system

Abstract

We provide the system. [Solution] A natural language processing means that receives information from the user and analyzes that information, An information retrieval means for searching for appropriate support information based on the analysis results, An interface means for presenting the searched support information to the user, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The object of this invention is to solve the problems of lack of information, complexity of procedures, and communication barriers due to multiple languages that low-income and poverty-stricken people face when accessing appropriate support information. In particular, it is required to improve the accessibility and convenience to effective support information and support economic independence.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a system that includes a natural language processing means for receiving and analyzing information from the user, and an information retrieval means for searching for support information based on the analysis results. Furthermore, by presenting the retrieved support information to the user through a multilingual interface, the system effectively provides information to users who use different languages. In addition, it is possible to optimize the search for support information using machine learning technology and provide the most relevant information to the user.

[0006] "Users" refer to individuals who utilize this system, primarily targeting those facing financial hardship or low-income groups.

[0007] "Information reception" refers to the process by which a system acquires data provided by the user.

[0008] "Natural language processing means" refers to technologies for analyzing received language data and understanding its intent and content.

[0009] "Information retrieval means" refers to a function that finds requested support information based on analyzed data.

[0010] "Interface means" refers to a user interface that allows users to access support information or interact with the system.

[0011] "Multilingual support" refers to the ability to process and present information in multiple languages.

[0012] "Machine learning technology" refers to algorithms and models used to optimize and improve the accuracy of the information retrieval process based on past data and experience. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]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. <00000�9>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.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] [[ID=·47]] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system designed to make it easier for people in poverty and low-income groups to access the support information they need. This system functions through the cooperation of three parties: a server, a terminal, and a user.

[0035] Users input information about their living situation and support needs using devices such as smartphones and computers. The device receives this information as natural language and transmits it to the server. The transmitted information is then analyzed on the server using natural language processing tools. During this analysis, important keywords and intentions are extracted from the input information.

[0036] The server uses information retrieval tools based on the analysis results to search for appropriate support information. This includes government welfare services, NPO support activities, and vocational training programs. Machine learning technology is used in the search, and by referring to past data, the server provides users with the most suitable support information with high accuracy.

[0037] Support information sent from the server is displayed on the terminal. The terminal can present information in the user's native language using a multilingual interface. For example, if a foreign worker residing in Japan searches for vocational training programs through the terminal, suitable support programs will be displayed, and instructions on how to use those programs and contact information will be provided in the user's language.

[0038] This system aims to improve users' access to the support information they need and provide effective support to diverse user groups, transcending language barriers. As a result, it is providing an effective means for people facing poverty and low incomes to receive appropriate support and live independent lives.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users access the system interface using a terminal and enter information about the support they require and their current situation. This information includes their place of residence, income level, and the type of support they need (e.g., medical expense assistance, vocational training).

[0042] Step 2:

[0043] The terminal sends information entered by the user to the server. This data is received by the server as a message written in natural language.

[0044] Step 3:

[0045] The server analyzes the received natural language data using natural language processing tools. Here, keywords and intent are extracted from the user's input, and the information is understood and classified.

[0046] Step 4:

[0047] After analysis, the server uses machine learning techniques to search for the most appropriate support information for the user's situation. This includes a process of referencing internal databases and external APIs to collect information on public support services and non-profit organizations.

[0048] Step 5:

[0049] The server optimizes the support provided based on the search results and selects the most relevant information for the user. This selected information includes specific support procedures, participation methods, and contact details.

[0050] Step 6:

[0051] The server sends the selected support information to the user's terminal.

[0052] Step 7:

[0053] The device displays the received support information to the user. This information is clearly presented in the user's chosen language using multilingual support.

[0054] Step 8:

[0055] Based on the support information provided, users can take the necessary actions. If needed, they can also contact support again via their device to request further information or assistance.

[0056] (Example 1)

[0057] 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."

[0058] A challenge is to improve the situation where people facing poverty and low income levels have difficulty accessing support information that meets their needs. In particular, language barriers and difficulty in filtering information often lead to delays in providing appropriate support. Therefore, there is a need for a system that allows for easy access to information in multiple languages.

[0059] 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.

[0060] In this invention, the server includes means for receiving information from users regarding their living situation and support needs using an information processing device, natural language processing means for using a generative AI model to analyze the received information, and information retrieval means for searching for appropriate social support information based on the analyzed information. This makes it possible for diverse users to efficiently obtain the support information they need, overcoming language barriers.

[0061] An "information processing device" is a device that receives input information from a user and transmits it to a server, and includes smartphones and computers.

[0062] A "generative AI model" is an artificial intelligence model used for natural language processing. It analyzes information from users and is used to extract important keywords and intentions.

[0063] "Natural language processing means" refers to means for analyzing natural language information received from users, and includes functions for analyzing the intent and important keywords of the information using a generative AI model.

[0064] An "information retrieval tool" is a means of searching for relevant social support information based on analyzed information, and it has the function of improving search accuracy using machine learning technology.

[0065] A "user interface means" is a means of presenting searched support information to the user, and it has the role of providing multilingual support and displaying information in the user's language.

[0066] For this invention to be implemented, it is essential that the server, terminal, and user within the system work together in coordination. This system is designed to provide rapid access to support information needed by people in need and low-income groups, overcoming language barriers.

[0067] The server is equipped with a generative AI model for natural language processing (NLP). This model analyzes natural language information about the user's living situation and support needs. Its main role is to extract important keywords and intentions from the information and search for relevant social support information. Machine learning algorithms are used for information retrieval, providing highly relevant information while referring to past data.

[0068] The terminal is responsible for transmitting user-inputted information to the server and features a multilingual user interface. This interface can display support information sent from the server in the user's native language. Furthermore, user convenience during information input has been considered, providing an intuitive interface.

[0069] Users input their living situation and support needs in natural language using information terminals such as smartphones and computers. For example, specific needs such as "I would like to receive public food assistance." This allows users to easily communicate the support information they need to the system.

[0070] As a concrete example, suppose a foreign worker residing in Japan enters a prompt message such as, "I'm looking for a vocational training program. My current occupation is food processing, and I want to improve my skills." In this case, the system analyzes this information and provides relevant vocational training program information, thereby facilitating the user's access to the support they need.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] Users input their living situation and support needs using a device such as a smartphone or computer. For example, they might enter a prompt message such as, "I need rent assistance." This input information is collected as initial data for the system.

[0074] Step 2:

[0075] The terminal formats the information entered by the user and sends it to the server. Here, natural language data in text format is passed to the server. The terminal's role is to convert the data into the appropriate format and transmit it quickly to the server.

[0076] Step 3:

[0077] The server receives the transmitted information and performs natural language processing using a generative AI model. It extracts keywords and intent from the input sentence and identifies the important information, "rent assistance." This analysis allows for a concrete understanding of the user's needs.

[0078] Step 4:

[0079] The server initiates information retrieval based on the analysis results. Using machine learning algorithms, it searches past databases for highly relevant support information, identifying appropriate support programs such as "rent subsidy programs." The data calculation performed here involves prioritizing search results using a scoring system.

[0080] Step 5:

[0081] The server returns the support information obtained as a search result to the terminal. This information includes specific support details, details of the providing organization, and application procedures. To improve the accuracy of the information, it is also personalized based on past usage history.

[0082] Step 6:

[0083] The terminal displays support information received from the server to the user. Using a multilingual user interface, the information is displayed in the user's native language, clearly guiding them through the necessary support procedures and access methods. This makes it easy for users to take immediate action to receive support.

[0084] (Application Example 1)

[0085] 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."

[0086] There is a challenge in that people facing poverty and low income levels have difficulty accessing necessary support information quickly and appropriately. Furthermore, language barriers and the sheer volume of information make it difficult to receive the support they need. In addition, it can be difficult to provide support that is tailored to the user's living situation. To address these challenges, it is necessary to offer appropriate support information that is suited to the user's circumstances.

[0087] 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.

[0088] In this invention, the server includes a natural language processing means for receiving and analyzing information from the user, an information retrieval means for searching for appropriate support content based on the analysis results, and a data analysis means for evaluating the user's living situation using purchase data. This makes it possible to propose appropriate support content based on the user's living situation.

[0089] A "user" is someone who provides information to the system and receives support from it.

[0090] "Receiving information" refers to the act of obtaining data from a user via a network.

[0091] "Natural language processing methods" refer to methods and technologies for analyzing text written in human language and understanding its meaning and intent.

[0092] "Information retrieval means" refers to a method of obtaining appropriate support information from databases and other information sources based on analyzed data.

[0093] "Display means" refers to a method for visually presenting the searched support content to the user.

[0094] "Purchase data" refers to information about a user's consumption behavior and history.

[0095] "Data analysis methods" refer to technologies used to evaluate purchase data and determine the user's lifestyle.

[0096] "Proposal methods" refer to methods and techniques for presenting relevant support services according to the user's living situation.

[0097] To implement this invention, the system is built on the cooperation of a server, terminals, and users. The server is a network-connected computing device equipped with a high-performance processor and large memory, and is primarily responsible for natural language processing and information retrieval. Specifically, the software used includes natural language processing frameworks such as NLTK and spaCy, and machine learning libraries such as scikit-learn and TENSORFLOW®.

[0098] Users utilize devices such as smartphones or computers. These devices have an application installed for users to input their living situation and support needs. This application sends the user's input information to a server in natural language, and displays the support information received from the server. The data is encrypted and transmitted in a privacy-conscious manner.

[0099] The server analyzes the received information using natural language processing and, based on the results, uses information retrieval to obtain the most suitable support information. Historical data is also considered during this process. Specifically, the server uses purchase data to determine the user's living situation and proposes necessary support. The proposed support information is displayed on the application through a multilingual interface for user convenience.

[0100] A concrete example is this system integrated into an electronic payment app. When a user purchases groceries and the payment information is sent to the server via the app, the server analyzes the data and assesses the likelihood of financial hardship. Based on this assessment, the system suggests the most suitable food coupons and support programs in the user's native language.

[0101] An example of a prompt for the generating AI model would be: "Based on the user's purchase history, search for and present information on local support services. For example, find information on food coupons and lifestyle assistance programs." Using this prompt enables the rapid provision of appropriate support information.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users input their living situation and support needs into an application on their smartphone or computer terminal. This input data is in text format, and the terminal transmits it to the server using a security protocol.

[0105] Step 2:

[0106] The server analyzes the received user input data using natural language processing tools (such as NLTK or spaCy). Here, important keywords and intentions are extracted from the data, thereby understanding the user's support needs. The resulting data contains information about the analyzed intentions and needs.

[0107] Step 3:

[0108] The server uses information retrieval tools to search for relevant support information from the database based on the analyzed user needs. At this time, it utilizes machine learning models (using scikit-learn or TensorFlow) to select the most suitable support information, taking into account past data and the user's purchase data. The output is a list of support information best suited to the user's situation.

[0109] Step 4:

[0110] The server uses the proposed method to translate the obtained support information using a multilingual interface so that it can be easily understood by the user and sent to the terminal. As a result, the support information is translated into the user's language and output.

[0111] Step 5:

[0112] The device displays translated support information to the user through the application. The user can then refer to the provided support information and determine the necessary actions. This enables specific support tailored to the user's living situation.

[0113] Step 6:

[0114] After the user takes action based on the support information provided, feedback information is sent back to the server via the terminal. This feedback data is used as training data when updating the generated AI model.

[0115] Through this series of processes, users can receive real-time information on daily living support and obtain assistance tailored to their specific situation.

