system

Abstract

We provide the system. [Solution] Means of obtaining information from user terminals, A means of analyzing user input using information acquisition means and identifying user needs using a language model, A means of retrieving relevant data from an information database based on identified needs and translating the retrieved data into the user's native language, A means of sending and displaying translated data on the user's terminal, A means of collecting user feedback and updating the information model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] The biggest problems faced by foreign visitors staying in Japan are the language barrier and the difficulty in accessing local information. As a result, the quality of travel deteriorates, and travelers may not be able to sightsee or shop as planned. Furthermore, it is difficult to provide information tailored to the needs of tourists, so the problem is that individual travel experiences are not sufficiently provided. By solving these problems, foreign visitors to Japan can enjoy a comfortable and fulfilling trip, and it is required to improve the quality of tourism.

Means for Solving the Problems

[0005] This invention provides a system that analyzes information entered by foreign visitors to Japan using a means for acquiring information from a user terminal, and identifies the user's needs using generation AI technology. Furthermore, based on the identified needs, it acquires data on relevant tourist destinations and services from an information database and translates them into the user's native language. By transmitting this translated information to the user terminal and making it easily accessible, foreign visitors to Japan can efficiently obtain information and enjoy a comfortable trip. In addition, by continuously learning the AI ​​model based on user feedback and improving the accuracy of the suggestions, a more personalized travel experience is provided. In this way, the invention provides a means to solve the problems of language barriers and information access.

[0006] A "user terminal" is an electronic device used by users to input or receive information, and includes smartphones and tablets.

[0007] An "information acquisition method" is a mechanism for electronically receiving requests from users and transmitting them within the system for analysis.

[0008] A "language model" is a technology that uses natural language processing to interpret the meaning and user intent from text and keywords, and to provide appropriate information and services.

[0009] An "information database" refers to a collection of data that systematically stores various types of information, such as local tourist attractions, restaurants, and transportation information, and makes it searchable.

[0010] "Means of translation into one's native language" refer to technologies and functions that accurately and efficiently convert information between different languages, making it easily understandable to the user.

[0011] "Feedback" refers to data that contributes to system improvement and service enhancement by allowing users to evaluate and comment on the information and services provided.

[0012] An "AI model" is an algorithm or system that utilizes machine learning and deep learning to learn from user behavior and feedback, and automatically provide the most suitable services and information. [Brief explanation of the drawing]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered 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, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system that provides a smart concierge service for foreign visitors to Japan, and mainly consists of a user terminal, an information acquisition means, a language model, an information database, a means for translating into the user's native language, a feedback function, and an AI model.

[0035] In this system, users request necessary information and services via their smartphones. For example, a user might input, "I'd like to know some recommended tourist spots in Kyoto." This input is sent to the server via an information retrieval mechanism. The server uses a language model to analyze the user's request and identify the user's preferences and needs. Once the needs are identified, the server accesses an information database to collect relevant tourist information.

[0036] The information is appropriately translated for user understanding and sent to the user's device in their native language. This allows users to access information without language barriers, even in foreign countries. Users can also provide feedback on the information provided and send their evaluation to the server via their device. This feedback is analyzed by an AI model and used as data to improve the quality of the service. The AI ​​model continuously learns to improve the accuracy of information and service provision in the future.

[0037] As a concrete example, consider a scenario where a user is staying in Kyoto and looking for a restaurant. The user enters "Recommended Japanese restaurants in Kyoto" into their device. Based on this, the server considers the user's current location and past preferences to find the most suitable restaurant information from its database, translates it, and sends it to the user's device. The user can then view the information and choose a restaurant. After the meal, they can also provide feedback on their satisfaction with the restaurant on their device. This feedback will be used to improve future restaurant choices.

[0038] This system aims to improve the travel experience of foreign visitors to Japan by providing information tailored to their individual needs.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users open the app on their smartphone and enter the information or service they want to know as a request. For example, if they want to know about "tourist attractions in Tokyo," they would enter that information.

[0042] Step 2:

[0043] The terminal analyzes the request entered by the user, converts it into the necessary data format, and sends it to the server. In this process, the terminal may also send the user's current location and past usage history to the server.

[0044] Step 3:

[0045] The server receives a request sent from the terminal. Next, the server uses a language model to parse the request and extract the necessary data. For example, it might identify the specified city name or category.

[0046] Step 4:

[0047] The server searches the information database based on the identified data. It extracts relevant information that matches the user's needs, taking into account the user's past preferences and current circumstances.

[0048] Step 5:

[0049] The server translates the extracted information into the user's native language. It uses internal translation modules and external translation APIs to convert the information into a format that is easy for the user to understand.

[0050] Step 6:

[0051] The server organizes the translated information and sends it back to the user's device in the most suitable format. For example, it might send it back in a suggested format that includes map information and detailed information.

[0052] Step 7:

[0053] The terminal displays information received from the server on the user interface. The user can then decide on an action based on the information provided.

[0054] Step 8:

[0055] Users provide feedback on the services and information offered through their devices. This feedback can be entered as ratings or comments.

[0056] Step 9:

[0057] The device sends the collected feedback to the server. This data is used to improve the service and train the AI ​​model.

[0058] Step 10:

[0059] The server feeds the received feedback into the AI ​​model, enabling it to make more accurate suggestions in future information provision. Once learning is complete, the process ends as a series of cycles.

[0060] (Example 1)

[0061] 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."

[0062] The difficulty foreign tourists face in obtaining information smoothly despite language barriers is a factor that restricts their selection of travel purposes and optimal actions during their stay. This problem can limit the traveler's experience and prevent them from achieving the comfortable trip they expect. Furthermore, if the information provided does not accurately meet the needs of individual travelers, there is a risk of decreased traveler satisfaction.

[0063] 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.

[0064] In this invention, the server includes means for communication from a user device, means for analyzing user input and identifying user needs using a natural language processing model, and means for retrieving relevant information from a database based on the identified needs and translating the retrieved information. This makes it possible for foreign tourists visiting Japan to obtain accurate information tailored to their individual needs in real time without experiencing language barriers.

[0065] A "user device" is a device equipped with communication functions for users to input and receive information.

[0066] "Communication means" refers to technologies and methods that enable the sending and receiving of data between user devices and servers.

[0067] A "natural language processing model" is a general term for algorithms and technologies used by computers to analyze, understand, and generate human language.

[0068] A "database" is a management system designed to organize and store large amounts of information, making it readily searchable as needed.

[0069] "Means of translation" refers to the technologies and methods used to convert information from one language to another.

[0070] An "artificial intelligence model" is software that has the ability to learn from large amounts of data and make judgments and predictions according to specific purposes.

[0071] This invention is an information provision system for foreign visitors to Japan, and consists of a user device, a communication device, natural language processing technology, an information database, a translation function, and an artificial intelligence model. Users use a user device such as a smartphone or tablet to request information and services during their trip via text. The device transmits this input to the server via the internet.

[0072] The server analyzes incoming requests using natural language processing (NLP) technology. Mature NLP models are implemented using products such as Google Translate and DeepL. Based on the user's needs identified through the analysis, the server accesses an information database to retrieve relevant information. This database contains a diverse range of tourist destination and facility information.

[0073] Next, the server uses automated translation technology to translate the collected information into the user's native language and sends it back to the user's device. This translation process may include additional language settings to reflect specific local cultural nuances. The user then reviews the translated information on their device to help them make travel decisions.

[0074] Furthermore, users can provide feedback on the information and services provided after their travel experience. This evaluation is sent back to the server, where an artificial intelligence model analyzes it. This model continuously learns using the feedback provided, improving the accuracy of future information provision. For this learning process, a generative AI model such as one from OpenAI® is used.

[0075] As a concrete example, consider a scenario where a user is staying in Kyoto and enters the prompt, "I'm looking for a recommended Japanese restaurant in Kyoto." In response to this prompt, the server uses past preference data, along with the user's current geographical location, to suggest the most suitable restaurant. The user can then use this information to easily choose a place to eat locally.

[0076] An example of a prompt message might be: "The user is heading to Osaka and wants to visit historical tourist spots. Please recommend some places."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user launches a dedicated app on their smartphone or tablet and enters their information request into a text box. An example input might be, "Please tell me some recommended tourist spots in Kyoto." The user then presses the "Submit" button to send the entered request to the server. The input here is the user's specific question, and the output is the request data sent to the server.

[0080] Step 2:

[0081] The terminal sends a text request from the user to the server over the internet. Specifically, the terminal uses data communication to packetize the request according to the protocol and forward it to the server's address. The input to this step is the user's text request, and the output is the request data that was successfully received by the server.

[0082] Step 3:

[0083] The server analyzes the received request data using natural language processing techniques. Specifically, it uses a natural language processing model to extract the intent and keywords of the request and clarify the user's needs. The input to this step is the request data received by the server, and the output is the analyzed user needs data.

[0084] Step 4:

[0085] Based on the analysis results, the server accesses the information database to search for the necessary information. Specifically, it uses SQL queries and other methods to quickly retrieve tourist destinations and service information that matches the user's needs. The input for this step is the analyzed needs data, and the output is the retrieved relevant information.

[0086] Step 5:

[0087] The server automatically translates the acquired information into the user's native language. This step involves specific processes, such as calling a translation API, to translate the acquired information within the appropriate context. The input is the acquired tourist information, and the output is the translated information.

[0088] Step 6:

[0089] The translated information is sent from the server to the user's device, and the device displays the information. Specifically, the system receives the transmitted data, formats it on the app's interface, and displays it so that the user can intuitively understand the information. The input for this step is the translated information data, and the output is the displayed content.

[0090] Step 7:

[0091] Users act based on the information provided and input and submit feedback from their devices. Specifically, users evaluate the accuracy and usefulness of the information and click the submit button on the server via the feedback form. The input in this step is the user's feedback content, and the output is the evaluation data sent to the server.

[0092] Step 8:

[0093] The server analyzes the received feedback and learns based on the generated AI model. Specifically, it evaluates the feedback data and extracts information that will help improve the accuracy of future information provision and enhance the service. The input for this step is user feedback data, and the output is the learning results for improvement.

[0094] (Application Example 1)

[0095] 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."

[0096] Foreign visitors to Japan face language barriers when obtaining information and selecting products in a foreign country, making it difficult to have a smooth shopping experience. Furthermore, the provision of information tailored to the diverse needs and preferences of users is often insufficient, leading to unsatisfactory service. Therefore, there is a need to develop systems that remove language barriers and enable effective information provision tailored to individual users.

[0097] 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.

[0098] In this invention, the server includes means for acquiring information from a user terminal, means for analyzing user input using the information acquisition means and identifying user needs using a language model, and means for acquiring relevant information from an information storage unit based on the identified needs and translating the acquired information into the user's native language. This makes it possible for foreign visitors to Japan to easily acquire product information in physical stores, overcoming language barriers, and to receive selective information based on their preferences and location.

[0099] A "user terminal" is a device used for information acquisition and as an interface, such as a smartphone or tablet, which is operated by the user to input and receive information.

[0100] "Information acquisition means" refers to a mechanism for collecting user input information and transmitting it to a server.

[0101] A "language model" is a natural language processing technique used to analyze user input and understand their intent.

[0102] "Needs" refer to the desires, demands, and preferences of users, and serve as the basis for providing services.

[0103] The "information storage unit" refers to a database or storage device where related information is stored, and is a source of information used for retrieving and searching for information.

[0104] "Translation means" refers to technology that converts acquired information into the user's native language and makes it understandable.

[0105] "Location information" refers to data that indicates the geographical location of users or physical stores, and is used to provide and recommend services.

[0106] "Methods for updating" refer to the process of collecting user evaluations and feedback in order to improve the service and enhance the accuracy of information.

[0107] This system uses a smartphone as the user terminal and transmits user input information to a server using an information acquisition method. The server analyzes the user's input using a language model to identify their needs. Natural language processing technology is used in the analysis to accurately understand the user's requirements.

[0108] The server retrieves relevant information from the data storage unit based on identified needs and translates that information into the user's native language using a translation tool. It is recommended to use a translation API (e.g., Google Translate API) for these processes. The translated information is sent to the user's terminal, allowing the user to access the information even in a foreign country, overcoming language barriers.

[0109] Furthermore, receiving feedback from user devices allows for updates to the information model on the server, leading to service improvements. Specifically, when a user selects a product in a physical store, scanning a QR code (registered trademark) retrieves specific product information, which is then translated into their native language through the language model. This allows foreign visitors to Japan to enjoy a smoother purchasing experience.

[0110] As a concrete example, when a user is selecting products at a specialty store in Japan, they scan the QR code of the product with their smartphone. At this time, product information of interest to the user (e.g., ingredients, origin, expiration date, etc.) is retrieved via the server and displayed on the smartphone in their native language. Through this process, the user can obtain product information in real time and make choices according to their preferences and needs.

[0111] An example of a prompt for a generative AI model would be: "Based on the user's current location and past preferences, select recommended products from nearby physical stores. Translate the selected product information into [language] and generate an appropriate description to provide to the user."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user scans a QR code using their smartphone. The input is the information from the QR code, which the device analyzes and sends to the server a specific product ID. The server then retrieves the product ID through this transmission process.

