system

Abstract

We provide the system. [Solution] Means for acquiring audio information, A means for analyzing emotional states from the aforementioned audio information, A means for generating a report based on the aforementioned emotional state, Means of proposing support to users based on the aforementioned report, A means of exchanging information with users within their homes and providing guidance, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the increase in stress in the workplace environment, the mental health of employees has deteriorated, and problems such as a decrease in productivity and an increase in the turnover rate have been regarded as issues. In response to this problem, an effective system for employees to spontaneously grasp and improve their own mental state is required, but the conventional mental health support systems have not always been accurate and easy to implement. The present invention solves such problems, and aims to accurately grasp the emotional state of employees and quickly provide appropriate counseling by combining high-precision voice analysis technology and AI.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring voice data, means for analyzing emotional states from the voice data, means for generating a report based on the emotional state, and means for proposing counseling to the user based on the report. This allows employees to objectively understand their own emotional state based on voice data obtained from everyday conversations and to receive counseling quickly as needed. This system utilizes high-precision voice analysis and AI to effectively provide continuous monitoring of mental health and appropriate care.

[0006] "Audio data" refers to the signal data used to record and analyze the user's voice.

[0007] "Means of acquisition" refers to the device or process for collecting and recording audio data.

[0008] "Emotional state" refers to the user's psychological or emotional state estimated based on voice data.

[0009] "Means of analysis" refers to an algorithm or device for processing audio data and estimating emotional states.

[0010] "Means of generating reports" refers to the process of creating reports based on analyzed emotional states and summarizing the results.

[0011] "Means of suggesting counseling" refers to a process or system for recommending appropriate counseling sessions to users based on the generated reports.

[0012] A "system" is a series of configurations that combine multiple means to comprehensively carry out everything from acquiring voice data to proposing counseling services. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

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

[0021] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0034] The system according to the present invention comprehensively supports the user's mental health by acquiring and analyzing voice data and proposing counseling. This system consists of terminal, server, and user elements and operates as follows.

[0035] While the user is performing daily tasks or engaging in conversations, the device acquires audio data in the background. This audio data is recorded with ambient noise removed, including voices the user makes unconsciously. The device uses this audio data to analyze the user's emotional state.

[0036] The raw audio data is first pre-processed to remove noise, and then an analysis algorithm is used to identify the user's emotional state. This analysis takes into account factors such as tone, speed, volume, and intonation. The analysis results indicate the user's current emotional state, making it possible to objectively understand their mental state. These analysis results are then transmitted from the terminal to the server.

[0037] Based on the received analysis data, the server analyzes the user's emotional trends by comparing them with historical data. For example, if stress levels are increasing compared to past data, the server will use that information to determine that the user requires attention.

[0038] Next, the server generates a report based on the analyzed data. The report includes insights into the employee's current emotional state, comparisons to the past, and potential mental health issues they may be facing. Based on this report, the server suggests appropriate AI counseling to the user. The suggestion is notified to the user via the terminal, and the user can then receive the necessary counseling session accordingly.

[0039] AI counseling automatically prepares the most suitable program based on the predicted emotional state, providing optimal support for the user. For example, if the user is determined to be experiencing high stress, sessions are offered to learn relaxation methods and stress management skills. The device records the progress of the session and sends feedback to the server for further detailed analysis.

[0040] Thus, the system of the present invention is designed to accurately analyze the user's emotional state and quickly provide appropriate mental health support. This system enables companies to effectively maintain employee health and improve productivity.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user makes a sound. The user naturally speaks during their daily work or conversations, and that sound is used as audio data.

[0044] Step 2:

[0045] The device acquires audio data. The device operates in the background, recording the user's voice in real time. It removes noise and saves the data in a format suitable for analysis.

[0046] Step 3:

[0047] The device analyzes the audio data. The device processes the acquired audio data and estimates the emotional state based on an audio analysis algorithm. The tone, speed, and volume of the voice are among the elements analyzed.

[0048] Step 4:

[0049] The device sends the analysis results to the server. The device uploads data indicating emotional state to the server via a secure channel. This information is handled with caution as personal data.

[0050] Step 5:

[0051] The server evaluates the analysis data. The server compares it with historical database data to identify user sentiment trends and outliers.

[0052] Step 6:

[0053] The server generates a report. Based on the sentiment assessment, it creates a report that reflects the mental health status, including potential problems and recommended actions.

[0054] Step 7:

[0055] The server proposes a counseling session. Based on the report, it suggests the most suitable AI counseling session for the user and notifies them of this information via their device.

[0056] Step 8:

[0057] The user receives a counseling session. The user participates in the proposed counseling program and receives guidance aimed at improving their mental health.

[0058] Step 9:

[0059] The terminal records the progress of the session. The terminal records the user's activity during the session and sends feedback to the server to support further analysis.

[0060] Step 10:

[0061] The server designs the follow-up. Based on post-session feedback, it builds an ongoing support program and provides users with additional resources and information as needed.

[0062] (Example 1)

[0063] 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."

[0064] In modern society, accurately understanding the mental health status of users and providing appropriate support promptly is crucial. However, conventional methods have made it difficult to effectively analyze acoustic data and assess emotional states in real time. This has led to the possibility of overlooking changes in users' emotions and resulting in situations where appropriate support is not provided. Furthermore, there has been a lack of mechanisms to identify the optimal support method for each individual user.

[0065] 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.

[0066] In this invention, the server includes means for preprocessing acoustic data and removing noise, means for analyzing the emotional state from the preprocessed acoustic data, and means for determining the optimal support method using a generative artificial intelligence model. This makes it possible to accurately evaluate the user's emotional state in real time and provide appropriate support quickly.

[0067] "Acoustic data" refers to digitized information containing frequency data, including sound, and is used to analyze the characteristics of sound.

[0068] "Preprocessing" is the process of removing unwanted noise from acoustic data and converting it into a clean state suitable for analysis.

[0069] "Noise" refers to unwanted sound components contained in acoustic data, which can potentially reduce the accuracy of the analysis results.

[0070] "Emotional state" refers to an evaluation result based on data that indicates an individual's emotional and mood state, such as the degree of stress or relaxation.

[0071] A "report" is a document that summarizes information based on an analyzed emotional state, and it includes details of the user's condition and proposed support.

[0072] "Consultation" refers to psychological support and advice provided to users based on analysis results, including suggestions for specific support methods.

[0073] A "generative artificial intelligence model" is a machine learning model that analyzes data according to a specific purpose and automatically generates optimal suggestions and support for the user.

[0074] "Support methods" refer to techniques for providing advice and action plans tailored to the analyzed emotional state, and include specific approaches to improve the user's mental health.

[0075] This invention is a system that analyzes a user's mental health state and provides appropriate support. This system uses acoustic data to evaluate the user's emotional state in real time and proposes the most suitable counseling method.

[0076] The terminal uses hardware such as a smartphone or tablet device. The microphone built into the terminal routinely acquires the user's acoustic data, which is then pre-processed using signal processing technology. This removes unwanted noise from the acquired acoustic data. For example, filtering algorithms are used in the pre-processing. Specifically, the Python audio processing library "LibROSA" could be used.

[0077] The server analyzes pre-processed acoustic data transmitted from the terminal and evaluates the user's emotional state using a generative AI model. This analysis considers acoustic characteristics such as tone, intonation, speed, and volume of speech. The analysis results are recorded in a database and compared with past data to analyze emotional tendencies. Furthermore, the server monitors changes in the user's emotional state in real time and issues warnings as needed.

[0078] Next, the server uses a generating AI model to suggest optimal counseling support. An example of a specific prompt might be, "What relaxation techniques are recommended when the user is in a high-stress state?" Based on the prompt, the AI ​​model automatically generates a specific support plan for the user, including relaxation methods.

[0079] Users can receive support by following counseling sessions suggested from their device. For example, online sessions to learn meditation or breathing techniques are offered, and the user's progress is recorded by the device. This record is fed back to the server and used to improve future support services.

[0080] In this way, the entire system works together to provide users with appropriate and timely mental health care.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The device acquires the user's everyday conversations and acoustic information using the microphone on their smartphone or tablet. The input is raw acoustic data, which is recorded for later analysis. The device captures this acoustic data in real time and transmits it to the next pre-processing stage.

[0084] Step 2:

[0085] The terminal preprocesses the acquired acoustic data. The input is the raw acoustic data obtained in step 1. By applying a noise reduction algorithm, noise is removed, and clean acoustic data is output. Digital signal processing technology is used in this process, and measures are taken to preserve the characteristics of the sound.

[0086] Step 3:

[0087] The device processes clean acoustic data through an analysis algorithm. The input is pre-processed acoustic data, where features such as voice tone, intonation, speed, and volume are extracted. This allows the user's emotional state, such as "relaxed" or "stressed," to be identified, and the analysis results are generated.

[0088] Step 4:

[0089] The analysis results are sent from the terminal to the server. The input is the emotional state analysis information obtained in step 3. The output is digital data received by the server, communicated using a secure protocol. This data is used for further analysis in the next step.

[0090] Step 5:

[0091] The server analyzes emotional tendencies based on the received analysis data. The input consists of historical data and new analysis data obtained in step 4. The output is an analysis result showing the user's emotional tendencies, including long-term emotional changes. This analysis can detect abnormalities in the user's mental health state.

[0092] Step 6:

[0093] The server generates a report based on the analysis of emotional state and tendencies. The input is the analysis data obtained in step 5, and the output is a report containing information to be shown to the user. This includes the current state and recommended support methods.

[0094] Step 7:

[0095] Based on the generated report, the server uses a generative AI model to propose the optimal counseling plan. The input is the report from step 6, and by inputting the prompt text into the generative AI model, the optimal support plan is obtained. The output is the proposal provided to the user via the terminal.

[0096] Step 8:

[0097] The user receives suggestions from the server via a terminal and participates in a counseling session. The input is the suggestions from the server, and the output is information about the progress of the counseling session obtained through the user's participation. This information is recorded by the terminal after the session.

[0098] Step 9:

[0099] The terminal sends feedback on the user's counseling session to the server. The input is the progress information recorded in step 8, and the output is data to optimize the next counseling session. This feedback is stored on the server and used to improve long-term user support.

[0100] (Application Example 1)

[0101] 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."

[0102] In modern society, the mental health problems individuals face are becoming increasingly diverse, making their management a challenging task. There is a need to understand the changing emotional states in daily life and provide appropriate support promptly. However, conventional technologies often require individuals to consciously recognize emotional changes and seek professional support themselves, limiting the effectiveness of preventative care. Therefore, there is a need for a system that automatically monitors an individual's mental health within the home and proposes support as needed.

[0103] 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.

[0104] In this invention, the server includes means for acquiring voice information, means for analyzing emotional states, means for generating reports, and means for exchanging information and providing guidance within the home. This makes it possible to automatically monitor an individual's mental health state in the home environment and provide appropriate support and guidance in real time.

[0105] "Means for acquiring voice information" refers to devices or processes that detect a user's voice and record it as digital data.

[0106] "Means for analyzing emotional states" refers to the process of analyzing elements such as tone, pace, and intensity from acquired audio information to identify the user's emotional state.

[0107] "Means for generating reports" refers to devices or processes that create reports, including current conditions and trends, based on analyzed emotional states.

[0108] "Means of exchanging information and providing guidance within the home" refers to devices and processes that provide mental health support in a home environment based on analysis results through communication with the user.

[0109] This invention aims to create a system that supports individual mental health within the home. This system is primarily implemented using a device for collecting voice data, a small computer, and cloud services.

[0110] First, the home robot, acting as the terminal, collects user voice information in the background via its microphone. The collected voice information is preprocessed through a noise filtering process. In this process, a "means for acquiring voice information" are used, and the device utilizes a speech recognition service such as Google® Cloud Speech-to-Text API. This converts the voice into text information.

[0111] Next, as a means of analyzing emotional states, external AI services such as IBM Watson® Tone Analyzer and Microsoft® Azure® Text Analytics are used to perform emotional analysis on the converted text information. This analysis makes it possible to identify the user's emotional state, and the information is sent to the server.

[0112] On the server, a "report generation mechanism" automatically generates a report showing the user's emotional trends based on the analyzed emotional information. This report visualizes and displays the user's daily emotional patterns and stress fluctuations.

[0113] Furthermore, the server provides personalized advice and counseling suggestions as a means of exchanging information and offering guidance within the home. This utilizes an AI counseling model to present the most suitable mental care plan for the user's current situation. For example, if the server determines that the user has a high stress level, it will suggest relaxation techniques and mindfulness exercises.

[0114] For example, while a user is relaxing in the living room, the robot can analyze the user's voice and suggest, "You seem a little tired today. How about trying a 5-minute meditation session?" In this way, constant mental support is provided within the home.

[0115] An example of a prompt for a generative AI model is, "Analyze the user's emotional state today and suggest ways to relax." This allows users to receive support tailored to their needs and facilitates the management of their mental health in daily life.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The device uses its microphone to collect ambient sound information. The input is ambient sound, and the output is audio data containing noise. This audio data is temporarily stored in the device's memory.

