system

Abstract

We provide the system. [Solution] A means for inputting the current location and destination from a user terminal to search for restaurants along a transportation route from the starting point to the destination, A means for calculating the optimal route from the current location to the destination based on user input, A means of receiving information about the user's food and drink preferences and desired meal times, A means of searching for a restaurant located along a transportation route using restaurant information obtained from a database, A means of filtering restaurants that match the user's route and preferences, taking real-time information into consideration, A system that includes means for providing filtered restaurant information to users.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When looking for a restaurant, the conventional method involves individually searching for stores that meet the conditions from a wide area and checking their locations and suitability one by one, which is time - consuming and laborious. Also, it is difficult to efficiently search for and reserve a restaurant while on the move, resulting in missing potential customer - store matching opportunities.

Means for Solving the Problems

[0005] This invention provides a means for allowing users to input their starting point and destination from a user terminal, calculating the optimal route, and collecting and searching for restaurant information along that route from a database. Furthermore, it filters the results according to the user's dining preferences and time, and narrows down the best restaurants considering real-time traffic information. In addition, it provides a method to assist with the reservation process for the restaurant selected by the user, thereby realizing an efficient and comfortable restaurant search and reservation experience.

[0006] A "user terminal" refers to an electronic device used by a user to input information or receive results.

[0007] "Current location" refers to data indicating the user's real-time geographical location, obtained using location-based services.

[0008] "Destination" refers to the final destination specified by the user, which will be used for navigation and search.

[0009] "Optimal route" refers to data that shows an efficient route that satisfies predetermined conditions between the current location and the destination.

[0010] "Restaurant information" refers to data containing detailed information about restaurants, such as location, menu, ratings, and business hours.

[0011] A "database" refers to an information system that systematically organizes and stores various types of information, including restaurant information.

[0012] "Filtering" refers to a method of processing data to select only information that meets specific criteria.

[0013] "Real-time information" refers to data that shows the current situation without any time delay, and includes traffic conditions, weather information, and so on.

[0014] "Reservation procedure" refers to a series of steps taken by a user to secure a seat or service in advance at a restaurant of their choice. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] An embodiment of the present invention is built on a system including a user terminal, a server, and a database. In this system, the user first inputs a starting point and destination using the terminal, and based on this, the terminal confirms the current location and calculates the optimal route to the destination. The calculated route information is transmitted to the server.

[0037] Afterward, the user uses their device to input their meal preferences, tastes, and preferred meal times. This information is also transmitted to the server, which accesses a database to collect information about restaurants located along the route specified by the user.

[0038] The server analyzes and filters the collected restaurant information based on the user's preferences and past usage history. This filtering identifies the most suitable restaurant candidates for the user. Furthermore, the server considers real-time traffic information to narrow down the list to restaurants that can be reached within the specified time.

[0039] As a result, a filtered list of restaurants is displayed on the device. The list includes the location, rating, distance, and estimated arrival time for each restaurant, allowing the user to select a restaurant based on this information. After making a selection, the user can proceed with making a reservation using the device.

[0040] As a concrete example, consider a scenario where a user is traveling from Tokyo to Yokohama at 11:00 AM and wants to eat Chinese food at 1:00 PM. In this situation, the terminal calculates the optimal route from Tokyo to Yokohama and searches for Chinese restaurants along that route. The server considers real-time traffic information, filters the restaurants to find the ideal one that can be reached within the given time, and presents the user with the best option. The user can then quickly make a reservation at the selected restaurant through the terminal.

[0041] This system allows users to easily find suitable restaurants and complete reservations while on the go.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user opens a map application on their device and enters their starting point and destination. The device obtains its current location via GPS, uses that information to call a map service API, and calculates the optimal route to the destination. The device then displays the calculated route to the user.

[0045] Step 2:

[0046] The user uses a device to input their preferred meal type and time via voice or text. The device analyzes this input using speech recognition or text analysis technology and generates formalized data. This data is then sent to a server.

[0047] Step 3:

[0048] Based on the route information and dining preferences received from the user, the server uses an online database to collect data on restaurants along the route. The server then temporarily stores the collected data.

[0049] Step 4:

[0050] The server filters the collected restaurant data. It selects suitable restaurants considering the user's preferences and past usage history. It also obtains real-time traffic information and narrows down the list to restaurants that can be reached within the time specified by the user.

[0051] Step 5:

[0052] The server generates a filtered list of restaurants and sends it to the terminal. The terminal displays the received list on the user's screen and provides detailed information about each restaurant (rating, distance, estimated arrival time, etc.).

[0053] Step 6:

[0054] The user selects a restaurant they wish to visit from a presented list. The terminal then redisplays information about the selected restaurant and provides a link or button to support the reservation process. Through this link, the user can quickly complete the reservation process.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] Until now, it has been difficult for travelers to easily and efficiently find and reserve restaurants and bars that match their preferences and requirements along the optimal route to their destination. In particular, systems that select the best establishments considering real-time traffic information and past usage history are not widespread. Therefore, there is a need for methods to improve convenience for travelers.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for inputting location information and destination from a user terminal for searching for restaurants along the travel route from the starting point to the destination, means for calculating the optimal travel route from the location information to the destination based on the user's input, and means for receiving information on the user's dining preferences and desired meal times. This makes it possible for users to easily obtain store information based on their preferences and conditions and to make reservations seamlessly.

[0060] "User terminal" refers to an electronic device used by a user to input information, and includes smartphones, tablets, and other similar devices.

[0061] "Location information" refers to information that indicates a physical location, such as geographical coordinates or addresses, and is data obtained using technologies such as GPS.

[0062] "Destination" refers to the final point of arrival that the user wishes to reach, and is defined as a geographical location or address.

[0063] An "optimal travel route" refers to a route calculated to minimize distance and time during travel from a starting point to a destination, and is determined based on traffic conditions and user needs.

[0064] "Dietary preferences" refer to the user's personal tastes regarding specific types of cuisine, flavors, and ingredients.

[0065] "Desired meal time" refers to the specific time or period in which the user wishes to eat.

[0066] An "information store" refers to a database used to store, manage, and provide data related to food and beverage establishments.

[0067] "Food and beverage establishments" refer to places that serve food and beverages, and include restaurants, cafes, and food courts.

[0068] "Real-time information" refers to information that changes over time, such as current traffic conditions or store congestion levels.

[0069] "Selection" refers to the process of choosing the best option from multiple choices based on specific conditions or criteria.

[0070] The embodiment of this invention is built upon a system comprising a user terminal, a server, and an information store. This system begins with the user inputting their starting point and destination using the user terminal. The terminal uses its built-in GPS function to acquire location information. This accurately determines the current location and calculates the optimal travel route to the destination. This calculation references real-world road information using a map service API.

[0071] The user then uses their device to input their preferences regarding food genres and desired meal times. This information is sent to the server, which then accesses an information store to collect data on relevant restaurants. The server analyzes this collected data using programming languages ​​such as Python. Specifically, it applies machine learning based on the user's past selection history and ratings to select the most suitable restaurant candidates. Generative AI models may also be used in this analysis.

[0072] Furthermore, the server considers real-time updated traffic conditions to narrow down the list of restaurants that the user can reach by their desired mealtime. The traffic information used here is obtained through a traffic conditions API. Finally, filtered restaurant information is delivered to the terminal, and the user can select a restaurant based on this. Once the selection is complete, a reservation can be made at the selected restaurant from the terminal.

[0073] As a concrete example, consider a scenario where a user travels from city A to city B at 11:00 AM and desires a Chinese restaurant at 1:00 PM. In this situation, the terminal calculates the optimal travel route from city A to city B and searches for Chinese restaurants along that route. The server, taking real-time traffic information into account, selects the ideal restaurant that can be reached within a reasonable time and presents the results to the user. The user can then make a reservation at the selected restaurant through the terminal.

[0074] An example of a prompt message would be: "Please recommend a Chinese restaurant between my current location and city B. I'd prefer a place I can arrive at by 1 PM, and I'd also like suggestions for restaurants I've visited in the past."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user inputs their starting point and destination using their device. Based on this input, the device uses its built-in GPS function to determine the user's current location. The device outputs location information, specifying the starting point as "City A," the destination as "City B," and the current location as "Central part of City A."

[0078] Step 2:

[0079] The device uses a map service API to calculate the optimal travel route based on the acquired current location and destination. The input is the location information of the current location and destination, and the output is obtained as the shortest time route. In this process, route candidates are obtained from the map service and analyzed to generate detailed travel routes such as "from the center of City A to Station in City A, from Station in City A to Station in City B, and from Station in City B to the center of City B."

[0080] Step 3:

[0081] The user uses a terminal to input the type of food and desired meal time. For example, they might enter "Chinese food" and "1 PM," and this information is sent to the server. The input data includes meal preferences and time, and the output is confirmation that the information has been sent to the server. At this point, the user selects and confirms the information through the application interface.

[0082] Step 4:

[0083] The server uses the received data to access the information store and search for relevant restaurants located along the specified travel route. The input here is the travel route and the user's dining preferences, and the output is a list of candidate restaurants. The server executes an SQL query to extract "Chinese restaurants located along the travel route."

[0084] Step 5:

[0085] The server analyzes the collected facility information, along with the user's past usage history and real-time data. Using a generative AI model, it scores the stores that are expected to provide a higher level of satisfaction and selects the store best suited to the user. Data input consists of store information and past usage history, and output is a list of evaluated stores. The server uses the constructed model to calculate scores and select the top-ranked restaurants.

[0086] Step 6:

[0087] The server obtains real-time traffic information via a traffic conditions API and narrows down the list of stores reachable within the user's desired time. The input is traffic information and an existing list of stores, and the output is a list of stores that meet the time constraint. If there are fluctuations in traffic conditions, such as delays, the server performs filtering to ensure that only stores reachable within the time limit are included in the final list.

[0088] Step 7:

[0089] The terminal receives a list of selected restaurants from the server and displays it to the user. The user can review the list displayed on the screen and select their desired restaurant. Input is a filtered list of restaurants, and output is the restaurant selection and reservation confirmation. The user can then select their chosen restaurant and complete the reservation process through the application.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Conventional food delivery services have made it difficult for users to receive prompt and timely delivery while on the go. Therefore, users had to plan meticulously in advance, and there was a risk that delivery would not occur at the desired time. This invention aims to solve the problem of enabling users to receive prompt delivery services from facilities near their destination, even while traveling or on business trips.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for searching for facilities along the transportation route from the starting point to the destination, means for receiving the user's preferences and desired activity time as data from the information processing device, and means for arranging logistics to the facilities identified from the filtered facility information. This enables efficient delivery services by selecting appropriate facilities along the pre-set route to the destination, even when the user is on the move, in accordance with the time.

[0095] An "information processing device" is a device on which users input data or receive results, and usually refers to a smartphone or computer.

[0096] "Current location" refers to the geographical location of the information processing device at that specific moment.

[0097] A "destination" is a specific place that the user has designated as their destination.

[0098] An "optimal route" is a route calculated to satisfy specific conditions, such as time and distance, when traveling from the current location to the destination.

[0099] "Preferences" refer to the individual user's tastes in food, drink, and activities, and are used for personalization purposes.

[0100] "Desired activity time" refers to the time period during which the user wishes to engage in a particular activity.

[0101] An "information recording device" is a system such as a database that stores facility and traffic information and is used for searching and retrieving it as needed.

[0102] "Real-time information" refers to the latest data, such as traffic conditions and facility congestion, that are acquired in real time.

[0103] "Filtering" is the process of selecting necessary items from collected information based on specific criteria or conditions.

[0104] "Facilities" refers to places where restaurants and other activities are held, and are locations that are eligible for delivery services.

[0105] "Logistics" refers to the business of delivering and transporting goods and services to designated locations.

[0106] The system for realizing this application example begins with an information processing device (e.g., a smartphone) receiving the user's starting point, destination, preferences, and desired activity time. The device determines its current location using GPS and calculates the optimal route. This process can utilize route search services such as Google® Maps API. The server, upon receiving the user's preference data and desired activity time, uses an information recording device (e.g., a database) to retrieve appropriate facility information based on past data history.

[0107] The server uses APIs and data analysis tools to filter information while considering real-time data, and proposes the most suitable facility to the user. This allows for the selection of a facility that can provide service at the optimal time based on the travel route. For the facility selected by the user, logistics are arranged, and reservations are adjusted as needed.