[0115] Step 2:

[0116] The server searches for relevant product information from its information storage unit based on the received product ID. The input for this search is the product ID, and the output is product information (e.g., ingredients, origin, expiration date, etc.). The server extracts this information and prepares it for the next translation process.

[0117] Step 3:

[0118] The server translates the retrieved product information into the user's native language using a language model and a translation API. The input here is the product information, and the output is the translated information. The server then processes this information into a user-friendly format and generates data for display.

[0119] Step 4:

[0120] The server sends the translated information to the user's terminal. The terminal displays this received information to the user. The output is translated product information, allowing the user to learn about product details in real time.

[0121] Step 5:

[0122] Users make purchasing decisions based on the product information provided. After purchasing a product, users send feedback, such as satisfaction levels, from their device to the server. This feedback information is passed to the server as input, and the information model is updated as output.

[0123] Step 6:

[0124] The server receives user feedback and uses a generative AI model to analyze the data and improve the accuracy of the service. The input is feedback data, and the output is an improved information model. This model will be used in future information provision.

[0125] 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.

[0126] This invention provides a system for smart concierge services for foreign visitors to Japan that enables the provision of information while taking into account the user's emotions. This system mainly consists of a user terminal, an emotion engine, information acquisition means, a language model, an information database, a translation means, a feedback function, and an AI model.

[0127] Users make requests for information and services via their smartphones. The device receives user input, and an emotion engine analyzes this input to recognize the user's emotional state. For example, if a user inputs "I want to know where I can relax right now," the emotion engine recognizes the desire to relax and adjusts its suggestions accordingly.

[0128] The device sends the analyzed information to the server. The server uses a language model to analyze the request and identify the user's needs. Based on this, it retrieves relevant information from its information database. The retrieved information is then tailored to the user's emotional state. For example, a user who wants to relax might be recommended quiet tourist spots or healing music.

[0129] The server then translates the information into the user's native language and sends it to the device. The device displays the received information in a format that is easy for the user to understand. The user can then decide on an action based on that information. Furthermore, the user can provide feedback on the presented information and suggestions through the device.

[0130] This feedback is sent back to the server and used as training data for the emotion engine and AI model. The AI ​​model improves based on past user responses and emotions, enhancing the accuracy of future suggestions. In this way, it becomes possible to provide optimal information tailored to each user's emotions and preferences.

[0131] As a concrete example, consider a scenario where a user is feeling stressed during a business trip and is seeking relaxation. When the user types "Tell me some relaxing places," the emotion engine detects their stress and recommends relaxing spas or nature walks. The information is translated and provided in the user's native language, making it easy for them to access and choose appropriate actions. Through this process, the user receives services tailored to their emotions, enriching their travel experience.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users enter information and service requests on a smartphone app. For example, they might enter "I want to know about places to relax in Tokyo."

[0135] Step 2:

[0136] The terminal receives the user's request. Simultaneously, the emotion engine analyzes the input text and recognizes the user's emotional state. In this case, the emotion of wanting to relax is recognized.

[0137] Step 3:

[0138] The device sends user requests to the server along with emotional information. The data is structured and transmitted using the appropriate protocol.

[0139] Step 4:

[0140] The server uses a language model to analyze the received request information. Here, it identifies that the user's need is to "relax in Tokyo."

[0141] Step 5:

[0142] The server accesses an information database and searches for information that matches the user's needs and emotions. It extracts tourist spots and facilities suitable for relaxation from the database.

[0143] Step 6:

[0144] The server translates search results into the user's native language. The translated information is then prioritized and organized based on sentiment.

[0145] Step 7:

[0146] The server sends the translated information to the terminal. Here, the information is formatted to be easily understood by the user.

[0147] Step 8:

[0148] The device displays the received information in a user interface. The user can choose a suitable place to relax from the listed information.

[0149] Step 9:

[0150] The user makes a selection based on the suggested information and enters the result as a review on the device. The review includes detailed feedback, including emotions.

[0151] Step 10:

[0152] The device sends feedback data to the server. This data is used as training data for the emotion engine and AI model.

[0153] Step 11:

[0154] The server analyzes the feedback and updates the AI ​​model. The emotion engine is also enhanced, improving the accuracy of future suggestions. This allows the entire system to provide suggestions that are more adapted to the user's emotions and needs.

[0155] (Example 2)

[0156] 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".

[0157] Information services targeting foreign visitors to Japan require the accurate and timely provision of personalized information that takes into account the user's feelings. However, conventional systems fail to adequately consider the user's emotional state, and the information provided is not always appropriate. Furthermore, communication barriers due to language differences exist, making it difficult for users to use the service without stress. It is necessary to solve this problem.

[0158] 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.

[0159] In this invention, the server includes means for analyzing user input and recognizing emotional states using natural language processing technology, means for obtaining relevant data from an information storage device based on the analyzed emotional state and needs, and means for adjusting the obtained data based on the user's emotional state and creating suggestions using a generative AI model. This enables the provision of optimal information tailored to the user's emotions, realizing a comfortable information provision service.

[0160] A "user terminal" is an information device used by a user to input and receive information.

[0161] "Information gathering means" refers to methods for obtaining user input data from a user terminal.

[0162] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0163] "Emotional state" refers to the user's psychological state inferred from their input.

[0164] An "information storage device" is a storage system for saving and retrieving necessary data.

[0165] A "generative AI model" is a model that uses machine learning algorithms to create suggestions tailored to the user's needs.

[0166] "Feedback" refers to data that expresses users' opinions and evaluations of the information and services provided.

[0167] "Translation methods" refer to methods of converting acquired information into a language that is easy for the user to understand.

[0168] This invention is an information system that provides emotion-based information to foreign visitors to Japan. The system includes a smartphone as a user terminal, a server, an emotion engine, information collection means, natural language processing technology, a generative AI model, an information storage device, a translation means, and a feedback collection function.

[0169] First, the user makes a request to the service using their smartphone. The user's device passes the entered text data to an emotion engine, which analyzes the user's emotional state using natural language processing technology. Through this analysis, a request such as "Tell me a place where I can relax" is understood to indicate a state of seeking relaxation.

[0170] The user terminal sends a request to the server that includes the results of sentiment analysis. The server uses the received information to identify the user's needs. It retrieves data on relevant locations and services from its information storage device and uses a generative AI model to create optimal suggestions tailored to the user's emotional state. For example, it might provide information on quiet and relaxing spas or nature trails to a user who is feeling stressed.

[0171] The server translates the generated information into the user's native language. The translated information is sent from the server to the user's terminal and displayed on the terminal in a visually easy-to-understand format. The user can then make decisions based on the information provided.

[0172] Furthermore, user feedback is sent to the server and used to improve the emotion engine and AI models. The feedback is used as training data for the system, improving the accuracy of its suggestions.

[0173] For example, if a user enters the prompt "Tell me where I can listen to healing music," the system will understand that the user is seeking relaxation and will suggest a suitable location. This allows the user to have a pleasant travel experience.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users enter information requests in text format on their smartphones. For example, they might enter a prompt like, "Tell me about relaxing places." This input data forms the basis for analysis based on the user's emotions and needs.

[0177] Step 2:

[0178] The device sends user input to an emotion engine, which then analyzes it using natural language processing technology. Specifically, the data processing involves keyword extraction and contextual analysis to identify emotions and intentions. The analysis reveals that the user is seeking relaxation. The output is data indicating the user's emotional state.

[0179] Step 3:

[0180] The terminal sends the analyzed emotional state and user prompts to the server. The input is text data containing the emotional state. The server receives this and uses a generative AI model to identify the user's needs. Specific data calculations include extracting needs based on the user's emotions and past data. The output is a query with identified needs.

[0181] Step 4:

[0182] The server retrieves relevant information from its data storage device based on identified needs. For example, it might retrieve data about places or activities where users can relax. The input is a query based on the needs, and after data retrieval, the output is a list of relevant information.

[0183] Step 5:

[0184] The server uses a generating AI model to adjust the acquired information based on the user's emotional state. Specifically, if the user is feeling stressed, locations that are expected to have a relaxing effect will be prioritized. The input is a list of relevant information, and the output is a suggestion optimized for the user's situation.

[0185] Step 6:

[0186] The server translates optimized suggestions into the user's native language. Real-time translation technology is used for rapid translation. The input is optimized suggestion data, and the output is the translated information.

[0187] Step 7:

[0188] The server sends the translated information to the user's terminal. The terminal receives the data and displays it in a user-friendly format. The input is the translated information, and the output is a display format that appeals to the user's visual sense.

[0189] Step 8:

[0190] Users review the information and provide feedback on the services offered via their device. This feedback is collected as data for the system to use in its learning process. The output consists of information about the user's impressions and suggestions.

[0191] Step 9:

[0192] The server analyzes feedback data to improve the emotion engine and generative AI model, thereby increasing the accuracy of future suggestions. The input is feedback data, and the output is the improved training data model.

[0193] (Application Example 2)

[0194] 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 device 14 will be referred to as the "terminal."

[0195] For foreign visitors to Japan to enjoy a comfortable shopping experience in physical stores, overcoming cultural and language barriers, it is necessary to provide information based on users' emotions and needs. However, current systems do not adequately consider these emotions and states when providing information, resulting in a limited user experience.

[0196] 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.

[0197] In this invention, the server includes an information acquisition device from a user device, means for analyzing user input using the information acquisition device and identifying user requests using a language model, means for acquiring relevant information from an information storage location based on the identified requests and translating the acquired information into the user's native language, and a device for recognizing the user's emotional state using an emotion analysis device and adjusting the information according to the recognized emotional state. This enables visitors to receive more precise and personalized information tailored to their emotions and needs at the time in a physical store, allowing them to comfortably use the service.

[0198] A "user device" is an electronic device that a user can carry and that allows for the input and display of information.

[0199] An "information acquisition device" is a technical device used to collect data entered by a user and transfer it to a system.

[0200] A "device for analyzing input" is a technological device that has the function of analyzing data obtained from users and understanding its meaning and purpose.

[0201] A "language model" is an artificial intelligence technology that understands human language and identifies requests.

[0202] An "information repository" is a database system where relevant information is stored and accessible.

[0203] "Translation methods" refer to technologies for converting information written in one language into another language.

[0204] A "transmission and display device" is a technological device that sends information to a user's device and displays it visually.

[0205] A "device for collecting evaluations and updating information models" is a technical device for collecting user feedback and improving the models used for information processing.

[0206] An "emotion analysis device" is a technological device that recognizes a user's emotional state and adjusts information based on that data.

[0207] This invention is a system that supports foreign visitors to Japan in comfortably engaging in purchasing activities in physical stores, overcoming cultural and linguistic barriers. This system includes a user device, a server, and an emotion analysis device. The details are described below.

[0208] First, the user inputs information via a user device such as a smartphone or tablet. The device then transmits the input data to the server via an information acquisition device. The server processes this data using a language model to identify the user's request.

[0209] Once a user's request is identified, the server retrieves relevant information from its data repository and translates it into the user's native language as needed. Commercially available translation APIs can be used for this translation process. The translated information is then transmitted to the user's device and displayed in an appropriate format.

[0210] On the other hand, the emotion analysis device recognizes the user's emotional state through the user's language input and, in some cases, video input. Emotion analysis utilizes technologies such as voice tone analysis and facial recognition (e.g., Microsoft® Azure® Face API and Google Cloud Speech-to-Text). The results of this emotion analysis are used to adjust the information provided; that is, customized information is provided according to the user's emotional state.

[0211] Furthermore, user ratings and feedback are collected on the server and used to update the information model. This process refines the system and improves its ability to provide optimal information tailored to individual users.

[0212] As a concrete example, consider a tourist staying in Tokyo who feels tired and needs to rest while shopping at a physical store. If the user inputs "I want to rest" into the device, the emotion analysis device can receive the request and send a prompt message to the server providing information about a nearby quiet cafe.

[0213] Example of a prompt:

[0214] "The user is tired. Please suggest places where they can rest comfortably."

[0215] In this way, the system understands visitors' emotions and needs and provides appropriate and personalized information, thereby creating a fulfilling shopping experience.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user uses a smartphone to input information. The user enters text based on their emotions and needs into the device. This input is collected by an information acquisition device and transmitted to a server. The main content of the input reflects the user's emotions and desires.

[0219] Step 2:

[0220] The server analyzes the received input data using natural language processing techniques. This process involves using a language model and data processing to identify user requests. The output then generates the identified user needs.

[0221] Step 3:

[0222] The server retrieves relevant information from the information repository based on the identified needs. The server executes database queries to search for highly relevant tourist attractions and services. Relevant information is generated as output.

[0223] Step 4:

[0224] The server translates the retrieved information into the user's native language. Standard translation APIs are used for the translation, and information is converted as needed. The output is the information translated into a language the user can understand.

[0225] Step 5:

[0226] The emotion analysis device analyzes the user's emotional state. The terminal recognizes emotions by referring to the input text and tone of voice. Data processing yields output regarding the user's emotional state.

