system

Abstract

We provide the system. [Solution] An input means for receiving voice or text input from a user, The aforementioned analysis means analyzes the received input using natural language processing technology and extracts the user's desired conditions, A search means for searching relevant databases based on the extracted desired conditions and identifying candidate destinations to propose, An evaluation means for evaluating the identified destination candidates based on distance, reputation, and convenience, and arranging them in the optimal order, A display means that transmits the evaluated destination candidates to the user terminal and displays the suggested content, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the use of map information services has been increasing. However, there is a problem in the conventional map search method that it is difficult to propose a destination based on vague desires and experiences of users. Specifically, even when a user does not know a clear address or name, it is required to quickly find an appropriate destination. To meet such needs, a system that accurately understands ambiguous requests of users and makes highly relevant proposals is necessary.

Means for Solving the Problems

[0005] This invention provides an analysis method that extracts a user's potential preferences by utilizing natural language processing technology based on voice or text input from the user. Furthermore, it includes a means for searching for destination candidates from a relevant database based on the analysis results. This efficiently identifies, evaluates, and ranks candidates that are suitable for the user's requests, thereby providing the user with an optimal list of suggestions. In other words, it realizes a system that presents personalized candidates by utilizing the user's past behavior history and real-time information.

[0006] A "user" is the entity that operates the system and inputs information.

[0007] "Voice or text input" refers to a method by which a user provides information to a system using voice or text.

[0008] "Input means" refers to devices and sets of functions for receiving information from the user.

[0009] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0010] An "analysis tool" is a component that has the function of analyzing received user input and extracting desired conditions.

[0011] "Desired conditions" refer to the attributes and characteristics that the user desires regarding the place or experience they are looking for.

[0012] A "database" is a collection of data organized for a system to reference and retrieve.

[0013] A "search tool" is a function that searches for information from relevant databases based on the analyzed conditions and determines potential destinations.

[0014] A "suggested destination" is a set of locations that are suggested as options based on the user's criteria.

[0015] "The "evaluation means" is a function for evaluating candidate destinations according to defined criteria and ranking them."

[0016] "The "display means" is a function and device for visually presenting the evaluated information and proposals to the user."

[0017] "The "history analysis means" is a function for analyzing past user behavior data and generating personalized proposals."

[0018] "The "real-time evaluation means" is a function for evaluating the congestion status and convenience of the destination based on the current situation."

[0019] "The "system" is an integrated mechanism that combines these elements to operate as a whole and provides valuable information to the user."

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment." [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment." [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment." [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment." [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment." [Figure 8]It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0021] 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.

[0022] First, the terms used in the following description will be explained. [[ID=​​​​​In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] 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).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] As an embodiment of the present invention, a system that uses the user's voice or text as a data input means will be described. The user can input information via a smartphone or car navigation device. This input is used to interpret the user's intent using natural language processing technology. Specifically, it is possible to extract the conditions and experiences desired by the user.

[0042] Server operation

[0043] The server receives input data from the user and performs analysis using natural language processing. This analysis extracts the user's desired conditions, and relevant databases are searched based on these conditions. The databases include map information, user ratings, event information, congestion levels, weather information, etc., and the server combines this information to select candidate locations.

[0044] Destination exploration and evaluation

[0045] After the search is complete, the server evaluates the potential destinations. Evaluation criteria include user proximity, the reputation of the suggested locations, and preferences based on past user data. The server integrates this information to present the user with a ranking of the most appropriate destinations.

[0046] Information provision and display to the device

[0047] The terminal presents the user with evaluated destination candidates received from the server. Destination information is visually displayed on a map, along with more detailed directions, reviews, and practical data such as current congestion levels. This allows users to quickly and effectively decide where they want to visit.

[0048] Specific example

[0049] Suppose a family is looking for a new activity for the weekend. When the user enters the voice command "I'm looking for a place for kids to have fun" into their device, the server analyzes the request and lists options such as local amusement parks, zoos, and science museums. The evaluation criteria include the quality of child-friendly facilities, safety ratings, and crowd predictions, and based on this information, the most suitable destination is presented to the user.

[0050] In this way, the system can provide users with valuable, wish-based discoveries that go beyond mere geographical information.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The user inputs voice or text into their smartphone or car navigation system. This input includes desired destination conditions and experience details.

[0054] Step 2:

[0055] The device sends the voice or text received from the user to the server. In the case of voice input, it is converted to text before transmission.

[0056] Step 3:

[0057] The server analyzes the received input using natural language processing technology. The analysis extracts the user's intentions and desired conditions.

[0058] Step 4:

[0059] The server searches relevant databases based on the analyzed desired conditions. These databases include map information, user ratings, facility information, event information, and more.

[0060] Step 5:

[0061] The server evaluates the searched destination candidates. Evaluation criteria include distance, user ratings, facility quality, and past user preferences.

[0062] Step 6:

[0063] The server selects the most suitable destination candidates based on the evaluation results and creates a recommendation list.

[0064] Step 7:

[0065] The server sends a list of recommendations to the terminal.

[0066] Step 8:

[0067] The device displays the received recommendation list to the user. The display includes the location on a map, detailed information, and the reason for the recommendation.

[0068] (Example 1)

[0069] 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."

[0070] In today's information society, users face the challenge of finding destinations that meet their specific criteria quickly and accurately. Furthermore, amidst an overwhelming amount of information, there is a demand for personalized suggestions that take into account individual user preferences and real-time environmental changes. However, conventional methods are insufficient to provide users with the appropriate information to meet their needs.

[0071] 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.

[0072] In this invention, the server includes data input means for receiving voice or text input from a user, intent analysis means for analyzing the received input using natural language processing technology and extracting conditions desired by the user, and data search means for searching an information set including geographic data, evaluation information, event information, congestion information, and weather data based on the extracted conditions and identifying candidate locations to propose. This makes it possible to propose customized destinations based on the user's preferences.

[0073] A "data input means" is a function for receiving information from the user in the form of voice or text.

[0074] An "intent analysis method" is a system that uses natural language processing technology to analyze the user's input and extract the conditions and requests the user desires.

[0075] "Data search method" refers to the process of searching various information sets, including geographic data, evaluation information, event information, congestion information, and weather data, based on the analyzed user preferences, to identify potential destinations.

[0076] The "candidate ranking determination method" is a function that evaluates identified destination candidates based on criteria such as proximity, reputation, and preferences based on past data, and determines the optimal order.

[0077] The "display means" is a mechanism for visually presenting evaluated destination candidates from the server to the user terminal.

[0078] A "preference analysis tool" is a means of analyzing a user's past preference data and providing personalized suggestions.

[0079] A "real-time evaluation method" is a function that uses real-time information to evaluate the congestion status of a location and provides the user with real-time information.

[0080] This invention is a system implemented by the user inputting voice or text via a smartphone or in-vehicle device. When the user inputs information into the device, the server receives it and performs analysis using natural language processing technology. The analysis utilizes a generative AI model and employs prompt sentences to interpret the user's intent. Analysis is made possible by using prompt sentences such as, "Explain the best way to find the destination the user desires."

[0081] The server identifies the user's desired experience and conditions, and then searches a wide range of data sources based on that information. Cloud computing services are used as the hardware, and natural language processing engines and database management systems are applied as the software. Relevant information is retrieved from multiple databases, including geographic information, user ratings, event information, congestion levels, and weather data.

[0082] The server integrates this data and analyzes it using criteria to evaluate potential locations. These criteria include the user's past preferences, proximity, reputation, safety, and congestion predictions. The locations are ranked through a scoring system to determine the most suitable candidate.

[0083] The terminal presents the user with evaluated destination information transmitted from the server. The information is displayed visually, including a map layout, detailed directions, user reviews, and current congestion status, all within an accessible interface. Through these functions, the user can select the optimal destination based on their preferences. As a concrete example, the system's effectiveness can be tested by entering the specific prompt: "Explain the function that searches for appropriate destinations based on the user's desired conditions and suggests the best location using evaluation criteria."

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] Users enter their requests via voice or text through their smartphones or in-car devices. The input includes specific information such as "Places the family can enjoy this weekend." This input indicates the user's preferences and desired experience.

[0087] Step 2:

[0088] The server receives voice or text data from the user through a data input mechanism. Next, it analyzes this input using natural language processing technology that leverages a generative AI model. During the analysis process, prompts are used to extract the user's intent and desired conditions, and based on this, appropriate conditions are clarified. The output is a keyword list corresponding to the user's desired conditions.

[0089] Step 3:

[0090] The server uses the extracted keyword list to search the database for relevant information. This search includes geographical data, user ratings, event information, congestion levels, and weather information. From these databases, potential destinations are identified based on the user's preferences. The output consists of multiple destinations that match the criteria.

[0091] Step 4:

[0092] The server analyzes the identified destination candidates based on factors such as proximity, reputation, preferences based on past user data, safety, and congestion prediction. This assigns a score to each candidate location, determining their ranking. The output of this step is a list of recommended locations ranked according to the evaluation results.

[0093] Step 5:

[0094] The terminal visually presents destination information to the user based on a recommended ranking list received from the server. By displaying the location on a map, detailed directions, reviews, and current congestion status, the user can efficiently select the most suitable destination. The final output is detailed information about the destination presented to the user.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern society, users expect to receive timely and appropriate suggestions tailored to their preferences when using food delivery services. However, conventional systems struggle to select the optimal stores and products to meet specific user needs, hindering efficient selection.

[0098] 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.

[0099] In this invention, the server includes an acquisition means for receiving requests from users via voice or text, an analysis means for analyzing the received requests using natural language processing to extract the user's desired conditions, and a derivation means for searching a food delivery database based on the extracted desired conditions to identify potential stores that can provide the service. This enables users to efficiently receive suggestions for the most suitable stores according to their time and preferences.

[0100] "Acquisition means" refers to a device or method for receiving requests from a user, either by voice or text.

[0101] "Analysis means" refers to a device or method for analyzing a received request using natural language processing technology and extracting the user's desired conditions.

[0102] "Derivation means" refers to a device or method for searching a food delivery database based on extracted desired conditions to identify potential stores that can provide the service.

[0103] "Evaluation means" refers to an apparatus or method for evaluating identified store candidates based on delivery time, reputation, and evaluation criteria, and for arranging them in optimal order.

[0104] "Presentation means" refers to a device or method for transmitting evaluated store candidates to a user terminal and presenting options.

[0105] "History analysis means" refers to a device or method for analyzing a user's order history and providing customized suggestions.

[0106] "Real-time evaluation means" refers to a device or method for evaluating the delivery status of a proposed store using real-time data and providing the results to a user terminal.

[0107] The system implementing this invention mainly consists of a user's smartphone and a server. The user inputs a request via voice or text using their smartphone. This input is transmitted to the server by the "acquisition means".

[0108] The server analyzes incoming requests using natural language processing technology. Specifically, it utilizes software that supports natural language processing, such as Google® Cloud Natural Language API, to extract the user's desired conditions using an "analysis tool." Based on this extracted information, the server searches a food delivery database and identifies potential stores that match the desired conditions using a "derivation tool."

[0109] The server then evaluates potential stores using an "evaluation tool" based on delivery time, reputation, and rating criteria, and sorts them in order of suitability. This evaluation result is transmitted to the user's terminal via a "presentation tool" and displayed on their smartphone. Based on this presentation, the user can select the most suitable store and order food.

[0110] A concrete example of this system's use is when a user enters a request into their smartphone such as, "Tell me the restaurant near my house that delivers the most popular ramen within 30 minutes." Based on this prompt, the server analyzes the request and quickly identifies and presents the most suitable restaurant.

[0111] The system utilizes smartphones as hardware and includes Google Cloud Natural Language API and MySQL® as software, enabling seamless integration between the user interface and the database. This system allows users to efficiently and comfortably access food delivery services.