[0119] Step 2:

[0120] The device performs noise filtering to remove unwanted background noise from the audio data. The input is audio data containing noise, and the output is clear audio data with the noise removed. This process uses a digital signal processing algorithm.

[0121] Step 3:

[0122] The device uses the Google Cloud Speech-to-Text API to convert noise-removed audio data into text. The input is clear audio data, and the output is text data that corresponds to the audio. The converted text forms the basis for emotion analysis.

[0123] Step 4:

[0124] The terminal converts text data and uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is text data, and the output is evaluation data regarding the user's emotional state. The emotional analysis utilizes the tone and word choice characteristics of the text.

[0125] Step 5:

[0126] The server generates a report by aggregating the user's daily emotional trends based on emotional state evaluation data received from the terminal. The input is emotional state evaluation data, and the output is a report that visualizes changes and trends in emotions from the past to the present.

[0127] Step 6:

[0128] Based on the reports generated by the server, the system prepares specific guidance and suggestions for AI counseling for the user. The input is the report data, and the output is a personalized mental support plan presented to the user. This includes relaxation methods and stress management advice.

[0129] Step 7:

[0130] The terminal presents the user with guidance from the server in audio and text format. The input is a mental support plan from the server, and the output is feedback to the user through audio guidance and screen displays.

[0131] 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.

[0132] This invention is a system designed to support the mental health of users, comprehensively providing services ranging from voice data acquisition to emotion analysis and counseling suggestions. The system consists of a terminal, a server, and an emotion engine, and is implemented as follows.

[0133] The audio that users produce during their daily work and conversations is acquired in the background by the device. This audio data is processed to remove noise and made suitable for analysis. The device then passes the audio data to the emotion engine for analysis of the user's emotional state.

[0134] The emotion engine has the functionality to analyze the user's emotional state based on the characteristics of the acquired voice data. This engine utilizes machine learning algorithms to analyze voice features and estimate the emotional state with high accuracy. Furthermore, because the emotion engine uses a customized emotion recognition model for each user, it can provide personalized analysis. As a result, the accuracy of emotion analysis improves according to the specific characteristics and circumstances of the user.

[0135] The analyzed emotional state data is sent from the terminal to the server. The server analyzes the user's emotional trends by comparing them with past data and identifies unique patterns. For example, if the stress level has increased compared to past data, the server will recognize this as a problem and determine that countermeasures are necessary.

[0136] Based on this sentiment analysis, the server generates a detailed report. This report provides insights into the user's current and past mental health status and includes suggestions on what kind of counseling would be effective.

[0137] Counseling suggestions are sent from the server to the user via the terminal. The user receives this notification and can participate in the suggested AI counseling session. For example, a session to learn relaxation methods for stress reduction might be suggested. The terminal records the activity and progress during the counseling session and sends this data to the server.

[0138] Based on this feedback data, the server designs further follow-up and additional support plans. This allows users to improve their mental health while receiving continuous support.

[0139] Thus, the system of the present invention is built to more accurately grasp the user's emotional state by incorporating an emotion engine, and to provide effective mental health support. Companies can utilize this to maintain employee health and improve work efficiency.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] The user makes a sound. The user naturally speaks during daily work and conversations, and that sound is used by the system.

[0143] Step 2:

[0144] The device acquires audio data. The device operates in a constantly on state, recording the user's voice in real time, performing noise reduction, and saving the data in a format suitable for analysis.

[0145] Step 3:

[0146] The device sends voice data to the emotion engine. The device then transfers the pre-processed voice data to the emotion engine and requests an analysis of the user's emotional state.

[0147] Step 4:

[0148] The emotion engine analyzes the audio data. The emotion engine uses machine learning algorithms to analyze the audio features and estimate the user's emotional state with high accuracy.

[0149] Step 5:

[0150] The emotion engine returns the analysis results to the terminal. The emotion engine sends the estimated emotional state back to the terminal and prepares for the next processing step.

[0151] Step 6:

[0152] The device sends the analysis results to the server. The device sends emotional state data to the server and requests further evaluation and processing.

[0153] Step 7:

[0154] The server evaluates the analysis data. The server identifies user sentiment trends and anomalies by comparing them with the history of past database data.

[0155] Step 8:

[0156] The server generates a report. Based on the collected data, the server creates a detailed report on the mental health status and prepares for the next steps.

[0157] Step 9:

[0158] The server proposes a counseling session. Based on the generated report, the server suggests an appropriate AI counseling session for the user.

[0159] Step 10:

[0160] The user receives a counseling session. The user attends the counseling session provided by the server and takes the necessary actions according to the instructions.

[0161] Step 11:

[0162] The terminal records the progress of the session. The terminal records user activity and sends this data to the server to support further analysis.

[0163] Step 12:

[0164] The server designs the follow-up. Based on post-session feedback, the server builds and prepares to provide ongoing support programs to users.

[0165] (Example 2)

[0166] 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".

[0167] In modern society, managing and supporting the mental health of users is a crucial issue. However, accurately analyzing users' emotional states in real time and providing appropriate support based on that analysis is difficult. Furthermore, there is a demand for personalized services that address individual emotional states. A system is needed to solve these challenges.

[0168] 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.

[0169] In this invention, the server includes means for acquiring audio signals, means for utilizing a model for analyzing the audio signals and estimating emotional states, and means for generating information using the analysis results. This makes it possible to monitor the user's emotional state in real time and immediately propose appropriate support activities based on the results.

[0170] "Audio signals" refer to data including the user's voice and surrounding sounds, and are used to analyze their emotional state.

[0171] An "estimation model" is an algorithm used to analyze emotional states from acquired audio signals, and is built using machine learning techniques.

[0172] "Information" refers to data, including mental health reports and suggestions, that are generated based on the analysis results of audio signals and provided to users.

[0173] "Support activities" refer to actions such as counseling and action plans proposed based on the analysis of the user's emotional state, with the aim of improving their mental health.

[0174] A "server" is a computer system used to receive and analyze audio signals and manage the generated information.

[0175] This system is designed to effectively support users' mental health and comprehensively covers everything from acquiring voice signals and analyzing emotions to suggesting counseling. A specific implementation is shown below.

[0176] The device acquires audio signals emitted in the user's daily life and work environment in the background. These audio signals are temporarily stored on the device and processed using an audio processing library (e.g., FFmpeg) to remove noise. After the audio is cleared through noise reduction, the audio signal is sent to the server.

[0177] The server utilizes a generative AI model to analyze the received audio signal. This model employs a machine learning framework (e.g., TENSORFLOW®) to analyze audio characteristics and estimate the user's emotional state. The estimated emotional information is compared with past data to determine the user's emotional trends. This provides insights into the user's current mental health state.

[0178] The server generates a report based on the analysis of the user's emotional state and suggests appropriate support activities. For example, for a user with a high stress level, it may suggest counseling that introduces relaxation methods. This report and suggestion are notified to the user via their terminal.

[0179] Users can accept suggestions received from their device and participate in an AI counseling session. The device records the user's responses and progress during the session and sends this feedback data to the server. Based on this feedback, the server develops an ongoing support plan for the user and provides personalized assistance on an ongoing basis.

[0180] For example, if a user is in a stressful work environment, this system can be used to quickly detect signs of stress and continuously suggest countermeasures. An example of a prompt message would be, "Analyze the user's emotional state from the following audio data and suggest the most appropriate counseling." In this way, it is possible to support the emotional health of users in both public and private settings.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] The device activates its recording function and acquires the user's voice signal in the background. This input voice signal is temporarily stored within the device. Specifically, the device uses its microphone to sample ambient sounds and the user's voice and saves them as a digital audio file.

[0184] Step 2:

[0185] The terminal performs noise reduction processing on the audio signal. It applies a noise reduction algorithm to the input audio data, resulting in the output of clear audio data. Specifically, it uses an audio processing library (e.g., FFmpeg) to reduce background noise and enhance only the human voice.

[0186] Step 3:

[0187] The device sends clear audio data to the server. The server receives this transmitted audio data and inputs it into a generative AI model to analyze the emotional state. Specifically, the device uploads the data to the server using a secure network protocol.

[0188] Step 4:

[0189] The server inputs the received audio data into a generating AI model, extracts audio features, and estimates the emotional state. The output is estimated emotional state data. Specifically, the algorithm used analyzes the tone, pitch, and speed of the audio and assigns an emotional label based on these factors.

[0190] Step 5:

[0191] The server compares estimated emotional state data with historical data to analyze the user's emotional trends. It performs trend analysis using the input emotional data and historical data, and outputs a trend report as a result. For example, the server searches a database to track fluctuations in the user's stress level.

[0192] Step 6:

[0193] The server generates a report that proposes support activities based on the analysis. It then prepares to notify the user of this generated report via the terminal. Specifically, the server uses a natural language generation system to create easy-to-understand suggestions.

[0194] Step 7:

[0195] The device notifies the user of reports received from the server. The user is then provided with instructions on how to participate in the counseling session. Specifically, the device uses push notifications to inform the user.

[0196] Step 8:

[0197] The user participates in a proposed counseling session. The device collects feedback data generated during this process and sends that data back to the server. Specifically, the device records the user's responses during the counseling session and uses this as digital feedback data.

[0198] Step 9:

[0199] The server analyzes feedback data and develops plans for further support activities for the user. Based on the input feedback data, it designs supplementary support and outputs it as a plan. For example, the server creates a new counseling schedule and proposes the next steps for the user.

[0200] (Application Example 2)

[0201] 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".

[0202] In modern society, maintaining and improving the mental health of individuals and communities is a crucial issue. In particular, there is a need for effective methods to alleviate daily stress and anxiety, and support tailored to individual needs is essential. Furthermore, understanding the emotional state of the entire community and utilizing this information to improve public services is also a vital challenge.

[0203] 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.

[0204] In this invention, the server includes means for acquiring voice information, means for providing information on local events, and means for providing feedback to improve public services. This enables the provision of appropriate mental health support to individuals and the improvement of well-being for the entire community.

[0205] "Audio information" refers to data that records the words and sounds spoken by a user in digital format.

[0206] "Emotional state" refers to the user's psychological tendencies and mood, obtained by analyzing voice information.

[0207] A "report" is a document that summarizes the results of an analysis of the user's emotional state, providing insights into the user's mental health.

[0208] "Users" refer to individuals who use this system and are eligible to receive mental health support.

[0209] "Counseling" refers to advice or sessions provided to improve a user's emotional state.

[0210] "Local event information" refers to information about events and activities held in the local community, providing users with opportunities for relaxation and stress relief.

[0211] "Emotional tendencies" refer to general psychological tendencies obtained by analyzing the emotional states of multiple users or the entire community.

[0212] "Public services" refer to activities and support provided by government agencies and other organizations for the purpose of benefiting society.

[0213] "Feedback" refers to information and suggestions provided based on analysis results, aimed at encouraging improvements to specific behaviors or environments.

[0214] The system implementing this invention consists of a terminal with multiple functions and a server. First, the terminal is responsible for acquiring the user's voice information. The hardware used for this is a smartphone or other device with voice input capabilities, and voice processing software such as "Librosa" is used to remove ambient noise during recording.

[0215] Next, the server receives the audio information and performs analysis. The server uses machine learning libraries such as "TensorFlow" to analyze the emotional state contained in the audio information. This analysis allows for a highly accurate estimation of the user's psychological tendencies. The analysis results are managed on the server and generated as a report.

[0216] The generated reports include personalized counseling suggestions for each user. These suggestions, such as information on local events or individual relaxation methods, are sent from the server to the user's device and notified to the user. Push notification technologies such as "Firebase Cloud Messaging" are used for these notifications. The server also analyzes the emotional trends of the entire region and provides feedback based on that data to help improve public services.

[0217] For example, if the analysis indicates that a user is experiencing stress, the server might suggest they attend a nearby yoga class. Furthermore, the server can use the user's feedback as data to improve its analysis model.

[0218] An example of a prompt for a generative AI model is: "Please tell me why you felt more stressed than usual last night. And if you want to know what kind of support you would like to receive to address this, what kind of support would you like?"

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The device acquires the user's voice through the microphone. The input is the user's voice, and the output is the recorded voice saved as digital data. The acquired voice data is processed using "Librosa" to remove noise. This processing improves the quality of the voice and enables accurate sentiment analysis.

[0222] Step 2:

[0223] The device sends the denoised audio data to the server. The server receives this audio data as input and proceeds with sentiment analysis. Here, it utilizes a TensorFlow machine learning model to extract audio features and analyze the user's emotional state. The output is data regarding the type and intensity of the emotion.

[0224] Step 3:

[0225] The server generates a report based on the emotion analysis results. The input is data on the type and intensity of emotions, and the output is a report summarizing insights into the user's mental health. This report also includes specific counseling suggestions for the user.

[0226] Step 4:

[0227] The server sends the generated report to the device. The device receives this report and notifies the user. The notification is performed using Firebase Cloud Messaging technology. The input in this process is the report, and the output is the notification information delivered to the user.

[0228] Step 5:

[0229] Based on suggestions received from the device, users participate in activities such as yoga classes or other stress management activities. The user's actions and feedback are recorded again by the device and sent to the server as input. The output is data used to develop the next improvement plan based on this information.