[0108] To give a concrete example, let's consider a scenario where a user departs Osaka at 9:00 AM and wishes to have lunch at a cafe in Kyoto at noon. This system calculates the optimal route from Osaka to Kyoto, selects a cafe in Kyoto to coincide with the estimated arrival time, and arranges lunch. Based on this process, the user can receive efficient and comfortable service.

[0109] An example of a prompt for a generative AI model is: "When a user leaves Osaka at 9:00 AM and wants to have lunch at a cafe in Kyoto at 12:00 PM, please write Python code that recommends the best route and cafe."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device receives the user's current location, destination, preferences, and desired activity time as input. Based on the input data, the Google Maps API is used to calculate the optimal route from the current location to the destination. The output of this step is the optimal route information.

[0113] Step 2:

[0114] The terminal sends optimal route information to the server. The server searches for facility information along the route from the information recording device based on the received route information and user preference data. The server filters the facility information based on location data and user preferences and extracts the relevant facilities. The output here is the filtered facility information.

[0115] Step 3:

[0116] The server retrieves real-time information and performs additional filtering, taking into account traffic conditions and facility congestion. Based on these conditions, the server creates a list of facilities available to the user. The output of this process is a list of available facilities that reflects the real-time information.

[0117] Step 4:

[0118] A list of available facilities is sent from the server to the terminal. The user selects their desired facility from the list displayed on the terminal. This selection is sent from the terminal to the server, and logistics arrangements are initiated. The output is the information of the specific facility selected by the user.

[0119] Step 5:

[0120] The server processes the logistics of goods to the facility selected by the user. If necessary, it adjusts the delivery to arrive at the time specified by the user and also makes reservations with the facility. The output of this step is the adjusted logistics plan.

[0121] Step 6:

[0122] The user's device displays a notification that the logistics plan is complete. The device helps users enjoy the service even while on the go by informing them of the estimated arrival time of their food delivery. The output of this step is notification information for the user.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention provides a system that recommends restaurants while considering the user's emotional state, by incorporating an emotion engine in addition to the usual location information service and restaurant database search functions. The system includes a user terminal, a server, a database, and an emotion engine.

[0125] The user enters their starting point and destination through the device and confirms their current location using GPS. Then, the user enters their meal preferences along the route via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotions and sends the data to a server.

[0126] The server queries a restaurant database based on route information, dining preferences, and even sentiment data submitted by the user, searching for relevant restaurants along the route. Real-time traffic conditions are also taken into consideration during this process.

[0127] During the filtering phase, the server uses the output of the emotion engine to identify the restaurant best suited to the user's current mood. For example, if the user wants to relax, a restaurant with a quiet and relaxed atmosphere will be recommended. Conversely, if they are looking for a lively atmosphere, a lively restaurant will be suggested.

[0128] As a result, the device not only displays a filtered list of restaurants, but also provides promotional and special offer information tailored to the user's emotions.

[0129] For example, if the emotion engine determines that a user is traveling on a highway and is feeling stressed, the server can prioritize listing restaurants in scenic locations or establishments playing relaxing background music, and may even offer special discounts.

[0130] This invention allows users to choose the optimal restaurant according to their mood and circumstances, enabling a comfortable and satisfying dining experience even while on the go.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses their device to input their starting point and destination. The device uses GPS to determine its current location and calls a map service API to calculate the optimal route to the destination.

[0134] Step 2:

[0135] The user inputs the type and time of their meal into the device via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice tone and text content to obtain emotional data.

[0136] Step 3:

[0137] The terminal sends acquired route information, meal preferences, and sentiment data to the server. Based on the received information, the server queries a large restaurant database to collect information on restaurants located along the user's route.

[0138] Step 4:

[0139] The server filters the collected restaurant information based on the user's dining preferences, real-time traffic information, and user sentiment data. Based on sentiment data, for example, if the user has a high stress level, the server prioritizes restaurants with a relaxing environment.

[0140] Step 5:

[0141] The server sends a filtered list of restaurants to the device. This list includes recommended restaurants tailored to the user's mood and special promotional information. The device displays this information to the user, allowing them to view the details.

[0142] Step 6:

[0143] The user selects a restaurant they wish to visit from a presented list. The terminal, upon receiving the user's selection, redisplays the restaurant's details and provides links or buttons to begin the reservation process. This allows the user to easily complete the reservation.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] While conventional restaurant search systems offer features to search for restaurants based on the user's location and basic food preferences, they lack flexible recommendations that take into account the user's emotions and mood. As a result, it was difficult for users to find a restaurant that was best suited to their mental state and mood at the time, sometimes leading to an unsatisfactory dining experience.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for searching for restaurants based on the user's route information and food and drink preferences, means for filtering the most suitable restaurants considering real-time information, and means for estimating the user's emotional state and adjusting recommendations accordingly. This makes it possible to recommend restaurants in a way that is appropriate to the user's current mood and mental state.

[0149] An "information terminal" is a device used by users to input location information and food and drink preferences, and to estimate their emotional state.

[0150] "Current location" refers to the user's real-time location information obtained via an information terminal.

[0151] The "destination" is the point that indicates the final destination of the journey entered by the user.

[0152] "Route" refers to the path taken from the current location to the destination.

[0153] "Preferences" refer to information indicating the types and conditions of food and drink that users prefer to choose.

[0154] "Data collection" refers to a database containing information about various food and beverage establishments.

[0155] "Filtering" refers to the process of selecting only information that meets specific criteria from a vast amount of information.

[0156] "Emotion analysis" is the process of estimating a user's emotional state from their speech and facial expressions.

[0157] "Benefit information" refers to information that includes additional benefits provided to users, such as promotions and discount information.

[0158] This invention relates to an information processing system that recommends the most suitable dining establishment based on the user's emotional state. Specifically, it consists of an information terminal, a server, data collection, and an emotion analysis engine.

[0159] The information terminal is a device that allows users to input their current location and destination, and can recognize their current geographical location using GPS functionality. It also has voice and text input capabilities, enabling users to input their food and drink preferences and mood. The emotion analysis engine is responsible for estimating emotions from the user's voice tone and facial expressions.

[0160] The server receives route information, preference data, and emotional state data transmitted from information terminals, and searches for restaurants by referring to the data collection. This involves manipulating the data collection using query languages ​​such as SQL to extract restaurants that meet the criteria. Real-time traffic information is also taken into consideration, and restaurants along the optimal route are selected.

[0161] The data collection system is a database containing information about various food and beverage establishments. It stores location information, establishment type, customer reviews, and promotional information, among other things.

[0162] For example, if a user inputs "My next destination is Kyoto, and I'd like a relaxing cafe" into their device, the server considers the user's emotional state, selects quiet and calming cafes from its data pool, and suggests them to the user along with special offers. A generative AI model complements the recommendation process, providing user-optimized feedback. This entire process allows users to enjoy a comfortable and satisfying dining experience even while on the go.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device receives the user's current location and destination. The user uses the map application interface to confirm their current location and specify the destination. The input is geocoded location data, which the device uses to obtain coordinate information for the starting point and destination.

[0166] Step 2:

[0167] The user inputs their food and drink preferences and desired mental state into the device via voice or text. The device's microphone or keyboard is used for this input. For example, preferences such as "I want spicy food" or "I want a quiet place" might be entered. The device categorizes this input information into preference and emotional categories.

[0168] Step 3:

[0169] The emotion analysis engine built into the device captures the user's voice tone and facial expressions using sensors and cameras to estimate the user's emotional state. The input data consists of voice spectrum and facial recognition information, and this data is analyzed to observe emotional states such as "I want to relax" or "I want to be energetic."

[0170] Step 4:

[0171] The device sends collected route information, preference data, and sentiment data to the server. The data is sent to the server via encrypted communication using the HTTP protocol. At this stage, the server receives location information, preference categories, and sentiment as input data for queries.

[0172] Step 5:

[0173] The server queries the data repository to search for restaurants and bars along the route that match the user's preferences and emotional state. The queries are performed using SQL statements, and conditions are constructed based on location information, preference categories, and emotional state. A list of candidate restaurants and bars is generated as output.

[0174] Step 6:

[0175] The server further filters the obtained list of restaurants and bars based on the user's emotional state and sends it to the terminal along with promotional information. The filtering is performed using an algorithm based on the priority of emotional states and is designed to optimize the user's emotional needs. The output is a list of restaurants and bars best suited to the user, along with information about special offers.

[0176] Step 7:

[0177] The terminal displays information received from the server to the user. The user interface visually displays detailed information about restaurants and their locations on a map, and also provides links for reservations and directions. This allows users to quickly select the most suitable restaurant.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] In recent years, there has been an increasing demand for optimal suggestions based on individual user preferences and emotions when choosing meals while on the go. However, existing systems only consider location information and basic food preferences, making it difficult to provide flexible suggestions based on the user's emotional state. As a result, they have been unable to provide truly satisfying choices for users, and improving the quality of the experience has been a challenge.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes means for inputting the current location and destination from a user terminal for searching for facilities along the travel route from the starting point to the destination, means for analyzing the user's emotional state and generating reward information based on that state, and means for optimizing the reward information and presenting it to the user. As a result, the user can receive the most suitable dining facilities and rewards according to their emotional state, thereby increasing the satisfaction of their dining experience.

[0183] A "user terminal" is an electronic device used by a user to input their current location and destination and receive information.

[0184] The "optimal route" is the best travel route calculated considering factors such as distance to the destination, travel time, and traffic information.

[0185] The "Data Storage Unit" is a centralized information management system that stores information about restaurants and facilities and allows for retrieval as needed.

[0186] "Real-time information" refers to recent dynamic data such as current traffic conditions and facility congestion levels.

[0187] "Emotional state" refers to a psychological or emotional state inferred from the user's voice and facial expression analysis.

[0188] "Special offers" refer to information, including discounts and promotions, that are optimized according to the user's emotional state and preferences.

[0189] "Optimization" is the process of adjusting things to obtain the most efficient and effective results possible based on specific conditions.

[0190] The system that implements this application consists of a user terminal, a server, a data storage unit, and an emotion analysis engine. The user inputs their starting point and destination using the terminal. At this time, the terminal uses GPS to confirm the current location and analyzes the user's emotions using their voice and facial expressions as input. Technologies such as Google Cloud Speech-to-Text and Face++ can be used for this analysis.

[0191] The server receives location information, route information, and sentiment data sent from the user's terminal, and queries the data storage unit to search for relevant restaurants and facilities. During this process, real-time traffic information is also considered to calculate the optimal travel route.

[0192] The server then generates and sends personalized reward information to the user based on the sentiment analysis results. For example, a user who wants to relax might be offered a discount coupon for a quiet cafe. Here, a generative AI model is used to suggest rewards and promotions that match the user's mood and state.

[0193] For example, if a user is feeling stressed after a meeting in a business district office, the system might suggest a restaurant with a calm atmosphere along the user's route and offer a free drink coupon. In this process, the generative model is prompted with the message, "Recommend appropriate restaurants and offers based on the user's route and emotions."

[0194] This system allows users to enjoy facilities and services that match their emotional state while on the go, resulting in a comfortable and satisfying experience.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user uses a device to input their starting point and destination. The device acquires its current location via GPS and collects emotional data by detecting voice and facial expressions. At this point, the inputs are the current location, destination, voice sample, and facial expression data, and the output is a user profile summarizing this information.

[0198] Step 2:

[0199] The terminal sends a user profile to the server. The server receives this profile and uses an emotion analysis engine to analyze the user's emotional state from voice and facial expression data. The input here is the user profile, and after data analysis, the user's emotional state is output.

[0200] Step 3:

[0201] The server accesses the data storage unit and queries facility information based on the user's current location and destination. The input is the user's location and sentiment state, and the output is a list of recommended facilities based on location and the user's sentiment.

[0202] Step 4:

[0203] The server acquires real-time traffic information and calculates the optimal route. The input consists of traffic data and user route information, and the optimized travel route is output.

[0204] Step 5:

[0205] The server uses a generative AI model to generate reward information tailored to the user's emotional state. Specifically, it takes a list of recommended establishments and the user's emotional state as input and outputs suggestions for establishments with rewards. At this stage, the prompt "Recommend appropriate restaurants and rewards based on the user's path and emotional state" is used.

[0206] Step 6:

[0207] The server sends the final list of recommendations and reward information to the device. The device receives this information and displays it to the user to assist in their decision-making. The input is a list of recommendations with rewards, and the output is a visual presentation of information to the user.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] An embodiment of the present invention is built on a system including a user terminal, a server, and a database. In this system, the user first inputs a starting point and destination using the terminal, and based on this, the terminal confirms the current location and calculates the optimal route to the destination. The calculated route information is transmitted to the server.

