System

Abstract

An object of a system according to an embodiment is to propose an optimal store on the basis of hobbies and preferences of a traveler and provide vacant seat information and the shortest route.SOLUTION: A system according to an embodiment includes a store proposal unit, a vacant seat prediction unit, an information provision unit, a route guide unit, and a reservation guide unit. The shop proposal unit proposes a shop on the basis of the user's hobbies and preferences. The vacant seat prediction unit provides vacant seat prediction information of the store proposed by the store proposal unit. The information providing unit provides information according to the area, the cooking genre, the budget, and the customer segment based on the information provided by the vacant seat prediction unit. The route guidance unit guides the shortest route based on the information provided by the information providing unit. The reservation guide part guides an online reservation site of the store guided by the route guide part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to suggest optimal stores based on a traveler's tastes and preferences, and to provide information on available seats and the shortest routes.

[0005] The system according to the embodiment aims to suggest the most suitable store based on the traveler's tastes and preferences, and to provide information on available seats and the shortest route. [Means for solving the problem]

[0006] The system according to the embodiment includes a store suggestion unit, a vacant seat prediction unit, an information providing unit, a route guidance unit, and a reservation guidance unit. The store suggestion unit suggests stores based on the user's hobbies and preferences. The vacant seat prediction unit provides vacant seat prediction information for stores suggested by the store suggestion unit. The information providing unit provides information according to area, cuisine genre, budget, and customer demographic based on the information provided by the vacant seat prediction unit. The route guidance unit provides guidance on the shortest route based on the information provided by the information providing unit. The reservation guidance unit provides guidance on online reservation sites for stores recommended by the route guidance unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable store based on the traveler's tastes and preferences, and provide information on available seats and the shortest route. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The recommendation system according to the embodiment of the present invention utilizes a generative AI to suggest local restaurants that suit the tastes and preferences of Italian travelers, and also provides information on predicted seat availability. As a result, the recommendation system can provide Italian travelers with information on the best restaurants, making their trip more comfortable.

[0029] The recommendation system according to the embodiment includes a restaurant recommendation unit, a vacancy prediction unit, an information provision unit, a route guidance unit, and a reservation guidance unit. The restaurant recommendation unit suggests restaurants based on the user's preferences. For example, if a user inputs information such as "I like authentic Italian food, I'm looking for a restaurant with a moderate budget that's suitable for families," the generation AI analyzes this information and lists relevant restaurants, trattorias, and pizzerias. The vacancy prediction unit provides vacancy prediction information for restaurants suggested by the restaurant recommendation unit. For example, based on the past and current reservation status of a specific restaurant, the generation AI predicts that "this restaurant is relatively empty on weekday evenings but gets crowded on weekends." The information provision unit provides information tailored to the area, cuisine genre, budget, and customer demographic based on the information provided by the vacancy prediction unit. For example, if a user inputs criteria such as "I'm looking for a restaurant in Rome that serves delicious pizza, has a budget of under 20 euros, and has a casual atmosphere," the generation AI lists restaurants that match the criteria and provides detailed information. The route guidance unit guides the user along the shortest route based on the information provided by the information provision unit. For example, when a user inputs a request such as "I want to go to the trattoria closest to my current location," the generation AI analyzes map data and displays the shortest route. The reservation guidance unit guides the user to the online reservation site of the restaurant guided by the route guidance unit. For example, when a user inputs a request such as "I want to make a reservation at this restaurant," the generation AI displays a link to the restaurant's reservation site, allowing the user to easily make a reservation. As a result, the proposal system according to the embodiment can make travel more comfortable by suggesting the most suitable restaurant based on the user's preferences and providing seat vacancy forecast information, the shortest route, and online reservation guidance.

[0030] The restaurant suggestion unit can analyze a user's past reviews and ratings, track changes in preferences, and evolve the content of suggestions. For example, the restaurant suggestion unit analyzes reviews and ratings posted by the user in the past and tracks changes in preferences. For example, it extracts characteristics of restaurants that have been highly rated in the past and suggests restaurants that match the user's current preferences. The restaurant suggestion unit also analyzes the content of the user's past reviews using natural language processing technology to understand changes in preferences. For example, it tracks changes in ratings for specific cuisine genres or atmospheres. The restaurant suggestion unit also models changes in preferences based on evaluation data of restaurants the user has visited in the past, predicts future preferences, and evolves the content of suggestions. For example, it takes into account changes in preferences with the seasons. In this way, more accurate restaurant suggestions can be made by tracking changes in the user's preferences.

