system

Abstract

The system according to this embodiment aims to automatically generate an optimal date plan based on the user's desired conditions and to optimize reservations and travel routes. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a reservation unit, and a travel unit. The reception unit receives input from the user regarding their desired date conditions. The generation unit generates an optimal date plan based on the information received by the reception unit. The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The travel unit optimizes the travel route based on the date plan generated by the generation unit.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it took a lot of effort and time for a user to make a date plan, and it was difficult to find an optimal plan.

[0005] The system according to the embodiment aims to automatically generate an optimal date plan based on the desired conditions of the user and perform reservation and optimization of the travel route.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a reservation unit, and a travel unit. The reception unit receives input from the user regarding their desired date conditions. The generation unit generates an optimal date plan based on the information received by the reception unit. The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The travel unit optimizes the travel route based on the date plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate an optimal date plan based on the user's preferences and optimize reservations and travel routes. [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] [[ID=X]]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] Note: In the translation of , "図1には、第1実施形態に係るデータ処理システム10の構成の一例が示されている。", I have adjusted the sentence structure for better English expression while maintaining the meaning. The original Japanese sentence structure is a bit different from the typical English sentence structure.The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Date Master AI Agent according to an embodiment of the present invention is an AI agent that proposes the optimal date plan based on specific circumstances and user preferences. This Date Master AI Agent generates a plan that is perfect for a date based on the user's preferences, schedule, and budget. Furthermore, it includes restaurant reservations, activity suggestions, and optimization of travel routes. This enables a smart and attractive date, allowing the user to enjoy a stress-free and perfect date. For example, the user inputs their desired date conditions. For example, they input the date and time of the date, budget, preferred activities, etc. This information is input into the Generating AI. The Generating AI analyzes the user's input information and generates the optimal date plan. For example, if the user inputs, "I want a romantic date on Saturday night with a budget of 5000 yen," the Generating AI will make restaurant reservations, suggest activities, and optimize travel routes based on those conditions. The Generating AI learns the user's past date history and preferences and proposes a more personalized date plan. For example, based on data of restaurants the user has visited and activities they have participated in in the past, it generates a new date plan that suits the user's preferences. This allows the user to enjoy a fresh and fun date every time. Furthermore, the Generating AI optimizes the date plan by utilizing real-time information. For example, it suggests the optimal travel route, taking into account traffic information and event schedules. This allows users to travel smoothly and stress-free during their date. This system significantly reduces the time and effort users spend planning dates. Users can enjoy a perfect date simply by following the date plan suggested by the generative AI. For example, even if a user is a busy working professional, the generative AI automatically generates a date plan, makes reservations, and optimizes travel routes, so there is no need to spend time planning the date. Furthermore, the generative AI improves the date plan based on user feedback. For example, by providing feedback after a date, the generative AI learns from that feedback and incorporates it into the next date plan. This allows users to have a better date experience every time.In this way, the Date Master AI agent proposes the optimal date plan according to the user's preferences and circumstances, allowing the user to enjoy a stress-free and perfect date. This enables the Date Master AI agent to automatically generate and optimize the user's date plan.

[0029] The Date Master AI agent according to this embodiment comprises a reception unit, a generation unit, a reservation unit, and a travel unit. The reception unit receives input from the user regarding their desired date conditions. For example, the user can input the date and time of the date, budget, preferred activities, etc. The reception unit transmits the user's input information to the generation unit. The generation unit uses a generation AI to generate an optimal date plan based on the information received by the reception unit. For example, if the user inputs "I want a romantic date on Saturday night with a budget of 5,000 yen," the generation unit will make restaurant reservations, suggest activities, and optimize the travel route based on those conditions. The generation unit can also learn from the user's past dating history and preferences to propose a more personalized date plan. For example, the generation unit can generate a new date plan that suits the user's preferences based on data of restaurants the user has visited and activities they have participated in in the past. The generation unit can also optimize the date plan by utilizing real-time information. For example, the generation unit can suggest the optimal travel route considering traffic information and event status. The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The reservation unit can, for example, make restaurant reservations using an online reservation system. The reservation unit selects the most suitable restaurant based on the user's desired date, time, and budget, and confirms the reservation. The travel unit optimizes the travel route based on the date plan generated by the generation unit. The travel unit can, for example, acquire real-time traffic information and propose the most suitable travel route. The travel unit optimizes the means of transportation and travel time so that the user can move smoothly during the date. As a result, the Date Master AI agent according to this embodiment can generate the most suitable date plan based on the user's desired date conditions and optimize reservations and travel routes.

[0030] The reception desk inputs the user's dating preferences. Specifically, users can input the date and time of the date, budget, preferred activities, etc. For example, users can access a dedicated application or website using their smartphone or computer and input their dating preferences. The input screen includes a calendar for selecting the date and time of the date, a text box for entering the budget, and a dropdown menu for selecting preferred activities. Users enter the necessary information into these input fields and send their preferences to the reception desk by pressing the submit button. The reception desk then sends the user's input information to the generation department. To ensure that the reception desk accurately receives the user's input information and sends it to the generation department, it has functions such as checking the input content and displaying error messages. For example, if there are errors in the input content or if required fields are not filled in, an error message is displayed to prompt the user to correct it. This allows the reception desk to accurately receive the user's preferences and send them to the generation department.

[0031] The generation unit uses a generation AI to generate the optimal date plan based on the information received by the reception unit. The generation AI utilizes natural language processing and machine learning techniques to propose the best date plan based on the user's preferences. For example, if a user inputs, "I want a romantic date on Saturday night with a budget of 5,000 yen," the generation AI will search its database for suitable restaurants and activities and propose the best combination. The generation AI can also learn from the user's past dating history and preferences to propose a more personalized date plan. For example, the generation AI can generate a new date plan that suits the user's preferences based on data from restaurants the user has visited and activities they have participated in in the past. The generation AI can also optimize the date plan by utilizing real-time information. For example, the generation AI can propose the best travel route by considering traffic information and event schedules. In this way, the generation unit can generate and provide the user with the optimal date plan based on their preferences.

