system

Abstract

The system according to this embodiment aims to provide individuals with food allergies with a safe and smooth dining experience when eating out. [Solution] The system according to the embodiment comprises a collection unit, a negotiation unit, a reservation unit, and a collaboration unit. The collection unit collects the user's allergy information. The negotiation unit negotiates with restaurants based on the information collected by the collection unit. The reservation unit makes reservations based on the information obtained by the negotiation unit. The collaboration unit collaborates with the restaurant's AI agent.

Description

Technical Field

[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 as a 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 prior art, there was a problem that it was difficult for an individual with food allergies to have a safe and smooth experience when dining out.

[0005] The system according to the embodiment aims to enable an individual with food allergies to have a safe and smooth experience when dining out.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a negotiation unit, a reservation unit, and a collaboration unit. The data collection unit collects the user's allergy information. The negotiation unit negotiates with restaurants based on the information collected by the data collection unit. The reservation unit makes reservations based on the information obtained by the negotiation unit. The collaboration unit collaborates with the restaurant's AI agent. [Effects of the Invention]

[0007] The system according to this embodiment allows individuals with food allergies to have a safe and smooth experience when dining out. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AllerBuddy System according to an embodiment of the present invention is an AI agent system aimed at reducing the risks and stress faced by individuals with food allergies and their families when dining out. The AllerBuddy System allows users to provide detailed allergy information through allergy test results and dialogue, and the AI ​​agent negotiates with restaurants based on this information. It supports the user by checking for allergens, suggesting alternative menus, and assisting with reservation procedures, making the user's dining-out experience safe and smooth. Furthermore, by introducing the AllerBuddy System to restaurants, AI collaboration becomes possible, leading to more efficient coordination. This reduces the burden on staff and improves reservation efficiency, allowing individuals with food allergies and their families to enjoy dining out with peace of mind. For example, the AllerBuddy System collects detailed allergy information when the user inputs allergy test results. Alternatively, the AllerBuddy System can collect allergy information through dialogue with the user. Next, the AllerBuddy System negotiates with restaurants based on the collected allergy information. For example, the AllerBuddy System checks for allergens and suggests alternative menus. Finally, the AllerBuddy System handles reservation procedures based on the results of negotiations with restaurants. For example, the AllerBuddy system allows users to confirm reservations simply by entering their desired date, time, and number of people. Furthermore, the AllerBuddy system collaborates with AI agents on the restaurant side to ensure efficient coordination. For instance, the AllerBuddy system shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. This allows individuals with food allergies and their families to have a safe and smooth dining experience when eating out.

[0029] The AllerBuddy system according to this embodiment comprises a collection unit, a negotiation unit, a reservation unit, and a collaboration unit. The collection unit collects the user's allergy information. For example, the collection unit collects detailed allergy information when the user inputs allergy test results. The collection unit can also collect allergy information through dialogue with the user, for example. The collection unit can also collect allergy information when the user uploads allergy test results, for example. The negotiation unit negotiates with restaurants based on the information collected by the collection unit. For example, the negotiation unit checks for the presence or absence of allergens. For example, the negotiation unit proposes alternative menus. For example, the negotiation unit checks the restaurant's menu and proposes menus that do not contain allergens. The reservation unit performs reservation procedures based on the information obtained by the negotiation unit. For example, the reservation unit confirms reservations simply by the user inputting the desired date, time, and number of people. For example, the reservation unit collaborates with the restaurant's reservation system to perform reservation procedures. For example, the reservation unit can also confirm reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit collaborates with the restaurant's AI agent. The collaboration unit, for example, shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The collaboration unit can also, for example, collaborate with the restaurant's AI agent to perform efficient adjustments. The collaboration unit can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. As a result, the AllerBuddy system according to this embodiment allows individuals with food allergies and their families to have a safe and smooth experience when dining out.

[0030] The data collection unit collects users' allergy information. Specifically, it collects detailed allergy information when users input their allergy test results. For example, users can input allergy test results through a dedicated application. This data includes the type of allergen, the severity of the allergic reaction, and a history of past allergic reactions. The data collection unit can also collect allergy information through interaction with users. For example, it can use a chatbot to ask users questions and collect detailed information about their allergies. Furthermore, it can collect allergy information when users upload their allergy test results. Users can upload images or PDF files of their test results, and the system automatically analyzes the content and registers it in the database. This allows the data collection unit to collect users' allergy information in various ways and accumulate accurate and detailed data. The collected data is stored in a cloud-based database and made accessible to other departments. This allows the data collection unit to efficiently manage users' allergy information and improve the overall performance of the system.

[0031] The Negotiation Department negotiates with restaurants based on information collected by the Data Collection Department. Specifically, it collects detailed information about the restaurant's menu and cooking methods to confirm the presence or absence of allergens. For example, the Negotiation Department directly contacts the restaurant's chefs and managers to confirm the use of allergen-containing ingredients and cooking processes. The Negotiation Department can also propose alternative menus. For example, it proposes menus using alternative ingredients that do not contain specific allergens and consults with the restaurant to determine which menus are feasible. Furthermore, the Negotiation Department reviews the restaurant's menu and proposes allergen-free options. This includes selecting allergen-free items from existing menus and making adjustments to provide them to users. Based on this information, the Negotiation Department can provide users with safe meal options. The Negotiation Department records the results of negotiations with restaurants in a database, making it accessible to other departments. This allows the Negotiation Department to effectively negotiate with restaurants based on users' allergy information and ensure the provision of safe meals.

[0032] The reservation department handles reservation procedures based on information obtained by the negotiation department. Specifically, it confirms reservations simply by the user entering their desired date, time, and number of people. For example, a user enters their desired date, time, and number of people through a dedicated application, and the reservation department automatically connects with the restaurant's reservation system to complete the reservation process. The reservation department can also connect with the restaurant's reservation system to complete the reservation process. This allows users to easily complete reservations without any hassle. Furthermore, the reservation department can also confirm reservations simply by the user entering their desired date, time, and number of people. The reservation department checks the restaurant's reservation status in real time and confirms reservations according to availability. After the reservation process is complete, the reservation department sends a reservation confirmation notification to the user. This includes the reservation date and time, number of people, restaurant information, and details regarding allergy accommodations. This allows the reservation department to provide users with a quick and accurate reservation process, increasing their peace of mind when dining out.

[0033] The Collaboration Department works in conjunction with the restaurant's AI agent. Specifically, it shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. For example, the Collaboration Department sends user allergy information and reservation information to the restaurant's AI agent so that the restaurant can take appropriate action. The Collaboration Department works in conjunction with the restaurant's AI agent to perform efficient adjustments. This includes acquiring menu and allergen information provided by the restaurant's AI agent in real time and making adjustments to provide it to the user. Furthermore, the Collaboration Department can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. This allows the Collaboration Department to work closely with restaurants and provide users with the best possible service. The Collaboration Department records the results of its collaboration with the restaurant's AI agent in a database, making it accessible to other departments. This allows the Collaboration Department to effectively collaborate with restaurants and provide users with a safe and smooth dining experience.

[0034] The data collection unit can collect detailed allergy information from the user through allergy test results and interactions. For example, the data collection unit can collect detailed allergy information when the user inputs allergy test results. The data collection unit can also collect allergy information through interactions with the user. The data collection unit can also collect allergy information when the user uploads allergy test results. This allows for more accurate information to be used when negotiating with restaurants by collecting detailed allergy information from the user through allergy test results and interactions. Detailed allergy information includes, for example, the type of allergen, the severity of the allergic reaction, and the timing of the allergy onset. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's allergy test results into a generating AI and have the generating AI collect detailed allergy information.