[0230] Step 6:

[0231] The server analyzes feedback data and examines the overall sentiment trends of the region. Inputs are feedback and sentiment data collected from multiple users, and outputs include feedback and improvement suggestions for public services.

[0232] Step 7:

[0233] The server provides regional data to government agencies to support improvements in public services. This could potentially lead to improved mental health throughout the region. The input is regional emotional trend data, and the output is a proposal for specific improvement measures.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] The system according to the present invention comprehensively supports the user's mental health by acquiring and analyzing voice data and proposing counseling. This system consists of terminal, server, and user elements and operates as follows.

[0251] While the user is performing daily tasks or engaging in conversations, the device acquires audio data in the background. This audio data is recorded with ambient noise removed, including voices the user makes unconsciously. The device uses this audio data to analyze the user's emotional state.

[0252] The raw audio data is first pre-processed to remove noise, and then an analysis algorithm is used to identify the user's emotional state. This analysis takes into account factors such as tone, speed, volume, and intonation. The analysis results indicate the user's current emotional state, making it possible to objectively understand their mental state. These analysis results are then transmitted from the terminal to the server.

[0253] Based on the received analysis data, the server analyzes the user's emotional trends by comparing them with historical data. For example, if stress levels are increasing compared to past data, the server will use that information to determine that the user requires attention.

[0254] Next, the server generates a report based on the analyzed data. The report includes insights into the employee's current emotional state, comparisons to the past, and potential mental health issues they may be facing. Based on this report, the server suggests appropriate AI counseling to the user. The suggestion is notified to the user via the terminal, and the user can then receive the necessary counseling session accordingly.

[0255] AI counseling automatically prepares the most suitable program based on the predicted emotional state, providing optimal support for the user. For example, if the user is determined to be experiencing high stress, sessions are offered to learn relaxation methods and stress management skills. The device records the progress of the session and sends feedback to the server for further detailed analysis.

[0256] Thus, the system of the present invention is designed to accurately analyze the user's emotional state and quickly provide appropriate mental health support. This system enables companies to effectively maintain employee health and improve productivity.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The user makes a sound. The user naturally speaks during their daily work or conversations, and that sound is used as audio data.

[0260] Step 2:

[0261] The device acquires audio data. The device operates in the background, recording the user's voice in real time. It removes noise and saves the data in a format suitable for analysis.

[0262] Step 3:

[0263] The device analyzes the audio data. The device processes the acquired audio data and estimates the emotional state based on an audio analysis algorithm. The tone, speed, and volume of the voice are among the elements analyzed.

[0264] Step 4:

[0265] The device sends the analysis results to the server. The device uploads data indicating emotional state to the server via a secure channel. This information is handled with caution as personal data.

[0266] Step 5:

[0267] The server evaluates the analysis data. The server compares it with historical database data to identify user sentiment trends and outliers.

[0268] Step 6:

[0269] The server generates a report. Based on the sentiment assessment, it creates a report that reflects the mental health status, including potential problems and recommended actions.

[0270] Step 7:

[0271] The server proposes a counseling session. Based on the report, it suggests the most suitable AI counseling session for the user and notifies them of this information via their device.

[0272] Step 8:

[0273] The user receives a counseling session. The user participates in the proposed counseling program and receives guidance aimed at improving their mental health.

[0274] Step 9:

[0275] The terminal records the progress of the session. The terminal records the user's activity during the session and sends feedback to the server to support further analysis.

[0276] Step 10:

[0277] The server designs the follow-up. Based on post-session feedback, it builds an ongoing support program and provides users with additional resources and information as needed.

[0278] (Example 1)

[0279] 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."

[0280] In modern society, accurately understanding the mental health status of users and providing appropriate support promptly is crucial. However, conventional methods have made it difficult to effectively analyze acoustic data and assess emotional states in real time. This has led to the possibility of overlooking changes in users' emotions and resulting in situations where appropriate support is not provided. Furthermore, there has been a lack of mechanisms to identify the optimal support method for each individual user.

[0281] 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.

[0282] In this invention, the server includes means for preprocessing acoustic data to remove noise, means for analyzing the emotional state from the preprocessed acoustic data, and means for determining an optimal support method using a generative artificial intelligence model. This makes it possible to accurately evaluate the emotional state of the user in real time and quickly provide appropriate support.

[0283] "Acoustic data" refers to information obtained by digitizing frequency information including sound, and is data used for analyzing the characteristics of sound.

[0284] "Preprocessing" is a process of removing unnecessary noise from acoustic data and converting it into a clean state suitable for analysis.

[0285] "Noise" refers to unnecessary sound components contained in acoustic data, which may reduce the accuracy of the analysis results.

[0286] "Emotional state" is an evaluation result based on data indicating an individual's emotional and mood states, and represents, for example, the degree of stress or relaxation.

[0287] "Report" is a document summarizing information based on the analyzed emotional state, which describes the details of the user's state and the proposed support.

[0288] "Counseling" refers to psychological support or advice provided to the user based on the analysis results, and is a proposal including specific support methods.

[0289] "Generative artificial intelligence model" is a machine learning model that analyzes data according to a specific purpose and automatically generates optimal proposals and support for the user.

[0290] "Support method" is a method of providing advice and action plans suitable for the analyzed emotional state, and includes specific approaches for improving the user's mental health.

[0291] This invention is a system that analyzes a user's mental health state and provides appropriate support. This system uses acoustic data to evaluate the user's emotional state in real time and proposes the most suitable counseling method.

[0292] The terminal uses hardware such as a smartphone or tablet device. The microphone built into the terminal routinely acquires the user's acoustic data, which is then pre-processed using signal processing technology. This removes unwanted noise from the acquired acoustic data. For example, filtering algorithms are used in the pre-processing. Specifically, the Python audio processing library "LibROSA" could be used.

[0293] The server analyzes pre-processed acoustic data transmitted from the terminal and evaluates the user's emotional state using a generative AI model. This analysis considers acoustic characteristics such as tone, intonation, speed, and volume of speech. The analysis results are recorded in a database and compared with past data to analyze emotional tendencies. Furthermore, the server monitors changes in the user's emotional state in real time and issues warnings as needed.

[0294] Next, the server uses a generating AI model to suggest optimal counseling support. An example of a specific prompt might be, "What relaxation techniques are recommended when the user is in a high-stress state?" Based on the prompt, the AI ​​model automatically generates a specific support plan for the user, including relaxation methods.

[0295] Users can receive support by following counseling sessions suggested from their device. For example, online sessions to learn meditation or breathing techniques are offered, and the user's progress is recorded by the device. This record is fed back to the server and used to improve future support services.

[0296] In this way, the entire system works together to provide users with appropriate and timely mental health care.

[0297] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0298] Step 1:

[0299] The device acquires the user's everyday conversations and acoustic information using the microphone on their smartphone or tablet. The input is raw acoustic data, which is recorded for later analysis. The device captures this acoustic data in real time and transmits it to the next pre-processing stage.

[0300] Step 2:

[0301] The terminal preprocesses the acquired acoustic data. The input is the raw acoustic data obtained in step 1. By applying a noise reduction algorithm, noise is removed, and clean acoustic data is output. Digital signal processing technology is used in this process, and measures are taken to preserve the characteristics of the sound.

[0302] Step 3:

[0303] The device processes clean acoustic data through an analysis algorithm. The input is pre-processed acoustic data, where features such as voice tone, intonation, speed, and volume are extracted. This allows the user's emotional state, such as "relaxed" or "stressed," to be identified, and the analysis results are generated.

[0304] Step 4:

[0305] The analysis results are sent from the terminal to the server. The input is the emotional state analysis information obtained in step 3. The output is digital data received by the server, communicated using a secure protocol. This data is used for further analysis in the next step.

[0306] Step 5:

[0307] The server analyzes the emotional tendency based on the received analysis data. The input is the past history data and the new analysis data obtained in Step 4. The output is the analysis result indicating the user's emotional tendency, including long-term emotional changes. Through this analysis, abnormalities in the user's mental health state are detected.

[0308] Step 6:

[0309] The server generates a report based on the analysis results of the emotional state and tendency. The input is the analysis data obtained in Step 5, and the output is a report containing the information to be shown to the user. This includes the current state and the recommended support methods.

[0310] Step 7:

[0311] The server proposes optimal counseling using the generated AI model based on the generated report. The input is the report in Step 6, and by inputting the prompt text into the generated AI model, an optimal support plan can be obtained. The output is the proposal provided to the user through the terminal.

[0312] Step 8:

[0313] The user receives the proposal from the server via the terminal and participates in the counseling session. The input is the proposal from the server, and the output is the progress information of the counseling obtained through the user's participation. This information is recorded by the terminal after the session.

[0314] Step 9:

[0315] The terminal sends the feedback of the user's counseling session to the server. The input is the progress information recorded in Step 8, and the output is the data for optimizing the next counseling. This feedback is accumulated in the server and used to improve long-term user support.

[0316] (Application Example 1)

[0317] 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."

[0318] In modern society, the mental health problems individuals face are becoming increasingly diverse, making their management a challenging task. There is a need to understand the changing emotional states in daily life and provide appropriate support promptly. However, conventional technologies often require individuals to consciously recognize emotional changes and seek professional support themselves, limiting the effectiveness of preventative care. Therefore, there is a need for a system that automatically monitors an individual's mental health within the home and proposes support as needed.

[0319] 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.

[0320] In this invention, the server includes means for acquiring voice information, means for analyzing emotional states, means for generating reports, and means for exchanging information and providing guidance within the home. This makes it possible to automatically monitor an individual's mental health state in the home environment and provide appropriate support and guidance in real time.

[0321] "Means for acquiring voice information" refers to devices or processes that detect a user's voice and record it as digital data.

[0322] "Means for analyzing emotional states" refers to the process of analyzing elements such as tone, pace, and intensity from acquired audio information to identify the user's emotional state.

[0323] "Means for generating reports" refers to devices or processes that create reports, including current conditions and trends, based on analyzed emotional states.

[0324] "Means of exchanging information and providing guidance within the home" refers to devices and processes that provide mental health support in a home environment based on analysis results through communication with the user.

[0325] This invention aims to create a system that supports individual mental health within the home. This system is primarily implemented using a device for collecting voice data, a small computer, and cloud services.

[0326] First, the home robot, acting as the terminal, collects user voice information in the background via its microphone. The collected voice information is preprocessed through a noise filtering process. In this process, a "means for acquiring voice information" are used, and the device utilizes a speech recognition service such as the Google Cloud Speech-to-Text API. This converts the voice into text information.

[0327] Next, as a means of analyzing emotional states, external AI services such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to perform emotional analysis on the converted text information. This analysis makes it possible to identify the user's emotional state, and the information is sent to the server.

[0328] On the server, a "report generation mechanism" automatically generates a report showing the user's emotional trends based on the analyzed emotional information. This report visualizes and displays the user's daily emotional patterns and stress fluctuations.

[0329] Furthermore, the server provides personalized advice and counseling suggestions as a means of exchanging information and offering guidance within the home. This utilizes an AI counseling model to present the most suitable mental care plan for the user's current situation. For example, if the server determines that the user has a high stress level, it will suggest relaxation techniques and mindfulness exercises.

[0330] For example, while a user is relaxing in the living room, the robot can analyze the user's voice and suggest, "You seem a little tired today. How about trying a 5-minute meditation session?" In this way, constant mental support is provided within the home.

[0331] An example of a prompt for a generative AI model is, "Analyze the user's emotional state today and suggest ways to relax." This allows users to receive support tailored to their needs and facilitates the management of their mental health in daily life.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The device uses its microphone to collect ambient sound information. The input is ambient sound, and the output is audio data containing noise. This audio data is temporarily stored in the device's memory.

[0335] Step 2:

[0336] The device performs noise filtering to remove unwanted background noise from the audio data. The input is audio data containing noise, and the output is clear audio data with the noise removed. This process uses a digital signal processing algorithm.

[0337] Step 3:

[0338] The device uses the Google Cloud Speech-to-Text API to convert noise-removed audio data into text. The input is clear audio data, and the output is text data that corresponds to the audio. The converted text forms the basis for emotion analysis.

[0339] Step 4:

[0340] The terminal converts text data and uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is text data, and the output is evaluation data regarding the user's emotional state. The emotional analysis utilizes the tone and word choice characteristics of the text.

[0341] Step 5:

[0342] The server generates a report by aggregating the user's daily emotional trends based on emotional state evaluation data received from the terminal. The input is emotional state evaluation data, and the output is a report that visualizes changes and trends in emotions from the past to the present.

[0343] Step 6:

[0344] Based on the reports generated by the server, the system prepares specific guidance and suggestions for AI counseling for the user. The input is the report data, and the output is a personalized mental support plan presented to the user. This includes relaxation methods and stress management advice.

[0345] Step 7:

[0346] The terminal presents the user with guidance from the server in audio and text format. The input is a mental support plan from the server, and the output is feedback to the user through audio guidance and screen displays.

[0347] 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.

[0348] This invention is a system designed to support the mental health of users, comprehensively providing services ranging from voice data acquisition to emotion analysis and counseling suggestions. The system consists of a terminal, a server, and an emotion engine, and is implemented as follows.