[0116] 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.

[0117] This invention is a system that, in addition to providing support information needed by people in poverty and low-income groups, recognizes the user's emotions and provides support information tailored to their individual emotional state. A key feature of this system is the collaboration between the user, terminal, and server, and the incorporation of an emotion engine.

[0118] The user inputs necessary information, their current status, and desired support through the device. This input is sent to the device as text or voice. The device sends the input information to the server, and in doing so, utilizes an emotion engine to recognize the user's emotions in real time. Emotion recognition is performed based on an algorithm that extracts emotions from the intonation of the user's voice and the expression of their text.

[0119] The server analyzes emotional data simultaneously with the received information. It uses natural language processing to understand user needs and then uses the results of the emotion engine to grasp the user's emotions. User emotions are considered a crucial factor in customizing the support information provided.

[0120] Based on the analysis results, the server uses information retrieval tools to find support information tailored to the user. Machine learning techniques are used for information retrieval, allowing for the priority selection of information most appropriate to the user's emotional state. For example, a user who is anxious can be presented with highly urgent solutions.

[0121] The optimized support information is then sent back to the device. The device presents the information in the format most easily understood by the user through a multilingual interface. At this time, it adjusts the way the information is presented and the wording used, taking into account the user's emotional state.

[0122] For example, if a foreign worker user experiencing anxiety has lost their job and is seeking financial support, the system can recognize the user's anxiety and prioritize providing information on counseling services to alleviate it, as well as welfare services that can provide immediate assistance.

[0123] In this way, this system enables the provision of support information that takes user emotions into consideration, making it possible to provide a support environment that users can use more accurately and with peace of mind.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] Users access the device and input text or voice data about their living situation and the type of support they need. This includes specific details such as "I need housing assistance" or "I have lost my job."

[0127] Step 2:

[0128] The device transmits information entered by the user to the server in real time. Simultaneously, the device's emotion engine analyzes the user's emotional state (e.g., anxiety, stress, joy) from voice and text, and sends that emotional data to the server.

[0129] Step 3:

[0130] The server analyzes the received user information and sentiment data using natural language processing techniques. This lays the foundation for understanding the user's requests and emotions and determining the most appropriate support.

[0131] Step 4:

[0132] The server utilizes information retrieval tools to search for suitable support information for the user from a database or external API based on the analysis results. Emotional data is taken into consideration, and information that aligns with the user's psychological state is prioritized.

[0133] Step 5:

[0134] To optimize search results, the server applies machine learning techniques. This automatically selects information that is relevant to similar past cases and the user's emotional state.

[0135] Step 6:

[0136] The server sends optimized support information to the terminal.

[0137] Step 7:

[0138] The device presents received information to the user through its interface. In this process, the display method and word choice are adjusted based on emotional data. For example, if the user is in a very unstable state, the guidance will be presented using more polite and reassuring language.

[0139] Step 8:

[0140] Users can review the provided support information and take the necessary actions. Furthermore, if further support is needed, users can enter additional questions via their device and resume the process.

[0141] (Example 2)

[0142] 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".

[0143] Conventional support information systems have the problem of not being able to take into account the user's emotions, making it difficult to customize them according to individual emotional states. In particular, in providing support to people in poverty and low-income groups, the lack of emotional considerations leads to the problem of not being able to provide adequate support information.

[0144] 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.

[0145] In this invention, the server includes a natural language processing means for receiving information from the user and analyzing that information and the user's emotions; an information retrieval means for searching for appropriate support information based on the analysis results and emotion data; and an interface means for adjusting and presenting the retrieved support information according to the user's emotional state. This makes it possible to provide optimal support information that takes the user's emotions into consideration.

[0146] A "user" refers to an individual or group that uses the system to seek support information.

[0147] "Natural language processing means" refers to technologies and methods for analyzing text or audio information received from a user and understanding its meaning and intent.

[0148] An "emotion engine" is a technology and algorithm that extracts emotional data from information provided by the user to the system and recognizes the user's emotional state in real time.

[0149] "Information retrieval means" refers to technology that searches for optimal support information for the user from databases and the internet based on analysis results obtained from natural language processing means and emotion engines.

[0150] "Interface means" refers to hardware and software components that enable display and operation for presenting searched support information to the user.

[0151] "Machine learning" is an artificial intelligence technology used in information retrieval to select the most appropriate support information, taking into account the user's emotional state.

[0152] "Multilingual support functionality" refers to a system feature that enables the processing and presentation of information in multiple languages ​​to the user.

[0153] "Emotional data" refers to information about the user's emotions extracted by the emotion engine.

[0154] This invention relates to a support information provision system that takes the user's emotions into consideration. This system functions through the coordinated efforts of the user, terminal, and server. Specific embodiments of each element are described below.

[0155] First, the user uses the terminal to input information about the support they need. This information can be implemented as text or voice data and should specifically express the user's condition and desired support. The terminal is equipped with an emotion engine to process the input data, and this engine is used to recognize the user's emotions in real time. Specifically, it uses an algorithm to extract emotion data from the intonation of voice data and from text.

[0156] Next, the device sends information and emotional data to the server. The server analyzes the received information using natural language processing to understand the user's specific support needs. It also uses data obtained from the emotion engine to confirm the user's emotional state. This process utilizes analysis algorithms and database management systems.

[0157] Based on the analysis results, the server uses information retrieval tools to find the most suitable support information. This search employs machine learning algorithms to prioritize support information that matches the user's emotional state. For example, if the user is showing anxiety, it is designed to suggest counseling support or immediate life support.

[0158] Finally, the server sends optimized support information to the terminal, which then communicates it to the user through a multilingual interface. The information is presented in a way that reflects the user's emotional state, specifically using a gentle tone and easy-to-understand language.

[0159] For example, if a user who is anxious has lost their job and is seeking financial assistance, this system can sense the user's anxiety and prioritize providing appropriate and timely information on solutions. An example of a prompt to input into the generation AI model is, "Generate and present support information for low-income individuals in a format optimized for anxious users."

[0160] In this way, the present invention enables the provision of support information that takes into account the user's emotions, thereby realizing a more appropriate and reassuring support environment.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] Users input information about the type of support they require into the device via text or voice. This input includes the user's current situation and the type of support they desire. This input data is collected using the device's microphone and keyboard.

[0164] Step 2:

[0165] The device receives input text and audio data and analyzes emotions using an emotion engine. Specifically, it recognizes the user's state of "anxiety" or "tension" in real time through an algorithm that extracts emotional data from the intonation of the voice and the expression of the text. In this process, the input is text or audio, and the output is emotional data.

[0166] Step 3:

[0167] The terminal transmits information and sentiment data obtained from the user to the server. During data transmission, the data is appropriately formatted and securely transmitted using network protocols. Input consists of analyzed information and sentiment data, and output is data transmission to the server.

[0168] Step 4:

[0169] The server analyzes the received information using natural language processing technology to identify the user's needs. Simultaneously, it analyzes emotional data to confirm the user's emotional state. The input consists of information and emotional data transmitted from the terminal, while the output is the user's needs and emotional state as a result of the analysis.

[0170] Step 5:

[0171] Based on the analysis results, the server uses information retrieval tools to search for the most suitable support information for the user. This process employs machine learning algorithms to prioritize information appropriate to the user's emotional state. For example, for an anxious user, information related to mental health care will be prioritized. The input is the user's needs and emotional state, and the output is the selected support information.

[0172] Step 6:

[0173] The server sends optimized support information to the terminal. Encryption technology is used for transmission to maintain the confidentiality of the information. The input is the selected support information, and the output is the transmission of information to the terminal.

[0174] Step 7:

[0175] The terminal presents support information received from the server to the user through a multilingual interface. The information presentation is optimally customized to the user's emotional state. Specifically, a gentle tone and translation appropriate to the language being used are implemented. Input is support information transmitted from the server, and output is information presented to the user visually or aurally.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] When people facing financial hardship or low-income groups need social support, there is a problem in that the information provided is general and does not address their individual needs or emotional states, thus failing to deliver the intended effect. Furthermore, while emotionally unstable users require information that is sensitive to their feelings, the current system struggles to provide this.

[0179] 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.

[0180] In this invention, the server includes a natural language processing means for analyzing user information, an emotion recognition means for recognizing the user's emotions in real time, and an information retrieval means for searching for appropriate support information based on the analysis results and emotions. This makes it possible to provide optimal support information tailored to the individual emotional state of each user.

[0181] A "user" refers to a person who uses this system to input information and receive support information.

[0182] "Natural language processing means for receiving and analyzing information" refers to technology that receives information provided by a user and linguistically analyzes that information in order to understand its content.

[0183] "Information retrieval means for searching for appropriate support information based on analysis results" refers to technology that uses analyzed data to find support information that meets the user's needs from databases, etc.

[0184] "An emotion recognition method that recognizes the user's emotions in real time" refers to a technology that determines and analyzes the emotional state of a user from their voice or text.

[0185] "Optimizing information based on emotions" means customizing the support information presented, taking into account the user's emotions, and providing it in the most useful way possible.

[0186] "Interface means" refers to physical or digital means used to present information to the user, and includes, for example, displays and speakers.

[0187] "Multilingual support functionality" refers to the ability to process and display information in multiple languages, providing information tailored to the user's language environment.

[0188] This invention aims to realize a system that determines the emotional state of a user based on information collected from the user and provides support information accordingly. A terminal receives information from the user via voice or text and transmits it to a server. The server uses natural language processing tools to analyze this information and understand the user's needs. Furthermore, emotion recognition technology determines the user's emotions in real time from the intonation of their voice and their textual expression. For example, a machine learning platform such as TensorFlow can be used for this purpose.

[0189] The server utilizes machine learning algorithms to select the most appropriate support information based on the user's emotional state. It also includes an information retrieval mechanism to search a database for relevant information based on the emotion recognition results. This allows the terminal to present optimized support information to the user through its interface. This interface is multilingual, designed with user-friendliness in mind, and the information provided is conveyed in a way that aligns with the user's emotions.

[0190] As a concrete example, if a child feels stressed about homework, the system can detect this and provide specific support, such as suggesting an educational mini-game to help them relax. An example of a prompt used in this system would be, "This user is feeling stressed. Please suggest ways or activities to help them relax."

[0191] Thus, the present invention can provide information based on emotional state, making it possible to offer a more useful and comfortable support experience to the user.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The user inputs information into the device via voice or text. This input data includes the type of assistance the user needs and their current situation. The device receives this data, converts it to a digital format, and sends it to the server. This conversion process utilizes speech recognition technology to convert speech into text data.

[0195] Step 2:

[0196] The server analyzes the received information. Using natural language processing tools, it extracts the user's needs from the received text. This generates contextual information necessary to understand the user's requests and problems. The analysis results are then provided as input for sentiment recognition.

[0197] Step 3:

[0198] The server performs emotion recognition based on the analysis results. It uses a machine learning model to analyze the intonation of speech and text expression to determine the user's emotional state. In this step, the analyzed user information is used as input data, and the recognized emotion data is obtained as output.

[0199] Step 4:

[0200] The server performs information retrieval based on emotional data and user needs. Machine learning algorithms are used to retrieve the most relevant information from the database. During this process, filtering that takes emotional data into account prioritizes information that aligns with the user's emotions.

[0201] Step 5:

[0202] The server sends search results to the terminal. This includes customized support information, which is translated by a multilingual interface. Multilingual support ensures that information is presented in a language that is easy for the user to understand.