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

[0113] Step 1:

[0114] The user enters a request using voice or text via their smartphone. The entered request is sent from the smartphone app to the server. In this step, the input is voice or text data, and the output is the transmission of data to the server.

[0115] Step 2:

[0116] The server analyzes the received request using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract the user's desired conditions from the request. The input for this step is the user's voice or text request, and the output is the extracted desired conditions. Data processing involves string analysis and semantic understanding.

[0117] Step 3:

[0118] The server searches the MySQL database based on the desired criteria extracted in the previous step. The food delivery database contains store information, menus, delivery times, etc. Candidate stores that match the desired criteria are identified. The input for this step is the desired criteria, and the output is a list of candidate stores.

[0119] Step 4:

[0120] The server evaluates the identified store candidates based on delivery time, reputation, and rating criteria. This evaluation also includes referencing the user's past data. The store candidates are then sorted in order of most appropriateness. The input for this step is a list of store candidates, and the output is an evaluated, ordered list of stores. Data calculations include numerical calculations and ranking.

[0121] Step 5:

[0122] The server sends the evaluation results to the smartphone device. The smartphone app displays this information to the user, who can then select a store from the suggested options and place an order. The input for this step is the list of evaluated stores, and the output is the information displayed to the user. When presenting the information, location information on a map and menu details are also provided visually.

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

[0124] As an embodiment for carrying out the present invention, a user support system incorporating an emotion engine will be described. This system recognizes emotions based on the user's voice or text input and reflects them in destination suggestions.

[0125] User input and emotion recognition

[0126] When a user inputs their desired conditions via voice or text on a smartphone or car navigation system, the device sends this input to a server. The server analyzes the input using natural language processing technology to extract the desired conditions, and also uses an emotion engine to identify the user's emotions. This allows the server to sense whether the user is happy or seeking relaxation.

[0127] Server-based data analysis and destination candidate selection

[0128] The server analyzes the user's emotional data, recognized by the emotion engine, along with their preferences. Based on this information, it searches relevant databases to identify potential destinations. The databases include geographical information, facility information, reputation, congestion levels, and event information.

[0129] Individualized assessment and recommendations

[0130] In addition to the usual evaluation criteria for potential destinations (distance, rating, and convenience), the server also takes into account the user's current emotional state when evaluating and ranking destinations. If the server determines that the user is seeking relaxation, it will prioritize quiet, nature-rich locations, optimizing the user experience.

[0131] Information provision and display to the device

[0132] The device displays evaluated destination options received from the server to the user. The suggestions are accompanied by reasons tailored to the user's emotions and are presented in a visually easy-to-understand format. For example, if the user enters "I want to have a fun day" and the emotion engine detects an excited state, amusement parks and active events will be suggested.

[0133] Thus, the present invention makes it possible to suggest appropriate destinations that take emotions into consideration, and to provide an experience that best suits the user's wishes and mood.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user inputs information via voice or text into their smartphone or car navigation system. This input concerns the user's preferences and current mood.

[0137] Step 2:

[0138] The device sends the user's voice or text input to the server. In the case of voice input, the device converts the voice into text data and sends it.

[0139] Step 3:

[0140] The server analyzes the received text data using natural language processing techniques to extract the user's desired conditions. This analysis identifies the user's intent from the input words and phrases.

[0141] Step 4:

[0142] The server uses an emotion engine to recognize the user's emotional state from their input. Emotional data is determined from the tone of voice and text, phrasing, and other factors.

[0143] Step 5:

[0144] The server searches relevant databases based on the extracted preferences and recognized emotional states to identify potential destinations. The databases include geographical information, reputation data, and current event information.

[0145] Step 6:

[0146] The server evaluates candidate destinations, considering factors such as distance, reputation, convenience, and the user's emotional state. Based on this evaluation, the destinations are sorted in order of likelihood of the highest user satisfaction.

[0147] Step 7:

[0148] The server sends a list of optimized destination options to the terminal. Each option is accompanied by a description and an emotion-based recommendation reason.

[0149] Step 8:

[0150] The device displays suggested destinations to the user. This display includes map information, destination features, and recommendation reasons tailored to the user's emotions.

[0151] (Example 2)

[0152] 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".

[0153] While conventional systems could suggest appropriate destinations based on user preferences, they lacked the ability to consider user emotions and moods. Furthermore, suggestions that disregarded user emotions often failed to meet specific user needs, leading to decreased satisfaction.

[0154] 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.

[0155] In this invention, the server includes means for receiving voice or text, means for analyzing the data to identify desired conditions and emotions, and means for performing information retrieval and evaluation based on the conditions and emotions. This makes it possible to suggest the optimal location based on the user's desired conditions and emotions.

[0156] "Speech or text" refers to spoken language or written text information provided by the user through an input method.

[0157] "Natural language processing" refers to the technical means by which computers understand, interpret, and generate human language.

[0158] "Emotion recognition technology" is an analytical technology that identifies a user's emotional state from voice or text.

[0159] A "source of information" refers to a database or application that contains data necessary to identify potential destinations, such as location information, facility information, rating data, and congestion information.

[0160] "Distance" is a criterion for evaluating the physical separation between a candidate location and the user's location.

[0161] "Rating" refers to the criteria used to rank potential locations based on user experience and facility quality.

[0162] "Convenience" is a criterion for evaluating the ease of access and use of a location.

[0163] A "server" is a central processing unit that processes data received from users and provides appropriate information.

[0164] A "user device" refers to an information processing terminal used by a user, such as a smartphone or car navigation system.

[0165] This invention is a user support system that uses emotion recognition technology to analyze the user's desired conditions and emotions, and then suggests destinations. This system is implemented according to the following procedure.

[0166] Users input voice or text using devices such as smartphones or car navigation systems. This data is sent from the device to a server for analysis of the user's preferences and emotions. The server uses natural language processing technology to analyze the input and extract the user's preferences. Furthermore, it uses emotion recognition technology to identify the user's emotional state using the same data. Specifically, general natural language processing software and emotion analysis engines are used for the analysis.

[0167] The server searches for information sources, taking into account the extracted preferences and identified emotions. These sources include location information, facility information, rating data, and congestion information. Based on this information, it identifies potential destinations to suggest to the user.

[0168] The device receives evaluated destination suggestions from the server and displays them to the user. The display includes distance, rating, convenience, and sentiment-based recommendation reasons, so the user can receive suggestions that best suit their preferences.

[0169] For example, if a user enters the text "I want to go somewhere where I can relax," the server uses natural language processing technology to extract "relax" as a desired condition and identifies that the user is seeking relaxation. Based on this information, a quiet place with abundant nature is recommended.

[0170] An example of a prompt for a generative AI model is, "Please recommend some tourist destinations that match my current mood. I'm in the mood to have fun today." This prompt is used as an input example to optimize the suggestions.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users input their desired conditions via voice or text using devices such as smartphones or car navigation systems. This input includes specific requests, such as seeking a relaxing place. The device then prepares to send the input data to the server.

[0174] Step 2:

[0175] The device sends voice or text data received from the user to the server. The HTTPS protocol is used for secure and reliable data transfer.

[0176] Step 3:

[0177] The server uses a natural language processing engine to analyze the received audio or text data. Specifically, it applies a keyword extraction algorithm to extract desired conditions such as "relax" and "location." The analysis results are stored as temporary data.

[0178] Step 4:

[0179] The server uses emotion recognition technology to identify the user's emotions from the analyzed data. For example, it can determine if the user is seeking relaxation based on the overall tone of the input text and specific words. Emotional data is also added to the temporary data.

[0180] Step 5:

[0181] The server searches for relevant information sources based on the extracted preferences and identified sentiment data. Specifically, it generates queries to databases containing location information, facility ratings, congestion information, etc., and executes the search.

[0182] Step 6:

[0183] The server lists potential destinations from the search results and ranks them using a proprietary rating algorithm based on distance, rating, convenience, and sentiment. It calculates a score for each candidate location and sorts them from highest to lowest.

[0184] Step 7:

[0185] The server sends evaluated destination candidates to the terminal. A secure communication protocol is used for transmission, taking user privacy into consideration.

[0186] Step 8:

[0187] The device visually displays the received destination suggestions to the user. Each suggestion includes distance, rating, and reason for recommendation, allowing the user to select the most suitable suggestion.

[0188] (Application Example 2)

[0189] 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."

[0190] Traditional user assistance systems could suggest destinations based on user preferences, but they failed to consider the user's emotional state, thus failing to fully meet their psychological needs. Furthermore, if the suggestions were not based on the user's past behavioral history or real-time information, they were often generic and unpersonalized, making them unattractive to the user.

[0191] 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.

[0192] In this invention, the server includes input means for receiving voice or text input from a user; analysis means for analyzing the received input using natural language processing technology to extract the user's desired conditions and emotional state; and search means for searching related information sets based on the extracted desired conditions and emotional state to identify candidate destinations to propose. This makes it possible to propose more personalized destinations that are tailored to the user's emotional state.

[0193] An "input means" is a mechanism for receiving voice or text input from a user.

[0194] The "analysis means" is a mechanism that analyzes the received input using natural language processing technology to extract the user's desired conditions and emotional state.

[0195] A "search tool" is a mechanism for searching for relevant information sets based on extracted desired conditions and emotional states, and for identifying candidate destinations to propose.

[0196] An "evaluation tool" is a mechanism for evaluating identified destination candidates based on distance, reputation, convenience, and emotional state, and for arranging them in the optimal order.

[0197] "Display means" refers to a mechanism for transmitting evaluated destination candidates to the user terminal and displaying the proposed content along with the reasons for the proposal.

[0198] A "history analysis means" is a mechanism for analyzing a user's past behavioral history and emotional state to provide personalized suggestions.

[0199] A "real-time evaluation means" is a mechanism that uses real-time information to evaluate the appropriateness of a proposed destination based on its congestion status and the user's emotional state, and provides the results to the user's terminal.

[0200] This invention can be implemented as a system for providing personalized destination suggestions based on user emotions. The system mainly consists of input means, analysis means, search means, evaluation means, and display means.

[0201] First, the user uses their smartphone's microphone to input voice data. This voice input is sent to the server via the input device. The server converts the voice to text using APIs such as Google Cloud Speech-to-Text API and performs natural language processing. As an analysis tool, libraries such as Hugging Face Transformers are used to extract the user's desired conditions and emotional state from the text.

[0202] Based on the analyzed information, the server uses search tools to search relevant databases and identify potential destinations. This includes geographical data, store information, and event information. The evaluation tool ranks these based on certain criteria. In this process, not only distance and convenience are considered, but also emotional state. For example, if the user's emotional state is seeking relaxation, a calm cafe or aromatherapy shop would be prioritized.

[0203] Finally, the server sends the evaluated destination candidates to the user's terminal via a display mechanism, allowing the user to select one. The user's smartphone displays visual map information using the Google Maps API, along with the reasons for the suggestions. This display allows the user to choose the location that best suits their mood.

[0204] For example, if a user voice-inputs "I want to relax today," the system recognizes the user's mood as relaxed and suggests a relaxing place accordingly. An example of a prompt for a generative AI model would be: "Consider a system that, when a user says 'I want to have fun today,' recognizes their mood as excited and suggests an appropriate destination or activity, such as an outing or an event."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The device receives voice input from the user. This voice data is converted into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. This conversion utilizes speech recognition technology to improve accuracy.

[0208] Step 2:

[0209] The server analyzes text data received from the terminal. Using natural language processing libraries such as Hugging Face Transformers, it extracts the user's desired conditions and emotional state from the text. The input is text data, and the output is data on desired conditions and emotional state. An emotional analysis algorithm is used to accurately determine the user's psychological needs.

[0210] Step 3:

[0211] The server searches for relevant information sets based on the extracted preferences and emotional states. These information sets include geographical information, store information, event information, etc. The input is preferences and emotional states, and the output is a list of potential destinations. This process utilizes database search technology to retrieve information in real time.