[0225] Afterward, the user uses their device to input their meal preferences, tastes, and preferred meal times. This information is also transmitted to the server, which accesses a database to collect information about restaurants located along the route specified by the user.

[0226] The server analyzes and filters the collected restaurant information based on the user's preferences and past usage history. This filtering identifies the most suitable restaurant candidates for the user. Furthermore, the server considers real-time traffic information to narrow down the list to restaurants that can be reached within the specified time.

[0227] As a result, a filtered list of restaurants is displayed on the device. The list includes the location, rating, distance, and estimated arrival time for each restaurant, allowing the user to select a restaurant based on this information. After making a selection, the user can proceed with making a reservation using the device.

[0228] As a concrete example, consider a scenario where a user is traveling from Tokyo to Yokohama at 11:00 AM and wants to eat Chinese food at 1:00 PM. In this situation, the terminal calculates the optimal route from Tokyo to Yokohama and searches for Chinese restaurants along that route. The server considers real-time traffic information, filters the restaurants to find the ideal one that can be reached within the given time, and presents the user with the best option. The user can then quickly make a reservation at the selected restaurant through the terminal.

[0229] This system allows users to easily find suitable restaurants and complete reservations while on the go.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user opens a map application on their device and enters their starting point and destination. The device obtains its current location via GPS, uses that information to call a map service API, and calculates the optimal route to the destination. The device then displays the calculated route to the user.

[0233] Step 2:

[0234] The user uses a device to input their preferred meal type and time via voice or text. The device analyzes this input using speech recognition or text analysis technology and generates formalized data. This data is then sent to a server.

[0235] Step 3:

[0236] Based on the route information and dining preferences received from the user, the server uses an online database to collect data on restaurants along the route. The server then temporarily stores the collected data.

[0237] Step 4:

[0238] The server filters the collected restaurant data. It selects suitable restaurants considering the user's preferences and past usage history. It also obtains real-time traffic information and narrows down the list to restaurants that can be reached within the time specified by the user.

[0239] Step 5:

[0240] The server generates a filtered list of restaurants and sends it to the terminal. The terminal displays the received list on the user's screen and provides detailed information about each restaurant (rating, distance, estimated arrival time, etc.).

[0241] Step 6:

[0242] The user selects a restaurant they wish to visit from a presented list. The terminal then redisplays information about the selected restaurant and provides a link or button to support the reservation process. Through this link, the user can quickly complete the reservation process.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] Until now, it has been difficult for travelers to easily and efficiently find and reserve restaurants and bars that match their preferences and requirements along the optimal route to their destination. In particular, systems that select the best establishments considering real-time traffic information and past usage history are not widespread. Therefore, there is a need for methods to improve convenience for travelers.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes means for inputting location information and destination from a user terminal for searching for restaurants along the travel route from the starting point to the destination, means for calculating the optimal travel route from the location information to the destination based on the user's input, and means for receiving information on the user's dining preferences and desired meal times. This makes it possible for users to easily obtain store information based on their preferences and conditions and to make reservations seamlessly.

[0248] "User terminal" refers to an electronic device used by a user to input information, and includes smartphones, tablets, and other similar devices.

[0249] "Location information" refers to information that indicates a physical location, such as geographical coordinates or addresses, and is data obtained using technologies such as GPS.

[0250] "Destination" refers to the final point of arrival that the user wishes to reach, and is defined as a geographical location or address.

[0251] An "optimal travel route" refers to a route calculated to minimize distance and time during travel from a starting point to a destination, and is determined based on traffic conditions and user needs.

[0252] "Dietary preferences" refer to the user's personal tastes regarding specific types of cuisine, flavors, and ingredients.

[0253] "Desired meal time" refers to the specific time or period in which the user wishes to eat.

[0254] An "information store" refers to a database used to store, manage, and provide data related to food and beverage establishments.

[0255] "Food and beverage establishments" refer to places that serve food and beverages, and include restaurants, cafes, and food courts.

[0256] "Real-time information" refers to information that changes over time, such as current traffic conditions or store congestion levels.

[0257] "Selection" refers to the process of choosing the best option from multiple choices based on specific conditions or criteria.

[0258] The embodiment of this invention is built upon a system comprising a user terminal, a server, and an information store. This system begins with the user inputting their starting point and destination using the user terminal. The terminal uses its built-in GPS function to acquire location information. This accurately determines the current location and calculates the optimal travel route to the destination. This calculation references real-world road information using a map service API.

[0259] The user then uses their device to input their preferences regarding food genres and desired meal times. This information is sent to the server, which then accesses an information store to collect data on relevant restaurants. The server analyzes this collected data using programming languages ​​such as Python. Specifically, it applies machine learning based on the user's past selection history and ratings to select the most suitable restaurant candidates. Generative AI models may also be used in this analysis.

[0260] Furthermore, the server considers real-time updated traffic conditions to narrow down the list of restaurants that the user can reach by their desired mealtime. The traffic information used here is obtained through a traffic conditions API. Finally, filtered restaurant information is delivered to the terminal, and the user can select a restaurant based on this. Once the selection is complete, a reservation can be made at the selected restaurant from the terminal.

[0261] As a concrete example, consider a scenario where a user travels from city A to city B at 11:00 AM and desires a Chinese restaurant at 1:00 PM. In this situation, the terminal calculates the optimal travel route from city A to city B and searches for Chinese restaurants along that route. The server, taking real-time traffic information into account, selects the ideal restaurant that can be reached within a reasonable time and presents the results to the user. The user can then make a reservation at the selected restaurant through the terminal.

[0262] An example of a prompt message would be: "Please recommend a Chinese restaurant between my current location and city B. I'd prefer a place I can arrive at by 1 PM, and I'd also like suggestions for restaurants I've visited in the past."

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The user inputs their starting point and destination using their device. Based on this input, the device uses its built-in GPS function to determine the user's current location. The device outputs location information, specifying the starting point as "City A," the destination as "City B," and the current location as "Central part of City A."

[0266] Step 2:

[0267] The device uses a map service API to calculate the optimal travel route based on the acquired current location and destination. The input is the location information of the current location and destination, and the output is obtained as the shortest time route. In this process, route candidates are obtained from the map service and analyzed to generate detailed travel routes such as "from the center of City A to Station in City A, from Station in City A to Station in City B, and from Station in City B to the center of City B."

[0268] Step 3:

[0269] The user uses a terminal to input the type of food and desired meal time. For example, they might enter "Chinese food" and "1 PM," and this information is sent to the server. The input data includes meal preferences and time, and the output is confirmation that the information has been sent to the server. At this point, the user selects and confirms the information through the application interface.

[0270] Step 4:

[0271] The server uses the received data to access the information store and search for relevant restaurants located along the specified travel route. The input here is the travel route and the user's dining preferences, and the output is a list of candidate restaurants. The server executes an SQL query to extract "Chinese restaurants located along the travel route."

[0272] Step 5:

[0273] The server analyzes the collected facility information, along with the user's past usage history and real-time data. Using a generative AI model, it scores the stores that are expected to provide a higher level of satisfaction and selects the store best suited to the user. Data input consists of store information and past usage history, and output is a list of evaluated stores. The server uses the constructed model to calculate scores and select the top-ranked restaurants.

[0274] Step 6:

[0275] The server obtains real-time traffic information via a traffic conditions API and narrows down the list of stores reachable within the user's desired time. The input is traffic information and an existing list of stores, and the output is a list of stores that meet the time constraint. If there are fluctuations in traffic conditions, such as delays, the server performs filtering to ensure that only stores reachable within the time limit are included in the final list.

[0276] Step 7:

[0277] The terminal receives a list of selected restaurants from the server and displays it to the user. The user can review the list displayed on the screen and select their desired restaurant. Input is a filtered list of restaurants, and output is the restaurant selection and reservation confirmation. The user can then select their chosen restaurant and complete the reservation process through the application.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] Conventional food delivery services have made it difficult for users to receive prompt and timely delivery while on the go. Therefore, users had to plan meticulously in advance, and there was a risk that delivery would not occur at the desired time. This invention aims to solve the problem of enabling users to receive prompt delivery services from facilities near their destination, even while traveling or on business trips.

[0281] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes means for searching for facilities on the transportation route from the starting point to the destination, means for receiving, as data, the preferences and desired activity times of the users of the information processing device, and means for arranging logistics to the facilities specified from the filtered facility information. Thereby, even when the user is on the move, appropriate facilities that are in time can be selected on the route to the preset destination, enabling an efficient delivery service.

[0283] The "information processing device" is a device through which the user inputs data or receives results, and usually refers to a smartphone or a computer.

[0284] The "current location" is the geographical location at the moment when the information processing device is located.

[0285] The "destination" is a specific location designated by the user to reach.

[0286] The "optimal route" is a route calculated to meet specific conditions such as time and distance in the movement from the current location to the destination.

[0287] "Preferences" refer to the individual user's preferences for eating, drinking, and activities, and are used for personalization.

[0288] The "desired activity time" refers to the time period when the user desires to perform a specific activity.

[0289] The "information recording device" is a system such as a database that holds facility and traffic information and is used for retrieval and extraction as needed.

[0290] "Real-time information" refers to the latest data such as traffic conditions and facility congestion conditions obtained in real time.

[0291] "Filtering" is a process of selecting necessary items from the collected information based on specific criteria and conditions.

[0292] "Facilities" refers to places where restaurants and other activities are held, and are locations that are eligible for delivery services.

[0293] "Logistics" refers to the business of delivering and transporting goods and services to designated locations.

[0294] The system for realizing this application example begins with an information processing device (e.g., a smartphone) receiving the user's starting point, destination, preferences, and desired activity time. The device determines its current location using GPS and calculates the optimal route. This process can utilize route search services such as the Google Maps API. The server, upon receiving the user's preference data and desired activity time, uses an information recording device (e.g., a database) to retrieve appropriate facility information based on past data history.

[0295] The server uses APIs and data analysis tools to filter information while considering real-time data, and proposes the most suitable facility to the user. This allows for the selection of a facility that can provide service at the optimal time based on the travel route. For the facility selected by the user, logistics are arranged, and reservations are adjusted as needed.

[0296] To give a concrete example, let's consider a scenario where a user departs Osaka at 9:00 AM and wishes to have lunch at a cafe in Kyoto at noon. This system calculates the optimal route from Osaka to Kyoto, selects a cafe in Kyoto to coincide with the estimated arrival time, and arranges lunch. Based on this process, the user can receive efficient and comfortable service.

[0297] An example of a prompt for a generative AI model is: "When a user leaves Osaka at 9:00 AM and wants to have lunch at a cafe in Kyoto at 12:00 PM, please write Python code that recommends the best route and cafe."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The terminal receives from the user the current location, destination, preferences, and desired activity time as input. Based on the input data, the Google Maps API is used to calculate the optimal route from the current location to the destination. The output of this step is the optimal route information.

[0301] Step 2:

[0302] The terminal sends the optimal route information to the server. The server searches for facility information on the route from the information recording device based on the received route information and the user's preference data. The server filters the facility information based on the location data and the user's preferences and extracts the corresponding facilities. The output here is the filtered facility information.

[0303] Step 3:

[0304] The server obtains real-time immediate information and performs additional filtering considering traffic conditions and facility congestion. The server creates a list of facilities available to the user based on these conditions. The output of this process is the list of available facilities reflecting the immediate information.

[0305] Step 4:

[0306] The list of available facilities is sent from the server to the terminal. The user selects the desired facility from the list of facilities displayed on the terminal. This selection is sent by the terminal to the server, and the logistics arrangement is initiated. The output is the specific facility information selected by the user.

[0307] Step 5:

[0308] The server processes the logistics of goods to the facility selected by the user. If necessary, it adjusts the delivery to arrive at the time specified by the user and also makes reservations with the facility. The output of this step is the adjusted logistics plan.

[0309] Step 6:

[0310] The user's device displays a notification that the logistics plan is complete. The device helps users enjoy the service even while on the go by informing them of the estimated arrival time of their food delivery. The output of this step is notification information for the user.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention provides a system that recommends restaurants while considering the user's emotional state, by incorporating an emotion engine in addition to the usual location information service and restaurant database search functions. The system includes a user terminal, a server, a database, and an emotion engine.