[0203] Step 6:

[0204] The device displays or audibly presents received information to the user. The method of information presentation is adjusted to the user's emotional state, for example, using a calm tone or friendly language. This creates an environment where users can use information with peace of mind.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention is a system designed to make it easier for people in poverty and low-income groups to access the support information they need. This system functions through the cooperation of three parties: a server, a terminal, and a user.

[0222] Users input information about their living situation and support needs using devices such as smartphones and computers. The device receives this information as natural language and transmits it to the server. The transmitted information is then analyzed on the server using natural language processing tools. During this analysis, important keywords and intentions are extracted from the input information.

[0223] The server uses information retrieval tools based on the analysis results to search for appropriate support information. This includes government welfare services, NPO support activities, and vocational training programs. Machine learning technology is used in the search, and by referring to past data, the server provides users with the most suitable support information with high accuracy.

[0224] Support information sent from the server is displayed on the terminal. The terminal can present information in the user's native language using a multilingual interface. For example, if a foreign worker residing in Japan searches for vocational training programs through the terminal, suitable support programs will be displayed, and instructions on how to use those programs and contact information will be provided in the user's language.

[0225] This system aims to improve users' access to the support information they need and provide effective support to diverse user groups, transcending language barriers. As a result, it is providing an effective means for people facing poverty and low incomes to receive appropriate support and live independent lives.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] Users access the system interface using a terminal and enter information about the support they require and their current situation. This information includes their place of residence, income level, and the type of support they need (e.g., medical expense assistance, vocational training).

[0229] Step 2:

[0230] The terminal sends information entered by the user to the server. This data is received by the server as a message written in natural language.

[0231] Step 3:

[0232] The server analyzes the received natural language data using natural language processing tools. Here, keywords and intent are extracted from the user's input, and the information is understood and classified.

[0233] Step 4:

[0234] After analysis, the server uses machine learning techniques to search for the most appropriate support information for the user's situation. This includes a process of referencing internal databases and external APIs to collect information on public support services and non-profit organizations.

[0235] Step 5:

[0236] The server optimizes the support provided based on the search results and selects the most relevant information for the user. This selected information includes specific support procedures, participation methods, and contact details.

[0237] Step 6:

[0238] The server sends the selected support information to the user's terminal.

[0239] Step 7:

[0240] The device displays the received support information to the user. This information is clearly presented in the user's chosen language using multilingual support.

[0241] Step 8:

[0242] Based on the support information provided, users can take the necessary actions. If needed, they can also contact support again via their device to request further information or assistance.

[0243] (Example 1)

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

[0245] A challenge is to improve the situation where people facing poverty and low income levels have difficulty accessing support information that meets their needs. In particular, language barriers and difficulty in filtering information often lead to delays in providing appropriate support. Therefore, there is a need for a system that allows for easy access to information in multiple languages.

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

[0247] In this invention, the server includes means for receiving information from users regarding their living situation and support needs using an information processing device, natural language processing means for using a generative AI model to analyze the received information, and information retrieval means for searching for appropriate social support information based on the analyzed information. This makes it possible for diverse users to efficiently obtain the support information they need, overcoming language barriers.

[0248] An "information processing device" is a device that receives input information from a user and transmits it to a server, and includes smartphones and computers.

[0249] A "generative AI model" is an artificial intelligence model used for natural language processing. It analyzes information from users and is used to extract important keywords and intentions.

[0250] "Natural language processing means" refers to means for analyzing natural language information received from users, and includes functions for analyzing the intent and important keywords of the information using a generative AI model.

[0251] An "information retrieval tool" is a means of searching for relevant social support information based on analyzed information, and it has the function of improving search accuracy using machine learning technology.

[0252] A "user interface means" is a means of presenting searched support information to the user, and it has the role of providing multilingual support and displaying information in the user's language.

[0253] For this invention to be implemented, it is essential that the server, terminal, and user within the system work together in coordination. This system is designed to provide rapid access to support information needed by people in need and low-income groups, overcoming language barriers.

[0254] The server is equipped with a generative AI model for natural language processing (NLP). This model analyzes natural language information about the user's living situation and support needs. Its main role is to extract important keywords and intentions from the information and search for relevant social support information. Machine learning algorithms are used for information retrieval, providing highly relevant information while referring to past data.

[0255] The terminal is responsible for transmitting user-inputted information to the server and features a multilingual user interface. This interface can display support information sent from the server in the user's native language. Furthermore, user convenience during information input has been considered, providing an intuitive interface.

[0256] Users input their living situation and support needs in natural language using information terminals such as smartphones and computers. For example, specific needs such as "I would like to receive public food assistance." This allows users to easily communicate the support information they need to the system.

[0257] As a concrete example, suppose a foreign worker residing in Japan enters a prompt message such as, "I'm looking for a vocational training program. My current occupation is food processing, and I want to improve my skills." In this case, the system analyzes this information and provides relevant vocational training program information, thereby facilitating the user's access to the support they need.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] Users input their living situation and support needs using a device such as a smartphone or computer. For example, they might enter a prompt message such as, "I need rent assistance." This input information is collected as initial data for the system.

[0261] Step 2:

[0262] The terminal formats the information entered by the user and sends it to the server. Here, natural language data in text format is passed to the server. The terminal's role is to convert the data into the appropriate format and transmit it quickly to the server.

[0263] Step 3:

[0264] The server receives the transmitted information and performs natural language processing using a generative AI model. It extracts keywords and intent from the input sentence and identifies the important information, "rent assistance." This analysis allows for a concrete understanding of the user's needs.

[0265] Step 4:

[0266] The server initiates information retrieval based on the analysis results. Using machine learning algorithms, it searches past databases for highly relevant support information, identifying appropriate support programs such as "rent subsidy programs." The data calculation performed here involves prioritizing search results using a scoring system.

[0267] Step 5:

[0268] The server returns the support information obtained as a search result to the terminal. This information includes specific support details, details of the providing organization, and application procedures. To improve the accuracy of the information, it is also personalized based on past usage history.

[0269] Step 6:

[0270] The terminal displays support information received from the server to the user. Using a multilingual user interface, the information is displayed in the user's native language, clearly guiding them through the necessary support procedures and access methods. This makes it easy for users to take immediate action to receive support.

[0271] (Application Example 1)

[0272] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] There is a challenge in that people facing poverty and low income levels have difficulty accessing necessary support information quickly and appropriately. Furthermore, language barriers and the sheer volume of information make it difficult to receive the support they need. In addition, it can be difficult to provide support that is tailored to the user's living situation. To address these challenges, it is necessary to offer appropriate support information that is suited to the user's circumstances.

[0274] 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.

[0275] In this invention, the server includes a natural language processing means for receiving and analyzing information from the user, an information retrieval means for searching for appropriate support content based on the analysis results, and a data analysis means for evaluating the user's living situation using purchase data. This makes it possible to propose appropriate support content based on the user's living situation.

[0276] A "user" is someone who provides information to the system and receives support from it.

[0277] "Receiving information" refers to the act of acquiring data from a user via a network.

[0278] "Natural language processing means" refers to methods and techniques for analyzing text described in human language and understanding its meaning and intent.

[0279] "Information retrieval means" refers to a method of obtaining appropriate support content from a database or other information sources based on the analyzed data.

[0280] "Display means" refers to a method for visually presenting the retrieved support content to the user.

[0281] "Purchase data" refers to information regarding a user's consumption behavior and history.

[0282] "Data analysis means" refers to a technology for evaluating purchase data to determine a user's living situation.

[0283] "Proposal means" refers to methods and techniques for presenting relevant support content according to a user's living situation.

[0284] To implement this invention, the system is constructed based on the cooperation of a server, a terminal, and a user. The server is a network-connected computing device equipped with a high-performance processor and a large-capacity memory, and is mainly responsible for natural language processing and information retrieval. As specific software, NLTK and spaCy of natural language processing frameworks, and scikit-learn and TensorFlow of machine learning libraries are used.

[0285] The user uses a terminal such as a smartphone or a computer. An application for the user to input their living situation and support needs is installed on the terminal. This application sends the input information of the user to the server as natural language and displays the support information received from the server. The data is encrypted and transmitted and received in a privacy-conscious manner.

[0286] The server analyzes the received information using natural language processing means and obtains optimal support information using information retrieval means based on the result. At this time, past history data is also considered. Specifically, the server uses the purchase data to judge the user's living situation and proposes the necessary support content. The proposed support information is displayed on the application through a multilingual interface so that the user can easily use it.

[0287] As a specific example, this system integrated into an electronic payment application can be cited. When the user purchases food products and the payment information is sent to the server through the application, the server analyzes the data and evaluates the possibility of living in poverty. Accordingly, optimal food coupon information and life support programs are proposed in the user's native language.

[0288] Examples of prompt texts for the generative AI model include "Search for and present available support information in the region based on the user's purchase history. For example, look for information on food coupons and life assistance programs." By using this prompt text, it becomes possible to quickly provide appropriate support information. [[ID=!4]]

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The user inputs their living situation and support needs into an application on a smartphone or computer terminal. This input data is in text format, and the terminal sends it to the server using a security protocol.

[0292] Step 2:

[0293] The server analyzes the received user input data using natural language processing tools (such as NLTK or spaCy). Here, important keywords and intentions are extracted from the data, thereby understanding the user's support needs. The resulting data contains information about the analyzed intentions and needs.

[0294] Step 3:

[0295] The server uses information retrieval tools to search for relevant support information from the database based on the analyzed user needs. At this time, it utilizes machine learning models (using scikit-learn or TensorFlow) to select the most suitable support information, taking into account past data and the user's purchase data. The output is a list of support information best suited to the user's situation.

[0296] Step 4:

[0297] The server uses the proposed method to translate the obtained support information using a multilingual interface so that it can be easily understood by the user and sent to the terminal. As a result, the support information is translated into the user's language and output.

[0298] Step 5:

[0299] The device displays translated support information to the user through the application. The user can then refer to the provided support information and determine the necessary actions. This enables specific support tailored to the user's living situation.

[0300] Step 6:

[0301] After the user takes action based on the support information provided, feedback information is sent back to the server via the terminal. This feedback data is used as training data when updating the generated AI model.

[0302] Through this series of processes, users can receive real-time information on daily living support and obtain assistance tailored to their specific situation.

[0303] 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.

[0304] This invention is a system that, in addition to providing support information needed by people in poverty and low-income groups, recognizes the user's emotions and provides support information tailored to their individual emotional state. A key feature of this system is the collaboration between the user, terminal, and server, and the incorporation of an emotion engine.

[0305] The user inputs necessary information, their current status, and desired support through the device. This input is sent to the device as text or voice. The device sends the input information to the server, and in doing so, utilizes an emotion engine to recognize the user's emotions in real time. Emotion recognition is performed based on an algorithm that extracts emotions from the intonation of the user's voice and the expression of their text.

[0306] The server analyzes emotional data simultaneously with the received information. It uses natural language processing to understand user needs and then uses the results of the emotion engine to grasp the user's emotions. User emotions are considered a crucial factor in customizing the support information provided.

[0307] Based on the analysis results, the server uses information retrieval means to find support information suitable for the user. For information retrieval, machine learning technology can be utilized to preferentially select information most suitable for the user's emotional state. For example, for a user who is anxious, countermeasures with high urgency can be presented.

[0308] The optimized support information is sent back to the terminal. The terminal presents the information in the most understandable format for the user via a multilingual interface. At this time, reflecting the user's emotional state, the presentation method and word choice of the information are adjusted.