[0031] The store suggestion unit can analyze a user's SNS posts and reflect the user's latest hobbies and preferences in real time. The store suggestion unit, for example, analyzes a user's SNS posts and reflects the user's latest hobbies and preferences in real time. For example, it can identify a new food genre that the user has become interested in from the content of recent posts. The store suggestion unit also uses SNS image analysis technology to extract preferences from photos of food posted by the user and reflects them in the suggestions. For example, if there are many photos of a particular food or drink, it can suggest stores in that genre. The store suggestion unit also analyzes the user's check-in information on SNS, understands trends in visited stores, and reflects the user's latest hobbies and preferences. For example, it can update the suggestions based on frequently visited areas and store characteristics. This allows the user's latest hobbies and preferences to be reflected in real time, enabling more appropriate store suggestions.

[0032] The vacancy prediction unit can analyze the store's past sales data and predict vacancy based on specific events or seasons. The vacancy prediction unit, for example, analyzes the store's past sales data and predicts vacancy based on specific events or seasons. For example, it predicts vacancy on specific days such as Christmas or Valentine's Day. The vacancy prediction unit also predicts vacancy for each season based on past sales data. For example, it predicts vacancy during summer vacation and winter vacation. The vacancy prediction unit also analyzes past sales data in detail to predict vacancy based on specific events or seasons. For example, it predicts vacancy during periods when local festivals or events are held. In this way, by predicting vacancy based on specific events or seasons, users can plan the optimal time to visit.

[0033] The vacant seat prediction unit can analyze traffic conditions and weather forecasts around the store and reflect them in the vacant seat prediction. The vacant seat prediction unit, for example, analyzes traffic conditions around the store and reflects them in the vacant seat prediction. For example, it predicts the possibility of an increase in vacant seats when there is traffic congestion. The vacant seat prediction unit also makes vacant seat predictions based on weather forecasts. For example, it makes vacant seat predictions taking into account the tendency for the number of customers to decrease when it rains. The vacant seat prediction unit also builds a system that analyzes traffic conditions and weather forecasts in real time and reflects them in the vacant seat prediction. For example, it updates the vacant seat prediction when a traffic accident or bad weather occurs. This makes it possible to make vacant seat predictions that take traffic conditions and weather forecasts into account, thereby enabling more accurate predictions.

[0034] The information providing unit can analyze the user's past visit history, identify the user's favorite areas and cuisine genres, and customize the suggestions. For example, the information providing unit analyzes the user's past visit history, identify the user's favorite areas and cuisine genres, and customize the suggestions. For example, the suggestions are adjusted based on frequently visited areas and favorite cuisine genres. The information providing unit also models the user's preferences based on the past visit history and customizes the suggestions. For example, the suggestions are updated based on the user's evaluation of specific cuisine genres or areas. The information providing unit also analyzes the user's visit history data and builds a system that identifies the user's favorite areas and cuisine genres. For example, the information providing unit suggests optimal restaurants based on the user's past visit history. This makes it possible to provide more appropriate restaurant information based on the user's past visit history, resulting in highly satisfying suggestions.

[0035] The information providing unit can provide discount information and benefits according to the user's budget. The information providing unit, for example, builds a system that provides discount information and benefits according to the user's budget. For example, it suggests discount coupons and benefits that can be used within a specific budget. The information providing unit also analyzes the user's budget data and provides optimal discount information and benefits. For example, it lists stores that offer benefits according to the budget. The information providing unit also develops a system that provides discount information and benefits in real time based on the user's budget. For example, it instantly displays discount information for stores that fit the budget. This makes it possible to provide discount information and benefits according to the user's budget, making it possible to make cost-effective suggestions.

[0036] The route guidance unit can analyze the user's movement history and propose the most efficient route. For example, the route guidance unit analyzes the user's movement history and proposes the most efficient route. For example, it identifies the shortest route based on past movement data. The route guidance unit also models the user's movement patterns based on the movement history and proposes the optimal route. For example, it takes into account frequently used means of transportation and routes. The route guidance unit also analyzes the user's movement history data and builds a system that proposes the most efficient route. For example, it calculates the optimal route based on past movement history. In this way, by proposing the most efficient route based on the user's movement history, travel time is shortened and convenience is improved.

[0037] The route guidance unit can provide the optimal route according to the user's means of transportation. For example, the route guidance unit builds a system that provides the optimal route according to the user's means of transportation. For example, it calculates the optimal route for each of walking, cycling, and car. The route guidance unit also develops an algorithm that suggests the optimal route based on the means of transportation. For example, if walking, it gives priority to pedestrian-only roads. The route guidance unit also analyzes the user's means of transportation data and provides the optimal route. For example, if driving, it suggests a route that avoids traffic congestion. In this way, the convenience of travel is improved by providing the optimal route according to the user's means of transportation.