[0313] The user enters their starting point and destination through the device and confirms their current location using GPS. Then, the user enters their meal preferences along the route via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotions and sends the data to a server.

[0314] The server queries a restaurant database based on route information, dining preferences, and even sentiment data submitted by the user, searching for relevant restaurants along the route. Real-time traffic conditions are also taken into consideration during this process.

[0315] During the filtering phase, the server uses the output of the emotion engine to identify the restaurant best suited to the user's current mood. For example, if the user wants to relax, a restaurant with a quiet and relaxed atmosphere will be recommended. Conversely, if they are looking for a lively atmosphere, a lively restaurant will be suggested.

[0316] As a result, the device not only displays a filtered list of restaurants, but also provides promotional and special offer information tailored to the user's emotions.

[0317] For example, if the emotion engine determines that a user is traveling on a highway and is feeling stressed, the server can prioritize listing restaurants in scenic locations or establishments playing relaxing background music, and may even offer special discounts.

[0318] This invention allows users to choose the optimal restaurant according to their mood and circumstances, enabling a comfortable and satisfying dining experience even while on the go.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user uses their device to input their starting point and destination. The device uses GPS to determine its current location and calls a map service API to calculate the optimal route to the destination.

[0322] Step 2:

[0323] The user inputs the type and time of their meal into the device via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice tone and text content to obtain emotional data.

[0324] Step 3:

[0325] The terminal sends acquired route information, meal preferences, and sentiment data to the server. Based on the received information, the server queries a large restaurant database to collect information on restaurants located along the user's route.

[0326] Step 4:

[0327] The server filters the collected restaurant information based on the user's dining preferences, real-time traffic information, and user sentiment data. Based on sentiment data, for example, if the user has a high stress level, the server prioritizes restaurants with a relaxing environment.

[0328] Step 5:

[0329] The server sends a filtered list of restaurants to the device. This list includes recommended restaurants tailored to the user's mood and special promotional information. The device displays this information to the user, allowing them to view the details.

[0330] Step 6:

[0331] The user selects a restaurant they wish to visit from a presented list. The terminal, upon receiving the user's selection, redisplays the restaurant's details and provides links or buttons to begin the reservation process. This allows the user to easily complete the reservation.

[0332] (Example 2)

[0333] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0334] While conventional restaurant search systems offer features to search for restaurants based on the user's location and basic food preferences, they lack flexible recommendations that take into account the user's emotions and mood. As a result, it was difficult for users to find a restaurant that was best suited to their mental state and mood at the time, sometimes leading to an unsatisfactory dining experience.

[0335] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0336] In this invention, the server includes means for searching for restaurants based on the user's route information and food and drink preferences, means for filtering the most suitable restaurants considering real-time information, and means for estimating the user's emotional state and adjusting recommendations accordingly. This makes it possible to recommend restaurants in a way that is appropriate to the user's current mood and mental state.

[0337] An "information terminal" is a device used by users to input location information and food and drink preferences, and to estimate their emotional state.

[0338] "Current location" refers to the user's real-time location information obtained via an information terminal.

[0339] The "destination" is the point that indicates the final destination of the journey entered by the user.

[0340] "Route" refers to the path taken from the current location to the destination.

[0341] "Preferences" refer to information indicating the types and conditions of food and drink that users prefer to choose.

[0342] "Data collection" refers to a database containing information about various food and beverage establishments.

[0343] "Filtering" refers to the process of selecting only information that meets specific criteria from a vast amount of information.

[0344] "Emotion analysis" is the process of estimating a user's emotional state from their speech and facial expressions.

[0345] "Benefit information" refers to information that includes additional benefits provided to users, such as promotions and discount information.

[0346] This invention relates to an information processing system that recommends the most suitable dining establishment based on the user's emotional state. Specifically, it consists of an information terminal, a server, data collection, and an emotion analysis engine.

[0347] The information terminal is a device that allows users to input their current location and destination, and can recognize their current geographical location using GPS functionality. It also has voice and text input capabilities, enabling users to input their food and drink preferences and mood. The emotion analysis engine is responsible for estimating emotions from the user's voice tone and facial expressions.

[0348] The server receives route information, preference data, and emotional state data transmitted from information terminals, and searches for restaurants by referring to the data collection. This involves manipulating the data collection using query languages ​​such as SQL to extract restaurants that meet the criteria. Real-time traffic information is also taken into consideration, and restaurants along the optimal route are selected.

[0349] The data collection system is a database containing information about various food and beverage establishments. It stores location information, establishment type, customer reviews, and promotional information, among other things.

[0350] For example, if a user inputs "My next destination is Kyoto, and I'd like a relaxing cafe" into their device, the server considers the user's emotional state, selects quiet and calming cafes from its data pool, and suggests them to the user along with special offers. A generative AI model complements the recommendation process, providing user-optimized feedback. This entire process allows users to enjoy a comfortable and satisfying dining experience even while on the go.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The device receives the user's current location and destination. The user uses the map application interface to confirm their current location and specify the destination. The input is geocoded location data, which the device uses to obtain coordinate information for the starting point and destination.

[0354] Step 2:

[0355] The user inputs their food and drink preferences and desired mental state into the device via voice or text. The device's microphone or keyboard is used for this input. For example, preferences such as "I want spicy food" or "I want a quiet place" might be entered. The device categorizes this input information into preference and emotional categories.

[0356] Step 3:

[0357] The emotion analysis engine built into the device captures the user's voice tone and facial expressions using sensors and cameras to estimate the user's emotional state. The input data consists of voice spectrum and facial recognition information, and this data is analyzed to observe emotional states such as "I want to relax" or "I want to be energetic."

[0358] Step 4:

[0359] The device sends collected route information, preference data, and sentiment data to the server. The data is sent to the server via encrypted communication using the HTTP protocol. At this stage, the server receives location information, preference categories, and sentiment as input data for queries.

[0360] Step 5:

[0361] The server queries the data repository to search for restaurants and bars along the route that match the user's preferences and emotional state. The queries are performed using SQL statements, and conditions are constructed based on location information, preference categories, and emotional state. A list of candidate restaurants and bars is generated as output.

[0362] Step 6:

[0363] The server further filters the obtained list of restaurants and bars based on the user's emotional state and sends it to the terminal along with promotional information. The filtering is performed using an algorithm based on the priority of emotional states and is designed to optimize the user's emotional needs. The output is a list of restaurants and bars best suited to the user, along with information about special offers.

[0364] Step 7:

[0365] The terminal displays information received from the server to the user. The user interface visually displays detailed information about restaurants and their locations on a map, and also provides links for reservations and directions. This allows users to quickly select the most suitable restaurant.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0368] In recent years, there has been an increasing demand for optimal suggestions based on individual user preferences and emotions when choosing meals while on the go. However, existing systems only consider location information and basic food preferences, making it difficult to provide flexible suggestions based on the user's emotional state. As a result, they have been unable to provide truly satisfying choices for users, and improving the quality of the experience has been a challenge.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes means for inputting the current location and destination from a user terminal for searching for facilities along the travel route from the starting point to the destination, means for analyzing the user's emotional state and generating reward information based on that state, and means for optimizing the reward information and presenting it to the user. As a result, the user can receive the most suitable dining facilities and rewards according to their emotional state, thereby increasing the satisfaction of their dining experience.

[0371] A "user terminal" is an electronic device used by a user to input their current location and destination and receive information.

[0372] The "optimal route" is the best travel route calculated considering factors such as distance to the destination, travel time, and traffic information.

[0373] The "Data Storage Unit" is a centralized information management system that stores information about restaurants and facilities and allows for retrieval as needed.

[0374] "Real-time information" refers to recent dynamic data such as current traffic conditions and facility congestion levels.

[0375] "Emotional state" refers to a psychological or emotional state inferred from the user's voice and facial expression analysis.

[0376] "Special offers" refer to information, including discounts and promotions, that are optimized according to the user's emotional state and preferences.

[0377] "Optimization" is the process of adjusting things to obtain the most efficient and effective results possible based on specific conditions.

[0378] The system that implements this application consists of a user terminal, a server, a data storage unit, and an emotion analysis engine. The user inputs their starting point and destination using the terminal. At this time, the terminal uses GPS to confirm the current location and analyzes the user's emotions using their voice and facial expressions as input. Technologies such as Google Cloud Speech-to-Text and Face++ can be used for this analysis.

[0379] The server receives location information, route information, and sentiment data sent from the user's terminal, and queries the data storage unit to search for relevant restaurants and facilities. During this process, real-time traffic information is also considered to calculate the optimal travel route.

[0380] The server then generates and sends personalized reward information to the user based on the sentiment analysis results. For example, a user who wants to relax might be offered a discount coupon for a quiet cafe. Here, a generative AI model is used to suggest rewards and promotions that match the user's mood and state.

[0381] For example, if a user is feeling stressed after a meeting in a business district office, the system might suggest a restaurant with a calm atmosphere along the user's route and offer a free drink coupon. In this process, the generative model is prompted with the message, "Recommend appropriate restaurants and offers based on the user's route and emotions."

[0382] This system allows users to enjoy facilities and services that match their emotional state while on the go, resulting in a comfortable and satisfying experience.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The user uses a device to input their starting point and destination. The device acquires its current location via GPS and collects emotional data by detecting voice and facial expressions. At this point, the inputs are the current location, destination, voice sample, and facial expression data, and the output is a user profile summarizing this information.

[0386] Step 2:

[0387] The terminal sends a user profile to the server. The server receives this profile and uses an emotion analysis engine to analyze the user's emotional state from voice and facial expression data. The input here is the user profile, and after data analysis, the user's emotional state is output.

[0388] Step 3:

[0389] The server accesses the data storage unit and queries facility information based on the user's current location and destination. The input is the user's location and sentiment state, and the output is a list of recommended facilities based on location and the user's sentiment.

[0390] Step 4:

[0391] The server acquires real-time traffic information and calculates the optimal route. The input consists of traffic data and user route information, and the optimized travel route is output.

[0392] Step 5:

[0393] The server uses a generative AI model to generate reward information tailored to the user's emotional state. Specifically, it takes a list of recommended establishments and the user's emotional state as input and outputs suggestions for establishments with rewards. At this stage, the prompt "Recommend appropriate restaurants and rewards based on the user's path and emotional state" is used.

[0394] Step 6:

[0395] The server sends the final list of recommendations and reward information to the device. The device receives this information and displays it to the user to assist in their decision-making. The input is a list of recommendations with rewards, and the output is a visual presentation of information to the user.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] An embodiment of the present invention is built on a system including a user terminal, a server, and a database. In this system, the user first inputs a starting point and destination using the terminal, and based on this, the terminal confirms the current location and calculates the optimal route to the destination. The calculated route information is transmitted to the server.

[0413] Afterward, the user uses their device to input their meal preferences, tastes, and preferred meal times. This information is also transmitted to the server, which accesses a database to collect information about restaurants located along the route specified by the user.

[0414] The server analyzes and filters the collected restaurant information based on the user's preferences and past usage history. This filtering identifies the most suitable restaurant candidates for the user. Furthermore, the server considers real-time traffic information to narrow down the list to restaurants that can be reached within the specified time.

[0415] As a result, a filtered list of restaurants is displayed on the device. The list includes the location, rating, distance, and estimated arrival time for each restaurant, allowing the user to select a restaurant based on this information. After making a selection, the user can proceed with making a reservation using the device.

[0416] As a concrete example, consider a scenario where a user is traveling from Tokyo to Yokohama at 11:00 AM and wants to eat Chinese food at 1:00 PM. In this situation, the terminal calculates the optimal route from Tokyo to Yokohama and searches for Chinese restaurants along that route. The server considers real-time traffic information, filters the restaurants to find the ideal one that can be reached within the given time, and presents the user with the best option. The user can then quickly make a reservation at the selected restaurant through the terminal.

[0417] This system allows users to easily find suitable restaurants and complete reservations while on the go.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The user opens a map application on their device and enters their starting point and destination. The device obtains its current location via GPS, uses that information to call a map service API, and calculates the optimal route to the destination. The device then displays the calculated route to the user.

[0421] Step 2:

[0422] The user uses a device to input their preferred meal type and time via voice or text. The device analyzes this input using speech recognition or text analysis technology and generates formalized data. This data is then sent to a server.

[0423] Step 3:

[0424] Based on the route information and dining preferences received from the user, the server uses an online database to collect data on restaurants along the route. The server then temporarily stores the collected data.