[0212] Step 4:

[0213] The server evaluates the identified destination candidates. Using evaluation tools, it ranks them based on distance, reputation, convenience, and sentiment. The input is a list of destination candidates, and the output is a ranked list of destinations. At this stage, weighted evaluation is performed, and an optimization algorithm is implemented.

[0214] Step 5:

[0215] The server sends ranked destination suggestions to the device. The device uses the Google Maps API to display the suggestions along with visual map information. The input is a ranked list of destinations, and the output is a display of suggestions to the user. This allows the user to select the destination that best suits their mood.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] As an embodiment of the present invention, a system that uses the user's voice or text as a data input means will be described. The user can input information via a smartphone or car navigation device. This input is used to interpret the user's intent using natural language processing technology. Specifically, it is possible to extract the conditions and experiences desired by the user.

[0233] Server operation

[0234] The server receives input data from the user and performs analysis using natural language processing. This analysis extracts the user's desired conditions, and relevant databases are searched based on these conditions. The databases include map information, user ratings, event information, congestion levels, weather information, etc., and the server combines this information to select candidate locations.

[0235] Destination exploration and evaluation

[0236] After the search is complete, the server evaluates the potential destinations. Evaluation criteria include user proximity, the reputation of the suggested locations, and preferences based on past user data. The server integrates this information to present the user with a ranking of the most appropriate destinations.

[0237] Information provision and display to the device

[0238] The terminal presents the user with evaluated destination candidates received from the server. Destination information is visually displayed on a map, along with more detailed directions, reviews, and practical data such as current congestion levels. This allows users to quickly and effectively decide where they want to visit.

[0239] Specific example

[0240] Suppose a family is looking for a new activity for the weekend. When the user enters the voice command "I'm looking for a place for kids to have fun" into their device, the server analyzes the request and lists options such as local amusement parks, zoos, and science museums. The evaluation criteria include the quality of child-friendly facilities, safety ratings, and crowd predictions, and based on this information, the most suitable destination is presented to the user.

[0241] In this way, the system can provide users with valuable, wish-based discoveries that go beyond mere geographical information.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user inputs voice or text into their smartphone or car navigation system. This input includes desired destination conditions and experience details.

[0245] Step 2:

[0246] The device sends the voice or text received from the user to the server. In the case of voice input, it is converted to text before transmission.

[0247] Step 3:

[0248] The server analyzes the received input using natural language processing technology. The analysis extracts the user's intentions and desired conditions.

[0249] Step 4:

[0250] The server searches relevant databases based on the analyzed desired conditions. These databases include map information, user ratings, facility information, event information, and more.

[0251] Step 5:

[0252] The server evaluates the searched destination candidates. Evaluation criteria include distance, user ratings, facility quality, and past user preferences.

[0253] Step 6:

[0254] The server selects the most suitable destination candidates based on the evaluation results and creates a recommendation list.

[0255] Step 7:

[0256] The server sends a list of recommendations to the terminal.

[0257] Step 8:

[0258] The device displays the received recommendation list to the user. The display includes the location on a map, detailed information, and the reason for the recommendation.

[0259] (Example 1)

[0260] 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."

[0261] In today's information society, users face the challenge of finding destinations that meet their specific criteria quickly and accurately. Furthermore, amidst an overwhelming amount of information, there is a demand for personalized suggestions that take into account individual user preferences and real-time environmental changes. However, conventional methods are insufficient to provide users with the appropriate information to meet their needs.

[0262] 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.

[0263] In this invention, the server includes data input means for receiving voice or text input from a user, intent analysis means for analyzing the received input using natural language processing technology and extracting conditions desired by the user, and data search means for searching an information set including geographic data, evaluation information, event information, congestion information, and weather data based on the extracted conditions and identifying candidate locations to propose. This makes it possible to propose customized destinations based on the user's preferences.

[0264] A "data input means" is a function for receiving information from the user in the form of voice or text.

[0265] An "intent analysis method" is a system that uses natural language processing technology to analyze the user's input and extract the conditions and requests the user desires.

[0266] "Data search method" refers to the process of searching various information sets, including geographic data, evaluation information, event information, congestion information, and weather data, based on the analyzed user preferences, to identify potential destinations.

[0267] The "candidate ranking determination method" is a function that evaluates identified destination candidates based on criteria such as proximity, reputation, and preferences based on past data, and determines the optimal order.

[0268] The "display means" is a mechanism for visually presenting evaluated destination candidates from the server to the user terminal.

[0269] A "preference analysis tool" is a means of analyzing a user's past preference data and providing personalized suggestions.

[0270] A "real-time evaluation method" is a function that uses real-time information to evaluate the congestion status of a location and provides the user with real-time information.

[0271] This invention is a system implemented by the user inputting voice or text via a smartphone or in-vehicle device. When the user inputs information into the device, the server receives it and performs analysis using natural language processing technology. The analysis utilizes a generative AI model and employs prompt sentences to interpret the user's intent. Analysis is made possible by using prompt sentences such as, "Explain the best way to find the destination the user desires."

[0272] The server identifies the user's desired experience and conditions, and then searches a wide range of data sources based on that information. Cloud computing services are used as the hardware, and natural language processing engines and database management systems are applied as the software. Relevant information is retrieved from multiple databases, including geographic information, user ratings, event information, congestion levels, and weather data.

[0273] The server integrates this data and analyzes it using criteria to evaluate potential locations. These criteria include the user's past preferences, proximity, reputation, safety, and congestion predictions. The locations are ranked through a scoring system to determine the most suitable candidate.

[0274] The terminal presents the user with evaluated destination information transmitted from the server. The information is displayed visually, including a map layout, detailed directions, user reviews, and current congestion status, all within an accessible interface. Through these functions, the user can select the optimal destination based on their preferences. As a concrete example, the system's effectiveness can be tested by entering the specific prompt: "Explain the function that searches for appropriate destinations based on the user's desired conditions and suggests the best location using evaluation criteria."

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users enter their requests via voice or text through their smartphones or in-car devices. The input includes specific information such as "Places the family can enjoy this weekend." This input indicates the user's preferences and desired experience.

[0278] Step 2:

[0279] The server receives voice or text data from the user through a data input mechanism. Next, it analyzes this input using natural language processing technology that leverages a generative AI model. During the analysis process, prompts are used to extract the user's intent and desired conditions, and based on this, appropriate conditions are clarified. The output is a keyword list corresponding to the user's desired conditions.

[0280] Step 3:

[0281] The server uses the extracted keyword list to search for a set of relevant information from the database. The search targets include geographical data, user evaluations, event information, congestion status, weather information, etc. Based on these databases, destination candidates that meet the user's desired conditions are identified. The output is a plurality of destination candidates that match the conditions.

[0282] Step 4:

[0283] The server performs analyses such as proximity, reputation, preference based on past user data, safety, and congestion prediction on the identified destination candidates. As a result, each candidate location is scored and an evaluation ranking is determined. The output of this step is a recommended ranking list of candidate locations based on the evaluation results.

[0284] Step 5:

[0285] The terminal visually presents destination information to the user based on the recommended ranking list received from the server. By displaying the arrangement on the map, detailed guidance information, reviews, and the current congestion status together, the user can efficiently select the optimal destination. The final output is the detailed information of the destination presented to the user.

[0286] (Application Example 1)

[0287] 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".

[0288] In modern society, when users use food delivery services, they are required to quickly obtain appropriate proposals that match their time and preferences. However, in conventional systems, there is a problem that it is difficult to select the optimal stores and products according to the specific needs of users, and efficient selection is hindered.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0290] In this invention, the server includes an acquisition means for receiving requests from users via voice or text, an analysis means for analyzing the received requests using natural language processing to extract the user's desired conditions, and a derivation means for searching a food delivery database based on the extracted desired conditions to identify potential stores that can provide the service. This enables users to efficiently receive suggestions for the most suitable stores according to their time and preferences.

[0291] "Acquisition means" refers to a device or method for receiving requests from a user, either by voice or text.

[0292] "Analysis means" refers to a device or method for analyzing a received request using natural language processing technology and extracting the user's desired conditions.

[0293] "Derivation means" refers to a device or method for searching a food delivery database based on extracted desired conditions to identify potential stores that can provide the service.

[0294] "Evaluation means" refers to an apparatus or method for evaluating identified store candidates based on delivery time, reputation, and evaluation criteria, and for arranging them in optimal order.

[0295] "Presentation means" refers to a device or method for transmitting evaluated store candidates to a user terminal and presenting options.

[0296] "History analysis means" refers to a device or method for analyzing a user's order history and providing customized suggestions.

[0297] "Real-time evaluation means" refers to a device or method for evaluating the delivery status of a proposed store using real-time data and providing the results to a user terminal.

[0298] The system implementing this invention mainly consists of a user's smartphone and a server. The user inputs a request via voice or text using their smartphone. This input is transmitted to the server by the "acquisition means".

[0299] The server uses natural language processing technology to analyze incoming requests. Specifically, it utilizes software that supports natural language processing, such as the Google Cloud Natural Language API, to extract the user's desired conditions using an "analysis tool." Based on this extracted information, the server searches a food delivery database and identifies potential stores that match the desired conditions using a "derivation tool."

[0300] The server then evaluates potential stores using an "evaluation tool" based on delivery time, reputation, and rating criteria, and sorts them in order of suitability. This evaluation result is transmitted to the user's terminal via a "presentation tool" and displayed on their smartphone. Based on this presentation, the user can select the most suitable store and order food.

[0301] A concrete example of this system's use is when a user enters a request into their smartphone such as, "Tell me the restaurant near my house that delivers the most popular ramen within 30 minutes." Based on this prompt, the server analyzes the request and quickly identifies and presents the most suitable restaurant.

[0302] The system utilizes smartphones as hardware and Google Cloud Natural Language API and MySQL as software, ensuring seamless integration between the user interface and the database. This system allows users to efficiently and comfortably access food delivery services.

[0303] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0304] Step 1:

[0305] The user inputs a request in voice or text using a smartphone. The input request is sent from the smartphone app to the server. The input of this step is voice or text data, and the output is data transmission to the server.

[0306] Step 2:

[0307] The server analyzes the received request using natural language processing technology. Specifically, the Google Cloud Natural Language API is used to extract the user's desired conditions from the request. The input of this step is the voice or text request from the user, and the output is the extracted desired conditions. As data processing, string analysis and semantic understanding are performed.

[0308] Step 3:

[0309] The server searches the MySQL database based on the desired conditions extracted in the previous step. The food delivery database includes store information, menus, delivery times, etc. Store candidates that match the desired conditions are identified. The input of this step is the desired conditions, and the output is a list of store candidates.

[0310] Step 4:

[0311] The server evaluates the identified store candidates based on delivery time, reputation, and evaluation criteria. This evaluation also includes referring to the user's past data. The store candidates are sorted in the most appropriate order. The input of this step is the list of store candidates, and the output is a list of stores in the evaluated order. As data operations, numerical calculations and ranking are performed.

[0312] Step 5:

[0313] The server sends the evaluation results to the smartphone device. The smartphone app displays this information to the user, who can then select a store from the suggested options and place an order. The input for this step is the list of evaluated stores, and the output is the information displayed to the user. When presenting the information, location information on a map and menu details are also provided visually.

[0314] 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.

[0315] As an embodiment for carrying out the present invention, a user support system incorporating an emotion engine will be described. This system recognizes emotions based on the user's voice or text input and reflects them in destination suggestions.

[0316] User input and emotion recognition

[0317] When a user inputs their desired conditions via voice or text on a smartphone or car navigation system, the device sends this input to a server. The server analyzes the input using natural language processing technology to extract the desired conditions, and also uses an emotion engine to identify the user's emotions. This allows the server to sense whether the user is happy or seeking relaxation.