[0227] Step 6:

[0228] The server adjusts the information based on the sentiment analysis results. The user's emotional state is taken into consideration, and the content of the information presented is optimized. The server generates prompts and customizes the information.

[0229] Step 7:

[0230] The translated information and sentiment-sensitive information are transmitted back to the terminal and displayed visually. The user can view the provided information through the terminal. The output is a visual display of the information on the user's terminal.

[0231] Step 8:

[0232] The user provides feedback on the presented information. The device collects the feedback data and sends it to the server. This feedback is used to improve the information model. The feedback data is obtained as output.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention is a system that provides a smart concierge service for foreign visitors to Japan, and mainly consists of a user terminal, an information acquisition means, a language model, an information database, a means for translating into the user's native language, a feedback function, and an AI model.

[0250] In this system, users request necessary information and services via their smartphones. For example, a user might input, "I'd like to know some recommended tourist spots in Kyoto." This input is sent to the server via an information retrieval mechanism. The server uses a language model to analyze the user's request and identify the user's preferences and needs. Once the needs are identified, the server accesses an information database to collect relevant tourist information.

[0251] The information is appropriately translated for user understanding and sent to the user's device in their native language. This allows users to access information without language barriers, even in foreign countries. Users can also provide feedback on the information provided and send their evaluation to the server via their device. This feedback is analyzed by an AI model and used as data to improve the quality of the service. The AI ​​model continuously learns to improve the accuracy of information and service provision in the future.

[0252] As a concrete example, consider a scenario where a user is staying in Kyoto and looking for a restaurant. The user enters "Recommended Japanese restaurants in Kyoto" into their device. Based on this, the server considers the user's current location and past preferences to find the most suitable restaurant information from its database, translates it, and sends it to the user's device. The user can then view the information and choose a restaurant. After the meal, they can also provide feedback on their satisfaction with the restaurant on their device. This feedback will be used to improve future restaurant choices.

[0253] This system aims to improve the travel experience of foreign visitors to Japan by providing information tailored to their individual needs.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] Users open the app on their smartphone and enter the information or service they want to know as a request. For example, if they want to know about "tourist attractions in Tokyo," they would enter that information.

[0257] Step 2:

[0258] The terminal analyzes the request entered by the user, converts it into the necessary data format, and sends it to the server. In this process, the terminal may also send the user's current location and past usage history to the server.

[0259] Step 3:

[0260] The server receives a request sent from the terminal. Next, the server uses a language model to parse the request and extract the necessary data. For example, it might identify the specified city name or category.

[0261] Step 4:

[0262] The server searches the information database based on the identified data. It extracts relevant information that matches the user's needs, taking into account the user's past preferences and current circumstances.

[0263] Step 5:

[0264] The server translates the extracted information into the user's native language. It uses internal translation modules and external translation APIs to convert the information into a format that is easy for the user to understand.

[0265] Step 6:

[0266] The server organizes the translated information and sends it back to the user's device in the most suitable format. For example, it might send it back in a suggested format that includes map information and detailed information.

[0267] Step 7:

[0268] The terminal displays information received from the server on the user interface. The user can then decide on an action based on the information provided.

[0269] Step 8:

[0270] Users provide feedback on the services and information offered through their devices. This feedback can be entered as ratings or comments.

[0271] Step 9:

[0272] The device sends the collected feedback to the server. This data is used to improve the service and train the AI ​​model.

[0273] Step 10:

[0274] The server feeds the received feedback into the AI ​​model, enabling it to make more accurate suggestions in future information provision. Once learning is complete, the process ends as a series of cycles.

[0275] (Example 1)

[0276] 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."

[0277] The difficulty foreign tourists face in obtaining information smoothly despite language barriers is a factor that restricts their selection of travel purposes and optimal actions during their stay. This problem can limit the traveler's experience and prevent them from achieving the comfortable trip they expect. Furthermore, if the information provided does not accurately meet the needs of individual travelers, there is a risk of decreased traveler satisfaction.

[0278] 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.

[0279] In this invention, the server includes communication means from a user device, means for analyzing a user's input and identifying the user's needs using a natural language processing model, and means for obtaining relevant information from a database based on the identified needs and translating the obtained information. As a result, foreign travelers visiting Japan can obtain accurate information tailored to their individual needs without feeling the language barrier in real time.

[0280] The "user device" refers to a device equipped with a communication function for a user to input or receive information.

[0281] The "communication means" refers to technologies or methods that enable data transmission and reception between a user device and a server.

[0282] The "natural language processing model" is a general term for algorithms and technologies for analyzing, understanding, and generating human language by a computer.

[0283] The "database" is a management system for organizing, storing a large amount of information, and enabling rapid retrieval as needed.

[0284] The "means for translating" refers to technologies or methods for converting information from one language to another.

[0285] The "artificial intelligence model" refers to software that learns a large amount of data and has the ability to make judgments and predictions according to specific purposes.

[0286] This invention is an information providing system for foreign visitors to Japan, which is composed of a user device, a communication device, natural language processing technology, an information database, a translation function, and an artificial intelligence model. The user uses a user device such as a smartphone or a tablet to request travel information and services in text. The terminal transmits this input to the server through the Internet.

[0287] The server analyzes incoming requests using natural language processing (NLP) technology. Mature NLP models are implemented using products such as Google Translate and DeepL. Based on the user's needs identified through the analysis, the server accesses an information database to retrieve relevant information. This database contains a diverse range of tourist destination and facility information.

[0288] Next, the server uses automated translation technology to translate the collected information into the user's native language and sends it back to the user's device. This translation process may include additional language settings to reflect specific local cultural nuances. The user then reviews the translated information on their device to help them make travel decisions.

[0289] Furthermore, users can provide feedback on the information and services provided after their travel experience. This evaluation is sent back to the server, where an artificial intelligence model analyzes it. This model continuously learns using the feedback provided, improving the accuracy of future information provision. For this learning process, a generative AI model such as one from OpenAI is used.

[0290] As a concrete example, consider a scenario where a user is staying in Kyoto and enters the prompt, "I'm looking for a recommended Japanese restaurant in Kyoto." In response to this prompt, the server uses past preference data, along with the user's current geographical location, to suggest the most suitable restaurant. The user can then use this information to easily choose a place to eat locally.

[0291] An example of a prompt message might be: "The user is heading to Osaka and wants to visit historical tourist spots. Please recommend some places."

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user launches a dedicated app on their smartphone or tablet and enters their information request into a text box. An example input might be, "Please tell me some recommended tourist spots in Kyoto." The user then presses the "Submit" button to send the entered request to the server. The input here is the user's specific question, and the output is the request data sent to the server.

[0295] Step 2:

[0296] The terminal sends a text request from the user to the server over the internet. Specifically, the terminal uses data communication to packetize the request according to the protocol and forward it to the server's address. The input to this step is the user's text request, and the output is the request data that was successfully received by the server.

[0297] Step 3:

[0298] The server analyzes the received request data using natural language processing techniques. Specifically, it uses a natural language processing model to extract the intent and keywords of the request and clarify the user's needs. The input to this step is the request data received by the server, and the output is the analyzed user needs data.

[0299] Step 4:

[0300] Based on the analysis results, the server accesses the information database to search for the necessary information. Specifically, it uses SQL queries and other methods to quickly retrieve tourist destinations and service information that matches the user's needs. The input for this step is the analyzed needs data, and the output is the retrieved relevant information.

[0301] Step 5:

[0302] The server automatically translates the acquired information into the user's native language. In this step, specific processes for translating the acquired information in an appropriate context are performed, such as by calling a translation API. The input is the acquired tourism information, and the output is the translated information.

[0303] Step 6:

[0304] The translated information is sent from the server to the user's terminal, and the terminal displays the information. As a specific operation, the received data is received and formatted and displayed on the interface of the application so that the user can intuitively understand the information. The input for this step is the translated information data, and the output is the displayed content.

[0305] Step 7:

[0306] The user acts based on the provided information and inputs and sends feedback from the terminal. Specifically, the user evaluates the accuracy and usefulness of the information and clicks the send button to the server through the feedback form. The input for this step is the content of the user's feedback, and the output is the evaluation data sent to the server.

[0307] Step 8:

[0308] The server analyzes the received feedback and performs learning based on the generated AI model. As a specific process, the feedback data is evaluated to extract information that contributes to improving the accuracy of the next information provision and service improvement. The input for this step is the user's feedback data, and the output is the learning result for improvement.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] Foreign visitors to Japan face language barriers when obtaining information and selecting products in a foreign country, making it difficult to have a smooth shopping experience. Furthermore, the provision of information tailored to the diverse needs and preferences of users is often insufficient, leading to unsatisfactory service. Therefore, there is a need to develop systems that remove language barriers and enable effective information provision tailored to individual users.

[0312] 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.

[0313] In this invention, the server includes means for acquiring information from a user terminal, means for analyzing user input using the information acquisition means and identifying user needs using a language model, and means for acquiring relevant information from an information storage unit based on the identified needs and translating the acquired information into the user's native language. This makes it possible for foreign visitors to Japan to easily acquire product information in physical stores, overcoming language barriers, and to receive selective information based on their preferences and location.

[0314] A "user terminal" is a device used for information acquisition and as an interface, such as a smartphone or tablet, which is operated by the user to input and receive information.

[0315] "Information acquisition means" refers to a mechanism for collecting user input information and transmitting it to a server.

[0316] A "language model" is a natural language processing technique used to analyze user input and understand their intent.

[0317] "Needs" refer to the desires, demands, and preferences of users, and serve as the basis for providing services.

[0318] The "information storage unit" refers to a database or storage device where related information is stored, and is a source of information used for retrieving and searching for information.

[0319] "Translation means" refers to technology that converts acquired information into the user's native language and makes it understandable.

[0320] "Location information" refers to data that indicates the geographical location of users or physical stores, and is used to provide and recommend services.

[0321] "Methods for updating" refer to the process of collecting user evaluations and feedback in order to improve the service and enhance the accuracy of information.

[0322] This system uses a smartphone as the user terminal and transmits user input information to a server using an information acquisition method. The server analyzes the user's input using a language model to identify their needs. Natural language processing technology is used in the analysis to accurately understand the user's requirements.

[0323] The server retrieves relevant information from the data storage unit based on identified needs and translates that information into the user's native language using a translation tool. It is recommended to use a translation API (e.g., Google Translate API) for these processes. The translated information is sent to the user's terminal, allowing the user to access the information even in a foreign country, overcoming language barriers.

[0324] Furthermore, receiving feedback from user devices allows for updates to the information model on the server, leading to service improvements. Specifically, when a user selects a product in a physical store, scanning a QR code retrieves specific product information, which is then translated into their native language through the language model. This allows foreign visitors to Japan to enjoy a smoother purchasing experience.

[0325] As a concrete example, when a user is selecting products at a specialty store in Japan, they scan the QR code of the product with their smartphone. At this time, product information of interest to the user (e.g., ingredients, origin, expiration date, etc.) is retrieved via the server and displayed on the smartphone in their native language. Through this process, the user can obtain product information in real time and make choices according to their preferences and needs.

[0326] An example of a prompt for a generative AI model would be: "Based on the user's current location and past preferences, select recommended products from nearby physical stores. Translate the selected product information into [language] and generate an appropriate description to provide to the user."

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user scans a QR code using their smartphone. The input is the information from the QR code, which the device analyzes and sends to the server a specific product ID. The server then retrieves the product ID through this transmission process.

[0330] Step 2:

[0331] The server searches for relevant product information from its information storage unit based on the received product ID. The input for this search is the product ID, and the output is product information (e.g., ingredients, origin, expiration date, etc.). The server extracts this information and prepares it for the next translation process.

[0332] Step 3:

[0333] The server translates the retrieved product information into the user's native language using a language model and a translation API. The input here is the product information, and the output is the translated information. The server then processes this information into a user-friendly format and generates data for display.

[0334] Step 4:

[0335] The server sends the translated information to the user's terminal. The terminal displays this received information to the user. The output is translated product information, allowing the user to learn about product details in real time.

[0336] Step 5:

[0337] Users make purchasing decisions based on the product information provided. After purchasing a product, users send feedback, such as satisfaction levels, from their device to the server. This feedback information is passed to the server as input, and the information model is updated as output.

[0338] Step 6:

[0339] The server receives user feedback and uses a generative AI model to analyze the data and improve the accuracy of the service. The input is feedback data, and the output is an improved information model. This model will be used in future information provision.

[0340] 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.

[0341] This invention provides a system for smart concierge services for foreign visitors to Japan that enables the provision of information while taking into account the user's emotions. This system mainly consists of a user terminal, an emotion engine, information acquisition means, a language model, an information database, a translation means, a feedback function, and an AI model.

[0342] Users make requests for information and services via their smartphones. The device receives user input, and an emotion engine analyzes this input to recognize the user's emotional state. For example, if a user inputs "I want to know where I can relax right now," the emotion engine recognizes the desire to relax and adjusts its suggestions accordingly.