[0038] The reservation information unit can analyze the user's past reservation history and prioritize the most frequently used reservation site. The reservation information unit, for example, analyzes the user's past reservation history and prioritizes the most frequently used reservation site. For example, it displays links to frequently used reservation sites. The reservation information unit also models the user's preferences based on the past reservation history and suggests the most suitable reservation site. For example, it updates the suggestions based on the user's evaluation of a specific reservation site. The reservation information unit also analyzes the user's reservation history data and builds a system to identify the most frequently used reservation site. For example, it suggests the most suitable reservation site based on the past reservation history. This improves convenience by guiding the user to the most frequently used reservation site based on the user's past reservation history.

[0039] The reservation information unit can provide special offers and discount information based on the user's reservation history. The reservation information unit, for example, builds a system that provides special offers and discount information based on the user's reservation history. For example, it suggests discount coupons and special offers that can be used on a specific reservation site. The reservation information unit also analyzes the user's reservation history data and provides optimal special offers and discount information. For example, it lists stores that offer special offers based on past reservation history. The reservation information unit also develops a system that provides special offers and discount information in real time based on the user's reservation history. For example, it instantly displays special offer information for stores that match the reservation history. In this way, user satisfaction is improved by providing special offers and discount information based on the user's reservation history.

[0040] The reservation information unit can apply the information from the online reservation site to other services. For example, the reservation information unit builds a system that applies the information from the online reservation site to other services. For example, it provides links to hotel and tour reservation sites. The reservation information unit also collects data from other services and provides information about online reservation sites. For example, it provides an integrated list of reservation sites for each service. The reservation information unit also develops an algorithm for applying the information from the online reservation site to other services. For example, it makes suggestions that take into account the characteristics of each service and user preferences. In this way, the information from the online reservation site can be applied to other services, improving user convenience.

[0041] The reservation information unit can incorporate information from online reservation sites into the user's travel schedule and suggest the optimal reservation timing. The reservation information unit, for example, incorporates information from online reservation sites into the user's travel schedule and suggests the optimal reservation timing. For example, it adjusts the reservation timing for tourist attractions and restaurants during the trip. The reservation information unit also builds a system that provides information from online reservation sites based on the user's travel schedule. For example, it reflects the reservation timing for specific tourist attractions and events in real time. The reservation information unit also incorporates information from online reservation sites into the travel schedule, allowing the user to plan the optimal reservation timing. For example, it makes suggestions to make reservations to avoid crowded times. This improves the convenience of reservations by suggesting the optimal reservation timing based on the user's travel schedule.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The recommendation system can also make restaurant recommendations that take into account the user's health condition. For example, if a user has a specific allergy, it will prioritize restaurants that offer menus that do not contain that allergen. For health-conscious users, it can also suggest restaurants that offer low-calorie or organic menus. Furthermore, it can analyze the user's health data and, for example, if the user needs to manage their blood sugar or blood pressure, it can list restaurants that offer menus that are suitable for that purpose. This makes it possible to recommend the most suitable restaurant based on the user's health condition.

[0044] The recommendation system can also make restaurant suggestions that take into account the user's cultural background. For example, for a user with specific religious restrictions, it can prioritize suggestions of restaurants that offer menus that meet those restrictions. Also, for a user who prefers cuisine from a specific country or region, it can suggest restaurants that specialize in that cuisine. Furthermore, it can also list restaurants that offer vegetarian or vegan menus, for example, based on the user's cultural background. This makes it possible to suggest optimal restaurants that match the user's cultural background.

[0045] The recommendation system can also suggest restaurants that match the user's travel purpose. For example, it can suggest restaurants with a quiet environment where it is easy to work to a business traveler. It can also suggest restaurants that serve local specialties to a tourist. It can also provide a list of restaurants that are safe for families traveling with children. This makes it possible to suggest the best restaurants that match the user's travel purpose.

[0046] The recommendation system can also analyze a user's past travel history and make restaurant suggestions to encourage repeat visits. For example, it can re-suggest restaurants that users have previously rated highly. It can also provide information on new menus and renovations at restaurants that users have previously visited. Furthermore, it can also create a list of restaurants that the user has not yet visited but is likely to like, based on the user's past travel history. This makes it possible to make optimal restaurant suggestions based on the user's past travel history.