[0349] The audio that users produce during their daily work and conversations is acquired in the background by the device. This audio data is processed to remove noise and made suitable for analysis. The device then passes the audio data to the emotion engine for analysis of the user's emotional state.

[0350] The emotion engine has the functionality to analyze the user's emotional state based on the characteristics of the acquired voice data. This engine utilizes machine learning algorithms to analyze voice features and estimate the emotional state with high accuracy. Furthermore, because the emotion engine uses a customized emotion recognition model for each user, it can provide personalized analysis. As a result, the accuracy of emotion analysis improves according to the specific characteristics and circumstances of the user.

[0351] The analyzed emotional state data is sent from the terminal to the server. The server analyzes the user's emotional trends by comparing them with past data and identifies unique patterns. For example, if the stress level has increased compared to past data, the server will recognize this as a problem and determine that countermeasures are necessary.

[0352] Based on this sentiment analysis, the server generates a detailed report. This report provides insights into the user's current and past mental health status and includes suggestions on what kind of counseling would be effective.

[0353] Counseling suggestions are sent from the server to the user via the terminal. The user receives this notification and can participate in the suggested AI counseling session. For example, a session to learn relaxation methods for stress reduction might be suggested. The terminal records the activity and progress during the counseling session and sends this data to the server.

[0354] Based on this feedback data, the server designs further follow-up and additional support plans. This allows users to improve their mental health while receiving continuous support.

[0355] Thus, the system of the present invention is built to more accurately grasp the user's emotional state by incorporating an emotion engine, and to provide effective mental health support. Companies can utilize this to maintain employee health and improve work efficiency.

[0356] The following describes the processing flow.

[0357] Step 1:

[0358] The user makes a sound. The user naturally speaks during daily work and conversations, and that sound is used by the system.

[0359] Step 2:

[0360] The device acquires audio data. The device operates in a constantly on state, recording the user's voice in real time, performing noise reduction, and saving the data in a format suitable for analysis.

[0361] Step 3:

[0362] The device sends voice data to the emotion engine. The device then transfers the pre-processed voice data to the emotion engine and requests an analysis of the user's emotional state.

[0363] Step 4:

[0364] The emotion engine analyzes the audio data. The emotion engine uses machine learning algorithms to analyze the audio features and estimate the user's emotional state with high accuracy.

[0365] Step 5:

[0366] The emotion engine returns the analysis results to the terminal. The emotion engine sends the estimated emotional state back to the terminal and prepares for the next processing step.

[0367] Step 6:

[0368] The device sends the analysis results to the server. The device sends emotional state data to the server and requests further evaluation and processing.

[0369] Step 7:

[0370] The server evaluates the analysis data. The server identifies user sentiment trends and anomalies by comparing them with the history of past database data.

[0371] Step 8:

[0372] The server generates a report. Based on the collected data, the server creates a detailed report on the mental health status and prepares for the next steps.

[0373] Step 9:

[0374] The server proposes a counseling session. Based on the generated report, the server suggests an appropriate AI counseling session for the user.

[0375] Step 10:

[0376] The user receives a counseling session. The user attends the counseling session provided by the server and takes the necessary actions according to the instructions.

[0377] Step 11:

[0378] The terminal records the progress of the session. The terminal records user activity and sends this data to the server to support further analysis.

[0379] Step 12:

[0380] The server designs the follow-up. Based on post-session feedback, the server builds and prepares to provide ongoing support programs to users.

[0381] (Example 2)

[0382] 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".

[0383] In modern society, managing and supporting the mental health of users is a crucial issue. However, accurately analyzing users' emotional states in real time and providing appropriate support based on that analysis is difficult. Furthermore, there is a demand for personalized services that address individual emotional states. A system is needed to solve these challenges.

[0384] 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.

[0385] In this invention, the server includes means for acquiring audio signals, means for utilizing a model for analyzing the audio signals and estimating emotional states, and means for generating information using the analysis results. This makes it possible to monitor the user's emotional state in real time and immediately propose appropriate support activities based on the results.

[0386] "Audio signals" refer to data including the user's voice and surrounding sounds, and are used to analyze their emotional state.

[0387] An "estimation model" is an algorithm used to analyze emotional states from acquired audio signals, and is built using machine learning techniques.

[0388] "Information" refers to data, including mental health reports and suggestions, that are generated based on the analysis results of audio signals and provided to users.

[0389] "Support activities" refer to actions such as counseling and action plans proposed based on the analysis of the user's emotional state, with the aim of improving their mental health.

[0390] A "server" is a computer system used to receive and analyze audio signals and manage the generated information.

[0391] This system is designed to effectively support users' mental health and comprehensively covers everything from acquiring voice signals and analyzing emotions to suggesting counseling. A specific implementation is shown below.

[0392] The device acquires audio signals emitted in the user's daily life and work environment in the background. These audio signals are temporarily stored on the device and processed using an audio processing library (e.g., FFmpeg) to remove noise. After the audio is cleared through noise reduction, the audio signal is sent to the server.

[0393] The server utilizes a generative AI model to analyze the received audio signal. This model employs a machine learning framework (e.g., TensorFlow) to analyze audio characteristics and estimate the user's emotional state. The estimated emotional information is compared with past data to determine the user's emotional trends. This provides insights into the user's current mental health state.

[0394] The server generates a report based on the analysis of the user's emotional state and suggests appropriate support activities. For example, for a user with a high stress level, it may suggest counseling that introduces relaxation methods. This report and suggestion are notified to the user via their terminal.

[0395] Users can accept suggestions received from their device and participate in an AI counseling session. The device records the user's responses and progress during the session and sends this feedback data to the server. Based on this feedback, the server develops an ongoing support plan for the user and provides personalized assistance on an ongoing basis.

[0396] For example, if a user is in a stressful work environment, this system can be used to quickly detect signs of stress and continuously suggest countermeasures. An example of a prompt message would be, "Analyze the user's emotional state from the following audio data and suggest the most appropriate counseling." In this way, it is possible to support the emotional health of users in both public and private settings.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The device activates its recording function and acquires the user's voice signal in the background. This input voice signal is temporarily stored within the device. Specifically, the device uses its microphone to sample ambient sounds and the user's voice and saves them as a digital audio file.

[0400] Step 2:

[0401] The terminal performs noise reduction processing on the audio signal. It applies a noise reduction algorithm to the input audio data, resulting in the output of clear audio data. Specifically, it uses an audio processing library (e.g., FFmpeg) to reduce background noise and enhance only the human voice.

[0402] Step 3:

[0403] The device sends clear audio data to the server. The server receives this transmitted audio data and inputs it into a generative AI model to analyze the emotional state. Specifically, the device uploads the data to the server using a secure network protocol.

[0404] Step 4:

[0405] The server inputs the received audio data into a generating AI model, extracts audio features, and estimates the emotional state. The output is estimated emotional state data. Specifically, the algorithm used analyzes the tone, pitch, and speed of the audio and assigns an emotional label based on these factors.

[0406] Step 5:

[0407] The server compares estimated emotional state data with historical data to analyze the user's emotional trends. It performs trend analysis using the input emotional data and historical data, and outputs a trend report as a result. For example, the server searches a database to track fluctuations in the user's stress level.

[0408] Step 6:

[0409] The server generates a report that proposes support activities based on the analysis. It then prepares to notify the user of this generated report via the terminal. Specifically, the server uses a natural language generation system to create easy-to-understand suggestions.

[0410] Step 7:

[0411] The device notifies the user of reports received from the server. The user is then provided with instructions on how to participate in the counseling session. Specifically, the device uses push notifications to inform the user.

[0412] Step 8:

[0413] The user participates in a proposed counseling session. The device collects feedback data generated during this process and sends that data back to the server. Specifically, the device records the user's responses during the counseling session and uses this as digital feedback data.

[0414] Step 9:

[0415] The server analyzes feedback data and develops plans for further support activities for the user. Based on the input feedback data, it designs supplementary support and outputs it as a plan. For example, the server creates a new counseling schedule and proposes the next steps for the user.

[0416] (Application Example 2)

[0417] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0418] In modern society, maintaining and improving the mental health of individuals and communities is a crucial issue. In particular, there is a need for effective methods to alleviate daily stress and anxiety, and support tailored to individual needs is essential. Furthermore, understanding the emotional state of the entire community and utilizing this information to improve public services is also a vital challenge.

[0419] 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.

[0420] In this invention, the server includes means for acquiring voice information, means for providing information on local events, and means for providing feedback to improve public services. This enables the provision of appropriate mental health support to individuals and the improvement of well-being for the entire community.

[0421] "Audio information" refers to data that records the words and sounds spoken by a user in digital format.

[0422] "Emotional state" refers to the user's psychological tendencies and mood, obtained by analyzing voice information.

[0423] A "report" is a document that summarizes the results of an analysis of the user's emotional state, providing insights into the user's mental health.

[0424] "Users" refer to individuals who use this system and are eligible to receive mental health support.

[0425] "Counseling" refers to advice or sessions provided to improve a user's emotional state.

[0426] "Local event information" refers to information about events and activities held in the local community, providing users with opportunities for relaxation and stress relief.

[0427] "Emotional tendencies" refer to general psychological tendencies obtained by analyzing the emotional states of multiple users or the entire community.

[0428] "Public services" refer to activities and support provided by government agencies and other organizations for the purpose of benefiting society.

[0429] "Feedback" refers to information and suggestions provided based on analysis results, aimed at encouraging improvements to specific behaviors or environments.

[0430] The system implementing this invention consists of a terminal with multiple functions and a server. First, the terminal is responsible for acquiring the user's voice information. The hardware used for this is a smartphone or other device with voice input capabilities, and voice processing software such as "Librosa" is used to remove ambient noise during recording.

[0431] Next, the server receives the audio information and performs analysis. The server uses machine learning libraries such as "TensorFlow" to analyze the emotional state contained in the audio information. This analysis allows for a highly accurate estimation of the user's psychological tendencies. The analysis results are managed on the server and generated as a report.

[0432] The generated reports include personalized counseling suggestions for each user. These suggestions, such as information on local events or individual relaxation methods, are sent from the server to the user's device and notified to the user. Push notification technologies such as "Firebase Cloud Messaging" are used for these notifications. The server also analyzes the emotional trends of the entire region and provides feedback based on that data to help improve public services.

[0433] For example, if the analysis indicates that a user is experiencing stress, the server might suggest they attend a nearby yoga class. Furthermore, the server can use the user's feedback as data to improve its analysis model.

[0434] An example of a prompt for a generative AI model is: "Please tell me why you felt more stressed than usual last night. And if you want to know what kind of support you would like to receive to address this, what kind of support would you like?"

[0435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0436] Step 1:

[0437] The device acquires the user's voice through the microphone. The input is the user's voice, and the output is the recorded voice saved as digital data. The acquired voice data is processed using "Librosa" to remove noise. This processing improves the quality of the voice and enables accurate sentiment analysis.

[0438] Step 2:

[0439] The device sends the denoised audio data to the server. The server receives this audio data as input and proceeds with sentiment analysis. Here, it utilizes a TensorFlow machine learning model to extract audio features and analyze the user's emotional state. The output is data regarding the type and intensity of the emotion.

[0440] Step 3:

[0441] The server generates a report based on the emotion analysis results. The input is data on the type and intensity of emotions, and the output is a report summarizing insights into the user's mental health. This report also includes specific counseling suggestions for the user.

[0442] Step 4:

[0443] The server sends the generated report to the device. The device receives this report and notifies the user. The notification is performed using Firebase Cloud Messaging technology. The input in this process is the report, and the output is the notification information delivered to the user.

[0444] Step 5:

[0445] Based on suggestions received from the device, users participate in activities such as yoga classes or other stress management activities. The user's actions and feedback are recorded again by the device and sent to the server as input. The output is data used to develop the next improvement plan based on this information.

[0446] Step 6:

[0447] The server analyzes feedback data and examines the overall sentiment trends of the region. Inputs are feedback and sentiment data collected from multiple users, and outputs include feedback and improvement suggestions for public services.

[0448] Step 7:

[0449] The server provides regional data to government agencies to support improvements in public services. This could potentially lead to improved mental health throughout the region. The input is regional emotional trend data, and the output is a proposal for specific improvement measures.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] [Third Embodiment]

[0454] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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".

[0466] The system according to the present invention comprehensively supports the user's mental health by acquiring and analyzing voice data and proposing counseling. This system consists of terminal, server, and user elements and operates as follows.

[0467] While the user is performing daily tasks or engaging in conversations, the device acquires audio data in the background. This audio data is recorded with ambient noise removed, including voices the user makes unconsciously. The device uses this audio data to analyze the user's emotional state.

[0468] The raw audio data is first pre-processed to remove noise, and then an analysis algorithm is used to identify the user's emotional state. This analysis takes into account factors such as tone, speed, volume, and intonation. The analysis results indicate the user's current emotional state, making it possible to objectively understand their mental state. These analysis results are then transmitted from the terminal to the server.

[0469] Based on the received analysis data, the server analyzes the user's emotional trends by comparing them with historical data. For example, if stress levels are increasing compared to past data, the server will use that information to determine that the user requires attention.