[0032] The reservation department makes restaurant reservations based on the date plan generated by the generation department. Specifically, the reservation department can make restaurant reservations using an online reservation system. The reservation department selects the most suitable restaurant based on the user's desired date, time, and budget, and confirms the reservation. For example, based on the date plan provided by the generation department, the reservation department searches for restaurants with availability at the user's desired date and time, and makes a reservation through the online reservation system. The reservation department also has functions for confirming, changing, and canceling reservations, allowing users to modify their reservation details as needed. In this way, the reservation department can make the most suitable restaurant reservation based on the user's desired conditions and support the realization of the date plan.

[0033] The mobile unit optimizes the travel route based on the date plan generated by the generation unit. Specifically, the mobile unit can acquire real-time traffic information and propose the optimal travel route. The mobile unit optimizes the mode of transport and travel time so that the user can travel smoothly during the date. For example, based on the date plan provided by the generation unit, the mobile unit calculates the optimal travel route from the user's current location to the date destination and proposes the mode of transport and travel time. To acquire real-time traffic information, the mobile unit cooperates with traffic information services and optimizes the travel route considering information such as traffic congestion and accidents. In this way, the mobile unit can support the user in traveling smoothly during the date and help realize the date plan.

[0034] The generation unit includes a learning unit that learns the user's past dating history and preferences. For example, the generation unit collects data on restaurants the user has visited and activities they have participated in in the past and sends it to the learning unit. The learning unit analyzes this data and learns the user's preferences. For example, the learning unit identifies the types of food the user likes and the characteristics of dating spots, and provides this feedback to the generation unit. Based on the feedback from the learning unit, the generation unit generates a date plan that suits the user's preferences. In this way, the generation unit can propose a more personalized date plan by learning the user's past dating history and preferences.

[0035] The generation unit includes an information acquisition unit that obtains real-time traffic and event information. The generation unit obtains real-time traffic information, for example, by using traffic information services or GPS data. The information acquisition unit analyzes this information and transmits it to the generation unit. The generation unit optimizes the date plan based on the information from the information acquisition unit. For example, the generation unit proposes the optimal travel route to avoid traffic congestion. The generation unit can also obtain real-time event information by using event calendars or event information from social media. The information acquisition unit analyzes this information and transmits it to the generation unit. The generation unit incorporates the event information into the date plan based on the information from the information acquisition unit. This allows the generation unit to optimize the date plan by utilizing real-time information.

[0036] The reception desk analyzes the user's past dating preferences and suggests the most suitable input method. For example, the reception desk automatically displays dating preferences that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The reception desk predicts and suggests conditions to be used at specific time slots based on the user's past dating preferences. In this way, the reception desk can suggest the most suitable input method by analyzing the user's past dating preferences.

[0037] The reception system filters the user's dating preferences based on their current lifestyle and areas of interest. For example, when a user enters their current lifestyle, the reception system automatically filters the relevant dating preferences. The reception system can also prioritize displaying dating preferences based on the user's areas of interest. The reception system analyzes the user's lifestyle and areas of interest and suggests the most suitable dating preferences. This allows the reception system to filter based on the user's lifestyle and areas of interest, enabling them to enter more appropriate dating preferences.

[0038] The reception desk prioritizes inputting highly relevant conditions when users enter their date preferences, taking into account their geographical location. For example, the reception desk will prioritize suggesting nearby date spots based on the user's current location. The reception desk can also automatically display the most suitable date preferences, taking into account the user's geographical location. The reception desk prioritizes inputting highly relevant date preferences based on the user's location. This allows the reception desk to input highly relevant date preferences by considering the user's geographical location.

[0039] The reception desk analyzes the user's social media activity when they enter their dating preferences and inputs relevant conditions. For example, the reception desk analyzes the user's social media activity and automatically suggests relevant dating preferences. The reception desk can also display the most suitable dating preferences based on the user's social media posts. The reception desk uses the user's social media activity as a reference to input highly relevant dating preferences. In this way, the reception desk can input relevant dating preferences by analyzing the user's social media activity.

[0040] The generation unit adjusts the level of detail in a date plan based on the user's past dating history. For example, it generates a detailed date plan based on places the user has visited in the past. The generation unit can also generate a plan that avoids crowds based on the user's past dating history. The generation unit analyzes the user's past dating history and generates the most efficient date plan. In this way, the generation unit can provide a more appropriate date plan by adjusting the level of detail based on the user's past dating history.

[0041] The generation unit applies different generation algorithms depending on the user's preferences when generating a date plan. For example, the generation unit may apply an algorithm to generate a romantic date plan based on the user's preferences. The generation unit can also apply an algorithm to generate an active date plan based on the user's preferences. The generation unit may apply an algorithm to generate a relaxed date plan based on the user's preferences. In this way, the generation unit can provide a more appropriate date plan by applying different generation algorithms depending on the user's preferences.

[0042] The generation unit prioritizes date plans based on when the user submits them. For example, if the user submits early, the generation unit will prioritize generating a detailed date plan. If the user submits at the last minute, the generation unit can also prioritize a date plan that can be generated quickly. The generation unit determines the optimal order for generating date plans based on when the user submits them. This allows the generation unit to provide more appropriate date plans by prioritizing them based on when the user submits them.

[0043] The generation unit adjusts the order of date plans based on user relevance when generating them. For example, the generation unit prioritizes highly relevant activities based on the user's preferences. The generation unit can also prioritize highly relevant plans by referring to the user's past dating history. The generation unit generates highly relevant date plans based on the user's areas of interest. As a result, the generation unit can provide more appropriate date plans by adjusting the order of plans based on user relevance.

[0044] The reservation department adjusts the level of detail in reservations based on the importance of the restaurant. For example, the reservation department provides detailed reservation information for high-end restaurants, while it can provide concise information for casual restaurants. The reservation department provides optimal reservation information based on the importance of the restaurant. This allows the reservation department to provide more appropriate reservation information by adjusting the level of detail based on the importance of the restaurant.