[0343] The device sends the analyzed information to the server. The server uses a language model to analyze the request and identify the user's needs. Based on this, it retrieves relevant information from its information database. The retrieved information is then tailored to the user's emotional state. For example, a user who wants to relax might be recommended quiet tourist spots or healing music.

[0344] The server then translates the information into the user's native language and sends it to the device. The device displays the received information in a format that is easy for the user to understand. The user can then decide on an action based on that information. Furthermore, the user can provide feedback on the presented information and suggestions through the device.

[0345] This feedback is sent back to the server and used as training data for the emotion engine and AI model. The AI ​​model improves based on past user responses and emotions, enhancing the accuracy of future suggestions. In this way, it becomes possible to provide optimal information tailored to each user's emotions and preferences.

[0346] As a concrete example, consider a scenario where a user is feeling stressed during a business trip and is seeking relaxation. When the user types "Tell me some relaxing places," the emotion engine detects their stress and recommends relaxing spas or nature walks. The information is translated and provided in the user's native language, making it easy for them to access and choose appropriate actions. Through this process, the user receives services tailored to their emotions, enriching their travel experience.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] Users enter information and service requests on a smartphone app. For example, they might enter "I want to know about places to relax in Tokyo."

[0350] Step 2:

[0351] The terminal receives the user's request. Simultaneously, the emotion engine analyzes the input text and recognizes the user's emotional state. In this case, the emotion of wanting to relax is recognized.

[0352] Step 3:

[0353] The device sends user requests to the server along with emotional information. The data is structured and transmitted using the appropriate protocol.

[0354] Step 4:

[0355] The server uses a language model to analyze the received request information. Here, it identifies that the user's need is to "relax in Tokyo."

[0356] Step 5:

[0357] The server accesses an information database and searches for information that matches the user's needs and emotions. It extracts tourist spots and facilities suitable for relaxation from the database.

[0358] Step 6:

[0359] The server translates search results into the user's native language. The translated information is then prioritized and organized based on sentiment.

[0360] Step 7:

[0361] The server sends the translated information to the terminal. Here, the information is formatted to be easily understood by the user.

[0362] Step 8:

[0363] The device displays the received information in a user interface. The user can choose a suitable place to relax from the listed information.

[0364] Step 9:

[0365] The user makes a selection based on the suggested information and enters the result as a review on the device. The review includes detailed feedback, including emotions.

[0366] Step 10:

[0367] The device sends feedback data to the server. This data is used as training data for the emotion engine and AI model.

[0368] Step 11:

[0369] The server analyzes the feedback and updates the AI ​​model. The emotion engine is also enhanced, improving the accuracy of future suggestions. This allows the entire system to provide suggestions that are more adapted to the user's emotions and needs.

[0370] (Example 2)

[0371] 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".

[0372] Information services targeting foreign visitors to Japan require the accurate and timely provision of personalized information that takes into account the user's feelings. However, conventional systems fail to adequately consider the user's emotional state, and the information provided is not always appropriate. Furthermore, communication barriers due to language differences exist, making it difficult for users to use the service without stress. It is necessary to solve this problem.

[0373] 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.

[0374] In this invention, the server includes means for analyzing user input and recognizing emotional states using natural language processing technology, means for obtaining relevant data from an information storage device based on the analyzed emotional state and needs, and means for adjusting the obtained data based on the user's emotional state and creating suggestions using a generative AI model. This enables the provision of optimal information tailored to the user's emotions, realizing a comfortable information provision service.

[0375] A "user terminal" is an information device used by a user to input and receive information.

[0376] "Information gathering means" refers to methods for obtaining user input data from a user terminal.

[0377] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0378] "Emotional state" refers to the user's psychological state inferred from their input.

[0379] An "information storage device" is a storage system for saving and retrieving necessary data.

[0380] A "generative AI model" is a model that uses machine learning algorithms to create suggestions tailored to the user's needs.

[0381] "Feedback" refers to data that expresses users' opinions and evaluations of the information and services provided.

[0382] "Translation methods" refer to methods of converting acquired information into a language that is easy for the user to understand.

[0383] This invention is an information system that provides emotion-based information to foreign visitors to Japan. The system includes a smartphone as a user terminal, a server, an emotion engine, information collection means, natural language processing technology, a generative AI model, an information storage device, a translation means, and a feedback collection function.

[0384] First, the user makes a request to the service using their smartphone. The user's device passes the entered text data to an emotion engine, which analyzes the user's emotional state using natural language processing technology. Through this analysis, a request such as "Tell me a place where I can relax" is understood to indicate a state of seeking relaxation.

[0385] The user terminal sends a request to the server that includes the results of sentiment analysis. The server uses the received information to identify the user's needs. It retrieves data on relevant locations and services from its information storage device and uses a generative AI model to create optimal suggestions tailored to the user's emotional state. For example, it might provide information on quiet and relaxing spas or nature trails to a user who is feeling stressed.

[0386] The server translates the generated information into the user's native language. The translated information is sent from the server to the user's terminal and displayed on the terminal in a visually easy-to-understand format. The user can then make decisions based on the information provided.

[0387] Furthermore, user feedback is sent to the server and used to improve the emotion engine and AI models. The feedback is used as training data for the system, improving the accuracy of its suggestions.

[0388] For example, if a user enters the prompt "Tell me where I can listen to healing music," the system will understand that the user is seeking relaxation and will suggest a suitable location. This allows the user to have a pleasant travel experience.

[0389] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0390] Step 1:

[0391] Users enter information requests in text format on their smartphones. For example, they might enter a prompt like, "Tell me about relaxing places." This input data forms the basis for analysis based on the user's emotions and needs.

[0392] Step 2:

[0393] The device sends user input to an emotion engine, which then analyzes it using natural language processing technology. Specifically, the data processing involves keyword extraction and contextual analysis to identify emotions and intentions. The analysis reveals that the user is seeking relaxation. The output is data indicating the user's emotional state.

[0394] Step 3:

[0395] The terminal sends the analyzed emotional state and user prompts to the server. The input is text data containing the emotional state. The server receives this and uses a generative AI model to identify the user's needs. Specific data calculations include extracting needs based on the user's emotions and past data. The output is a query with identified needs.

[0396] Step 4:

[0397] The server retrieves relevant information from its data storage device based on identified needs. For example, it might retrieve data about places or activities where users can relax. The input is a query based on the needs, and after data retrieval, the output is a list of relevant information.

[0398] Step 5:

[0399] The server uses a generating AI model to adjust the acquired information based on the user's emotional state. Specifically, if the user is feeling stressed, locations that are expected to have a relaxing effect will be prioritized. The input is a list of relevant information, and the output is a suggestion optimized for the user's situation.

[0400] Step 6:

[0401] The server translates optimized suggestions into the user's native language. Real-time translation technology is used for rapid translation. The input is optimized suggestion data, and the output is the translated information.

[0402] Step 7:

[0403] The server sends the translated information to the user's terminal. The terminal receives the data and displays it in a user-friendly format. The input is the translated information, and the output is a display format that appeals to the user's visual sense.

[0404] Step 8:

[0405] Users review the information and provide feedback on the services offered via their device. This feedback is collected as data for the system to use in its learning process. The output consists of information about the user's impressions and suggestions.

[0406] Step 9:

[0407] The server analyzes feedback data to improve the emotion engine and generative AI model, thereby increasing the accuracy of future suggestions. The input is feedback data, and the output is the improved training data model.

[0408] (Application Example 2)

[0409] 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 will be referred to as the "terminal."

[0410] For foreign visitors to Japan to enjoy a comfortable shopping experience in physical stores, overcoming cultural and language barriers, it is necessary to provide information based on users' emotions and needs. However, current systems do not adequately consider these emotions and states when providing information, resulting in a limited user experience.

[0411] 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.

[0412] In this invention, the server includes an information acquisition device from a user device, means for analyzing user input using the information acquisition device and identifying user requests using a language model, means for acquiring relevant information from an information storage location based on the identified requests and translating the acquired information into the user's native language, and a device for recognizing the user's emotional state using an emotion analysis device and adjusting the information according to the recognized emotional state. This enables visitors to receive more precise and personalized information tailored to their emotions and needs at the time in a physical store, allowing them to comfortably use the service.

[0413] A "user device" is an electronic device that a user can carry and that allows for the input and display of information.

[0414] An "information acquisition device" is a technical device used to collect data entered by a user and transfer it to a system.

[0415] A "device for analyzing input" is a technological device that has the function of analyzing data obtained from users and understanding its meaning and purpose.

[0416] A "language model" is an artificial intelligence technology that understands human language and identifies requests.

[0417] An "information repository" is a database system where relevant information is stored and accessible.

[0418] "Translation methods" refer to technologies for converting information written in one language into another language.

[0419] A "transmission and display device" is a technological device that sends information to a user's device and displays it visually.

[0420] A "device for collecting evaluations and updating information models" is a technical device for collecting user feedback and improving the models used for information processing.

[0421] An "emotion analysis device" is a technological device that recognizes a user's emotional state and adjusts information based on that data.

[0422] This invention is a system that supports foreign visitors to Japan in comfortably engaging in purchasing activities in physical stores, overcoming cultural and linguistic barriers. This system includes a user device, a server, and an emotion analysis device. The details are described below.

[0423] First, the user inputs information via a user device such as a smartphone or tablet. The device then transmits the input data to the server via an information acquisition device. The server processes this data using a language model to identify the user's request.

[0424] Once a user's request is identified, the server retrieves relevant information from its data repository and translates it into the user's native language as needed. Commercially available translation APIs can be used for this translation process. The translated information is then transmitted to the user's device and displayed in an appropriate format.

[0425] On the other hand, emotion analysis devices recognize the user's emotional state through the user's language input and, in some cases, video input. Emotion analysis utilizes technologies such as voice tone analysis and facial recognition (e.g., Microsoft Azure Face API and Google Cloud Speech-to-Text). The results of this emotion analysis are used to adjust the information provided; that is, customized information is provided according to the user's emotional state.

[0426] Furthermore, user ratings and feedback are collected on the server and used to update the information model. This process refines the system and improves its ability to provide optimal information tailored to individual users.

[0427] As a concrete example, consider a tourist staying in Tokyo who feels tired and needs to rest while shopping at a physical store. If the user inputs "I want to rest" into the device, the emotion analysis device can receive the request and send a prompt message to the server providing information about a nearby quiet cafe.

[0428] Example of a prompt:

[0429] "The user is tired. Please suggest places where they can rest comfortably."

[0430] In this way, the system understands visitors' emotions and needs and provides appropriate and personalized information, thereby creating a fulfilling shopping experience.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The user uses a smartphone to input information. The user enters text based on their emotions and needs into the device. This input is collected by an information acquisition device and transmitted to a server. The main content of the input reflects the user's emotions and desires.

[0434] Step 2:

[0435] The server analyzes the received input data using natural language processing techniques. This process involves using a language model and data processing to identify user requests. The output then generates the identified user needs.

[0436] Step 3:

[0437] The server retrieves relevant information from the information repository based on the identified needs. The server executes database queries to search for highly relevant tourist attractions and services. Relevant information is generated as output.

[0438] Step 4:

[0439] The server translates the retrieved information into the user's native language. Standard translation APIs are used for the translation, and information is converted as needed. The output is the information translated into a language the user can understand.

[0440] Step 5:

[0441] The emotion analysis device analyzes the user's emotional state. The terminal recognizes emotions by referring to the input text and tone of voice. Data processing yields output regarding the user's emotional state.

[0442] Step 6:

[0443] The server adjusts the information based on the sentiment analysis results. The user's emotional state is taken into consideration, and the content of the information presented is optimized. The server generates prompts and customizes the information.

[0444] Step 7:

[0445] The translated information and sentiment-sensitive information are transmitted back to the terminal and displayed visually. The user can view the provided information through the terminal. The output is a visual display of the information on the user's terminal.

[0446] Step 8:

[0447] The user provides feedback on the presented information. The device collects the feedback data and sends it to the server. This feedback is used to improve the information model. The feedback data is obtained as output.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Third Embodiment]

[0452] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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".

[0464] This invention is a system that provides a smart concierge service for foreign visitors to Japan, and mainly consists of a user terminal, an information acquisition means, a language model, an information database, a means for translating into the user's native language, a feedback function, and an AI model.

[0465] In this system, users request necessary information and services via their smartphones. For example, a user might input, "I'd like to know some recommended tourist spots in Kyoto." This input is sent to the server via an information retrieval mechanism. The server uses a language model to analyze the user's request and identify the user's preferences and needs. Once the needs are identified, the server accesses an information database to collect relevant tourist information.

[0466] The information is appropriately translated for user understanding and sent to the user's device in their native language. This allows users to access information without language barriers, even in foreign countries. Users can also provide feedback on the information provided and send their evaluation to the server via their device. This feedback is analyzed by an AI model and used as data to improve the quality of the service. The AI ​​model continuously learns to improve the accuracy of information and service provision in the future.