[0470] Next, the server generates a report based on the analyzed data. The report includes insights into the employee's current emotional state, comparisons to the past, and potential mental health issues they may be facing. Based on this report, the server suggests appropriate AI counseling to the user. The suggestion is notified to the user via the terminal, and the user can then receive the necessary counseling session accordingly.

[0471] AI counseling automatically prepares the most suitable program based on the predicted emotional state, providing optimal support for the user. For example, if the user is determined to be experiencing high stress, sessions are offered to learn relaxation methods and stress management skills. The device records the progress of the session and sends feedback to the server for further detailed analysis.

[0472] Thus, the system of the present invention is designed to accurately analyze the user's emotional state and quickly provide appropriate mental health support. This system enables companies to effectively maintain employee health and improve productivity.

[0473] The following describes the processing flow.

[0474] Step 1:

[0475] The user makes a sound. The user naturally speaks during their daily work or conversations, and that sound is used as audio data.

[0476] Step 2:

[0477] The device acquires audio data. The device operates in the background, recording the user's voice in real time. It removes noise and saves the data in a format suitable for analysis.

[0478] Step 3:

[0479] The device analyzes the audio data. The device processes the acquired audio data and estimates the emotional state based on an audio analysis algorithm. The tone, speed, and volume of the voice are among the elements analyzed.

[0480] Step 4:

[0481] The device sends the analysis results to the server. The device uploads data indicating emotional state to the server via a secure channel. This information is handled with caution as personal data.

[0482] Step 5:

[0483] The server evaluates the analysis data. The server compares it with historical database data to identify user sentiment trends and outliers.

[0484] Step 6:

[0485] The server generates a report. Based on the sentiment assessment, it creates a report that reflects the mental health status, including potential problems and recommended actions.

[0486] Step 7:

[0487] The server proposes a counseling session. Based on the report, it suggests the most suitable AI counseling session for the user and notifies them of this information via their device.

[0488] Step 8:

[0489] The user receives a counseling session. The user participates in the proposed counseling program and receives guidance aimed at improving their mental health.

[0490] Step 9:

[0491] The terminal records the progress of the session. The terminal records the user's activity during the session and sends feedback to the server to support further analysis.

[0492] Step 10:

[0493] The server designs the follow-up. Based on post-session feedback, it builds an ongoing support program and provides users with additional resources and information as needed.

[0494] (Example 1)

[0495] 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."

[0496] In modern society, accurately understanding the mental health status of users and providing appropriate support promptly is crucial. However, conventional methods have made it difficult to effectively analyze acoustic data and assess emotional states in real time. This has led to the possibility of overlooking changes in users' emotions and resulting in situations where appropriate support is not provided. Furthermore, there has been a lack of mechanisms to identify the optimal support method for each individual user.

[0497] 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.

[0498] In this invention, the server includes means for preprocessing acoustic data and removing noise, means for analyzing the emotional state from the preprocessed acoustic data, and means for determining the optimal support method using a generative artificial intelligence model. This makes it possible to accurately evaluate the user's emotional state in real time and provide appropriate support quickly.

[0499] "Acoustic data" refers to digitized information containing frequency data, including sound, and is used to analyze the characteristics of sound.

[0500] "Preprocessing" is the process of removing unwanted noise from acoustic data and converting it into a clean state suitable for analysis.

[0501] "Noise" refers to unwanted sound components contained in acoustic data, which can potentially reduce the accuracy of the analysis results.

[0502] "Emotional state" refers to an evaluation result based on data that indicates an individual's emotional and mood state, such as the degree of stress or relaxation.

[0503] A "report" is a document that summarizes information based on an analyzed emotional state, and it includes details of the user's condition and proposed support.

[0504] "Consultation" refers to psychological support and advice provided to users based on analysis results, including suggestions for specific support methods.

[0505] A "generative artificial intelligence model" is a machine learning model that analyzes data according to a specific purpose and automatically generates optimal suggestions and support for the user.

[0506] "Support methods" refer to techniques for providing advice and action plans tailored to the analyzed emotional state, and include specific approaches to improve the user's mental health.

[0507] This invention is a system that analyzes a user's mental health state and provides appropriate support. This system uses acoustic data to evaluate the user's emotional state in real time and proposes the most suitable counseling method.

[0508] The terminal uses hardware such as a smartphone or tablet device. The microphone built into the terminal routinely acquires the user's acoustic data, which is then pre-processed using signal processing technology. This removes unwanted noise from the acquired acoustic data. For example, filtering algorithms are used in the pre-processing. Specifically, the Python audio processing library "LibROSA" could be used.

[0509] The server analyzes pre-processed acoustic data transmitted from the terminal and evaluates the user's emotional state using a generative AI model. This analysis considers acoustic characteristics such as tone, intonation, speed, and volume of speech. The analysis results are recorded in a database and compared with past data to analyze emotional tendencies. Furthermore, the server monitors changes in the user's emotional state in real time and issues warnings as needed.

[0510] Next, the server uses a generating AI model to suggest optimal counseling support. An example of a specific prompt might be, "What relaxation techniques are recommended when the user is in a high-stress state?" Based on the prompt, the AI ​​model automatically generates a specific support plan for the user, including relaxation methods.

[0511] Users can receive support by following counseling sessions suggested from their device. For example, online sessions to learn meditation or breathing techniques are offered, and the user's progress is recorded by the device. This record is fed back to the server and used to improve future support services.

[0512] In this way, the entire system works together to provide users with appropriate and timely mental health care.

[0513] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0514] Step 1:

[0515] The device acquires the user's everyday conversations and acoustic information using the microphone on their smartphone or tablet. The input is raw acoustic data, which is recorded for later analysis. The device captures this acoustic data in real time and transmits it to the next pre-processing stage.

[0516] Step 2:

[0517] The terminal preprocesses the acquired acoustic data. The input is the raw acoustic data obtained in step 1. By applying a noise reduction algorithm, noise is removed, and clean acoustic data is output. Digital signal processing technology is used in this process, and measures are taken to preserve the characteristics of the sound.

[0518] Step 3:

[0519] The device processes clean acoustic data through an analysis algorithm. The input is pre-processed acoustic data, where features such as voice tone, intonation, speed, and volume are extracted. This allows the user's emotional state, such as "relaxed" or "stressed," to be identified, and the analysis results are generated.

[0520] Step 4:

[0521] The analysis results are sent from the terminal to the server. The input is the emotional state analysis information obtained in step 3. The output is digital data received by the server, communicated using a secure protocol. This data is used for further analysis in the next step.

[0522] Step 5:

[0523] The server analyzes emotional tendencies based on the received analysis data. The input consists of historical data and new analysis data obtained in step 4. The output is an analysis result showing the user's emotional tendencies, including long-term emotional changes. This analysis can detect abnormalities in the user's mental health state.

[0524] Step 6:

[0525] The server generates a report based on the analysis of emotional state and tendencies. The input is the analysis data obtained in step 5, and the output is a report containing information to be shown to the user. This includes the current state and recommended support methods.

[0526] Step 7:

[0527] Based on the generated report, the server uses a generative AI model to propose the optimal counseling plan. The input is the report from step 6, and by inputting the prompt text into the generative AI model, the optimal support plan is obtained. The output is the proposal provided to the user via the terminal.

[0528] Step 8:

[0529] The user receives suggestions from the server via a terminal and participates in a counseling session. The input is the suggestions from the server, and the output is information about the progress of the counseling session obtained through the user's participation. This information is recorded by the terminal after the session.

[0530] Step 9:

[0531] The terminal sends feedback on the user's counseling session to the server. The input is the progress information recorded in step 8, and the output is data to optimize the next counseling session. This feedback is stored on the server and used to improve long-term user support.

[0532] (Application Example 1)

[0533] 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."

[0534] In modern society, the mental health problems individuals face are becoming increasingly diverse, making their management a challenging task. There is a need to understand the changing emotional states in daily life and provide appropriate support promptly. However, conventional technologies often require individuals to consciously recognize emotional changes and seek professional support themselves, limiting the effectiveness of preventative care. Therefore, there is a need for a system that automatically monitors an individual's mental health within the home and proposes support as needed.

[0535] 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.

[0536] In this invention, the server includes means for acquiring voice information, means for analyzing emotional states, means for generating reports, and means for exchanging information and providing guidance within the home. This makes it possible to automatically monitor an individual's mental health state in the home environment and provide appropriate support and guidance in real time.

[0537] "Means for acquiring voice information" refers to devices or processes that detect a user's voice and record it as digital data.

[0538] "Means for analyzing emotional states" refers to the process of analyzing elements such as tone, pace, and intensity from acquired audio information to identify the user's emotional state.

[0539] "Means for generating reports" refers to devices or processes that create reports, including current conditions and trends, based on analyzed emotional states.

[0540] "Means of exchanging information and providing guidance within the home" refers to devices and processes that provide mental health support in a home environment based on analysis results through communication with the user.

[0541] This invention aims to create a system that supports individual mental health within the home. This system is primarily implemented using a device for collecting voice data, a small computer, and cloud services.

[0542] First, the home robot, acting as the terminal, collects user voice information in the background via its microphone. The collected voice information is preprocessed through a noise filtering process. In this process, a "means for acquiring voice information" are used, and the device utilizes a speech recognition service such as the Google Cloud Speech-to-Text API. This converts the voice into text information.

[0543] Next, as a means of analyzing emotional states, external AI services such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to perform emotional analysis on the converted text information. This analysis makes it possible to identify the user's emotional state, and the information is sent to the server.

[0544] On the server, a "report generation mechanism" automatically generates a report showing the user's emotional trends based on the analyzed emotional information. This report visualizes and displays the user's daily emotional patterns and stress fluctuations.

[0545] Furthermore, the server provides personalized advice and counseling suggestions as a means of exchanging information and offering guidance within the home. This utilizes an AI counseling model to present the most suitable mental care plan for the user's current situation. For example, if the server determines that the user has a high stress level, it will suggest relaxation techniques and mindfulness exercises.

[0546] For example, while a user is relaxing in the living room, the robot can analyze the user's voice and suggest, "You seem a little tired today. How about trying a 5-minute meditation session?" In this way, constant mental support is provided within the home.

[0547] An example of a prompt for a generative AI model is, "Analyze the user's emotional state today and suggest ways to relax." This allows users to receive support tailored to their needs and facilitates the management of their mental health in daily life.

[0548] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0549] Step 1:

[0550] The device uses its microphone to collect ambient sound information. The input is ambient sound, and the output is audio data containing noise. This audio data is temporarily stored in the device's memory.

[0551] Step 2:

[0552] The device performs noise filtering to remove unwanted background noise from the audio data. The input is audio data containing noise, and the output is clear audio data with the noise removed. This process uses a digital signal processing algorithm.

[0553] Step 3:

[0554] The device uses the Google Cloud Speech-to-Text API to convert noise-removed audio data into text. The input is clear audio data, and the output is text data that corresponds to the audio. The converted text forms the basis for emotion analysis.

[0555] Step 4:

[0556] The terminal converts text data and uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is text data, and the output is evaluation data regarding the user's emotional state. The emotional analysis utilizes the tone and word choice characteristics of the text.

[0557] Step 5:

[0558] The server generates a report by aggregating the user's daily emotional trends based on emotional state evaluation data received from the terminal. The input is emotional state evaluation data, and the output is a report that visualizes changes and trends in emotions from the past to the present.

[0559] Step 6:

[0560] Based on the reports generated by the server, the system prepares specific guidance and suggestions for AI counseling for the user. The input is the report data, and the output is a personalized mental support plan presented to the user. This includes relaxation methods and stress management advice.

[0561] Step 7:

[0562] The terminal presents the user with guidance from the server in audio and text format. The input is a mental support plan from the server, and the output is feedback to the user through audio guidance and screen displays.

[0563] 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.

[0564] This invention is a system designed to support the mental health of users, comprehensively providing services ranging from voice data acquisition to emotion analysis and counseling suggestions. The system consists of a terminal, a server, and an emotion engine, and is implemented as follows.

[0565] The audio that users produce during their daily work and conversations is acquired in the background by the device. This audio data is processed to remove noise and made suitable for analysis. The device then passes the audio data to the emotion engine for analysis of the user's emotional state.

[0566] The emotion engine has the functionality to analyze the user's emotional state based on the characteristics of the acquired voice data. This engine utilizes machine learning algorithms to analyze voice features and estimate the emotional state with high accuracy. Furthermore, because the emotion engine uses a customized emotion recognition model for each user, it can provide personalized analysis. As a result, the accuracy of emotion analysis improves according to the specific characteristics and circumstances of the user.

[0567] The analyzed emotional state data is sent from the terminal to the server. The server analyzes the user's emotional trends by comparing them with past data and identifies unique patterns. For example, if the stress level has increased compared to past data, the server will recognize this as a problem and determine that countermeasures are necessary.

[0568] Based on this sentiment analysis, the server generates a detailed report. This report provides insights into the user's current and past mental health status and includes suggestions on what kind of counseling would be effective.

[0569] Counseling suggestions are sent from the server to the user via the terminal. The user receives this notification and can participate in the suggested AI counseling session. For example, a session to learn relaxation methods for stress reduction might be suggested. The terminal records the activity and progress during the counseling session and sends this data to the server.