[0035] The negotiating department can provide information on the presence or absence of allergens and suggest alternative menu options. For example, the negotiating department can check for the presence or absence of allergens. For example, the negotiating department can check the ingredient list of a restaurant's menu to determine if it contains allergens. For example, the negotiating department can check the cooking methods of a restaurant to determine if it contains allergens. For example, the negotiating department can suggest alternative menu options. For example, the negotiating department can suggest menu options that do not contain allergens. For example, the negotiating department can suggest menu options from which allergens have been removed. This allows users to enjoy their meals safely by providing information on the presence or absence of allergens and suggesting alternative menu options. Checking for the presence or absence of allergens includes, for example, the ingredient list of a restaurant's menu and the cooking methods of a restaurant. Suggesting alternative menu options includes, for example, menu options that do not contain allergens and menu options from which allergens have been removed. Some or all of the above processes performed by the negotiating department may be carried out using AI, for example, or without AI. For example, the negotiating department can input the ingredient list of a restaurant's menu into a generating AI and have the generating AI check for the presence or absence of allergens.

[0036] The reservation system allows users to confirm reservations simply by entering their desired date, time, and number of people. For example, the reservation system can integrate with restaurant reservation systems to handle reservations. The reservation system can also confirm reservations simply by the user entering their desired date, time, and number of people. This simplifies the reservation process by allowing users to confirm reservations simply by entering their desired date, time, and number of people. Reservation confirmation includes, for example, the method of confirmation, the content of the confirmation, and the timing of the confirmation. Some or all of the above-described processes in the reservation system may be performed using AI, or not. For example, the reservation system can generate a reservation confirmation using an AI that generates the confirmation simply by the user entering their desired date, time, and number of people.

[0037] The collaboration unit can work in conjunction with the restaurant's AI agent to perform efficient adjustments. For example, the collaboration unit can share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The collaboration unit can work in conjunction with the restaurant's AI agent to perform efficient adjustments. The collaboration unit can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. By working in conjunction with the restaurant's AI agent to perform efficient adjustments, it is expected that the burden on staff will be reduced and the efficiency of reservations will be improved. Efficient adjustments include, for example, the means of adjustment, the content of the adjustment, and the criteria for success of the adjustment. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can share information with the restaurant's AI agent and have an AI generate efficient adjustments.

[0038] The negotiating department can communicate with restaurants using messengers or synthesized speech. For example, the negotiating department can communicate with restaurants using messengers. For example, the negotiating department can communicate with restaurants using text messages. For example, the negotiating department can also communicate with restaurants using voice messages. For example, the negotiating department can communicate with restaurants using synthesized speech. For example, the negotiating department can communicate with restaurants using speech synthesis technology. For example, the negotiating department can communicate with restaurants considering the naturalness of the voice. This makes efficient negotiations possible by communicating with restaurants using messengers or synthesized speech. Messengers include, for example, text messages, voice messages, and video messages. Synthetic speech includes, for example, speech synthesis technology, naturalness of voice, and methods for customizing the voice. Some or all of the above processing in the negotiating department may be performed using, for example, AI, or not using AI. For example, the negotiating department can input messenger or synthesized speech into a generating AI and have the generating AI perform the communication with the restaurant.

[0039] The data collection unit can analyze the user's past allergy reaction history and select the optimal data collection method. For example, the data collection unit can analyze the frequency and severity of allergy reactions the user has experienced in the past and determine the information to focus on collecting. For example, if the user has shown a strong allergic reaction to a particular food in the past, the data collection unit can prioritize collecting information about that food. For example, the data collection unit can predict foods that are likely to cause allergies based on the user's past allergy reaction history and customize the information to be collected. This enables optimal information collection by analyzing the user's past allergy reaction history. The optimal data collection method includes, for example, the means of collection, the timing of collection, and the accuracy of collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past allergy reaction history into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter allergy information based on the user's current health status and dietary history. For example, the data collection unit can check the user's current health status, identify situations that are likely to trigger allergic reactions, and collect information. For example, the data collection unit can analyze the user's recent dietary history, identify ingredients that may cause allergies, and collect information. The data collection unit can also adjust the scope of allergy information collection based on the user's health status and dietary history, collecting only the necessary information. This allows for the collection of only the necessary information by filtering it based on the user's current health status and dietary history. Filtering includes, for example, filtering conditions, filtering methods, and filtering accuracy. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's health status and dietary history into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting allergy information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information on common allergens in that region. For example, if the user is traveling, the data collection unit can collect information on common allergens in the destination region. The data collection unit can also collect and provide information on region-specific allergens based on the user's geographical location. This allows for the provision of region-specific allergen information by prioritizing the collection of highly relevant information based on the user's geographical location. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting allergy information. For example, the data collection unit can analyze the food content shared by the user on social media and identify ingredients that may cause allergies. For example, the data collection unit can collect information about allergic reactions mentioned by the user on social media and update the allergy information. For example, the data collection unit can identify the user's interests and concerns regarding allergies based on their social media activity and customize the information it collects. This allows the collection of relevant allergy information by analyzing the user's social media activity. Social media activity includes, for example, posts, the number of likes, and the number of followers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0043] The negotiating department can adjust the level of detail in negotiations based on the importance of the allergen. For example, in negotiations concerning serious allergens, the negotiating department can provide detailed information and conduct negotiations carefully. For example, in negotiations concerning mild allergens, the negotiating department can provide concise information and conduct negotiations quickly. The negotiating department can also adjust the level of detail in negotiations according to the importance of the allergen to provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in negotiations based on the importance of the allergen. The importance of an allergen includes, for example, the risk of the allergen, the frequency of the allergen, and the effects of the allergen. Some or all of the above processes in the negotiating department may be performed using AI, for example, or not using AI. For example, the negotiating department can input the importance of the allergen into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0044] The negotiation department can apply different negotiation algorithms depending on the restaurant category during negotiations. For example, the negotiation department might apply a polite and detailed negotiation algorithm to a high-end restaurant. For example, it might apply a quick and concise negotiation algorithm to a fast-food restaurant. For example, it might apply a casual and friendly negotiation algorithm to a cafe or bar. By applying different negotiation algorithms depending on the restaurant category, more effective negotiations become possible. Negotiation algorithms include, for example, negotiation strategies, negotiation methods, and negotiation success criteria. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input the restaurant category into a generating AI and have the generating AI perform the application of negotiation algorithms.

[0045] The negotiating department can determine negotiation priorities based on the timing of menu submissions from restaurants. For example, the negotiating department can negotiate immediately before a restaurant introduces a new menu and confirm allergen information. For example, the negotiating department can prioritize negotiations when a restaurant offers a seasonal menu. For example, the negotiating department can prioritize confirming allergen information when a restaurant updates its menu. This allows for negotiations to be conducted at the appropriate time by determining negotiation priorities based on the timing of menu submissions from restaurants. The timing of menu submissions includes, for example, the menu submission deadline, the frequency of menu updates, and the menu submission method. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input the timing of menu submissions from restaurants into a generating AI and have the generating AI determine the negotiation priorities.