[0467] As a concrete example, consider a scenario where a user is staying in Kyoto and looking for a restaurant. The user enters "Recommended Japanese restaurants in Kyoto" into their device. Based on this, the server considers the user's current location and past preferences to find the most suitable restaurant information from its database, translates it, and sends it to the user's device. The user can then view the information and choose a restaurant. After the meal, they can also provide feedback on their satisfaction with the restaurant on their device. This feedback will be used to improve future restaurant choices.

[0468] This system aims to improve the travel experience of foreign visitors to Japan by providing information tailored to their individual needs.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] Users open the app on their smartphone and enter the information or service they want to know as a request. For example, if they want to know about "tourist attractions in Tokyo," they would enter that information.

[0472] Step 2:

[0473] The terminal analyzes the request entered by the user, converts it into the necessary data format, and sends it to the server. In this process, the terminal may also send the user's current location and past usage history to the server.

[0474] Step 3:

[0475] The server receives a request sent from the terminal. Next, the server uses a language model to parse the request and extract the necessary data. For example, it might identify the specified city name or category.

[0476] Step 4:

[0477] The server searches the information database based on the identified data. It extracts relevant information that matches the user's needs, taking into account the user's past preferences and current circumstances.

[0478] Step 5:

[0479] The server translates the extracted information into the user's native language. It uses internal translation modules and external translation APIs to convert the information into a format that is easy for the user to understand.

[0480] Step 6:

[0481] The server organizes the translated information and sends it back to the user's device in the most suitable format. For example, it might send it back in a suggested format that includes map information and detailed information.

[0482] Step 7:

[0483] The terminal displays information received from the server on the user interface. The user can then decide on an action based on the information provided.

[0484] Step 8:

[0485] Users provide feedback on the services and information offered through their devices. This feedback can be entered as ratings or comments.

[0486] Step 9:

[0487] The device sends the collected feedback to the server. This data is used to improve the service and train the AI ​​model.

[0488] Step 10:

[0489] The server feeds the received feedback into the AI ​​model, enabling it to make more accurate suggestions in future information provision. Once learning is complete, the process ends as a series of cycles.

[0490] (Example 1)

[0491] 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."

[0492] The difficulty foreign tourists face in obtaining information smoothly despite language barriers is a factor that restricts their selection of travel purposes and optimal actions during their stay. This problem can limit the traveler's experience and prevent them from achieving the comfortable trip they expect. Furthermore, if the information provided does not accurately meet the needs of individual travelers, there is a risk of decreased traveler satisfaction.

[0493] 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.

[0494] In this invention, the server includes means for communication from a user device, means for analyzing user input and identifying user needs using a natural language processing model, and means for retrieving relevant information from a database based on the identified needs and translating the retrieved information. This makes it possible for foreign tourists visiting Japan to obtain accurate information tailored to their individual needs in real time without experiencing language barriers.

[0495] A "user device" is a device equipped with communication functions for users to input and receive information.

[0496] "Communication means" refers to technologies and methods that enable the sending and receiving of data between user devices and servers.

[0497] A "natural language processing model" is a general term for algorithms and technologies used by computers to analyze, understand, and generate human language.

[0498] A "database" is a management system designed to organize and store large amounts of information, making it readily searchable as needed.

[0499] "Means of translation" refers to the technologies and methods used to convert information from one language to another.

[0500] An "artificial intelligence model" is software that has the ability to learn from large amounts of data and make judgments and predictions according to specific purposes.

[0501] This invention is an information provision system for foreign visitors to Japan, and consists of a user device, a communication device, natural language processing technology, an information database, a translation function, and an artificial intelligence model. Users use a user device such as a smartphone or tablet to request information and services during their trip via text. The device transmits this input to the server via the internet.

[0502] The server analyzes incoming requests using natural language processing (NLP) technology. Mature NLP models are implemented using products such as Google Translate and DeepL. Based on the user's needs identified through the analysis, the server accesses an information database to retrieve relevant information. This database contains a diverse range of tourist destination and facility information.

[0503] Next, the server uses automated translation technology to translate the collected information into the user's native language and sends it back to the user's device. This translation process may include additional language settings to reflect specific local cultural nuances. The user then reviews the translated information on their device to help them make travel decisions.

[0504] Furthermore, users can provide feedback on the information and services provided after their travel experience. This evaluation is sent back to the server, where an artificial intelligence model analyzes it. This model continuously learns using the feedback provided, improving the accuracy of future information provision. For this learning process, a generative AI model such as one from OpenAI is used.

[0505] As a concrete example, consider a scenario where a user is staying in Kyoto and enters the prompt, "I'm looking for a recommended Japanese restaurant in Kyoto." In response to this prompt, the server uses past preference data, along with the user's current geographical location, to suggest the most suitable restaurant. The user can then use this information to easily choose a place to eat locally.

[0506] An example of a prompt message might be: "The user is heading to Osaka and wants to visit historical tourist spots. Please recommend some places."

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] The user launches a dedicated app on their smartphone or tablet and enters their information request into a text box. An example input might be, "Please tell me some recommended tourist spots in Kyoto." The user then presses the "Submit" button to send the entered request to the server. The input here is the user's specific question, and the output is the request data sent to the server.

[0510] Step 2:

[0511] The terminal sends a text request from the user to the server over the internet. Specifically, the terminal uses data communication to packetize the request according to the protocol and forward it to the server's address. The input to this step is the user's text request, and the output is the request data that was successfully received by the server.

[0512] Step 3:

[0513] The server analyzes the received request data using natural language processing techniques. Specifically, it uses a natural language processing model to extract the intent and keywords of the request and clarify the user's needs. The input to this step is the request data received by the server, and the output is the analyzed user needs data.

[0514] Step 4:

[0515] Based on the analysis results, the server accesses the information database to search for the necessary information. Specifically, it uses SQL queries and other methods to quickly retrieve tourist destinations and service information that matches the user's needs. The input for this step is the analyzed needs data, and the output is the retrieved relevant information.

[0516] Step 5:

[0517] The server automatically translates the acquired information into the user's native language. This step involves specific processes, such as calling a translation API, to translate the acquired information within the appropriate context. The input is the acquired tourist information, and the output is the translated information.

[0518] Step 6:

[0519] The translated information is sent from the server to the user's device, and the device displays the information. Specifically, the system receives the transmitted data, formats it on the app's interface, and displays it so that the user can intuitively understand the information. The input for this step is the translated information data, and the output is the displayed content.

[0520] Step 7:

[0521] Users act based on the information provided and input and submit feedback from their devices. Specifically, users evaluate the accuracy and usefulness of the information and click the submit button on the server via the feedback form. The input in this step is the user's feedback content, and the output is the evaluation data sent to the server.

[0522] Step 8:

[0523] The server analyzes the received feedback and learns based on the generated AI model. Specifically, it evaluates the feedback data and extracts information that will help improve the accuracy of future information provision and enhance the service. The input for this step is user feedback data, and the output is the learning results for improvement.

[0524] (Application Example 1)

[0525] 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."

[0526] Foreign visitors to Japan face language barriers when obtaining information and selecting products in a foreign country, making it difficult to have a smooth shopping experience. Furthermore, the provision of information tailored to the diverse needs and preferences of users is often insufficient, leading to unsatisfactory service. Therefore, there is a need to develop systems that remove language barriers and enable effective information provision tailored to individual users.

[0527] 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.

[0528] In this invention, the server includes means for acquiring information from a user terminal, means for analyzing user input using the information acquisition means and identifying user needs using a language model, and means for acquiring relevant information from an information storage unit based on the identified needs and translating the acquired information into the user's native language. This makes it possible for foreign visitors to Japan to easily acquire product information in physical stores, overcoming language barriers, and to receive selective information based on their preferences and location.

[0529] A "user terminal" is a device used for information acquisition and as an interface, such as a smartphone or tablet, which is operated by the user to input and receive information.

[0530] "Information acquisition means" refers to a mechanism for collecting user input information and transmitting it to a server.

[0531] A "language model" is a natural language processing technique used to analyze user input and understand their intent.

[0532] "Needs" refer to the desires, demands, and preferences of users, and serve as the basis for providing services.

[0533] The "information storage unit" refers to a database or storage device where related information is stored, and is a source of information used for retrieving and searching for information.

[0534] "Translation means" refers to technology that converts acquired information into the user's native language and makes it understandable.

[0535] "Location information" refers to data that indicates the geographical location of users or physical stores, and is used to provide and recommend services.

[0536] "Methods for updating" refer to the process of collecting user evaluations and feedback in order to improve the service and enhance the accuracy of information.

[0537] This system uses a smartphone as the user terminal and transmits user input information to a server using an information acquisition method. The server analyzes the user's input using a language model to identify their needs. Natural language processing technology is used in the analysis to accurately understand the user's requirements.

[0538] The server retrieves relevant information from the data storage unit based on identified needs and translates that information into the user's native language using a translation tool. It is recommended to use a translation API (e.g., Google Translate API) for these processes. The translated information is sent to the user's terminal, allowing the user to access the information even in a foreign country, overcoming language barriers.

[0539] Furthermore, receiving feedback from user devices allows for updates to the information model on the server, leading to service improvements. Specifically, when a user selects a product in a physical store, scanning a QR code retrieves specific product information, which is then translated into their native language through the language model. This allows foreign visitors to Japan to enjoy a smoother purchasing experience.

[0540] As a concrete example, when a user is selecting products at a specialty store in Japan, they scan the QR code of the product with their smartphone. At this time, product information of interest to the user (e.g., ingredients, origin, expiration date, etc.) is retrieved via the server and displayed on the smartphone in their native language. Through this process, the user can obtain product information in real time and make choices according to their preferences and needs.

[0541] An example of a prompt for a generative AI model would be: "Based on the user's current location and past preferences, select recommended products from nearby physical stores. Translate the selected product information into [language] and generate an appropriate description to provide to the user."

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The user scans a QR code using their smartphone. The input is the information from the QR code, which the device analyzes and sends to the server a specific product ID. The server then retrieves the product ID through this transmission process.

[0545] Step 2:

[0546] The server searches for relevant product information from its information storage unit based on the received product ID. The input for this search is the product ID, and the output is product information (e.g., ingredients, origin, expiration date, etc.). The server extracts this information and prepares it for the next translation process.

[0547] Step 3:

[0548] The server translates the retrieved product information into the user's native language using a language model and a translation API. The input here is the product information, and the output is the translated information. The server then processes this information into a user-friendly format and generates data for display.

[0549] Step 4:

[0550] The server sends the translated information to the user's terminal. The terminal displays this received information to the user. The output is translated product information, allowing the user to learn about product details in real time.

[0551] Step 5:

[0552] Users make purchasing decisions based on the product information provided. After purchasing a product, users send feedback, such as satisfaction levels, from their device to the server. This feedback information is passed to the server as input, and the information model is updated as output.

[0553] Step 6:

[0554] The server receives user feedback and uses a generative AI model to analyze the data and improve the accuracy of the service. The input is feedback data, and the output is an improved information model. This model will be used in future information provision.

[0555] 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.

[0556] This invention provides a system for smart concierge services for foreign visitors to Japan that enables the provision of information while taking into account the user's emotions. This system mainly consists of a user terminal, an emotion engine, information acquisition means, a language model, an information database, a translation means, a feedback function, and an AI model.

[0557] Users make requests for information and services via their smartphones. The device receives user input, and an emotion engine analyzes this input to recognize the user's emotional state. For example, if a user inputs "I want to know where I can relax right now," the emotion engine recognizes the desire to relax and adjusts its suggestions accordingly.

[0558] The device sends the analyzed information to the server. The server uses a language model to analyze the request and identify the user's needs. Based on this, it retrieves relevant information from its information database. The retrieved information is then tailored to the user's emotional state. For example, a user who wants to relax might be recommended quiet tourist spots or healing music.

[0559] The server then translates the information into the user's native language and sends it to the device. The device displays the received information in a format that is easy for the user to understand. The user can then decide on an action based on that information. Furthermore, the user can provide feedback on the presented information and suggestions through the device.

[0560] This feedback is sent back to the server and used as training data for the emotion engine and AI model. The AI ​​model improves based on past user responses and emotions, enhancing the accuracy of future suggestions. In this way, it becomes possible to provide optimal information tailored to each user's emotions and preferences.

[0561] As a concrete example, consider a scenario where a user is feeling stressed during a business trip and is seeking relaxation. When the user types "Tell me some relaxing places," the emotion engine detects their stress and recommends relaxing spas or nature walks. The information is translated and provided in the user's native language, making it easy for them to access and choose appropriate actions. Through this process, the user receives services tailored to their emotions, enriching their travel experience.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users enter information and service requests on a smartphone app. For example, they might enter "I want to know about places to relax in Tokyo."

[0565] Step 2:

[0566] The terminal receives the user's request. Simultaneously, the emotion engine analyzes the input text and recognizes the user's emotional state. In this case, the emotion of wanting to relax is recognized.

[0567] Step 3:

[0568] The device sends user requests to the server along with emotional information. The data is structured and transmitted using the appropriate protocol.

[0569] Step 4:

[0570] The server uses a language model to analyze the received request information. Here, it identifies that the user's need is to "relax in Tokyo."