[0318] Server-based data analysis and destination candidate selection

[0319] The server analyzes the user's emotional data, recognized by the emotion engine, along with their preferences. Based on this information, it searches relevant databases to identify potential destinations. The databases include geographical information, facility information, reputation, congestion levels, and event information.

[0320] Individualized assessment and recommendations

[0321] In addition to the usual evaluation criteria for potential destinations (distance, rating, and convenience), the server also takes into account the user's current emotional state when evaluating and ranking destinations. If the server determines that the user is seeking relaxation, it will prioritize quiet, nature-rich locations, optimizing the user experience.

[0322] Information provision and display to the device

[0323] The device displays evaluated destination options received from the server to the user. The suggestions are accompanied by reasons tailored to the user's emotions and are presented in a visually easy-to-understand format. For example, if the user enters "I want to have a fun day" and the emotion engine detects an excited state, amusement parks and active events will be suggested.

[0324] Thus, the present invention makes it possible to suggest appropriate destinations that take emotions into consideration, and to provide an experience that best suits the user's wishes and mood.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The user inputs information via voice or text into their smartphone or car navigation system. This input concerns the user's preferences and current mood.

[0328] Step 2:

[0329] The device sends the user's voice or text input to the server. In the case of voice input, the device converts the voice into text data and sends it.

[0330] Step 3:

[0331] The server analyzes the received text data using natural language processing techniques to extract the user's desired conditions. This analysis identifies the user's intent from the input words and phrases.

[0332] Step 4:

[0333] The server uses an emotion engine to recognize the user's emotional state from their input. Emotional data is determined from the tone of voice and text, phrasing, and other factors.

[0334] Step 5:

[0335] The server searches relevant databases based on the extracted preferences and recognized emotional states to identify potential destinations. The databases include geographical information, reputation data, and current event information.

[0336] Step 6:

[0337] The server evaluates candidate destinations, considering factors such as distance, reputation, convenience, and the user's emotional state. Based on this evaluation, the destinations are sorted in order of likelihood of the highest user satisfaction.

[0338] Step 7:

[0339] The server sends a list of optimized destination options to the terminal. Each option is accompanied by a description and an emotion-based recommendation reason.

[0340] Step 8:

[0341] The device displays suggested destinations to the user. This display includes map information, destination features, and recommendation reasons tailored to the user's emotions.

[0342] (Example 2)

[0343] 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".

[0344] While conventional systems could suggest appropriate destinations based on user preferences, they lacked the ability to consider user emotions and moods. Furthermore, suggestions that disregarded user emotions often failed to meet specific user needs, leading to decreased satisfaction.

[0345] 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.

[0346] In this invention, the server includes means for receiving voice or text, means for analyzing the data to identify desired conditions and emotions, and means for performing information retrieval and evaluation based on the conditions and emotions. This makes it possible to suggest the optimal location based on the user's desired conditions and emotions.

[0347] "Speech or text" refers to spoken language or written text information provided by the user through an input method.

[0348] "Natural language processing" refers to the technical means by which computers understand, interpret, and generate human language.

[0349] "Emotion recognition technology" is an analytical technology that identifies a user's emotional state from voice or text.

[0350] A "source of information" refers to a database or application that contains data necessary to identify potential destinations, such as location information, facility information, rating data, and congestion information.

[0351] "Distance" is a criterion for evaluating the physical separation between a candidate location and the user's location.

[0352] "Rating" refers to the criteria used to rank potential locations based on user experience and facility quality.

[0353] "Convenience" is a criterion for evaluating the ease of access and use of a location.

[0354] A "server" is a central processing unit that processes data received from users and provides appropriate information.

[0355] A "user device" refers to an information processing terminal used by a user, such as a smartphone or car navigation system.

[0356] This invention is a user support system that uses emotion recognition technology to analyze the user's desired conditions and emotions, and then suggests destinations. This system is implemented according to the following procedure.

[0357] Users input voice or text using devices such as smartphones or car navigation systems. This data is sent from the device to a server for analysis of the user's preferences and emotions. The server uses natural language processing technology to analyze the input and extract the user's preferences. Furthermore, it uses emotion recognition technology to identify the user's emotional state using the same data. Specifically, general natural language processing software and emotion analysis engines are used for the analysis.

[0358] The server searches for information sources, taking into account the extracted preferences and identified emotions. These sources include location information, facility information, rating data, and congestion information. Based on this information, it identifies potential destinations to suggest to the user.

[0359] The device receives evaluated destination suggestions from the server and displays them to the user. The display includes distance, rating, convenience, and sentiment-based recommendation reasons, so the user can receive suggestions that best suit their preferences.

[0360] For example, if a user enters the text "I want to go somewhere where I can relax," the server uses natural language processing technology to extract "relax" as a desired condition and identifies that the user is seeking relaxation. Based on this information, a quiet place with abundant nature is recommended.

[0361] An example of a prompt for a generative AI model is, "Please recommend some tourist destinations that match my current mood. I'm in the mood to have fun today." This prompt is used as an input example to optimize the suggestions.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] Users input their desired conditions via voice or text using devices such as smartphones or car navigation systems. This input includes specific requests, such as seeking a relaxing place. The device then prepares to send the input data to the server.

[0365] Step 2:

[0366] The device sends voice or text data received from the user to the server. The HTTPS protocol is used for secure and reliable data transfer.

[0367] Step 3:

[0368] The server uses a natural language processing engine to analyze the received audio or text data. Specifically, it applies a keyword extraction algorithm to extract desired conditions such as "relax" and "location." The analysis results are stored as temporary data.

[0369] Step 4:

[0370] The server uses emotion recognition technology to identify the user's emotions from the analyzed data. For example, it can determine if the user is seeking relaxation based on the overall tone of the input text and specific words. Emotional data is also added to the temporary data.

[0371] Step 5:

[0372] The server searches for relevant information sources based on the extracted preferences and identified sentiment data. Specifically, it generates queries to databases containing location information, facility ratings, congestion information, etc., and executes the search.

[0373] Step 6:

[0374] The server lists potential destinations from the search results and ranks them using a proprietary rating algorithm based on distance, rating, convenience, and sentiment. It calculates a score for each candidate location and sorts them from highest to lowest.

[0375] Step 7:

[0376] The server sends evaluated destination candidates to the terminal. A secure communication protocol is used for transmission, taking user privacy into consideration.

[0377] Step 8:

[0378] The device visually displays the received destination suggestions to the user. Each suggestion includes distance, rating, and reason for recommendation, allowing the user to select the most suitable suggestion.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0381] Traditional user assistance systems could suggest destinations based on user preferences, but they failed to consider the user's emotional state, thus failing to fully meet their psychological needs. Furthermore, if the suggestions were not based on the user's past behavioral history or real-time information, they were often generic and unpersonalized, making them unattractive to the user.

[0382] 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.

[0383] In this invention, the server includes input means for receiving voice or text input from a user; analysis means for analyzing the received input using natural language processing technology to extract the user's desired conditions and emotional state; and search means for searching related information sets based on the extracted desired conditions and emotional state to identify candidate destinations to propose. This makes it possible to propose more personalized destinations that are tailored to the user's emotional state.

[0384] An "input means" is a mechanism for receiving voice or text input from a user.

[0385] The "analysis means" is a mechanism that analyzes the received input using natural language processing technology to extract the user's desired conditions and emotional state.

[0386] A "search tool" is a mechanism for searching for relevant information sets based on extracted desired conditions and emotional states, and for identifying candidate destinations to propose.

[0387] An "evaluation tool" is a mechanism for evaluating identified destination candidates based on distance, reputation, convenience, and emotional state, and for arranging them in the optimal order.

[0388] "Display means" refers to a mechanism for transmitting evaluated destination candidates to the user terminal and displaying the proposed content along with the reasons for the proposal.

[0389] A "history analysis means" is a mechanism for analyzing a user's past behavioral history and emotional state to provide personalized suggestions.

[0390] A "real-time evaluation means" is a mechanism that uses real-time information to evaluate the appropriateness of a proposed destination based on its congestion status and the user's emotional state, and provides the results to the user's terminal.

[0391] This invention can be implemented as a system for providing personalized destination suggestions based on user emotions. The system mainly consists of input means, analysis means, search means, evaluation means, and display means.

[0392] First, the user uses their smartphone's microphone to input voice data. This voice input is sent to the server via the input device. The server converts the voice to text using APIs such as Google Cloud Speech-to-Text API and performs natural language processing. As an analysis tool, libraries such as Hugging Face Transformers are used to extract the user's desired conditions and emotional state from the text.

[0393] Based on the analyzed information, the server uses search tools to search relevant databases and identify potential destinations. This includes geographical data, store information, and event information. The evaluation tool ranks these based on certain criteria. In this process, not only distance and convenience are considered, but also emotional state. For example, if the user's emotional state is seeking relaxation, a calm cafe or aromatherapy shop would be prioritized.

[0394] Finally, the server sends the evaluated destination candidates to the user's terminal via a display mechanism, allowing the user to select one. The user's smartphone displays visual map information using the Google Maps API, along with the reasons for the suggestions. This display allows the user to choose the location that best suits their mood.

[0395] For example, if a user voice-inputs "I want to relax today," the system recognizes the user's mood as relaxed and suggests a relaxing place accordingly. An example of a prompt for a generative AI model would be: "Consider a system that, when a user says 'I want to have fun today,' recognizes their mood as excited and suggests an appropriate destination or activity, such as an outing or an event."

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The device receives voice input from the user. This voice data is converted into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. This conversion utilizes speech recognition technology to improve accuracy.

[0399] Step 2:

[0400] The server analyzes text data received from the terminal. Using natural language processing libraries such as Hugging Face Transformers, it extracts the user's desired conditions and emotional state from the text. The input is text data, and the output is data on desired conditions and emotional state. An emotional analysis algorithm is used to accurately determine the user's psychological needs.

[0401] Step 3:

[0402] The server searches for relevant information sets based on the extracted preferences and emotional states. These information sets include geographical information, store information, event information, etc. The input is preferences and emotional states, and the output is a list of potential destinations. This process utilizes database search technology to retrieve information in real time.

[0403] Step 4:

[0404] The server evaluates the identified destination candidates. Using evaluation tools, it ranks them based on distance, reputation, convenience, and sentiment. The input is a list of destination candidates, and the output is a ranked list of destinations. At this stage, weighted evaluation is performed, and an optimization algorithm is implemented.

[0405] Step 5:

[0406] The server sends ranked destination suggestions to the device. The device uses the Google Maps API to display the suggestions along with visual map information. The input is a ranked list of destinations, and the output is a display of suggestions to the user. This allows the user to select the destination that best suits their mood.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] As an embodiment of the present invention, a system that uses the user's voice or text as a data input means will be described. The user can input information via a smartphone or car navigation device. This input is used to interpret the user's intent using natural language processing technology. Specifically, it is possible to extract the conditions and experiences desired by the user.

[0424] Server operation

[0425] The server receives input data from the user and performs analysis using natural language processing. This analysis extracts the user's desired conditions, and relevant databases are searched based on these conditions. The databases include map information, user ratings, event information, congestion levels, weather information, etc., and the server combines this information to select candidate locations.

[0426] Destination exploration and evaluation

[0427] After the search is complete, the server evaluates the potential destinations. Evaluation criteria include user proximity, the reputation of the suggested locations, and preferences based on past user data. The server integrates this information to present the user with a ranking of the most appropriate destinations.

[0428] Information provision and display to the device

[0429] The terminal presents the user with evaluated destination candidates received from the server. Destination information is visually displayed on a map, along with more detailed directions, reviews, and practical data such as current congestion levels. This allows users to quickly and effectively decide where they want to visit.

