system

Abstract

We provide the system. [Solution] A means of analyzing input sentiment data in real time using a natural language processing engine, A means of generating personalized self-care suggestions based on analyzed emotional data, A means of presenting the generated suggestions to the user, A means of anonymizing the input data and processing it statistically while protecting the user's privacy, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, daily stress and dissatisfaction have a negative impact on individuals' mental health, and eliminating them has become a major issue. There is a demand for a system that is easy for many people to use, effectively expresses dissatisfaction, and can obtain solutions for improving their own conditions, but there is no system in the prior art that fully meets this demand.

Means for Solving the Problems

[0005] This invention provides a system that uses a natural language processing engine to perform real-time sentiment analysis on voice and text data collected from users. Based on the analysis results, this system generates and presents personalized self-care suggestions to users, thereby reducing stress and dissatisfaction. Furthermore, the collected data is anonymized and statistically processed to protect privacy, contributing to the improvement of the mental health of the entire group.

[0006] A "natural language processing engine" is a technology that analyzes natural human language from text and audio data to extract information and perform sentiment analysis.

[0007] "Emotional data" refers to input data that indicates a user's emotional state, and is collected as information such as voice or text.

[0008] "Real-time analysis" is a technology that processes and analyzes data simultaneously with or very quickly after it is entered.

[0009] "Self-care suggestions" refer to specific actions and strategies provided to users for self-improvement and stress reduction.

[0010] "Anonymization" is a technique for protecting privacy by deleting or concealing information that can identify an individual.

[0011] "Statistical processing" is a method of deriving patterns and trends by aggregating and analyzing collected data. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] 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.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system of this invention appropriately expresses the frustrations that users experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system functions primarily around a terminal, a server, and the user.

[0034] Users express their dissatisfaction using voice or text input via a device. The device is equipped with speech recognition technology to convert the input voice data into text data. The collected data is then transmitted to a server via a communication protocol.

[0035] The server performs real-time sentiment analysis on the received data using a natural language processing engine. Sentiment analysis involves reading the user's emotional state from their text data and evaluating the type and intensity of emotions such as anger, depression, and stress. Based on these analysis results, the server generates self-care suggestions optimized for the user.

[0036] Self-care suggestions include support such as recommendations for relaxation music, breathing exercises, and, if needed, access to a chat function with a specialist. The generated suggestions are sent back from the server to the device and displayed on the device's user interface. Through this, users can take appropriate self-health measures.

[0037] Furthermore, the servers anonymize and statistically process the collected data to provide information that reveals emotional trends and tendencies by region and time of day. Since this data is aggregated in a way that does not identify users, privacy is strictly protected. The resulting statistical information is then used to improve mental health across society and to optimize services by companies.

[0038] The above describes the embodiments for carrying out the present invention. As a specific example, if a user expresses dissatisfaction such as "I'm under too much work stress," the system can generate self-care suggestions related to that stress and present the user with options for stress management.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input their complaints in voice or text format using the input interface installed on the device. In the case of voice input, the device uses speech recognition technology to convert the voice data into text data.

[0042] Step 2:

[0043] The device performs a process to anonymize the complaint data. It removes personally identifiable information and prepares the data in a privacy-protected state.

[0044] Step 3:

[0045] The terminal sends the formatted data to the server. Since the communication is protected by security protocols, the data is transferred securely.

[0046] Step 4:

[0047] The server uses a natural language processing engine to perform real-time sentiment analysis on the received text data. This analysis examines the linguistic structure and keywords within the data to evaluate the type and intensity of emotions the user is experiencing.

[0048] Step 5:

[0049] Based on the analysis results, the server generates personalized self-care suggestions for the user. These suggestions may include relaxation music, breathing exercises, or, if needed, a chat function with a counselor.

[0050] Step 6:

[0051] The server sends the generated suggestions to the terminal. The terminal displays the received information on its user interface and presents the user with appropriate self-care actions.

[0052] Step 7:

[0053] Users can review the displayed suggestions, select self-care actions that suit their needs, and then take action.

[0054] Step 8:

[0055] The server also aggregates and analyzes the collected data, statistically processing dissatisfaction trends by region and time of day. The results of this analysis are used for further service optimization and social contribution.

[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] Stress and dissatisfaction in modern society are becoming increasingly diverse, creating a need to provide individualized self-care methods quickly and effectively. However, traditional methods lack the ability to accurately understand users' emotions and suggest appropriate coping strategies. Furthermore, protecting user data privacy while effectively utilizing that data presents a new challenge.

[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 collecting dissatisfaction data from users in voice or text format, and in the former case, converting it to text format using voice conversion technology; means for analyzing the emotional state contained in the text using a natural language processing mechanism and evaluating the type and intensity of the emotion; and means for creating individually optimized self-care suggestions based on the analysis. This enables the rapid provision of self-care suggestions tailored to the user's emotions, and allows for health management that meets individual needs.

[0061] "Dissatisfaction data" refers to information in audio or text format that expresses the dissatisfaction and stress that users experience in their daily lives.

[0062] "Speech conversion technology" is a technology for converting audio data into text format.

[0063] A "natural language processing mechanism" is a technology used to analyze information such as emotions and intentions from text data.

[0064] "Emotional state" refers to the type and intensity of emotions expressed in the user's text data.

[0065] "Self-care suggestions" refer to proposing self-care methods and activities that are optimized according to the user's emotional state.

[0066] "Anonymization" is a method of processing data in a way that prevents individuals from being identified.

[0067] "Statistical processing" is a data processing method aimed at aggregating and analyzing data to extract trends and patterns.

[0068] The system of this invention allows users to express frustrations they experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system is composed of a terminal, a server, and the user.

[0069] Users input their complaints via voice or text using a computer or smartphone. In the case of voice input, the device has the capability to convert voice data into text using speech-to-text conversion technology, often utilizing common speech recognition software. The resulting text data is then transmitted to a server using a secure communication method.

[0070] The server analyzes the received text data using advanced natural language processing mechanisms. This typically involves using a natural language processing engine on a cloud service. The server identifies the user's emotional state and evaluates its type and intensity. For example, if a user types "tired," the server can infer from the text that the user is feeling fatigued.

[0071] Based on the analysis results, the server generates self-care suggestions tailored to the user. These suggestions may include, for example, relaxation music, breathing exercises, or a consultation function with a professional. The generated suggestions are then sent back to the terminal and can be viewed on the user interface.

[0072] Furthermore, the server anonymizes the acquired data and processes it statistically, making it possible to understand emotional trends in each region. This can contribute to improving mental health and optimizing services across society.

[0073] For example, if a user expresses dissatisfaction such as "I'm stressed out at work," the system can suggest relaxation music aimed at stress reduction or guide them through simple meditation techniques. An example of a prompt to the generating AI model would be, "Please describe the biggest source of dissatisfaction the user is experiencing and provide simple self-care methods to alleviate that dissatisfaction."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] Users input their complaints via voice or text through their device. The input is complaint data, such as "I've been under a lot of work stress lately." In the case of voice input, the device uses its built-in speech recognition software to convert the voice data into text data. This conversion results in output in the form of text data from voice data.

[0077] Step 2:

[0078] The terminal sends the converted text data to the server via a secure communication protocol. The input is text data, and the output is data transfer to the server. Data privacy protection is crucial here, and encryption technology is employed.

[0079] Step 3:

[0080] The server begins processing the received text data. It uses natural language processing capabilities to analyze the emotional state within the text. The input is text data, and the data analysis generates output regarding the type and intensity of emotions. A generative AI model is used in this process.

[0081] Step 4:

[0082] The server generates personalized self-care suggestions based on the results of the emotion analysis. The input is the results of the emotion analysis, and the output is a self-care plan optimized for the user. Specific suggestions may include listening to relaxation music, practicing breathing exercises, and chatting with a professional.

[0083] Step 5:

[0084] The server sends the generated self-care suggestions to the terminal. The input is the content of the self-care suggestions, and an output indicating that the data has been sent to the terminal is received. The suggestions are displayed on the terminal's user interface so that the user can easily review them.

[0085] Step 6:

[0086] The user reviews and acts upon the suggestions provided on the device. The input here is the self-care suggestion displayed on the device, and the output is the user's action. For example, the user can reduce stress by playing music and relaxing.

[0087] Step 7:

[0088] The server anonymizes the collected emotional data and performs statistical analysis. The input is user emotional data, and the output is statistical data that helps understand emotional trends by region and time of day. This statistical information can be used to improve mental health throughout society and to improve services provided by companies.

[0089] (Application Example 1)

[0090] 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."

[0091] Conventional self-care suggestion systems struggle to provide personalized decision-making support that takes into account the user's emotions, and they particularly lack mechanisms for smooth payment processes in daily life. Furthermore, there is a need for a system that proposes safe and efficient payment methods while considering the user's emotions.

[0092] 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.

[0093] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing engine, means for generating personalized decision support suggestions based on the analyzed emotional data, and means for presenting the generated suggestions to the user and optimizing the payment method. This enables rapid and secure decision support based on the user's individual emotional state.

[0094] A "natural language processing engine" is a technology that analyzes emotions and meanings from input text data, and is used to evaluate the user's emotional state.

[0095] "Emotional data" refers to information that includes text or audio data collected from users and reflects the user's emotional state.

[0096] "Decision support suggestions" refer to optimized actions and options provided to the user based on the results of a user sentiment analysis.

[0097] "Optimizing payment methods" is the process of suggesting the most appropriate and secure payment method to the user, taking into account their emotional state.

[0098] "Anonymization" is a technique used to process user data in a way that prevents individuals from being identified, in order to protect the privacy of user data.

[0099] "Statistical processing" refers to a data processing method used to aggregate and analyze data in order to understand the overall trends and characteristics of society.

[0100] One possible embodiment of this invention is a system in which a user, a terminal, and a server work together. The user inputs their emotions and frustrations in voice or text format using a terminal such as a smartphone. To achieve this, the terminal is equipped with a speech recognition function to convert the voice data into text. Specific technologies used include Google® Cloud Speech-to-Text API.

[0101] Text data sent from the device is analyzed for sentiment in real time on the server using a natural language processing engine. For example, the Google Cloud Natural Language API is used for this process. Based on the analyzed sentiment data, the server generates decision support suggestions optimized for the user. These suggestions may include electronic payment methods that are appropriate for the user's current situation.

[0102] The suggestions based on the analysis results are sent back to the terminal and presented to the user through the user interface. This allows users to adopt a safe and efficient payment method that suits their individual emotional state. The server also anonymizes and statistically processes the emotional data to protect personal information. The data obtained through this processing can be used to improve mental health throughout society and optimize commercial services.

[0103] For example, if a user inputs a common complaint such as "I forgot my wallet," the system can suggest quick payment methods using credit cards or electronic money that are appropriate for that situation. In this case, the generating AI model can be prompted with a phrase like "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" to create an appropriate suggestion.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] Users input their frustrations and feelings in voice or text format using a device. The device receives this input and, if it's voice input, converts it into text data using speech recognition technology. The Google Cloud Speech-to-Text API is used for this text conversion. The input is voice or text data, and the output is text data.

[0107] Step 2:

[0108] The terminal sends the converted text data to the server. The server receives this text data and feeds it into a natural language processing engine. This process analyzes the user's emotional state from the text data. The input is the user's text data, and the output is the analysis result indicating the emotional state. Sentiment analysis is performed using the Google Cloud Natural Language API.