[0309] As a specific example, when a user who is a foreign worker with anxiety loses their job and seeks life support, the system can recognize the user's sense of uneasiness and preferentially guide information on counseling services to relieve it and welfare services that can respond immediately.

[0310] In this way, this system realizes the provision of support information considering the user's emotions, and enables the provision of a support environment that users can use more accurately and with confidence.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The user accesses the terminal and inputs text or voice data about their living situation and the support content they seek. This includes specific content such as "Need housing support" or "Lost my job".

[0314] Step 2:

[0315] The terminal sends the information input by the user to the server in real time. At the same time, the emotion engine in the terminal analyzes the user's emotional state (e.g., anxiety, stress, joy) from the voice and text, and sends the emotion data to the server.

[0316] Step 3:

[0317] The server analyzes the received user information and sentiment data using natural language processing techniques. This lays the foundation for understanding the user's requests and emotions and determining the most appropriate support.

[0318] Step 4:

[0319] The server utilizes information retrieval tools to search for suitable support information for the user from a database or external API based on the analysis results. Emotional data is taken into consideration, and information that aligns with the user's psychological state is prioritized.

[0320] Step 5:

[0321] To optimize search results, the server applies machine learning techniques. This automatically selects information that is relevant to similar past cases and the user's emotional state.

[0322] Step 6:

[0323] The server sends optimized support information to the terminal.

[0324] Step 7:

[0325] The device presents received information to the user through its interface. In this process, the display method and word choice are adjusted based on emotional data. For example, if the user is in a very unstable state, the guidance will be presented using more polite and reassuring language.

[0326] Step 8:

[0327] Users can review the provided support information and take the necessary actions. Furthermore, if further support is needed, users can enter additional questions via their device and resume the process.

[0328] (Example 2)

[0329] 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".

[0330] Conventional support information systems have the problem of not being able to take into account the user's emotions, making it difficult to customize them according to individual emotional states. In particular, in providing support to people in poverty and low-income groups, the lack of emotional considerations leads to the problem of not being able to provide adequate support information.

[0331] 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.

[0332] In this invention, the server includes a natural language processing means for receiving information from the user and analyzing that information and the user's emotions; an information retrieval means for searching for appropriate support information based on the analysis results and emotion data; and an interface means for adjusting and presenting the retrieved support information according to the user's emotional state. This makes it possible to provide optimal support information that takes the user's emotions into consideration.

[0333] A "user" refers to an individual or group that uses the system to seek support information.

[0334] "Natural language processing means" refers to technologies and methods for analyzing text or audio information received from a user and understanding its meaning and intent.

[0335] An "emotion engine" is a technology and algorithm that extracts emotional data from information provided by the user to the system and recognizes the user's emotional state in real time.

[0336] "Information retrieval means" refers to technology that searches for optimal support information for the user from databases and the internet based on analysis results obtained from natural language processing means and emotion engines.

[0337] "Interface means" refers to hardware and software components that enable display and operation for presenting searched support information to the user.

[0338] "Machine learning" is an artificial intelligence technology used in information retrieval to select the most appropriate support information, taking into account the user's emotional state.

[0339] "Multilingual support functionality" refers to a system feature that enables the processing and presentation of information in multiple languages ​​to the user.

[0340] "Emotional data" refers to information about the user's emotions extracted by the emotion engine.

[0341] This invention relates to a support information provision system that takes the user's emotions into consideration. This system functions through the coordinated efforts of the user, terminal, and server. Specific embodiments of each element are described below.

[0342] First, the user uses the terminal to input information about the support they need. This information can be implemented as text or voice data and should specifically express the user's condition and desired support. The terminal is equipped with an emotion engine to process the input data, and this engine is used to recognize the user's emotions in real time. Specifically, it uses an algorithm to extract emotion data from the intonation of voice data and from text.

[0343] Next, the device sends information and emotional data to the server. The server analyzes the received information using natural language processing to understand the user's specific support needs. It also uses data obtained from the emotion engine to confirm the user's emotional state. This process utilizes analysis algorithms and database management systems.

[0344] Based on the analysis results, the server uses information retrieval tools to find the most suitable support information. This search employs machine learning algorithms to prioritize support information that matches the user's emotional state. For example, if the user is showing anxiety, it is designed to suggest counseling support or immediate life support.

[0345] Finally, the server sends optimized support information to the terminal, which then communicates it to the user through a multilingual interface. The information is presented in a way that reflects the user's emotional state, specifically using a gentle tone and easy-to-understand language.

[0346] For example, if a user who is anxious has lost their job and is seeking financial assistance, this system can sense the user's anxiety and prioritize providing appropriate and timely information on solutions. An example of a prompt to input into the generation AI model is, "Generate and present support information for low-income individuals in a format optimized for anxious users."

[0347] In this way, the present invention enables the provision of support information that takes into account the user's emotions, thereby realizing a more appropriate and reassuring support environment.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] Users input information about the type of support they require into the device via text or voice. This input includes the user's current situation and the type of support they desire. This input data is collected using the device's microphone and keyboard.

[0351] Step 2:

[0352] The device receives input text and audio data and analyzes emotions using an emotion engine. Specifically, it recognizes the user's state of "anxiety" or "tension" in real time through an algorithm that extracts emotional data from the intonation of the voice and the expression of the text. In this process, the input is text or audio, and the output is emotional data.

[0353] Step 3:

[0354] The terminal transmits information and sentiment data obtained from the user to the server. During data transmission, the data is appropriately formatted and securely transmitted using network protocols. Input consists of analyzed information and sentiment data, and output is data transmission to the server.

[0355] Step 4:

[0356] The server analyzes the received information using natural language processing technology to identify the user's needs. Simultaneously, it analyzes emotional data to confirm the user's emotional state. The input consists of information and emotional data transmitted from the terminal, while the output is the user's needs and emotional state as a result of the analysis.

[0357] Step 5:

[0358] Based on the analysis results, the server uses information retrieval tools to search for the most suitable support information for the user. This process employs machine learning algorithms to prioritize information appropriate to the user's emotional state. For example, for an anxious user, information related to mental health care will be prioritized. The input is the user's needs and emotional state, and the output is the selected support information.

[0359] Step 6:

[0360] The server sends optimized support information to the terminal. Encryption technology is used for transmission to maintain the confidentiality of the information. The input is the selected support information, and the output is the transmission of information to the terminal.

[0361] Step 7:

[0362] The terminal presents support information received from the server to the user through a multilingual interface. The information presentation is optimally customized to the user's emotional state. Specifically, a gentle tone and translation appropriate to the language being used are implemented. Input is support information transmitted from the server, and output is information presented to the user visually or aurally.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] When people facing financial hardship or low-income groups need social support, there is a problem in that the information provided is general and does not address their individual needs or emotional states, thus failing to deliver the intended effect. Furthermore, while emotionally unstable users require information that is sensitive to their feelings, the current system struggles to provide this.

[0366] 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.

[0367] In this invention, the server includes a natural language processing means for analyzing user information, an emotion recognition means for recognizing the user's emotions in real time, and an information retrieval means for searching for appropriate support information based on the analysis results and emotions. This makes it possible to provide optimal support information tailored to the individual emotional state of each user.

[0368] A "user" refers to a person who uses this system to input information and receive support information.

[0369] "Natural language processing means for receiving and analyzing information" refers to technology that receives information provided by a user and linguistically analyzes that information in order to understand its content.

[0370] "Information retrieval means for searching for appropriate support information based on analysis results" refers to technology that uses analyzed data to find support information that meets the user's needs from databases, etc.

[0371] "An emotion recognition method that recognizes the user's emotions in real time" refers to a technology that determines and analyzes the emotional state of a user from their voice or text.

[0372] "Optimizing information based on emotions" means customizing the support information presented, taking into account the user's emotions, and providing it in the most useful way possible.

[0373] "Interface means" refers to physical or digital means used to present information to the user, and includes, for example, displays and speakers.

[0374] "Multilingual support functionality" refers to the ability to process and display information in multiple languages, providing information tailored to the user's language environment.

[0375] This invention aims to realize a system that determines the emotional state of a user based on information collected from the user and provides support information accordingly. A terminal receives information from the user via voice or text and transmits it to a server. The server uses natural language processing tools to analyze this information and understand the user's needs. Furthermore, emotion recognition technology determines the user's emotions in real time from the intonation of their voice and their textual expression. For example, a machine learning platform such as TensorFlow can be used for this purpose.

[0376] The server utilizes machine learning algorithms to select the most appropriate support information based on the user's emotional state. It also includes an information retrieval mechanism to search a database for relevant information based on the emotion recognition results. This allows the terminal to present optimized support information to the user through its interface. This interface is multilingual, designed with user-friendliness in mind, and the information provided is conveyed in a way that aligns with the user's emotions.

[0377] As a concrete example, if a child feels stressed about homework, the system can detect this and provide specific support, such as suggesting an educational mini-game to help them relax. An example of a prompt used in this system would be, "This user is feeling stressed. Please suggest ways or activities to help them relax."

[0378] Thus, the present invention can provide information based on emotional state, making it possible to offer a more useful and comfortable support experience to the user.

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The user inputs information into the device via voice or text. This input data includes the type of assistance the user needs and their current situation. The device receives this data, converts it to a digital format, and sends it to the server. This conversion process utilizes speech recognition technology to convert speech into text data.

[0382] Step 2:

[0383] The server analyzes the received information. Using natural language processing tools, it extracts the user's needs from the received text. This generates contextual information necessary to understand the user's requests and problems. The analysis results are then provided as input for sentiment recognition.

[0384] Step 3:

[0385] The server performs emotion recognition based on the analysis results. It uses a machine learning model to analyze the intonation of speech and text expression to determine the user's emotional state. In this step, the analyzed user information is used as input data, and the recognized emotion data is obtained as output.

[0386] Step 4:

[0387] The server performs information retrieval based on emotional data and user needs. Machine learning algorithms are used to retrieve the most relevant information from the database. During this process, filtering that takes emotional data into account prioritizes information that aligns with the user's emotions.

[0388] Step 5:

[0389] The server sends search results to the terminal. This includes customized support information, which is translated by a multilingual interface. Multilingual support ensures that information is presented in a language that is easy for the user to understand.

[0390] Step 6:

[0391] The device displays or audibly presents received information to the user. The method of information presentation is adjusted to the user's emotional state, for example, using a calm tone or friendly language. This creates an environment where users can use information with peace of mind.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention is a system designed to make it easier for people in poverty and low-income groups to access the support information they need. This system functions through the cooperation of three parties: a server, a terminal, and a user.

[0409] Users input information about their living situation and support needs using devices such as smartphones and computers. The device receives this information as natural language and transmits it to the server. The transmitted information is then analyzed on the server using natural language processing tools. During this analysis, important keywords and intentions are extracted from the input information.

[0410] The server uses information retrieval tools based on the analysis results to search for appropriate support information. This includes government welfare services, NPO support activities, and vocational training programs. Machine learning technology is used in the search, and by referring to past data, the server provides users with the most suitable support information with high accuracy.

[0411] Support information sent from the server is displayed on the terminal. The terminal can present information in the user's native language using a multilingual interface. For example, if a foreign worker residing in Japan searches for vocational training programs through the terminal, suitable support programs will be displayed, and instructions on how to use those programs and contact information will be provided in the user's language.

[0412] This system aims to improve users' access to the support information they need and provide effective support to diverse user groups, transcending language barriers. As a result, it is providing an effective means for people facing poverty and low incomes to receive appropriate support and live independent lives.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] Users access the system interface using a terminal and enter information about the support they require and their current situation. This information includes their place of residence, income level, and the type of support they need (e.g., medical expense assistance, vocational training).