[0046] The negotiation unit can adjust the order of negotiations based on the relevance of the restaurants during the negotiation process. For example, the negotiation unit can prioritize negotiations with restaurants that the user frequently uses. For example, the negotiation unit can quickly negotiate with restaurants that the user is using for the first time. The negotiation unit can also adjust the order of negotiations based on the popularity and ratings of the restaurants. This allows for efficient negotiations by adjusting the order of negotiations based on the relevance of the restaurants. The relevance of restaurants includes, for example, the restaurant's category, location, and reputation. Some or all of the above processing in the negotiation unit may be performed using, for example, AI, or not using AI. For example, the negotiation unit can input the relevance of restaurants into a generating AI and have the generating AI perform the adjustment of the negotiation order.

[0047] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. For example, the reservation unit can suggest the optimal reservation time to avoid congestion based on the user's past reservation history. For example, the reservation unit can analyze the user's past reservation history to select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. The optimal reservation method includes, for example, the reservation method, the timing of the reservation, and the accuracy of the reservation. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's past reservation history into a generating AI and have the generating AI select the optimal reservation method.

[0048] The reservation unit can customize the reservation method based on the user's current schedule at the time of reservation. For example, the reservation unit can refer to the user's calendar information and suggest the optimal reservation time. For example, the reservation unit can customize the reservation method to suit the user's schedule. For example, the reservation unit can also suggest the optimal reservation method based on the user's current schedule. This makes it possible to make more appropriate reservations by customizing the reservation method based on the user's current schedule. Reservation methods include, for example, online reservations, telephone reservations, and direct reservations. Some or all of the above processing in the reservation unit may be performed using, for example, AI, or not using AI. For example, the reservation unit can input the user's calendar information into a generating AI and have the generating AI perform the customization of the reservation method.

[0049] The reservation department can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, if the user is in a specific region, the reservation department can prioritize suggesting restaurants available in that region. For example, if the user is traveling, the reservation department can suggest the optimal reservation method for the region the user is traveling to. The reservation department can also select the optimal reservation method based on the user's geographical location information. This allows for appropriate reservations tailored to the region by selecting the optimal reservation method while considering the user's geographical location information. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the reservation department may be performed using, for example, AI, or not using AI. For example, the reservation department can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0050] The reservation department can analyze a user's social media activity and suggest reservation methods when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on restaurants mentioned by the user on social media. For example, the reservation department can analyze a user's social media activity and suggest restaurants of interest. For example, the reservation department can customize reservation methods based on a user's social media activity. This allows the department to suggest reservation methods for restaurants of interest by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input a user's social media activity into a generating AI and have the generating AI suggest reservation methods.

[0051] The integration unit can select the optimal integration method by analyzing the restaurant's past integration history during integration. For example, the integration unit can select the optimal integration method based on successful integration cases in the restaurant's past. For example, the integration unit can identify points where problems are likely to occur from the restaurant's past integration history and adjust the integration method accordingly. For example, the integration unit can also select the most efficient integration method by analyzing the restaurant's past integration history. In this way, the optimal integration method can be selected by analyzing the restaurant's past integration history. The optimal integration method includes, for example, the means of integration, the timing of integration, and the accuracy of integration. Some or all of the above processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the restaurant's past integration history into a generating AI and have the generating AI select the optimal integration method.

[0052] The integration unit can customize the means of integration based on the current status of the restaurant during integration. For example, if the restaurant is busy, the integration unit can select a quick and simple integration method. For example, if the restaurant is not busy, the integration unit can select a more detailed integration method. The integration unit can also customize the optimal means of integration based on the current status of the restaurant. This allows for more appropriate integration by customizing the means of integration based on the current status of the restaurant. Means of integration include, for example, online integration, telephone integration, and direct integration. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the current status of the restaurant into a generating AI and have the generating AI perform the customization of the means of integration.

[0053] The integration unit can select the optimal integration method when integrating, taking into account the geographical location information of restaurants. For example, if a restaurant is located in a specific region, the integration unit will select an integration method considering the characteristics of that region. For example, if a restaurant is located in a travel destination, the integration unit can select an integration method considering the characteristics of that destination. The integration unit can also select the optimal integration method based on the geographical location information of restaurants. This enables appropriate integration tailored to the region by selecting the optimal integration method while considering the geographical location information of restaurants. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the integration unit may be performed using, for example, AI, or without AI. For example, the integration unit can input the geographical location information of restaurants into a generating AI and have the generating AI select the optimal integration method.

[0054] The collaboration unit can analyze a restaurant's social media activity and propose collaboration methods during the collaboration process. For example, the collaboration unit can propose the most suitable collaboration method based on information the restaurant has posted on social media. For example, the collaboration unit can analyze a restaurant's social media activity and propose collaboration methods of interest. For example, the collaboration unit can customize collaboration methods based on a restaurant's social media activity. This allows the optimal collaboration method to be proposed by analyzing a restaurant's social media activity. Social media activity includes, for example, post content, number of likes, and number of followers. Some or all of the above processing in the collaboration unit may be performed using, for example, AI, or not using AI. For example, the collaboration unit can input a restaurant's social media activity into a generating AI and have the generating AI propose collaboration methods.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AllerBuddy system comprises a collection unit that collects user allergy information, a negotiation unit that negotiates with restaurants, a reservation unit that handles reservation procedures, and a collaboration unit that works with AI agents on the restaurant side. The collection unit can collect detailed allergy information when users input their allergy test results. For example, if a user uploads their allergy test results, the collection unit can collect allergy information based on that information. The negotiation unit negotiates with restaurants based on the information collected by the collection unit. For example, the negotiation unit can check the ingredient list of the restaurant's menu to confirm the presence or absence of allergens. The reservation unit handles reservation procedures based on the information obtained by the negotiation unit. For example, users can confirm reservations simply by entering their desired date, time, and number of people. The collaboration unit works with AI agents on the restaurant side to make efficient adjustments. For example, it can share information with the restaurant's AI agent to adjust reservation procedures and menus. As a result, the AllerBuddy system enables individuals with food allergies and their families to have a safe and smooth dining experience when eating out.

[0057] The AllerBuddy system can analyze a user's past allergy reaction history and select the optimal data collection method. For example, it can analyze the frequency and severity of allergy reactions the user has experienced in the past and determine the information to focus on collecting. If a user has shown a strong allergic reaction to a particular food in the past, information about that food can be prioritized for collection. Based on the user's past allergy reaction history, it can also predict foods that are likely to cause allergies and customize the information to be collected. This enables optimal information collection by analyzing the user's past allergy reaction history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0058] The AllerBuddy system can filter allergy information based on the user's current health status and dietary history. For example, it can check the user's current health status and identify situations that are likely to trigger allergic reactions, thereby collecting relevant information. It can also analyze the user's recent dietary history and identify ingredients that may cause allergies, thereby collecting relevant information. Based on the user's health status and dietary history, the system can adjust the scope of allergy information collection, collecting only the necessary information. This allows for the collection of only the necessary information by filtering it based on the user's current health status and dietary history. Some or all of the above processing in the collection unit may be performed using AI, or it may be performed without using AI.

