system

Abstract

The system according to this embodiment aims to analyze the user's daily emotions and mental state and encourage them to consult a specialist early on. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, and a promotion unit. The collection unit collects the user's daily conversations and texts. The analysis unit analyzes the data collected by the collection unit. The determination unit determines the user's emotions and mental state based on the data analyzed by the analysis unit. The promotion unit encourages the user to consult a specialist based on the results determined by the determination unit.

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, the method including the 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 that responds 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 the conventional technology, there is a problem that it is difficult to appropriately grasp the daily emotions and mental states of users and respond promptly.

[0005] The system according to the embodiment aims to analyze the daily emotions and mental states of users and prompt early consultation with experts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a promotion unit. The collection unit collects the user's daily conversations and texts. The analysis unit analyzes the data collected by the collection unit. The determination unit determines the user's emotions and mental state based on the data analyzed by the analysis unit. The promotion unit encourages the user to consult a specialist based on the results determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's daily emotions and mental state and prompt them to seek professional help early on. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mental care agent according to an embodiment of the present invention is an AI assistant that analyzes the user's emotions and mental state from daily conversations and texts, and detects stress and anxiety early. The mental care agent analyzes emotions from both voice and text, and uses an evolving algorithm based on machine learning to capture emotional changes in real time. This supports the user's mental health and prompts them to consult a professional when necessary. Furthermore, advanced security measures are in place to protect privacy. The mental care agent consists of the following steps: First, it collects the user's daily conversations and texts. Next, the AI ​​analyzes the collected data to determine the user's emotions and mental state. Based on the determination results, it detects stress and anxiety early and prompts the user to consult a professional when necessary. This mechanism improves the accuracy of emotion analysis and increases the rate of early intervention. It is also expected to reduce medical costs through the prevention of mental health problems and improve the user's quality of life. Furthermore, it is possible to evolve the service by incorporating emotion recognition using the latest AI analysis technology and user feedback. Mental Care Agent targets men and women aged 20 to 60, people in stressful occupations, and companies of all sizes, from small businesses to large corporations, that focus on employee health management. This helps address challenges such as decreased productivity and increased absenteeism due to stress and mental health issues, identify mental health problems, and provide appropriate support. Through this, Mental Care Agent can support users' mental health and, when necessary, encourage them to consult with professionals.

[0029] The mental care agent according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a promotion unit. The collection unit collects the user's daily conversations and texts. The collection unit can collect data in the form of, for example, voice calls, chat messages, and emails. The collection unit can record voice data and save text data. The collection unit can record voice calls and save text messages. The collection unit can also save the contents of emails. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data by methods such as sentiment analysis, keyword extraction, and contextual analysis. The analysis unit performs sentiment analysis to determine the user's emotions. The analysis unit performs keyword extraction to extract important information. The analysis unit performs contextual analysis to understand the meaning of the data. The determination unit determines emotions and mental states based on the data analyzed by the analysis unit. The determination unit can make judgments based on criteria such as emotion scoring and mental state classification. The determination unit performs emotion scoring to quantify the user's emotions. The assessment unit classifies mental states and identifies the user's mental state. Based on the analysis results, the assessment unit determines the user's emotions and mental state. The promotion unit encourages consultation with a professional based on the results determined by the assessment unit. The promotion unit can provide promotion based on criteria such as the method, timing, and content of the notification. The promotion unit sends a notification and encourages the user to consult with a professional. The promotion unit adjusts the timing and sends the notification at an appropriate time. The promotion unit adjusts the content of the notification and provides the user with appropriate information. As a result, the mental care agent according to the embodiment can support the user's mental health by collecting, analyzing, assessing, and promoting the user's daily conversations and texts.