[0416] Step 2:

[0417] The terminal sends information entered by the user to the server. This data is received by the server as a message written in natural language.

[0418] Step 3:

[0419] The server analyzes the received natural language data using natural language processing tools. Here, keywords and intent are extracted from the user's input, and the information is understood and classified.

[0420] Step 4:

[0421] After analysis, the server uses machine learning techniques to search for the most appropriate support information for the user's situation. This includes a process of referencing internal databases and external APIs to collect information on public support services and non-profit organizations.

[0422] Step 5:

[0423] The server optimizes the support provided based on the search results and selects the most relevant information for the user. This selected information includes specific support procedures, participation methods, and contact details.

[0424] Step 6:

[0425] The server sends the selected support information to the user's terminal.

[0426] Step 7:

[0427] The device displays the received support information to the user. This information is clearly presented in the user's chosen language using multilingual support.

[0428] Step 8:

[0429] Based on the support information provided, users can take the necessary actions. If needed, they can also contact support again via their device to request further information or assistance.

[0430] (Example 1)

[0431] 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."

[0432] A challenge is to improve the situation where people facing poverty and low income levels have difficulty accessing support information that meets their needs. In particular, language barriers and difficulty in filtering information often lead to delays in providing appropriate support. Therefore, there is a need for a system that allows for easy access to information in multiple languages.

[0433] 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.

[0434] In this invention, the server includes means for receiving information from users regarding their living situation and support needs using an information processing device, natural language processing means for using a generative AI model to analyze the received information, and information retrieval means for searching for appropriate social support information based on the analyzed information. This makes it possible for diverse users to efficiently obtain the support information they need, overcoming language barriers.

[0435] An "information processing device" is a device that receives input information from a user and transmits it to a server, and includes smartphones and computers.

[0436] A "generative AI model" is an artificial intelligence model used for natural language processing. It analyzes information from users and is used to extract important keywords and intentions.

[0437] "Natural language processing means" refers to means for analyzing natural language information received from users, and includes functions for analyzing the intent and important keywords of the information using a generative AI model.

[0438] An "information retrieval tool" is a means of searching for relevant social support information based on analyzed information, and it has the function of improving search accuracy using machine learning technology.

[0439] A "user interface means" is a means of presenting searched support information to the user, and it has the role of providing multilingual support and displaying information in the user's language.

[0440] For this invention to be implemented, it is essential that the server, terminal, and user within the system work together in coordination. This system is designed to provide rapid access to support information needed by people in need and low-income groups, overcoming language barriers.

[0441] The server is equipped with a generative AI model for natural language processing (NLP). This model analyzes natural language information about the user's living situation and support needs. Its main role is to extract important keywords and intentions from the information and search for relevant social support information. Machine learning algorithms are used for information retrieval, providing highly relevant information while referring to past data.

[0442] The terminal is responsible for transmitting user-inputted information to the server and features a multilingual user interface. This interface can display support information sent from the server in the user's native language. Furthermore, user convenience during information input has been considered, providing an intuitive interface.

[0443] Users input their living situation and support needs in natural language using information terminals such as smartphones and computers. For example, specific needs such as "I would like to receive public food assistance." This allows users to easily communicate the support information they need to the system.

[0444] As a concrete example, suppose a foreign worker residing in Japan enters a prompt message such as, "I'm looking for a vocational training program. My current occupation is food processing, and I want to improve my skills." In this case, the system analyzes this information and provides relevant vocational training program information, thereby facilitating the user's access to the support they need.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] Users input their living situation and support needs using a device such as a smartphone or computer. For example, they might enter a prompt message such as, "I need rent assistance." This input information is collected as initial data for the system.

[0448] Step 2:

[0449] The terminal formats the information entered by the user and sends it to the server. Here, natural language data in text format is passed to the server. The terminal's role is to convert the data into the appropriate format and transmit it quickly to the server.

[0450] Step 3:

[0451] The server receives the transmitted information and performs natural language processing using a generative AI model. It extracts keywords and intent from the input sentence and identifies the important information, "rent assistance." This analysis allows for a concrete understanding of the user's needs.

[0452] Step 4:

[0453] The server initiates information retrieval based on the analysis results. Using machine learning algorithms, it searches past databases for highly relevant support information, identifying appropriate support programs such as "rent subsidy programs." The data calculation performed here involves prioritizing search results using a scoring system.

[0454] Step 5:

[0455] The server returns the support information obtained as a search result to the terminal. This information includes specific support details, details of the providing organization, and application procedures. To improve the accuracy of the information, it is also personalized based on past usage history.

[0456] Step 6:

[0457] The terminal displays support information received from the server to the user. Using a multilingual user interface, the information is displayed in the user's native language, clearly guiding them through the necessary support procedures and access methods. This makes it easy for users to take immediate action to receive support.

[0458] (Application Example 1)

[0459] 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."

[0460] There is a challenge in that people facing poverty and low income levels have difficulty accessing necessary support information quickly and appropriately. Furthermore, language barriers and the sheer volume of information make it difficult to receive the support they need. In addition, it can be difficult to provide support that is tailored to the user's living situation. To address these challenges, it is necessary to offer appropriate support information that is suited to the user's circumstances.

[0461] 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.

[0462] In this invention, the server includes a natural language processing means for receiving and analyzing information from the user, an information retrieval means for searching for appropriate support content based on the analysis results, and a data analysis means for evaluating the user's living situation using purchase data. This makes it possible to propose appropriate support content based on the user's living situation.

[0463] A "user" is someone who provides information to the system and receives support from it.

[0464] "Receiving information" refers to the act of obtaining data from a user via a network.

[0465] "Natural language processing methods" refer to methods and technologies for analyzing text written in human language and understanding its meaning and intent.

[0466] "Information retrieval means" refers to a method of obtaining appropriate support information from databases and other information sources based on analyzed data.

[0467] "Display means" refers to a method for visually presenting the searched support content to the user.

[0468] "Purchase data" refers to information about a user's consumption behavior and history.

[0469] "Data analysis methods" refer to technologies used to evaluate purchase data and determine the user's lifestyle.

[0470] "Proposal methods" refer to methods and techniques for presenting relevant support services according to the user's living situation.

[0471] To implement this invention, the system is built on the cooperation of a server, terminals, and users. The server is a network-connected computing device equipped with a high-performance processor and large memory, and is primarily responsible for natural language processing and information retrieval. Specifically, the software used includes natural language processing frameworks such as NLTK and spaCy, and machine learning libraries such as scikit-learn and TensorFlow.

[0472] Users utilize devices such as smartphones or computers. These devices have an application installed for users to input their living situation and support needs. This application sends the user's input information to a server in natural language, and displays the support information received from the server. The data is encrypted and transmitted in a privacy-conscious manner.

[0473] The server analyzes the received information using natural language processing and, based on the results, uses information retrieval to obtain the most suitable support information. Historical data is also considered during this process. Specifically, the server uses purchase data to determine the user's living situation and proposes necessary support. The proposed support information is displayed on the application through a multilingual interface for user convenience.

[0474] A concrete example is this system integrated into an electronic payment app. When a user purchases groceries and the payment information is sent to the server via the app, the server analyzes the data and assesses the likelihood of financial hardship. Based on this assessment, the system suggests the most suitable food coupons and support programs in the user's native language.

[0475] An example of a prompt for the generating AI model would be: "Based on the user's purchase history, search for and present information on local support services. For example, find information on food coupons and lifestyle assistance programs." Using this prompt enables the rapid provision of appropriate support information.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] Users input their living situation and support needs into an application on their smartphone or computer terminal. This input data is in text format, and the terminal transmits it to the server using a security protocol.

[0479] Step 2:

[0480] The server analyzes the received user input data using natural language processing tools (such as NLTK or spaCy). Here, important keywords and intentions are extracted from the data, thereby understanding the user's support needs. The resulting data contains information about the analyzed intentions and needs.

[0481] Step 3:

[0482] The server uses information retrieval tools to search for relevant support information from the database based on the analyzed user needs. At this time, it utilizes machine learning models (using scikit-learn or TensorFlow) to select the most suitable support information, taking into account past data and the user's purchase data. The output is a list of support information best suited to the user's situation.

[0483] Step 4:

[0484] The server uses the proposed method to translate the obtained support information using a multilingual interface so that it can be easily understood by the user and sent to the terminal. As a result, the support information is translated into the user's language and output.

[0485] Step 5:

[0486] The device displays translated support information to the user through the application. The user can then refer to the provided support information and determine the necessary actions. This enables specific support tailored to the user's living situation.

[0487] Step 6:

[0488] After the user takes action based on the support information provided, feedback information is sent back to the server via the terminal. This feedback data is used as training data when updating the generated AI model.

[0489] Through this series of processes, users can receive real-time information on daily living support and obtain assistance tailored to their specific situation.

[0490] 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.

[0491] This invention is a system that, in addition to providing support information needed by people in poverty and low-income groups, recognizes the user's emotions and provides support information tailored to their individual emotional state. A key feature of this system is the collaboration between the user, terminal, and server, and the incorporation of an emotion engine.

[0492] The user inputs necessary information, their current status, and desired support through the device. This input is sent to the device as text or voice. The device sends the input information to the server, and in doing so, utilizes an emotion engine to recognize the user's emotions in real time. Emotion recognition is performed based on an algorithm that extracts emotions from the intonation of the user's voice and the expression of their text.

[0493] The server analyzes emotional data simultaneously with the received information. It uses natural language processing to understand user needs and then uses the results of the emotion engine to grasp the user's emotions. User emotions are considered a crucial factor in customizing the support information provided.

[0494] Based on the analysis results, the server uses information retrieval tools to find support information tailored to the user. Machine learning techniques are used for information retrieval, allowing for the priority selection of information most appropriate to the user's emotional state. For example, a user who is anxious can be presented with highly urgent solutions.

[0495] The optimized support information is then sent back to the device. The device presents the information in the format most easily understood by the user through a multilingual interface. At this time, it adjusts the way the information is presented and the wording used, taking into account the user's emotional state.

[0496] For example, if a foreign worker user experiencing anxiety has lost their job and is seeking financial support, the system can recognize the user's anxiety and prioritize providing information on counseling services to alleviate it, as well as welfare services that can provide immediate assistance.

[0497] In this way, this system enables the provision of support information that takes user emotions into consideration, making it possible to provide a support environment that users can use more accurately and with peace of mind.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] Users access the device and input text or voice data about their living situation and the type of support they need. This includes specific details such as "I need housing assistance" or "I have lost my job."

[0501] Step 2:

[0502] The device transmits information entered by the user to the server in real time. Simultaneously, the device's emotion engine analyzes the user's emotional state (e.g., anxiety, stress, joy) from voice and text, and sends that emotional data to the server.

[0503] Step 3:

[0504] The server analyzes the received user information and sentiment data using natural language processing techniques. This lays the foundation for understanding the user's requests and emotions and determining the most appropriate support.

[0505] Step 4:

[0506] The server utilizes information retrieval tools to search for suitable support information for the user from a database or external API based on the analysis results. Emotional data is taken into consideration, and information that aligns with the user's psychological state is prioritized.

[0507] Step 5:

[0508] To optimize search results, the server applies machine learning techniques. This automatically selects information that is relevant to similar past cases and the user's emotional state.

[0509] Step 6:

[0510] The server sends optimized support information to the terminal.