[0059] The AllerBuddy system can prioritize the collection of highly relevant information based on the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of information about common allergens in that region. If the user is traveling, it can collect information about common allergens in the region they are visiting. It can also collect and provide information about region-specific allergens based on the user's geographical location. This allows the system to provide information about region-specific allergens by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0060] The AllerBuddy system can analyze a user's social media activity and collect relevant information. For example, it can analyze the meals a user shares on social media and identify ingredients that may cause allergies. It can also collect information about allergic reactions mentioned by the user on social media and update allergy information. Based on the user's social media activity, it can also identify their interests and concerns regarding allergies and customize the information it collects. This allows the system to collect relevant allergy information by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not.

[0061] The AllerBuddy system can adjust the level of detail in negotiations based on the severity of the allergen. For example, in negotiations concerning a major allergen, the negotiating unit can provide detailed information and conduct negotiations carefully. In negotiations concerning a mild allergen, the negotiating unit can provide concise information and conduct negotiations quickly. Depending on the severity of the allergen, the negotiating unit can also adjust the level of detail in negotiations and provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in negotiations based on the severity of the allergen. Some or all of the above processes in the negotiating unit may be performed using AI or not.

[0062] The AllerBuddy system can apply different negotiation algorithms depending on the category of the restaurant during negotiations. For example, in a high-end restaurant, the negotiation team can apply a polite and detailed negotiation algorithm. In a fast-food restaurant, the negotiation team can apply a quick and concise negotiation algorithm. In a cafe or bar, the negotiation team can also apply a casual and friendly negotiation algorithm. This allows for more effective negotiations by applying different negotiation algorithms depending on the category of the restaurant. Some or all of the above processes in the negotiation team may be performed using AI or not.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects the user's allergy information. For example, detailed allergy information is collected when the user enters their allergy test results. Allergy information can also be collected through interaction with the user. Furthermore, allergy information can be collected when the user uploads their allergy test results. Step 2: The negotiation team negotiates with restaurants based on the information collected by the data collection team. For example, they may check for allergens or suggest alternative menus. They can also review the restaurant's menu and suggest allergen-free options. Step 3: The reservation department processes reservations based on the information obtained by the negotiation department. For example, it can confirm reservations simply by the user entering their desired date, time, and number of people. It can also integrate with restaurant reservation systems to process reservations. Step 4: The integration unit collaborates with the restaurant's AI agent. For example, it shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. It can also collaborate to facilitate more efficient coordination.

[0065] (Example of form 2) The AllerBuddy System according to an embodiment of the present invention is an AI agent system aimed at reducing the risks and stress faced by individuals with food allergies and their families when dining out. The AllerBuddy System allows users to provide detailed allergy information through allergy test results and dialogue, and the AI ​​agent negotiates with restaurants based on this information. It supports the user by checking for allergens, suggesting alternative menus, and assisting with reservation procedures, making the user's dining-out experience safe and smooth. Furthermore, by introducing the AllerBuddy System to restaurants, AI collaboration becomes possible, leading to more efficient coordination. This reduces the burden on staff and improves reservation efficiency, allowing individuals with food allergies and their families to enjoy dining out with peace of mind. For example, the AllerBuddy System collects detailed allergy information when the user inputs allergy test results. Alternatively, the AllerBuddy System can collect allergy information through dialogue with the user. Next, the AllerBuddy System negotiates with restaurants based on the collected allergy information. For example, the AllerBuddy System checks for allergens and suggests alternative menus. Finally, the AllerBuddy System handles reservation procedures based on the results of negotiations with restaurants. For example, the AllerBuddy system allows users to confirm reservations simply by entering their desired date, time, and number of people. Furthermore, the AllerBuddy system collaborates with AI agents on the restaurant side to ensure efficient coordination. For instance, the AllerBuddy system shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. This allows individuals with food allergies and their families to have a safe and smooth dining experience when eating out.

[0066] The AllerBuddy system according to this embodiment comprises a collection unit, a negotiation unit, a reservation unit, and a collaboration unit. The collection unit collects the user's allergy information. For example, the collection unit collects detailed allergy information when the user inputs allergy test results. The collection unit can also collect allergy information through dialogue with the user, for example. The collection unit can also collect allergy information when the user uploads allergy test results, for example. The negotiation unit negotiates with restaurants based on the information collected by the collection unit. For example, the negotiation unit checks for the presence or absence of allergens. For example, the negotiation unit proposes alternative menus. For example, the negotiation unit checks the restaurant's menu and proposes menus that do not contain allergens. The reservation unit performs reservation procedures based on the information obtained by the negotiation unit. For example, the reservation unit confirms reservations simply by the user inputting the desired date, time, and number of people. For example, the reservation unit collaborates with the restaurant's reservation system to perform reservation procedures. For example, the reservation unit can also confirm reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit collaborates with the restaurant's AI agent. The collaboration unit, for example, shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The collaboration unit can also, for example, collaborate with the restaurant's AI agent to perform efficient adjustments. The collaboration unit can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. As a result, the AllerBuddy system according to this embodiment allows individuals with food allergies and their families to have a safe and smooth experience when dining out.

[0067] The data collection unit collects users' allergy information. Specifically, it collects detailed allergy information when users input their allergy test results. For example, users can input allergy test results through a dedicated application. This data includes the type of allergen, the severity of the allergic reaction, and a history of past allergic reactions. The data collection unit can also collect allergy information through interaction with users. For example, it can use a chatbot to ask users questions and collect detailed information about their allergies. Furthermore, it can collect allergy information when users upload their allergy test results. Users can upload images or PDF files of their test results, and the system automatically analyzes the content and registers it in the database. This allows the data collection unit to collect users' allergy information in various ways and accumulate accurate and detailed data. The collected data is stored in a cloud-based database and made accessible to other departments. This allows the data collection unit to efficiently manage users' allergy information and improve the overall performance of the system.

[0068] The Negotiation Department negotiates with restaurants based on information collected by the Data Collection Department. Specifically, it collects detailed information about the restaurant's menu and cooking methods to confirm the presence or absence of allergens. For example, the Negotiation Department directly contacts the restaurant's chefs and managers to confirm the use of allergen-containing ingredients and cooking processes. The Negotiation Department can also propose alternative menus. For example, it proposes menus using alternative ingredients that do not contain specific allergens and consults with the restaurant to determine which menus are feasible. Furthermore, the Negotiation Department reviews the restaurant's menu and proposes allergen-free options. This includes selecting allergen-free items from existing menus and making adjustments to provide them to users. Based on this information, the Negotiation Department can provide users with safe meal options. The Negotiation Department records the results of negotiations with restaurants in a database, making it accessible to other departments. This allows the Negotiation Department to effectively negotiate with restaurants based on users' allergy information and ensure the provision of safe meals.

[0069] The reservation department handles reservation procedures based on information obtained by the negotiation department. Specifically, it confirms reservations simply by the user entering their desired date, time, and number of people. For example, a user enters their desired date, time, and number of people through a dedicated application, and the reservation department automatically connects with the restaurant's reservation system to complete the reservation process. The reservation department can also connect with the restaurant's reservation system to complete the reservation process. This allows users to easily complete reservations without any hassle. Furthermore, the reservation department can also confirm reservations simply by the user entering their desired date, time, and number of people. The reservation department checks the restaurant's reservation status in real time and confirms reservations according to availability. After the reservation process is complete, the reservation department sends a reservation confirmation notification to the user. This includes the reservation date and time, number of people, restaurant information, and details regarding allergy accommodations. This allows the reservation department to provide users with a quick and accurate reservation process, increasing their peace of mind when dining out.