[0030] The data collection unit collects users' daily conversations and texts. The unit can collect data in various formats, such as voice calls, chat messages, and emails. Specifically, in the case of voice calls, the unit records the call content and stores it as audio data. This includes the ability to record all audio from the start to the end of the call in high quality, with the user's permission. In the case of chat messages, the unit retrieves and stores text data in real time from the messaging application used by the user. This includes not only messages sent by the user but also messages received. In the case of emails, the unit accesses the user's email account and periodically scans and stores the content of sent and received emails. This allows for comprehensive collection of the user's entire communication. Furthermore, the unit centrally manages this data and stores it using encryption technology to ensure security. This allows for efficient collection of necessary data while protecting user privacy. With the user's permission, the unit can adjust the frequency and scope of data collection, enabling flexible responses to user needs and circumstances.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as sentiment analysis, keyword extraction, and contextual analysis. Specifically, sentiment analysis uses natural language processing technology to determine the user's emotions from text and audio data. For example, in the case of audio data, speech recognition technology is used to convert the audio into text, and the emotions contained in the user's statements are identified by analyzing that text. The sentiment analysis algorithm classifies emotions into categories such as positive, negative, and neutral, and scores the intensity of the emotions. Keyword extraction extracts important keywords from the user's statements and messages to identify the user's concerns and problems. For example, by extracting words and phrases that the user frequently uses and analyzing their frequency of occurrence and context, important information related to the user's mental state can be grasped. Contextual analysis considers the context of the user's statements and messages to understand the meaning of the data. This allows for a more accurate understanding of the user's intentions and emotional changes, rather than just simple keyword extraction. The analysis unit integrates these analysis results to comprehensively evaluate the user's mental state. Furthermore, the analysis unit can utilize past data and statistical information to analyze changes and trends in users' mental states and provide information for long-term mental care.

[0032] The judgment unit determines emotions and mental states based on data analyzed by the analysis unit. The judgment unit can make judgments based on criteria such as emotion scoring and mental state classification. Specifically, in emotion scoring, the judgment unit quantifies the user's emotions based on the results of emotion analysis provided by the analysis unit. For example, it assigns high scores to positive emotions and low scores to negative emotions, quantitatively evaluating the intensity of the user's emotions. In mental state classification, the judgment unit classifies the user's mental state into multiple categories based on the user's emotion score and keyword extraction results. For example, it classifies into categories such as stress, depression, anxiety, and stability to identify the user's current mental state. Based on these judgment results, the judgment unit comprehensively evaluates the user's mental state and provides information for taking necessary measures. Furthermore, the judgment unit can utilize past data and statistical information to analyze changes and trends in the user's mental state and provide information for long-term mental care. For example, if the user's mental state has deteriorated over a certain period, measures such as encouraging consultation with a specialist early on can be taken. This allows the judgment unit to accurately understand the user's mental state and provide information to take appropriate measures.

[0033] The Facilitation Unit encourages users to consult with professionals based on the results determined by the Assessment Unit. The Facilitation Unit can promote consultations based on criteria such as the method, timing, and content of notifications. Specifically, it uses methods such as smartphone push notifications, email, SMS, and voice calls to communicate information to users. The timing of notifications is adjusted considering the user's lifestyle and mental state. For example, notifications can be sent during times when the user is relaxed or when it is determined that their stress levels are high, effectively encouraging them to consult with professionals. The content of notifications is customized according to the user's mental state and situation. For example, if a user is feeling stressed, a notification including stress management methods and relaxation suggestions will be sent. Also, if a user is determined to be depressed, a notification strongly recommending consultation with a professional will be sent. Through these notifications, the Facilitation Unit helps users receive professional support at the appropriate time. Furthermore, the Facilitation Unit collects user feedback and evaluates the effectiveness of notifications. For example, it records how users reacted to notifications and uses that data to improve the notification methods and content. This allows the promotion department to provide effective support to users and facilitate improvements in their mental health.

[0034] The analysis unit can analyze emotions from both audio and text. The analysis unit can analyze data in various formats, such as recorded audio, real-time audio, text messages, and emails. The analysis unit analyzes recorded audio to determine the user's emotions. The analysis unit analyzes real-time audio to determine the user's emotions in real time. The analysis unit analyzes text messages to determine the user's emotions. The analysis unit analyzes the content of emails to determine the user's emotions. By analyzing emotions from both audio and text, the accuracy of emotion analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data into a generative AI, which can then analyze the audio data to determine the emotions.

[0035] The evolutionary unit can use evolving algorithms based on machine learning. The evolutionary unit can use algorithms of various types, such as self-learning algorithms and adaptive algorithms. Using a self-learning algorithm, the evolutionary unit learns from data and evolves the algorithm. Using an adaptive algorithm, the evolutionary unit evolves the algorithm while adapting to the environment. By evolving the algorithm, the evolutionary unit improves the accuracy of the system. Thus, using evolving algorithms based on machine learning improves the accuracy of the system. Some or all of the above-described processes in the evolutionary unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evolutionary unit can input data into a generative AI, which can learn from the data and evolve the algorithm.