[0425] Step 4:

[0426] The server filters the collected restaurant data. It selects suitable restaurants considering the user's preferences and past usage history. It also obtains real-time traffic information and narrows down the list to restaurants that can be reached within the time specified by the user.

[0427] Step 5:

[0428] The server generates a filtered list of restaurants and sends it to the terminal. The terminal displays the received list on the user's screen and provides detailed information about each restaurant (rating, distance, estimated arrival time, etc.).

[0429] Step 6:

[0430] The user selects a restaurant they wish to visit from a presented list. The terminal then redisplays information about the selected restaurant and provides a link or button to support the reservation process. Through this link, the user can quickly complete the reservation process.

[0431] (Example 1)

[0432] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] Until now, it has been difficult for travelers to easily and efficiently find and reserve restaurants and bars that match their preferences and requirements along the optimal route to their destination. In particular, systems that select the best establishments considering real-time traffic information and past usage history are not widespread. Therefore, there is a need for methods to improve convenience for travelers.

[0434] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0435] In this invention, the server includes means for inputting location information and destination from a user terminal for searching for restaurants along the travel route from the starting point to the destination, means for calculating the optimal travel route from the location information to the destination based on the user's input, and means for receiving information on the user's dining preferences and desired meal times. This makes it possible for users to easily obtain store information based on their preferences and conditions and to make reservations seamlessly.

[0436] "User terminal" refers to an electronic device used by a user to input information, and includes smartphones, tablets, and other similar devices.

[0437] "Location information" refers to information that indicates a physical location, such as geographical coordinates or addresses, and is data obtained using technologies such as GPS.

[0438] "Destination" refers to the final point of arrival that the user wishes to reach, and is defined as a geographical location or address.

[0439] An "optimal travel route" refers to a route calculated to minimize distance and time during travel from a starting point to a destination, and is determined based on traffic conditions and user needs.

[0440] "Dietary preferences" refer to the user's personal tastes regarding specific types of cuisine, flavors, and ingredients.

[0441] "Desired meal time" refers to the specific time or period in which the user wishes to eat.

[0442] An "information store" refers to a database used to store, manage, and provide data related to food and beverage establishments.

[0443] "Food and beverage establishments" refer to places that serve food and beverages, and include restaurants, cafes, and food courts.

[0444] "Real-time information" refers to information that changes over time, such as current traffic conditions or store congestion levels.

[0445] "Selection" refers to the process of choosing the best option from multiple choices based on specific conditions or criteria.

[0446] The embodiment of this invention is built upon a system comprising a user terminal, a server, and an information store. This system begins with the user inputting their starting point and destination using the user terminal. The terminal uses its built-in GPS function to acquire location information. This accurately determines the current location and calculates the optimal travel route to the destination. This calculation references real-world road information using a map service API.

[0447] The user then uses their device to input their preferences regarding food genres and desired meal times. This information is sent to the server, which then accesses an information store to collect data on relevant restaurants. The server analyzes this collected data using programming languages ​​such as Python. Specifically, it applies machine learning based on the user's past selection history and ratings to select the most suitable restaurant candidates. Generative AI models may also be used in this analysis.

[0448] Furthermore, the server considers real-time updated traffic conditions to narrow down the list of restaurants that the user can reach by their desired mealtime. The traffic information used here is obtained through a traffic conditions API. Finally, filtered restaurant information is delivered to the terminal, and the user can select a restaurant based on this. Once the selection is complete, a reservation can be made at the selected restaurant from the terminal.

[0449] As a concrete example, consider a scenario where a user travels from city A to city B at 11:00 AM and desires a Chinese restaurant at 1:00 PM. In this situation, the terminal calculates the optimal travel route from city A to city B and searches for Chinese restaurants along that route. The server, taking real-time traffic information into account, selects the ideal restaurant that can be reached within a reasonable time and presents the results to the user. The user can then make a reservation at the selected restaurant through the terminal.

[0450] An example of a prompt message would be: "Please recommend a Chinese restaurant between my current location and city B. I'd prefer a place I can arrive at by 1 PM, and I'd also like suggestions for restaurants I've visited in the past."

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The user inputs their starting point and destination using their device. Based on this input, the device uses its built-in GPS function to determine the user's current location. The device outputs location information, specifying the starting point as "City A," the destination as "City B," and the current location as "Central part of City A."

[0454] Step 2:

[0455] The device uses a map service API to calculate the optimal travel route based on the acquired current location and destination. The input is the location information of the current location and destination, and the output is obtained as the shortest time route. In this process, route candidates are obtained from the map service and analyzed to generate detailed travel routes such as "from the center of City A to Station in City A, from Station in City A to Station in City B, and from Station in City B to the center of City B."

[0456] Step 3:

[0457] The user uses a terminal to input the type of food and desired meal time. For example, they might enter "Chinese food" and "1 PM," and this information is sent to the server. The input data includes meal preferences and time, and the output is confirmation that the information has been sent to the server. At this point, the user selects and confirms the information through the application interface.

[0458] Step 4:

[0459] The server uses the received data to access the information store and search for relevant restaurants located along the specified travel route. The input here is the travel route and the user's dining preferences, and the output is a list of candidate restaurants. The server executes an SQL query to extract "Chinese restaurants located along the travel route."

[0460] Step 5:

[0461] The server analyzes the collected facility information, along with the user's past usage history and real-time data. Using a generative AI model, it scores the stores that are expected to provide a higher level of satisfaction and selects the store best suited to the user. Data input consists of store information and past usage history, and output is a list of evaluated stores. The server uses the constructed model to calculate scores and select the top-ranked restaurants.

[0462] Step 6:

[0463] The server obtains real-time traffic information via a traffic conditions API and narrows down the list of stores reachable within the user's desired time. The input is traffic information and an existing list of stores, and the output is a list of stores that meet the time constraint. If there are fluctuations in traffic conditions, such as delays, the server performs filtering to ensure that only stores reachable within the time limit are included in the final list.

[0464] Step 7:

[0465] The terminal receives a list of selected restaurants from the server and displays it to the user. The user can review the list displayed on the screen and select their desired restaurant. Input is a filtered list of restaurants, and output is the restaurant selection and reservation confirmation. The user can then select their chosen restaurant and complete the reservation process through the application.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] Conventional food delivery services have made it difficult for users to receive prompt and timely delivery while on the go. Therefore, users had to plan meticulously in advance, and there was a risk that delivery would not occur at the desired time. This invention aims to solve the problem of enabling users to receive prompt delivery services from facilities near their destination, even while traveling or on business trips.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for searching for facilities along the transportation route from the starting point to the destination, means for receiving the user's preferences and desired activity time as data from the information processing device, and means for arranging logistics to the facilities identified from the filtered facility information. This enables efficient delivery services by selecting appropriate facilities along the pre-set route to the destination, even when the user is on the move, in accordance with the time.

[0471] An "information processing device" is a device on which users input data or receive results, and usually refers to a smartphone or computer.

[0472] "Current location" refers to the geographical location of the information processing device at that specific moment.

[0473] A "destination" is a specific place that the user has designated as their destination.

[0474] An "optimal route" is a route calculated to satisfy specific conditions, such as time and distance, when traveling from the current location to the destination.

[0475] "Preferences" refer to the individual user's tastes in food, drink, and activities, and are used for personalization purposes.

[0476] "Desired activity time" refers to the time period during which the user wishes to engage in a particular activity.

[0477] An "information recording device" is a system such as a database that stores facility and traffic information and is used for searching and retrieving it as needed.

[0478] "Real-time information" refers to the latest data, such as traffic conditions and facility congestion, that are acquired in real time.

[0479] "Filtering" is the process of selecting necessary items from collected information based on specific criteria or conditions.

[0480] "Facilities" refers to places where restaurants and other activities are held, and are locations that are eligible for delivery services.

[0481] "Logistics" refers to the business of delivering and transporting goods and services to designated locations.

[0482] The system for realizing this application example begins with an information processing device (e.g., a smartphone) receiving the user's starting point, destination, preferences, and desired activity time. The device determines its current location using GPS and calculates the optimal route. This process can utilize route search services such as the Google Maps API. The server, upon receiving the user's preference data and desired activity time, uses an information recording device (e.g., a database) to retrieve appropriate facility information based on past data history.

[0483] The server uses APIs and data analysis tools to filter information while considering real-time data, and proposes the most suitable facility to the user. This allows for the selection of a facility that can provide service at the optimal time based on the travel route. For the facility selected by the user, logistics are arranged, and reservations are adjusted as needed.

[0484] To give a concrete example, let's consider a scenario where a user departs Osaka at 9:00 AM and wishes to have lunch at a cafe in Kyoto at noon. This system calculates the optimal route from Osaka to Kyoto, selects a cafe in Kyoto to coincide with the estimated arrival time, and arranges lunch. Based on this process, the user can receive efficient and comfortable service.

[0485] An example of a prompt for a generative AI model is: "When a user leaves Osaka at 9:00 AM and wants to have lunch at a cafe in Kyoto at 12:00 PM, please write Python code that recommends the best route and cafe."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The device receives the user's current location, destination, preferences, and desired activity time as input. Based on the input data, the Google Maps API is used to calculate the optimal route from the current location to the destination. The output of this step is the optimal route information.

[0489] Step 2:

[0490] The terminal sends optimal route information to the server. The server searches for facility information along the route from the information recording device based on the received route information and user preference data. The server filters the facility information based on location data and user preferences and extracts the relevant facilities. The output here is the filtered facility information.

[0491] Step 3:

[0492] The server retrieves real-time information and performs additional filtering, taking into account traffic conditions and facility congestion. Based on these conditions, the server creates a list of facilities available to the user. The output of this process is a list of available facilities that reflects the real-time information.

[0493] Step 4:

[0494] A list of available facilities is sent from the server to the terminal. The user selects their desired facility from the list displayed on the terminal. This selection is sent from the terminal to the server, and logistics arrangements are initiated. The output is the information of the specific facility selected by the user.

[0495] Step 5:

[0496] The server processes the logistics of goods to the facility selected by the user. If necessary, it adjusts the delivery to arrive at the time specified by the user and also makes reservations with the facility. The output of this step is the adjusted logistics plan.

[0497] Step 6:

[0498] The user's device displays a notification that the logistics plan is complete. The device helps users enjoy the service even while on the go by informing them of the estimated arrival time of their food delivery. The output of this step is notification information for the user.

[0499] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0500] This invention provides a system that recommends restaurants while considering the user's emotional state, by incorporating an emotion engine in addition to the usual location information service and restaurant database search functions. The system includes a user terminal, a server, a database, and an emotion engine.

[0501] The user enters their starting point and destination through the device and confirms their current location using GPS. Then, the user enters their meal preferences along the route via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotions and sends the data to a server.

[0502] The server queries a restaurant database based on route information, dining preferences, and even sentiment data submitted by the user, searching for relevant restaurants along the route. Real-time traffic conditions are also taken into consideration during this process.

[0503] During the filtering phase, the server uses the output of the emotion engine to identify the restaurant best suited to the user's current mood. For example, if the user wants to relax, a restaurant with a quiet and relaxed atmosphere will be recommended. Conversely, if they are looking for a lively atmosphere, a lively restaurant will be suggested.

[0504] As a result, the device not only displays a filtered list of restaurants, but also provides promotional and special offer information tailored to the user's emotions.

[0505] For example, if the emotion engine determines that a user is traveling on a highway and is feeling stressed, the server can prioritize listing restaurants in scenic locations or establishments playing relaxing background music, and may even offer special discounts.

[0506] This invention allows users to choose the optimal restaurant according to their mood and circumstances, enabling a comfortable and satisfying dining experience even while on the go.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The user uses their device to input their starting point and destination. The device uses GPS to determine its current location and calls a map service API to calculate the optimal route to the destination.

[0510] Step 2:

[0511] The user inputs the type and time of their meal into the device via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice tone and text content to obtain emotional data.

[0512] Step 3:

[0513] The terminal sends acquired route information, meal preferences, and sentiment data to the server. Based on the received information, the server queries a large restaurant database to collect information on restaurants located along the user's route.

[0514] Step 4:

[0515] The server filters the collected restaurant information based on the user's dining preferences, real-time traffic information, and user sentiment data. Based on sentiment data, for example, if the user has a high stress level, the server prioritizes restaurants with a relaxing environment.

[0516] Step 5:

[0517] The server sends a filtered list of restaurants to the device. This list includes recommended restaurants tailored to the user's mood and special promotional information. The device displays this information to the user, allowing them to view the details.