[0070] The Collaboration Department works in conjunction with the restaurant's AI agent. Specifically, it shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. For example, the Collaboration Department sends user allergy information and reservation information to the restaurant's AI agent so that the restaurant can take appropriate action. The Collaboration Department works in conjunction with the restaurant's AI agent to perform efficient adjustments. This includes acquiring menu and allergen information provided by the restaurant's AI agent in real time and making adjustments to provide it to the user. Furthermore, the Collaboration Department can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. This allows the Collaboration Department to work closely with restaurants and provide users with the best possible service. The Collaboration Department records the results of its collaboration with the restaurant's AI agent in a database, making it accessible to other departments. This allows the Collaboration Department to effectively collaborate with restaurants and provide users with a safe and smooth dining experience.

[0071] The data collection unit can collect detailed allergy information from the user through allergy test results and interactions. For example, the data collection unit can collect detailed allergy information when the user inputs allergy test results. The data collection unit can also collect allergy information through interactions with the user. The data collection unit can also collect allergy information when the user uploads allergy test results. This allows for more accurate information to be used when negotiating with restaurants by collecting detailed allergy information from the user through allergy test results and interactions. Detailed allergy information includes, for example, the type of allergen, the severity of the allergic reaction, and the timing of the allergy onset. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's allergy test results into a generating AI and have the generating AI collect detailed allergy information.

[0072] The negotiating department can provide information on the presence or absence of allergens and suggest alternative menu options. For example, the negotiating department can check for the presence or absence of allergens. For example, the negotiating department can check the ingredient list of a restaurant's menu to determine if it contains allergens. For example, the negotiating department can check the cooking methods of a restaurant to determine if it contains allergens. For example, the negotiating department can suggest alternative menu options. For example, the negotiating department can suggest menu options that do not contain allergens. For example, the negotiating department can suggest menu options from which allergens have been removed. This allows users to enjoy their meals safely by providing information on the presence or absence of allergens and suggesting alternative menu options. Checking for the presence or absence of allergens includes, for example, the ingredient list of a restaurant's menu and the cooking methods of a restaurant. Suggesting alternative menu options includes, for example, menu options that do not contain allergens and menu options from which allergens have been removed. Some or all of the above processes performed by the negotiating department may be carried out using AI, for example, or without AI. For example, the negotiating department can input the ingredient list of a restaurant's menu into a generating AI and have the generating AI check for the presence or absence of allergens.

[0073] The reservation system allows users to confirm reservations simply by entering their desired date, time, and number of people. For example, the reservation system can integrate with restaurant reservation systems to handle reservations. The reservation system can also confirm reservations simply by the user entering their desired date, time, and number of people. This simplifies the reservation process by allowing users to confirm reservations simply by entering their desired date, time, and number of people. Reservation confirmation includes, for example, the method of confirmation, the content of the confirmation, and the timing of the confirmation. Some or all of the above-described processes in the reservation system may be performed using AI, or not. For example, the reservation system can generate a reservation confirmation using an AI that generates the confirmation simply by the user entering their desired date, time, and number of people.

[0074] The collaboration unit can work in conjunction with the restaurant's AI agent to perform efficient adjustments. For example, the collaboration unit can share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The collaboration unit can work in conjunction with the restaurant's AI agent to perform efficient adjustments. The collaboration unit can also share information with the restaurant's AI agent to handle reservation procedures and menu adjustments. By working in conjunction with the restaurant's AI agent to perform efficient adjustments, it is expected that the burden on staff will be reduced and the efficiency of reservations will be improved. Efficient adjustments include, for example, the means of adjustment, the content of the adjustment, and the criteria for success of the adjustment. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can share information with the restaurant's AI agent and have an AI generate efficient adjustments.

[0075] The negotiating department can communicate with restaurants using messengers or synthesized speech. For example, the negotiating department can communicate with restaurants using messengers. For example, the negotiating department can communicate with restaurants using text messages. For example, the negotiating department can also communicate with restaurants using voice messages. For example, the negotiating department can communicate with restaurants using synthesized speech. For example, the negotiating department can communicate with restaurants using speech synthesis technology. For example, the negotiating department can communicate with restaurants considering the naturalness of the voice. This makes efficient negotiations possible by communicating with restaurants using messengers or synthesized speech. Messengers include, for example, text messages, voice messages, and video messages. Synthetic speech includes, for example, speech synthesis technology, naturalness of voice, and methods for customizing the voice. Some or all of the above processing in the negotiating department may be performed using, for example, AI, or not using AI. For example, the negotiating department can input messenger or synthesized speech into a generating AI and have the generating AI perform the communication with the restaurant.

[0076] The data collection unit can estimate the user's emotions and adjust the method of collecting allergy information based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can ask questions in a conversational format to provide reassurance. For example, if the user is relaxed, the data collection unit can quickly collect information using concise questions. For example, if the user is in a hurry, the data collection unit can focus on asking questions about the most important information and complete the collection in a short time. This allows for more appropriate information collection by adjusting the method of collecting allergy information according to the user's emotions. Estimation of the user's emotions includes, for example, emotion recognition technology, the type of emotion, and the intensity of the emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the method of collecting allergy information.

[0077] The data collection unit can analyze the user's past allergy reaction history and select the optimal data collection method. For example, the data collection unit can analyze the frequency and severity of allergy reactions the user has experienced in the past and determine the information to focus on collecting. For example, if the user has shown a strong allergic reaction to a particular food in the past, the data collection unit can prioritize collecting information about that food. For example, the data collection unit can predict foods that are likely to cause allergies based on the user's past allergy reaction history and customize the information to be collected. This enables optimal information collection by analyzing the user's past allergy reaction history. The optimal data collection method includes, for example, the means of collection, the timing of collection, and the accuracy of collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past allergy reaction history into a generating AI and have the generating AI select the optimal data collection method.

[0078] The data collection unit can filter allergy information based on the user's current health status and dietary history. For example, the data collection unit can check the user's current health status, identify situations that are likely to trigger allergic reactions, and collect information. For example, the data collection unit can analyze the user's recent dietary history, identify ingredients that may cause allergies, and collect information. The data collection unit can also adjust the scope of allergy information collection based on the user's health status and dietary history, collecting only the necessary information. This allows for the collection of only the necessary information by filtering it based on the user's current health status and dietary history. Filtering includes, for example, filtering conditions, filtering methods, and filtering accuracy. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's health status and dietary history into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of allergy information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting the most important allergy information to provide reassurance. For example, if the user is relaxed, the data collection unit can collect detailed allergy information to provide comprehensive data. For example, if the user is in a hurry, the data collection unit can also focus on quickly collecting only the most important information. This allows for the priority collection of more important information by determining the priority of allergy information according to the user's emotions. The determination of priority includes, for example, priority conditions, means of priority, and accuracy of priority. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of allergy information priorities.

[0080] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting allergy information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information on common allergens in that region. For example, if the user is traveling, the data collection unit can collect information on common allergens in the destination region. The data collection unit can also collect and provide information on region-specific allergens based on the user's geographical location. This allows for the provision of region-specific allergen information by prioritizing the collection of highly relevant information based on the user's geographical location. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant information when collecting allergy information. For example, the data collection unit can analyze the food content shared by the user on social media and identify ingredients that may cause allergies. For example, the data collection unit can collect information about allergic reactions mentioned by the user on social media and update the allergy information. For example, the data collection unit can identify the user's interests and concerns regarding allergies based on their social media activity and customize the information it collects. This allows the collection of relevant allergy information by analyzing the user's social media activity. Social media activity includes, for example, posts, the number of likes, and the number of followers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant information.

