggest a date plan that allows for a more relaxed experience if the user is on holiday. Furthermore, if the user has plans to attend a specific event, the analysis unit can suggest a date plan tailored to that event. This allows the analysis unit to provide more appropriate date plans by offering analysis results that match the user's current lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For instance, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI customize the analysis results.

[0086] 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 concise display method. By providing a display method that matches the user's emotions, it can suggest a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can classify the analysis results by region, taking into account the user's geographical location information. For example, the analysis unit can prioritize displaying restaurants and activities close to the user's current location. Furthermore, if the user is in a specific region, the analysis unit can prioritize displaying popular spots in that region. Additionally, if the user is traveling, the analysis unit can prioritize displaying tourist attractions and restaurants in their travel destination. This allows for the suggestion of more relevant date plans by classifying the analysis results based on geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the regional classification.

[0088] The analysis unit can analyze a user's social media activity and reflect relevant date plans in the analysis. For example, the analysis unit can reflect places that the user has mentioned as places they "want to go" on social media. It can also reflect restaurants and activities that the user has "liked" in the analysis. Furthermore, the analysis unit can predict preferences and plans from the content of the user's social media posts and reflect them in the analysis. In this way, by analyzing based on social media activity, it can suggest more appropriate date plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the analysis of relevant date plans.

[0089] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can offer detailed suggestions. If the user is in a hurry, it can offer concise suggestions. Furthermore, if the user is excited, it can offer visually appealing suggestions. By providing suggestions tailored to the user's emotions, it can suggest more appropriate date plans. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the date. For example, it can provide detailed suggestions for special anniversary dates, while offering concise suggestions for casual dates. Furthermore, it can provide carefully selected suggestions for first dates. This allows the suggestion unit to propose more appropriate date plans by offering suggestions tailored to the importance of each date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input date importance data into a generating AI and have the generating AI adjust the level of detail in the suggestions.

[0091] The suggestion unit can apply different suggestion algorithms depending on the date category when making suggestions. For example, for a romantic date, the suggestion unit can suggest restaurants with a good atmosphere and activities. For an active date, the suggestion unit can also suggest sports and outdoor activities. Furthermore, for a cultural date, the suggestion unit can suggest art museums and museums. By providing suggestions tailored to the date category, it can suggest more appropriate date plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input date category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can also make a longer suggestion with more detailed explanations. Furthermore, if the user is excited, the suggestion unit can make a visually stimulating suggestion. By providing suggestion lengths that match the user's emotions, it can suggest a more appropriate date plan. 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 processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The suggestion unit can prioritize suggestions based on the planned timing of the date. For example, it will prioritize suggestions for upcoming dates. It can also postpone suggestions for dates far in the future. Furthermore, if the date is tied to a specific event, it can tailor the suggestion unit's suggestions to that event. This allows the suggestion unit to propose a more appropriate date plan by providing suggestions that are appropriate for the planned timing of the date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input date timing data into a generating AI and have the generating AI determine the priority of the suggestions.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of the date. For example, the suggestion unit can first suggest the one that best suits the user's preferences. It can also first suggest the one that best suits the user's schedule. Furthermore, it can first suggest the one that is most likely to be successful based on the user's past dating history. This allows the suggestion unit to propose a more appropriate date plan by providing suggestions according to the relevance of the date. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input date relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0095] The reservation unit can estimate the user's emotions and adjust the timing of reservations based on those emotions. For example, if the user is relaxed, the reservation unit can make a reservation with ample time. If the user is in a hurry, the reservation unit can make a reservation quickly. Furthermore, if the user is excited, the reservation unit can make a reservation immediately. This allows the system to suggest a more appropriate date plan by providing reservation timing that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI or not. For example, the reservation unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The reservation department can select the optimal reservation method by referring to the user's past reservation history. For example, the reservation department may prioritize reservations at restaurants the user has previously enjoyed. The reservation department can also select successful reservation methods from the user's past reservation history. Furthermore, the reservation department can refer to the user's past reservation history and avoid unsuccessful reservation methods. By providing the optimal reservation method based on past reservation history, it can suggest a more appropriate date plan. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's past reservation history data into a generating AI and have the generating AI select the optimal reservation method.