[0047] The recommendation system can also suggest stores based on the user's travel activities. For example, for a user touring tourist spots, it can suggest restaurants with good reviews in the area. For a user enjoying shopping, it can also suggest popular cafes and restaurants in the shopping area. Furthermore, it can also suggest stores where users can relax in nature based on the type of activity, for example, for a user who enjoys hiking or cycling. This makes it possible to suggest the most suitable stores based on the user's activity.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The restaurant suggestion unit suggests restaurants based on the user's tastes. For example, if a user enters information such as "I like authentic Italian food, I'm on a moderate budget, and I'm looking for a restaurant that's suitable for families," the AI ​​analyzes this information and lists relevant restaurants, trattorias, and pizzerias. Step 2: The seat vacancy prediction unit provides seat vacancy prediction information for the restaurants proposed by the restaurant proposal unit. For example, based on the past and current reservation status of a specific restaurant, the generation AI makes a prediction such as, "This restaurant is relatively empty on weekday evenings, but gets crowded on weekends." Step 3: The information provider uses the information provided by the seat availability forecaster to provide information tailored to the area, cuisine, budget, and customer demographic. For example, if a user enters criteria such as "I'm looking for a restaurant in Rome that serves delicious pizza, has a budget of under 20 euros, and has a casual atmosphere," the AI ​​generator will list restaurants that fit the criteria and provide detailed information. Step 4: The route guidance unit guides users to the shortest route based on the information provided by the information provider. For example, if a user inputs a request such as "I want to go to the trattoria closest to my current location," the AI ​​generator analyzes map data and displays the shortest route. Step 5: The reservation guidance unit guides the user to the online reservation site of the restaurant indicated by the route guidance unit. For example, if a user inputs a request such as "I would like to make a reservation at this restaurant," the generation AI will display a link to the restaurant's reservation site, allowing the user to easily make a reservation.

[0050] (Example 2) The recommendation system according to the embodiment of the present invention utilizes a generative AI to suggest local restaurants that suit the tastes and preferences of Italian travelers, and also provides information on predicted seat availability. As a result, the recommendation system can provide Italian travelers with information on the best restaurants, making their trip more comfortable.

[0051] The recommendation system according to the embodiment includes a restaurant recommendation unit, a vacancy prediction unit, an information provision unit, a route guidance unit, and a reservation guidance unit. The restaurant recommendation unit suggests restaurants based on the user's preferences. For example, if a user inputs information such as "I like authentic Italian food, I'm looking for a restaurant with a moderate budget that's suitable for families," the generation AI analyzes this information and lists relevant restaurants, trattorias, and pizzerias. The vacancy prediction unit provides vacancy prediction information for restaurants suggested by the restaurant recommendation unit. For example, based on the past and current reservation status of a specific restaurant, the generation AI predicts that "this restaurant is relatively empty on weekday evenings but gets crowded on weekends." The information provision unit provides information tailored to the area, cuisine genre, budget, and customer demographic based on the information provided by the vacancy prediction unit. For example, if a user inputs criteria such as "I'm looking for a restaurant in Rome that serves delicious pizza, has a budget of under 20 euros, and has a casual atmosphere," the generation AI lists restaurants that match the criteria and provides detailed information. The route guidance unit guides the user along the shortest route based on the information provided by the information provision unit. For example, when a user inputs a request such as "I want to go to the trattoria closest to my current location," the generation AI analyzes map data and displays the shortest route. The reservation guidance unit guides the user to the online reservation site of the restaurant guided by the route guidance unit. For example, when a user inputs a request such as "I want to make a reservation at this restaurant," the generation AI displays a link to the restaurant's reservation site, allowing the user to easily make a reservation. As a result, the proposal system according to the embodiment can make travel more comfortable by suggesting the most suitable restaurant based on the user's preferences and providing seat vacancy forecast information, the shortest route, and online reservation guidance.

[0052] The restaurant suggestion unit can analyze a user's past reviews and ratings, track changes in preferences, and evolve the content of suggestions. For example, the restaurant suggestion unit analyzes reviews and ratings posted by the user in the past and tracks changes in preferences. For example, it extracts characteristics of restaurants that have been highly rated in the past and suggests restaurants that match the user's current preferences. The restaurant suggestion unit also analyzes the content of the user's past reviews using natural language processing technology to understand changes in preferences. For example, it tracks changes in ratings for specific cuisine genres or atmospheres. The restaurant suggestion unit also models changes in preferences based on evaluation data of restaurants the user has visited in the past, predicts future preferences, and evolves the content of suggestions. For example, it takes into account changes in preferences with the seasons. In this way, more accurate restaurant suggestions can be made by tracking changes in the user's preferences.