[0570] Based on this feedback data, the server designs further follow-up and additional support plans. This allows users to improve their mental health while receiving continuous support.

[0571] Thus, the system of the present invention is built to more accurately grasp the user's emotional state by incorporating an emotion engine, and to provide effective mental health support. Companies can utilize this to maintain employee health and improve work efficiency.

[0572] The following describes the processing flow.

[0573] Step 1:

[0574] The user makes a sound. The user naturally speaks during daily work and conversations, and that sound is used by the system.

[0575] Step 2:

[0576] The device acquires audio data. The device operates in a constantly on state, recording the user's voice in real time, performing noise reduction, and saving the data in a format suitable for analysis.

[0577] Step 3:

[0578] The device sends voice data to the emotion engine. The device then transfers the pre-processed voice data to the emotion engine and requests an analysis of the user's emotional state.

[0579] Step 4:

[0580] The emotion engine analyzes the audio data. The emotion engine uses machine learning algorithms to analyze the audio features and estimate the user's emotional state with high accuracy.

[0581] Step 5:

[0582] The emotion engine returns the analysis results to the terminal. The emotion engine sends the estimated emotional state back to the terminal and prepares for the next processing step.

[0583] Step 6:

[0584] The device sends the analysis results to the server. The device sends emotional state data to the server and requests further evaluation and processing.

[0585] Step 7:

[0586] The server evaluates the analysis data. The server identifies user sentiment trends and anomalies by comparing them with the history of past database data.

[0587] Step 8:

[0588] The server generates a report. Based on the collected data, the server creates a detailed report on the mental health status and prepares for the next steps.

[0589] Step 9:

[0590] The server proposes a counseling session. Based on the generated report, the server suggests an appropriate AI counseling session for the user.

[0591] Step 10:

[0592] The user receives a counseling session. The user attends the counseling session provided by the server and takes the necessary actions according to the instructions.

[0593] Step 11:

[0594] The terminal records the progress of the session. The terminal records user activity and sends this data to the server to support further analysis.

[0595] Step 12:

[0596] The server designs the follow-up. Based on post-session feedback, the server builds and prepares to provide ongoing support programs to users.

[0597] (Example 2)

[0598] 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."

[0599] In modern society, managing and supporting the mental health of users is a crucial issue. However, accurately analyzing users' emotional states in real time and providing appropriate support based on that analysis is difficult. Furthermore, there is a demand for personalized services that address individual emotional states. A system is needed to solve these challenges.

[0600] 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.

[0601] In this invention, the server includes means for acquiring audio signals, means for utilizing a model for analyzing the audio signals and estimating emotional states, and means for generating information using the analysis results. This makes it possible to monitor the user's emotional state in real time and immediately propose appropriate support activities based on the results.

[0602] "Audio signals" refer to data including the user's voice and surrounding sounds, and are used to analyze their emotional state.

[0603] An "estimation model" is an algorithm used to analyze emotional states from acquired audio signals, and is built using machine learning techniques.

[0604] "Information" refers to data, including mental health reports and suggestions, that are generated based on the analysis results of audio signals and provided to users.

[0605] "Support activities" refer to actions such as counseling and action plans proposed based on the analysis of the user's emotional state, with the aim of improving their mental health.

[0606] A "server" is a computer system used to receive and analyze audio signals and manage the generated information.

[0607] This system is designed to effectively support users' mental health and comprehensively covers everything from acquiring voice signals and analyzing emotions to suggesting counseling. A specific implementation is shown below.

[0608] The device acquires audio signals emitted in the user's daily life and work environment in the background. These audio signals are temporarily stored on the device and processed using an audio processing library (e.g., FFmpeg) to remove noise. After the audio is cleared through noise reduction, the audio signal is sent to the server.

[0609] The server utilizes a generative AI model to analyze the received audio signal. This model employs a machine learning framework (e.g., TensorFlow) to analyze audio characteristics and estimate the user's emotional state. The estimated emotional information is compared with past data to determine the user's emotional trends. This provides insights into the user's current mental health state.

[0610] The server generates a report based on the analysis of the user's emotional state and suggests appropriate support activities. For example, for a user with a high stress level, it may suggest counseling that introduces relaxation methods. This report and suggestion are notified to the user via their terminal.

[0611] Users can accept suggestions received from their device and participate in an AI counseling session. The device records the user's responses and progress during the session and sends this feedback data to the server. Based on this feedback, the server develops an ongoing support plan for the user and provides personalized assistance on an ongoing basis.

[0612] For example, if a user is in a stressful work environment, this system can be used to quickly detect signs of stress and continuously suggest countermeasures. An example of a prompt message would be, "Analyze the user's emotional state from the following audio data and suggest the most appropriate counseling." In this way, it is possible to support the emotional health of users in both public and private settings.

[0613] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0614] Step 1:

[0615] The device activates its recording function and acquires the user's voice signal in the background. This input voice signal is temporarily stored within the device. Specifically, the device uses its microphone to sample ambient sounds and the user's voice and saves them as a digital audio file.

[0616] Step 2:

[0617] The terminal performs noise reduction processing on the audio signal. It applies a noise reduction algorithm to the input audio data, resulting in the output of clear audio data. Specifically, it uses an audio processing library (e.g., FFmpeg) to reduce background noise and enhance only the human voice.

[0618] Step 3:

[0619] The device sends clear audio data to the server. The server receives this transmitted audio data and inputs it into a generative AI model to analyze the emotional state. Specifically, the device uploads the data to the server using a secure network protocol.

[0620] Step 4:

[0621] The server inputs the received audio data into a generating AI model, extracts audio features, and estimates the emotional state. The output is estimated emotional state data. Specifically, the algorithm used analyzes the tone, pitch, and speed of the audio and assigns an emotional label based on these factors.

[0622] Step 5:

[0623] The server compares estimated emotional state data with historical data to analyze the user's emotional trends. It performs trend analysis using the input emotional data and historical data, and outputs a trend report as a result. For example, the server searches a database to track fluctuations in the user's stress level.

[0624] Step 6:

[0625] The server generates a report that proposes support activities based on the analysis. It then prepares to notify the user of this generated report via the terminal. Specifically, the server uses a natural language generation system to create easy-to-understand suggestions.

[0626] Step 7:

[0627] The device notifies the user of reports received from the server. The user is then provided with instructions on how to participate in the counseling session. Specifically, the device uses push notifications to inform the user.

[0628] Step 8:

[0629] The user participates in a proposed counseling session. The device collects feedback data generated during this process and sends that data back to the server. Specifically, the device records the user's responses during the counseling session and uses this as digital feedback data.

[0630] Step 9:

[0631] The server analyzes feedback data and develops plans for further support activities for the user. Based on the input feedback data, it designs supplementary support and outputs it as a plan. For example, the server creates a new counseling schedule and proposes the next steps for the user.

[0632] (Application Example 2)

[0633] 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."

[0634] In modern society, maintaining and improving the mental health of individuals and communities is a crucial issue. In particular, there is a need for effective methods to alleviate daily stress and anxiety, and support tailored to individual needs is essential. Furthermore, understanding the emotional state of the entire community and utilizing this information to improve public services is also a vital challenge.

[0635] 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.

[0636] In this invention, the server includes means for acquiring voice information, means for providing information on local events, and means for providing feedback to improve public services. This enables the provision of appropriate mental health support to individuals and the improvement of well-being for the entire community.

[0637] "Audio information" refers to data that records the words and sounds spoken by a user in digital format.

[0638] "Emotional state" refers to the user's psychological tendencies and mood, obtained by analyzing voice information.

[0639] A "report" is a document that summarizes the results of an analysis of the user's emotional state, providing insights into the user's mental health.

[0640] "Users" refer to individuals who use this system and are eligible to receive mental health support.

[0641] "Counseling" refers to advice or sessions provided to improve a user's emotional state.

[0642] "Local event information" refers to information about events and activities held in the local community, providing users with opportunities for relaxation and stress relief.

[0643] "Emotional tendencies" refer to general psychological tendencies obtained by analyzing the emotional states of multiple users or the entire community.

[0644] "Public services" refer to activities and support provided by government agencies and other organizations for the purpose of benefiting society.

[0645] "Feedback" refers to information and suggestions provided based on analysis results, aimed at encouraging improvements to specific behaviors or environments.

[0646] The system implementing this invention consists of a terminal with multiple functions and a server. First, the terminal is responsible for acquiring the user's voice information. The hardware used for this is a smartphone or other device with voice input capabilities, and voice processing software such as "Librosa" is used to remove ambient noise during recording.

[0647] Next, the server receives the audio information and performs analysis. The server uses machine learning libraries such as "TensorFlow" to analyze the emotional state contained in the audio information. This analysis allows for a highly accurate estimation of the user's psychological tendencies. The analysis results are managed on the server and generated as a report.

[0648] The generated reports include personalized counseling suggestions for each user. These suggestions, such as information on local events or individual relaxation methods, are sent from the server to the user's device and notified to the user. Push notification technologies such as "Firebase Cloud Messaging" are used for these notifications. The server also analyzes the emotional trends of the entire region and provides feedback based on that data to help improve public services.

[0649] For example, if the analysis indicates that a user is experiencing stress, the server might suggest they attend a nearby yoga class. Furthermore, the server can use the user's feedback as data to improve its analysis model.

[0650] An example of a prompt for a generative AI model is: "Please tell me why you felt more stressed than usual last night. And if you want to know what kind of support you would like to receive to address this, what kind of support would you like?"

[0651] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0652] Step 1:

[0653] The device acquires the user's voice through the microphone. The input is the user's voice, and the output is the recorded voice saved as digital data. The acquired voice data is processed using "Librosa" to remove noise. This processing improves the quality of the voice and enables accurate sentiment analysis.

[0654] Step 2:

[0655] The device sends the denoised audio data to the server. The server receives this audio data as input and proceeds with sentiment analysis. Here, it utilizes a TensorFlow machine learning model to extract audio features and analyze the user's emotional state. The output is data regarding the type and intensity of the emotion.

[0656] Step 3:

[0657] The server generates a report based on the emotion analysis results. The input is data on the type and intensity of emotions, and the output is a report summarizing insights into the user's mental health. This report also includes specific counseling suggestions for the user.

[0658] Step 4:

[0659] The server sends the generated report to the device. The device receives this report and notifies the user. The notification is performed using Firebase Cloud Messaging technology. The input in this process is the report, and the output is the notification information delivered to the user.

[0660] Step 5:

[0661] Based on suggestions received from the device, users participate in activities such as yoga classes or other stress management activities. The user's actions and feedback are recorded again by the device and sent to the server as input. The output is data used to develop the next improvement plan based on this information.

[0662] Step 6:

[0663] The server analyzes feedback data and examines the overall sentiment trends of the region. Inputs are feedback and sentiment data collected from multiple users, and outputs include feedback and improvement suggestions for public services.

[0664] Step 7:

[0665] The server provides regional data to government agencies to support improvements in public services. This could potentially lead to improved mental health throughout the region. The input is regional emotional trend data, and the output is a proposal for specific improvement measures.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] [Fourth Embodiment]

[0670] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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).

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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".

[0683] The system according to the present invention comprehensively supports the user's mental health by acquiring and analyzing voice data and proposing counseling. This system consists of terminal, server, and user elements and operates as follows.

[0684] While the user is performing daily tasks or engaging in conversations, the device acquires audio data in the background. This audio data is recorded with ambient noise removed, including voices the user makes unconsciously. The device uses this audio data to analyze the user's emotional state.

[0685] The raw audio data is first pre-processed to remove noise, and then an analysis algorithm is used to identify the user's emotional state. This analysis takes into account factors such as tone, speed, volume, and intonation. The analysis results indicate the user's current emotional state, making it possible to objectively understand their mental state. These analysis results are then transmitted from the terminal to the server.

[0686] Based on the received analysis data, the server analyzes the user's emotional trends by comparing them with historical data. For example, if stress levels are increasing compared to past data, the server will use that information to determine that the user requires attention.

[0687] Next, the server generates a report based on the analyzed data. The report includes insights into the employee's current emotional state, comparisons to the past, and potential mental health issues they may be facing. Based on this report, the server suggests appropriate AI counseling to the user. The suggestion is notified to the user via the terminal, and the user can then receive the necessary counseling session accordingly.

[0688] AI counseling automatically prepares the most suitable program based on the predicted emotional state, providing optimal support for the user. For example, if the user is determined to be experiencing high stress, sessions are offered to learn relaxation methods and stress management skills. The device records the progress of the session and sends feedback to the server for further detailed analysis.

[0689] Thus, the system of the present invention is designed to accurately analyze the user's emotional state and quickly provide appropriate mental health support. This system enables companies to effectively maintain employee health and improve productivity.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The user makes a sound. The user naturally speaks during their daily work or conversations, and that sound is used as audio data.

[0693] Step 2:

[0694] The device acquires audio data. The device operates in the background, recording the user's voice in real time. It removes noise and saves the data in a format suitable for analysis.

[0695] Step 3:

[0696] The device analyzes the audio data. The device processes the acquired audio data and estimates the emotional state based on an audio analysis algorithm. The tone, speed, and volume of the voice are among the elements analyzed.