[0097] The reservation department can customize reservation details based on the user's current lifestyle. For example, if the user is busy, the reservation department can reserve a restaurant that can be enjoyed in a short amount of time. If the user is on holiday, the reservation department can reserve a restaurant where they can relax. Furthermore, if the user has plans to attend a specific event, the reservation department can reserve a restaurant that suits that event. By providing reservation details that match the user's current lifestyle, the department can suggest a more appropriate date plan. Some or all of the above processing in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the reservation details.

[0098] The reservation system can estimate the user's emotions and determine reservation priorities based on those emotions. For example, if the user is nervous, the reservation system will prioritize booking a relaxing restaurant. If the user is having fun, the reservation system can also prioritize booking a restaurant with an active atmosphere. Furthermore, if the user is tired, the reservation system can also prioritize booking a quiet restaurant. By providing reservation priorities that match the user's emotions, the system can suggest a more appropriate date plan. 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 reservation system may be performed using AI or not. For example, the reservation system can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The reservation system can prioritize booking restaurants that are highly relevant to the user, taking into account the user's geographical location. For example, the reservation system can prioritize booking restaurants close to the user's current location. Furthermore, if the user is in a specific region, the reservation system can prioritize booking popular restaurants in that region. Additionally, if the user is traveling, the reservation system can prioritize booking restaurants in their travel destination. This allows for the suggestion of more appropriate date plans by providing highly relevant restaurants based on geographical location information. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system can input the user's geographical location data into a generating AI and have the AI ​​generate suggestions for highly relevant restaurants.

[0100] The reservation department can analyze a user's social media activity and automatically make reservations at relevant restaurants. For example, the reservation department can automatically reserve restaurants that a user has mentioned as places they "want to go" to on social media. It can also automatically reserve restaurants that a user has "liked." Furthermore, the reservation department can predict a user's preferences and plans from their social media posts and automatically reserve relevant restaurants. This allows for more appropriate date plans to be suggested by making reservations based on social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can input a user's social media data into a generating AI and have the generating AI perform the automatic reservation of relevant restaurants.

[0101] The activity suggestion unit can estimate the user's emotions and adjust its activity suggestion method based on the estimated emotions. For example, if the user is relaxed, the activity suggestion unit can suggest a relaxing activity. If the user is in a hurry, the activity suggestion unit can also suggest a short, enjoyable activity. Furthermore, if the user is excited, the activity suggestion unit can suggest an active activity. By providing an activity suggestion method that matches the user's emotions, it can suggest a more appropriate date plan. 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 activity suggestion unit may be performed using AI or not using AI. For example, the activity suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0102] The activity suggestion unit can provide optimal suggestions by referring to the user's past activity history. For example, the activity suggestion unit can suggest similar activities based on activities the user has enjoyed in the past. The activity suggestion unit can also analyze successful activities from the user's past activity history and suggest optimal activities. Furthermore, the activity suggestion unit can refer to the user's past activity history and suggest activities that have failed. In this way, by providing optimal suggestions based on past activity history, it can suggest more appropriate date plans. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or not using AI. For example, the activity suggestion unit can input the user's past activity history data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0103] The activity suggestion unit can customize activity suggestions based on the user's current lifestyle. For example, if the user is busy, the activity suggestion unit can suggest activities that can be enjoyed in a short amount of time. It can also suggest activities that allow the user to relax if they have a day off. Furthermore, if the user has plans to attend a specific event, the activity suggestion unit can suggest activities tailored to that event. This allows the unit to provide activity suggestions that are appropriate to the user's current lifestyle, thereby suggesting a more suitable date plan. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or without AI. For example, the activity suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI customize the suggestions.

[0104] The activity suggestion unit can estimate the user's emotions and determine the priority of activities based on the estimated emotions. For example, if the user is feeling tense, the activity suggestion unit will prioritize suggesting relaxing activities. It can also prioritize suggesting active activities if the user is having fun. Furthermore, if the user is feeling tired, the activity suggestion unit can prioritize suggesting activities that emphasize rest. This provides activity priorities tailored to the user's emotions, thereby suggesting a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the activity suggestion unit may be performed using AI or not. For example, the activity suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The activity suggestion unit can prioritize suggesting highly relevant activities by taking into account the user's geographical location. For example, the activity suggestion unit can prioritize suggesting activities close to the user's current location. Furthermore, if the user is in a specific region, the activity suggestion unit can prioritize suggesting popular activities in that region. Additionally, if the user is traveling, the activity suggestion unit can prioritize suggesting activities in their travel destination. This allows for the suggestion of more appropriate date plans by providing highly relevant activities based on geographical location information. Some or all of the above processing in the activity suggestion unit may be performed using AI, or not. For example, the activity suggestion unit can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant activities.