[0109] Step 3:

[0110] The server uses a generative AI model to generate decision support suggestions optimized for the user, based on the sentiment analysis results. Here, the AI ​​model is instructed using necessary prompts to create suggestions such as appropriate payment methods. The input is the sentiment analysis results, and the output is the optimized suggestions. For example, a prompt such as "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" might be used.

[0111] Step 4:

[0112] The server sends the generated decision support proposals to the terminal. The terminal receives this proposal data and presents it to the user through the user interface. The user reviews the proposals on the screen and makes a decision based on the presented options. The input is the proposal data, and the output is a visual representation for the user.

[0113] Step 5:

[0114] The server anonymizes and statistically processes emotional data. This allows for the understanding of overall societal data trends while protecting individual privacy. The input is emotional data, and the output is anonymized statistical information. The resulting information is then used to improve mental health and optimize services.

[0115] 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.

[0116] The system of the present invention collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. In particular, the configuration that combines an emotion engine makes it possible to understand the user's emotional state in more detail and with greater accuracy.

[0117] Users use the device to input emotions such as dissatisfaction and stress in either voice or text format. The device converts voice data into text using speech recognition technology. In addition, an emotion engine analyzes the input data and estimates the user's emotional state from the tone and speed of the voice and the context of the text.

[0118] Data transmitted from the device is received and processed by the server. The server uses a natural language processing engine and an emotion engine to evaluate the emotional data and determine the user's emotional state in detail. From this data, it generates self-care suggestions tailored to the user's state. These suggestions consist of a wide range of options, such as selecting relaxation music, receiving breathing exercises, or utilizing a professional consultation function.

[0119] The server sends the generated suggestions to the terminal, which then displays the received suggestions in a user interface. Through this interface, the user can select and perform self-care actions appropriate to their emotional state.

[0120] For example, if a user submits a complaint such as, "Recently, the pressure at work has been terrible and I feel very unstable," the emotion engine analyzes the complaint and determines that it indicates a high level of anxiety. The server then suggests music to reduce stress or a consultation function with a specialist that is appropriate for the situation.

[0121] Furthermore, the server anonymizes the collected data and applies statistical processing to analyze trends in dissatisfaction by region and time. This information can be used by companies and organizations to improve mental health, and is operated while fully protecting user privacy. The above is a specific embodiment for carrying out the present invention.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user inputs their complaints into the device via voice or text. In the case of voice input, the device uses speech recognition software to convert the voice data into text data.

[0125] Step 2:

[0126] The device transmits the input data to the emotion engine in real time. The emotion engine analyzes the user's emotional state based on voice tone and text context.

[0127] Step 3:

[0128] The device anonymizes the emotional state data obtained from the emotion engine and sends it to the server while protecting the user's personal information.

[0129] Step 4:

[0130] The server further analyzes the received data using a natural language processing engine to determine the user's specific emotional state. This analysis includes evaluating linguistic nuances and the intensity of emotions.

[0131] Step 5:

[0132] The server generates personalized self-care suggestions based on the analysis results. These suggestions may include relaxation music, breathing exercises, or access to professional consultation features.

[0133] Step 6:

[0134] The server sends the generated self-care suggestions to the terminal.

[0135] Step 7:

[0136] The device presents suggestions to the user through its user interface. The user can then choose and perform the self-care action best suited to them from the presented options.

[0137] Step 8:

[0138] The server further aggregates user sentiment data and processes it as statistical data in an anonymized format. This data is used to analyze sentiment trends by region and time of day.

[0139] (Example 2)

[0140] 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".

[0141] In modern society, it is crucial to provide timely and appropriate self-care based on individual emotional states. However, conventional systems have difficulty accurately grasping emotional changes in real time, making it challenging to generate self-care suggestions optimized for each user. Furthermore, there is a need for methods to securely manage collected emotional data and utilize it while ensuring user privacy.

[0142] 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.

[0143] In this invention, the server includes means for analyzing input emotional data in real time in detail and with accuracy using a natural language processing engine and an emotional analysis engine; means for generating individually optimized self-care suggestions based on the analyzed emotional data and providing a variety of self-care options; and means for statistically processing emotional trends by region and time while anonymizing the input data and protecting privacy. This makes it possible to achieve highly accurate emotional analysis in real time and propose individually optimized self-care while protecting user privacy.

[0144] A "natural language processing engine" is a technological device that analyzes text data and understands human language.

[0145] An "emotion analysis engine" is an analytical tool used to estimate emotional states from input data.

[0146] "Self-care suggestions" refer to guidance aimed at mental care and health promotion, provided according to the emotional state of each individual user.

[0147] "Anonymization" is a technique for securely processing data by removing or transforming information that could identify an individual.

[0148] "Privacy protection" refers to measures taken to prevent personal information from being leaked to third parties.

[0149] "Statistical processing" refers to methods for aggregating data and analyzing trends and patterns.

[0150] "Real-time" is a time concept that indicates processing or reactions occur almost instantly.

[0151] "Self-care options" refer to the types and methods of self-care activities that users can choose from.

[0152] This invention is a system that collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. Users can input emotional data in voice or text format using a terminal. If voice data is input, the terminal converts it to text using speech recognition technology. Commonly used APIs can be used as the speech recognition technology here.

[0153] The terminal sends the converted text data to the server. This server, equipped with a natural language processing engine and an emotion analysis engine, analyzes the input data. Specifically, the server utilizes language processing technology to evaluate the user's emotional state from the context of the text and the characteristics of the voice. It uses a generative AI model as an appropriate model to estimate emotions.

[0154] A key feature of this system is that it generates individually optimized self-care suggestions based on analyzed emotional data, including a variety of self-care options. For example, if a user enters "I've been feeling very unstable lately due to pressure at work," the server will determine this emotional state to be a high level of anxiety and suggest stress-reducing music or professional counseling services to the user's device.

[0155] Furthermore, by statistically processing data while protecting user privacy, it is possible to analyze emotional trends by region and time. This allows companies and organizations to utilize this information as a means of improving mental health. An example of a prompt would be, "What self-care would you suggest when you are feeling stressed due to increased workload at work?"

[0156] In this way, the system implements technologies that provide flexible and detailed support tailored to user needs.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] Users input emotional data using a device. This input data is in voice or text format and provides details about the user's experience and emotions. If voice data is input, it is converted to text in the next processing step.

[0160] Step 2:

[0161] The terminal converts the input voice data into text format using speech recognition technology. This conversion transforms the speech into text information, enabling natural language processing. Specifically, the terminal launches speech recognition software, analyzes the voice signal, and generates the corresponding text. The text data is then output.

[0162] Step 3:

[0163] The device sends text data to the server. Here, the text data is treated as important data, including the user's sentiment information. The device sends the data to the server using a secure communication protocol and confirms receipt of the data.

[0164] Step 4:

[0165] The server analyzes the received text data using a natural language processing engine and an emotion analysis engine. The input data is processed to analyze the context and nuances of the text and estimate the user's emotional state. Specifically, the analysis model runs and outputs negative, positive, or neutral emotional states.

[0166] Step 5:

[0167] The server generates self-care suggestions based on the analysis results. This generation process includes selecting the most suitable self-care options corresponding to the user's emotional state. For example, if high stress levels are detected, suggestions such as relaxation music or breathing exercises will be output.

[0168] Step 6:

[0169] The server sends the generated self-care suggestions to the device. Here, measures are taken to ensure privacy and user confidence. The server appropriately formats the suggestions and sends them to the device via a secure communication channel.

[0170] Step 7:

[0171] The device displays received self-care suggestions in the user interface. The user reviews the suggestions through the interface, selects the self-care appropriate for their condition, and performs it. Specifically, the device displays suggestions in a pop-up window, guiding the user to make selections easier. Based on the user's selection, actions to proceed to the next step are displayed.

[0172] (Application Example 2)

[0173] 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".

[0174] In retail stores, there is a challenge in understanding customers' anxieties and dissatisfactions in real time and providing appropriate customer service. Furthermore, traditional methods do not allow for a comprehensive analysis of customers' facial expressions and tone of voice, making it difficult to efficiently provide personalized customer service.

[0175] 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.

[0176] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing mechanism, means for analyzing facial expressions and estimating emotional states using a video acquisition function, and means for converting audio data into text format and analyzing voice tone using speech recognition technology. This makes it possible to analyze customer emotions in detail and recommend personalized customer service to store staff.

[0177] "Natural language processing" refers to technology that analyzes speech and text data to understand its meaning and emotions.

[0178] "Emotional data" refers to information that indicates a user's emotional state, and can be expressed in various forms such as voice, text, and facial expressions.

[0179] "Self-management suggestions" refer to specific advice provided to users based on their emotional state, for self-care and improvement activities.

[0180] "Video acquisition functionality" refers to a technology that acquires video data using devices such as cameras, and is particularly used to capture customers' facial expressions.

[0181] "Speech recognition technology" is a technology that converts speech into text, and it also analyzes features such as tone and speed of voice.

[0182] "Anonymization" is a method of protecting privacy by removing personally identifiable information from acquired data.

[0183] "Statistical processing" is a method of aggregating and analyzing data to extract meaningful information and reveal overall trends.

[0184] The system for implementing this invention mainly consists of a server, a smart device (such as smart glasses), and real-time processing software. The server uses natural language processing mechanisms and emotion recognition technology to analyze emotional data acquired from the user in detail.

[0185] The smart glasses have a built-in camera and microphone, and use video acquisition and voice recognition technology to capture customers' facial expressions and voices. These smart devices are worn by users during customer service and transmit customer emotional information to a server in real time. The server uses natural language processing based on the received data to estimate the customer's emotional state.

[0186] Specifically, the server preprocesses image data using machine learning frameworks such as TENSORFLOW® to estimate emotional states in real time. Audio data is converted to text using the Google Cloud Speech-to-Text API, and sentiment analysis is performed using the Google Natural Language API. Based on this information, the server recommends personalized customer service actions to staff.

[0187] For example, if a customer displays a confused expression in the store, the server analyzes the facial data and sends a notification to the smart glasses stating, "This customer may need assistance." Based on this notification, staff can then provide more appropriate service.

[0188] The following is an example of a prompt message for a generative AI model.

[0189] "How can we analyze customer emotions within a store and provide that information to staff in real time to provide the best possible customer service?"

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The device (smart glasses) captures customers' facial expressions in the store using a camera and simultaneously collects audio data using a microphone. This input data includes both image and audio data.

[0193] Step 2:

[0194] The device converts the collected audio data into text data using speech recognition technology. Specifically, it analyzes the audio waveform and converts its content into sentences. The output of this process is sent to the server as text data.

[0195] Step 3:

[0196] The server receives image data sent from the terminal and performs preprocessing using the machine learning framework TensorFlow. This process detects customer facial features from the image and estimates emotions in real time using an emotion recognition model. The output is the estimated emotion information.

[0197] Step 4:

[0198] The server analyzes the speech-to-text data using a natural language processing engine and evaluates the emotion based on the text's context and tone. The input is text data, and the output is additional information about the user's emotional state.

[0199] Step 5:

[0200] The server integrates emotional information obtained from image and audio data to comprehensively determine the customer's current emotional state. This allows for a detailed understanding of the customer's anxiety, confusion, and other emotional states. The output includes the concluded emotional state.