[0053] The store suggestion unit can analyze a user's SNS posts and reflect the user's latest hobbies and preferences in real time. The store suggestion unit, for example, analyzes a user's SNS posts and reflects the user's latest hobbies and preferences in real time. For example, it can identify a new food genre that the user has become interested in from the content of recent posts. The store suggestion unit also uses SNS image analysis technology to extract preferences from photos of food posted by the user and reflects them in the suggestions. For example, if there are many photos of a particular food or drink, it can suggest stores in that genre. The store suggestion unit also analyzes the user's check-in information on SNS, understands trends in visited stores, and reflects the user's latest hobbies and preferences. For example, it can update the suggestions based on frequently visited areas and store characteristics. This allows the user's latest hobbies and preferences to be reflected in real time, enabling more appropriate store suggestions.

[0054] The store suggestion unit can use the emotion estimation function to analyze the emotions of the user at stores visited in the past and suggest stores that elicit positive emotions. For example, the store suggestion unit analyzes the emotions of the user at stores visited in the past and suggests stores that elicit positive emotions. For example, it analyzes emotional expressions contained in past reviews and ratings. The store suggestion unit also uses the emotion estimation function to calculate an emotion score for stores visited in the past by the user and prioritizes suggesting stores with high positive emotions. For example, it lists stores that elicit high levels of joy and satisfaction. The store suggestion unit also combines the user's past visit history with emotion data to identify stores that elicit positive emotions. For example, it customizes the content of suggestions based on the user's emotional reactions to specific dishes or services. This allows the user to select stores that will elicit positive emotions by suggesting stores that will elicit high satisfaction.

[0055] The vacancy prediction unit can analyze the store's past sales data and predict vacancy based on specific events or seasons. The vacancy prediction unit, for example, analyzes the store's past sales data and predicts vacancy based on specific events or seasons. For example, it predicts vacancy on specific days such as Christmas or Valentine's Day. The vacancy prediction unit also predicts vacancy for each season based on past sales data. For example, it predicts vacancy during summer vacation and winter vacation. The vacancy prediction unit also analyzes past sales data in detail to predict vacancy based on specific events or seasons. For example, it predicts vacancy during periods when local festivals or events are held. In this way, by predicting vacancy based on specific events or seasons, users can plan the optimal time to visit.

[0056] The vacant seat prediction unit can analyze traffic conditions and weather forecasts around the store and reflect them in the vacant seat prediction. The vacant seat prediction unit, for example, analyzes traffic conditions around the store and reflects them in the vacant seat prediction. For example, it predicts the possibility of an increase in vacant seats when there is traffic congestion. The vacant seat prediction unit also makes vacant seat predictions based on weather forecasts. For example, it makes vacant seat predictions taking into account the tendency for the number of customers to decrease when it rains. The vacant seat prediction unit also builds a system that analyzes traffic conditions and weather forecasts in real time and reflects them in the vacant seat prediction. For example, it updates the vacant seat prediction when a traffic accident or bad weather occurs. This makes it possible to make vacant seat predictions that take traffic conditions and weather forecasts into account, thereby enabling more accurate predictions.

[0057] The vacant seat prediction unit can use the emotion estimation function to provide a vacant seat prediction that will be most relaxing during the time period when the user visits. The vacant seat prediction unit, for example, uses the emotion estimation function to provide a vacant seat prediction that will be most relaxing during the time period when the user visits. For example, the vacant seat prediction unit identifies a time period when the user can relax based on past emotion data. The vacant seat prediction unit also analyzes the user's emotional state in real time to suggest restaurants that have vacant seats during the time period when the user can relax. For example, it prioritizes suggestions for quiet time periods. The vacant seat prediction unit also predicts a time period when the user can relax based on the emotion estimation data and lists restaurants that have vacant seats during that time period. For example, it suggests the optimal time to visit based on past emotional responses. This allows the user to visit during a time period when the user can relax, providing a comfortable dining experience.

[0058] The information providing unit can analyze the user's past visit history, identify the user's favorite areas and cuisine genres, and customize the suggestions. For example, the information providing unit analyzes the user's past visit history, identify the user's favorite areas and cuisine genres, and customize the suggestions. For example, the suggestions are adjusted based on frequently visited areas and favorite cuisine genres. The information providing unit also models the user's preferences based on the past visit history and customizes the suggestions. For example, the suggestions are updated based on the user's evaluation of specific cuisine genres or areas. The information providing unit also analyzes the user's visit history data and builds a system that identifies the user's favorite areas and cuisine genres. For example, the information providing unit suggests optimal restaurants based on the user's past visit history. This makes it possible to provide more appropriate restaurant information based on the user's past visit history, resulting in highly satisfying suggestions.