[0518] Step 6:

[0519] The user selects a restaurant they wish to visit from a presented list. The terminal, upon receiving the user's selection, redisplays the restaurant's details and provides links or buttons to begin the reservation process. This allows the user to easily complete the reservation.

[0520] (Example 2)

[0521] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] While conventional restaurant search systems offer features to search for restaurants based on the user's location and basic food preferences, they lack flexible recommendations that take into account the user's emotions and mood. As a result, it was difficult for users to find a restaurant that was best suited to their mental state and mood at the time, sometimes leading to an unsatisfactory dining experience.

[0523] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0524] In this invention, the server includes means for searching for restaurants based on the user's route information and food and drink preferences, means for filtering the most suitable restaurants considering real-time information, and means for estimating the user's emotional state and adjusting recommendations accordingly. This makes it possible to recommend restaurants in a way that is appropriate to the user's current mood and mental state.

[0525] An "information terminal" is a device used by users to input location information and food and drink preferences, and to estimate their emotional state.

[0526] "Current location" refers to the user's real-time location information obtained via an information terminal.

[0527] The "destination" is the point that indicates the final destination of the journey entered by the user.

[0528] "Route" refers to the path taken from the current location to the destination.

[0529] "Preferences" refer to information indicating the types and conditions of food and drink that users prefer to choose.

[0530] "Data collection" refers to a database containing information about various food and beverage establishments.

[0531] "Filtering" refers to the process of selecting only information that meets specific criteria from a vast amount of information.

[0532] "Emotion analysis" is the process of estimating a user's emotional state from their speech and facial expressions.

[0533] "Benefit information" refers to information that includes additional benefits provided to users, such as promotions and discount information.

[0534] This invention relates to an information processing system that recommends the most suitable dining establishment based on the user's emotional state. Specifically, it consists of an information terminal, a server, data collection, and an emotion analysis engine.

[0535] The information terminal is a device that allows users to input their current location and destination, and can recognize their current geographical location using GPS functionality. It also has voice and text input capabilities, enabling users to input their food and drink preferences and mood. The emotion analysis engine is responsible for estimating emotions from the user's voice tone and facial expressions.

[0536] The server receives route information, preference data, and emotional state data transmitted from information terminals, and searches for restaurants by referring to the data collection. This involves manipulating the data collection using query languages ​​such as SQL to extract restaurants that meet the criteria. Real-time traffic information is also taken into consideration, and restaurants along the optimal route are selected.

[0537] The data collection system is a database containing information about various food and beverage establishments. It stores location information, establishment type, customer reviews, and promotional information, among other things.

[0538] For example, if a user inputs "My next destination is Kyoto, and I'd like a relaxing cafe" into their device, the server considers the user's emotional state, selects quiet and calming cafes from its data pool, and suggests them to the user along with special offers. A generative AI model complements the recommendation process, providing user-optimized feedback. This entire process allows users to enjoy a comfortable and satisfying dining experience even while on the go.

[0539] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0540] Step 1:

[0541] The device receives the user's current location and destination. The user uses the map application interface to confirm their current location and specify the destination. The input is geocoded location data, which the device uses to obtain coordinate information for the starting point and destination.

[0542] Step 2:

[0543] The user inputs their food and drink preferences and desired mental state into the device via voice or text. The device's microphone or keyboard is used for this input. For example, preferences such as "I want spicy food" or "I want a quiet place" might be entered. The device categorizes this input information into preference and emotional categories.

[0544] Step 3:

[0545] The emotion analysis engine built into the device captures the user's voice tone and facial expressions using sensors and cameras to estimate the user's emotional state. The input data consists of voice spectrum and facial recognition information, and this data is analyzed to observe emotional states such as "I want to relax" or "I want to be energetic."

[0546] Step 4:

[0547] The device sends collected route information, preference data, and sentiment data to the server. The data is sent to the server via encrypted communication using the HTTP protocol. At this stage, the server receives location information, preference categories, and sentiment as input data for queries.

[0548] Step 5:

[0549] The server queries the data repository to search for restaurants and bars along the route that match the user's preferences and emotional state. The queries are performed using SQL statements, and conditions are constructed based on location information, preference categories, and emotional state. A list of candidate restaurants and bars is generated as output.

[0550] Step 6:

[0551] The server further filters the obtained list of restaurants and bars based on the user's emotional state and sends it to the terminal along with promotional information. The filtering is performed using an algorithm based on the priority of emotional states and is designed to optimize the user's emotional needs. The output is a list of restaurants and bars best suited to the user, along with information about special offers.

[0552] Step 7:

[0553] The terminal displays information received from the server to the user. The user interface visually displays detailed information about restaurants and their locations on a map, and also provides links for reservations and directions. This allows users to quickly select the most suitable restaurant.

[0554] (Application Example 2)

[0555] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] In recent years, there has been an increasing demand for optimal suggestions based on individual user preferences and emotions when choosing meals while on the go. However, existing systems only consider location information and basic food preferences, making it difficult to provide flexible suggestions based on the user's emotional state. As a result, they have been unable to provide truly satisfying choices for users, and improving the quality of the experience has been a challenge.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0558] In this invention, the server includes means for inputting the current location and destination from a user terminal for searching for facilities along the travel route from the starting point to the destination, means for analyzing the user's emotional state and generating reward information based on that state, and means for optimizing the reward information and presenting it to the user. As a result, the user can receive the most suitable dining facilities and rewards according to their emotional state, thereby increasing the satisfaction of their dining experience.

[0559] A "user terminal" is an electronic device used by a user to input their current location and destination and receive information.

[0560] The "optimal route" is the best travel route calculated considering factors such as distance to the destination, travel time, and traffic information.

[0561] The "Data Storage Unit" is a centralized information management system that stores information about restaurants and facilities and allows for retrieval as needed.

[0562] "Real-time information" refers to recent dynamic data such as current traffic conditions and facility congestion levels.

[0563] "Emotional state" refers to a psychological or emotional state inferred from the user's voice and facial expression analysis.

[0564] "Special offers" refer to information, including discounts and promotions, that are optimized according to the user's emotional state and preferences.

[0565] "Optimization" is the process of adjusting things to obtain the most efficient and effective results possible based on specific conditions.

[0566] The system that implements this application consists of a user terminal, a server, a data storage unit, and an emotion analysis engine. The user inputs their starting point and destination using the terminal. At this time, the terminal uses GPS to confirm the current location and analyzes the user's emotions using their voice and facial expressions as input. Technologies such as Google Cloud Speech-to-Text and Face++ can be used for this analysis.

[0567] The server receives location information, route information, and sentiment data sent from the user's terminal, and queries the data storage unit to search for relevant restaurants and facilities. During this process, real-time traffic information is also considered to calculate the optimal travel route.

[0568] The server then generates and sends personalized reward information to the user based on the sentiment analysis results. For example, a user who wants to relax might be offered a discount coupon for a quiet cafe. Here, a generative AI model is used to suggest rewards and promotions that match the user's mood and state.

[0569] For example, if a user is feeling stressed after a meeting in a business district office, the system might suggest a restaurant with a calm atmosphere along the user's route and offer a free drink coupon. In this process, the generative model is prompted with the message, "Recommend appropriate restaurants and offers based on the user's route and emotions."

[0570] This system allows users to enjoy facilities and services that match their emotional state while on the go, resulting in a comfortable and satisfying experience.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The user uses a device to input their starting point and destination. The device acquires its current location via GPS and collects emotional data by detecting voice and facial expressions. At this point, the inputs are the current location, destination, voice sample, and facial expression data, and the output is a user profile summarizing this information.

[0574] Step 2:

[0575] The terminal sends a user profile to the server. The server receives this profile and uses an emotion analysis engine to analyze the user's emotional state from voice and facial expression data. The input here is the user profile, and after data analysis, the user's emotional state is output.

[0576] Step 3:

[0577] The server accesses the data storage unit and queries facility information based on the user's current location and destination. The input is the user's location and sentiment state, and the output is a list of recommended facilities based on location and the user's sentiment.

[0578] Step 4:

[0579] The server acquires real-time traffic information and calculates the optimal route. The input consists of traffic data and user route information, and the optimized travel route is output.

[0580] Step 5:

[0581] The server uses a generative AI model to generate reward information tailored to the user's emotional state. Specifically, it takes a list of recommended establishments and the user's emotional state as input and outputs suggestions for establishments with rewards. At this stage, the prompt "Recommend appropriate restaurants and rewards based on the user's path and emotional state" is used.

[0582] Step 6:

[0583] The server sends the final list of recommendations and reward information to the device. The device receives this information and displays it to the user to assist in their decision-making. The input is a list of recommendations with rewards, and the output is a visual presentation of information to the user.

[0584] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0585] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0587] [Fourth Embodiment]

[0588] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0589] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0590] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0592] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0594] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0595] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0596] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0597] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0598] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0599] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0600] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0601] An embodiment of the present invention is built on a system including a user terminal, a server, and a database. In this system, the user first inputs a starting point and destination using the terminal, and based on this, the terminal confirms the current location and calculates the optimal route to the destination. The calculated route information is transmitted to the server.

[0602] Afterward, the user uses their device to input their meal preferences, tastes, and preferred meal times. This information is also transmitted to the server, which accesses a database to collect information about restaurants located along the route specified by the user.

[0603] The server analyzes and filters the collected restaurant information based on the user's preferences and past usage history. This filtering identifies the most suitable restaurant candidates for the user. Furthermore, the server considers real-time traffic information to narrow down the list to restaurants that can be reached within the specified time.

[0604] As a result, a filtered list of restaurants is displayed on the device. The list includes the location, rating, distance, and estimated arrival time for each restaurant, allowing the user to select a restaurant based on this information. After making a selection, the user can proceed with making a reservation using the device.

[0605] As a concrete example, consider a scenario where a user is traveling from Tokyo to Yokohama at 11:00 AM and wants to eat Chinese food at 1:00 PM. In this situation, the terminal calculates the optimal route from Tokyo to Yokohama and searches for Chinese restaurants along that route. The server considers real-time traffic information, filters the restaurants to find the ideal one that can be reached within the given time, and presents the user with the best option. The user can then quickly make a reservation at the selected restaurant through the terminal.

[0606] This system allows users to easily find suitable restaurants and complete reservations while on the go.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user opens a map application on their device and enters their starting point and destination. The device obtains its current location via GPS, uses that information to call a map service API, and calculates the optimal route to the destination. The device then displays the calculated route to the user.

[0610] Step 2:

[0611] The user uses a device to input their preferred meal type and time via voice or text. The device analyzes this input using speech recognition or text analysis technology and generates formalized data. This data is then sent to a server.

[0612] Step 3:

[0613] Based on the route information and dining preferences received from the user, the server uses an online database to collect data on restaurants along the route. The server then temporarily stores the collected data.

[0614] Step 4:

[0615] The server filters the collected restaurant data. It selects suitable restaurants considering the user's preferences and past usage history. It also obtains real-time traffic information and narrows down the list to restaurants that can be reached within the time specified by the user.

[0616] Step 5:

[0617] The server generates a filtered list of restaurants and sends it to the terminal. The terminal displays the received list on the user's screen and provides detailed information about each restaurant (rating, distance, estimated arrival time, etc.).

[0618] Step 6:

[0619] The user selects a restaurant they wish to visit from a presented list. The terminal then redisplays information about the selected restaurant and provides a link or button to support the reservation process. Through this link, the user can quickly complete the reservation process.

[0620] (Example 1)

[0621] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0622] Until now, it has been difficult for travelers to easily and efficiently find and reserve restaurants and bars that match their preferences and requirements along the optimal route to their destination. In particular, systems that select the best establishments considering real-time traffic information and past usage history are not widespread. Therefore, there is a need for methods to improve convenience for travelers.

[0623] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0624] In this invention, the server includes means for inputting location information and destination from a user terminal for searching for restaurants along the travel route from the starting point to the destination, means for calculating the optimal travel route from the location information to the destination based on the user's input, and means for receiving information on the user's dining preferences and desired meal times. This makes it possible for users to easily obtain store information based on their preferences and conditions and to make reservations seamlessly.

[0625] "User terminal" refers to an electronic device used by a user to input information, and includes smartphones, tablets, and other similar devices.

[0626] "Location information" refers to information that indicates a physical location, such as geographical coordinates or addresses, and is data obtained using technologies such as GPS.

[0627] "Destination" refers to the final point of arrival that the user wishes to reach, and is defined as a geographical location or address.