[0201] Step 6:

[0202] Based on the analysis results, the server generates prompts on the terminal recommending specific customer service actions. For example, if a specific emotional state is detected, a specific action such as "provide additional support to the customer" will be recommended. The generated recommended actions are displayed on the terminal in real time.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The system of this invention appropriately expresses the frustrations that users experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system functions primarily around a terminal, a server, and the user.

[0220] Users express their dissatisfaction using voice or text input via a device. The device is equipped with speech recognition technology to convert the input voice data into text data. The collected data is then transmitted to a server via a communication protocol.

[0221] The server performs real-time sentiment analysis on the received data using a natural language processing engine. Sentiment analysis involves reading the user's emotional state from their text data and evaluating the type and intensity of emotions such as anger, depression, and stress. Based on these analysis results, the server generates self-care suggestions optimized for the user.

[0222] Self-care suggestions include support such as recommendations for relaxation music, breathing exercises, and, if needed, access to a chat function with a specialist. The generated suggestions are sent back from the server to the device and displayed on the device's user interface. Through this, users can take appropriate self-health measures.

[0223] Furthermore, the servers anonymize and statistically process the collected data to provide information that reveals emotional trends and tendencies by region and time of day. Since this data is aggregated in a way that does not identify users, privacy is strictly protected. The resulting statistical information is then used to improve mental health across society and to optimize services by companies.

[0224] The above describes the embodiments for carrying out the present invention. As a specific example, if a user expresses dissatisfaction such as "I'm under too much work stress," the system can generate self-care suggestions related to that stress and present the user with options for stress management.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] Users input their complaints in voice or text format using the input interface installed on the device. In the case of voice input, the device uses speech recognition technology to convert the voice data into text data.

[0228] Step 2:

[0229] The device performs a process to anonymize the complaint data. It removes personally identifiable information and prepares the data in a privacy-protected state.

[0230] Step 3:

[0231] The terminal sends the formatted data to the server. Since the communication is protected by security protocols, the data is transferred securely.

[0232] Step 4:

[0233] The server uses a natural language processing engine to perform real-time sentiment analysis on the received text data. This analysis examines the linguistic structure and keywords within the data to evaluate the type and intensity of emotions the user is experiencing.

[0234] Step 5:

[0235] Based on the analysis results, the server generates personalized self-care suggestions for the user. These suggestions may include relaxation music, breathing exercises, or, if needed, a chat function with a counselor.

[0236] Step 6:

[0237] The server sends the generated suggestions to the terminal. The terminal displays the received information on its user interface and presents the user with appropriate self-care actions.

[0238] Step 7:

[0239] Users can review the displayed suggestions, select self-care actions that suit their needs, and then take action.

[0240] Step 8:

[0241] The server also aggregates and analyzes the collected data, statistically processing dissatisfaction trends by region and time of day. The results of this analysis are used for further service optimization and social contribution.

[0242] (Example 1)

[0243] 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."

[0244] Stress and dissatisfaction in modern society are becoming increasingly diverse, creating a need to provide individualized self-care methods quickly and effectively. However, traditional methods lack the ability to accurately understand users' emotions and suggest appropriate coping strategies. Furthermore, protecting user data privacy while effectively utilizing that data presents a new challenge.

[0245] 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.

[0246] In this invention, the server includes means for collecting dissatisfaction data from users in voice or text format, and in the former case, converting it to text format using voice conversion technology; means for analyzing the emotional state contained in the text using a natural language processing mechanism and evaluating the type and intensity of the emotion; and means for creating individually optimized self-care suggestions based on the analysis. This enables the rapid provision of self-care suggestions tailored to the user's emotions, and allows for health management that meets individual needs.

[0247] "Dissatisfaction data" refers to information in audio or text format that expresses the dissatisfaction and stress that users experience in their daily lives.

[0248] "Speech conversion technology" is a technology for converting audio data into text format.

[0249] A "natural language processing mechanism" is a technology used to analyze information such as emotions and intentions from text data.

[0250] "Emotional state" refers to the type and intensity of emotions expressed in the user's text data.

[0251] "Self-care suggestions" refer to proposing self-care methods and activities that are optimized according to the user's emotional state.

[0252] "Anonymization" is a method of processing data in a way that prevents individuals from being identified.

[0253] "Statistical processing" is a data processing method aimed at aggregating and analyzing data to extract trends and patterns.

[0254] The system of this invention allows users to express frustrations they experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system is composed of a terminal, a server, and the user.

[0255] Users input their complaints via voice or text using a computer or smartphone. In the case of voice input, the device has the capability to convert voice data into text using speech-to-text conversion technology, often utilizing common speech recognition software. The resulting text data is then transmitted to a server using a secure communication method.

[0256] The server analyzes the received text data using advanced natural language processing mechanisms. This typically involves using a natural language processing engine on a cloud service. The server identifies the user's emotional state and evaluates its type and intensity. For example, if a user types "tired," the server can infer from the text that the user is feeling fatigued.

[0257] Based on the analysis results, the server generates self-care suggestions tailored to the user. These suggestions may include, for example, relaxation music, breathing exercises, or a consultation function with a professional. The generated suggestions are then sent back to the terminal and can be viewed on the user interface.

[0258] Furthermore, the server anonymizes the acquired data and processes it statistically, making it possible to understand emotional trends in each region. This can contribute to improving mental health and optimizing services across society.

[0259] For example, if a user expresses dissatisfaction such as "I'm stressed out at work," the system can suggest relaxation music aimed at stress reduction or guide them through simple meditation techniques. An example of a prompt to the generating AI model would be, "Please describe the biggest source of dissatisfaction the user is experiencing and provide simple self-care methods to alleviate that dissatisfaction."

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] Users input their complaints via voice or text through their device. The input is complaint data, such as "I've been under a lot of work stress lately." In the case of voice input, the device uses its built-in speech recognition software to convert the voice data into text data. This conversion results in output in the form of text data from voice data.

[0263] Step 2:

[0264] The terminal sends the converted text data to the server via a secure communication protocol. The input is text data, and the output is data transfer to the server. Data privacy protection is crucial here, and encryption technology is employed.

[0265] Step 3:

[0266] The server begins processing the received text data. It uses natural language processing capabilities to analyze the emotional state within the text. The input is text data, and the data analysis generates output regarding the type and intensity of emotions. A generative AI model is used in this process.

[0267] Step 4:

[0268] The server generates personalized self-care suggestions based on the results of the emotion analysis. The input is the results of the emotion analysis, and the output is a self-care plan optimized for the user. Specific suggestions may include listening to relaxation music, practicing breathing exercises, and chatting with a professional.

[0269] Step 5:

[0270] The server sends the generated self-care suggestions to the terminal. The input is the content of the self-care suggestions, and an output indicating that the data has been sent to the terminal is received. The suggestions are displayed on the terminal's user interface so that the user can easily review them.

[0271] Step 6:

[0272] The user reviews and acts upon the suggestions provided on the device. The input here is the self-care suggestion displayed on the device, and the output is the user's action. For example, the user can reduce stress by playing music and relaxing.

[0273] Step 7:

[0274] The server anonymizes the collected emotional data and performs statistical analysis. The input is user emotional data, and the output is statistical data that helps understand emotional trends by region and time of day. This statistical information can be used to improve mental health throughout society and to improve services provided by companies.

[0275] (Application Example 1)

[0276] 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."

[0277] Conventional self-care suggestion systems struggle to provide personalized decision-making support that takes into account the user's emotions, and they particularly lack mechanisms for smooth payment processes in daily life. Furthermore, there is a need for a system that proposes safe and efficient payment methods while considering the user's emotions.

[0278] 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.

[0279] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing engine, means for generating personalized decision support suggestions based on the analyzed emotional data, and means for presenting the generated suggestions to the user and optimizing the payment method. This enables rapid and secure decision support based on the user's individual emotional state.

[0280] A "natural language processing engine" is a technology that analyzes emotions and meanings from input text data, and is used to evaluate the user's emotional state.

[0281] "Emotional data" refers to information that includes text or audio data collected from users and reflects the user's emotional state.

[0282] The "Decision-making Support Proposal" refers to the optimized actions and options provided to the user based on the result of the user's sentiment analysis.

[0283] "Optimizing the payment method" is a process of proposing the most appropriate and secure payment means to the user considering the user's emotional state.

[0284] "Anonymization" is a method of processing data so that individuals cannot be identified in order to protect the privacy of user data.

[0285] "Statistically processing" is a data processing method used to aggregate and analyze data to understand the trends and characteristics of the whole society.

[0286] As a form for implementing this invention, a system in which three parties, namely the user, the terminal, and the server, cooperate and operate is assumed. The user inputs their emotions and dissatisfaction in voice or text form using a terminal such as a smartphone. To achieve this, the terminal has a voice recognition function and converts voice data into text. As a specific technology, Google Cloud Speech-to-Text API etc. are used.

[0287] The text data sent from the terminal is real-time sentiment analyzed by using a natural language processing engine in the server. For example, Google Cloud Natural Language API is used in this process. The server generates a decision-making support proposal optimized for the user based on the analyzed sentiment data. This proposal may include an electronic payment method suitable for the user's current situation.

[0288] The suggestions based on the analysis results are sent back to the terminal and presented to the user through the user interface. This allows users to adopt a safe and efficient payment method that suits their individual emotional state. The server also anonymizes and statistically processes the emotional data to protect personal information. The data obtained through this processing can be used to improve mental health throughout society and optimize commercial services.

[0289] For example, if a user inputs a common complaint such as "I forgot my wallet," the system can suggest quick payment methods using credit cards or electronic money that are appropriate for that situation. In this case, the generating AI model can be prompted with a phrase like "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" to create an appropriate suggestion.

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] Users input their frustrations and feelings in voice or text format using a device. The device receives this input and, if it's voice input, converts it into text data using speech recognition technology. The Google Cloud Speech-to-Text API is used for this text conversion. The input is voice or text data, and the output is text data.

[0293] Step 2:

[0294] The terminal sends the converted text data to the server. The server receives this text data and feeds it into a natural language processing engine. This process analyzes the user's emotional state from the text data. The input is the user's text data, and the output is the analysis result indicating the emotional state. Sentiment analysis is performed using the Google Cloud Natural Language API.

[0295] Step 3:

[0296] The server uses a generative AI model to generate decision support suggestions optimized for the user, based on the sentiment analysis results. Here, the AI ​​model is instructed using necessary prompts to create suggestions such as appropriate payment methods. The input is the sentiment analysis results, and the output is the optimized suggestions. For example, a prompt such as "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" might be used.

[0297] Step 4:

[0298] The server sends the generated decision support proposals to the terminal. The terminal receives this proposal data and presents it to the user through the user interface. The user reviews the proposals on the screen and makes a decision based on the presented options. The input is the proposal data, and the output is a visual representation for the user.

[0299] Step 5:

[0300] The server anonymizes and statistically processes emotional data. This allows for the understanding of overall societal data trends while protecting individual privacy. The input is emotional data, and the output is anonymized statistical information. The resulting information is then used to improve mental health and optimize services.

[0301] 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.

[0302] The system of the present invention collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. In particular, the configuration that combines an emotion engine makes it possible to understand the user's emotional state in more detail and with greater accuracy.

[0303] The user uses a terminal to input emotions such as dissatisfaction and stress in the form of voice or text. In the case of voice data, the terminal converts it into text format using speech recognition technology. Also, an emotion engine analyzes the input data and estimates the user's emotional state from the tone and speed of the voice and the context of the text.