[0059] The information providing unit can provide discount information and benefits according to the user's budget. The information providing unit, for example, builds a system that provides discount information and benefits according to the user's budget. For example, it suggests discount coupons and benefits that can be used within a specific budget. The information providing unit also analyzes the user's budget data and provides optimal discount information and benefits. For example, it lists stores that offer benefits according to the budget. The information providing unit also develops a system that provides discount information and benefits in real time based on the user's budget. For example, it instantly displays discount information for stores that fit the budget. This makes it possible to provide discount information and benefits according to the user's budget, making it possible to make cost-effective suggestions.

[0060] The route guidance unit can analyze the user's movement history and propose the most efficient route. For example, the route guidance unit analyzes the user's movement history and proposes the most efficient route. For example, it identifies the shortest route based on past movement data. The route guidance unit also models the user's movement patterns based on the movement history and proposes the optimal route. For example, it takes into account frequently used means of transportation and routes. The route guidance unit also analyzes the user's movement history data and builds a system that proposes the most efficient route. For example, it calculates the optimal route based on past movement history. In this way, by proposing the most efficient route based on the user's movement history, travel time is shortened and convenience is improved.

[0061] The route guidance unit can provide the optimal route according to the user's means of transportation. For example, the route guidance unit builds a system that provides the optimal route according to the user's means of transportation. For example, it calculates the optimal route for each of walking, cycling, and car. The route guidance unit also develops an algorithm that suggests the optimal route based on the means of transportation. For example, if walking, it gives priority to pedestrian-only roads. The route guidance unit also analyzes the user's means of transportation data and provides the optimal route. For example, if driving, it suggests a route that avoids traffic congestion. In this way, the convenience of travel is improved by providing the optimal route according to the user's means of transportation.

[0062] The route guidance unit can use the emotion estimation function to suggest a route that will cause the user the least stress. For example, the route guidance unit uses the emotion estimation function to suggest a route that will cause the user the least stress. For example, it identifies a route that will cause the least stress based on past emotion data. The route guidance unit also analyzes the user's emotional state in real time to suggest a route that will cause the user the least stress. For example, it prioritizes suggesting routes that avoid crowds or quiet routes. The route guidance unit also lists routes that will cause the user the least stress based on the emotion estimation data. For example, it suggests an optimal route based on past emotional reactions. In this way, the route that will cause the user the least stress is suggested, thereby providing a comfortable travel experience.

[0063] The reservation information unit can analyze the user's past reservation history and prioritize the most frequently used reservation site. The reservation information unit, for example, analyzes the user's past reservation history and prioritizes the most frequently used reservation site. For example, it displays links to frequently used reservation sites. The reservation information unit also models the user's preferences based on the past reservation history and suggests the most suitable reservation site. For example, it updates the suggestions based on the user's evaluation of a specific reservation site. The reservation information unit also analyzes the user's reservation history data and builds a system to identify the most frequently used reservation site. For example, it suggests the most suitable reservation site based on the past reservation history. This improves convenience by guiding the user to the most frequently used reservation site based on the user's past reservation history.

[0064] The reservation information unit can provide special offers and discount information based on the user's reservation history. The reservation information unit, for example, builds a system that provides special offers and discount information based on the user's reservation history. For example, it suggests discount coupons and special offers that can be used on a specific reservation site. The reservation information unit also analyzes the user's reservation history data and provides optimal special offers and discount information. For example, it lists stores that offer special offers based on past reservation history. The reservation information unit also develops a system that provides special offers and discount information in real time based on the user's reservation history. For example, it instantly displays special offer information for stores that match the reservation history. In this way, user satisfaction is improved by providing special offers and discount information based on the user's reservation history.

[0065] The reservation guide unit can use the emotion estimation function to suggest the reservation site that will most satisfy the user. The reservation guide unit, for example, uses the emotion estimation function to suggest the reservation site that will most satisfy the user. For example, it identifies reservation sites with high satisfaction based on past emotion data. The reservation guide unit also analyzes the user's emotional state in real time to suggest reservation sites with high satisfaction. For example, it prioritizes suggesting reservation sites with strong positive emotions. The reservation guide unit also lists reservation sites that will most satisfy the user based on emotion estimation data. For example, it suggests the optimal reservation site based on past emotional responses. In this way, the quality of the reservation experience is improved by suggesting the reservation site that will most satisfy the user.