[0571] Step 5:

[0572] The server accesses an information database and searches for information that matches the user's needs and emotions. It extracts tourist spots and facilities suitable for relaxation from the database.

[0573] Step 6:

[0574] The server translates search results into the user's native language. The translated information is then prioritized and organized based on sentiment.

[0575] Step 7:

[0576] The server sends the translated information to the terminal. Here, the information is formatted to be easily understood by the user.

[0577] Step 8:

[0578] The device displays the received information in a user interface. The user can choose a suitable place to relax from the listed information.

[0579] Step 9:

[0580] The user makes a selection based on the suggested information and enters the result as a review on the device. The review includes detailed feedback, including emotions.

[0581] Step 10:

[0582] The device sends feedback data to the server. This data is used as training data for the emotion engine and AI model.

[0583] Step 11:

[0584] The server analyzes the feedback and updates the AI ​​model. The emotion engine is also enhanced, improving the accuracy of future suggestions. This allows the entire system to provide suggestions that are more adapted to the user's emotions and needs.

[0585] (Example 2)

[0586] 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."

[0587] Information services targeting foreign visitors to Japan require the accurate and timely provision of personalized information that takes into account the user's feelings. However, conventional systems fail to adequately consider the user's emotional state, and the information provided is not always appropriate. Furthermore, communication barriers due to language differences exist, making it difficult for users to use the service without stress. It is necessary to solve this problem.

[0588] 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.

[0589] In this invention, the server includes means for analyzing user input and recognizing emotional states using natural language processing technology, means for obtaining relevant data from an information storage device based on the analyzed emotional state and needs, and means for adjusting the obtained data based on the user's emotional state and creating suggestions using a generative AI model. This enables the provision of optimal information tailored to the user's emotions, realizing a comfortable information provision service.

[0590] A "user terminal" is an information device used by a user to input and receive information.

[0591] "Information gathering means" refers to methods for obtaining user input data from a user terminal.

[0592] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0593] "Emotional state" refers to the user's psychological state inferred from their input.

[0594] An "information storage device" is a storage system for saving and retrieving necessary data.

[0595] A "generative AI model" is a model that uses machine learning algorithms to create suggestions tailored to the user's needs.

[0596] "Feedback" refers to data that expresses users' opinions and evaluations of the information and services provided.

[0597] "Translation methods" refer to methods of converting acquired information into a language that is easy for the user to understand.

[0598] This invention is an information system that provides emotion-based information to foreign visitors to Japan. The system includes a smartphone as a user terminal, a server, an emotion engine, information collection means, natural language processing technology, a generative AI model, an information storage device, a translation means, and a feedback collection function.

[0599] First, the user makes a request to the service using their smartphone. The user's device passes the entered text data to an emotion engine, which analyzes the user's emotional state using natural language processing technology. Through this analysis, a request such as "Tell me a place where I can relax" is understood to indicate a state of seeking relaxation.

[0600] The user terminal sends a request to the server that includes the results of sentiment analysis. The server uses the received information to identify the user's needs. It retrieves data on relevant locations and services from its information storage device and uses a generative AI model to create optimal suggestions tailored to the user's emotional state. For example, it might provide information on quiet and relaxing spas or nature trails to a user who is feeling stressed.

[0601] The server translates the generated information into the user's native language. The translated information is sent from the server to the user's terminal and displayed on the terminal in a visually easy-to-understand format. The user can then make decisions based on the information provided.

[0602] Furthermore, user feedback is sent to the server and used to improve the emotion engine and AI models. The feedback is used as training data for the system, improving the accuracy of its suggestions.

[0603] For example, if a user enters the prompt "Tell me where I can listen to healing music," the system will understand that the user is seeking relaxation and will suggest a suitable location. This allows the user to have a pleasant travel experience.

[0604] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0605] Step 1:

[0606] Users enter information requests in text format on their smartphones. For example, they might enter a prompt like, "Tell me about relaxing places." This input data forms the basis for analysis based on the user's emotions and needs.

[0607] Step 2:

[0608] The device sends user input to an emotion engine, which then analyzes it using natural language processing technology. Specifically, the data processing involves keyword extraction and contextual analysis to identify emotions and intentions. The analysis reveals that the user is seeking relaxation. The output is data indicating the user's emotional state.

[0609] Step 3:

[0610] The terminal sends the analyzed emotional state and user prompts to the server. The input is text data containing the emotional state. The server receives this and uses a generative AI model to identify the user's needs. Specific data calculations include extracting needs based on the user's emotions and past data. The output is a query with identified needs.

[0611] Step 4:

[0612] The server retrieves relevant information from its data storage device based on identified needs. For example, it might retrieve data about places or activities where users can relax. The input is a query based on the needs, and after data retrieval, the output is a list of relevant information.

[0613] Step 5:

[0614] The server uses a generating AI model to adjust the acquired information based on the user's emotional state. Specifically, if the user is feeling stressed, locations that are expected to have a relaxing effect will be prioritized. The input is a list of relevant information, and the output is a suggestion optimized for the user's situation.

[0615] Step 6:

[0616] The server translates optimized suggestions into the user's native language. Real-time translation technology is used for rapid translation. The input is optimized suggestion data, and the output is the translated information.

[0617] Step 7:

[0618] The server sends the translated information to the user's terminal. The terminal receives the data and displays it in a user-friendly format. The input is the translated information, and the output is a display format that appeals to the user's visual sense.

[0619] Step 8:

[0620] Users review the information and provide feedback on the services offered via their device. This feedback is collected as data for the system to use in its learning process. The output consists of information about the user's impressions and suggestions.

[0621] Step 9:

[0622] The server analyzes feedback data to improve the emotion engine and generative AI model, thereby increasing the accuracy of future suggestions. The input is feedback data, and the output is the improved training data model.

[0623] (Application Example 2)

[0624] 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."

[0625] For foreign visitors to Japan to enjoy a comfortable shopping experience in physical stores, overcoming cultural and language barriers, it is necessary to provide information based on users' emotions and needs. However, current systems do not adequately consider these emotions and states when providing information, resulting in a limited user experience.

[0626] 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.

[0627] In this invention, the server includes an information acquisition device from a user device, means for analyzing user input using the information acquisition device and identifying user requests using a language model, means for acquiring relevant information from an information storage location based on the identified requests and translating the acquired information into the user's native language, and a device for recognizing the user's emotional state using an emotion analysis device and adjusting the information according to the recognized emotional state. This enables visitors to receive more precise and personalized information tailored to their emotions and needs at the time in a physical store, allowing them to comfortably use the service.

[0628] A "user device" is an electronic device that a user can carry and that allows for the input and display of information.

[0629] An "information acquisition device" is a technical device used to collect data entered by a user and transfer it to a system.

[0630] A "device for analyzing input" is a technological device that has the function of analyzing data obtained from users and understanding its meaning and purpose.

[0631] A "language model" is an artificial intelligence technology that understands human language and identifies requests.

[0632] An "information repository" is a database system where relevant information is stored and accessible.

[0633] "Translation methods" refer to technologies for converting information written in one language into another language.

[0634] A "transmission and display device" is a technological device that sends information to a user's device and displays it visually.

[0635] A "device for collecting evaluations and updating information models" is a technical device for collecting user feedback and improving the models used for information processing.

[0636] An "emotion analysis device" is a technological device that recognizes a user's emotional state and adjusts information based on that data.

[0637] This invention is a system that supports foreign visitors to Japan in comfortably engaging in purchasing activities in physical stores, overcoming cultural and linguistic barriers. This system includes a user device, a server, and an emotion analysis device. The details are described below.

[0638] First, the user inputs information via a user device such as a smartphone or tablet. The device then transmits the input data to the server via an information acquisition device. The server processes this data using a language model to identify the user's request.

[0639] Once a user's request is identified, the server retrieves relevant information from its data repository and translates it into the user's native language as needed. Commercially available translation APIs can be used for this translation process. The translated information is then transmitted to the user's device and displayed in an appropriate format.

[0640] On the other hand, emotion analysis devices recognize the user's emotional state through the user's language input and, in some cases, video input. Emotion analysis utilizes technologies such as voice tone analysis and facial recognition (e.g., Microsoft Azure Face API and Google Cloud Speech-to-Text). The results of this emotion analysis are used to adjust the information provided; that is, customized information is provided according to the user's emotional state.

[0641] Furthermore, user ratings and feedback are collected on the server and used to update the information model. This process refines the system and improves its ability to provide optimal information tailored to individual users.

[0642] As a concrete example, consider a tourist staying in Tokyo who feels tired and needs to rest while shopping at a physical store. If the user inputs "I want to rest" into the device, the emotion analysis device can receive the request and send a prompt message to the server providing information about a nearby quiet cafe.

[0643] Example of a prompt:

[0644] "The user is tired. Please suggest places where they can rest comfortably."

[0645] In this way, the system understands visitors' emotions and needs and provides appropriate and personalized information, thereby creating a fulfilling shopping experience.

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The user uses a smartphone to input information. The user enters text based on their emotions and needs into the device. This input is collected by an information acquisition device and transmitted to a server. The main content of the input reflects the user's emotions and desires.

[0649] Step 2:

[0650] The server analyzes the received input data using natural language processing techniques. This process involves using a language model and data processing to identify user requests. The output then generates the identified user needs.

[0651] Step 3:

[0652] The server retrieves relevant information from the information repository based on the identified needs. The server executes database queries to search for highly relevant tourist attractions and services. Relevant information is generated as output.

[0653] Step 4:

[0654] The server translates the retrieved information into the user's native language. Standard translation APIs are used for the translation, and information is converted as needed. The output is the information translated into a language the user can understand.

[0655] Step 5:

[0656] The emotion analysis device analyzes the user's emotional state. The terminal recognizes emotions by referring to the input text and tone of voice. Data processing yields output regarding the user's emotional state.

[0657] Step 6:

[0658] The server adjusts the information based on the sentiment analysis results. The user's emotional state is taken into consideration, and the content of the information presented is optimized. The server generates prompts and customizes the information.

[0659] Step 7:

[0660] The translated information and sentiment-sensitive information are transmitted back to the terminal and displayed visually. The user can view the provided information through the terminal. The output is a visual display of the information on the user's terminal.

[0661] Step 8:

[0662] The user provides feedback on the presented information. The device collects the feedback data and sends it to the server. This feedback is used to improve the information model. The feedback data is obtained as output.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Fourth Embodiment]

[0667] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0668] 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.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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".

[0680] This invention is a system that provides a smart concierge service for foreign visitors to Japan, and mainly consists of a user terminal, an information acquisition means, a language model, an information database, a means for translating into the user's native language, a feedback function, and an AI model.

[0681] In this system, users request necessary information and services via their smartphones. For example, a user might input, "I'd like to know some recommended tourist spots in Kyoto." This input is sent to the server via an information retrieval mechanism. The server uses a language model to analyze the user's request and identify the user's preferences and needs. Once the needs are identified, the server accesses an information database to collect relevant tourist information.

[0682] The information is appropriately translated for user understanding and sent to the user's device in their native language. This allows users to access information without language barriers, even in foreign countries. Users can also provide feedback on the information provided and send their evaluation to the server via their device. This feedback is analyzed by an AI model and used as data to improve the quality of the service. The AI ​​model continuously learns to improve the accuracy of information and service provision in the future.

[0683] As a concrete example, consider a scenario where a user is staying in Kyoto and looking for a restaurant. The user enters "Recommended Japanese restaurants in Kyoto" into their device. Based on this, the server considers the user's current location and past preferences to find the most suitable restaurant information from its database, translates it, and sends it to the user's device. The user can then view the information and choose a restaurant. After the meal, they can also provide feedback on their satisfaction with the restaurant on their device. This feedback will be used to improve future restaurant choices.

[0684] This system aims to improve the travel experience of foreign visitors to Japan by providing information tailored to their individual needs.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] Users open the app on their smartphone and enter the information or service they want to know as a request. For example, if they want to know about "tourist attractions in Tokyo," they would enter that information.

[0688] Step 2:

[0689] The terminal analyzes the request entered by the user, converts it into the necessary data format, and sends it to the server. In this process, the terminal may also send the user's current location and past usage history to the server.

[0690] Step 3:

[0691] The server receives a request sent from the terminal. Next, the server uses a language model to parse the request and extract the necessary data. For example, it might identify the specified city name or category.

[0692] Step 4:

[0693] The server searches the information database based on the identified data. It extracts relevant information that matches the user's needs, taking into account the user's past preferences and current circumstances.

[0694] Step 5:

[0695] The server translates the extracted information into the user's native language. It uses internal translation modules and external translation APIs to convert the information into a format that is easy for the user to understand.

[0696] Step 6:

[0697] The server organizes the translated information and sends it back to the user's device in the most suitable format. For example, it might send it back in a suggested format that includes map information and detailed information.

[0698] Step 7:

[0699] The terminal displays information received from the server on the user interface. The user can then decide on an action based on the information provided.

[0700] Step 8:

[0701] Users provide feedback on the services and information offered through their devices. This feedback can be entered as ratings or comments.