[0304] The data sent from the terminal is received and processed by the server. The server evaluates the emotion data using a natural language processing engine and an emotion engine, and determines the user's emotional state in detail. From the data thus obtained, self-care proposals suitable for the user's state are generated. The proposals are composed of a variety of options such as selection of relaxation music, guidance on breathing methods, or use of a professional consultation function.

[0305] The server sends the generated proposals to the terminal, and the terminal presents the received proposals to the user interface. Through this interface, the user can select and execute self-care actions suitable for their emotional state.

[0306] For example, when dissatisfaction such as "Recently, the pressure at work has been extremely unstable" is input, the emotion engine analyzes the dissatisfaction and determines that it indicates a high level of anxiety. Then, the server proposes music for stress reduction suitable for the situation and a consultation function with experts.

[0307] Furthermore, the server anonymizes the collected data and performs statistical processing to analyze the dissatisfaction trends by region and time. This information can be utilized by companies and organizations for mental health improvement, and is operated while fully protecting the privacy of users. The above is a specific form for implementing the present invention.

[0308] The processing flow will be described below.

[0309] Step 1:

[0310] The user inputs their complaints into the device via voice or text. In the case of voice input, the device uses speech recognition software to convert the voice data into text data.

[0311] Step 2:

[0312] The device transmits the input data to the emotion engine in real time. The emotion engine analyzes the user's emotional state based on voice tone and text context.

[0313] Step 3:

[0314] The device anonymizes the emotional state data obtained from the emotion engine and sends it to the server while protecting the user's personal information.

[0315] Step 4:

[0316] The server further analyzes the received data using a natural language processing engine to determine the user's specific emotional state. This analysis includes evaluating linguistic nuances and the intensity of emotions.

[0317] Step 5:

[0318] The server generates personalized self-care suggestions based on the analysis results. These suggestions may include relaxation music, breathing exercises, or access to professional consultation features.

[0319] Step 6:

[0320] The server sends the generated self-care suggestions to the terminal.

[0321] Step 7:

[0322] The device presents suggestions to the user through its user interface. The user can then choose and perform the self-care action best suited to them from the presented options.

[0323] Step 8:

[0324] The server further aggregates user sentiment data and processes it as statistical data in an anonymized format. This data is used to analyze sentiment trends by region and time of day.

[0325] (Example 2)

[0326] 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".

[0327] In modern society, it is crucial to provide timely and appropriate self-care based on individual emotional states. However, conventional systems have difficulty accurately grasping emotional changes in real time, making it challenging to generate self-care suggestions optimized for each user. Furthermore, there is a need for methods to securely manage collected emotional data and utilize it while ensuring user privacy.

[0328] 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.

[0329] In this invention, the server includes means for analyzing input emotional data in real time in detail and with accuracy using a natural language processing engine and an emotional analysis engine; means for generating individually optimized self-care suggestions based on the analyzed emotional data and providing a variety of self-care options; and means for statistically processing emotional trends by region and time while anonymizing the input data and protecting privacy. This makes it possible to achieve highly accurate emotional analysis in real time and propose individually optimized self-care while protecting user privacy.

[0330] A "natural language processing engine" is a technological device that analyzes text data and understands human language.

[0331] An "emotion analysis engine" is an analytical tool used to estimate emotional states from input data.

[0332] "Self-care suggestions" refer to guidance aimed at mental care and health promotion, provided according to the emotional state of each individual user.

[0333] "Anonymization" is a technique for securely processing data by removing or transforming information that could identify an individual.

[0334] "Privacy protection" refers to measures taken to prevent personal information from being leaked to third parties.

[0335] "Statistical processing" refers to methods for aggregating data and analyzing trends and patterns.

[0336] "Real-time" is a time concept that indicates processing or reactions occur almost instantly.

[0337] "Self-care options" refer to the types and methods of self-care activities that users can choose from.

[0338] This invention is a system that collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. Users can input emotional data in voice or text format using a terminal. If voice data is input, the terminal converts it to text using speech recognition technology. Commonly used APIs can be used as the speech recognition technology here.

[0339] The terminal sends the converted text data to the server. This server, equipped with a natural language processing engine and an emotion analysis engine, analyzes the input data. Specifically, the server utilizes language processing technology to evaluate the user's emotional state from the context of the text and the characteristics of the voice. It uses a generative AI model as an appropriate model to estimate emotions.

[0340] A key feature of this system is that it generates individually optimized self-care suggestions based on analyzed emotional data, including a variety of self-care options. For example, if a user enters "I've been feeling very unstable lately due to pressure at work," the server will determine this emotional state to be a high level of anxiety and suggest stress-reducing music or professional counseling services to the user's device.

[0341] Furthermore, by statistically processing data while protecting user privacy, it is possible to analyze emotional trends by region and time. This allows companies and organizations to utilize this information as a means of improving mental health. An example of a prompt would be, "What self-care would you suggest when you are feeling stressed due to increased workload at work?"

[0342] In this way, the system implements technologies that provide flexible and detailed support tailored to user needs.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] Users input emotional data using a device. This input data is in voice or text format and provides details about the user's experience and emotions. If voice data is input, it is converted to text in the next processing step.

[0346] Step 2:

[0347] The terminal converts the input voice data into text format using speech recognition technology. This conversion transforms the speech into text information, enabling natural language processing. Specifically, the terminal launches speech recognition software, analyzes the voice signal, and generates the corresponding text. The text data is then output.

[0348] Step 3:

[0349] The device sends text data to the server. Here, the text data is treated as important data, including the user's sentiment information. The device sends the data to the server using a secure communication protocol and confirms receipt of the data.

[0350] Step 4:

[0351] The server analyzes the received text data using a natural language processing engine and an emotion analysis engine. The input data is processed to analyze the context and nuances of the text and estimate the user's emotional state. Specifically, the analysis model runs and outputs negative, positive, or neutral emotional states.

[0352] Step 5:

[0353] The server generates self-care suggestions based on the analysis results. This generation process includes selecting the most suitable self-care options corresponding to the user's emotional state. For example, if high stress levels are detected, suggestions such as relaxation music or breathing exercises will be output.

[0354] Step 6:

[0355] The server sends the generated self-care suggestions to the device. Here, measures are taken to ensure privacy and user confidence. The server appropriately formats the suggestions and sends them to the device via a secure communication channel.

[0356] Step 7:

[0357] The device displays received self-care suggestions in the user interface. The user reviews the suggestions through the interface, selects the self-care appropriate for their condition, and performs it. Specifically, the device displays suggestions in a pop-up window, guiding the user to make selections easier. Based on the user's selection, actions to proceed to the next step are displayed.

[0358] (Application Example 2)

[0359] 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."

[0360] In retail stores, there is a challenge in understanding customers' anxieties and dissatisfactions in real time and providing appropriate customer service. Furthermore, traditional methods do not allow for a comprehensive analysis of customers' facial expressions and tone of voice, making it difficult to efficiently provide personalized customer service.

[0361] 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.

[0362] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing mechanism, means for analyzing facial expressions and estimating emotional states using a video acquisition function, and means for converting audio data into text format and analyzing voice tone using speech recognition technology. This makes it possible to analyze customer emotions in detail and recommend personalized customer service to store staff.

[0363] "Natural language processing" refers to technology that analyzes speech and text data to understand its meaning and emotions.

[0364] "Emotional data" refers to information that indicates a user's emotional state, and can be expressed in various forms such as voice, text, and facial expressions.

[0365] "Self-management suggestions" refer to specific advice provided to users based on their emotional state, for self-care and improvement activities.

[0366] "Video acquisition functionality" refers to a technology that acquires video data using devices such as cameras, and is particularly used to capture customers' facial expressions.

[0367] "Speech recognition technology" is a technology that converts speech into text, and it also analyzes features such as tone and speed of voice.

[0368] "Anonymization" is a method of protecting privacy by removing personally identifiable information from acquired data.

[0369] "Statistical processing" is a method of aggregating and analyzing data to extract meaningful information and reveal overall trends.

[0370] The system for implementing this invention mainly consists of a server, a smart device (such as smart glasses), and real-time processing software. The server uses natural language processing mechanisms and emotion recognition technology to analyze emotional data acquired from the user in detail.

[0371] The smart glasses have a built-in camera and microphone, and use video acquisition and voice recognition technology to capture customers' facial expressions and voices. These smart devices are worn by users during customer service and transmit customer emotional information to a server in real time. The server uses natural language processing based on the received data to estimate the customer's emotional state.

[0372] Specifically, the server preprocesses image data using machine learning frameworks such as TensorFlow to estimate emotional states in real time. Audio data is converted to text using the Google Cloud Speech-to-Text API, and sentiment analysis is performed using the Google Natural Language API. Based on this information, the server recommends personalized customer service actions to staff.

[0373] For example, if a customer displays a confused expression in the store, the server analyzes the facial data and sends a notification to the smart glasses stating, "This customer may need assistance." Based on this notification, staff can then provide more appropriate service.

[0374] The following is an example of a prompt message for a generative AI model.

[0375] "How can we analyze customer emotions within a store and provide that information to staff in real time to provide the best possible customer service?"

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The device (smart glasses) captures customers' facial expressions in the store using a camera and simultaneously collects audio data using a microphone. This input data includes both image and audio data.

[0379] Step 2:

[0380] The device converts the collected audio data into text data using speech recognition technology. Specifically, it analyzes the audio waveform and converts its content into sentences. The output of this process is sent to the server as text data.

[0381] Step 3:

[0382] The server receives image data sent from the terminal and performs preprocessing using the machine learning framework TensorFlow. This process detects customer facial features from the image and estimates emotions in real time using an emotion recognition model. The output is the estimated emotion information.

[0383] Step 4:

[0384] The server analyzes the speech-to-text data using a natural language processing engine and evaluates the emotion based on the text's context and tone. The input is text data, and the output is additional information about the user's emotional state.

[0385] Step 5:

[0386] The server integrates emotional information obtained from image and audio data to comprehensively determine the customer's current emotional state. This allows for a detailed understanding of the customer's anxiety, confusion, and other emotional states. The output includes the concluded emotional state.

[0387] Step 6:

[0388] Based on the analysis results, the server generates prompts on the terminal recommending specific customer service actions. For example, if a specific emotional state is detected, a specific action such as "provide additional support to the customer" will be recommended. The generated recommended actions are displayed on the terminal in real time.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Third Embodiment]

[0393] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] The system of this invention appropriately expresses the frustrations that users experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system functions primarily around a terminal, a server, and the user.

[0406] Users express their dissatisfaction using voice or text input via a device. The device is equipped with speech recognition technology to convert the input voice data into text data. The collected data is then transmitted to a server via a communication protocol.

[0407] The server performs real-time sentiment analysis on the received data using a natural language processing engine. Sentiment analysis involves reading the user's emotional state from their text data and evaluating the type and intensity of emotions such as anger, depression, and stress. Based on these analysis results, the server generates self-care suggestions optimized for the user.

[0408] Self-care suggestions include support such as recommendations for relaxation music, breathing exercises, and, if needed, access to a chat function with a specialist. The generated suggestions are sent back from the server to the device and displayed on the device's user interface. Through this, users can take appropriate self-health measures.

[0409] Furthermore, the servers anonymize and statistically process the collected data to provide information that reveals emotional trends and tendencies by region and time of day. Since this data is aggregated in a way that does not identify users, privacy is strictly protected. The resulting statistical information is then used to improve mental health across society and to optimize services by companies.