[0066] The reservation information unit can apply the information from the online reservation site to other services. For example, the reservation information unit builds a system that applies the information from the online reservation site to other services. For example, it provides links to hotel and tour reservation sites. The reservation information unit also collects data from other services and provides information about online reservation sites. For example, it provides an integrated list of reservation sites for each service. The reservation information unit also develops an algorithm for applying the information from the online reservation site to other services. For example, it makes suggestions that take into account the characteristics of each service and user preferences. In this way, the information from the online reservation site can be applied to other services, improving user convenience.

[0067] The reservation information unit can incorporate information from online reservation sites into the user's travel schedule and suggest the optimal reservation timing. The reservation information unit, for example, incorporates information from online reservation sites into the user's travel schedule and suggests the optimal reservation timing. For example, it adjusts the reservation timing for tourist attractions and restaurants during the trip. The reservation information unit also builds a system that provides information from online reservation sites based on the user's travel schedule. For example, it reflects the reservation timing for specific tourist attractions and events in real time. The reservation information unit also incorporates information from online reservation sites into the travel schedule, allowing the user to plan the optimal reservation timing. For example, it makes suggestions to make reservations to avoid crowded times. This improves the convenience of reservations by suggesting the optimal reservation timing based on the user's travel schedule.

[0068] The reservation guide unit can use the emotion estimation function to provide a reservation process that causes the user the least stress. The reservation guide unit, for example, uses the emotion estimation function to provide a reservation process that causes the user the least stress. For example, it identifies a reservation site that causes the least stress based on past emotion data. The reservation guide unit also analyzes the user's emotional state in real time to suggest a reservation process that causes the user the least stress. For example, it prioritizes suggesting sites that allow reservations to be completed with simple operations. The reservation guide unit also lists reservation processes that cause the user the least stress based on the emotion estimation data. For example, it suggests the optimal reservation site based on past emotional responses. This provides a reservation process that causes the user the least stress, thereby improving the quality of the reservation experience.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The recommendation system can also make restaurant recommendations that take into account the user's health condition. For example, if a user has a specific allergy, it will prioritize restaurants that offer menus that do not contain that allergen. For health-conscious users, it can also suggest restaurants that offer low-calorie or organic menus. Furthermore, it can analyze the user's health data and, for example, if the user needs to manage their blood sugar or blood pressure, it can list restaurants that offer menus that are suitable for that purpose. This makes it possible to recommend the most suitable restaurant based on the user's health condition.

[0071] The recommendation system can also make restaurant suggestions that take into account the user's cultural background. For example, for a user with specific religious restrictions, it can prioritize suggestions of restaurants that offer menus that meet those restrictions. Also, for a user who prefers cuisine from a specific country or region, it can suggest restaurants that specialize in that cuisine. Furthermore, it can also list restaurants that offer vegetarian or vegan menus, for example, based on the user's cultural background. This makes it possible to suggest optimal restaurants that match the user's cultural background.

[0072] The recommendation system can also suggest restaurants that match the user's travel purpose. For example, it can suggest restaurants with a quiet environment where it is easy to work to a business traveler. It can also suggest restaurants that serve local specialties to a tourist. It can also provide a list of restaurants that are safe for families traveling with children. This makes it possible to suggest the best restaurants that match the user's travel purpose.

[0073] The recommendation system can also analyze a user's past travel history and make restaurant suggestions to encourage repeat visits. For example, it can re-suggest restaurants that users have previously rated highly. It can also provide information on new menus and renovations at restaurants that users have previously visited. Furthermore, it can also create a list of restaurants that the user has not yet visited but is likely to like, based on the user's past travel history. This makes it possible to make optimal restaurant suggestions based on the user's past travel history.

[0074] The proposed system can also use the user's emotion estimation function to suggest stores that will reduce stress during travel. For example, it can suggest stores where users can relax based on past emotion data. It can also analyze the user's emotional state in real time to suggest stores that will not cause stress. Furthermore, it can use emotion estimation data to create a list of stores with available seats during times when the user is most likely to relax. This makes it possible to suggest optimal stores based on the user's emotional state.

[0075] The proposed system can also use the user's emotion estimation function to suggest stores that will elicit positive emotions during the trip. For example, it can suggest stores that elicit high levels of joy and satisfaction based on past emotion data. It can also analyze the user's emotional state in real time to suggest stores that elicit positive emotions. Furthermore, it can use emotion estimation data to create a list of stores with available seats during the time period when the user feels most positive emotions. This makes it possible to suggest optimal stores based on the user's emotional state.