[0106] The activity suggestion unit can analyze a user's social media activity and automatically suggest relevant activities. For example, it can automatically suggest activities that a user has mentioned as "wanting to go" on social media. It can also automatically suggest activities that a user has "liked." Furthermore, it can predict a user's preferences and plans from their social media posts and automatically suggest relevant activities. This allows for more appropriate date plans to be suggested by automatically making suggestions based on social media activity. Some or all of the above processing in the activity suggestion unit may be performed using AI, for example, or without AI. For example, the activity suggestion unit can input the user's social media data into a generating AI and have the generating AI perform the automatic suggestion of relevant activities.

[0107] The travel route optimization unit can estimate the user's emotions and adjust the travel route optimization method based on the estimated user emotions. For example, if the user is relaxed, the travel route optimization unit can suggest a scenic route. It can also suggest the shortest route if the user is in a hurry. Furthermore, if the user is excited, it can suggest an active route. This provides a travel route optimization method tailored to the user's emotions, thereby suggesting a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, or not. For example, the travel route optimization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The travel route optimization unit can propose the optimal travel route by referring to the user's past travel history. For example, the travel route optimization unit proposes the optimal route based on routes the user has used in the past. The travel route optimization unit can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the travel route optimization unit can analyze the user's past travel history and propose the most efficient route. In this way, by providing the optimal travel route based on past travel history, it proposes a more appropriate date plan. Some or all of the above processing in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's past travel history data into a generating AI and have the generating AI execute the proposal of the optimal travel route.

[0109] The travel route optimization unit can customize travel routes based on the user's current lifestyle. For example, if the user is busy, the travel route optimization unit can suggest the shortest route. It can also suggest a scenic route if the user is on holiday. Furthermore, if the user has plans to attend a specific event, the travel route optimization unit can suggest a route tailored to that event. This allows for the provision of more appropriate date plans by offering travel routes that match the user's current lifestyle. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, for example, or without AI. For instance, the travel route optimization unit can input user lifestyle data into a generating AI and have the generating AI perform the travel route customization.

[0110] The travel route optimization unit can estimate the user's emotions and determine the priority of travel routes based on the estimated emotions. For example, if the user is tense, the travel route optimization unit will prioritize suggesting relaxing routes. It can also prioritize suggesting active routes if the user is enjoying themselves. Furthermore, if the user is tired, it can prioritize suggesting routes that prioritize rest. This allows for the suggestion of a more appropriate date plan by providing travel route priorities that align with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, or not. For example, the travel route optimization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The travel route optimization unit can propose the optimal travel route by taking into account the user's geographical location information. For example, the travel route optimization unit can prioritize proposing routes that are close to the user's current location. Furthermore, if the user is in a specific region, the travel route optimization unit can also propose the optimal route for that region. In addition, if the user is traveling, the travel route optimization unit can propose the optimal route for their destination. By providing the optimal travel route based on geographical location information, it can propose a more appropriate date plan. Some or all of the above-described processes in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's geographical location data into a generating AI and have the generating AI propose the optimal travel route.

[0112] The travel route optimization unit can analyze a user's social media activity and automatically suggest relevant travel routes. For example, the travel route optimization unit can automatically suggest routes to places the user has mentioned as places they "want to go" on social media. It can also automatically suggest routes to places the user has "liked." Furthermore, the travel route optimization unit can predict the user's preferences and plans from the content of their social media posts and automatically suggest relevant travel routes. This allows for the suggestion of more appropriate date plans by automatically making suggestions based on social media activity. Some or all of the above processing in the travel route optimization unit may be performed using AI, for example, or without AI. For example, the travel route optimization unit can input the user's social media data into a generating AI and have the generating AI perform the automatic suggestion of relevant travel routes.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The date plan suggestion system can further acquire user health data and customize its suggestions. For example, it can suggest a date plan with an appropriate walking distance based on the user's step count data. It can also suggest relaxing activities based on the user's heart rate data. Furthermore, it can suggest a date plan tailored to the user's fatigue level based on their sleep data. By providing date plans that match the user's health condition, it can help create more appropriate dates.