[0036] The Security Department can implement advanced security measures to protect privacy. For example, the Security Department can implement security measures through methods such as encryption technology, access control, and data protection policies. The Security Department uses encryption technology to encrypt and protect data. The Security Department implements access control to restrict access to data. The Security Department develops data protection policies to ensure thorough data protection. This allows for the secure protection of user data through advanced security measures to protect privacy.

[0037] The data collection unit can analyze the user's past conversation history and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods (voice, text, etc.) that the user has frequently used in the past. The data collection unit may select a data collection method for a specific time period from the user's past conversation history. The data collection unit analyzes the user's past conversation history and selects the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past conversation history data into a generating AI, which can then select the optimal data collection method.

[0038] The data collection unit can filter conversations and texts based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting conversations and texts relevant to the user's current lifestyle. The data collection unit filters relevant conversations and texts based on the user's areas of interest. The data collection unit determines the priority of data to collect based on the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data when collecting conversations or text, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit filters highly relevant data based on the user's geographical location. The data collection unit determines the priority of data to collect, taking into account the user's geographical location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant data.

[0040] The data collection unit can analyze the user's social media activity and collect relevant data when collecting conversations and texts. For example, the data collection unit prioritizes collecting relevant conversations and texts based on the user's social media activity. The data collection unit analyzes the user's social media activity and filters the relevant data. The data collection unit determines the priority of the data to be collected based on the user's social media activity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For data with low importance, the analysis unit performs a simplified analysis. The analysis unit determines the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For audio data, it applies a speech recognition algorithm. For image data, it applies an image analysis algorithm. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply different analysis algorithms depending on the category.

[0043] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit will prioritize the analysis of recently submitted data. The analysis unit will postpone the analysis of older data. The analysis unit will adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI, which can then determine the priority of analysis based on the submission date.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit determines the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then adjust the order of analysis based on the relevance.

[0045] The determination unit can improve the accuracy of its determination by considering the interrelationships of the data during the determination process. For example, the determination unit analyzes the interrelationships of the data and makes a determination based on the data with high relevance. The determination unit improves the accuracy of its determination by considering the interrelationships of the data. The determination unit determines the priority of the determination based on the interrelationships of the data. This improves the accuracy of the determination by considering the interrelationships of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the interrelationships of the data into a generating AI, and the generating AI can improve the accuracy of the determination by considering the interrelationships.

[0046] The determination unit can make a determination by considering the attribute information of the data submitter. For example, the determination unit may consider the age and gender of the data submitter. The determination unit may consider the occupation and living environment of the data submitter. The determination unit adjusts the criteria for determination based on the attribute information of the data submitter. This makes it possible to make a more appropriate determination by considering the attribute information of the data submitter. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the attribute information of the data submitter into a generating AI, and the generating AI can make a determination by considering the attribute information.

[0047] The determination unit can make a determination while considering the geographical distribution of the data. For example, the determination unit analyzes the geographical distribution of the data and makes a determination while considering the characteristics of each region. The determination unit adjusts the criteria for determination based on the geographical distribution of the data. The determination unit determines the priority of determination while considering the geographical distribution of the data. This makes it possible to make determinations that reflect the characteristics of each region by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of the data into a generating AI, and the generating AI can make a determination while considering the geographical distribution.

[0048] The judgment unit can improve the accuracy of its judgment by referring to relevant literature for the data during the judgment process. For example, the judgment unit can refer to relevant literature for the data to improve the accuracy of its judgment. The judgment unit adjusts the criteria for judgment based on the relevant literature for the data. The judgment unit determines the priority of judgment considering the relevant literature for the data. As a result, the accuracy of judgment is improved by referring to relevant literature for the data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature for the data into a generating AI, and the generating AI can refer to the relevant literature to improve the accuracy of its judgment.

[0049] The promotion unit can analyze the user's past consultation history and select the optimal promotion method during the promotion process. For example, the promotion unit selects the optimal promotion method based on the user's past consultation history. The promotion unit analyzes the user's past consultation history and selects an effective promotion method. The promotion unit adjusts the timing of consultations by referring to the user's past consultation history. In this way, the optimal promotion method can be selected by analyzing the user's past consultation history. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's past consultation history data into a generating AI, and the generating AI can select the optimal promotion method.