[0410] The above describes the embodiments for carrying out the present invention. As a specific example, if a user expresses dissatisfaction such as "I'm under too much work stress," the system can generate self-care suggestions related to that stress and present the user with options for stress management.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] Users input their complaints in voice or text format using the input interface installed on the device. In the case of voice input, the device uses speech recognition technology to convert the voice data into text data.

[0414] Step 2:

[0415] The device performs a process to anonymize the complaint data. It removes personally identifiable information and prepares the data in a privacy-protected state.

[0416] Step 3:

[0417] The terminal sends the formatted data to the server. Since the communication is protected by security protocols, the data is transferred securely.

[0418] Step 4:

[0419] The server uses a natural language processing engine to perform real-time sentiment analysis on the received text data. This analysis examines the linguistic structure and keywords within the data to evaluate the type and intensity of emotions the user is experiencing.

[0420] Step 5:

[0421] Based on the analysis results, the server generates personalized self-care suggestions for the user. These suggestions may include relaxation music, breathing exercises, or, if needed, a chat function with a counselor.

[0422] Step 6:

[0423] The server sends the generated suggestions to the terminal. The terminal displays the received information on its user interface and presents the user with appropriate self-care actions.

[0424] Step 7:

[0425] Users can review the displayed suggestions, select self-care actions that suit their needs, and then take action.

[0426] Step 8:

[0427] The server also aggregates and analyzes the collected data, statistically processing dissatisfaction trends by region and time of day. The results of this analysis are used for further service optimization and social contribution.

[0428] (Example 1)

[0429] 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."

[0430] Stress and dissatisfaction in modern society are becoming increasingly diverse, creating a need to provide individualized self-care methods quickly and effectively. However, traditional methods lack the ability to accurately understand users' emotions and suggest appropriate coping strategies. Furthermore, protecting user data privacy while effectively utilizing that data presents a new challenge.

[0431] 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.

[0432] In this invention, the server includes means for collecting dissatisfaction data from users in voice or text format, and in the former case, converting it to text format using voice conversion technology; means for analyzing the emotional state contained in the text using a natural language processing mechanism and evaluating the type and intensity of the emotion; and means for creating individually optimized self-care suggestions based on the analysis. This enables the rapid provision of self-care suggestions tailored to the user's emotions, and allows for health management that meets individual needs.

[0433] "Dissatisfaction data" refers to information in audio or text format that expresses the dissatisfaction and stress that users experience in their daily lives.

[0434] "Speech conversion technology" is a technology for converting audio data into text format.

[0435] A "natural language processing mechanism" is a technology used to analyze information such as emotions and intentions from text data.

[0436] "Emotional state" refers to the type and intensity of emotions expressed in the user's text data.

[0437] "Self-care suggestions" refer to proposing self-care methods and activities that are optimized according to the user's emotional state.

[0438] "Anonymization" is a method of processing data in a way that prevents individuals from being identified.

[0439] "Statistical processing" is a data processing method aimed at aggregating and analyzing data to extract trends and patterns.

[0440] The system of this invention allows users to express frustrations they experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system is composed of a terminal, a server, and the user.

[0441] Users input their complaints via voice or text using a computer or smartphone. In the case of voice input, the device has the capability to convert voice data into text using speech-to-text conversion technology, often utilizing common speech recognition software. The resulting text data is then transmitted to a server using a secure communication method.

[0442] The server analyzes the received text data using advanced natural language processing mechanisms. This typically involves using a natural language processing engine on a cloud service. The server identifies the user's emotional state and evaluates its type and intensity. For example, if a user types "tired," the server can infer from the text that the user is feeling fatigued.

[0443] Based on the analysis results, the server generates self-care suggestions tailored to the user. These suggestions may include, for example, relaxation music, breathing exercises, or a consultation function with a professional. The generated suggestions are then sent back to the terminal and can be viewed on the user interface.

[0444] Furthermore, the server anonymizes the acquired data and processes it statistically, making it possible to understand emotional trends in each region. This can contribute to improving mental health and optimizing services across society.

[0445] For example, if a user expresses dissatisfaction such as "I'm stressed out at work," the system can suggest relaxation music aimed at stress reduction or guide them through simple meditation techniques. An example of a prompt to the generating AI model would be, "Please describe the biggest source of dissatisfaction the user is experiencing and provide simple self-care methods to alleviate that dissatisfaction."

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] Users input their complaints via voice or text through their device. The input is complaint data, such as "I've been under a lot of work stress lately." In the case of voice input, the device uses its built-in speech recognition software to convert the voice data into text data. This conversion results in output in the form of text data from voice data.

[0449] Step 2:

[0450] The terminal sends the converted text data to the server via a secure communication protocol. The input is text data, and the output is data transfer to the server. Data privacy protection is crucial here, and encryption technology is employed.

[0451] Step 3:

[0452] The server begins processing the received text data. It uses natural language processing capabilities to analyze the emotional state within the text. The input is text data, and the data analysis generates output regarding the type and intensity of emotions. A generative AI model is used in this process.

[0453] Step 4:

[0454] The server generates personalized self-care suggestions based on the results of the emotion analysis. The input is the results of the emotion analysis, and the output is a self-care plan optimized for the user. Specific suggestions may include listening to relaxation music, practicing breathing exercises, and chatting with a professional.

[0455] Step 5:

[0456] The server sends the generated self-care suggestions to the terminal. The input is the content of the self-care suggestions, and an output indicating that the data has been sent to the terminal is received. The suggestions are displayed on the terminal's user interface so that the user can easily review them.

[0457] Step 6:

[0458] The user reviews and acts upon the suggestions provided on the device. The input here is the self-care suggestion displayed on the device, and the output is the user's action. For example, the user can reduce stress by playing music and relaxing.

[0459] Step 7:

[0460] The server anonymizes the collected emotional data and performs statistical analysis. The input is user emotional data, and the output is statistical data that helps understand emotional trends by region and time of day. This statistical information can be used to improve mental health throughout society and to improve services provided by companies.

[0461] (Application Example 1)

[0462] 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."

[0463] Conventional self-care suggestion systems struggle to provide personalized decision-making support that takes into account the user's emotions, and they particularly lack mechanisms for smooth payment processes in daily life. Furthermore, there is a need for a system that proposes safe and efficient payment methods while considering the user's emotions.

[0464] 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.

[0465] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing engine, means for generating personalized decision support suggestions based on the analyzed emotional data, and means for presenting the generated suggestions to the user and optimizing the payment method. This enables rapid and secure decision support based on the user's individual emotional state.

[0466] A "natural language processing engine" is a technology that analyzes emotions and meanings from input text data, and is used to evaluate the user's emotional state.

[0467] "Emotional data" refers to information that includes text or audio data collected from users and reflects the user's emotional state.

[0468] "Decision support suggestions" refer to optimized actions and options provided to the user based on the results of a user sentiment analysis.

[0469] "Optimizing payment methods" is the process of suggesting the most appropriate and secure payment method to the user, taking into account their emotional state.

[0470] "Anonymization" is a technique used to process user data in a way that prevents individuals from being identified, in order to protect the privacy of user data.

[0471] "Statistical processing" refers to a data processing method used to aggregate and analyze data in order to understand the overall trends and characteristics of society.

[0472] One possible embodiment of this invention is a system in which a user, a terminal, and a server work together. The user inputs their emotions and frustrations in voice or text format using a terminal such as a smartphone. To achieve this, the terminal is equipped with a speech recognition function to convert the voice data into text. Specific technologies such as the Google Cloud Speech-to-Text API are used.

[0473] Text data sent from the device is analyzed for sentiment in real time on the server using a natural language processing engine. For example, the Google Cloud Natural Language API is used for this process. Based on the analyzed sentiment data, the server generates decision support suggestions optimized for the user. These suggestions may include electronic payment methods that are appropriate for the user's current situation.

[0474] The suggestions based on the analysis results are sent back to the terminal and presented to the user through the user interface. This allows users to adopt a safe and efficient payment method that suits their individual emotional state. The server also anonymizes and statistically processes the emotional data to protect personal information. The data obtained through this processing can be used to improve mental health throughout society and optimize commercial services.

[0475] For example, if a user inputs a common complaint such as "I forgot my wallet," the system can suggest quick payment methods using credit cards or electronic money that are appropriate for that situation. In this case, the generating AI model can be prompted with a phrase like "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" to create an appropriate suggestion.

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

[0477] Step 1:

[0478] Users input their frustrations and feelings in voice or text format using a device. The device receives this input and, if it's voice input, converts it into text data using speech recognition technology. The Google Cloud Speech-to-Text API is used for this text conversion. The input is voice or text data, and the output is text data.

[0479] Step 2:

[0480] The terminal sends the converted text data to the server. The server receives this text data and feeds it into a natural language processing engine. This process analyzes the user's emotional state from the text data. The input is the user's text data, and the output is the analysis result indicating the emotional state. Sentiment analysis is performed using the Google Cloud Natural Language API.

[0481] Step 3:

[0482] The server uses a generative AI model to generate decision support suggestions optimized for the user, based on the sentiment analysis results. Here, the AI ​​model is instructed using necessary prompts to create suggestions such as appropriate payment methods. The input is the sentiment analysis results, and the output is the optimized suggestions. For example, a prompt such as "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" might be used.

[0483] Step 4:

[0484] The server sends the generated decision support proposals to the terminal. The terminal receives this proposal data and presents it to the user through the user interface. The user reviews the proposals on the screen and makes a decision based on the presented options. The input is the proposal data, and the output is a visual representation for the user.

[0485] Step 5:

[0486] The server anonymizes and statistically processes emotional data. This allows for the understanding of overall societal data trends while protecting individual privacy. The input is emotional data, and the output is anonymized statistical information. The resulting information is then used to improve mental health and optimize services.

[0487] 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.

[0488] The system of the present invention collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. In particular, the configuration that combines an emotion engine makes it possible to understand the user's emotional state in more detail and with greater accuracy.

[0489] Users use the device to input emotions such as dissatisfaction and stress in either voice or text format. The device converts voice data into text using speech recognition technology. In addition, an emotion engine analyzes the input data and estimates the user's emotional state from the tone and speed of the voice and the context of the text.

[0490] Data transmitted from the device is received and processed by the server. The server uses a natural language processing engine and an emotion engine to evaluate the emotional data and determine the user's emotional state in detail. From this data, it generates self-care suggestions tailored to the user's state. These suggestions consist of a wide range of options, such as selecting relaxation music, receiving breathing exercises, or utilizing a professional consultation function.

[0491] The server sends the generated suggestions to the terminal, which then displays the received suggestions in a user interface. Through this interface, the user can select and perform self-care actions appropriate to their emotional state.

[0492] For example, if a user submits a complaint such as, "Recently, the pressure at work has been terrible and I feel very unstable," the emotion engine analyzes the complaint and determines that it indicates a high level of anxiety. The server then suggests music to reduce stress or a consultation function with a specialist that is appropriate for the situation.

[0493] Furthermore, the server anonymizes the collected data and applies statistical processing to analyze trends in dissatisfaction by region and time. This information can be used by companies and organizations to improve mental health, and is operated while fully protecting user privacy. The above is a specific embodiment for carrying out the present invention.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The user inputs their complaints into the device via voice or text. In the case of voice input, the device uses speech recognition software to convert the voice data into text data.