[0045] The reservation department applies different reservation algorithms depending on the restaurant category when a reservation is made. For example, the reservation department applies a detailed reservation algorithm for high-end restaurants. For casual restaurants, the reservation department can also apply a simplified reservation algorithm. The reservation department applies the most suitable reservation algorithm depending on the restaurant category. In this way, the reservation department can provide more appropriate reservation information by applying different reservation algorithms depending on the restaurant category.

[0046] The reservation department prioritizes reservations based on when the restaurant submits the reservation request. For example, if a user submits a request early, the reservation department will prioritize more detailed reservations. If a user submits a request at the last minute, the reservation department may also prioritize restaurants that can be booked quickly. The reservation department determines the optimal order of reservations based on when the user submits their request. This allows the reservation department to provide more relevant reservation information by prioritizing reservations based on when the restaurant submits their request.

[0047] The reservation system adjusts the order of reservations based on the relevance of the restaurants during the reservation process. For example, the reservation system prioritizes reservations at restaurants that are highly relevant to the user's preferences. The reservation system can also prioritize restaurants that are highly relevant by referring to the user's past dating history. The reservation system reserves restaurants that are highly relevant to the user's areas of interest. In this way, the reservation system can provide more appropriate reservation information by adjusting the order of reservations based on the relevance of the restaurants.

[0048] The mobile system adjusts the level of detail in the route based on the user's past travel history when optimizing travel routes. For example, the mobile system proposes a detailed travel route based on routes the user has used in the past. The mobile system can also propose routes that avoid congestion based on the user's past travel history. The mobile system analyzes the user's past travel history and proposes the most efficient travel route. In this way, the mobile system can provide a more appropriate travel route by adjusting the level of detail in the route based on the user's past travel history.

[0049] The mobile unit applies different optimization algorithms to user preferences when optimizing travel routes. For example, it might apply an algorithm that suggests a scenic route based on user preferences. It could also apply an algorithm that suggests the shortest route based on user preferences. It could also apply an algorithm that suggests a route that avoids congestion based on user preferences. In this way, the mobile unit can provide more appropriate travel routes by applying different optimization algorithms according to user preferences.

[0050] The travel unit prioritizes routes based on the user's submission timing when optimizing travel routes. For example, if the user submits early, the travel unit will prioritize suggesting detailed travel routes. If the user submits just before the user submits, the travel unit can also prioritize travel routes that can be suggested quickly. The travel unit determines the optimal order of suggested travel routes based on the user's submission timing. This allows the travel unit to provide more appropriate travel routes by prioritizing routes based on the user's submission timing.

[0051] The navigation unit adjusts the order of routes based on user relevance when optimizing travel routes. For example, the navigation unit prioritizes suggesting highly relevant routes based on user preferences. The navigation unit can also prioritize highly relevant routes by referring to the user's past travel history. The navigation unit suggests highly relevant routes based on the user's areas of interest. In this way, the navigation unit can provide more appropriate travel routes by adjusting the order of routes based on user relevance.

[0052] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. The learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit improves the accuracy of the learning algorithm by referring to past learning data.

[0053] The learning unit weights the training data based on when the date history was submitted. For example, the learning unit will give more weight to date history submitted by the user earlier. The learning unit can also learn by downplaying date history submitted by the user most recently. The learning unit adjusts the weighting of the training data based on when the date history was submitted. This allows the learning unit to perform more appropriate learning by weighting the training data based on when the date history was submitted.

[0054] The information acquisition unit selects the optimal acquisition method by referring to past information acquisition history when acquiring information. For example, the information acquisition unit selects the optimal information acquisition method based on past information acquisition history. The information acquisition unit can also analyze past information acquisition history and adjust the parameters of the information acquisition method. The information acquisition unit improves the accuracy of the information acquisition method by referring to past information acquisition history. As a result, the accuracy of the information acquisition method is improved by the information acquisition unit referring to past information acquisition history.

[0055] The information acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location. For example, the information acquisition unit prioritizes acquiring nearby information based on the user's current location. The information acquisition unit can also automatically acquire the most relevant information by considering the user's geographical location. The information acquisition unit prioritizes acquiring highly relevant information based on the user's location. As a result, the information acquisition unit can acquire highly relevant information by considering the user's geographical location.

[0056] The information acquisition unit prioritizes acquiring highly relevant information by considering the user's areas of interest during information acquisition. For example, the information acquisition unit prioritizes acquiring relevant information based on the user's areas of interest. The information acquisition unit can also prioritize highly relevant information by referring to the user's past information acquisition history. The information acquisition unit acquires the most suitable information based on the user's areas of interest. As a result, the information acquisition unit can acquire highly relevant information by considering the user's areas of interest.

[0057] The information acquisition unit analyzes the user's social media activity and retrieves relevant information when acquiring data. For example, the information acquisition unit analyzes the user's social media activity and automatically retrieves relevant information. The information acquisition unit can also acquire optimal information based on the content of the user's social media posts. The information acquisition unit uses the user's social media activity as a reference to acquire highly relevant information. In this way, the information acquisition unit can acquire relevant information by analyzing the user's social media activity.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze the user's past dating preferences and suggest the most suitable input method. For example, it can automatically display dating preferences that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). It can even predict and suggest preferences to be used at specific time slots based on the user's past dating preferences. In this way, the reception desk can suggest the most suitable input method by analyzing the user's past dating preferences.

[0060] The reception system can filter dating preferences based on the user's current lifestyle and areas of interest. For example, when a user enters their current lifestyle, the system can automatically filter relevant dating preferences. It can also prioritize displaying dating preferences based on the user's areas of interest. Furthermore, it can analyze the user's lifestyle and areas of interest and suggest the most suitable dating preferences. This allows the reception system to filter based on the user's lifestyle and areas of interest, enabling them to enter more appropriate dating preferences.

[0061] The reception desk can prioritize highly relevant conditions when users input their date preferences, taking into account their geographical location. For example, it can suggest nearby date spots based on the user's current location. It can also automatically display the most suitable date preferences, taking into account the user's geographical location. It can also prioritize highly relevant date preferences based on the user's location. This allows the reception desk to input highly relevant date preferences by considering the user's geographical location.