[0050] The promotion unit can customize the means of promotion based on the user's current living situation during the promotion process. For example, the promotion unit can suggest the most suitable consultation method based on the user's current living situation. The promotion unit can adjust the timing of the consultation according to the user's living situation. The promotion unit can customize the means of consultation based on the user's living situation. In this way, by customizing the means of promotion based on the user's living situation, a more appropriate consultation method can be provided. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without using AI. For example, the promotion unit can input the user's living situation data into a generating AI, and the generating AI can suggest the most suitable consultation method.

[0051] The promotion unit can select the optimal promotion method during promotion, taking into account the user's geographical location information. For example, the promotion unit can suggest the optimal consultation location based on the user's geographical location information. The promotion unit can adjust the timing of the consultation, taking into account the user's geographical location information. The promotion unit can select the means of consultation based on the user's geographical location information. In this way, by taking into account the user's geographical location information, the optimal consultation location and means can be provided. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without using AI. For example, the promotion unit can input the user's geographical location information data into a generating AI, and the generating AI can suggest the optimal consultation location and means.

[0052] The promotion unit can analyze the user's social media activity and propose promotion methods during the promotion process. For example, the promotion unit can propose the most suitable consultation method based on the user's social media activity. The promotion unit analyzes the user's social media activity and proposes effective promotion methods. The promotion unit adjusts the timing of consultations based on the user's social media activity. In this way, by analyzing the user's social media activity, the promotion unit can propose the most suitable consultation method. Some or all of the above processes in the promotion unit may be performed using AI, for example, or not using AI. For example, the promotion unit can input the user's social media activity data into a generating AI, which can then propose the most suitable consultation method.

[0053] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio and text during the analysis. For example, the analysis unit performs a detailed analysis on audio and text with high importance. The analysis unit performs a simplified analysis on audio and text with low importance. The analysis unit determines the priority of the analysis based on the importance of the audio and text. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio and text. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the audio and text into a generating AI, which can then adjust the level of detail of the analysis based on the importance.

[0054] The analysis unit can apply different analysis algorithms depending on the audio and text categories during analysis. For example, the analysis unit applies a natural language processing algorithm to text data, a speech recognition algorithm to audio data, and an image analysis algorithm to image data. By applying different analysis algorithms depending on the audio and text categories, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the audio and text categories into a generating AI, which can then apply different analysis algorithms depending on the category.

[0055] The analysis unit can prioritize analysis based on the submission dates of audio and text. For example, the analysis unit will prioritize analyzing recently submitted audio and text. It will postpone the analysis of older audio and text submissions. The analysis unit adjusts the analysis schedule based on the submission dates. This enables efficient analysis by prioritizing analysis based on the submission dates of audio and text. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of audio and text into a generating AI, which can then prioritize analysis based on the submission dates.

[0056] The analysis unit can adjust the order of analysis based on the relevance between audio and text during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant audio and text. It postpones the analysis of less relevant audio and text. The analysis unit determines the order of analysis based on the relevance between audio and text. This allows for efficient analysis by adjusting the order of analysis based on the relevance between audio and text. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance between audio and text into a generating AI, which can then adjust the order of analysis based on the relevance.

[0057] The evolutionary unit can optimize the evolutionary algorithm by referring to past training data during evolution. For example, the evolutionary unit optimizes the evolutionary algorithm based on past training data. The evolutionary unit improves the accuracy of the evolutionary algorithm by referring to past training data. The evolutionary unit adjusts the parameters of the evolutionary algorithm based on past training data. This improves the accuracy of the evolutionary algorithm by referring to past training data. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can input past training data into a generating AI, and the generating AI can optimize the evolutionary algorithm by referring to the training data.

[0058] The evolutionary unit can weight the evolutionary algorithm based on the data submission date during evolution. For example, the evolutionary unit weights the evolutionary algorithm for recently submitted data. The evolutionary unit adjusts the weighting of the evolutionary algorithm for older submitted data. The evolutionary unit adjusts the parameters of the evolutionary algorithm based on the submission date. This allows for more appropriate evolution by weighting the evolutionary algorithm based on the data submission date. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can input the data submission date into a generating AI, which can then weight the evolutionary algorithm based on the submission date.