[0082] The negotiation unit can estimate the user's emotions and adjust the negotiation's expression based on those emotions. For example, if the user is feeling anxious, the negotiation unit can use a polite and reassuring expression. If the user is relaxed, the negotiation unit can use a casual and friendly expression. If the user is in a hurry, the negotiation unit can also use a quick and concise expression. By adjusting the negotiation's expression according to the user's emotions, more effective negotiations become possible. The negotiation's expression includes, for example, word choice, tone of expression, and form of expression. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI, for example, or not using AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the negotiation's expression.

[0083] The negotiating department can adjust the level of detail in negotiations based on the importance of the allergen. For example, in negotiations concerning serious allergens, the negotiating department can provide detailed information and conduct negotiations carefully. For example, in negotiations concerning mild allergens, the negotiating department can provide concise information and conduct negotiations quickly. The negotiating department can also adjust the level of detail in negotiations according to the importance of the allergen to provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in negotiations based on the importance of the allergen. The importance of an allergen includes, for example, the risk of the allergen, the frequency of the allergen, and the effects of the allergen. Some or all of the above processes in the negotiating department may be performed using AI, for example, or not using AI. For example, the negotiating department can input the importance of the allergen into a generating AI and have the generating AI perform the adjustment of the level of detail in negotiations.

[0084] The negotiation department can apply different negotiation algorithms depending on the restaurant category during negotiations. For example, the negotiation department might apply a polite and detailed negotiation algorithm to a high-end restaurant. For example, it might apply a quick and concise negotiation algorithm to a fast-food restaurant. For example, it might apply a casual and friendly negotiation algorithm to a cafe or bar. By applying different negotiation algorithms depending on the restaurant category, more effective negotiations become possible. Negotiation algorithms include, for example, negotiation strategies, negotiation methods, and negotiation success criteria. Some or all of the above processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input the restaurant category into a generating AI and have the generating AI perform the application of negotiation algorithms.

[0085] The negotiation unit can estimate the user's emotions and adjust the length of the negotiation based on the estimated emotions. For example, if the user is feeling anxious, the negotiation unit will conduct the negotiation carefully and for an extended period. If the user is relaxed, the negotiation unit can conduct the negotiation for an appropriate length. If the user is in a hurry, the negotiation unit can also complete the negotiation quickly. By adjusting the length of the negotiation according to the user's emotions, more effective negotiations become possible. The length of the negotiation includes, for example, the duration of the negotiation, the stage of the negotiation, and the progress of the negotiation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI, for example, or not using AI. For example, the negotiation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the negotiation.

[0086] The negotiating department can determine negotiation priorities based on the timing of menu submissions from restaurants. For example, the negotiating department can negotiate immediately before a restaurant introduces a new menu and confirm allergen information. For example, the negotiating department can prioritize negotiations when a restaurant offers a seasonal menu. For example, the negotiating department can prioritize confirming allergen information when a restaurant updates its menu. This allows for negotiations to be conducted at the appropriate time by determining negotiation priorities based on the timing of menu submissions from restaurants. The timing of menu submissions includes, for example, the menu submission deadline, the frequency of menu updates, and the menu submission method. Some or all of the above processes in the negotiating department may be performed using AI, or not. For example, the negotiating department can input the timing of menu submissions from restaurants into a generating AI and have the generating AI determine the negotiation priorities.

[0087] The negotiation unit can adjust the order of negotiations based on the relevance of the restaurants during the negotiation process. For example, the negotiation unit can prioritize negotiations with restaurants that the user frequently uses. For example, the negotiation unit can quickly negotiate with restaurants that the user is using for the first time. The negotiation unit can also adjust the order of negotiations based on the popularity and ratings of the restaurants. This allows for efficient negotiations by adjusting the order of negotiations based on the relevance of the restaurants. The relevance of restaurants includes, for example, the restaurant's category, location, and reputation. Some or all of the above processing in the negotiation unit may be performed using, for example, AI, or not using AI. For example, the negotiation unit can input the relevance of restaurants into a generating AI and have the generating AI perform the adjustment of the negotiation order.

[0088] The reservation unit can estimate the user's emotions and adjust the reservation confirmation method based on the estimated emotions. For example, if the user is feeling anxious, the reservation unit can perform the confirmation procedure carefully to provide reassurance. For example, if the user is relaxed, the reservation unit can perform a concise confirmation procedure. For example, if the user is in a hurry, the reservation unit can complete the confirmation procedure quickly. In this way, by adjusting the reservation confirmation method according to the user's emotions, a more appropriate confirmation procedure becomes possible. The reservation confirmation method includes, for example, the means of confirmation, the timing of confirmation, and the content of confirmation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation unit may be performed using AI, for example, or not using AI. For example, the reservation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reservation confirmation method.

[0089] The reservation unit can analyze the user's past reservation history to select the optimal reservation method at the time of reservation. For example, the reservation unit can suggest the optimal reservation method based on the reservation method the user has used in the past. For example, the reservation unit can suggest the optimal reservation time to avoid congestion based on the user's past reservation history. For example, the reservation unit can analyze the user's past reservation history to select the most efficient reservation method. In this way, the optimal reservation method can be selected by analyzing the user's past reservation history. The optimal reservation method includes, for example, the reservation method, the timing of the reservation, and the accuracy of the reservation. Some or all of the above processes in the reservation unit may be performed using AI, for example, or without AI. For example, the reservation unit can input the user's past reservation history into a generating AI and have the generating AI select the optimal reservation method.

[0090] The reservation unit can customize the reservation method based on the user's current schedule at the time of reservation. For example, the reservation unit can refer to the user's calendar information and suggest the optimal reservation time. For example, the reservation unit can customize the reservation method to suit the user's schedule. For example, the reservation unit can also suggest the optimal reservation method based on the user's current schedule. This makes it possible to make more appropriate reservations by customizing the reservation method based on the user's current schedule. Reservation methods include, for example, online reservations, telephone reservations, and direct reservations. Some or all of the above processing in the reservation unit may be performed using, for example, AI, or not using AI. For example, the reservation unit can input the user's calendar information into a generating AI and have the generating AI perform the customization of the reservation method.

[0091] The reservation system can estimate the user's emotions and determine reservation priorities based on those emotions. For example, if the user is feeling anxious, the reservation system will prioritize important reservations. If the user is relaxed, the reservation system can prioritize reservations with normal priority. If the user is in a hurry, the reservation system can also quickly prioritize important reservations. This allows for prioritizing important reservations based on the user's emotions. Reservation priorities may include, for example, the importance of the reservation, the urgency of the reservation, and the impact of the reservation. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reservation system may be performed using AI or not. For example, the reservation system can input user emotion data into a generative AI and have the generative AI determine reservation priorities.

[0092] The reservation department can select the optimal reservation method when a reservation is made, taking into account the user's geographical location information. For example, if the user is in a specific region, the reservation department can prioritize suggesting restaurants available in that region. For example, if the user is traveling, the reservation department can suggest the optimal reservation method for the region the user is traveling to. The reservation department can also select the optimal reservation method based on the user's geographical location information. This allows for appropriate reservations tailored to the region by selecting the optimal reservation method while considering the user's geographical location information. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the reservation department may be performed using, for example, AI, or not using AI. For example, the reservation department can input the user's geographical location information into a generating AI and have the generating AI select the optimal reservation method.