[0062] The reception desk can analyze the user's social media activity when they enter their dating preferences and input relevant criteria. For example, it can analyze the user's social media activity and automatically suggest relevant dating preferences. It can also display the most suitable dating preferences based on the user's social media posts. It can also prompt the user to enter highly relevant dating preferences based on their social media activity. In this way, the reception desk can input relevant dating preferences by analyzing the user's social media activity.

[0063] The generation unit can apply different generation algorithms depending on the user's preferences when generating a date plan. For example, it can apply an algorithm to generate a romantic date plan based on the user's preferences. It can also apply an algorithm to generate an active date plan based on the user's preferences. It can also apply an algorithm to generate a relaxed date plan based on the user's preferences. In this way, the generation unit can provide a more appropriate date plan by applying different generation algorithms according to the user's preferences.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk inputs the user's dating preferences. For example, the user can input the date and time of the date, budget, preferred activities, etc. The reception desk then sends the user's input information to the generation desk. Step 2: The generation unit generates the optimal date plan based on the information received by the reception unit. The generation unit can also use generation AI to learn the user's past dating history and preferences to propose a more personalized date plan. It can also optimize the date plan by utilizing real-time information. Step 3: The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The reservation unit can use an online reservation system to make restaurant reservations, select the best restaurant based on the user's desired date and time and budget, and confirm the reservation. Step 4: The travel unit optimizes the travel route based on the date plan generated by the generation unit. The travel unit can acquire real-time traffic information and suggest the optimal travel route. This optimizes the mode of transport and travel time so that the user can travel smoothly during the date.

[0066] (Example of form 2) The Date Master AI Agent according to an embodiment of the present invention is an AI agent that proposes the optimal date plan based on specific circumstances and user preferences. This Date Master AI Agent generates a plan that is perfect for a date based on the user's preferences, schedule, and budget. Furthermore, it includes restaurant reservations, activity suggestions, and optimization of travel routes. This enables a smart and attractive date, allowing the user to enjoy a stress-free and perfect date. For example, the user inputs their desired date conditions. For example, they input the date and time of the date, budget, preferred activities, etc. This information is input into the Generating AI. The Generating AI analyzes the user's input information and generates the optimal date plan. For example, if the user inputs, "I want a romantic date on Saturday night with a budget of 5000 yen," the Generating AI will make restaurant reservations, suggest activities, and optimize travel routes based on those conditions. The Generating AI learns the user's past date history and preferences and proposes a more personalized date plan. For example, based on data of restaurants the user has visited and activities they have participated in in the past, it generates a new date plan that suits the user's preferences. This allows the user to enjoy a fresh and fun date every time. Furthermore, the Generating AI optimizes the date plan by utilizing real-time information. For example, it suggests the optimal travel route, taking into account traffic information and event schedules. This allows users to travel smoothly and stress-free during their date. This system significantly reduces the time and effort users spend planning dates. Users can enjoy a perfect date simply by following the date plan suggested by the generative AI. For example, even if a user is a busy working professional, the generative AI automatically generates a date plan, makes reservations, and optimizes travel routes, so there is no need to spend time planning the date. Furthermore, the generative AI improves the date plan based on user feedback. For example, by providing feedback after a date, the generative AI learns from that feedback and incorporates it into the next date plan. This allows users to have a better date experience every time.In this way, the Date Master AI agent proposes the optimal date plan according to the user's preferences and circumstances, allowing the user to enjoy a stress-free and perfect date. This enables the Date Master AI agent to automatically generate and optimize the user's date plan.

[0067] The Date Master AI agent according to this embodiment comprises a reception unit, a generation unit, a reservation unit, and a travel unit. The reception unit receives input from the user regarding their desired date conditions. For example, the user can input the date and time of the date, budget, preferred activities, etc. The reception unit transmits the user's input information to the generation unit. The generation unit uses a generation AI to generate an optimal date plan based on the information received by the reception unit. For example, if the user inputs "I want a romantic date on Saturday night with a budget of 5,000 yen," the generation unit will make restaurant reservations, suggest activities, and optimize the travel route based on those conditions. The generation unit can also learn from the user's past dating history and preferences to propose a more personalized date plan. For example, the generation unit can generate a new date plan that suits the user's preferences based on data of restaurants the user has visited and activities they have participated in in the past. The generation unit can also optimize the date plan by utilizing real-time information. For example, the generation unit can suggest the optimal travel route considering traffic information and event status. The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The reservation unit can, for example, make restaurant reservations using an online reservation system. The reservation unit selects the most suitable restaurant based on the user's desired date, time, and budget, and confirms the reservation. The travel unit optimizes the travel route based on the date plan generated by the generation unit. The travel unit can, for example, acquire real-time traffic information and propose the most suitable travel route. The travel unit optimizes the means of transportation and travel time so that the user can move smoothly during the date. As a result, the Date Master AI agent according to this embodiment can generate the most suitable date plan based on the user's desired date conditions and optimize reservations and travel routes.

[0068] The reception desk inputs the user's dating preferences. Specifically, users can input the date and time of the date, budget, preferred activities, etc. For example, users can access a dedicated application or website using their smartphone or computer and input their dating preferences. The input screen includes a calendar for selecting the date and time of the date, a text box for entering the budget, and a dropdown menu for selecting preferred activities. Users enter the necessary information into these input fields and send their preferences to the reception desk by pressing the submit button. The reception desk then sends the user's input information to the generation department. To ensure that the reception desk accurately receives the user's input information and sends it to the generation department, it has functions such as checking the input content and displaying error messages. For example, if there are errors in the input content or if required fields are not filled in, an error message is displayed to prompt the user to correct it. This allows the reception desk to accurately receive the user's preferences and send them to the generation department.

[0069] The generation unit uses a generation AI to generate the optimal date plan based on the information received by the reception unit. The generation AI utilizes natural language processing and machine learning techniques to propose the best date plan based on the user's preferences. For example, if a user inputs, "I want a romantic date on Saturday night with a budget of 5,000 yen," the generation AI will search its database for suitable restaurants and activities and propose the best combination. The generation AI can also learn from the user's past dating history and preferences to propose a more personalized date plan. For example, the generation AI can generate a new date plan that suits the user's preferences based on data from restaurants the user has visited and activities they have participated in in the past. The generation AI can also optimize the date plan by utilizing real-time information. For example, the generation AI can propose the best travel route by considering traffic information and event schedules. In this way, the generation unit can generate and provide the user with the optimal date plan based on their preferences.