[0497] Step 2:

[0498] The device transmits the input data to the emotion engine in real time. The emotion engine analyzes the user's emotional state based on voice tone and text context.

[0499] Step 3:

[0500] The device anonymizes the emotional state data obtained from the emotion engine and sends it to the server while protecting the user's personal information.

[0501] Step 4:

[0502] The server further analyzes the received data using a natural language processing engine to determine the user's specific emotional state. This analysis includes evaluating linguistic nuances and the intensity of emotions.

[0503] Step 5:

[0504] The server generates personalized self-care suggestions based on the analysis results. These suggestions may include relaxation music, breathing exercises, or access to professional consultation features.

[0505] Step 6:

[0506] The server sends the generated self-care suggestions to the terminal.

[0507] Step 7:

[0508] The device presents suggestions to the user through its user interface. The user can then choose and perform the self-care action best suited to them from the presented options.

[0509] Step 8:

[0510] The server further aggregates user sentiment data and processes it as statistical data in an anonymized format. This data is used to analyze sentiment trends by region and time of day.

[0511] (Example 2)

[0512] 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."

[0513] In modern society, it is crucial to provide timely and appropriate self-care based on individual emotional states. However, conventional systems have difficulty accurately grasping emotional changes in real time, making it challenging to generate self-care suggestions optimized for each user. Furthermore, there is a need for methods to securely manage collected emotional data and utilize it while ensuring user privacy.

[0514] 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.

[0515] In this invention, the server includes means for analyzing input emotional data in real time in detail and with accuracy using a natural language processing engine and an emotional analysis engine; means for generating individually optimized self-care suggestions based on the analyzed emotional data and providing a variety of self-care options; and means for statistically processing emotional trends by region and time while anonymizing the input data and protecting privacy. This makes it possible to achieve highly accurate emotional analysis in real time and propose individually optimized self-care while protecting user privacy.

[0516] A "natural language processing engine" is a technological device that analyzes text data and understands human language.

[0517] An "emotion analysis engine" is an analytical tool used to estimate emotional states from input data.

[0518] "Self-care suggestions" refer to guidance aimed at mental care and health promotion, provided according to the emotional state of each individual user.

[0519] "Anonymization" is a technique for securely processing data by removing or transforming information that could identify an individual.

[0520] "Privacy protection" refers to measures taken to prevent personal information from being leaked to third parties.

[0521] "Statistical processing" refers to methods for aggregating data and analyzing trends and patterns.

[0522] "Real-time" is a time concept that indicates processing or reactions occur almost instantly.

[0523] "Self-care options" refer to the types and methods of self-care activities that users can choose from.

[0524] This invention is a system that collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. Users can input emotional data in voice or text format using a terminal. If voice data is input, the terminal converts it to text using speech recognition technology. Commonly used APIs can be used as the speech recognition technology here.

[0525] The terminal sends the converted text data to the server. This server, equipped with a natural language processing engine and an emotion analysis engine, analyzes the input data. Specifically, the server utilizes language processing technology to evaluate the user's emotional state from the context of the text and the characteristics of the voice. It uses a generative AI model as an appropriate model to estimate emotions.

[0526] A key feature of this system is that it generates individually optimized self-care suggestions based on analyzed emotional data, including a variety of self-care options. For example, if a user enters "I've been feeling very unstable lately due to pressure at work," the server will determine this emotional state to be a high level of anxiety and suggest stress-reducing music or professional counseling services to the user's device.

[0527] Furthermore, by statistically processing data while protecting user privacy, it is possible to analyze emotional trends by region and time. This allows companies and organizations to utilize this information as a means of improving mental health. An example of a prompt would be, "What self-care would you suggest when you are feeling stressed due to increased workload at work?"

[0528] In this way, the system implements technologies that provide flexible and detailed support tailored to user needs.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] Users input emotional data using a device. This input data is in voice or text format and provides details about the user's experience and emotions. If voice data is input, it is converted to text in the next processing step.

[0532] Step 2:

[0533] The terminal converts the input voice data into text format using speech recognition technology. This conversion transforms the speech into text information, enabling natural language processing. Specifically, the terminal launches speech recognition software, analyzes the voice signal, and generates the corresponding text. The text data is then output.

[0534] Step 3:

[0535] The device sends text data to the server. Here, the text data is treated as important data, including the user's sentiment information. The device sends the data to the server using a secure communication protocol and confirms receipt of the data.

[0536] Step 4:

[0537] The server analyzes the received text data using a natural language processing engine and an emotion analysis engine. The input data is processed to analyze the context and nuances of the text and estimate the user's emotional state. Specifically, the analysis model runs and outputs negative, positive, or neutral emotional states.

[0538] Step 5:

[0539] The server generates self-care suggestions based on the analysis results. This generation process includes selecting the most suitable self-care options corresponding to the user's emotional state. For example, if high stress levels are detected, suggestions such as relaxation music or breathing exercises will be output.

[0540] Step 6:

[0541] The server sends the generated self-care suggestions to the device. Here, measures are taken to ensure privacy and user confidence. The server appropriately formats the suggestions and sends them to the device via a secure communication channel.

[0542] Step 7:

[0543] The device displays received self-care suggestions in the user interface. The user reviews the suggestions through the interface, selects the self-care appropriate for their condition, and performs it. Specifically, the device displays suggestions in a pop-up window, guiding the user to make selections easier. Based on the user's selection, actions to proceed to the next step are displayed.

[0544] (Application Example 2)

[0545] 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."

[0546] In retail stores, there is a challenge in understanding customers' anxieties and dissatisfactions in real time and providing appropriate customer service. Furthermore, traditional methods do not allow for a comprehensive analysis of customers' facial expressions and tone of voice, making it difficult to efficiently provide personalized customer service.

[0547] 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.

[0548] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing mechanism, means for analyzing facial expressions and estimating emotional states using a video acquisition function, and means for converting audio data into text format and analyzing voice tone using speech recognition technology. This makes it possible to analyze customer emotions in detail and recommend personalized customer service to store staff.

[0549] "Natural language processing" refers to technology that analyzes speech and text data to understand its meaning and emotions.

[0550] "Emotional data" refers to information that indicates a user's emotional state, and can be expressed in various forms such as voice, text, and facial expressions.

[0551] "Self-management suggestions" refer to specific advice provided to users based on their emotional state, for self-care and improvement activities.

[0552] "Video acquisition functionality" refers to a technology that acquires video data using devices such as cameras, and is particularly used to capture customers' facial expressions.

[0553] "Speech recognition technology" is a technology that converts speech into text, and it also analyzes features such as tone and speed of voice.

[0554] "Anonymization" is a method of protecting privacy by removing personally identifiable information from acquired data.

[0555] "Statistical processing" is a method of aggregating and analyzing data to extract meaningful information and reveal overall trends.

[0556] The system for implementing this invention mainly consists of a server, a smart device (such as smart glasses), and real-time processing software. The server uses natural language processing mechanisms and emotion recognition technology to analyze emotional data acquired from the user in detail.

[0557] The smart glasses have a built-in camera and microphone, and use video acquisition and voice recognition technology to capture customers' facial expressions and voices. These smart devices are worn by users during customer service and transmit customer emotional information to a server in real time. The server uses natural language processing based on the received data to estimate the customer's emotional state.

[0558] Specifically, the server preprocesses image data using machine learning frameworks such as TensorFlow to estimate emotional states in real time. Audio data is converted to text using the Google Cloud Speech-to-Text API, and sentiment analysis is performed using the Google Natural Language API. Based on this information, the server recommends personalized customer service actions to staff.

[0559] For example, if a customer displays a confused expression in the store, the server analyzes the facial data and sends a notification to the smart glasses stating, "This customer may need assistance." Based on this notification, staff can then provide more appropriate service.

[0560] The following is an example of a prompt message for a generative AI model.

[0561] "How can we analyze customer emotions within a store and provide that information to staff in real time to provide the best possible customer service?"

[0562] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0563] Step 1:

[0564] The device (smart glasses) captures customers' facial expressions in the store using a camera and simultaneously collects audio data using a microphone. This input data includes both image and audio data.

[0565] Step 2:

[0566] The device converts the collected audio data into text data using speech recognition technology. Specifically, it analyzes the audio waveform and converts its content into sentences. The output of this process is sent to the server as text data.

[0567] Step 3:

[0568] The server receives image data sent from the terminal and performs preprocessing using the machine learning framework TensorFlow. This process detects customer facial features from the image and estimates emotions in real time using an emotion recognition model. The output is the estimated emotion information.

[0569] Step 4:

[0570] The server analyzes the speech-to-text data using a natural language processing engine and evaluates the emotion based on the text's context and tone. The input is text data, and the output is additional information about the user's emotional state.

[0571] Step 5:

[0572] The server integrates emotional information obtained from image and audio data to comprehensively determine the customer's current emotional state. This allows for a detailed understanding of the customer's anxiety, confusion, and other emotional states. The output includes the concluded emotional state.

[0573] Step 6:

[0574] Based on the analysis results, the server generates prompts on the terminal recommending specific customer service actions. For example, if a specific emotional state is detected, a specific action such as "provide additional support to the customer" will be recommended. The generated recommended actions are displayed on the terminal in real time.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] [Fourth Embodiment]

[0579] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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".

[0592] The system of this invention appropriately expresses the frustrations that users experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system functions primarily around a terminal, a server, and the user.

[0593] Users express their dissatisfaction using voice or text input via a device. The device is equipped with speech recognition technology to convert the input voice data into text data. The collected data is then transmitted to a server via a communication protocol.

[0594] The server performs real-time sentiment analysis on the received data using a natural language processing engine. Sentiment analysis involves reading the user's emotional state from their text data and evaluating the type and intensity of emotions such as anger, depression, and stress. Based on these analysis results, the server generates self-care suggestions optimized for the user.

[0595] Self-care suggestions include support such as recommendations for relaxation music, breathing exercises, and, if needed, access to a chat function with a specialist. The generated suggestions are sent back from the server to the device and displayed on the device's user interface. Through this, users can take appropriate self-health measures.

[0596] Furthermore, the servers anonymize and statistically process the collected data to provide information that reveals emotional trends and tendencies by region and time of day. Since this data is aggregated in a way that does not identify users, privacy is strictly protected. The resulting statistical information is then used to improve mental health across society and to optimize services by companies.

[0597] The above describes the embodiments for carrying out the present invention. As a specific example, if a user expresses dissatisfaction such as "I'm under too much work stress," the system can generate self-care suggestions related to that stress and present the user with options for stress management.

[0598] The following describes the processing flow.

[0599] Step 1:

[0600] Users input their complaints in voice or text format using the input interface installed on the device. In the case of voice input, the device uses speech recognition technology to convert the voice data into text data.

[0601] Step 2:

[0602] The device performs a process to anonymize the complaint data. It removes personally identifiable information and prepares the data in a privacy-protected state.

[0603] Step 3:

[0604] The terminal sends the formatted data to the server. Since the communication is protected by security protocols, the data is transferred securely.

[0605] Step 4:

[0606] The server uses a natural language processing engine to perform real-time sentiment analysis on the received text data. This analysis examines the linguistic structure and keywords within the data to evaluate the type and intensity of emotions the user is experiencing.

[0607] Step 5:

[0608] Based on the analysis results, the server generates personalized self-care suggestions for the user. These suggestions may include relaxation music, breathing exercises, or, if needed, a chat function with a counselor.