[0430] Specific example

[0431] Suppose a family is looking for a new activity for the weekend. When the user enters the voice command "I'm looking for a place for kids to have fun" into their device, the server analyzes the request and lists options such as local amusement parks, zoos, and science museums. The evaluation criteria include the quality of child-friendly facilities, safety ratings, and crowd predictions, and based on this information, the most suitable destination is presented to the user.

[0432] In this way, the system can provide users with valuable, wish-based discoveries that go beyond mere geographical information.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The user inputs voice or text into their smartphone or car navigation system. This input includes desired destination conditions and experience details.

[0436] Step 2:

[0437] The device sends the voice or text received from the user to the server. In the case of voice input, it is converted to text before transmission.

[0438] Step 3:

[0439] The server analyzes the received input using natural language processing technology. The analysis extracts the user's intentions and desired conditions.

[0440] Step 4:

[0441] The server searches relevant databases based on the analyzed desired conditions. These databases include map information, user ratings, facility information, event information, and more.

[0442] Step 5:

[0443] The server evaluates the searched destination candidates. Evaluation criteria include distance, user ratings, facility quality, and past user preferences.

[0444] Step 6:

[0445] The server selects the most suitable destination candidates based on the evaluation results and creates a recommendation list.

[0446] Step 7:

[0447] The server sends a list of recommendations to the terminal.

[0448] Step 8:

[0449] The device displays the received recommendation list to the user. The display includes the location on a map, detailed information, and the reason for the recommendation.

[0450] (Example 1)

[0451] 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."

[0452] In today's information society, users face the challenge of finding destinations that meet their specific criteria quickly and accurately. Furthermore, amidst an overwhelming amount of information, there is a demand for personalized suggestions that take into account individual user preferences and real-time environmental changes. However, conventional methods are insufficient to provide users with the appropriate information to meet their needs.

[0453] 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.

[0454] In this invention, the server includes data input means for receiving voice or text input from a user, intent analysis means for analyzing the received input using natural language processing technology and extracting conditions desired by the user, and data search means for searching an information set including geographic data, evaluation information, event information, congestion information, and weather data based on the extracted conditions and identifying candidate locations to propose. This makes it possible to propose customized destinations based on the user's preferences.

[0455] A "data input means" is a function for receiving information from the user in the form of voice or text.

[0456] An "intent analysis method" is a system that uses natural language processing technology to analyze the user's input and extract the conditions and requests the user desires.

[0457] "Data search method" refers to the process of searching various information sets, including geographic data, evaluation information, event information, congestion information, and weather data, based on the analyzed user preferences, to identify potential destinations.

[0458] The "candidate ranking determination method" is a function that evaluates identified destination candidates based on criteria such as proximity, reputation, and preferences based on past data, and determines the optimal order.

[0459] The "display means" is a mechanism for visually presenting evaluated destination candidates from the server to the user terminal.

[0460] A "preference analysis tool" is a means of analyzing a user's past preference data and providing personalized suggestions.

[0461] A "real-time evaluation method" is a function that uses real-time information to evaluate the congestion status of a location and provides the user with real-time information.

[0462] This invention is a system implemented by the user inputting voice or text via a smartphone or in-vehicle device. When the user inputs information into the device, the server receives it and performs analysis using natural language processing technology. The analysis utilizes a generative AI model and employs prompt sentences to interpret the user's intent. Analysis is made possible by using prompt sentences such as, "Explain the best way to find the destination the user desires."

[0463] The server identifies the user's desired experience and conditions, and then searches a wide range of data sources based on that information. Cloud computing services are used as the hardware, and natural language processing engines and database management systems are applied as the software. Relevant information is retrieved from multiple databases, including geographic information, user ratings, event information, congestion levels, and weather data.

[0464] The server integrates this data and analyzes it using criteria to evaluate potential locations. These criteria include the user's past preferences, proximity, reputation, safety, and congestion predictions. The locations are ranked through a scoring system to determine the most suitable candidate.

[0465] The terminal presents the user with evaluated destination information transmitted from the server. The information is displayed visually, including a map layout, detailed directions, user reviews, and current congestion status, all within an accessible interface. Through these functions, the user can select the optimal destination based on their preferences. As a concrete example, the system's effectiveness can be tested by entering the specific prompt: "Explain the function that searches for appropriate destinations based on the user's desired conditions and suggests the best location using evaluation criteria."

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] Users enter their requests via voice or text through their smartphones or in-car devices. The input includes specific information such as "Places the family can enjoy this weekend." This input indicates the user's preferences and desired experience.

[0469] Step 2:

[0470] The server receives voice or text data from the user through a data input mechanism. Next, it analyzes this input using natural language processing technology that leverages a generative AI model. During the analysis process, prompts are used to extract the user's intent and desired conditions, and based on this, appropriate conditions are clarified. The output is a keyword list corresponding to the user's desired conditions.

[0471] Step 3:

[0472] The server uses the extracted keyword list to search the database for relevant information. This search includes geographical data, user ratings, event information, congestion levels, and weather information. From these databases, potential destinations are identified based on the user's preferences. The output consists of multiple destinations that match the criteria.

[0473] Step 4:

[0474] The server analyzes the identified destination candidates based on factors such as proximity, reputation, preferences based on past user data, safety, and congestion prediction. This assigns a score to each candidate location, determining their ranking. The output of this step is a list of recommended locations ranked according to the evaluation results.

[0475] Step 5:

[0476] The terminal visually presents destination information to the user based on a recommended ranking list received from the server. By displaying the location on a map, detailed directions, reviews, and current congestion status, the user can efficiently select the most suitable destination. The final output is detailed information about the destination presented to the user.

[0477] (Application Example 1)

[0478] 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."

[0479] In modern society, users expect to receive timely and appropriate suggestions tailored to their preferences when using food delivery services. However, conventional systems struggle to select the optimal stores and products to meet specific user needs, hindering efficient selection.

[0480] 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.

[0481] In this invention, the server includes an acquisition means for receiving requests from users via voice or text, an analysis means for analyzing the received requests using natural language processing to extract the user's desired conditions, and a derivation means for searching a food delivery database based on the extracted desired conditions to identify potential stores that can provide the service. This enables users to efficiently receive suggestions for the most suitable stores according to their time and preferences.

[0482] "Acquisition means" refers to a device or method for receiving requests from a user, either by voice or text.

[0483] "Analysis means" refers to a device or method for analyzing a received request using natural language processing technology and extracting the user's desired conditions.

[0484] "Derivation means" refers to a device or method for searching a food delivery database based on extracted desired conditions to identify potential stores that can provide the service.

[0485] "Evaluation means" refers to an apparatus or method for evaluating identified store candidates based on delivery time, reputation, and evaluation criteria, and for arranging them in optimal order.

[0486] "Presentation means" refers to a device or method for transmitting evaluated store candidates to a user terminal and presenting options.

[0487] "History analysis means" refers to a device or method for analyzing a user's order history and providing customized suggestions.

[0488] "Real-time evaluation means" refers to a device or method for evaluating the delivery status of a proposed store using real-time data and providing the results to a user terminal.

[0489] The system implementing this invention mainly consists of a user's smartphone and a server. The user inputs a request via voice or text using their smartphone. This input is transmitted to the server by the "acquisition means".

[0490] The server uses natural language processing technology to analyze incoming requests. Specifically, it utilizes software that supports natural language processing, such as the Google Cloud Natural Language API, to extract the user's desired conditions using an "analysis tool." Based on this extracted information, the server searches a food delivery database and identifies potential stores that match the desired conditions using a "derivation tool."

[0491] The server then evaluates potential stores using an "evaluation tool" based on delivery time, reputation, and rating criteria, and sorts them in order of suitability. This evaluation result is transmitted to the user's terminal via a "presentation tool" and displayed on their smartphone. Based on this presentation, the user can select the most suitable store and order food.

[0492] A concrete example of this system's use is when a user enters a request into their smartphone such as, "Tell me the restaurant near my house that delivers the most popular ramen within 30 minutes." Based on this prompt, the server analyzes the request and quickly identifies and presents the most suitable restaurant.

[0493] The system utilizes smartphones as hardware and Google Cloud Natural Language API and MySQL as software, ensuring seamless integration between the user interface and the database. This system allows users to efficiently and comfortably access food delivery services.

[0494] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0495] Step 1:

[0496] The user enters a request using voice or text via their smartphone. The entered request is sent from the smartphone app to the server. In this step, the input is voice or text data, and the output is the transmission of data to the server.

[0497] Step 2:

[0498] The server analyzes the received request using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract the user's desired conditions from the request. The input for this step is the user's voice or text request, and the output is the extracted desired conditions. Data processing involves string analysis and semantic understanding.

[0499] Step 3:

[0500] The server searches the MySQL database based on the desired criteria extracted in the previous step. The food delivery database contains store information, menus, delivery times, etc. Candidate stores that match the desired criteria are identified. The input for this step is the desired criteria, and the output is a list of candidate stores.

[0501] Step 4:

[0502] The server evaluates the identified store candidates based on delivery time, reputation, and rating criteria. This evaluation also includes referencing the user's past data. The store candidates are then sorted in order of most appropriateness. The input for this step is a list of store candidates, and the output is an evaluated, ordered list of stores. Data calculations include numerical calculations and ranking.

[0503] Step 5:

[0504] The server sends the evaluation results to the smartphone device. The smartphone app displays this information to the user, who can then select a store from the suggested options and place an order. The input for this step is the list of evaluated stores, and the output is the information displayed to the user. When presenting the information, location information on a map and menu details are also provided visually.

[0505] 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.

[0506] As an embodiment for carrying out the present invention, a user support system incorporating an emotion engine will be described. This system recognizes emotions based on the user's voice or text input and reflects them in destination suggestions.

[0507] User input and emotion recognition

[0508] When a user inputs their desired conditions via voice or text on a smartphone or car navigation system, the device sends this input to a server. The server analyzes the input using natural language processing technology to extract the desired conditions, and also uses an emotion engine to identify the user's emotions. This allows the server to sense whether the user is happy or seeking relaxation.

[0509] Server-based data analysis and destination candidate selection

[0510] The server analyzes the user's emotional data, recognized by the emotion engine, along with their preferences. Based on this information, it searches relevant databases to identify potential destinations. The databases include geographical information, facility information, reputation, congestion levels, and event information.

[0511] Individualized assessment and recommendations

[0512] In addition to the usual evaluation criteria for potential destinations (distance, rating, and convenience), the server also takes into account the user's current emotional state when evaluating and ranking destinations. If the server determines that the user is seeking relaxation, it will prioritize quiet, nature-rich locations, optimizing the user experience.

[0513] Information provision and display to the device

[0514] The device displays evaluated destination options received from the server to the user. The suggestions are accompanied by reasons tailored to the user's emotions and are presented in a visually easy-to-understand format. For example, if the user enters "I want to have a fun day" and the emotion engine detects an excited state, amusement parks and active events will be suggested.

[0515] Thus, the present invention makes it possible to suggest appropriate destinations that take emotions into consideration, and to provide an experience that best suits the user's wishes and mood.

[0516] The following describes the processing flow.

[0517] Step 1:

[0518] The user inputs information via voice or text into their smartphone or car navigation system. This input concerns the user's preferences and current mood.

[0519] Step 2:

[0520] The device sends the user's voice or text input to the server. In the case of voice input, the device converts the voice into text data and sends it.

[0521] Step 3:

[0522] The server analyzes the received text data using natural language processing techniques to extract the user's desired conditions. This analysis identifies the user's intent from the input words and phrases.

[0523] Step 4:

[0524] The server uses an emotion engine to recognize the user's emotional state from their input. Emotional data is determined from the tone of voice and text, phrasing, and other factors.

[0525] Step 5:

[0526] The server searches relevant databases based on the extracted preferences and recognized emotional states to identify potential destinations. The databases include geographical information, reputation data, and current event information.