[0070] The reservation department makes restaurant reservations based on the date plan generated by the generation department. Specifically, the reservation department can make restaurant reservations using an online reservation system. The reservation department selects the most suitable restaurant based on the user's desired date, time, and budget, and confirms the reservation. For example, based on the date plan provided by the generation department, the reservation department searches for restaurants with availability at the user's desired date and time, and makes a reservation through the online reservation system. The reservation department also has functions for confirming, changing, and canceling reservations, allowing users to modify their reservation details as needed. In this way, the reservation department can make the most suitable restaurant reservation based on the user's desired conditions and support the realization of the date plan.

[0071] The mobile unit optimizes the travel route based on the date plan generated by the generation unit. Specifically, the mobile unit can acquire real-time traffic information and propose the optimal travel route. The mobile unit optimizes the mode of transport and travel time so that the user can travel smoothly during the date. For example, based on the date plan provided by the generation unit, the mobile unit calculates the optimal travel route from the user's current location to the date destination and proposes the mode of transport and travel time. To acquire real-time traffic information, the mobile unit cooperates with traffic information services and optimizes the travel route considering information such as traffic congestion and accidents. In this way, the mobile unit can support the user in traveling smoothly during the date and help realize the date plan.

[0072] The generation unit includes a learning unit that learns the user's past dating history and preferences. For example, the generation unit collects data on restaurants the user has visited and activities they have participated in in the past and sends it to the learning unit. The learning unit analyzes this data and learns the user's preferences. For example, the learning unit identifies the types of food the user likes and the characteristics of dating spots, and provides this feedback to the generation unit. Based on the feedback from the learning unit, the generation unit generates a date plan that suits the user's preferences. In this way, the generation unit can propose a more personalized date plan by learning the user's past dating history and preferences.

[0073] The generation unit includes an information acquisition unit that obtains real-time traffic and event information. The generation unit obtains real-time traffic information, for example, by using traffic information services or GPS data. The information acquisition unit analyzes this information and transmits it to the generation unit. The generation unit optimizes the date plan based on the information from the information acquisition unit. For example, the generation unit proposes the optimal travel route to avoid traffic congestion. The generation unit can also obtain real-time event information by using event calendars or event information from social media. The information acquisition unit analyzes this information and transmits it to the generation unit. The generation unit incorporates the event information into the date plan based on the information from the information acquisition unit. This allows the generation unit to optimize the date plan by utilizing real-time information.

[0074] The reception desk estimates the user's emotions and adjusts the input method for date preferences based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. If the user is relaxed, the reception desk may also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of date preferences. This allows the reception desk to input more appropriate date preferences by adjusting the input method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The reception desk analyzes the user's past dating preferences and suggests the most suitable input method. For example, the reception desk automatically displays dating preferences that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The reception desk predicts and suggests conditions to be used at specific time slots based on the user's past dating preferences. In this way, the reception desk can suggest the most suitable input method by analyzing the user's past dating preferences.

[0076] The reception system filters the user's dating preferences based on their current lifestyle and areas of interest. For example, when a user enters their current lifestyle, the reception system automatically filters the relevant dating preferences. The reception system can also prioritize displaying dating preferences based on the user's areas of interest. The reception system analyzes the user's lifestyle and areas of interest and suggests the most suitable dating preferences. This allows the reception system to filter based on the user's lifestyle and areas of interest, enabling them to enter more appropriate dating preferences.

[0077] The reception desk estimates the user's emotions and, based on the estimated emotions, determines the priority of the desired conditions to be entered. For example, if the user is feeling stressed, the reception desk will prompt them to enter important conditions first. If the user is relaxed, the reception desk may also prompt them to enter detailed conditions. If the user is in a hurry, the reception desk will prompt them to enter the most important conditions first. This allows the reception desk to enter more appropriate dating conditions by prioritizing the conditions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception desk prioritizes inputting highly relevant conditions when users enter their date preferences, taking into account their geographical location. For example, the reception desk will prioritize suggesting nearby date spots based on the user's current location. The reception desk can also automatically display the most suitable date preferences, taking into account the user's geographical location. The reception desk prioritizes inputting highly relevant date preferences based on the user's location. This allows the reception desk to input highly relevant date preferences by considering the user's geographical location.

[0079] The reception desk analyzes the user's social media activity when they enter their dating preferences and inputs relevant conditions. For example, the reception desk analyzes the user's social media activity and automatically suggests relevant dating preferences. The reception desk can also display the most suitable dating preferences based on the user's social media posts. The reception desk uses the user's social media activity as a reference to input highly relevant dating preferences. In this way, the reception desk can input relevant dating preferences by analyzing the user's social media activity.

[0080] The generation unit estimates the user's emotions and adjusts how the date plan is presented based on those emotions. For example, if the user is relaxed, the generation unit generates a date plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a date plan that emphasizes the shortest route. If the user is excited, the generation unit generates a date plan with visually stimulating effects. In this way, the generation unit can provide a more appropriate date plan by adjusting how it is presented according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit adjusts the level of detail in a date plan based on the user's past dating history. For example, it generates a detailed date plan based on places the user has visited in the past. The generation unit can also generate a plan that avoids crowds based on the user's past dating history. The generation unit analyzes the user's past dating history and generates the most efficient date plan. In this way, the generation unit can provide a more appropriate date plan by adjusting the level of detail based on the user's past dating history.

[0082] The generation unit applies different generation algorithms depending on the user's preferences when generating a date plan. For example, the generation unit may apply an algorithm to generate a romantic date plan based on the user's preferences. The generation unit can also apply an algorithm to generate an active date plan based on the user's preferences. The generation unit may apply an algorithm to generate a relaxed date plan based on the user's preferences. In this way, the generation unit can provide a more appropriate date plan by applying different generation algorithms depending on the user's preferences.