[0609] Step 6:

[0610] The server sends the generated suggestions to the terminal. The terminal displays the received information on its user interface and presents the user with appropriate self-care actions.

[0611] Step 7:

[0612] Users can review the displayed suggestions, select self-care actions that suit their needs, and then take action.

[0613] Step 8:

[0614] The server also aggregates and analyzes the collected data, statistically processing dissatisfaction trends by region and time of day. The results of this analysis are used for further service optimization and social contribution.

[0615] (Example 1)

[0616] 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".

[0617] Stress and dissatisfaction in modern society are becoming increasingly diverse, creating a need to provide individualized self-care methods quickly and effectively. However, traditional methods lack the ability to accurately understand users' emotions and suggest appropriate coping strategies. Furthermore, protecting user data privacy while effectively utilizing that data presents a new challenge.

[0618] 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.

[0619] In this invention, the server includes means for collecting dissatisfaction data from users in voice or text format, and in the former case, converting it to text format using voice conversion technology; means for analyzing the emotional state contained in the text using a natural language processing mechanism and evaluating the type and intensity of the emotion; and means for creating individually optimized self-care suggestions based on the analysis. This enables the rapid provision of self-care suggestions tailored to the user's emotions, and allows for health management that meets individual needs.

[0620] "Dissatisfaction data" refers to information in audio or text format that expresses the dissatisfaction and stress that users experience in their daily lives.

[0621] "Speech conversion technology" is a technology for converting audio data into text format.

[0622] A "natural language processing mechanism" is a technology used to analyze information such as emotions and intentions from text data.

[0623] "Emotional state" refers to the type and intensity of emotions expressed in the user's text data.

[0624] "Self-care suggestions" refer to proposing self-care methods and activities that are optimized according to the user's emotional state.

[0625] "Anonymization" is a method of processing data in a way that prevents individuals from being identified.

[0626] "Statistical processing" is a data processing method aimed at aggregating and analyzing data to extract trends and patterns.

[0627] The system of this invention allows users to express frustrations they experience in their daily lives and provides personalized self-care suggestions based on those frustrations. This system is composed of a terminal, a server, and the user.

[0628] Users input their complaints via voice or text using a computer or smartphone. In the case of voice input, the device has the capability to convert voice data into text using speech-to-text conversion technology, often utilizing common speech recognition software. The resulting text data is then transmitted to a server using a secure communication method.

[0629] The server analyzes the received text data using advanced natural language processing mechanisms. This typically involves using a natural language processing engine on a cloud service. The server identifies the user's emotional state and evaluates its type and intensity. For example, if a user types "tired," the server can infer from the text that the user is feeling fatigued.

[0630] Based on the analysis results, the server generates self-care suggestions tailored to the user. These suggestions may include, for example, relaxation music, breathing exercises, or a consultation function with a professional. The generated suggestions are then sent back to the terminal and can be viewed on the user interface.

[0631] Furthermore, the server anonymizes the acquired data and processes it statistically, making it possible to understand emotional trends in each region. This can contribute to improving mental health and optimizing services across society.

[0632] For example, if a user expresses dissatisfaction such as "I'm stressed out at work," the system can suggest relaxation music aimed at stress reduction or guide them through simple meditation techniques. An example of a prompt to the generating AI model would be, "Please describe the biggest source of dissatisfaction the user is experiencing and provide simple self-care methods to alleviate that dissatisfaction."

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

[0634] Step 1:

[0635] Users input their complaints via voice or text through their device. The input is complaint data, such as "I've been under a lot of work stress lately." In the case of voice input, the device uses its built-in speech recognition software to convert the voice data into text data. This conversion results in output in the form of text data from voice data.

[0636] Step 2:

[0637] The terminal sends the converted text data to the server via a secure communication protocol. The input is text data, and the output is data transfer to the server. Data privacy protection is crucial here, and encryption technology is employed.

[0638] Step 3:

[0639] The server begins processing the received text data. It uses natural language processing capabilities to analyze the emotional state within the text. The input is text data, and the data analysis generates output regarding the type and intensity of emotions. A generative AI model is used in this process.

[0640] Step 4:

[0641] The server generates personalized self-care suggestions based on the results of the emotion analysis. The input is the results of the emotion analysis, and the output is a self-care plan optimized for the user. Specific suggestions may include listening to relaxation music, practicing breathing exercises, and chatting with a professional.

[0642] Step 5:

[0643] The server sends the generated self-care suggestions to the terminal. The input is the content of the self-care suggestions, and an output indicating that the data has been sent to the terminal is received. The suggestions are displayed on the terminal's user interface so that the user can easily review them.

[0644] Step 6:

[0645] The user reviews and acts upon the suggestions provided on the device. The input here is the self-care suggestion displayed on the device, and the output is the user's action. For example, the user can reduce stress by playing music and relaxing.

[0646] Step 7:

[0647] The server anonymizes the collected emotional data and performs statistical analysis. The input is user emotional data, and the output is statistical data that helps understand emotional trends by region and time of day. This statistical information can be used to improve mental health throughout society and to improve services provided by companies.

[0648] (Application Example 1)

[0649] 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".

[0650] Conventional self-care suggestion systems struggle to provide personalized decision-making support that takes into account the user's emotions, and they particularly lack mechanisms for smooth payment processes in daily life. Furthermore, there is a need for a system that proposes safe and efficient payment methods while considering the user's emotions.

[0651] 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.

[0652] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing engine, means for generating personalized decision support suggestions based on the analyzed emotional data, and means for presenting the generated suggestions to the user and optimizing the payment method. This enables rapid and secure decision support based on the user's individual emotional state.

[0653] A "natural language processing engine" is a technology that analyzes emotions and meanings from input text data, and is used to evaluate the user's emotional state.

[0654] "Emotional data" refers to information that includes text or audio data collected from users and reflects the user's emotional state.

[0655] "Decision support suggestions" refer to optimized actions and options provided to the user based on the results of a user sentiment analysis.

[0656] "Optimizing payment methods" is the process of suggesting the most appropriate and secure payment method to the user, taking into account their emotional state.

[0657] "Anonymization" is a technique used to process user data in a way that prevents individuals from being identified, in order to protect the privacy of user data.

[0658] "Statistical processing" refers to a data processing method used to aggregate and analyze data in order to understand the overall trends and characteristics of society.

[0659] One possible embodiment of this invention is a system in which a user, a terminal, and a server work together. The user inputs their emotions and frustrations in voice or text format using a terminal such as a smartphone. To achieve this, the terminal is equipped with a speech recognition function to convert the voice data into text. Specific technologies such as the Google Cloud Speech-to-Text API are used.

[0660] Text data sent from the device is analyzed for sentiment in real time on the server using a natural language processing engine. For example, the Google Cloud Natural Language API is used for this process. Based on the analyzed sentiment data, the server generates decision support suggestions optimized for the user. These suggestions may include electronic payment methods that are appropriate for the user's current situation.

[0661] The suggestions based on the analysis results are sent back to the terminal and presented to the user through the user interface. This allows users to adopt a safe and efficient payment method that suits their individual emotional state. The server also anonymizes and statistically processes the emotional data to protect personal information. The data obtained through this processing can be used to improve mental health throughout society and optimize commercial services.

[0662] For example, if a user inputs a common complaint such as "I forgot my wallet," the system can suggest quick payment methods using credit cards or electronic money that are appropriate for that situation. In this case, the generating AI model can be prompted with a phrase like "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" to create an appropriate suggestion.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] Users input their frustrations and feelings in voice or text format using a device. The device receives this input and, if it's voice input, converts it into text data using speech recognition technology. The Google Cloud Speech-to-Text API is used for this text conversion. The input is voice or text data, and the output is text data.

[0666] Step 2:

[0667] The terminal sends the converted text data to the server. The server receives this text data and feeds it into a natural language processing engine. This process analyzes the user's emotional state from the text data. The input is the user's text data, and the output is the analysis result indicating the emotional state. Sentiment analysis is performed using the Google Cloud Natural Language API.

[0668] Step 3:

[0669] The server uses a generative AI model to generate decision support suggestions optimized for the user, based on the sentiment analysis results. Here, the AI ​​model is instructed using necessary prompts to create suggestions such as appropriate payment methods. The input is the sentiment analysis results, and the output is the optimized suggestions. For example, a prompt such as "Suggest an electronic payment method that will reduce the stress a user experiences when they forget their wallet" might be used.

[0670] Step 4:

[0671] The server sends the generated decision support proposals to the terminal. The terminal receives this proposal data and presents it to the user through the user interface. The user reviews the proposals on the screen and makes a decision based on the presented options. The input is the proposal data, and the output is a visual representation for the user.

[0672] Step 5:

[0673] The server anonymizes and statistically processes emotional data. This allows for the understanding of overall societal data trends while protecting individual privacy. The input is emotional data, and the output is anonymized statistical information. The resulting information is then used to improve mental health and optimize services.

[0674] 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.

[0675] The system of the present invention collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. In particular, the configuration that combines an emotion engine makes it possible to understand the user's emotional state in more detail and with greater accuracy.

[0676] Users use the device to input emotions such as dissatisfaction and stress in either voice or text format. The device converts voice data into text using speech recognition technology. In addition, an emotion engine analyzes the input data and estimates the user's emotional state from the tone and speed of the voice and the context of the text.

[0677] Data transmitted from the device is received and processed by the server. The server uses a natural language processing engine and an emotion engine to evaluate the emotional data and determine the user's emotional state in detail. From this data, it generates self-care suggestions tailored to the user's state. These suggestions consist of a wide range of options, such as selecting relaxation music, receiving breathing exercises, or utilizing a professional consultation function.

[0678] The server sends the generated suggestions to the terminal, which then displays the received suggestions in a user interface. Through this interface, the user can select and perform self-care actions appropriate to their emotional state.

[0679] For example, if a user submits a complaint such as, "Recently, the pressure at work has been terrible and I feel very unstable," the emotion engine analyzes the complaint and determines that it indicates a high level of anxiety. The server then suggests music to reduce stress or a consultation function with a specialist that is appropriate for the situation.

[0680] Furthermore, the server anonymizes the collected data and applies statistical processing to analyze trends in dissatisfaction by region and time. This information can be used by companies and organizations to improve mental health, and is operated while fully protecting user privacy. The above is a specific embodiment for carrying out the present invention.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The user inputs their complaints into the device via voice or text. In the case of voice input, the device uses speech recognition software to convert the voice data into text data.

[0684] Step 2:

[0685] The device transmits the input data to the emotion engine in real time. The emotion engine analyzes the user's emotional state based on voice tone and text context.

[0686] Step 3:

[0687] The device anonymizes the emotional state data obtained from the emotion engine and sends it to the server while protecting the user's personal information.

[0688] Step 4:

[0689] The server further analyzes the received data using a natural language processing engine to determine the user's specific emotional state. This analysis includes evaluating linguistic nuances and the intensity of emotions.

[0690] Step 5:

[0691] The server generates personalized self-care suggestions based on the analysis results. These suggestions may include relaxation music, breathing exercises, or access to professional consultation features.

[0692] Step 6:

[0693] The server sends the generated self-care suggestions to the terminal.

[0694] Step 7:

[0695] The device presents suggestions to the user through its user interface. The user can then choose and perform the self-care action best suited to them from the presented options.

[0696] Step 8:

[0697] The server further aggregates user sentiment data and processes it as statistical data in an anonymized format. This data is used to analyze sentiment trends by region and time of day.

[0698] (Example 2)

[0699] 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".