[0511] Step 7:

[0512] The device presents received information to the user through its interface. In this process, the display method and word choice are adjusted based on emotional data. For example, if the user is in a very unstable state, the guidance will be presented using more polite and reassuring language.

[0513] Step 8:

[0514] Users can review the provided support information and take the necessary actions. Furthermore, if further support is needed, users can enter additional questions via their device and resume the process.

[0515] (Example 2)

[0516] 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."

[0517] Conventional support information systems have the problem of not being able to take into account the user's emotions, making it difficult to customize them according to individual emotional states. In particular, in providing support to people in poverty and low-income groups, the lack of emotional considerations leads to the problem of not being able to provide adequate support information.

[0518] 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.

[0519] In this invention, the server includes a natural language processing means for receiving information from the user and analyzing that information and the user's emotions; an information retrieval means for searching for appropriate support information based on the analysis results and emotion data; and an interface means for adjusting and presenting the retrieved support information according to the user's emotional state. This makes it possible to provide optimal support information that takes the user's emotions into consideration.

[0520] A "user" refers to an individual or group that uses the system to seek support information.

[0521] "Natural language processing means" refers to technologies and methods for analyzing text or audio information received from a user and understanding its meaning and intent.

[0522] An "emotion engine" is a technology and algorithm that extracts emotional data from information provided by the user to the system and recognizes the user's emotional state in real time.

[0523] "Information retrieval means" refers to technology that searches for optimal support information for the user from databases and the internet based on analysis results obtained from natural language processing means and emotion engines.

[0524] "Interface means" refers to hardware and software components that enable display and operation for presenting searched support information to the user.

[0525] "Machine learning" is an artificial intelligence technology used in information retrieval to select the most appropriate support information, taking into account the user's emotional state.

[0526] "Multilingual support functionality" refers to a system feature that enables the processing and presentation of information in multiple languages ​​to the user.

[0527] "Emotional data" refers to information about the user's emotions extracted by the emotion engine.

[0528] This invention relates to a support information provision system that takes the user's emotions into consideration. This system functions through the coordinated efforts of the user, terminal, and server. Specific embodiments of each element are described below.

[0529] First, the user uses the terminal to input information about the support they need. This information can be implemented as text or voice data and should specifically express the user's condition and desired support. The terminal is equipped with an emotion engine to process the input data, and this engine is used to recognize the user's emotions in real time. Specifically, it uses an algorithm to extract emotion data from the intonation of voice data and from text.

[0530] Next, the device sends information and emotional data to the server. The server analyzes the received information using natural language processing to understand the user's specific support needs. It also uses data obtained from the emotion engine to confirm the user's emotional state. This process utilizes analysis algorithms and database management systems.

[0531] Based on the analysis results, the server uses information retrieval tools to find the most suitable support information. This search employs machine learning algorithms to prioritize support information that matches the user's emotional state. For example, if the user is showing anxiety, it is designed to suggest counseling support or immediate life support.

[0532] Finally, the server sends optimized support information to the terminal, which then communicates it to the user through a multilingual interface. The information is presented in a way that reflects the user's emotional state, specifically using a gentle tone and easy-to-understand language.

[0533] For example, if a user who is anxious has lost their job and is seeking financial assistance, this system can sense the user's anxiety and prioritize providing appropriate and timely information on solutions. An example of a prompt to input into the generation AI model is, "Generate and present support information for low-income individuals in a format optimized for anxious users."

[0534] In this way, the present invention enables the provision of support information that takes into account the user's emotions, thereby realizing a more appropriate and reassuring support environment.

[0535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0536] Step 1:

[0537] Users input information about the type of support they require into the device via text or voice. This input includes the user's current situation and the type of support they desire. This input data is collected using the device's microphone and keyboard.

[0538] Step 2:

[0539] The device receives input text and audio data and analyzes emotions using an emotion engine. Specifically, it recognizes the user's state of "anxiety" or "tension" in real time through an algorithm that extracts emotional data from the intonation of the voice and the expression of the text. In this process, the input is text or audio, and the output is emotional data.

[0540] Step 3:

[0541] The terminal transmits information and sentiment data obtained from the user to the server. During data transmission, the data is appropriately formatted and securely transmitted using network protocols. Input consists of analyzed information and sentiment data, and output is data transmission to the server.

[0542] Step 4:

[0543] The server analyzes the received information using natural language processing technology to identify the user's needs. Simultaneously, it analyzes emotional data to confirm the user's emotional state. The input consists of information and emotional data transmitted from the terminal, while the output is the user's needs and emotional state as a result of the analysis.

[0544] Step 5:

[0545] Based on the analysis results, the server uses information retrieval tools to search for the most suitable support information for the user. This process employs machine learning algorithms to prioritize information appropriate to the user's emotional state. For example, for an anxious user, information related to mental health care will be prioritized. The input is the user's needs and emotional state, and the output is the selected support information.

[0546] Step 6:

[0547] The server sends optimized support information to the terminal. Encryption technology is used for transmission to maintain the confidentiality of the information. The input is the selected support information, and the output is the transmission of information to the terminal.

[0548] Step 7:

[0549] The terminal presents support information received from the server to the user through a multilingual interface. The information presentation is optimally customized to the user's emotional state. Specifically, a gentle tone and translation appropriate to the language being used are implemented. Input is support information transmitted from the server, and output is information presented to the user visually or aurally.

[0550] (Application Example 2)

[0551] 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."

[0552] When people facing financial hardship or low-income groups need social support, there is a problem in that the information provided is general and does not address their individual needs or emotional states, thus failing to deliver the intended effect. Furthermore, while emotionally unstable users require information that is sensitive to their feelings, the current system struggles to provide this.

[0553] 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.

[0554] In this invention, the server includes a natural language processing means for analyzing user information, an emotion recognition means for recognizing the user's emotions in real time, and an information retrieval means for searching for appropriate support information based on the analysis results and emotions. This makes it possible to provide optimal support information tailored to the individual emotional state of each user.

[0555] A "user" refers to a person who uses this system to input information and receive support information.

[0556] "Natural language processing means for receiving and analyzing information" refers to technology that receives information provided by a user and linguistically analyzes that information in order to understand its content.

[0557] "Information retrieval means for searching for appropriate support information based on analysis results" refers to technology that uses analyzed data to find support information that meets the user's needs from databases, etc.

[0558] "An emotion recognition method that recognizes the user's emotions in real time" refers to a technology that determines and analyzes the emotional state of a user from their voice or text.

[0559] "Optimizing information based on emotions" means customizing the support information presented, taking into account the user's emotions, and providing it in the most useful way possible.

[0560] "Interface means" refers to physical or digital means used to present information to the user, and includes, for example, displays and speakers.

[0561] "Multilingual support functionality" refers to the ability to process and display information in multiple languages, providing information tailored to the user's language environment.

[0562] This invention aims to realize a system that determines the emotional state of a user based on information collected from the user and provides support information accordingly. A terminal receives information from the user via voice or text and transmits it to a server. The server uses natural language processing tools to analyze this information and understand the user's needs. Furthermore, emotion recognition technology determines the user's emotions in real time from the intonation of their voice and their textual expression. For example, a machine learning platform such as TensorFlow can be used for this purpose.

[0563] The server utilizes machine learning algorithms to select the most appropriate support information based on the user's emotional state. It also includes an information retrieval mechanism to search a database for relevant information based on the emotion recognition results. This allows the terminal to present optimized support information to the user through its interface. This interface is multilingual, designed with user-friendliness in mind, and the information provided is conveyed in a way that aligns with the user's emotions.

[0564] As a concrete example, if a child feels stressed about homework, the system can detect this and provide specific support, such as suggesting an educational mini-game to help them relax. An example of a prompt used in this system would be, "This user is feeling stressed. Please suggest ways or activities to help them relax."

[0565] Thus, the present invention can provide information based on emotional state, making it possible to offer a more useful and comfortable support experience to the user.

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The user inputs information into the device via voice or text. This input data includes the type of assistance the user needs and their current situation. The device receives this data, converts it to a digital format, and sends it to the server. This conversion process utilizes speech recognition technology to convert speech into text data.

[0569] Step 2:

[0570] The server analyzes the received information. Using natural language processing tools, it extracts the user's needs from the received text. This generates contextual information necessary to understand the user's requests and problems. The analysis results are then provided as input for sentiment recognition.

[0571] Step 3:

[0572] The server performs emotion recognition based on the analysis results. It uses a machine learning model to analyze the intonation of speech and text expression to determine the user's emotional state. In this step, the analyzed user information is used as input data, and the recognized emotion data is obtained as output.

[0573] Step 4:

[0574] The server performs information retrieval based on emotional data and user needs. Machine learning algorithms are used to retrieve the most relevant information from the database. During this process, filtering that takes emotional data into account prioritizes information that aligns with the user's emotions.

[0575] Step 5:

[0576] The server sends search results to the terminal. This includes customized support information, which is translated by a multilingual interface. Multilingual support ensures that information is presented in a language that is easy for the user to understand.

[0577] Step 6:

[0578] The device displays or audibly presents received information to the user. The method of information presentation is adjusted to the user's emotional state, for example, using a calm tone or friendly language. This creates an environment where users can use information with peace of mind.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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".

[0596] This invention is a system designed to make it easier for people in poverty and low-income groups to access the support information they need. This system functions through the cooperation of three parties: a server, a terminal, and a user.

[0597] Users input information about their living situation and support needs using devices such as smartphones and computers. The device receives this information as natural language and transmits it to the server. The transmitted information is then analyzed on the server using natural language processing tools. During this analysis, important keywords and intentions are extracted from the input information.

[0598] The server uses information retrieval tools based on the analysis results to search for appropriate support information. This includes government welfare services, NPO support activities, and vocational training programs. Machine learning technology is used in the search, and by referring to past data, the server provides users with the most suitable support information with high accuracy.

[0599] Support information sent from the server is displayed on the terminal. The terminal can present information in the user's native language using a multilingual interface. For example, if a foreign worker residing in Japan searches for vocational training programs through the terminal, suitable support programs will be displayed, and instructions on how to use those programs and contact information will be provided in the user's language.

[0600] This system aims to improve users' access to the support information they need and provide effective support to diverse user groups, transcending language barriers. As a result, it is providing an effective means for people facing poverty and low incomes to receive appropriate support and live independent lives.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] Users access the system interface using a terminal and enter information about the support they require and their current situation. This information includes their place of residence, income level, and the type of support they need (e.g., medical expense assistance, vocational training).

[0604] Step 2:

[0605] The terminal sends information entered by the user to the server. This data is received by the server as a message written in natural language.

[0606] Step 3:

[0607] The server analyzes the received natural language data using natural language processing tools. Here, keywords and intent are extracted from the user's input, and the information is understood and classified.

[0608] Step 4:

[0609] After analysis, the server uses machine learning techniques to search for the most appropriate support information for the user's situation. This includes a process of referencing internal databases and external APIs to collect information on public support services and non-profit organizations.

[0610] Step 5:

[0611] The server optimizes the support provided based on the search results and selects the most relevant information for the user. This selected information includes specific support procedures, participation methods, and contact details.

[0612] Step 6:

[0613] The server sends the selected support information to the user's terminal.

[0614] Step 7:

[0615] The device displays the received support information to the user. This information is clearly presented in the user's chosen language using multilingual support.

[0616] Step 8:

[0617] Based on the support information provided, users can take the necessary actions. If needed, they can also contact support again via their device to request further information or assistance.