[0697] Step 4:

[0698] The device sends the analysis results to the server. The device uploads data indicating emotional state to the server via a secure channel. This information is handled with caution as personal data.

[0699] Step 5:

[0700] The server evaluates the analysis data. The server compares it with historical database data to identify user sentiment trends and outliers.

[0701] Step 6:

[0702] The server generates a report. Based on the sentiment assessment, it creates a report that reflects the mental health status, including potential problems and recommended actions.

[0703] Step 7:

[0704] The server proposes a counseling session. Based on the report, it suggests the most suitable AI counseling session for the user and notifies them of this information via their device.

[0705] Step 8:

[0706] The user receives a counseling session. The user participates in the proposed counseling program and receives guidance aimed at improving their mental health.

[0707] Step 9:

[0708] The terminal records the progress of the session. The terminal records the user's activity during the session and sends feedback to the server to support further analysis.

[0709] Step 10:

[0710] The server designs the follow-up. Based on post-session feedback, it builds an ongoing support program and provides users with additional resources and information as needed.

[0711] (Example 1)

[0712] 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".

[0713] In modern society, accurately understanding the mental health status of users and providing appropriate support promptly is crucial. However, conventional methods have made it difficult to effectively analyze acoustic data and assess emotional states in real time. This has led to the possibility of overlooking changes in users' emotions and resulting in situations where appropriate support is not provided. Furthermore, there has been a lack of mechanisms to identify the optimal support method for each individual user.

[0714] 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.

[0715] In this invention, the server includes means for preprocessing acoustic data and removing noise, means for analyzing the emotional state from the preprocessed acoustic data, and means for determining the optimal support method using a generative artificial intelligence model. This makes it possible to accurately evaluate the user's emotional state in real time and provide appropriate support quickly.

[0716] "Acoustic data" refers to digitized information containing frequency data, including sound, and is used to analyze the characteristics of sound.

[0717] "Preprocessing" is the process of removing unwanted noise from acoustic data and converting it into a clean state suitable for analysis.

[0718] "Noise" refers to unwanted sound components contained in acoustic data, which can potentially reduce the accuracy of the analysis results.

[0719] "Emotional state" refers to an evaluation result based on data that indicates an individual's emotional and mood state, such as the degree of stress or relaxation.

[0720] A "report" is a document that summarizes information based on an analyzed emotional state, and it includes details of the user's condition and proposed support.

[0721] "Consultation" refers to psychological support and advice provided to users based on analysis results, including suggestions for specific support methods.

[0722] A "generative artificial intelligence model" is a machine learning model that analyzes data according to a specific purpose and automatically generates optimal suggestions and support for the user.

[0723] "Support methods" refer to techniques for providing advice and action plans tailored to the analyzed emotional state, and include specific approaches to improve the user's mental health.

[0724] This invention is a system that analyzes a user's mental health state and provides appropriate support. This system uses acoustic data to evaluate the user's emotional state in real time and proposes the most suitable counseling method.

[0725] The terminal uses hardware such as a smartphone or tablet device. The microphone built into the terminal routinely acquires the user's acoustic data, which is then pre-processed using signal processing technology. This removes unwanted noise from the acquired acoustic data. For example, filtering algorithms are used in the pre-processing. Specifically, the Python audio processing library "LibROSA" could be used.

[0726] The server analyzes pre-processed acoustic data transmitted from the terminal and evaluates the user's emotional state using a generative AI model. This analysis considers acoustic characteristics such as tone, intonation, speed, and volume of speech. The analysis results are recorded in a database and compared with past data to analyze emotional tendencies. Furthermore, the server monitors changes in the user's emotional state in real time and issues warnings as needed.

[0727] Next, the server uses a generating AI model to suggest optimal counseling support. An example of a specific prompt might be, "What relaxation techniques are recommended when the user is in a high-stress state?" Based on the prompt, the AI ​​model automatically generates a specific support plan for the user, including relaxation methods.

[0728] Users can receive support by following counseling sessions suggested from their device. For example, online sessions to learn meditation or breathing techniques are offered, and the user's progress is recorded by the device. This record is fed back to the server and used to improve future support services.

[0729] In this way, the entire system works together to provide users with appropriate and timely mental health care.

[0730] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0731] Step 1:

[0732] The device acquires the user's everyday conversations and acoustic information using the microphone on their smartphone or tablet. The input is raw acoustic data, which is recorded for later analysis. The device captures this acoustic data in real time and transmits it to the next pre-processing stage.

[0733] Step 2:

[0734] The terminal preprocesses the acquired acoustic data. The input is the raw acoustic data obtained in step 1. By applying a noise reduction algorithm, noise is removed, and clean acoustic data is output. Digital signal processing technology is used in this process, and measures are taken to preserve the characteristics of the sound.

[0735] Step 3:

[0736] The device processes clean acoustic data through an analysis algorithm. The input is pre-processed acoustic data, where features such as voice tone, intonation, speed, and volume are extracted. This allows the user's emotional state, such as "relaxed" or "stressed," to be identified, and the analysis results are generated.

[0737] Step 4:

[0738] The analysis results are sent from the terminal to the server. The input is the emotional state analysis information obtained in step 3. The output is digital data received by the server, communicated using a secure protocol. This data is used for further analysis in the next step.

[0739] Step 5:

[0740] The server analyzes emotional tendencies based on the received analysis data. The input consists of historical data and new analysis data obtained in step 4. The output is an analysis result showing the user's emotional tendencies, including long-term emotional changes. This analysis can detect abnormalities in the user's mental health state.

[0741] Step 6:

[0742] The server generates a report based on the analysis of emotional state and tendencies. The input is the analysis data obtained in step 5, and the output is a report containing information to be shown to the user. This includes the current state and recommended support methods.

[0743] Step 7:

[0744] Based on the generated report, the server uses a generative AI model to propose the optimal counseling plan. The input is the report from step 6, and by inputting the prompt text into the generative AI model, the optimal support plan is obtained. The output is the proposal provided to the user via the terminal.

[0745] Step 8:

[0746] The user receives suggestions from the server via a terminal and participates in a counseling session. The input is the suggestions from the server, and the output is information about the progress of the counseling session obtained through the user's participation. This information is recorded by the terminal after the session.

[0747] Step 9:

[0748] The terminal sends feedback on the user's counseling session to the server. The input is the progress information recorded in step 8, and the output is data to optimize the next counseling session. This feedback is stored on the server and used to improve long-term user support.

[0749] (Application Example 1)

[0750] 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".

[0751] In modern society, the mental health problems individuals face are becoming increasingly diverse, making their management a challenging task. There is a need to understand the changing emotional states in daily life and provide appropriate support promptly. However, conventional technologies often require individuals to consciously recognize emotional changes and seek professional support themselves, limiting the effectiveness of preventative care. Therefore, there is a need for a system that automatically monitors an individual's mental health within the home and proposes support as needed.

[0752] 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.

[0753] In this invention, the server includes means for acquiring voice information, means for analyzing emotional states, means for generating reports, and means for exchanging information and providing guidance within the home. This makes it possible to automatically monitor an individual's mental health state in the home environment and provide appropriate support and guidance in real time.

[0754] "Means for acquiring voice information" refers to devices or processes that detect a user's voice and record it as digital data.

[0755] "Means for analyzing emotional states" refers to the process of analyzing elements such as tone, pace, and intensity from acquired audio information to identify the user's emotional state.

[0756] "Means for generating reports" refers to devices or processes that create reports, including current conditions and trends, based on analyzed emotional states.

[0757] "Means of exchanging information and providing guidance within the home" refers to devices and processes that provide mental health support in a home environment based on analysis results through communication with the user.

[0758] This invention aims to create a system that supports individual mental health within the home. This system is primarily implemented using a device for collecting voice data, a small computer, and cloud services.

[0759] First, the home robot, acting as the terminal, collects user voice information in the background via its microphone. The collected voice information is preprocessed through a noise filtering process. In this process, a "means for acquiring voice information" are used, and the device utilizes a speech recognition service such as the Google Cloud Speech-to-Text API. This converts the voice into text information.

[0760] Next, as a means of analyzing emotional states, external AI services such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to perform emotional analysis on the converted text information. This analysis makes it possible to identify the user's emotional state, and the information is sent to the server.

[0761] On the server, a "report generation mechanism" automatically generates a report showing the user's emotional trends based on the analyzed emotional information. This report visualizes and displays the user's daily emotional patterns and stress fluctuations.

[0762] Furthermore, the server provides personalized advice and counseling suggestions as a means of exchanging information and offering guidance within the home. This utilizes an AI counseling model to present the most suitable mental care plan for the user's current situation. For example, if the server determines that the user has a high stress level, it will suggest relaxation techniques and mindfulness exercises.

[0763] For example, while a user is relaxing in the living room, the robot can analyze the user's voice and suggest, "You seem a little tired today. How about trying a 5-minute meditation session?" In this way, constant mental support is provided within the home.

[0764] An example of a prompt for a generative AI model is, "Analyze the user's emotional state today and suggest ways to relax." This allows users to receive support tailored to their needs and facilitates the management of their mental health in daily life.

[0765] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0766] Step 1:

[0767] The device uses its microphone to collect ambient sound information. The input is ambient sound, and the output is audio data containing noise. This audio data is temporarily stored in the device's memory.

[0768] Step 2:

[0769] The device performs noise filtering to remove unwanted background noise from the audio data. The input is audio data containing noise, and the output is clear audio data with the noise removed. This process uses a digital signal processing algorithm.

[0770] Step 3:

[0771] The device uses the Google Cloud Speech-to-Text API to convert noise-removed audio data into text. The input is clear audio data, and the output is text data that corresponds to the audio. The converted text forms the basis for emotion analysis.

[0772] Step 4:

[0773] The terminal converts text data and uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is text data, and the output is evaluation data regarding the user's emotional state. The emotional analysis utilizes the tone and word choice characteristics of the text.

[0774] Step 5:

[0775] The server generates a report by aggregating the user's daily emotional trends based on emotional state evaluation data received from the terminal. The input is emotional state evaluation data, and the output is a report that visualizes changes and trends in emotions from the past to the present.

[0776] Step 6:

[0777] Based on the reports generated by the server, the system prepares specific guidance and suggestions for AI counseling for the user. The input is the report data, and the output is a personalized mental support plan presented to the user. This includes relaxation methods and stress management advice.

[0778] Step 7:

[0779] The terminal presents the user with guidance from the server in audio and text format. The input is a mental support plan from the server, and the output is feedback to the user through audio guidance and screen displays.

[0780] 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.

[0781] This invention is a system designed to support the mental health of users, comprehensively providing services ranging from voice data acquisition to emotion analysis and counseling suggestions. The system consists of a terminal, a server, and an emotion engine, and is implemented as follows.

[0782] The audio that users produce during their daily work and conversations is acquired in the background by the device. This audio data is processed to remove noise and made suitable for analysis. The device then passes the audio data to the emotion engine for analysis of the user's emotional state.

[0783] The emotion engine has the functionality to analyze the user's emotional state based on the characteristics of the acquired voice data. This engine utilizes machine learning algorithms to analyze voice features and estimate the emotional state with high accuracy. Furthermore, because the emotion engine uses a customized emotion recognition model for each user, it can provide personalized analysis. As a result, the accuracy of emotion analysis improves according to the specific characteristics and circumstances of the user.

[0784] The analyzed emotional state data is sent from the terminal to the server. The server analyzes the user's emotional trends by comparing them with past data and identifies unique patterns. For example, if the stress level has increased compared to past data, the server will recognize this as a problem and determine that countermeasures are necessary.

[0785] Based on this sentiment analysis, the server generates a detailed report. This report provides insights into the user's current and past mental health status and includes suggestions on what kind of counseling would be effective.

[0786] Counseling suggestions are sent from the server to the user via the terminal. The user receives this notification and can participate in the suggested AI counseling session. For example, a session to learn relaxation methods for stress reduction might be suggested. The terminal records the activity and progress during the counseling session and sends this data to the server.

[0787] Based on this feedback data, the server designs further follow-up and additional support plans. This allows users to improve their mental health while receiving continuous support.

[0788] Thus, the system of the present invention is built to more accurately grasp the user's emotional state by incorporating an emotion engine, and to provide effective mental health support. Companies can utilize this to maintain employee health and improve work efficiency.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The user makes a sound. The user naturally speaks during daily work and conversations, and that sound is used by the system.

[0792] Step 2:

[0793] The device acquires audio data. The device operates in a constantly on state, recording the user's voice in real time, performing noise reduction, and saving the data in a format suitable for analysis.

[0794] Step 3:

[0795] The device sends voice data to the emotion engine. The device then transfers the pre-processed voice data to the emotion engine and requests an analysis of the user's emotional state.

[0796] Step 4:

[0797] The emotion engine analyzes the audio data. The emotion engine uses machine learning algorithms to analyze the audio features and estimate the user's emotional state with high accuracy.

[0798] Step 5:

[0799] The emotion engine returns the analysis results to the terminal. The emotion engine sends the estimated emotional state back to the terminal and prepares for the next processing step.

[0800] Step 6:

[0801] The device sends the analysis results to the server. The device sends emotional state data to the server and requests further evaluation and processing.