[0076] The proposed system can also use the user's emotion estimation function to suggest stores that will reduce negative emotions during the trip. For example, it can suggest stores that cause less stress and dissatisfaction based on past emotion data. It can also analyze the user's emotional state in real time to suggest stores that will reduce negative emotions. Furthermore, it can use emotion estimation data to list stores with vacant seats during times when the user feels the least negative emotions. This makes it possible to suggest optimal stores based on the user's emotional state.

[0077] The proposed system can also use the user's emotion estimation function to suggest stores according to changes in emotions during the trip. For example, it can suggest relaxing stores on the first day of the trip and exciting stores towards the end of the trip. It can also analyze the user's emotional state in real time and suggest stores according to changes in emotions. Furthermore, it can also create a list of stores that are best suited to changes in the user's emotions based on the emotion estimation data. This makes it possible to suggest optimal stores according to changes in the user's emotions.

[0078] The proposed system can also use the user's emotion estimation function to suggest restaurants that will help stabilize emotions during the trip. For example, it can suggest restaurants that will stabilize emotions based on past emotion data. It can also analyze the user's emotional state in real time to suggest restaurants that will stabilize emotions. Furthermore, it can use emotion estimation data to create a list of restaurants with vacant seats during the time period when the user feels most emotionally stable. This makes it possible to suggest the most suitable restaurant based on the user's emotional state.

[0079] The recommendation system can also suggest stores based on the user's travel activities. For example, for a user touring tourist spots, it can suggest restaurants with good reviews in the area. For a user enjoying shopping, it can also suggest popular cafes and restaurants in the shopping area. Furthermore, it can also suggest stores where users can relax in nature based on the type of activity, for example, for a user who enjoys hiking or cycling. This makes it possible to suggest the most suitable stores based on the user's activity.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The restaurant suggestion unit suggests restaurants based on the user's tastes. For example, if a user enters information such as "I like authentic Italian food, I'm on a moderate budget, and I'm looking for a restaurant that's suitable for families," the AI ​​analyzes this information and lists relevant restaurants, trattorias, and pizzerias. Step 2: The seat vacancy prediction unit provides seat vacancy prediction information for the restaurants proposed by the restaurant proposal unit. For example, based on the past and current reservation status of a specific restaurant, the generation AI makes a prediction such as, "This restaurant is relatively empty on weekday evenings, but gets crowded on weekends." Step 3: The information provider uses the information provided by the seat availability forecaster to provide information tailored to the area, cuisine, budget, and customer demographic. For example, if a user enters criteria such as "I'm looking for a restaurant in Rome that serves delicious pizza, has a budget of under 20 euros, and has a casual atmosphere," the AI ​​generator will list restaurants that fit the criteria and provide detailed information. Step 4: The route guidance unit guides users to the shortest route based on the information provided by the information provider. For example, if a user inputs a request such as "I want to go to the trattoria closest to my current location," the AI ​​generator analyzes map data and displays the shortest route. Step 5: The reservation guidance unit guides the user to the online reservation site of the restaurant indicated by the route guidance unit. For example, if a user inputs a request such as "I would like to make a reservation at this restaurant," the generation AI will display a link to the restaurant's reservation site, allowing the user to easily make a reservation.

[0082] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a 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.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a store suggestion unit that suggests stores based on the user's hobbies and preferences; a vacant seat prediction unit that provides vacant seat prediction information for the store proposed by the store proposal unit; an information providing unit that provides information according to area, food genre, budget, and customer demographic based on the information provided by the vacant seat prediction unit; a route guidance unit that provides guidance on the shortest route based on the information provided by the information providing unit; a reservation guidance unit that guides the user to an online reservation site for the store guided by the route guidance unit.

2. The store suggestion unit Analyze the user's SNS posts and reflect their latest interests and preferences in real time 2. The system of claim 1.

3. The vacant seat prediction unit Analyze the store's past sales data and predict seat availability based on specific events or seasons.

2. The system of claim 1.

4. The information providing unit Analyze the user's past visit history, identify their preferred areas and cuisine genres, and customize the suggestions.

2. The system of claim 1.

5. The route guidance unit Analyze the user's travel history and suggest the most efficient route 2. The system of claim 1.

6. The reservation information unit Suggest the booking site that the user is most satisfied with 2. The system of claim 1.

7. The store suggestion unit Analyzing the emotions of the user at the stores they have visited in the past and suggesting stores that elicit positive emotions 2. The system of claim 1.

8. The vacant seat prediction unit To provide a seat availability forecast that will allow the user to relax the most during the time period when the user visits 2. The system of claim 1.

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