[0093] The reservation department can analyze a user's social media activity and suggest reservation methods when a reservation is made. For example, the reservation department can suggest the optimal reservation method based on restaurants mentioned by the user on social media. For example, the reservation department can analyze a user's social media activity and suggest restaurants of interest. For example, the reservation department can customize reservation methods based on a user's social media activity. This allows the department to suggest reservation methods for restaurants of interest by analyzing the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reservation department may be performed using AI, for example, or without AI. For example, the reservation department can input a user's social media activity into a generating AI and have the generating AI suggest reservation methods.

[0094] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is feeling anxious, the interaction unit can interact in a polite and reassuring way. If the user is relaxed, the interaction unit can interact in a casual and friendly way. If the user is in a hurry, the interaction unit can interact in a quick and concise way. By adjusting the interaction method according to the user's emotions, more effective interaction becomes possible. The interaction method includes, for example, the means of interaction, the timing of interaction, and the content of interaction. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI adjust the interaction method.

[0095] The integration unit can select the optimal integration method by analyzing the restaurant's past integration history during integration. For example, the integration unit can select the optimal integration method based on successful integration cases in the restaurant's past. For example, the integration unit can identify points where problems are likely to occur from the restaurant's past integration history and adjust the integration method accordingly. For example, the integration unit can also select the most efficient integration method by analyzing the restaurant's past integration history. In this way, the optimal integration method can be selected by analyzing the restaurant's past integration history. The optimal integration method includes, for example, the means of integration, the timing of integration, and the accuracy of integration. Some or all of the above processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the restaurant's past integration history into a generating AI and have the generating AI select the optimal integration method.

[0096] The integration unit can customize the means of integration based on the current status of the restaurant during integration. For example, if the restaurant is busy, the integration unit can select a quick and simple integration method. For example, if the restaurant is not busy, the integration unit can select a more detailed integration method. The integration unit can also customize the optimal means of integration based on the current status of the restaurant. This allows for more appropriate integration by customizing the means of integration based on the current status of the restaurant. Means of integration include, for example, online integration, telephone integration, and direct integration. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the current status of the restaurant into a generating AI and have the generating AI perform the customization of the means of integration.

[0097] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is feeling anxious, the collaboration unit will prioritize important collaborations. For example, if the user is relaxed, the collaboration unit can perform collaborations with normal priority. For example, if the user is in a hurry, the collaboration unit can also perform important collaborations quickly. In this way, by determining the priority of collaborations according to the user's emotions, important collaborations can be prioritized. The priority of collaborations includes, for example, the importance of the collaboration, the urgency of the collaboration, and the impact of the collaboration. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI perform the determination of collaboration priorities.

[0098] The integration unit can select the optimal integration method when integrating, taking into account the geographical location information of restaurants. For example, if a restaurant is located in a specific region, the integration unit will select an integration method considering the characteristics of that region. For example, if a restaurant is located in a travel destination, the integration unit can select an integration method considering the characteristics of that destination. The integration unit can also select the optimal integration method based on the geographical location information of restaurants. This enables appropriate integration tailored to the region by selecting the optimal integration method while considering the geographical location information of restaurants. Geographical location information includes, for example, GPS information, location accuracy, and location update frequency. Some or all of the above processing in the integration unit may be performed using, for example, AI, or without AI. For example, the integration unit can input the geographical location information of restaurants into a generating AI and have the generating AI select the optimal integration method.

[0099] The collaboration unit can analyze a restaurant's social media activity and propose collaboration methods during the collaboration process. For example, the collaboration unit can propose the most suitable collaboration method based on information the restaurant has posted on social media. For example, the collaboration unit can analyze a restaurant's social media activity and propose collaboration methods of interest. For example, the collaboration unit can customize collaboration methods based on a restaurant's social media activity. This allows the optimal collaboration method to be proposed by analyzing a restaurant's social media activity. Social media activity includes, for example, post content, number of likes, and number of followers. Some or all of the above processing in the collaboration unit may be performed using, for example, AI, or not using AI. For example, the collaboration unit can input a restaurant's social media activity into a generating AI and have the generating AI propose collaboration methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The AllerBuddy system comprises a collection unit that collects user allergy information, a negotiation unit that negotiates with restaurants, a reservation unit that handles reservation procedures, and a collaboration unit that works with AI agents on the restaurant side. The collection unit can collect detailed allergy information when users input their allergy test results. For example, if a user uploads their allergy test results, the collection unit can collect allergy information based on that information. The negotiation unit negotiates with restaurants based on the information collected by the collection unit. For example, the negotiation unit can check the ingredient list of the restaurant's menu to confirm the presence or absence of allergens. The reservation unit handles reservation procedures based on the information obtained by the negotiation unit. For example, users can confirm reservations simply by entering their desired date, time, and number of people. The collaboration unit works with AI agents on the restaurant side to make efficient adjustments. For example, it can share information with the restaurant's AI agent to adjust reservation procedures and menus. As a result, the AllerBuddy system enables individuals with food allergies and their families to have a safe and smooth dining experience when eating out.

[0102] The AllerBuddy system can estimate the user's emotions and adjust the method of collecting allergy information based on those estimated emotions. For example, if the user is feeling anxious, the collection unit can ask questions in a conversational format to provide reassurance. If the user is relaxed, the collection unit can quickly collect information using concise questions. If the user is in a hurry, the collection unit can focus on asking questions that provide the most important information and complete the collection in a short time. This allows for more appropriate information collection by adjusting the method of collecting allergy information according to the user's emotions. Emotion estimation includes emotion recognition technology, the type of emotion, and the intensity of the emotion. Some or all of the above processing in the collection unit may be performed using AI or not.

[0103] The AllerBuddy system can analyze a user's past allergy reaction history and select the optimal data collection method. For example, it can analyze the frequency and severity of allergy reactions the user has experienced in the past and determine the information to focus on collecting. If a user has shown a strong allergic reaction to a particular food in the past, information about that food can be prioritized for collection. Based on the user's past allergy reaction history, it can also predict foods that are likely to cause allergies and customize the information to be collected. This enables optimal information collection by analyzing the user's past allergy reaction history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0104] The AllerBuddy system can filter allergy information based on the user's current health status and dietary history. For example, it can check the user's current health status and identify situations that are likely to trigger allergic reactions, thereby collecting relevant information. It can also analyze the user's recent dietary history and identify ingredients that may cause allergies, thereby collecting relevant information. Based on the user's health status and dietary history, the system can adjust the scope of allergy information collection, collecting only the necessary information. This allows for the collection of only the necessary information by filtering it based on the user's current health status and dietary history. Some or all of the above processing in the collection unit may be performed using AI, or it may be performed without using AI.

[0105] The AllerBuddy system can estimate the user's emotions and prioritize the allergy information to collect based on those emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting the most important allergy information to provide reassurance. If the user is relaxed, the collection unit can collect detailed allergy information to provide comprehensive data. If the user is in a hurry, the collection unit can also focus on quickly collecting only the most important information. This allows for the collection of more important information by prioritizing the allergy information collected according to the user's emotions. Some or all of the above processing in the collection unit may or may not be performed using AI.