[0083] The generation unit estimates the user's emotions and adjusts the length of the date plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a date plan that can be completed in a short time. If the user is relaxed, the generation unit can also generate a date plan that can be enjoyed for a longer period. If the user is excited, the generation unit generates a date plan with more activity. In this way, the generation unit can provide a more appropriate date plan by adjusting the length of the date plan according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit prioritizes date plans based on when the user submits them. For example, if the user submits early, the generation unit will prioritize generating a detailed date plan. If the user submits at the last minute, the generation unit can also prioritize a date plan that can be generated quickly. The generation unit determines the optimal order for generating date plans based on when the user submits them. This allows the generation unit to provide more appropriate date plans by prioritizing them based on when the user submits them.

[0085] The generation unit adjusts the order of date plans based on user relevance when generating them. For example, the generation unit prioritizes highly relevant activities based on the user's preferences. The generation unit can also prioritize highly relevant plans by referring to the user's past dating history. The generation unit generates highly relevant date plans based on the user's areas of interest. As a result, the generation unit can provide more appropriate date plans by adjusting the order of plans based on user relevance.

[0086] The reservation system estimates the user's emotions and adjusts the way reservations are presented based on those emotions. For example, if the user is relaxed, the system provides detailed reservation information. If the user is in a hurry, the system can also provide concise reservation information. If the user is excited, the system provides visually appealing reservation information. This allows the system to provide more appropriate reservation information by adjusting the way reservations are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reservation department adjusts the level of detail in reservations based on the importance of the restaurant. For example, the reservation department provides detailed reservation information for high-end restaurants, while it can provide concise information for casual restaurants. The reservation department provides optimal reservation information based on the importance of the restaurant. This allows the reservation department to provide more appropriate reservation information by adjusting the level of detail based on the importance of the restaurant.

[0088] The reservation department applies different reservation algorithms depending on the restaurant category when a reservation is made. For example, the reservation department applies a detailed reservation algorithm for high-end restaurants. For casual restaurants, the reservation department can also apply a simplified reservation algorithm. The reservation department applies the most suitable reservation algorithm depending on the restaurant category. In this way, the reservation department can provide more appropriate reservation information by applying different reservation algorithms depending on the restaurant category.

[0089] The booking system estimates the user's emotions and adjusts the length of the booking based on those emotions. For example, if the user is in a hurry, the system suggests a booking that can be completed quickly. If the user is relaxed, the system may suggest a booking that can be enjoyed for a longer period. If the user is excited, the system may suggest a booking with more activity. In this way, the booking system can provide more appropriate booking information by adjusting the length of the booking according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reservation department prioritizes reservations based on when the restaurant submits the reservation request. For example, if a user submits a request early, the reservation department will prioritize more detailed reservations. If a user submits a request at the last minute, the reservation department may also prioritize restaurants that can be booked quickly. The reservation department determines the optimal order of reservations based on when the user submits their request. This allows the reservation department to provide more relevant reservation information by prioritizing reservations based on when the restaurant submits their request.

[0091] The reservation system adjusts the order of reservations based on the relevance of the restaurants during the reservation process. For example, the reservation system prioritizes reservations at restaurants that are highly relevant to the user's preferences. The reservation system can also prioritize restaurants that are highly relevant by referring to the user's past dating history. The reservation system reserves restaurants that are highly relevant to the user's areas of interest. In this way, the reservation system can provide more appropriate reservation information by adjusting the order of reservations based on the relevance of the restaurants.

[0092] The navigation unit estimates the user's emotions and adjusts how the navigation route is presented based on the estimated emotions. For example, if the user is relaxed, the navigation unit suggests a navigation route that proceeds at a leisurely pace. If the user is in a hurry, the navigation unit may also suggest a navigation route that emphasizes the shortest route. If the user is excited, the navigation unit suggests a navigation route with visually stimulating effects. In this way, the navigation unit can provide a more appropriate navigation route by adjusting how the navigation route is presented according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The mobile system adjusts the level of detail in the route based on the user's past travel history when optimizing travel routes. For example, the mobile system proposes a detailed travel route based on routes the user has used in the past. The mobile system can also propose routes that avoid congestion based on the user's past travel history. The mobile system analyzes the user's past travel history and proposes the most efficient travel route. In this way, the mobile system can provide a more appropriate travel route by adjusting the level of detail in the route based on the user's past travel history.

[0094] The mobile unit applies different optimization algorithms to user preferences when optimizing travel routes. For example, it might apply an algorithm that suggests a scenic route based on user preferences. It could also apply an algorithm that suggests the shortest route based on user preferences. It could also apply an algorithm that suggests a route that avoids congestion based on user preferences. In this way, the mobile unit can provide more appropriate travel routes by applying different optimization algorithms according to user preferences.

[0095] The navigation unit estimates the user's emotions and adjusts the length of the travel route based on the estimated emotions. For example, if the user is in a hurry, the navigation unit suggests a travel route that can be completed in a short time. If the user is relaxed, the navigation unit may also suggest a travel route that can be enjoyed for a longer period. If the user is excited, the navigation unit suggests a travel route with more activity. In this way, the navigation unit can provide a more appropriate travel route by adjusting the length of the travel route according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The travel unit prioritizes routes based on the user's submission timing when optimizing travel routes. For example, if the user submits early, the travel unit will prioritize suggesting detailed travel routes. If the user submits just before the user submits, the travel unit can also prioritize travel routes that can be suggested quickly. The travel unit determines the optimal order of suggested travel routes based on the user's submission timing. This allows the travel unit to provide more appropriate travel routes by prioritizing routes based on the user's submission timing.

[0097] The navigation unit adjusts the order of routes based on user relevance when optimizing travel routes. For example, the navigation unit prioritizes suggesting highly relevant routes based on user preferences. The navigation unit can also prioritize highly relevant routes by referring to the user's past travel history. The navigation unit suggests highly relevant routes based on the user's areas of interest. In this way, the navigation unit can provide more appropriate travel routes by adjusting the order of routes based on user relevance.