[0618] (Example 1)

[0619] 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".

[0620] A challenge is to improve the situation where people facing poverty and low income levels have difficulty accessing support information that meets their needs. In particular, language barriers and difficulty in filtering information often lead to delays in providing appropriate support. Therefore, there is a need for a system that allows for easy access to information in multiple languages.

[0621] 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.

[0622] In this invention, the server includes means for receiving information from users regarding their living situation and support needs using an information processing device, natural language processing means for using a generative AI model to analyze the received information, and information retrieval means for searching for appropriate social support information based on the analyzed information. This makes it possible for diverse users to efficiently obtain the support information they need, overcoming language barriers.

[0623] An "information processing device" is a device that receives input information from a user and transmits it to a server, and includes smartphones and computers.

[0624] A "generative AI model" is an artificial intelligence model used for natural language processing. It analyzes information from users and is used to extract important keywords and intentions.

[0625] "Natural language processing means" refers to means for analyzing natural language information received from users, and includes functions for analyzing the intent and important keywords of the information using a generative AI model.

[0626] An "information retrieval tool" is a means of searching for relevant social support information based on analyzed information, and it has the function of improving search accuracy using machine learning technology.

[0627] A "user interface means" is a means of presenting searched support information to the user, and it has the role of providing multilingual support and displaying information in the user's language.

[0628] For this invention to be implemented, it is essential that the server, terminal, and user within the system work together in coordination. This system is designed to provide rapid access to support information needed by people in need and low-income groups, overcoming language barriers.

[0629] The server is equipped with a generative AI model for natural language processing (NLP). This model analyzes natural language information about the user's living situation and support needs. Its main role is to extract important keywords and intentions from the information and search for relevant social support information. Machine learning algorithms are used for information retrieval, providing highly relevant information while referring to past data.

[0630] The terminal is responsible for transmitting user-inputted information to the server and features a multilingual user interface. This interface can display support information sent from the server in the user's native language. Furthermore, user convenience during information input has been considered, providing an intuitive interface.

[0631] Users input their living situation and support needs in natural language using information terminals such as smartphones and computers. For example, specific needs such as "I would like to receive public food assistance." This allows users to easily communicate the support information they need to the system.

[0632] As a concrete example, suppose a foreign worker residing in Japan enters a prompt message such as, "I'm looking for a vocational training program. My current occupation is food processing, and I want to improve my skills." In this case, the system analyzes this information and provides relevant vocational training program information, thereby facilitating the user's access to the support they need.

[0633] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0634] Step 1:

[0635] Users input their living situation and support needs using a device such as a smartphone or computer. For example, they might enter a prompt message such as, "I need rent assistance." This input information is collected as initial data for the system.

[0636] Step 2:

[0637] The terminal formats the information entered by the user and sends it to the server. Here, natural language data in text format is passed to the server. The terminal's role is to convert the data into the appropriate format and transmit it quickly to the server.

[0638] Step 3:

[0639] The server receives the transmitted information and performs natural language processing using a generative AI model. It extracts keywords and intent from the input sentence and identifies the important information, "rent assistance." This analysis allows for a concrete understanding of the user's needs.

[0640] Step 4:

[0641] The server initiates information retrieval based on the analysis results. Using machine learning algorithms, it searches past databases for highly relevant support information, identifying appropriate support programs such as "rent subsidy programs." The data calculation performed here involves prioritizing search results using a scoring system.

[0642] Step 5:

[0643] The server returns the support information obtained as a search result to the terminal. This information includes specific support details, details of the providing organization, and application procedures. To improve the accuracy of the information, it is also personalized based on past usage history.

[0644] Step 6:

[0645] The terminal displays support information received from the server to the user. Using a multilingual user interface, the information is displayed in the user's native language, clearly guiding them through the necessary support procedures and access methods. This makes it easy for users to take immediate action to receive support.

[0646] (Application Example 1)

[0647] 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".

[0648] There is a challenge in that people facing poverty and low income levels have difficulty accessing necessary support information quickly and appropriately. Furthermore, language barriers and the sheer volume of information make it difficult to receive the support they need. In addition, it can be difficult to provide support that is tailored to the user's living situation. To address these challenges, it is necessary to offer appropriate support information that is suited to the user's circumstances.

[0649] 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.

[0650] In this invention, the server includes a natural language processing means for receiving and analyzing information from the user, an information retrieval means for searching for appropriate support content based on the analysis results, and a data analysis means for evaluating the user's living situation using purchase data. This makes it possible to propose appropriate support content based on the user's living situation.

[0651] A "user" is someone who provides information to the system and receives support from it.

[0652] "Receiving information" refers to the act of obtaining data from a user via a network.

[0653] "Natural language processing methods" refer to methods and technologies for analyzing text written in human language and understanding its meaning and intent.

[0654] "Information retrieval means" refers to a method of obtaining appropriate support information from databases and other information sources based on analyzed data.

[0655] "Display means" refers to a method for visually presenting the searched support content to the user.

[0656] "Purchase data" refers to information about a user's consumption behavior and history.

[0657] "Data analysis methods" refer to technologies used to evaluate purchase data and determine the user's lifestyle.

[0658] "Proposal methods" refer to methods and techniques for presenting relevant support services according to the user's living situation.

[0659] To implement this invention, the system is built on the cooperation of a server, terminals, and users. The server is a network-connected computing device equipped with a high-performance processor and large memory, and is primarily responsible for natural language processing and information retrieval. Specifically, the software used includes natural language processing frameworks such as NLTK and spaCy, and machine learning libraries such as scikit-learn and TensorFlow.

[0660] Users utilize devices such as smartphones or computers. These devices have an application installed for users to input their living situation and support needs. This application sends the user's input information to a server in natural language, and displays the support information received from the server. The data is encrypted and transmitted in a privacy-conscious manner.

[0661] The server analyzes the received information using natural language processing and, based on the results, uses information retrieval to obtain the most suitable support information. Historical data is also considered during this process. Specifically, the server uses purchase data to determine the user's living situation and proposes necessary support. The proposed support information is displayed on the application through a multilingual interface for user convenience.

[0662] A concrete example is this system integrated into an electronic payment app. When a user purchases groceries and the payment information is sent to the server via the app, the server analyzes the data and assesses the likelihood of financial hardship. Based on this assessment, the system suggests the most suitable food coupons and support programs in the user's native language.

[0663] An example of a prompt for the generating AI model would be: "Based on the user's purchase history, search for and present information on local support services. For example, find information on food coupons and lifestyle assistance programs." Using this prompt enables the rapid provision of appropriate support information.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] Users input their living situation and support needs into an application on their smartphone or computer terminal. This input data is in text format, and the terminal transmits it to the server using a security protocol.

[0667] Step 2:

[0668] The server analyzes the received user input data using natural language processing tools (such as NLTK or spaCy). Here, important keywords and intentions are extracted from the data, thereby understanding the user's support needs. The resulting data contains information about the analyzed intentions and needs.

[0669] Step 3:

[0670] The server uses information retrieval tools to search for relevant support information from the database based on the analyzed user needs. At this time, it utilizes machine learning models (using scikit-learn or TensorFlow) to select the most suitable support information, taking into account past data and the user's purchase data. The output is a list of support information best suited to the user's situation.

[0671] Step 4:

[0672] The server uses the proposed method to translate the obtained support information using a multilingual interface so that it can be easily understood by the user and sent to the terminal. As a result, the support information is translated into the user's language and output.

[0673] Step 5:

[0674] The device displays translated support information to the user through the application. The user can then refer to the provided support information and determine the necessary actions. This enables specific support tailored to the user's living situation.

[0675] Step 6:

[0676] After the user takes action based on the support information provided, feedback information is sent back to the server via the terminal. This feedback data is used as training data when updating the generated AI model.

[0677] Through this series of processes, users can receive real-time information on daily living support and obtain assistance tailored to their specific situation.

[0678] 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.

[0679] This invention is a system that, in addition to providing support information needed by people in poverty and low-income groups, recognizes the user's emotions and provides support information tailored to their individual emotional state. A key feature of this system is the collaboration between the user, terminal, and server, and the incorporation of an emotion engine.

[0680] The user inputs necessary information, their current status, and desired support through the device. This input is sent to the device as text or voice. The device sends the input information to the server, and in doing so, utilizes an emotion engine to recognize the user's emotions in real time. Emotion recognition is performed based on an algorithm that extracts emotions from the intonation of the user's voice and the expression of their text.

[0681] The server analyzes emotional data simultaneously with the received information. It uses natural language processing to understand user needs and then uses the results of the emotion engine to grasp the user's emotions. User emotions are considered a crucial factor in customizing the support information provided.

[0682] Based on the analysis results, the server uses information retrieval tools to find support information tailored to the user. Machine learning techniques are used for information retrieval, allowing for the priority selection of information most appropriate to the user's emotional state. For example, a user who is anxious can be presented with highly urgent solutions.

[0683] The optimized support information is then sent back to the device. The device presents the information in the format most easily understood by the user through a multilingual interface. At this time, it adjusts the way the information is presented and the wording used, taking into account the user's emotional state.

[0684] For example, if a foreign worker user experiencing anxiety has lost their job and is seeking financial support, the system can recognize the user's anxiety and prioritize providing information on counseling services to alleviate it, as well as welfare services that can provide immediate assistance.

[0685] In this way, this system enables the provision of support information that takes user emotions into consideration, making it possible to provide a support environment that users can use more accurately and with peace of mind.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] Users access the device and input text or voice data about their living situation and the type of support they need. This includes specific details such as "I need housing assistance" or "I have lost my job."

[0689] Step 2:

[0690] The device transmits information entered by the user to the server in real time. Simultaneously, the device's emotion engine analyzes the user's emotional state (e.g., anxiety, stress, joy) from voice and text, and sends that emotional data to the server.

[0691] Step 3:

[0692] The server analyzes the received user information and sentiment data using natural language processing techniques. This lays the foundation for understanding the user's requests and emotions and determining the most appropriate support.

[0693] Step 4:

[0694] The server utilizes information retrieval tools to search for suitable support information for the user from a database or external API based on the analysis results. Emotional data is taken into consideration, and information that aligns with the user's psychological state is prioritized.

[0695] Step 5:

[0696] To optimize search results, the server applies machine learning techniques. This automatically selects information that is relevant to similar past cases and the user's emotional state.

[0697] Step 6:

[0698] The server sends optimized support information to the terminal.

[0699] Step 7:

[0700] The device presents received information to the user through its interface. In this process, the display method and word choice are adjusted based on emotional data. For example, if the user is in a very unstable state, the guidance will be presented using more polite and reassuring language.

[0701] Step 8:

[0702] Users can review the provided support information and take the necessary actions. Furthermore, if further support is needed, users can enter additional questions via their device and resume the process.

[0703] (Example 2)

[0704] 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".

[0705] Conventional support information systems have the problem of not being able to take into account the user's emotions, making it difficult to customize them according to individual emotional states. In particular, in providing support to people in poverty and low-income groups, the lack of emotional considerations leads to the problem of not being able to provide adequate support information.

[0706] 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.

[0707] In this invention, the server includes a natural language processing means for receiving information from the user and analyzing that information and the user's emotions; an information retrieval means for searching for appropriate support information based on the analysis results and emotion data; and an interface means for adjusting and presenting the retrieved support information according to the user's emotional state. This makes it possible to provide optimal support information that takes the user's emotions into consideration.

[0708] A "user" refers to an individual or group that uses the system to seek support information.