[0115] The date plan suggestion system can further customize its suggestions by taking into account the user's hobbies and interests. For example, if the user likes music, it can suggest live concerts or music events. If the user likes sports, it can suggest watching sports or other active activities. Furthermore, if the user likes art, it can suggest visiting museums or galleries. By providing date plans tailored to the user's hobbies and interests, it can lead to more satisfying dates.

[0116] The date plan suggestion system can further estimate the user's emotions and determine the theme of the date plan based on those emotions. For example, if the user is in a happy mood, it can suggest a date plan with a fun theme. If the user is feeling down, it can suggest a date plan to lift their spirits. Furthermore, if the user wants to relax, it can suggest a date plan with a relaxing theme. By providing date plans with themes that match the user's emotions, it can help create more appropriate dates.

[0117] The date plan suggestion system can further improve its suggestions by collecting feedback on users' past date plans. For example, if a user gives a high rating to a past date plan, it will suggest a similar plan. Conversely, if a user gives a low rating, the system can adjust the suggestions to avoid those elements. Furthermore, it can generate new date plan ideas based on user feedback. This allows for more satisfying dates by providing date plans that reflect user feedback.

[0118] The date plan suggestion system can further estimate the user's emotions and adjust the difficulty level of the date plan based on those emotions. For example, if the user is relaxed, it can suggest a more challenging activity. If the user is tired, it can suggest an easy, relaxing activity. Furthermore, if the user is excited, it can suggest a challenging activity. By providing date plans of appropriate difficulty levels according to the user's emotions, it can lead to more suitable dates.

[0119] The date plan suggestion system can further customize its suggestions by incorporating the opinions of the user's friends and family. For example, it can suggest restaurants and activities recommended by the user's friends. It can also suggest places that the user's family likes. Furthermore, it can generate new date plan ideas based on the opinions of the user's friends and family. This allows for more satisfying dates by providing date plans that reflect the opinions of those around the user.

[0120] The date plan suggestion system can further estimate the user's emotions and adjust the timing of the date plan based on those emotions. For example, if the user is relaxed, it can suggest an evening date plan. If the user is energetic, it can suggest a daytime activity. Furthermore, if the user is tired, it can suggest a date plan that can be enjoyed in a short amount of time. In this way, by providing date plans at times that match the user's emotions, it can lead to more appropriate dates.

[0121] The date plan suggestion system can further customize its suggestions by taking into account the user's travel history. For example, it can suggest similar date plans based on places the user has visited in the past. It can also suggest places the user wants to visit. Furthermore, it can generate new date plan ideas based on the user's travel history. By providing date plans that reflect the user's travel history, it can lead to more satisfying dates.

[0122] The date plan suggestion system can also estimate the user's emotions and adjust the budget of the date plan based on those emotions. For example, if the user is relaxed, it can suggest upscale restaurants and activities. If the user wants to save money, it can suggest a reasonably priced date plan. Furthermore, if the user wants to celebrate a special day, it can suggest a luxurious date plan. In this way, by providing date plans with budgets that match the user's emotions, it can lead to more appropriate dates.