[0098] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select training data based on positive dating experiences. If the user is stressed, the learning unit can also select training data based on dating experiences that help reduce stress. If the user is excited, the learning unit will select training data based on exciting dating experiences. This allows the learning unit to learn more appropriately by selecting training data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. The learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the learning unit improves the accuracy of the learning algorithm by referring to past learning data.

[0100] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit will learn more frequently. If the user is stressed, the learning unit can also reduce the learning frequency. If the user is excited, the learning unit will adjust the learning frequency. In this way, the learning unit can perform more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The learning unit weights the training data based on when the date history was submitted. For example, the learning unit will give more weight to date history submitted by the user earlier. The learning unit can also learn by downplaying date history submitted by the user most recently. The learning unit adjusts the weighting of the training data based on when the date history was submitted. This allows the learning unit to perform more appropriate learning by weighting the training data based on when the date history was submitted.

[0102] The information acquisition unit estimates the user's emotions and adjusts its information acquisition method based on the estimated emotions. For example, if the user is relaxed, the information acquisition unit acquires detailed information. If the user is stressed, the information acquisition unit can also acquire concise information. If the user is excited, the information acquisition unit acquires visually appealing information. In this way, the information acquisition unit can acquire more appropriate information by adjusting its information acquisition method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The information acquisition unit selects the optimal acquisition method by referring to past information acquisition history when acquiring information. For example, the information acquisition unit selects the optimal information acquisition method based on past information acquisition history. The information acquisition unit can also analyze past information acquisition history and adjust the parameters of the information acquisition method. The information acquisition unit improves the accuracy of the information acquisition method by referring to past information acquisition history. As a result, the accuracy of the information acquisition method is improved by the information acquisition unit referring to past information acquisition history.

[0104] The information retrieval unit estimates the user's emotions and determines the priority of information retrieval based on the estimated emotions. For example, if the user is relaxed, the information retrieval unit prioritizes retrieving detailed information. If the user is stressed, the information retrieval unit may also prioritize retrieving concise information. If the user is excited, the information retrieval unit prioritizes retrieving visually appealing information. In this way, the information retrieval unit can obtain more appropriate information by determining the priority of information retrieval based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The information acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location. For example, the information acquisition unit prioritizes acquiring nearby information based on the user's current location. The information acquisition unit can also automatically acquire the most relevant information by considering the user's geographical location. The information acquisition unit prioritizes acquiring highly relevant information based on the user's location. As a result, the information acquisition unit can acquire highly relevant information by considering the user's geographical location.

[0106] The information acquisition unit prioritizes acquiring highly relevant information by considering the user's areas of interest during information acquisition. For example, the information acquisition unit prioritizes acquiring relevant information based on the user's areas of interest. The information acquisition unit can also prioritize highly relevant information by referring to the user's past information acquisition history. The information acquisition unit acquires the most suitable information based on the user's areas of interest. As a result, the information acquisition unit can acquire highly relevant information by considering the user's areas of interest.

[0107] The information acquisition unit analyzes the user's social media activity and retrieves relevant information when acquiring data. For example, the information acquisition unit analyzes the user's social media activity and automatically retrieves relevant information. The information acquisition unit can also acquire optimal information based on the content of the user's social media posts. The information acquisition unit uses the user's social media activity as a reference to acquire highly relevant information. In this way, the information acquisition unit can acquire relevant information by analyzing the user's social media activity.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception system can estimate the user's emotions and adjust how they input their dating preferences based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can offer detailed input options and suggest customizable input methods. If the user is in a hurry, it can prioritize voice input to allow them to quickly enter their dating preferences. This allows the reception system to input more appropriate dating preferences by adjusting the input method according to the user's emotions.

[0110] The generation unit can estimate the user's emotions and adjust how the date plan is presented based on those emotions. For example, if the user is relaxed, it can generate a date plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a date plan that emphasizes the shortest route. If the user is excited, it can generate a date plan with visually stimulating effects. In this way, the generation unit can provide a more appropriate date plan by adjusting how it is presented according to the user's emotions.

[0111] The reservation system can estimate the user's emotions and adjust the way reservations are presented based on those emotions. For example, if the user is relaxed, detailed reservation information can be provided. If the user is in a hurry, concise reservation information can be provided. If the user is excited, visually appealing reservation information can be provided. In this way, the reservation system can provide more appropriate reservation information by adjusting the way reservations are presented according to the user's emotions.

[0112] The navigation unit can estimate the user's emotions and adjust how the navigation route is presented based on those emotions. For example, if the user is relaxed, it can suggest a navigation route that proceeds at a leisurely pace. If the user is in a hurry, it can suggest a navigation route that emphasizes the shortest route. If the user is excited, it can suggest a navigation route with visually stimulating effects. In this way, the navigation unit can provide a more appropriate navigation route by adjusting how the navigation route is presented according to the user's emotions.

[0113] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is relaxed, it can select training data based on positive dating experiences. If the user is stressed, it can select training data based on dating experiences that help reduce stress. If the user is excited, it can select training data based on exciting dating experiences. This allows the learning unit to perform more appropriate learning by selecting training data based on the user's emotions.

[0114] The reception desk can analyze the user's past dating preferences and suggest the most suitable input method. For example, it can automatically display dating preferences that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). It can even predict and suggest preferences to be used at specific time slots based on the user's past dating preferences. In this way, the reception desk can suggest the most suitable input method by analyzing the user's past dating preferences.

[0115] The reception system can filter dating preferences based on the user's current lifestyle and areas of interest. For example, when a user enters their current lifestyle, the system can automatically filter relevant dating preferences. It can also prioritize displaying dating preferences based on the user's areas of interest. Furthermore, it can analyze the user's lifestyle and areas of interest and suggest the most suitable dating preferences. This allows the reception system to filter based on the user's lifestyle and areas of interest, enabling them to enter more appropriate dating preferences.