[0802] Step 7:

[0803] The server evaluates the analysis data. The server identifies user sentiment trends and anomalies by comparing them with the history of past database data.

[0804] Step 8:

[0805] The server generates a report. Based on the collected data, the server creates a detailed report on the mental health status and prepares for the next steps.

[0806] Step 9:

[0807] The server proposes a counseling session. Based on the generated report, the server suggests an appropriate AI counseling session for the user.

[0808] Step 10:

[0809] The user receives a counseling session. The user attends the counseling session provided by the server and takes the necessary actions according to the instructions.

[0810] Step 11:

[0811] The terminal records the progress of the session. The terminal records user activity and sends this data to the server to support further analysis.

[0812] Step 12:

[0813] The server designs the follow-up. Based on post-session feedback, the server builds and prepares to provide ongoing support programs to users.

[0814] (Example 2)

[0815] 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".

[0816] In modern society, managing and supporting the mental health of users is a crucial issue. However, accurately analyzing users' emotional states in real time and providing appropriate support based on that analysis is difficult. Furthermore, there is a demand for personalized services that address individual emotional states. A system is needed to solve these challenges.

[0817] 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.

[0818] In this invention, the server includes means for acquiring audio signals, means for utilizing a model for analyzing the audio signals and estimating emotional states, and means for generating information using the analysis results. This makes it possible to monitor the user's emotional state in real time and immediately propose appropriate support activities based on the results.

[0819] "Audio signals" refer to data including the user's voice and surrounding sounds, and are used to analyze their emotional state.

[0820] An "estimation model" is an algorithm used to analyze emotional states from acquired audio signals, and is built using machine learning techniques.

[0821] "Information" refers to data, including mental health reports and suggestions, that are generated based on the analysis results of audio signals and provided to users.

[0822] "Support activities" refer to actions such as counseling and action plans proposed based on the analysis of the user's emotional state, with the aim of improving their mental health.

[0823] A "server" is a computer system used to receive and analyze audio signals and manage the generated information.

[0824] This system is designed to effectively support users' mental health and comprehensively covers everything from acquiring voice signals and analyzing emotions to suggesting counseling. A specific implementation is shown below.

[0825] The device acquires audio signals emitted in the user's daily life and work environment in the background. These audio signals are temporarily stored on the device and processed using an audio processing library (e.g., FFmpeg) to remove noise. After the audio is cleared through noise reduction, the audio signal is sent to the server.

[0826] The server utilizes a generative AI model to analyze the received audio signal. This model employs a machine learning framework (e.g., TensorFlow) to analyze audio characteristics and estimate the user's emotional state. The estimated emotional information is compared with past data to determine the user's emotional trends. This provides insights into the user's current mental health state.

[0827] The server generates a report based on the analysis of the user's emotional state and suggests appropriate support activities. For example, for a user with a high stress level, it may suggest counseling that introduces relaxation methods. This report and suggestion are notified to the user via their terminal.

[0828] Users can accept suggestions received from their device and participate in an AI counseling session. The device records the user's responses and progress during the session and sends this feedback data to the server. Based on this feedback, the server develops an ongoing support plan for the user and provides personalized assistance on an ongoing basis.

[0829] For example, if a user is in a stressful work environment, this system can be used to quickly detect signs of stress and continuously suggest countermeasures. An example of a prompt message would be, "Analyze the user's emotional state from the following audio data and suggest the most appropriate counseling." In this way, it is possible to support the emotional health of users in both public and private settings.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The device activates its recording function and acquires the user's voice signal in the background. This input voice signal is temporarily stored within the device. Specifically, the device uses its microphone to sample ambient sounds and the user's voice and saves them as a digital audio file.

[0833] Step 2:

[0834] The terminal performs noise reduction processing on the audio signal. It applies a noise reduction algorithm to the input audio data, resulting in the output of clear audio data. Specifically, it uses an audio processing library (e.g., FFmpeg) to reduce background noise and enhance only the human voice.

[0835] Step 3:

[0836] The device sends clear audio data to the server. The server receives this transmitted audio data and inputs it into a generative AI model to analyze the emotional state. Specifically, the device uploads the data to the server using a secure network protocol.

[0837] Step 4:

[0838] The server inputs the received audio data into a generating AI model, extracts audio features, and estimates the emotional state. The output is estimated emotional state data. Specifically, the algorithm used analyzes the tone, pitch, and speed of the audio and assigns an emotional label based on these factors.

[0839] Step 5:

[0840] The server compares estimated emotional state data with historical data to analyze the user's emotional trends. It performs trend analysis using the input emotional data and historical data, and outputs a trend report as a result. For example, the server searches a database to track fluctuations in the user's stress level.

[0841] Step 6:

[0842] The server generates a report that proposes support activities based on the analysis. It then prepares to notify the user of this generated report via the terminal. Specifically, the server uses a natural language generation system to create easy-to-understand suggestions.

[0843] Step 7:

[0844] The device notifies the user of reports received from the server. The user is then provided with instructions on how to participate in the counseling session. Specifically, the device uses push notifications to inform the user.

[0845] Step 8:

[0846] The user participates in a proposed counseling session. The device collects feedback data generated during this process and sends that data back to the server. Specifically, the device records the user's responses during the counseling session and uses this as digital feedback data.

[0847] Step 9:

[0848] The server analyzes feedback data and develops plans for further support activities for the user. Based on the input feedback data, it designs supplementary support and outputs it as a plan. For example, the server creates a new counseling schedule and proposes the next steps for the user.

[0849] (Application Example 2)

[0850] 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".

[0851] In modern society, maintaining and improving the mental health of individuals and communities is a crucial issue. In particular, there is a need for effective methods to alleviate daily stress and anxiety, and support tailored to individual needs is essential. Furthermore, understanding the emotional state of the entire community and utilizing this information to improve public services is also a vital challenge.

[0852] 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.

[0853] In this invention, the server includes means for acquiring voice information, means for providing information on local events, and means for providing feedback to improve public services. This enables the provision of appropriate mental health support to individuals and the improvement of well-being for the entire community.

[0854] "Audio information" refers to data that records the words and sounds spoken by a user in digital format.

[0855] "Emotional state" refers to the user's psychological tendencies and mood, obtained by analyzing voice information.

[0856] A "report" is a document that summarizes the results of an analysis of the user's emotional state, providing insights into the user's mental health.

[0857] "Users" refer to individuals who use this system and are eligible to receive mental health support.

[0858] "Counseling" refers to advice or sessions provided to improve a user's emotional state.

[0859] "Local event information" refers to information about events and activities held in the local community, providing users with opportunities for relaxation and stress relief.

[0860] "Emotional tendencies" refer to general psychological tendencies obtained by analyzing the emotional states of multiple users or the entire community.

[0861] "Public services" refer to activities and support provided by government agencies and other organizations for the purpose of benefiting society.

[0862] "Feedback" refers to information and suggestions provided based on analysis results, aimed at encouraging improvements to specific behaviors or environments.

[0863] The system implementing this invention consists of a terminal with multiple functions and a server. First, the terminal is responsible for acquiring the user's voice information. The hardware used for this is a smartphone or other device with voice input capabilities, and voice processing software such as "Librosa" is used to remove ambient noise during recording.

[0864] Next, the server receives the audio information and performs analysis. The server uses machine learning libraries such as "TensorFlow" to analyze the emotional state contained in the audio information. This analysis allows for a highly accurate estimation of the user's psychological tendencies. The analysis results are managed on the server and generated as a report.

[0865] The generated reports include personalized counseling suggestions for each user. These suggestions, such as information on local events or individual relaxation methods, are sent from the server to the user's device and notified to the user. Push notification technologies such as "Firebase Cloud Messaging" are used for these notifications. The server also analyzes the emotional trends of the entire region and provides feedback based on that data to help improve public services.

[0866] For example, if the analysis indicates that a user is experiencing stress, the server might suggest they attend a nearby yoga class. Furthermore, the server can use the user's feedback as data to improve its analysis model.

[0867] An example of a prompt for a generative AI model is: "Please tell me why you felt more stressed than usual last night. And if you want to know what kind of support you would like to receive to address this, what kind of support would you like?"

[0868] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0869] Step 1:

[0870] The device acquires the user's voice through the microphone. The input is the user's voice, and the output is the recorded voice saved as digital data. The acquired voice data is processed using "Librosa" to remove noise. This processing improves the quality of the voice and enables accurate sentiment analysis.

[0871] Step 2:

[0872] The device sends the denoised audio data to the server. The server receives this audio data as input and proceeds with sentiment analysis. Here, it utilizes a TensorFlow machine learning model to extract audio features and analyze the user's emotional state. The output is data regarding the type and intensity of the emotion.

[0873] Step 3:

[0874] The server generates a report based on the emotion analysis results. The input is data on the type and intensity of emotions, and the output is a report summarizing insights into the user's mental health. This report also includes specific counseling suggestions for the user.

[0875] Step 4:

[0876] The server sends the generated report to the device. The device receives this report and notifies the user. The notification is performed using Firebase Cloud Messaging technology. The input in this process is the report, and the output is the notification information delivered to the user.

[0877] Step 5:

[0878] Based on suggestions received from the device, users participate in activities such as yoga classes or other stress management activities. The user's actions and feedback are recorded again by the device and sent to the server as input. The output is data used to develop the next improvement plan based on this information.

[0879] Step 6:

[0880] The server analyzes feedback data and examines the overall sentiment trends of the region. Inputs are feedback and sentiment data collected from multiple users, and outputs include feedback and improvement suggestions for public services.

[0881] Step 7:

[0882] The server provides regional data to government agencies to support improvements in public services. This could potentially lead to improved mental health throughout the region. The input is regional emotional trend data, and the output is a proposal for specific improvement measures.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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."

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] The following is further disclosed regarding the embodiments described above.

[0905] (Claim 1)

[0906] Means for acquiring audio data,

[0907] A means for analyzing emotional state from the aforementioned audio data,

[0908] A means for generating a report based on the aforementioned emotional state,

[0909] A means of proposing counseling to the user based on the aforementioned report,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for removing noise in the analysis of the aforementioned audio data.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for providing the user with a specific support plan in the counseling proposal.

[0915] "Example 1"

[0916] (Claim 1)

[0917] Means for acquiring acoustic data,

[0918] The means for preprocessing the aforementioned acoustic data and removing noise,

[0919] A means for analyzing emotional states from the aforementioned pre-processed acoustic data,

[0920] A means for creating a report based on the results of the analysis of the aforementioned emotional state,

[0921] Based on the aforementioned report, means of proposing consultation to users,

[0922] In this consultation, a means of determining the optimal support method using a generative artificial intelligence model,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, further comprising means for analyzing emotional tendencies by comparing acoustic data with past history.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising means for evaluating the user's emotional state in real time and notifying changes therein.

[0928] "Application Example 1"

[0929] (Claim 1)

[0930] Means for acquiring audio information,

[0931] A means for analyzing emotional states from the aforementioned audio information,

[0932] A means for generating a report based on the aforementioned emotional state,

[0933] Means of proposing support to users based on the aforementioned report,

[0934] A means of exchanging information with users within their homes and providing guidance,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, further comprising means for removing noise in the analysis of the aforementioned audio information.

[0938] (Claim 3)

[0939] The system according to claim 1, further comprising means for providing a specific assistance plan to the user in the aforementioned support proposal.

[0940] "Example 2 of combining an emotion engine"

[0941] (Claim 1)

[0942] Means for acquiring audio signals,

[0943] A means for utilizing an estimation model to analyze emotional states from the aforementioned audio signals,

[0944] means for generating information based on the aforementioned emotional state,

[0945] A means of proposing support activities to users based on the aforementioned information,

[0946] A means of accumulating analysis results and analyzing trends,

[0947] A method that uses an individualized emotion recognition model according to the analysis results,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, further comprising means for removing noise in the analysis of the audio signal.

[0951] (Claim 3)

[0952] The system according to claim 1, further comprising means for providing a specific support plan to a user in the proposed support activities.

[0953] "Application example 2 when combining with an emotional engine"

[0954] (Claim 1)

[0955] Means for acquiring audio information,

[0956] A means for analyzing emotional state from the aforementioned audio information,

[0957] A means for generating a report based on the aforementioned emotional state,

[0958] Based on the aforementioned report, means of proposing counseling to the user,

[0959] A means of providing the aforementioned users with information on local events,

[0960] A means for analyzing the emotional trends of the entire region,

[0961] Means of providing feedback to improve public services,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, further comprising means for removing noise in the analysis of the aforementioned audio information.

[0965] (Claim 3)

[0966] The system according to claim 1, further comprising means for providing a specific support plan to the user in the counseling proposal. [Explanation of Symbols]

[0967] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring audio information, A means for analyzing emotional states from the aforementioned audio information, A means for generating a report based on the aforementioned emotional state, Means of proposing support to users based on the aforementioned report, A means of exchanging information with users within their homes and providing guidance, A system that includes this.

2. The system according to claim 1, further comprising means for removing noise in the analysis of the aforementioned audio information.

3. The system according to claim 1, further comprising means for providing a specific assistance plan to the user in the aforementioned support proposal.

Images