[0702] Step 9:

[0703] The device sends the collected feedback to the server. This data is used to improve the service and train the AI ​​model.

[0704] Step 10:

[0705] The server feeds the received feedback into the AI ​​model, enabling it to make more accurate suggestions in future information provision. Once learning is complete, the process ends as a series of cycles.

[0706] (Example 1)

[0707] 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".

[0708] The difficulty foreign tourists face in obtaining information smoothly despite language barriers is a factor that restricts their selection of travel purposes and optimal actions during their stay. This problem can limit the traveler's experience and prevent them from achieving the comfortable trip they expect. Furthermore, if the information provided does not accurately meet the needs of individual travelers, there is a risk of decreased traveler satisfaction.

[0709] 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.

[0710] In this invention, the server includes means for communication from a user device, means for analyzing user input and identifying user needs using a natural language processing model, and means for retrieving relevant information from a database based on the identified needs and translating the retrieved information. This makes it possible for foreign tourists visiting Japan to obtain accurate information tailored to their individual needs in real time without experiencing language barriers.

[0711] A "user device" is a device equipped with communication functions for users to input and receive information.

[0712] "Communication means" refers to technologies and methods that enable the sending and receiving of data between user devices and servers.

[0713] A "natural language processing model" is a general term for algorithms and technologies used by computers to analyze, understand, and generate human language.

[0714] A "database" is a management system designed to organize and store large amounts of information, making it readily searchable as needed.

[0715] "Means of translation" refers to the technologies and methods used to convert information from one language to another.

[0716] An "artificial intelligence model" is software that has the ability to learn from large amounts of data and make judgments and predictions according to specific purposes.

[0717] This invention is an information provision system for foreign visitors to Japan, and consists of a user device, a communication device, natural language processing technology, an information database, a translation function, and an artificial intelligence model. Users use a user device such as a smartphone or tablet to request information and services during their trip via text. The device transmits this input to the server via the internet.

[0718] The server analyzes incoming requests using natural language processing (NLP) technology. Mature NLP models are implemented using products such as Google Translate and DeepL. Based on the user's needs identified through the analysis, the server accesses an information database to retrieve relevant information. This database contains a diverse range of tourist destination and facility information.

[0719] Next, the server uses automated translation technology to translate the collected information into the user's native language and sends it back to the user's device. This translation process may include additional language settings to reflect specific local cultural nuances. The user then reviews the translated information on their device to help them make travel decisions.

[0720] Furthermore, users can provide feedback on the information and services provided after their travel experience. This evaluation is sent back to the server, where an artificial intelligence model analyzes it. This model continuously learns using the feedback provided, improving the accuracy of future information provision. For this learning process, a generative AI model such as one from OpenAI is used.

[0721] As a concrete example, consider a scenario where a user is staying in Kyoto and enters the prompt, "I'm looking for a recommended Japanese restaurant in Kyoto." In response to this prompt, the server uses past preference data, along with the user's current geographical location, to suggest the most suitable restaurant. The user can then use this information to easily choose a place to eat locally.

[0722] An example of a prompt message might be: "The user is heading to Osaka and wants to visit historical tourist spots. Please recommend some places."

[0723] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0724] Step 1:

[0725] The user launches a dedicated app on their smartphone or tablet and enters their information request into a text box. An example input might be, "Please tell me some recommended tourist spots in Kyoto." The user then presses the "Submit" button to send the entered request to the server. The input here is the user's specific question, and the output is the request data sent to the server.

[0726] Step 2:

[0727] The terminal sends a text request from the user to the server over the internet. Specifically, the terminal uses data communication to packetize the request according to the protocol and forward it to the server's address. The input to this step is the user's text request, and the output is the request data that was successfully received by the server.

[0728] Step 3:

[0729] The server analyzes the received request data using natural language processing techniques. Specifically, it uses a natural language processing model to extract the intent and keywords of the request and clarify the user's needs. The input to this step is the request data received by the server, and the output is the analyzed user needs data.

[0730] Step 4:

[0731] Based on the analysis results, the server accesses the information database to search for the necessary information. Specifically, it uses SQL queries and other methods to quickly retrieve tourist destinations and service information that matches the user's needs. The input for this step is the analyzed needs data, and the output is the retrieved relevant information.

[0732] Step 5:

[0733] The server automatically translates the acquired information into the user's native language. This step involves specific processes, such as calling a translation API, to translate the acquired information within the appropriate context. The input is the acquired tourist information, and the output is the translated information.

[0734] Step 6:

[0735] The translated information is sent from the server to the user's device, and the device displays the information. Specifically, the system receives the transmitted data, formats it on the app's interface, and displays it so that the user can intuitively understand the information. The input for this step is the translated information data, and the output is the displayed content.

[0736] Step 7:

[0737] Users act based on the information provided and input and submit feedback from their devices. Specifically, users evaluate the accuracy and usefulness of the information and click the submit button on the server via the feedback form. The input in this step is the user's feedback content, and the output is the evaluation data sent to the server.

[0738] Step 8:

[0739] The server analyzes the received feedback and learns based on the generated AI model. Specifically, it evaluates the feedback data and extracts information that will help improve the accuracy of future information provision and enhance the service. The input for this step is user feedback data, and the output is the learning results for improvement.

[0740] (Application Example 1)

[0741] 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".

[0742] Foreign visitors to Japan face language barriers when obtaining information and selecting products in a foreign country, making it difficult to have a smooth shopping experience. Furthermore, the provision of information tailored to the diverse needs and preferences of users is often insufficient, leading to unsatisfactory service. Therefore, there is a need to develop systems that remove language barriers and enable effective information provision tailored to individual users.

[0743] 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.

[0744] In this invention, the server includes means for acquiring information from a user terminal, means for analyzing user input using the information acquisition means and identifying user needs using a language model, and means for acquiring relevant information from an information storage unit based on the identified needs and translating the acquired information into the user's native language. This makes it possible for foreign visitors to Japan to easily acquire product information in physical stores, overcoming language barriers, and to receive selective information based on their preferences and location.

[0745] A "user terminal" is a device used for information acquisition and as an interface, such as a smartphone or tablet, which is operated by the user to input and receive information.

[0746] "Information acquisition means" refers to a mechanism for collecting user input information and transmitting it to a server.

[0747] A "language model" is a natural language processing technique used to analyze user input and understand their intent.

[0748] "Needs" refer to the desires, demands, and preferences of users, and serve as the basis for providing services.

[0749] The "information storage unit" refers to a database or storage device where related information is stored, and is a source of information used for retrieving and searching for information.

[0750] "Translation means" refers to technology that converts acquired information into the user's native language and makes it understandable.

[0751] "Location information" refers to data that indicates the geographical location of users or physical stores, and is used to provide and recommend services.

[0752] "Methods for updating" refer to the process of collecting user evaluations and feedback in order to improve the service and enhance the accuracy of information.

[0753] This system uses a smartphone as the user terminal and transmits user input information to a server using an information acquisition method. The server analyzes the user's input using a language model to identify their needs. Natural language processing technology is used in the analysis to accurately understand the user's requirements.

[0754] The server retrieves relevant information from the data storage unit based on identified needs and translates that information into the user's native language using a translation tool. It is recommended to use a translation API (e.g., Google Translate API) for these processes. The translated information is sent to the user's terminal, allowing the user to access the information even in a foreign country, overcoming language barriers.

[0755] Furthermore, receiving feedback from user devices allows for updates to the information model on the server, leading to service improvements. Specifically, when a user selects a product in a physical store, scanning a QR code retrieves specific product information, which is then translated into their native language through the language model. This allows foreign visitors to Japan to enjoy a smoother purchasing experience.

[0756] As a concrete example, when a user is selecting products at a specialty store in Japan, they scan the QR code of the product with their smartphone. At this time, product information of interest to the user (e.g., ingredients, origin, expiration date, etc.) is retrieved via the server and displayed on the smartphone in their native language. Through this process, the user can obtain product information in real time and make choices according to their preferences and needs.

[0757] An example of a prompt for a generative AI model would be: "Based on the user's current location and past preferences, select recommended products from nearby physical stores. Translate the selected product information into [language] and generate an appropriate description to provide to the user."

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] The user scans a QR code using their smartphone. The input is the information from the QR code, which the device analyzes and sends to the server a specific product ID. The server then retrieves the product ID through this transmission process.

[0761] Step 2:

[0762] The server searches for relevant product information from its information storage unit based on the received product ID. The input for this search is the product ID, and the output is product information (e.g., ingredients, origin, expiration date, etc.). The server extracts this information and prepares it for the next translation process.

[0763] Step 3:

[0764] The server translates the retrieved product information into the user's native language using a language model and a translation API. The input here is the product information, and the output is the translated information. The server then processes this information into a user-friendly format and generates data for display.

[0765] Step 4:

[0766] The server sends the translated information to the user's terminal. The terminal displays this received information to the user. The output is translated product information, allowing the user to learn about product details in real time.

[0767] Step 5:

[0768] Users make purchasing decisions based on the product information provided. After purchasing a product, users send feedback, such as satisfaction levels, from their device to the server. This feedback information is passed to the server as input, and the information model is updated as output.

[0769] Step 6:

[0770] The server receives user feedback and uses a generative AI model to analyze the data and improve the accuracy of the service. The input is feedback data, and the output is an improved information model. This model will be used in future information provision.

[0771] 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.

[0772] This invention provides a system for smart concierge services for foreign visitors to Japan that enables the provision of information while taking into account the user's emotions. This system mainly consists of a user terminal, an emotion engine, information acquisition means, a language model, an information database, a translation means, a feedback function, and an AI model.

[0773] Users make requests for information and services via their smartphones. The device receives user input, and an emotion engine analyzes this input to recognize the user's emotional state. For example, if a user inputs "I want to know where I can relax right now," the emotion engine recognizes the desire to relax and adjusts its suggestions accordingly.

[0774] The device sends the analyzed information to the server. The server uses a language model to analyze the request and identify the user's needs. Based on this, it retrieves relevant information from its information database. The retrieved information is then tailored to the user's emotional state. For example, a user who wants to relax might be recommended quiet tourist spots or healing music.

[0775] The server then translates the information into the user's native language and sends it to the device. The device displays the received information in a format that is easy for the user to understand. The user can then decide on an action based on that information. Furthermore, the user can provide feedback on the presented information and suggestions through the device.

[0776] This feedback is sent back to the server and used as training data for the emotion engine and AI model. The AI ​​model improves based on past user responses and emotions, enhancing the accuracy of future suggestions. In this way, it becomes possible to provide optimal information tailored to each user's emotions and preferences.

[0777] As a concrete example, consider a scenario where a user is feeling stressed during a business trip and is seeking relaxation. When the user types "Tell me some relaxing places," the emotion engine detects their stress and recommends relaxing spas or nature walks. The information is translated and provided in the user's native language, making it easy for them to access and choose appropriate actions. Through this process, the user receives services tailored to their emotions, enriching their travel experience.

[0778] The following describes the processing flow.

[0779] Step 1:

[0780] Users enter information and service requests on a smartphone app. For example, they might enter "I want to know about places to relax in Tokyo."

[0781] Step 2:

[0782] The terminal receives the user's request. Simultaneously, the emotion engine analyzes the input text and recognizes the user's emotional state. In this case, the emotion of wanting to relax is recognized.

[0783] Step 3:

[0784] The device sends user requests to the server along with emotional information. The data is structured and transmitted using the appropriate protocol.

[0785] Step 4:

[0786] The server uses a language model to analyze the received request information. Here, it identifies that the user's need is to "relax in Tokyo."

[0787] Step 5:

[0788] The server accesses an information database and searches for information that matches the user's needs and emotions. It extracts tourist spots and facilities suitable for relaxation from the database.

[0789] Step 6:

[0790] The server translates search results into the user's native language. The translated information is then prioritized and organized based on sentiment.

[0791] Step 7:

[0792] The server sends the translated information to the terminal. Here, the information is formatted to be easily understood by the user.

[0793] Step 8:

[0794] The device displays the received information in a user interface. The user can choose a suitable place to relax from the listed information.

[0795] Step 9:

[0796] The user makes a selection based on the suggested information and enters the result as a review on the device. The review includes detailed feedback, including emotions.

[0797] Step 10:

[0798] The device sends feedback data to the server. This data is used as training data for the emotion engine and AI model.

[0799] Step 11:

[0800] The server analyzes the feedback and updates the AI ​​model. The emotion engine is also enhanced, improving the accuracy of future suggestions. This allows the entire system to provide suggestions that are more adapted to the user's emotions and needs.

[0801] (Example 2)

[0802] 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".

[0803] Information services targeting foreign visitors to Japan require the accurate and timely provision of personalized information that takes into account the user's feelings. However, conventional systems fail to adequately consider the user's emotional state, and the information provided is not always appropriate. Furthermore, communication barriers due to language differences exist, making it difficult for users to use the service without stress. It is necessary to solve this problem.

[0804] 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.