[0116] The reception desk can prioritize highly relevant conditions when users input their date preferences, taking into account their geographical location. For example, it can suggest nearby date spots based on the user's current location. It can also automatically display the most suitable date preferences, taking into account the user's geographical location. It can also prioritize highly relevant date preferences based on the user's location. This allows the reception desk to input highly relevant date preferences by considering the user's geographical location.

[0117] The reception desk can analyze the user's social media activity when they enter their dating preferences and input relevant criteria. For example, it can analyze the user's social media activity and automatically suggest relevant dating preferences. It can also display the most suitable dating preferences based on the user's social media posts. It can also prompt the user to enter highly relevant dating preferences based on their social media activity. In this way, the reception desk can input relevant dating preferences by analyzing the user's social media activity.

[0118] The generation unit can apply different generation algorithms depending on the user's preferences when generating a date plan. For example, it can apply an algorithm to generate a romantic date plan based on the user's preferences. It can also apply an algorithm to generate an active date plan based on the user's preferences. It can also apply an algorithm to generate a relaxed date plan based on the user's preferences. In this way, the generation unit can provide a more appropriate date plan by applying different generation algorithms according to the user's preferences.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk inputs the user's dating preferences. For example, the user can input the date and time of the date, budget, preferred activities, etc. The reception desk then sends the user's input information to the generation desk. Step 2: The generation unit generates the optimal date plan based on the information received by the reception unit. The generation unit can also use generation AI to learn the user's past dating history and preferences to propose a more personalized date plan. It can also optimize the date plan by utilizing real-time information. Step 3: The reservation unit makes restaurant reservations based on the date plan generated by the generation unit. The reservation unit can use an online reservation system to make restaurant reservations, select the best restaurant based on the user's desired date and time and budget, and confirm the reservation. Step 4: The travel unit optimizes the travel route based on the date plan generated by the generation unit. The travel unit can acquire real-time traffic information and suggest the optimal travel route. This optimizes the mode of transport and travel time so that the user can travel smoothly during the date.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and travel unit, is implemented by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user inputs their desired date conditions. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, where an optimal date plan is generated using a generation AI. The reservation unit is implemented by the control unit 46A of the smart device 14, where restaurant reservations are made. The travel unit is implemented by the specific processing unit 290 of the data processing device 12, where the travel route is optimized. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and movement unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user inputs their desired date conditions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal date plan is generated using a generation AI. The reservation unit is implemented by the control unit 46A of the smart glasses 214, where restaurant reservations are made. The movement unit is implemented by the specific processing unit 290 of the data processing unit 12, where the travel route is optimized. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and travel 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 control unit 46A of the headset terminal 314, where the user inputs their desired date conditions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an optimal date plan is generated using a generation AI. The reservation unit is implemented by the control unit 46A of the headset terminal 314, where restaurant reservations are made. The travel unit is implemented by the specific processing unit 290 of the data processing unit 12, where the travel route is optimized. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, generation unit, reservation unit, and movement 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 control unit 46A of the robot 414, which inputs the user's desired date conditions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates an optimal date plan using generation AI. The reservation unit is implemented by the control unit 46A of the robot 414, which makes restaurant reservations. The movement unit is implemented by the specific processing unit 290 of the data processing unit 12, which optimizes the movement route. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A reception area where users enter their desired dating conditions, A generation unit that generates an optimal date plan based on the information received by the reception unit, A reservation unit makes restaurant reservations based on the date plan generated by the generation unit, The system includes a movement unit that optimizes the movement route based on the date plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It features a learning unit that learns the user's past dating history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It is equipped with an information acquisition unit that obtains real-time traffic information and event information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts how users input their dating preferences based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We analyze the user's past dating preferences and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter their dating preferences, the system filters them based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input preferences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter their dating preferences, the system prioritizes highly relevant criteria by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their dating preferences, the system analyzes their social media activity and inputs relevant criteria. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts how the date plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a date plan, adjust the level of detail based on the user's past dating history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating date plans, different generation algorithms are applied according to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the length of the date plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating date plans, the system prioritizes plans based on when the user submits them. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a date plan, the order of the plan is adjusted based on the relevance of the users. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the way reservations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reservation section is, When making a reservation, adjust the level of detail based on the importance of the restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reservation section is, When making a reservation, different reservation algorithms are applied depending on the restaurant's category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, It estimates the user's emotions and adjusts the length of the reservation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, When making a reservation, priority will be determined based on when the restaurant submitted the reservation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When making a reservation, the order of reservations will be adjusted based on the relevance of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned movable part is The system estimates the user's emotions and adjusts how the travel route is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned movable part is When optimizing travel routes, adjust the level of detail of the route based on the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned movable part is When optimizing travel routes, different optimization algorithms are applied according to the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned movable part is It estimates the user's emotions and adjusts the length of the travel route based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned movable part is When optimizing travel routes, route priorities are determined based on when the user submits them. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned movable part is When optimizing travel routes, the order of routes is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned learning unit, During training, the training data is weighted based on when the dating history was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned information acquisition unit, It estimates the user's emotions and adjusts the information acquisition method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned information acquisition unit, When acquiring information, the system selects the optimal acquisition method by referring to past information acquisition history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned information acquisition unit, It estimates the user's emotions and determines the priority of information retrieval based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned information acquisition unit, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned information acquisition unit, When retrieving information, the system prioritizes obtaining highly relevant information by considering the user's areas of interest. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned information acquisition unit, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0193] 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 where users enter their desired dating conditions, A generation unit that generates an optimal date plan based on the information received by the reception unit, A reservation unit makes restaurant reservations based on the date plan generated by the generation unit, The system includes a movement unit that optimizes the movement route based on the date plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is It features a learning unit that learns the user's past dating history and preferences. The system according to feature 1.

3. The generating unit is It is equipped with an information acquisition unit that obtains real-time traffic information and event information. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts how users input their dating preferences based on those emotions. The system according to feature 1.

5. The aforementioned reception unit is We analyze the user's past dating preferences and suggest the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is When users enter their dating preferences, the system filters them based on their current lifestyle and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input preferences based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter their dating preferences, the system prioritizes highly relevant criteria by considering their geographical location. The system according to feature 1.

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