[0700] In modern society, it is crucial to provide timely and appropriate self-care based on individual emotional states. However, conventional systems have difficulty accurately grasping emotional changes in real time, making it challenging to generate self-care suggestions optimized for each user. Furthermore, there is a need for methods to securely manage collected emotional data and utilize it while ensuring user privacy.

[0701] 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.

[0702] In this invention, the server includes means for analyzing input emotional data in real time in detail and with accuracy using a natural language processing engine and an emotional analysis engine; means for generating individually optimized self-care suggestions based on the analyzed emotional data and providing a variety of self-care options; and means for statistically processing emotional trends by region and time while anonymizing the input data and protecting privacy. This makes it possible to achieve highly accurate emotional analysis in real time and propose individually optimized self-care while protecting user privacy.

[0703] A "natural language processing engine" is a technological device that analyzes text data and understands human language.

[0704] An "emotion analysis engine" is an analytical tool used to estimate emotional states from input data.

[0705] "Self-care suggestions" refer to guidance aimed at mental care and health promotion, provided according to the emotional state of each individual user.

[0706] "Anonymization" is a technique for securely processing data by removing or transforming information that could identify an individual.

[0707] "Privacy protection" refers to measures taken to prevent personal information from being leaked to third parties.

[0708] "Statistical processing" refers to methods for aggregating data and analyzing trends and patterns.

[0709] "Real-time" is a time concept that indicates processing or reactions occur almost instantly.

[0710] "Self-care options" refer to the types and methods of self-care activities that users can choose from.

[0711] This invention is a system that collects and analyzes emotional data from users in real time and provides personalized self-care suggestions. Users can input emotional data in voice or text format using a terminal. If voice data is input, the terminal converts it to text using speech recognition technology. Commonly used APIs can be used as the speech recognition technology here.

[0712] The terminal sends the converted text data to the server. This server, equipped with a natural language processing engine and an emotion analysis engine, analyzes the input data. Specifically, the server utilizes language processing technology to evaluate the user's emotional state from the context of the text and the characteristics of the voice. It uses a generative AI model as an appropriate model to estimate emotions.

[0713] A key feature of this system is that it generates individually optimized self-care suggestions based on analyzed emotional data, including a variety of self-care options. For example, if a user enters "I've been feeling very unstable lately due to pressure at work," the server will determine this emotional state to be a high level of anxiety and suggest stress-reducing music or professional counseling services to the user's device.

[0714] Furthermore, by statistically processing data while protecting user privacy, it is possible to analyze emotional trends by region and time. This allows companies and organizations to utilize this information as a means of improving mental health. An example of a prompt would be, "What self-care would you suggest when you are feeling stressed due to increased workload at work?"

[0715] In this way, the system implements technologies that provide flexible and detailed support tailored to user needs.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] Users input emotional data using a device. This input data is in voice or text format and provides details about the user's experience and emotions. If voice data is input, it is converted to text in the next processing step.

[0719] Step 2:

[0720] The terminal converts the input voice data into text format using speech recognition technology. This conversion transforms the speech into text information, enabling natural language processing. Specifically, the terminal launches speech recognition software, analyzes the voice signal, and generates the corresponding text. The text data is then output.

[0721] Step 3:

[0722] The device sends text data to the server. Here, the text data is treated as important data, including the user's sentiment information. The device sends the data to the server using a secure communication protocol and confirms receipt of the data.

[0723] Step 4:

[0724] The server analyzes the received text data using a natural language processing engine and an emotion analysis engine. The input data is processed to analyze the context and nuances of the text and estimate the user's emotional state. Specifically, the analysis model runs and outputs negative, positive, or neutral emotional states.

[0725] Step 5:

[0726] The server generates self-care suggestions based on the analysis results. This generation process includes selecting the most suitable self-care options corresponding to the user's emotional state. For example, if high stress levels are detected, suggestions such as relaxation music or breathing exercises will be output.

[0727] Step 6:

[0728] The server sends the generated self-care suggestions to the device. Here, measures are taken to ensure privacy and user confidence. The server appropriately formats the suggestions and sends them to the device via a secure communication channel.

[0729] Step 7:

[0730] The device displays received self-care suggestions in the user interface. The user reviews the suggestions through the interface, selects the self-care appropriate for their condition, and performs it. Specifically, the device displays suggestions in a pop-up window, guiding the user to make selections easier. Based on the user's selection, actions to proceed to the next step are displayed.

[0731] (Application Example 2)

[0732] 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".

[0733] In retail stores, there is a challenge in understanding customers' anxieties and dissatisfactions in real time and providing appropriate customer service. Furthermore, traditional methods do not allow for a comprehensive analysis of customers' facial expressions and tone of voice, making it difficult to efficiently provide personalized customer service.

[0734] 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.

[0735] In this invention, the server includes means for analyzing input emotional data in real time using a natural language processing mechanism, means for analyzing facial expressions and estimating emotional states using a video acquisition function, and means for converting audio data into text format and analyzing voice tone using speech recognition technology. This makes it possible to analyze customer emotions in detail and recommend personalized customer service to store staff.

[0736] "Natural language processing" refers to technology that analyzes speech and text data to understand its meaning and emotions.

[0737] "Emotional data" refers to information that indicates a user's emotional state, and can be expressed in various forms such as voice, text, and facial expressions.

[0738] "Self-management suggestions" refer to specific advice provided to users based on their emotional state, for self-care and improvement activities.

[0739] "Video acquisition functionality" refers to a technology that acquires video data using devices such as cameras, and is particularly used to capture customers' facial expressions.

[0740] "Speech recognition technology" is a technology that converts speech into text, and it also analyzes features such as tone and speed of voice.

[0741] "Anonymization" is a method of protecting privacy by removing personally identifiable information from acquired data.

[0742] "Statistical processing" is a method of aggregating and analyzing data to extract meaningful information and reveal overall trends.

[0743] The system for implementing this invention mainly consists of a server, a smart device (such as smart glasses), and real-time processing software. The server uses natural language processing mechanisms and emotion recognition technology to analyze emotional data acquired from the user in detail.

[0744] The smart glasses have a built-in camera and microphone, and use video acquisition and voice recognition technology to capture customers' facial expressions and voices. These smart devices are worn by users during customer service and transmit customer emotional information to a server in real time. The server uses natural language processing based on the received data to estimate the customer's emotional state.

[0745] Specifically, the server preprocesses image data using machine learning frameworks such as TensorFlow to estimate emotional states in real time. Audio data is converted to text using the Google Cloud Speech-to-Text API, and sentiment analysis is performed using the Google Natural Language API. Based on this information, the server recommends personalized customer service actions to staff.

[0746] For example, if a customer displays a confused expression in the store, the server analyzes the facial data and sends a notification to the smart glasses stating, "This customer may need assistance." Based on this notification, staff can then provide more appropriate service.

[0747] The following is an example of a prompt message for a generative AI model.

[0748] "How can we analyze customer emotions within a store and provide that information to staff in real time to provide the best possible customer service?"

[0749] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0750] Step 1:

[0751] The device (smart glasses) captures customers' facial expressions in the store using a camera and simultaneously collects audio data using a microphone. This input data includes both image and audio data.

[0752] Step 2:

[0753] The device converts the collected audio data into text data using speech recognition technology. Specifically, it analyzes the audio waveform and converts its content into sentences. The output of this process is sent to the server as text data.

[0754] Step 3:

[0755] The server receives image data sent from the terminal and performs preprocessing using the machine learning framework TensorFlow. This process detects customer facial features from the image and estimates emotions in real time using an emotion recognition model. The output is the estimated emotion information.

[0756] Step 4:

[0757] The server analyzes the speech-to-text data using a natural language processing engine and evaluates the emotion based on the text's context and tone. The input is text data, and the output is additional information about the user's emotional state.

[0758] Step 5:

[0759] The server integrates emotional information obtained from image and audio data to comprehensively determine the customer's current emotional state. This allows for a detailed understanding of the customer's anxiety, confusion, and other emotional states. The output includes the concluded emotional state.

[0760] Step 6:

[0761] Based on the analysis results, the server generates prompts on the terminal recommending specific customer service actions. For example, if a specific emotional state is detected, a specific action such as "provide additional support to the customer" will be recommended. The generated recommended actions are displayed on the terminal in real time.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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."

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means of analyzing input sentiment data in real time using a natural language processing engine,

[0786] A means of generating personalized self-care suggestions based on analyzed emotional data,

[0787] A means of presenting the generated suggestions to the user,

[0788] A means of anonymizing the input data and processing it statistically while protecting the user's privacy,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, which collects and analyzes voice data and text data as emotional data.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the generated self-care suggestions include a professional consultation function.

[0794] "Example 1"

[0795] (Claim 1)

[0796] A means of collecting complaint data from users in audio or text format, and in the former case, converting it to text format using speech conversion technology,

[0797] A means for analyzing the emotional state embedded in text using a natural language processing mechanism and evaluating the type and intensity of emotion,

[0798] A means of creating individually optimized self-care suggestions based on analysis,

[0799] A means of displaying the proposal to the user again,

[0800] A means of anonymizing acquired data and performing statistical processing while maintaining the privacy of the group,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, which collects and analyzes emotional data as audio data and text data.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the generated self-care suggestions include a function for consultation with a professional.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] A means of analyzing input sentiment data in real time using a natural language processing engine,

[0809] A means for generating personalized decision support suggestions based on analyzed emotional data,

[0810] A means of presenting generated proposals to users and optimizing payment methods,

[0811] A means of anonymizing the input data and processing it statistically while protecting the user's privacy,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, which collects and analyzes voice data and text data as emotional data.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the generated decision support proposals include a professional consultation function.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] A means for analyzing input emotional data in real time, in detail and accurately, using a natural language processing engine and an emotion analysis engine.

[0820] A means of generating individually optimized self-care suggestions based on analyzed emotional data and providing diverse self-care options,

[0821] Means for presenting the generated proposals to the user visually or audibly,

[0822] A means of anonymizing the input data and statistically processing emotional trends by region and time period while protecting privacy,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which simultaneously collects voice data and text data as emotion data and converts them into text format using speech recognition technology.

[0826] (Claim 3)

[0827] The system according to claim 1, which includes, in addition to the generated self-care suggestions, instruction on music and breathing techniques for stress reduction, and a professional consultation function.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] A means of analyzing input emotion data in real time using a natural language processing mechanism,

[0831] A means of generating personalized self-management suggestions based on analyzed emotional data,

[0832] A means of presenting the generated proposal to the user,

[0833] A means of analyzing facial expressions and estimating emotional states using video acquisition capabilities,

[0834] A means of using speech recognition technology to convert speech data into text format and analyze the tone of voice,

[0835] A means of anonymizing the input data and processing it statistically while protecting user privacy,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which collects voice and text information as emotion data and analyzes it in combination with video data.

[0839] (Claim 3)

[0840] The system according to claim 1, wherein the generated self-management suggestions include a function for consulting with experts. [Explanation of Symbols]

[0841] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing input sentiment data in real time using a natural language processing engine, A means of generating personalized self-care suggestions based on analyzed emotional data, A means of presenting the generated suggestions to the user, A means of anonymizing the input data and processing it statistically while protecting the user's privacy, A system that includes this.

2. The system according to claim 1, which collects and analyzes voice data and text data as emotional data.

3. The system according to claim 1, wherein the generated self-care suggestions include a professional consultation function.

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