[0123] The date plan suggestion system can further customize its suggestions by taking into account the user's cultural background. For example, it can suggest restaurants and activities based on the user's culture. It can also suggest cultural events and festivals related to the user. Furthermore, it can generate new date plan ideas based on the user's cultural background. By providing date plans that reflect the user's cultural background, it can lead to more satisfying dates.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception desk accepts user input about their preferences and schedule. For example, users can enter details such as their favorite type of food, places they want to go, and preferred time for dates. The reception desk then sends the information entered by the user to the AI. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it extracts data to suggest the optimal date plan based on the user's preferences and schedule. The analysis unit uses AI to analyze the user's preferences and schedule. Step 3: The suggestion unit proposes the optimal date plan based on the information analyzed by the analysis unit. For example, if the user likes Italian food and wants a night date, the unit will search for a restaurant that meets those criteria and make a reservation. It will also suggest activities that are perfect for a date. For example, it will suggest activities that suit the user's preferences, such as watching a movie or visiting an art museum. Step 4: The reservation department makes restaurant reservations based on the date plan proposed by the suggestion department. For example, it searches for a restaurant the user wants and makes a reservation. Step 5: The activity suggestion unit proposes activities based on the date plan suggested by the suggestion unit. For example, if the user wants to see a movie, it searches for nearby movie theaters and suggests showtimes. Step 6: The travel route optimization unit optimizes the travel route based on the date plan proposed by the proposal unit. For example, it proposes the shortest route from the restaurant to the movie theater, making travel time more efficient.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, reservation unit, activity proposal unit, and travel route optimization unit, is implemented by 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 and accepts input of the user's preferences and schedule. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information transmitted from the reception unit. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal date plan based on the analyzed information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a restaurant reservation based on the proposed date plan. The activity proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an activity based on the proposed date plan. The travel route optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel route based on the proposed date plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, reservation unit, activity proposal unit, and travel route optimization unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and accepts input of the user's preferences and schedule. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information transmitted from the reception unit. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal date plan based on the analyzed information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a restaurant reservation based on the proposed date plan. The activity proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an activity based on the proposed date plan. The travel route optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel route based on the proposed date plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, reservation unit, activity proposal unit, and travel route optimization unit, is implemented by 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 and accepts input of the user's preferences and schedule. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information transmitted from the reception unit. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal date plan based on the analyzed information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a restaurant reservation based on the proposed date plan. The activity proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an activity based on the proposed date plan. The travel route optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel route based on the proposed date plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, reservation unit, activity proposal unit, and travel route optimization 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 and receives input of the user's preferences and schedule. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information transmitted from the reception unit. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal date plan based on the analyzed information. The reservation unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes a restaurant reservation based on the proposed date plan. The activity proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an activity based on the proposed date plan. The travel route optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the travel route based on the proposed date plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A reception area that accepts user preferences and schedule inputs, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the optimal date plan. A reservation department makes restaurant reservations based on the date plan proposed by the aforementioned proposal department, An activity proposal unit proposes activities based on the date plan proposed by the aforementioned proposal unit, The system includes a travel route optimization unit that optimizes the travel route based on the date plan proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the way preferences and schedules are entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past dating history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Filter input based on the user's current mood and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system prioritizes input of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyzes users' social media activity and automatically populates relevant preferences and schedules. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned analysis unit, By referencing the user's past dating history, we can improve the accuracy of our analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Customize analysis results based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, The analysis results are categorized by region, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Analyze users' social media activity and incorporate relevant dating plans into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the date. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the dating category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, prioritize it based on the timing of the date. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the date. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, It estimates the user's emotions and adjusts the timing of reservations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, The system selects the optimal booking method by referring to the user's past booking history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, Customize reservation details based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, The system prioritizes booking restaurants that are relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reservation section is, Analyze users' social media activity and automatically make reservations at relevant restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned activity proposal unit, It estimates the user's emotions and adjusts how it suggests activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned activity proposal unit, We provide optimal suggestions by referring to the user's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned activity proposal unit, Customize activity suggestions based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned activity proposal unit, It estimates the user's emotions and prioritizes activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned activity proposal unit, The system prioritizes suggesting highly relevant activities, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned activity proposal unit, Analyzes users' social media activity and automatically suggests relevant activities. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned travel route optimization unit, It estimates the user's emotions and adjusts the optimization method for travel routes based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned travel route optimization unit, The system suggests the optimal travel route by referencing the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned travel route optimization unit, Customize travel routes based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned travel route optimization unit, It estimates the user's emotions and prioritizes travel routes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned travel route optimization unit, The system proposes the optimal travel route, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned travel route optimization unit, Analyzes users' social media activity and automatically suggests relevant travel routes. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts user preferences and schedule inputs, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the optimal date plan. A reservation department makes restaurant reservations based on the date plan proposed by the aforementioned proposal department, An activity proposal unit proposes activities based on the date plan proposed by the aforementioned proposal unit, The system includes a travel route optimization unit that optimizes the travel route based on the date plan proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the way preferences and schedules are entered based on those estimated emotions. The system according to feature 1.

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

4. The aforementioned reception unit is Filter input based on the user's current mood and physical condition. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is The system prioritizes input of highly relevant information, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is Analyzes users' social media activity and automatically populates relevant preferences and schedules. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, By referencing the user's past dating history, we can improve the accuracy of our analysis. The system according to feature 1.

10. The aforementioned analysis unit, Customize analysis results based on the user's current lifestyle. The system according to feature 1.

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