[0527] Step 6:

[0528] The server evaluates candidate destinations, considering factors such as distance, reputation, convenience, and the user's emotional state. Based on this evaluation, the destinations are sorted in order of likelihood of the highest user satisfaction.

[0529] Step 7:

[0530] The server sends a list of optimized destination options to the terminal. Each option is accompanied by a description and an emotion-based recommendation reason.

[0531] Step 8:

[0532] The device displays suggested destinations to the user. This display includes map information, destination features, and recommendation reasons tailored to the user's emotions.

[0533] (Example 2)

[0534] 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."

[0535] While conventional systems could suggest appropriate destinations based on user preferences, they lacked the ability to consider user emotions and moods. Furthermore, suggestions that disregarded user emotions often failed to meet specific user needs, leading to decreased satisfaction.

[0536] 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.

[0537] In this invention, the server includes means for receiving voice or text, means for analyzing the data to identify desired conditions and emotions, and means for performing information retrieval and evaluation based on the conditions and emotions. This makes it possible to suggest the optimal location based on the user's desired conditions and emotions.

[0538] "Speech or text" refers to spoken language or written text information provided by the user through an input method.

[0539] "Natural language processing" refers to the technical means by which computers understand, interpret, and generate human language.

[0540] "Emotion recognition technology" is an analytical technology that identifies a user's emotional state from voice or text.

[0541] A "source of information" refers to a database or application that contains data necessary to identify potential destinations, such as location information, facility information, rating data, and congestion information.

[0542] "Distance" is a criterion for evaluating the physical separation between a candidate location and the user's location.

[0543] "Rating" refers to the criteria used to rank potential locations based on user experience and facility quality.

[0544] "Convenience" is a criterion for evaluating the ease of access and use of a location.

[0545] A "server" is a central processing unit that processes data received from users and provides appropriate information.

[0546] A "user device" refers to an information processing terminal used by a user, such as a smartphone or car navigation system.

[0547] This invention is a user support system that uses emotion recognition technology to analyze the user's desired conditions and emotions, and then suggests destinations. This system is implemented according to the following procedure.

[0548] Users input voice or text using devices such as smartphones or car navigation systems. This data is sent from the device to a server for analysis of the user's preferences and emotions. The server uses natural language processing technology to analyze the input and extract the user's preferences. Furthermore, it uses emotion recognition technology to identify the user's emotional state using the same data. Specifically, general natural language processing software and emotion analysis engines are used for the analysis.

[0549] The server searches for information sources, taking into account the extracted preferences and identified emotions. These sources include location information, facility information, rating data, and congestion information. Based on this information, it identifies potential destinations to suggest to the user.

[0550] The device receives evaluated destination suggestions from the server and displays them to the user. The display includes distance, rating, convenience, and sentiment-based recommendation reasons, so the user can receive suggestions that best suit their preferences.

[0551] For example, if a user enters the text "I want to go somewhere where I can relax," the server uses natural language processing technology to extract "relax" as a desired condition and identifies that the user is seeking relaxation. Based on this information, a quiet place with abundant nature is recommended.

[0552] An example of a prompt for a generative AI model is, "Please recommend some tourist destinations that match my current mood. I'm in the mood to have fun today." This prompt is used as an input example to optimize the suggestions.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] Users input their desired conditions via voice or text using devices such as smartphones or car navigation systems. This input includes specific requests, such as seeking a relaxing place. The device then prepares to send the input data to the server.

[0556] Step 2:

[0557] The device sends voice or text data received from the user to the server. The HTTPS protocol is used for secure and reliable data transfer.

[0558] Step 3:

[0559] The server uses a natural language processing engine to analyze the received audio or text data. Specifically, it applies a keyword extraction algorithm to extract desired conditions such as "relax" and "location." The analysis results are stored as temporary data.

[0560] Step 4:

[0561] The server uses emotion recognition technology to identify the user's emotions from the analyzed data. For example, it can determine if the user is seeking relaxation based on the overall tone of the input text and specific words. Emotional data is also added to the temporary data.

[0562] Step 5:

[0563] The server searches for relevant information sources based on the extracted preferences and identified sentiment data. Specifically, it generates queries to databases containing location information, facility ratings, congestion information, etc., and executes the search.

[0564] Step 6:

[0565] The server lists potential destinations from the search results and ranks them using a proprietary rating algorithm based on distance, rating, convenience, and sentiment. It calculates a score for each candidate location and sorts them from highest to lowest.

[0566] Step 7:

[0567] The server sends evaluated destination candidates to the terminal. A secure communication protocol is used for transmission, taking user privacy into consideration.

[0568] Step 8:

[0569] The device visually displays the received destination suggestions to the user. Each suggestion includes distance, rating, and reason for recommendation, allowing the user to select the most suitable suggestion.

[0570] (Application Example 2)

[0571] 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."

[0572] Traditional user assistance systems could suggest destinations based on user preferences, but they failed to consider the user's emotional state, thus failing to fully meet their psychological needs. Furthermore, if the suggestions were not based on the user's past behavioral history or real-time information, they were often generic and unpersonalized, making them unattractive to the user.

[0573] 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.

[0574] In this invention, the server includes input means for receiving voice or text input from a user; analysis means for analyzing the received input using natural language processing technology to extract the user's desired conditions and emotional state; and search means for searching related information sets based on the extracted desired conditions and emotional state to identify candidate destinations to propose. This makes it possible to propose more personalized destinations that are tailored to the user's emotional state.

[0575] An "input means" is a mechanism for receiving voice or text input from a user.

[0576] The "analysis means" is a mechanism that analyzes the received input using natural language processing technology to extract the user's desired conditions and emotional state.

[0577] A "search tool" is a mechanism for searching for relevant information sets based on extracted desired conditions and emotional states, and for identifying candidate destinations to propose.

[0578] An "evaluation tool" is a mechanism for evaluating identified destination candidates based on distance, reputation, convenience, and emotional state, and for arranging them in the optimal order.

[0579] "Display means" refers to a mechanism for transmitting evaluated destination candidates to the user terminal and displaying the proposed content along with the reasons for the proposal.

[0580] A "history analysis means" is a mechanism for analyzing a user's past behavioral history and emotional state to provide personalized suggestions.

[0581] A "real-time evaluation means" is a mechanism that uses real-time information to evaluate the appropriateness of a proposed destination based on its congestion status and the user's emotional state, and provides the results to the user's terminal.

[0582] This invention can be implemented as a system for providing personalized destination suggestions based on user emotions. The system mainly consists of input means, analysis means, search means, evaluation means, and display means.

[0583] First, the user uses their smartphone's microphone to input voice data. This voice input is sent to the server via the input device. The server converts the voice to text using APIs such as Google Cloud Speech-to-Text API and performs natural language processing. As an analysis tool, libraries such as Hugging Face Transformers are used to extract the user's desired conditions and emotional state from the text.

[0584] Based on the analyzed information, the server uses search tools to search relevant databases and identify potential destinations. This includes geographical data, store information, and event information. The evaluation tool ranks these based on certain criteria. In this process, not only distance and convenience are considered, but also emotional state. For example, if the user's emotional state is seeking relaxation, a calm cafe or aromatherapy shop would be prioritized.

[0585] Finally, the server sends the evaluated destination candidates to the user's terminal via a display mechanism, allowing the user to select one. The user's smartphone displays visual map information using the Google Maps API, along with the reasons for the suggestions. This display allows the user to choose the location that best suits their mood.

[0586] For example, if a user voice-inputs "I want to relax today," the system recognizes the user's mood as relaxed and suggests a relaxing place accordingly. An example of a prompt for a generative AI model would be: "Consider a system that, when a user says 'I want to have fun today,' recognizes their mood as excited and suggests an appropriate destination or activity, such as an outing or an event."

[0587] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0588] Step 1:

[0589] The device receives voice input from the user. This voice data is converted into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. This conversion utilizes speech recognition technology to improve accuracy.

[0590] Step 2:

[0591] The server analyzes text data received from the terminal. Using natural language processing libraries such as Hugging Face Transformers, it extracts the user's desired conditions and emotional state from the text. The input is text data, and the output is data on desired conditions and emotional state. An emotional analysis algorithm is used to accurately determine the user's psychological needs.

[0592] Step 3:

[0593] The server searches for relevant information sets based on the extracted preferences and emotional states. These information sets include geographical information, store information, event information, etc. The input is preferences and emotional states, and the output is a list of potential destinations. This process utilizes database search technology to retrieve information in real time.

[0594] Step 4:

[0595] The server evaluates the identified destination candidates. Using evaluation tools, it ranks them based on distance, reputation, convenience, and sentiment. The input is a list of destination candidates, and the output is a ranked list of destinations. At this stage, weighted evaluation is performed, and an optimization algorithm is implemented.

[0596] Step 5:

[0597] The server sends ranked destination suggestions to the device. The device uses the Google Maps API to display the suggestions along with visual map information. The input is a ranked list of destinations, and the output is a display of suggestions to the user. This allows the user to select the destination that best suits their mood.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] As an embodiment of the present invention, a system that uses the user's voice or text as a data input means will be described. The user can input information via a smartphone or car navigation device. This input is used to interpret the user's intent using natural language processing technology. Specifically, it is possible to extract the conditions and experiences desired by the user.

[0616] Server operation

[0617] The server receives input data from the user and performs analysis using natural language processing. This analysis extracts the user's desired conditions, and relevant databases are searched based on these conditions. The databases include map information, user ratings, event information, congestion levels, weather information, etc., and the server combines this information to select candidate locations.

[0618] Destination exploration and evaluation

[0619] After the search is complete, the server evaluates the potential destinations. Evaluation criteria include user proximity, the reputation of the suggested locations, and preferences based on past user data. The server integrates this information to present the user with a ranking of the most appropriate destinations.

[0620] Information provision and display to the device

[0621] The terminal presents the user with evaluated destination candidates received from the server. Destination information is visually displayed on a map, along with more detailed directions, reviews, and practical data such as current congestion levels. This allows users to quickly and effectively decide where they want to visit.

[0622] Specific example

[0623] Suppose a family is looking for a new activity for the weekend. When the user enters the voice command "I'm looking for a place for kids to have fun" into their device, the server analyzes the request and lists options such as local amusement parks, zoos, and science museums. The evaluation criteria include the quality of child-friendly facilities, safety ratings, and crowd predictions, and based on this information, the most suitable destination is presented to the user.

[0624] In this way, the system can provide users with valuable, wish-based discoveries that go beyond mere geographical information.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] The user inputs voice or text into their smartphone or car navigation system. This input includes desired destination conditions and experience details.

[0628] Step 2:

[0629] The device sends the voice or text received from the user to the server. In the case of voice input, it is converted to text before transmission.

[0630] Step 3:

[0631] The server analyzes the received input using natural language processing technology. The analysis extracts the user's intentions and desired conditions.

[0632] Step 4:

[0633] The server searches relevant databases based on the analyzed desired conditions. These databases include map information, user ratings, facility information, event information, and more.

[0634] Step 5:

[0635] The server evaluates the searched destination candidates. Evaluation criteria include distance, user ratings, facility quality, and past user preferences.

[0636] Step 6:

[0637] The server selects the most suitable destination candidates based on the evaluation results and creates a recommendation list.

[0638] Step 7:

[0639] The server sends a list of recommendations to the terminal.

[0640] Step 8:

[0641] The device displays the received recommendation list to the user. The display includes the location on a map, detailed information, and the reason for the recommendation.

[0642] (Example 1)

[0643] 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".

[0644] In today's information society, users face the challenge of finding destinations that meet their specific criteria quickly and accurately. Furthermore, amidst an overwhelming amount of information, there is a demand for personalized suggestions that take into account individual user preferences and real-time environmental changes. However, conventional methods are insufficient to provide users with the appropriate information to meet their needs.

[0645] 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.