[0628] An "optimal travel route" refers to a route calculated to minimize distance and time during travel from a starting point to a destination, and is determined based on traffic conditions and user needs.

[0629] "Dietary preferences" refer to the user's personal tastes regarding specific types of cuisine, flavors, and ingredients.

[0630] "Desired meal time" refers to the specific time or period in which the user wishes to eat.

[0631] An "information store" refers to a database used to store, manage, and provide data related to food and beverage establishments.

[0632] "Food and beverage establishments" refer to places that serve food and beverages, and include restaurants, cafes, and food courts.

[0633] "Real-time information" refers to information that changes over time, such as current traffic conditions or store congestion levels.

[0634] "Selection" refers to the process of choosing the best option from multiple choices based on specific conditions or criteria.

[0635] The embodiment of this invention is built upon a system comprising a user terminal, a server, and an information store. This system begins with the user inputting their starting point and destination using the user terminal. The terminal uses its built-in GPS function to acquire location information. This accurately determines the current location and calculates the optimal travel route to the destination. This calculation references real-world road information using a map service API.

[0636] The user then uses their device to input their preferences regarding food genres and desired meal times. This information is sent to the server, which then accesses an information store to collect data on relevant restaurants. The server analyzes this collected data using programming languages ​​such as Python. Specifically, it applies machine learning based on the user's past selection history and ratings to select the most suitable restaurant candidates. Generative AI models may also be used in this analysis.

[0637] Furthermore, the server considers real-time updated traffic conditions to narrow down the list of restaurants that the user can reach by their desired mealtime. The traffic information used here is obtained through a traffic conditions API. Finally, filtered restaurant information is delivered to the terminal, and the user can select a restaurant based on this. Once the selection is complete, a reservation can be made at the selected restaurant from the terminal.

[0638] As a concrete example, consider a scenario where a user travels from city A to city B at 11:00 AM and desires a Chinese restaurant at 1:00 PM. In this situation, the terminal calculates the optimal travel route from city A to city B and searches for Chinese restaurants along that route. The server, taking real-time traffic information into account, selects the ideal restaurant that can be reached within a reasonable time and presents the results to the user. The user can then make a reservation at the selected restaurant through the terminal.

[0639] An example of a prompt message would be: "Please recommend a Chinese restaurant between my current location and city B. I'd prefer a place I can arrive at by 1 PM, and I'd also like suggestions for restaurants I've visited in the past."

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The user inputs their starting point and destination using their device. Based on this input, the device uses its built-in GPS function to determine the user's current location. The device outputs location information, specifying the starting point as "City A," the destination as "City B," and the current location as "Central part of City A."

[0643] Step 2:

[0644] The device uses a map service API to calculate the optimal travel route based on the acquired current location and destination. The input is the location information of the current location and destination, and the output is obtained as the shortest time route. In this process, route candidates are obtained from the map service and analyzed to generate detailed travel routes such as "from the center of City A to Station in City A, from Station in City A to Station in City B, and from Station in City B to the center of City B."

[0645] Step 3:

[0646] The user uses a terminal to input the type of food and desired meal time. For example, they might enter "Chinese food" and "1 PM," and this information is sent to the server. The input data includes meal preferences and time, and the output is confirmation that the information has been sent to the server. At this point, the user selects and confirms the information through the application interface.

[0647] Step 4:

[0648] The server uses the received data to access the information store and search for relevant restaurants located along the specified travel route. The input here is the travel route and the user's dining preferences, and the output is a list of candidate restaurants. The server executes an SQL query to extract "Chinese restaurants located along the travel route."

[0649] Step 5:

[0650] The server analyzes the collected facility information, along with the user's past usage history and real-time data. Using a generative AI model, it scores the stores that are expected to provide a higher level of satisfaction and selects the store best suited to the user. Data input consists of store information and past usage history, and output is a list of evaluated stores. The server uses the constructed model to calculate scores and select the top-ranked restaurants.

[0651] Step 6:

[0652] The server obtains real-time traffic information via a traffic conditions API and narrows down the list of stores reachable within the user's desired time. The input is traffic information and an existing list of stores, and the output is a list of stores that meet the time constraint. If there are fluctuations in traffic conditions, such as delays, the server performs filtering to ensure that only stores reachable within the time limit are included in the final list.

[0653] Step 7:

[0654] The terminal receives a list of selected restaurants from the server and displays it to the user. The user can review the list displayed on the screen and select their desired restaurant. Input is a filtered list of restaurants, and output is the restaurant selection and reservation confirmation. The user can then select their chosen restaurant and complete the reservation process through the application.

[0655] (Application Example 1)

[0656] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] Conventional food delivery services have made it difficult for users to receive prompt and timely delivery while on the go. Therefore, users had to plan meticulously in advance, and there was a risk that delivery would not occur at the desired time. This invention aims to solve the problem of enabling users to receive prompt delivery services from facilities near their destination, even while traveling or on business trips.

[0658] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0659] In this invention, the server includes means for searching for facilities along the transportation route from the starting point to the destination, means for receiving the user's preferences and desired activity time as data from the information processing device, and means for arranging logistics to the facilities identified from the filtered facility information. This enables efficient delivery services by selecting appropriate facilities along the pre-set route to the destination, even when the user is on the move, in accordance with the time.

[0660] An "information processing device" is a device on which users input data or receive results, and usually refers to a smartphone or computer.

[0661] "Current location" refers to the geographical location of the information processing device at that specific moment.

[0662] A "destination" is a specific place that the user has designated as their destination.

[0663] An "optimal route" is a route calculated to satisfy specific conditions, such as time and distance, when traveling from the current location to the destination.

[0664] "Preferences" refer to the individual user's tastes in food, drink, and activities, and are used for personalization purposes.

[0665] "Desired activity time" refers to the time period during which the user wishes to engage in a particular activity.

[0666] An "information recording device" is a system such as a database that stores facility and traffic information and is used for searching and retrieving it as needed.

[0667] "Real-time information" refers to the latest data, such as traffic conditions and facility congestion, that are acquired in real time.

[0668] "Filtering" is the process of selecting necessary items from collected information based on specific criteria or conditions.

[0669] "Facilities" refers to places where restaurants and other activities are held, and are locations that are eligible for delivery services.

[0670] "Logistics" refers to the business of delivering and transporting goods and services to designated locations.

[0671] The system for realizing this application example begins with an information processing device (e.g., a smartphone) receiving the user's starting point, destination, preferences, and desired activity time. The device determines its current location using GPS and calculates the optimal route. This process can utilize route search services such as the Google Maps API. The server, upon receiving the user's preference data and desired activity time, uses an information recording device (e.g., a database) to retrieve appropriate facility information based on past data history.

[0672] The server uses APIs and data analysis tools to filter information while considering real-time data, and proposes the most suitable facility to the user. This allows for the selection of a facility that can provide service at the optimal time based on the travel route. For the facility selected by the user, logistics are arranged, and reservations are adjusted as needed.

[0673] To give a concrete example, let's consider a scenario where a user departs Osaka at 9:00 AM and wishes to have lunch at a cafe in Kyoto at noon. This system calculates the optimal route from Osaka to Kyoto, selects a cafe in Kyoto to coincide with the estimated arrival time, and arranges lunch. Based on this process, the user can receive efficient and comfortable service.

[0674] An example of a prompt for a generative AI model is: "When a user leaves Osaka at 9:00 AM and wants to have lunch at a cafe in Kyoto at 12:00 PM, please write Python code that recommends the best route and cafe."

[0675] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0676] Step 1:

[0677] The device receives the user's current location, destination, preferences, and desired activity time as input. Based on the input data, the Google Maps API is used to calculate the optimal route from the current location to the destination. The output of this step is the optimal route information.

[0678] Step 2:

[0679] The terminal sends optimal route information to the server. The server searches for facility information along the route from the information recording device based on the received route information and user preference data. The server filters the facility information based on location data and user preferences and extracts the relevant facilities. The output here is the filtered facility information.

[0680] Step 3:

[0681] The server retrieves real-time information and performs additional filtering, taking into account traffic conditions and facility congestion. Based on these conditions, the server creates a list of facilities available to the user. The output of this process is a list of available facilities that reflects the real-time information.

[0682] Step 4:

[0683] A list of available facilities is sent from the server to the terminal. The user selects their desired facility from the list displayed on the terminal. This selection is sent from the terminal to the server, and logistics arrangements are initiated. The output is the information of the specific facility selected by the user.

[0684] Step 5:

[0685] The server processes the logistics of goods to the facility selected by the user. If necessary, it adjusts the delivery to arrive at the time specified by the user and also makes reservations with the facility. The output of this step is the adjusted logistics plan.

[0686] Step 6:

[0687] The user's device displays a notification that the logistics plan is complete. The device helps users enjoy the service even while on the go by informing them of the estimated arrival time of their food delivery. The output of this step is notification information for the user.

[0688] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0689] This invention provides a system that recommends restaurants while considering the user's emotional state, by incorporating an emotion engine in addition to the usual location information service and restaurant database search functions. The system includes a user terminal, a server, a database, and an emotion engine.

[0690] The user enters their starting point and destination through the device and confirms their current location using GPS. Then, the user enters their meal preferences along the route via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice and facial expressions to determine their emotions and sends the data to a server.

[0691] The server queries a restaurant database based on route information, dining preferences, and even sentiment data submitted by the user, searching for relevant restaurants along the route. Real-time traffic conditions are also taken into consideration during this process.

[0692] During the filtering phase, the server uses the output of the emotion engine to identify the restaurant best suited to the user's current mood. For example, if the user wants to relax, a restaurant with a quiet and relaxed atmosphere will be recommended. Conversely, if they are looking for a lively atmosphere, a lively restaurant will be suggested.

[0693] As a result, the device not only displays a filtered list of restaurants, but also provides promotional and special offer information tailored to the user's emotions.

[0694] For example, if the emotion engine determines that a user is traveling on a highway and is feeling stressed, the server can prioritize listing restaurants in scenic locations or establishments playing relaxing background music, and may even offer special discounts.

[0695] This invention allows users to choose the optimal restaurant according to their mood and circumstances, enabling a comfortable and satisfying dining experience even while on the go.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The user uses their device to input their starting point and destination. The device uses GPS to determine its current location and calls a map service API to calculate the optimal route to the destination.

[0699] Step 2:

[0700] The user inputs the type and time of their meal into the device via voice or text. During this process, the device's built-in emotion engine analyzes the user's voice tone and text content to obtain emotional data.

[0701] Step 3:

[0702] The terminal sends acquired route information, meal preferences, and sentiment data to the server. Based on the received information, the server queries a large restaurant database to collect information on restaurants located along the user's route.

[0703] Step 4:

[0704] The server filters the collected restaurant information based on the user's dining preferences, real-time traffic information, and user sentiment data. Based on sentiment data, for example, if the user has a high stress level, the server prioritizes restaurants with a relaxing environment.

[0705] Step 5:

[0706] The server sends a filtered list of restaurants to the device. This list includes recommended restaurants tailored to the user's mood and special promotional information. The device displays this information to the user, allowing them to view the details.

[0707] Step 6:

[0708] The user selects a restaurant they wish to visit from a presented list. The terminal, upon receiving the user's selection, redisplays the restaurant's details and provides links or buttons to begin the reservation process. This allows the user to easily complete the reservation.

[0709] (Example 2)

[0710] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0711] While conventional restaurant search systems offer features to search for restaurants based on the user's location and basic food preferences, they lack flexible recommendations that take into account the user's emotions and mood. As a result, it was difficult for users to find a restaurant that was best suited to their mental state and mood at the time, sometimes leading to an unsatisfactory dining experience.

[0712] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0713] In this invention, the server includes means for searching for restaurants based on the user's route information and food and drink preferences, means for filtering the most suitable restaurants considering real-time information, and means for estimating the user's emotional state and adjusting recommendations accordingly. This makes it possible to recommend restaurants in a way that is appropriate to the user's current mood and mental state.

[0714] An "information terminal" is a device used by users to input location information and food and drink preferences, and to estimate their emotional state.

[0715] "Current location" refers to the user's real-time location information obtained via an information terminal.

[0716] The "destination" is the point that indicates the final destination of the journey entered by the user.

[0717] "Route" refers to the path taken from the current location to the destination.

[0718] "Preferences" refer to information indicating the types and conditions of food and drink that users prefer to choose.

[0719] "Data collection" refers to a database containing information about various food and beverage establishments.