[0106] The AllerBuddy system can prioritize the collection of highly relevant information based on the user's geographical location. For example, if the user is in a specific region, it can prioritize the collection of information about common allergens in that region. If the user is traveling, it can collect information about common allergens in the region they are visiting. It can also collect and provide information about region-specific allergens based on the user's geographical location. This allows the system to provide information about region-specific allergens by prioritizing the collection of highly relevant information based on the user's geographical location. Some or all of the processing described above in the collection unit may be performed using AI or not.

[0107] The AllerBuddy system can analyze a user's social media activity and collect relevant information. For example, it can analyze the meals a user shares on social media and identify ingredients that may cause allergies. It can also collect information about allergic reactions mentioned by the user on social media and update allergy information. Based on the user's social media activity, it can also identify their interests and concerns regarding allergies and customize the information it collects. This allows the system to collect relevant allergy information by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI or not.

[0108] The AllerBuddy system can estimate the user's emotions and adjust its negotiation style based on those emotions. For example, if the user is feeling anxious, the negotiator can use a polite and reassuring style of communication. If the user is relaxed, the negotiator can use a casual and friendly style of communication. If the user is in a hurry, the negotiator can use a quick and concise style of communication. By adjusting the negotiation style of communication according to the user's emotions, more effective negotiations become possible. Some or all of the above processing in the negotiator may be performed using AI or not.

[0109] The AllerBuddy system can adjust the level of detail in negotiations based on the severity of the allergen. For example, in negotiations concerning a major allergen, the negotiating unit can provide detailed information and conduct negotiations carefully. In negotiations concerning a mild allergen, the negotiating unit can provide concise information and conduct negotiations quickly. Depending on the severity of the allergen, the negotiating unit can also adjust the level of detail in negotiations and provide appropriate information. This allows for the provision of appropriate information by adjusting the level of detail in negotiations based on the severity of the allergen. Some or all of the above processes in the negotiating unit may be performed using AI or not.

[0110] The AllerBuddy system can apply different negotiation algorithms depending on the category of the restaurant during negotiations. For example, in a high-end restaurant, the negotiation team can apply a polite and detailed negotiation algorithm. In a fast-food restaurant, the negotiation team can apply a quick and concise negotiation algorithm. In a cafe or bar, the negotiation team can also apply a casual and friendly negotiation algorithm. This allows for more effective negotiations by applying different negotiation algorithms depending on the category of the restaurant. Some or all of the above processes in the negotiation team may be performed using AI or not.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects the user's allergy information. For example, detailed allergy information is collected when the user enters their allergy test results. Allergy information can also be collected through interaction with the user. Furthermore, allergy information can be collected when the user uploads their allergy test results. Step 2: The negotiation team negotiates with restaurants based on the information collected by the data collection team. For example, they may check for allergens or suggest alternative menus. They can also review the restaurant's menu and suggest allergen-free options. Step 3: The reservation department processes reservations based on the information obtained by the negotiation department. For example, it can confirm reservations simply by the user entering their desired date, time, and number of people. It can also integrate with restaurant reservation systems to process reservations. Step 4: The integration unit collaborates with the restaurant's AI agent. For example, it shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. It can also collaborate to facilitate more efficient coordination.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, negotiation unit, reservation unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14, and collects detailed allergy information when the user inputs allergy test results. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12, and negotiates with restaurants based on the collected information. The reservation unit is implemented by the control unit 46A of the smart device 14, and confirms reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, and shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, negotiation unit, reservation unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214, and collects detailed allergy information when the user inputs allergy test results. The negotiation unit is implemented by the identification processing unit 290 of the data processing unit 12, and negotiates with restaurants based on the collected information. The reservation unit is implemented by the control unit 46A of the smart glasses 214, and confirms reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit is implemented by the identification processing unit 290 of the data processing unit 12, and shares information with the AI ​​agent on the restaurant side to handle reservation procedures and menu adjustments. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, negotiation unit, reservation unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314, and collects detailed allergy information when the user inputs allergy test results. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12, and negotiates with restaurants based on the collected information. The reservation unit is implemented by the control unit 46A of the headset terminal 314, and confirms reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12, and shares information with the restaurant's AI agent to handle reservation procedures and menu adjustments. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the data collection unit, negotiation unit, reservation unit, and collaboration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects detailed allergy information when the user inputs allergy test results. The negotiation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and negotiates with restaurants based on the collected information. The reservation unit is implemented by, for example, the control unit 46A of the robot 414 and confirms reservations simply by the user inputting the desired date, time, and number of people. The collaboration unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and shares information with the AI ​​agent on the restaurant side to handle reservation procedures and menu adjustments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that collects user allergy information, Based on the information collected by the aforementioned collection unit, the negotiation unit negotiates with restaurants, The reservation department carries out reservation procedures based on the information obtained by the aforementioned negotiation department, It includes a collaboration department that works in conjunction with the restaurant's AI agent. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect detailed allergy information from users through their allergy test results and interactions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned negotiating body said, We will check for allergens and suggest alternative menu options. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reservation section is, Reservations can be confirmed simply by the user entering their desired date, time, and number of people. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, We work in conjunction with the restaurant's AI agent to make efficient adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned negotiating body said, Communicate with restaurants using messengers and synthesized voices. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting allergy information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past allergy reaction history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting allergy information, filtering is performed based on the user's current health status and dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the user's emotions and prioritizes the allergy information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting allergy information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting allergy information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned negotiating body said, During negotiations, adjust the level of detail based on the importance of the allergen. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned negotiating body said, During negotiations, different negotiation algorithms are applied depending on the restaurant category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the length of the negotiation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned negotiating body said, During negotiations, priority will be determined based on when the restaurant submits its menu. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned negotiating body said, During negotiations, the order of negotiations will be adjusted based on the relevance of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reservation section is, The system estimates the user's emotions and adjusts the reservation confirmation method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reservation section is, When a reservation is made, the system analyzes the user's past reservation history to select the most suitable reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reservation section is, When making a reservation, customize the reservation method based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reservation section is, The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reservation section is, When making a reservation, the system will select the most suitable reservation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reservation section is, When making a reservation, we analyze the user's social media activity and suggest a reservation method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, During the integration process, the past integration history of restaurants is analyzed to select the most suitable integration method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, During the integration process, the integration method will be customized based on the current situation of the restaurant. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When integrating, the optimal integration method will be selected considering the geographical location information of the restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, When collaborating, we analyze the social media activity of restaurants and propose methods for collaboration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user allergy information, Based on the information collected by the aforementioned collection unit, the negotiation unit negotiates with restaurants, The reservation department carries out reservation procedures based on the information obtained by the aforementioned negotiation department, It includes a collaboration unit that works in conjunction with the restaurant's AI agent. A system characterized by the following features.

2. The aforementioned collection unit is We collect detailed allergy information from users through their allergy test results and interactions. The system according to feature 1.

3. The aforementioned negotiating body said, We will check for allergens and suggest alternative menu options. The system according to feature 1.

4. The aforementioned reservation section is, Reservations can be confirmed simply by the user entering their desired date, time, and number of people. The system according to feature 1.

5. The aforementioned linkage unit is, We collaborate with AI agents on the restaurant side to perform efficient coordination. The system according to feature 1.

6. The aforementioned negotiating body said, Communicate with restaurants using messengers and synthesized voices. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting allergy information based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past allergy reaction history and select the optimal data collection method. The system according to feature 1.

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