[0646] In this invention, the server includes data input means for receiving voice or text input from a user, intent analysis means for analyzing the received input using natural language processing technology and extracting conditions desired by the user, and data search means for searching an information set including geographic data, evaluation information, event information, congestion information, and weather data based on the extracted conditions and identifying candidate locations to propose. This makes it possible to propose customized destinations based on the user's preferences.

[0647] A "data input means" is a function for receiving information from the user in the form of voice or text.

[0648] An "intent analysis method" is a system that uses natural language processing technology to analyze the user's input and extract the conditions and requests the user desires.

[0649] "Data search method" refers to the process of searching various information sets, including geographic data, evaluation information, event information, congestion information, and weather data, based on the analyzed user preferences, to identify potential destinations.

[0650] The "candidate ranking determination method" is a function that evaluates identified destination candidates based on criteria such as proximity, reputation, and preferences based on past data, and determines the optimal order.

[0651] The "display means" is a mechanism for visually presenting evaluated destination candidates from the server to the user terminal.

[0652] A "preference analysis tool" is a means of analyzing a user's past preference data and providing personalized suggestions.

[0653] A "real-time evaluation method" is a function that uses real-time information to evaluate the congestion status of a location and provides the user with real-time information.

[0654] This invention is a system implemented by the user inputting voice or text via a smartphone or in-vehicle device. When the user inputs information into the device, the server receives it and performs analysis using natural language processing technology. The analysis utilizes a generative AI model and employs prompt sentences to interpret the user's intent. Analysis is made possible by using prompt sentences such as, "Explain the best way to find the destination the user desires."

[0655] The server identifies the user's desired experience and conditions, and then searches a wide range of data sources based on that information. Cloud computing services are used as the hardware, and natural language processing engines and database management systems are applied as the software. Relevant information is retrieved from multiple databases, including geographic information, user ratings, event information, congestion levels, and weather data.

[0656] The server integrates this data and analyzes it using criteria to evaluate potential locations. These criteria include the user's past preferences, proximity, reputation, safety, and congestion predictions. The locations are ranked through a scoring system to determine the most suitable candidate.

[0657] The terminal presents the user with evaluated destination information transmitted from the server. The information is displayed visually, including a map layout, detailed directions, user reviews, and current congestion status, all within an accessible interface. Through these functions, the user can select the optimal destination based on their preferences. As a concrete example, the system's effectiveness can be tested by entering the specific prompt: "Explain the function that searches for appropriate destinations based on the user's desired conditions and suggests the best location using evaluation criteria."

[0658] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0659] Step 1:

[0660] Users enter their requests via voice or text through their smartphones or in-car devices. The input includes specific information such as "Places the family can enjoy this weekend." This input indicates the user's preferences and desired experience.

[0661] Step 2:

[0662] The server receives voice or text data from the user through a data input mechanism. Next, it analyzes this input using natural language processing technology that leverages a generative AI model. During the analysis process, prompts are used to extract the user's intent and desired conditions, and based on this, appropriate conditions are clarified. The output is a keyword list corresponding to the user's desired conditions.

[0663] Step 3:

[0664] The server uses the extracted keyword list to search the database for relevant information. This search includes geographical data, user ratings, event information, congestion levels, and weather information. From these databases, potential destinations are identified based on the user's preferences. The output consists of multiple destinations that match the criteria.

[0665] Step 4:

[0666] The server analyzes the identified destination candidates based on factors such as proximity, reputation, preferences based on past user data, safety, and congestion prediction. This assigns a score to each candidate location, determining their ranking. The output of this step is a list of recommended candidate locations based on the evaluation results.

[0667] Step 5:

[0668] The terminal visually presents destination information to the user based on a recommended ranking list received from the server. By displaying the location on a map, detailed directions, reviews, and current congestion status, the user can efficiently select the most suitable destination. The final output is detailed information about the destination presented to the user.

[0669] (Application Example 1)

[0670] 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".

[0671] In modern society, users expect to receive timely and appropriate suggestions tailored to their preferences when using food delivery services. However, conventional systems struggle to select the optimal stores and products to meet specific user needs, hindering efficient selection.

[0672] 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.

[0673] In this invention, the server includes an acquisition means for receiving requests from users via voice or text, an analysis means for analyzing the received requests using natural language processing to extract the user's desired conditions, and a derivation means for searching a food delivery database based on the extracted desired conditions to identify potential stores that can provide the service. This enables users to efficiently receive suggestions for the most suitable stores according to their time and preferences.

[0674] "Acquisition means" refers to a device or method for receiving requests from a user, either by voice or text.

[0675] "Analysis means" refers to a device or method for analyzing a received request using natural language processing technology and extracting the user's desired conditions.

[0676] "Derivation means" refers to a device or method for searching a food delivery database based on extracted desired conditions to identify potential stores that can provide the service.

[0677] "Evaluation means" refers to an apparatus or method for evaluating identified store candidates based on delivery time, reputation, and evaluation criteria, and for arranging them in optimal order.

[0678] "Presentation means" refers to a device or method for transmitting evaluated store candidates to a user terminal and presenting options.

[0679] "History analysis means" refers to a device or method for analyzing a user's order history and providing customized suggestions.

[0680] "Real-time evaluation means" refers to a device or method for evaluating the delivery status of a proposed store using real-time data and providing the results to a user terminal.

[0681] The system implementing this invention mainly consists of a user's smartphone and a server. The user inputs a request via voice or text using their smartphone. This input is transmitted to the server by the "acquisition means".

[0682] The server uses natural language processing technology to analyze incoming requests. Specifically, it utilizes software that supports natural language processing, such as the Google Cloud Natural Language API, to extract the user's desired conditions using an "analysis tool." Based on this extracted information, the server searches a food delivery database and identifies potential stores that match the desired conditions using a "derivation tool."

[0683] The server then evaluates potential stores using an "evaluation tool" based on delivery time, reputation, and rating criteria, and sorts them in order of suitability. This evaluation result is transmitted to the user's terminal via a "presentation tool" and displayed on their smartphone. Based on this presentation, the user can select the most suitable store and order food.

[0684] A concrete example of this system's use is when a user enters a request into their smartphone such as, "Tell me the restaurant near my house that delivers the most popular ramen within 30 minutes." Based on this prompt, the server analyzes the request and quickly identifies and presents the most suitable restaurant.

[0685] The system utilizes smartphones as hardware and Google Cloud Natural Language API and MySQL as software, ensuring seamless integration between the user interface and the database. This system allows users to efficiently and comfortably access food delivery services.

[0686] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0687] Step 1:

[0688] The user enters a request using voice or text via their smartphone. The entered request is sent from the smartphone app to the server. In this step, the input is voice or text data, and the output is the transmission of data to the server.

[0689] Step 2:

[0690] The server analyzes the received request using natural language processing techniques. Specifically, it uses the Google Cloud Natural Language API to extract the user's desired conditions from the request. The input for this step is the user's voice or text request, and the output is the extracted desired conditions. Data processing involves string analysis and semantic understanding.

[0691] Step 3:

[0692] The server searches the MySQL database based on the desired criteria extracted in the previous step. The food delivery database contains store information, menus, delivery times, etc. Candidate stores that match the desired criteria are identified. The input for this step is the desired criteria, and the output is a list of candidate stores.

[0693] Step 4:

[0694] The server evaluates the identified store candidates based on delivery time, reputation, and rating criteria. This evaluation also includes referencing the user's past data. The store candidates are then sorted in order of most appropriateness. The input for this step is a list of store candidates, and the output is an evaluated, ordered list of stores. Data calculations include numerical calculations and ranking.

[0695] Step 5:

[0696] The server sends the evaluation results to the smartphone device. The smartphone app displays this information to the user, who can then select a store from the suggested options and place an order. The input for this step is the list of evaluated stores, and the output is the information displayed to the user. When presenting the information, location information on a map and menu details are also provided visually.

[0697] 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.

[0698] As an embodiment for carrying out the present invention, a user support system incorporating an emotion engine will be described. This system recognizes emotions based on the user's voice or text input and reflects them in destination suggestions.

[0699] User input and emotion recognition

[0700] When a user inputs their desired conditions via voice or text on a smartphone or car navigation system, the device sends this input to a server. The server analyzes the input using natural language processing technology to extract the desired conditions, and also uses an emotion engine to identify the user's emotions. This allows the server to sense whether the user is happy or seeking relaxation.

[0701] Server-based data analysis and destination candidate selection

[0702] The server analyzes the user's emotional data, recognized by the emotion engine, along with their preferences. Based on this information, it searches relevant databases to identify potential destinations. The databases include geographical information, facility information, reputation, congestion levels, and event information.

[0703] Individualized assessment and recommendations

[0704] In addition to the usual evaluation criteria for potential destinations (distance, rating, and convenience), the server also takes into account the user's current emotional state when evaluating and ranking destinations. If the server determines that the user is seeking relaxation, it will prioritize quiet, nature-rich locations, optimizing the user experience.

[0705] Information provision and display to the device

[0706] The device displays evaluated destination options received from the server to the user. The suggestions are accompanied by reasons tailored to the user's emotions and are presented in a visually easy-to-understand format. For example, if the user enters "I want to have a fun day" and the emotion engine detects an excited state, amusement parks and active events will be suggested.

[0707] Thus, the present invention makes it possible to suggest appropriate destinations that take emotions into consideration, and to provide an experience that best suits the user's wishes and mood.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] The user inputs information via voice or text into their smartphone or car navigation system. This input concerns the user's preferences and current mood.

[0711] Step 2:

[0712] The device sends the user's voice or text input to the server. In the case of voice input, the device converts the voice into text data and sends it.

[0713] Step 3:

[0714] The server analyzes the received text data using natural language processing techniques to extract the user's desired conditions. This analysis identifies the user's intent from the input words and phrases.

[0715] Step 4:

[0716] The server uses an emotion engine to recognize the user's emotional state from their input. Emotional data is determined from the tone of voice and text, phrasing, and other factors.

[0717] Step 5:

[0718] The server searches relevant databases based on the extracted preferences and recognized emotional states to identify potential destinations. The databases include geographical information, reputation data, and current event information.

[0719] Step 6:

[0720] The server evaluates candidate destinations, considering factors such as distance, reputation, convenience, and the user's emotional state. Based on this evaluation, the destinations are sorted in order of likelihood of the highest user satisfaction.

[0721] Step 7:

[0722] The server sends a list of optimized destination options to the terminal. Each option is accompanied by a description and an emotion-based recommendation reason.

[0723] Step 8:

[0724] The device displays destination suggestions to the user. These suggestions include map information, destination features, and recommendation reasons tailored to the user's emotional state.

[0725] (Example 2)

[0726] 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".

[0727] While conventional systems could suggest appropriate destinations based on user preferences, they lacked the ability to consider user emotions and moods. Furthermore, suggestions that disregarded user emotions often failed to meet specific user needs, leading to decreased satisfaction.

[0728] 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.

[0729] In this invention, the server includes means for receiving voice or text, means for analyzing the data to identify desired conditions and emotions, and means for performing information retrieval and evaluation based on the conditions and emotions. This makes it possible to suggest the optimal location based on the user's desired conditions and emotions.

[0730] "Speech or text" refers to spoken language or written text information provided by the user through an input method.

[0731] "Natural language processing" refers to the technical means by which computers understand, interpret, and generate human language.

[0732] "Emotion recognition technology" is an analytical technology that identifies a user's emotional state from voice or text.

[0733] A "source of information" refers to a database or application that contains data necessary to identify potential destinations, such as location information, facility information, rating data, and congestion information.

[0734] "Distance" is a criterion for evaluating the physical separation between a candidate location and the user's location.

[0735] "Rating" refers to the criteria used to rank potential locations based on user experience and facility quality.

[0736] "Convenience" is a criterion for evaluating the ease of access and use of a location.

[0737] A "server" is a central processing unit that processes data received from users and provides appropriate information.