[0709] "Natural language processing means" refers to technologies and methods for analyzing text or audio information received from a user and understanding its meaning and intent.

[0710] An "emotion engine" is a technology and algorithm that extracts emotional data from information provided by the user to the system and recognizes the user's emotional state in real time.

[0711] "Information retrieval means" refers to technology that searches for optimal support information for the user from databases and the internet based on analysis results obtained from natural language processing means and emotion engines.

[0712] "Interface means" refers to hardware and software components that enable display and operation for presenting searched support information to the user.

[0713] "Machine learning" is an artificial intelligence technology used in information retrieval to select the most appropriate support information, taking into account the user's emotional state.

[0714] "Multilingual support functionality" refers to a system feature that enables the processing and presentation of information in multiple languages ​​to the user.

[0715] "Emotional data" refers to information about the user's emotions extracted by the emotion engine.

[0716] This invention relates to a support information provision system that takes the user's emotions into consideration. This system functions through the coordinated efforts of the user, terminal, and server. Specific embodiments of each element are described below.

[0717] First, the user uses the terminal to input information about the support they need. This information can be implemented as text or voice data and should specifically express the user's condition and desired support. The terminal is equipped with an emotion engine to process the input data, and this engine is used to recognize the user's emotions in real time. Specifically, it uses an algorithm to extract emotion data from the intonation of voice data and from text.

[0718] Next, the device sends information and emotional data to the server. The server analyzes the received information using natural language processing to understand the user's specific support needs. It also uses data obtained from the emotion engine to confirm the user's emotional state. This process utilizes analysis algorithms and database management systems.

[0719] Based on the analysis results, the server uses information retrieval tools to find the most suitable support information. This search employs machine learning algorithms to prioritize support information that matches the user's emotional state. For example, if the user is showing anxiety, it is designed to suggest counseling support or immediate life support.

[0720] Finally, the server sends optimized support information to the terminal, which then communicates it to the user through a multilingual interface. The information is presented in a way that reflects the user's emotional state, specifically using a gentle tone and easy-to-understand language.

[0721] For example, if a user who is anxious has lost their job and is seeking financial assistance, this system can sense the user's anxiety and prioritize providing appropriate and timely information on solutions. An example of a prompt to input into the generation AI model is, "Generate and present support information for low-income individuals in a format optimized for anxious users."

[0722] In this way, the present invention enables the provision of support information that takes into account the user's emotions, thereby realizing a more appropriate and reassuring support environment.

[0723] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0724] Step 1:

[0725] Users input information about the type of support they require into the device via text or voice. This input includes the user's current situation and the type of support they desire. This input data is collected using the device's microphone and keyboard.

[0726] Step 2:

[0727] The device receives input text and audio data and analyzes emotions using an emotion engine. Specifically, it recognizes the user's state of "anxiety" or "tension" in real time through an algorithm that extracts emotional data from the intonation of the voice and the expression of the text. In this process, the input is text or audio, and the output is emotional data.

[0728] Step 3:

[0729] The terminal transmits information and sentiment data obtained from the user to the server. During data transmission, the data is appropriately formatted and securely transmitted using network protocols. Input consists of analyzed information and sentiment data, and output is data transmission to the server.

[0730] Step 4:

[0731] The server analyzes the received information using natural language processing technology to identify the user's needs. Simultaneously, it analyzes emotional data to confirm the user's emotional state. The input consists of information and emotional data transmitted from the terminal, while the output is the user's needs and emotional state as a result of the analysis.

[0732] Step 5:

[0733] Based on the analysis results, the server uses information retrieval tools to search for the most suitable support information for the user. This process employs machine learning algorithms to prioritize information appropriate to the user's emotional state. For example, for an anxious user, information related to mental health care will be prioritized. The input is the user's needs and emotional state, and the output is the selected support information.

[0734] Step 6:

[0735] The server sends optimized support information to the terminal. Encryption technology is used for transmission to maintain the confidentiality of the information. The input is the selected support information, and the output is the transmission of information to the terminal.

[0736] Step 7:

[0737] The terminal presents support information received from the server to the user through a multilingual interface. The information presentation is optimally customized to the user's emotional state. Specifically, a gentle tone and translation appropriate to the language being used are implemented. Input is support information transmitted from the server, and output is information presented to the user visually or aurally.

[0738] (Application Example 2)

[0739] 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".

[0740] When people facing financial hardship or low-income groups need social support, there is a problem in that the information provided is general and does not address their individual needs or emotional states, thus failing to deliver the intended effect. Furthermore, while emotionally unstable users require information that is sensitive to their feelings, the current system struggles to provide this.

[0741] 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.

[0742] In this invention, the server includes a natural language processing means for analyzing user information, an emotion recognition means for recognizing the user's emotions in real time, and an information retrieval means for searching for appropriate support information based on the analysis results and emotions. This makes it possible to provide optimal support information tailored to the individual emotional state of each user.

[0743] A "user" refers to a person who uses this system to input information and receive support information.

[0744] "Natural language processing means for receiving and analyzing information" refers to technology that receives information provided by a user and linguistically analyzes that information in order to understand its content.

[0745] "Information retrieval means for searching for appropriate support information based on analysis results" refers to technology that uses analyzed data to find support information that meets the user's needs from databases, etc.

[0746] "An emotion recognition method that recognizes the user's emotions in real time" refers to a technology that determines and analyzes the emotional state of a user from their voice or text.

[0747] "Optimizing information based on emotions" means customizing the support information presented, taking into account the user's emotions, and providing it in the most useful way possible.

[0748] "Interface means" refers to physical or digital means used to present information to the user, and includes, for example, displays and speakers.

[0749] "Multilingual support functionality" refers to the ability to process and display information in multiple languages, providing information tailored to the user's language environment.

[0750] This invention aims to realize a system that determines the emotional state of a user based on information collected from the user and provides support information accordingly. A terminal receives information from the user via voice or text and transmits it to a server. The server uses natural language processing tools to analyze this information and understand the user's needs. Furthermore, emotion recognition technology determines the user's emotions in real time from the intonation of their voice and their textual expression. For example, a machine learning platform such as TensorFlow can be used for this purpose.

[0751] The server utilizes machine learning algorithms to select the most appropriate support information based on the user's emotional state. It also includes an information retrieval mechanism to search a database for relevant information based on the emotion recognition results. This allows the terminal to present optimized support information to the user through its interface. This interface is multilingual, designed with user-friendliness in mind, and the information provided is conveyed in a way that aligns with the user's emotions.

[0752] As a concrete example, if a child feels stressed about homework, the system can detect this and provide specific support, such as suggesting an educational mini-game to help them relax. An example of a prompt used in this system would be, "This user is feeling stressed. Please suggest ways or activities to help them relax."

[0753] Thus, the present invention can provide information based on emotional state, making it possible to offer a more useful and comfortable support experience to the user.

[0754] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0755] Step 1:

[0756] The user inputs information into the device via voice or text. This input data includes the type of assistance the user needs and their current situation. The device receives this data, converts it to a digital format, and sends it to the server. This conversion process utilizes speech recognition technology to convert speech into text data.

[0757] Step 2:

[0758] The server analyzes the received information. Using natural language processing tools, it extracts the user's needs from the received text. This generates contextual information necessary to understand the user's requests and problems. The analysis results are then provided as input for sentiment recognition.

[0759] Step 3:

[0760] The server performs emotion recognition based on the analysis results. It uses a machine learning model to analyze the intonation of speech and text expression to determine the user's emotional state. In this step, the analyzed user information is used as input data, and the recognized emotion data is obtained as output.

[0761] Step 4:

[0762] The server performs information retrieval based on emotional data and user needs. Machine learning algorithms are used to retrieve the most relevant information from the database. During this process, filtering that takes emotional data into account prioritizes information that aligns with the user's emotions.

[0763] Step 5:

[0764] The server sends search results to the terminal. This includes customized support information, which is translated by a multilingual interface. Multilingual support ensures that information is presented in a language that is easy for the user to understand.

[0765] Step 6:

[0766] The device displays or audibly presents received information to the user. The method of information presentation is adjusted to the user's emotional state, for example, using a calm tone or friendly language. This creates an environment where users can use information with peace of mind.

[0767] 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.

[0768] 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.

[0769] In the above embodiment, an example was given in which the 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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."

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0788] The following is further disclosed regarding the embodiments described above.

[0789] (Claim 1)

[0790] A natural language processing means that receives information from the user and analyzes that information,

[0791] An information retrieval means for searching for appropriate support information based on the analysis results,

[0792] An interface means for presenting the searched support information to the user,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein the information retrieval means optimizes the support information using machine learning.

[0796] (Claim 3)

[0797] The system according to claim 1, which has multilingual support capabilities and processes and presents information in multiple languages.

[0798] "Example 1"

[0799] (Claim 1)

[0800] A means of receiving information from users regarding their living situation and support needs using an information processing device,

[0801] A natural language processing means that uses a generative AI model to analyze the received information,

[0802] Information retrieval means for searching for appropriate social support information based on the analyzed information,

[0803] A user interface means for displaying the retrieved social support information in multiple languages,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the information retrieval means optimizes the support information while referring to past information using a machine learning algorithm.

[0807] (Claim 3)

[0808] The system according to claim 1, which enables the user interface means to process and present information in multiple languages.

[0809] "Application Example 1"

[0810] (Claim 1)

[0811] A natural language processing means that receives information from the user and analyzes that information,

[0812] An information retrieval method for searching for appropriate support content based on analysis results,

[0813] A display means for presenting the searched support content to the user,

[0814] A data analysis method that uses purchase data to evaluate living conditions,

[0815] A proposal method that suggests relevant support services according to living circumstances,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the information retrieval means optimizes the support information using machine learning.

[0819] (Claim 3)

[0820] The system according to claim 1, which has multilingual support capabilities and processes and presents information in multiple languages.

[0821] "Example 2 of combining an emotion engine"

[0822] (Claim 1)

[0823] A natural language processing means that receives information from the user and analyzes that information and the user's emotions,

[0824] An information retrieval means for searching for appropriate support information based on analysis results and emotional data,

[0825] An interface means for adjusting and presenting retrieved support information according to the user's emotional state,

[0826] An emotion engine that recognizes the user's emotions in real time,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the information retrieval means optimizes support information based on the user's emotional state using machine learning.

[0830] (Claim 3)

[0831] The system according to claim 1, which has a multilingual support function and processes and presents information and adaptive expressions that respond to the user's emotions in multiple languages.

[0832] "Application example 2 when combining with an emotional engine"

[0833] (Claim 1)

[0834] A natural language processing means that receives information from the user and analyzes that information,

[0835] An information retrieval means for searching for appropriate support information based on the analysis results,

[0836] An emotion recognition means that recognizes the user's emotions in real time and optimizes information based on those emotions,

[0837] An interface means for presenting the searched support information to the user,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the information retrieval means optimizes support information using machine learning, and the emotion recognition means preferentially selects information corresponding to the user's emotional state.

[0841] (Claim 3)

[0842] The system according to claim 1, which has multilingual support capabilities, processes and presents information in multiple languages, and adjusts the presentation method based on the user's emotions. [Explanation of symbols]

[0843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A natural language processing means that receives information from the user and analyzes that information, An information retrieval means for searching for appropriate support information based on the analysis results, An interface means for presenting the searched support information to the user, A system that includes this.

2. The system according to claim 1, wherein the information retrieval means optimizes the support information using machine learning.

3. The system according to claim 1, which has a multilingual support function and processes and presents information in multiple languages.

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