[0720] "Filtering" refers to the process of selecting only information that meets specific criteria from a vast amount of information.

[0721] "Emotion analysis" is the process of estimating a user's emotional state from their speech and facial expressions.

[0722] "Benefit information" refers to information that includes additional benefits provided to users, such as promotions and discount information.

[0723] This invention relates to an information processing system that recommends the most suitable dining establishment based on the user's emotional state. Specifically, it consists of an information terminal, a server, data collection, and an emotion analysis engine.

[0724] The information terminal is a device that allows users to input their current location and destination, and can recognize their current geographical location using GPS functionality. It also has voice and text input capabilities, enabling users to input their food and drink preferences and mood. The emotion analysis engine is responsible for estimating emotions from the user's voice tone and facial expressions.

[0725] The server receives route information, preference data, and emotional state data transmitted from information terminals, and searches for restaurants by referring to the data collection. This involves manipulating the data collection using query languages ​​such as SQL to extract restaurants that meet the criteria. Real-time traffic information is also taken into consideration, and restaurants along the optimal route are selected.

[0726] The data collection system is a database containing information about various food and beverage establishments. It stores location information, establishment type, customer reviews, and promotional information, among other things.

[0727] For example, if a user inputs "My next destination is Kyoto, and I'd like a relaxing cafe" into their device, the server considers the user's emotional state, selects quiet and calming cafes from its data pool, and suggests them to the user along with special offers. A generative AI model complements the recommendation process, providing user-optimized feedback. This entire process allows users to enjoy a comfortable and satisfying dining experience even while on the go.

[0728] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0729] Step 1:

[0730] The device receives the user's current location and destination. The user uses the map application interface to confirm their current location and specify the destination. The input is geocoded location data, which the device uses to obtain coordinate information for the starting point and destination.

[0731] Step 2:

[0732] The user inputs their food and drink preferences and desired mental state into the device via voice or text. The device's microphone or keyboard is used for this input. For example, preferences such as "I want spicy food" or "I want a quiet place" might be entered. The device categorizes this input information into preference and emotional categories.

[0733] Step 3:

[0734] The emotion analysis engine built into the device captures the user's voice tone and facial expressions using sensors and cameras to estimate the user's emotional state. The input data consists of voice spectrum and facial recognition information, and this data is analyzed to observe emotional states such as "I want to relax" or "I want to be energetic."

[0735] Step 4:

[0736] The device sends collected route information, preference data, and sentiment data to the server. The data is sent to the server via encrypted communication using the HTTP protocol. At this stage, the server receives location information, preference categories, and sentiment as input data for queries.

[0737] Step 5:

[0738] The server queries the data repository to search for restaurants and bars along the route that match the user's preferences and emotional state. The queries are performed using SQL statements, and conditions are constructed based on location information, preference categories, and emotional state. A list of candidate restaurants and bars is generated as output.

[0739] Step 6:

[0740] The server further filters the obtained list of restaurants and bars based on the user's emotional state and sends it to the terminal along with promotional information. The filtering is performed using an algorithm based on the priority of emotional states and is designed to optimize the user's emotional needs. The output is a list of restaurants and bars best suited to the user, along with information about special offers.

[0741] Step 7:

[0742] The terminal displays information received from the server to the user. The user interface visually displays detailed information about restaurants and their locations on a map, and also provides links for reservations and directions. This allows users to quickly select the most suitable restaurant.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] In recent years, there has been an increasing demand for optimal suggestions based on individual user preferences and emotions when choosing meals while on the go. However, existing systems only consider location information and basic food preferences, making it difficult to provide flexible suggestions based on the user's emotional state. As a result, they have been unable to provide truly satisfying choices for users, and improving the quality of the experience has been a challenge.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes means for inputting the current location and destination from a user terminal for searching for facilities along the travel route from the starting point to the destination, means for analyzing the user's emotional state and generating reward information based on that state, and means for optimizing the reward information and presenting it to the user. As a result, the user can receive the most suitable dining facilities and rewards according to their emotional state, thereby increasing the satisfaction of their dining experience.

[0748] A "user terminal" is an electronic device used by a user to input their current location and destination and receive information.

[0749] The "optimal route" is the best travel route calculated considering factors such as distance to the destination, travel time, and traffic information.

[0750] The "Data Storage Unit" is a centralized information management system that stores information about restaurants and facilities and allows for retrieval as needed.

[0751] "Real-time information" refers to recent dynamic data such as current traffic conditions and facility congestion levels.

[0752] "Emotional state" refers to a psychological or emotional state inferred from the user's voice and facial expression analysis.

[0753] "Special offers" refer to information, including discounts and promotions, that are optimized according to the user's emotional state and preferences.

[0754] "Optimization" is the process of adjusting things to obtain the most efficient and effective results possible based on specific conditions.

[0755] The system that implements this application consists of a user terminal, a server, a data storage unit, and an emotion analysis engine. The user inputs their starting point and destination using the terminal. At this time, the terminal uses GPS to confirm the current location and analyzes the user's emotions using their voice and facial expressions as input. Technologies such as Google Cloud Speech-to-Text and Face++ can be used for this analysis.

[0756] The server receives location information, route information, and sentiment data sent from the user's terminal, and queries the data storage unit to search for relevant restaurants and facilities. During this process, real-time traffic information is also considered to calculate the optimal travel route.

[0757] The server then generates and sends personalized reward information to the user based on the sentiment analysis results. For example, a user who wants to relax might be offered a discount coupon for a quiet cafe. Here, a generative AI model is used to suggest rewards and promotions that match the user's mood and state.

[0758] For example, if a user is feeling stressed after a meeting in a business district office, the system might suggest a restaurant with a calm atmosphere along the user's route and offer a free drink coupon. In this process, the generative model is prompted with the message, "Recommend appropriate restaurants and offers based on the user's route and emotions."

[0759] This system allows users to enjoy facilities and services that match their emotional state while on the go, resulting in a comfortable and satisfying experience.

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] The user uses a device to input their starting point and destination. The device acquires its current location via GPS and collects emotional data by detecting voice and facial expressions. At this point, the inputs are the current location, destination, voice sample, and facial expression data, and the output is a user profile summarizing this information.

[0763] Step 2:

[0764] The terminal sends a user profile to the server. The server receives this profile and uses an emotion analysis engine to analyze the user's emotional state from voice and facial expression data. The input here is the user profile, and after data analysis, the user's emotional state is output.

[0765] Step 3:

[0766] The server accesses the data storage unit and queries facility information based on the user's current location and destination. The input is the user's location and sentiment state, and the output is a list of recommended facilities based on location and the user's sentiment.

[0767] Step 4:

[0768] The server acquires real-time traffic information and calculates the optimal route. The input consists of traffic data and user route information, and the optimized travel route is output.

[0769] Step 5:

[0770] The server uses a generative AI model to generate reward information tailored to the user's emotional state. Specifically, it takes a list of recommended establishments and the user's emotional state as input and outputs suggestions for establishments with rewards. At this stage, the prompt "Recommend appropriate restaurants and rewards based on the user's path and emotional state" is used.

[0771] Step 6:

[0772] The server sends the final list of recommendations and reward information to the device. The device receives this information and displays it to the user to assist in their decision-making. The input is a list of recommendations with rewards, and the output is a visual presentation of information to the user.

[0773] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0774] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0775] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0776] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0777] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0778] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0779] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0780] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0781] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0782] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0783] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0784] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0785] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0786] 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.

[0787] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0788] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0789] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0790] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0791] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0792] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0793] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0794] The following is further disclosed regarding the embodiments described above.

[0795] (Claim 1)

[0796] A means for inputting the current location and destination from a user terminal to search for restaurants along a transportation route from the starting point to the destination,

[0797] A means for calculating the optimal route from the current location to the destination based on user input,

[0798] A means of receiving information about the user's food and drink preferences and desired meal times,

[0799] A means of searching for a restaurant located along a transportation route using restaurant information obtained from a database,

[0800] A means of filtering restaurants that match the user's route and preferences, taking real-time information into consideration,

[0801] A system that includes means for providing filtered restaurant information to users.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising means for narrowing down candidates by considering location information along a transportation route when retrieving restaurant information from a database.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising means for assisting the user in making a reservation for a restaurant selected by the user.

[0806] "Example 1"

[0807] (Claim 1)

[0808] A means for inputting location information and destination from a user terminal to search for restaurants and bars along the travel route from the starting point to the destination,

[0809] A means for calculating the optimal travel route from location information to the destination based on user input,

[0810] A means of receiving information about the user's food preferences and desired meal times,

[0811] A means of searching for a relevant restaurant located along a travel route using restaurant information obtained from an information store,

[0812] A means of selecting a dining establishment that matches the user's route and preferences, taking into account past usage history and real-time information,

[0813] A system that includes means for providing users with information on selected food and beverage establishments.

[0814] (Claim 2)

[0815] The system according to claim 1, comprising means for narrowing down candidates by considering location information along the travel route when acquiring information on food and beverage establishments from an information store.

[0816] (Claim 3)

[0817] The system according to claim 1, further comprising means for assisting the user in making a reservation for a dining establishment selected by the user.

[0818] "Application Example 1"

[0819] (Claim 1)

[0820] A means for inputting the current location and destination from an information processing device to search for facilities along a transportation route from a starting point to a destination,

[0821] A means for calculating the optimal route from the current location to the destination based on the input of an information processing device,

[0822] A means of receiving the user preferences and desired activity time of an information processing device as data,

[0823] A means for searching for relevant facilities located along a transportation route using facility information obtained from an information recording device,

[0824] A means for filtering facilities that match the route and preferences of an information processing device, taking into account real-time information,

[0825] Means for providing filtered facility information to an information processing device,

[0826] A means of arranging logistics to facilities identified from filtered facility information,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for narrowing down candidates by considering location data on a traffic route when acquiring facility information from an information recording device.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising means for supporting reservation operations for facilities identified by the information processing device.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] A means of inputting the current location and destination from an information terminal to search for restaurants and bars along the route from the starting point to the destination,

[0835] A means for calculating the optimal route from the current location to the destination based on user data input,

[0836] A means of receiving information about the user's food and drink preferences and desired meal times,

[0837] A means for searching for target restaurants located along a route using restaurant information obtained from data collection,

[0838] A means for filtering dining establishments that match the user's route and preferences, taking real-time information into consideration,

[0839] A means of providing filtered information on food and beverage establishments to users,

[0840] A means for performing sentiment analysis to estimate the user's emotional state,

[0841] A means of recommending dining establishments based on estimated emotional states,

[0842] A system that includes means for displaying reward information based on emotions.

[0843] (Claim 2)

[0844] The system according to claim 1, comprising means for narrowing down candidates by considering location information along the route when acquiring information on food and beverage establishments from data collection.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for assisting the user in making a reservation for a restaurant or bar selected by the user.

[0847] "Application example 2 when combining with an emotional engine"

[0848] (Claim 1)

[0849] A means for inputting the current location and destination from a user terminal to search for facilities along the travel route from the starting point to the destination,

[0850] A means for calculating the optimal route from the current location to the destination based on user input,

[0851] A means of receiving information about the user's meal preferences and desired meal times,

[0852] A means for searching for the relevant facility located along the route using facility information obtained from the data storage unit,

[0853] A means to narrow down facilities that match the user's route and preferences, taking real-time information into consideration,

[0854] A means of providing users with narrowed-down facility information,

[0855] A means of analyzing the emotional state of users and generating reward information based on those emotions,

[0856] A means of optimizing and presenting special offer information to users,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, further comprising means for narrowing down candidates by considering location information along the route when acquiring facility information from a data storage unit.

[0860] (Claim 3)

[0861] The system according to claim 1, comprising means for assisting the user with procedures at a facility selected by the user. [Explanation of symbols]

[0862] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting the current location and destination from a user terminal to search for restaurants along a transportation route from the starting point to the destination, A means for calculating the optimal route from the current location to the destination based on user input, A means of receiving information about the user's food and drink preferences and desired meal times, A means of searching for a restaurant located along a transportation route using restaurant information obtained from a database, A means of filtering restaurants that match the user's route and preferences, taking real-time information into consideration, A system that includes means for providing filtered restaurant information to users.

2. The system according to claim 1, further comprising means for narrowing down candidates by considering location information along a transportation route when retrieving restaurant information from a database.

3. The system according to claim 1, further comprising means for assisting the user in making a reservation at a restaurant selected by the user.

Images