[0738] A "user device" refers to an information processing terminal used by a user, such as a smartphone or car navigation system.

[0739] This invention is a user support system that uses emotion recognition technology to analyze the user's desired conditions and emotions, and then suggests destinations. This system is implemented according to the following procedure.

[0740] Users input voice or text using devices such as smartphones or car navigation systems. This data is sent from the device to a server for analysis of the user's preferences and emotions. The server uses natural language processing technology to analyze the input and extract the user's preferences. Furthermore, it uses emotion recognition technology to identify the user's emotional state using the same data. Specifically, general natural language processing software and emotion analysis engines are used for the analysis.

[0741] The server searches for information sources, taking into account the extracted preferences and identified emotions. These sources include location information, facility information, rating data, and congestion information. Based on this information, it identifies potential destinations to suggest to the user.

[0742] The device receives evaluated destination suggestions from the server and displays them to the user. The display includes distance, rating, convenience, and sentiment-based recommendation reasons, so the user can receive suggestions that best suit their preferences.

[0743] For example, if a user enters the text "I want to go somewhere where I can relax," the server uses natural language processing technology to extract "relax" as a desired condition and identifies that the user is seeking relaxation. Based on this information, a quiet place with abundant nature is recommended.

[0744] An example of a prompt for a generative AI model is, "Please recommend some tourist destinations that match my current mood. I'm in the mood to have fun today." This prompt is used as an input example to optimize the suggestions.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] Users input their desired conditions via voice or text using devices such as smartphones or car navigation systems. This input includes specific requests, such as seeking a relaxing place. The device then prepares to send the input data to the server.

[0748] Step 2:

[0749] The device sends voice or text data received from the user to the server. The HTTPS protocol is used for secure and reliable data transfer.

[0750] Step 3:

[0751] The server uses a natural language processing engine to analyze the received audio or text data. Specifically, it applies a keyword extraction algorithm to extract desired conditions such as "relax" and "location." The analysis results are stored as temporary data.

[0752] Step 4:

[0753] The server uses emotion recognition technology to identify the user's emotions from the analyzed data. For example, it can determine if the user is seeking relaxation based on the overall tone of the input text and specific words. Emotional data is also added to the temporary data.

[0754] Step 5:

[0755] The server searches for relevant information sources based on the extracted preferences and identified sentiment data. Specifically, it generates queries to databases containing location information, facility ratings, congestion information, etc., and executes the search.

[0756] Step 6:

[0757] The server lists potential destinations from the search results and ranks them using a proprietary rating algorithm based on distance, rating, convenience, and sentiment. It calculates a score for each candidate location and sorts them from highest to lowest.

[0758] Step 7:

[0759] The server sends evaluated destination candidates to the terminal. A secure communication protocol is used for transmission, taking user privacy into consideration.

[0760] Step 8:

[0761] The device visually displays the received destination suggestions to the user. Each suggestion includes distance, rating, and reason for recommendation, allowing the user to select the most suitable suggestion.

[0762] (Application Example 2)

[0763] 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".

[0764] Traditional user assistance systems could suggest destinations based on user preferences, but they failed to consider the user's emotional state, thus failing to fully meet their psychological needs. Furthermore, if the suggestions were not based on the user's past behavioral history or real-time information, they were often generic and unpersonalized, making them unattractive to the user.

[0765] 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.

[0766] In this invention, the server includes input means for receiving voice or text input from a user; analysis means for analyzing the received input using natural language processing technology to extract the user's desired conditions and emotional state; and search means for searching related information sets based on the extracted desired conditions and emotional state to identify candidate destinations to propose. This makes it possible to propose more personalized destinations that are tailored to the user's emotional state.

[0767] An "input means" is a mechanism for receiving voice or text input from a user.

[0768] The "analysis means" is a mechanism that analyzes the received input using natural language processing technology to extract the user's desired conditions and emotional state.

[0769] A "search tool" is a mechanism for searching for relevant information sets based on extracted desired conditions and emotional states, and for identifying candidate destinations to propose.

[0770] An "evaluation tool" is a mechanism for evaluating identified destination candidates based on distance, reputation, convenience, and emotional state, and for arranging them in the optimal order.

[0771] "Display means" refers to a mechanism for transmitting evaluated destination candidates to the user terminal and displaying the proposed content along with the reasons for the proposal.

[0772] A "history analysis means" is a mechanism for analyzing a user's past behavioral history and emotional state to provide personalized suggestions.

[0773] A "real-time evaluation means" is a mechanism that uses real-time information to evaluate the appropriateness of a proposed destination based on its congestion status and the user's emotional state, and provides the results to the user's terminal.

[0774] This invention can be implemented as a system for providing personalized destination suggestions based on user emotions. The system mainly consists of input means, analysis means, search means, evaluation means, and display means.

[0775] First, the user uses their smartphone's microphone to input voice data. This voice input is sent to the server via the input device. The server converts the voice to text using APIs such as Google Cloud Speech-to-Text API and performs natural language processing. As an analysis tool, libraries such as Hugging Face Transformers are used to extract the user's desired conditions and emotional state from the text.

[0776] Based on the analyzed information, the server uses search tools to search relevant databases and identify potential destinations. This includes geographical data, store information, and event information. The evaluation tool ranks these based on certain criteria. In this process, not only distance and convenience are considered, but also emotional state. For example, if the user's emotional state is seeking relaxation, a calm cafe or aromatherapy shop would be prioritized.

[0777] Finally, the server sends the evaluated destination candidates to the user's terminal via a display mechanism, allowing the user to select one. The user's smartphone displays visual map information using the Google Maps API, along with the reasons for the suggestions. This display allows the user to choose the location that best suits their mood.

[0778] For example, if a user voice-inputs "I want to relax today," the system recognizes the user's mood as relaxed and suggests a relaxing place accordingly. An example of a prompt for a generative AI model would be: "Consider a system that, when a user says 'I want to have fun today,' recognizes their mood as excited and suggests an appropriate destination or activity, such as an outing or an event."

[0779] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0780] Step 1:

[0781] The device receives voice input from the user. This voice data is converted into text data using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. This conversion utilizes speech recognition technology to improve accuracy.

[0782] Step 2:

[0783] The server analyzes text data received from the terminal. Using natural language processing libraries such as Hugging Face Transformers, it extracts the user's desired conditions and emotional state from the text. The input is text data, and the output is data on desired conditions and emotional state. An emotional analysis algorithm is used to accurately determine the user's psychological needs.

[0784] Step 3:

[0785] The server searches for relevant information sets based on the extracted preferences and emotional states. These information sets include geographical information, store information, event information, etc. The input is preferences and emotional states, and the output is a list of potential destinations. This process utilizes database search technology to retrieve information in real time.

[0786] Step 4:

[0787] The server evaluates the identified destination candidates. Using evaluation tools, it ranks them based on distance, reputation, convenience, and sentiment. The input is a list of destination candidates, and the output is a ranked list of destinations. At this stage, weighted evaluation is performed, and an optimization algorithm is implemented.

[0788] Step 5:

[0789] The server sends ranked destination suggestions to the device. The device uses the Google Maps API to display the suggestions along with visual map information. The input is a ranked list of destinations, and the output is a display of suggestions to the user. This allows the user to select the destination that best suits their mood.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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."

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] The following is further disclosed regarding the embodiments described above.

[0812] (Claim 1)

[0813] An input means for receiving voice or text input from a user,

[0814] The aforementioned analysis means analyzes the received input using natural language processing technology and extracts the user's desired conditions,

[0815] A search means for searching relevant databases based on the extracted desired conditions and identifying candidate destinations to propose,

[0816] An evaluation means for evaluating the identified destination candidates based on distance, reputation, and convenience, and arranging them in the optimal order,

[0817] A display means that transmits the evaluated destination candidates to the user terminal and displays the suggested content,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, further comprising a history analysis means for analyzing a user's past behavior history and providing personalized suggestions.

[0821] (Claim 3)

[0822] The system according to claim 1, further comprising a real-time evaluation means for evaluating the congestion status of a proposed destination using real-time information and providing the results to a user terminal.

[0823] "Example 1"

[0824] (Claim 1)

[0825] A data input means for receiving voice or text input from a user,

[0826] An intent analysis means that analyzes the received input using natural language processing technology and extracts the conditions desired by the user,

[0827] A data search means that searches for an information set including geographic data, evaluation information, event information, congestion information, and weather data based on the extracted conditions, and identifies candidate locations to propose,

[0828] A means for determining candidate rankings that evaluates the identified destination candidates based on proximity, reputation, and preferences based on past data, and arranges them in the optimal order.

[0829] A display means for presenting the evaluated destination candidates as visual information to the user terminal,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, further comprising preference analysis means for analyzing a user's past preference data and providing personalized suggestions.

[0833] (Claim 3)

[0834] The system according to claim 1, further comprising an immediate evaluation means for evaluating the congestion status of a proposed location using real-time information and displaying the results.

[0835] "Application Example 1"

[0836] (Claim 1)

[0837] A means for receiving requests from users via voice or text,

[0838] The aforementioned received request is analyzed using natural language processing and an analysis means is used to extract the user's desired conditions.

[0839] A means for searching a food delivery database based on the extracted desired conditions and identifying potential stores that can provide the service,

[0840] An evaluation method for evaluating the identified store candidates based on delivery time, reputation, and evaluation criteria, and arranging them in optimal order,

[0841] A presentation means that transmits the evaluated store candidates to the user terminal and presents the options,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising a history analysis means for analyzing a user's order history and making customized suggestions.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising a real-time evaluation means for evaluating the delivery status of a proposed store using real-time data and providing the results to a user terminal.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] Means for receiving voice or text from the user,

[0850] The means for analyzing the received data, extracting the user's desired conditions using natural language processing technology, and further identifying the user's emotions using emotion recognition technology,

[0851] A means for searching for relevant information sources based on the extracted preferences and identified emotions, and for identifying potential locations to suggest,

[0852] A means for evaluating the identified candidate locations based on distance, reputation, convenience, and emotion, and arranging them in the optimal order,

[0853] A means for transmitting the evaluated location candidates to the user device and displaying the proposed content,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means for analyzing a user's past behavioral history and providing personalized suggestions.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for evaluating the congestion status of a proposed location using real-time information and providing the results to a user device.

[0859] "Application example 2 of combining emotional engines"

[0860] (Claim 1)

[0861] An input means for receiving voice or text input from a user,

[0862] An analysis means that analyzes the received input using natural language processing technology to extract the user's desired conditions and emotional state,

[0863] A search means for searching for relevant information sets based on the extracted desired conditions and emotional states, and identifying candidate destinations to propose,

[0864] An evaluation means for evaluating the identified destination candidates based on distance, reputation, convenience, and emotional state, and arranging them in the optimal order,

[0865] A display means that transmits the evaluated destination candidates to the user terminal and displays the proposed content along with the reasons for the proposal,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising a history analysis means for analyzing a user's past behavioral history and emotional state and providing personalized suggestions.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising a real-time evaluation means that evaluates the appropriateness of a proposed destination based on its congestion status and the user's emotional state using real-time information, and provides the results to the user terminal. [Explanation of Symbols]

[0871] 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. An input means for receiving voice or text input from a user, The aforementioned analysis means analyzes the received input using natural language processing technology and extracts the user's desired conditions, A search means for searching relevant databases based on the extracted desired conditions and identifying candidate destinations to propose, An evaluation means for evaluating the identified destination candidates based on distance, reputation, and convenience, and arranging them in the optimal order, A display means that transmits the evaluated destination candidates to the user terminal and displays the suggested content, A system that includes this.

2. The system according to claim 1, further comprising a history analysis means for analyzing a user's past behavior history and providing personalized suggestions.

3. The system according to claim 1, further comprising a real-time evaluation means for evaluating the congestion status of a proposed destination using real-time information and providing the results to a user terminal.

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