[0805] In this invention, the server includes means for analyzing user input and recognizing emotional states using natural language processing technology, means for obtaining relevant data from an information storage device based on the analyzed emotional state and needs, and means for adjusting the obtained data based on the user's emotional state and creating suggestions using a generative AI model. This enables the provision of optimal information tailored to the user's emotions, realizing a comfortable information provision service.

[0806] A "user terminal" is an information device used by a user to input and receive information.

[0807] "Information gathering means" refers to methods for obtaining user input data from a user terminal.

[0808] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0809] "Emotional state" refers to the user's psychological state inferred from their input.

[0810] An "information storage device" is a storage system for saving and retrieving necessary data.

[0811] A "generative AI model" is a model that uses machine learning algorithms to create suggestions tailored to the user's needs.

[0812] "Feedback" refers to data that expresses users' opinions and evaluations of the information and services provided.

[0813] "Translation methods" refer to methods of converting acquired information into a language that is easy for the user to understand.

[0814] This invention is an information system that provides emotion-based information to foreign visitors to Japan. The system includes a smartphone as a user terminal, a server, an emotion engine, information collection means, natural language processing technology, a generative AI model, an information storage device, a translation means, and a feedback collection function.

[0815] First, the user makes a request to the service using their smartphone. The user's device passes the entered text data to an emotion engine, which analyzes the user's emotional state using natural language processing technology. Through this analysis, a request such as "Tell me a place where I can relax" is understood to indicate a state of seeking relaxation.

[0816] The user terminal sends a request to the server that includes the results of sentiment analysis. The server uses the received information to identify the user's needs. It retrieves data on relevant locations and services from its information storage device and uses a generative AI model to create optimal suggestions tailored to the user's emotional state. For example, it might provide information on quiet and relaxing spas or nature trails to a user who is feeling stressed.

[0817] The server translates the generated information into the user's native language. The translated information is sent from the server to the user's terminal and displayed on the terminal in a visually easy-to-understand format. The user can then make decisions based on the information provided.

[0818] Furthermore, user feedback is sent to the server and used to improve the emotion engine and AI models. The feedback is used as training data for the system, improving the accuracy of its suggestions.

[0819] For example, if a user enters the prompt "Tell me where I can listen to healing music," the system will understand that the user is seeking relaxation and will suggest a suitable location. This allows the user to have a pleasant travel experience.

[0820] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0821] Step 1:

[0822] Users enter information requests in text format on their smartphones. For example, they might enter a prompt like, "Tell me about relaxing places." This input data forms the basis for analysis based on the user's emotions and needs.

[0823] Step 2:

[0824] The device sends user input to an emotion engine, which then analyzes it using natural language processing technology. Specifically, the data processing involves keyword extraction and contextual analysis to identify emotions and intentions. The analysis reveals that the user is seeking relaxation. The output is data indicating the user's emotional state.

[0825] Step 3:

[0826] The terminal sends the analyzed emotional state and user prompts to the server. The input is text data containing the emotional state. The server receives this and uses a generative AI model to identify the user's needs. Specific data calculations include extracting needs based on the user's emotions and past data. The output is a query with identified needs.

[0827] Step 4:

[0828] The server retrieves relevant information from its data storage device based on identified needs. For example, it might retrieve data about places or activities where users can relax. The input is a query based on the needs, and after data retrieval, the output is a list of relevant information.

[0829] Step 5:

[0830] The server uses a generating AI model to adjust the acquired information based on the user's emotional state. Specifically, if the user is feeling stressed, locations that are expected to have a relaxing effect will be prioritized. The input is a list of relevant information, and the output is a suggestion optimized for the user's situation.

[0831] Step 6:

[0832] The server translates optimized suggestions into the user's native language. Real-time translation technology is used for rapid translation. The input is optimized suggestion data, and the output is the translated information.

[0833] Step 7:

[0834] The server sends the translated information to the user's terminal. The terminal receives the data and displays it in a user-friendly format. The input is the translated information, and the output is a display format that appeals to the user's visual sense.

[0835] Step 8:

[0836] Users review the information and provide feedback on the services offered via their device. This feedback is collected as data for the system to use in its learning process. The output consists of information about the user's impressions and suggestions.

[0837] Step 9:

[0838] The server analyzes feedback data to improve the emotion engine and generative AI model, thereby increasing the accuracy of future suggestions. The input is feedback data, and the output is the improved training data model.

[0839] (Application Example 2)

[0840] 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".

[0841] For foreign visitors to Japan to enjoy a comfortable shopping experience in physical stores, overcoming cultural and language barriers, it is necessary to provide information based on users' emotions and needs. However, current systems do not adequately consider these emotions and states when providing information, resulting in a limited user experience.

[0842] 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.

[0843] In this invention, the server includes an information acquisition device from a user device, means for analyzing user input using the information acquisition device and identifying user requests using a language model, means for acquiring relevant information from an information storage location based on the identified requests and translating the acquired information into the user's native language, and a device for recognizing the user's emotional state using an emotion analysis device and adjusting the information according to the recognized emotional state. This enables visitors to receive more precise and personalized information tailored to their emotions and needs at the time in a physical store, allowing them to comfortably use the service.

[0844] A "user device" is an electronic device that a user can carry and that allows for the input and display of information.

[0845] An "information acquisition device" is a technical device used to collect data entered by a user and transfer it to a system.

[0846] A "device for analyzing input" is a technological device that has the function of analyzing data obtained from users and understanding its meaning and purpose.

[0847] A "language model" is an artificial intelligence technology that understands human language and identifies requests.

[0848] An "information repository" is a database system where relevant information is stored and accessible.

[0849] "Translation methods" refer to technologies for converting information written in one language into another language.

[0850] A "transmission and display device" is a technological device that sends information to a user's device and displays it visually.

[0851] A "device for collecting evaluations and updating information models" is a technical device for collecting user feedback and improving the models used for information processing.

[0852] An "emotion analysis device" is a technological device that recognizes a user's emotional state and adjusts information based on that data.

[0853] This invention is a system that supports foreign visitors to Japan in comfortably engaging in purchasing activities in physical stores, overcoming cultural and linguistic barriers. This system includes a user device, a server, and an emotion analysis device. The details are described below.

[0854] First, the user inputs information via a user device such as a smartphone or tablet. The device then transmits the input data to the server via an information acquisition device. The server processes this data using a language model to identify the user's request.

[0855] Once a user's request is identified, the server retrieves relevant information from its data repository and translates it into the user's native language as needed. Commercially available translation APIs can be used for this translation process. The translated information is then transmitted to the user's device and displayed in an appropriate format.

[0856] On the other hand, emotion analysis devices recognize the user's emotional state through the user's language input and, in some cases, video input. Emotion analysis utilizes technologies such as voice tone analysis and facial recognition (e.g., Microsoft Azure Face API and Google Cloud Speech-to-Text). The results of this emotion analysis are used to adjust the information provided; that is, customized information is provided according to the user's emotional state.

[0857] Furthermore, user ratings and feedback are collected on the server and used to update the information model. This process refines the system and improves its ability to provide optimal information tailored to individual users.

[0858] As a concrete example, consider a tourist staying in Tokyo who feels tired and needs to rest while shopping at a physical store. If the user inputs "I want to rest" into the device, the emotion analysis device can receive the request and send a prompt message to the server providing information about a nearby quiet cafe.

[0859] Example of a prompt:

[0860] "The user is tired. Please suggest places where they can rest comfortably."

[0861] In this way, the system understands visitors' emotions and needs and provides appropriate and personalized information, thereby creating a fulfilling shopping experience.

[0862] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0863] Step 1:

[0864] The user uses a smartphone to input information. The user enters text based on their emotions and needs into the device. This input is collected by an information acquisition device and transmitted to a server. The main content of the input reflects the user's emotions and desires.

[0865] Step 2:

[0866] The server analyzes the received input data using natural language processing techniques. This process involves using a language model and data processing to identify user requests. The output then generates the identified user needs.

[0867] Step 3:

[0868] The server retrieves relevant information from the information repository based on the identified needs. The server executes database queries to search for highly relevant tourist attractions and services. Relevant information is generated as output.

[0869] Step 4:

[0870] The server translates the retrieved information into the user's native language. Standard translation APIs are used for the translation, and information is converted as needed. The output is the information translated into a language the user can understand.

[0871] Step 5:

[0872] The emotion analysis device analyzes the user's emotional state. The terminal recognizes emotions by referring to the input text and tone of voice. Data processing yields output regarding the user's emotional state.

[0873] Step 6:

[0874] The server adjusts the information based on the sentiment analysis results. The user's emotional state is taken into consideration, and the content of the information presented is optimized. The server generates prompts and customizes the information.

[0875] Step 7:

[0876] The translated information and sentiment-sensitive information are transmitted back to the terminal and displayed visually. The user can view the provided information through the terminal. The output is a visual display of the information on the user's terminal.

[0877] Step 8:

[0878] The user provides feedback on the presented information. The device collects the feedback data and sends it to the server. This feedback is used to improve the information model. The feedback data is obtained as output.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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."

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] The following is further disclosed regarding the embodiments described above.

[0901] (Claim 1)

[0902] Means of obtaining information from user terminals,

[0903] A means of analyzing user input using information acquisition means and identifying user needs using a language model,

[0904] A means of retrieving relevant data from an information database based on identified needs and translating the retrieved data into the user's native language,

[0905] A means of sending and displaying translated data on the user's terminal,

[0906] A means of collecting user feedback and updating the information model,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which uses natural language processing technology to analyze user input.

[0910] (Claim 3)

[0911] The system according to claim 1, which takes geographical information into consideration when generating suggestions based on user preferences.

[0912] "Example 1"

[0913] (Claim 1)

[0914] Communication means from the user device,

[0915] A means of analyzing user input using communication methods and identifying user needs using a natural language processing model,

[0916] A means to retrieve relevant information from a database based on identified needs and translate the retrieved information into the user's native language,

[0917] A means for transmitting and displaying translated information on a user device,

[0918] A means of collecting user feedback and updating the artificial intelligence model,

[0919] An artificial intelligence model analyzes the evaluation and learns to improve the accuracy of information provided in the future,

[0920] An information provision system that includes this.

[0921] (Claim 2)

[0922] The information provision system according to claim 1, which utilizes natural language processing technology in the analysis of user input.

[0923] (Claim 3)

[0924] The information provision system according to claim 1, which takes location information into consideration when generating recommendations based on user preferences.

[0925] "Application Example 1"

[0926] (Claim 1)

[0927] Means of obtaining information from user terminals,

[0928] A means of analyzing user input using information acquisition methods and identifying user needs using language models,

[0929] A means for retrieving relevant information from an information storage unit based on identified needs and translating the retrieved information into the user's native language,

[0930] A means of transmitting and displaying translated information on the user's terminal,

[0931] A means of collecting user feedback and updating the information model,

[0932] A means of providing selective product information in physical stores based on user preferences and current location information,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, which uses natural language processing technology to analyze user input.

[0936] (Claim 3)

[0937] The system according to claim 1, which takes regional information into consideration when generating recommendations.

[0938] "Example 2 of combining an emotion engine"

[0939] (Claim 1)

[0940] Means of collecting information from user terminals,

[0941] A means of analyzing user input using information gathering methods and recognizing emotional states using natural language processing technology,

[0942] A means for obtaining relevant data from an information storage device based on the analyzed emotional state and user needs,

[0943] A means of adjusting acquired data based on the user's emotional state and creating suggestions that meet their needs using a generative AI model,

[0944] A means of translating the adjusted data into the user's native language,

[0945] A means of sending and displaying translated data on the user's terminal,

[0946] A means of collecting user feedback and improving information models and sentiment state analysis,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, which uses a generative AI model to identify user needs from user input and emotional state and generate suggestions.

[0950] (Claim 3)

[0951] The system according to claim 1, which, when generating suggestions based on user preferences, takes geographical information into consideration and proposes natural tourist destinations.

[0952] "Application example 2 of combining emotional engines"

[0953] (Claim 1)

[0954] A device for acquiring information from user equipment,

[0955] A device that uses an information acquisition device to analyze user input and uses a language model to identify user requests,

[0956] A device that retrieves relevant information from an information repository based on specified requests and translates the retrieved information into the user's native language,

[0957] A device that transmits and displays translated information to a user device,

[0958] A device that collects user feedback and updates the information model,

[0959] A device that uses an emotion analysis device to recognize the user's emotional state and adjusts the information according to the recognized emotional state,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, which uses natural language processing technology to analyze user input and analyzes emotions in real time in a smart device.

[0963] (Claim 3)

[0964] The system according to claim 1, which takes into account location information and the user's emotional state when generating suggestions based on the user's preferences. [Explanation of Symbols]

[0965] 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. Means of obtaining information from user terminals, A means of analyzing user input using information acquisition means and identifying user needs using a language model, A means of retrieving relevant data from an information database based on identified needs and translating the retrieved data into the user's native language, A means of sending and displaying translated data on the user's terminal, A means of collecting user feedback and updating the information model, A system that includes this.

2. The system according to claim 1, which uses natural language processing technology to analyze user input.

3. The system according to claim 1, which takes geographical information into consideration when generating suggestions based on user preferences.

Images