[0059] The security department can select the optimal security measures by referring to past security incidents when implementing security measures. For example, the security department selects the optimal measures based on past security incidents. The security department improves the accuracy of security measures by referring to past security incidents. The security department adjusts the parameters of security measures based on past security incidents. This allows the security department to select the optimal security measures by referring to past security incidents. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input past security incident data into a generating AI, and the generating AI can refer to the incidents and select the optimal measures.

[0060] The security department can select the optimal security measures by considering the user's geographical location information. For example, the security department selects the optimal security measures based on the user's geographical location information. The security department determines the priority of security measures by considering the user's geographical location information. The security department adjusts the parameters of security measures based on the user's geographical location information. This allows the security department to select the optimal security measures by considering the user's geographical location information. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal measures by considering the geographical location information.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] A mental health agent can analyze a user's past mental health data to understand their mental health trends. For example, it can identify periods and situations in the user's past that tended to cause stress and suggest preventative measures based on that information. It can also identify periods and situations in the user's past that made them feel relaxed and provide advice on how to relax based on that information. Furthermore, it can identify periods and situations in the user's past that made them feel rushed and provide advice on how to complete tasks efficiently based on that information. In this way, it is possible to provide more effective support by utilizing the user's past mental health data.

[0063] Mental care agents can collect users' lifestyle data and identify factors that influence their mental health. For example, they can analyze users' sleep patterns, diet, and exercise habits to assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's lifestyle data. Furthermore, they can continuously monitor the user's lifestyle data and update the advice as needed. This allows for more effective mental health support by leveraging the user's lifestyle data.

[0064] Mental health agents can analyze users' social network data to identify relationships that influence their mental health. For example, they can analyze who users frequently communicate with and the nature of those relationships, and assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's social network data. Furthermore, they can continuously monitor the user's social network data and update advice as needed. This allows for more effective mental health support by leveraging the user's social network data.

[0065] Mental care agents can leverage users' geographical location information to identify environmental factors that influence their mental health. For example, they can analyze places users frequently visit and the environment of those places (e.g., noise levels and congestion) to assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's geographical location information. Furthermore, they can continuously monitor the user's geographical location information and update advice as needed. This allows for more effective mental health support by utilizing the user's geographical location information.

[0066] Mental care agents can collect users' health data and identify physical factors that affect their mental health. For example, they can monitor vital signs such as heart rate, blood pressure, and body temperature and assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's health data. Furthermore, they can continuously monitor the user's health data and update the advice as needed. This allows them to leverage the user's health data to provide more effective mental health support.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects the user's daily conversations and texts. The collection unit can collect data in various formats, such as voice calls, chat messages, and emails. The collection unit can record voice data and save text data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as sentiment analysis, keyword extraction, and contextual analysis. The analysis unit performs sentiment analysis to determine the user's emotions. The analysis unit performs keyword extraction to extract important information. The analysis unit performs contextual analysis to understand the meaning of the data. Step 3: The judgment unit determines the emotions and mental state based on the data analyzed by the analysis unit. The judgment unit can make judgments based on criteria such as emotion scoring and mental state classification. The judgment unit scores emotions and quantifies the user's emotions. The judgment unit classifies mental states and identifies the user's mental state. Step 4: The Facilitation Unit prompts the user to consult with an expert based on the results determined by the Judgment Unit. The Facilitation Unit can facilitate consultation based on criteria such as the method, timing, and content of the notification. The Facilitation Unit issues a notification and prompts the user to consult with an expert. The Facilitation Unit adjusts the timing and issues the notification at an appropriate time. The Facilitation Unit adjusts the content of the notification and provides the user with appropriate information.

[0069] (Example of form 2) The mental care agent according to an embodiment of the present invention is an AI assistant that analyzes the user's emotions and mental state from daily conversations and texts, and detects stress and anxiety early. The mental care agent analyzes emotions from both voice and text, and uses an evolving algorithm based on machine learning to capture emotional changes in real time. This supports the user's mental health and prompts them to consult a professional when necessary. Furthermore, advanced security measures are in place to protect privacy. The mental care agent consists of the following steps: First, it collects the user's daily conversations and texts. Next, the AI ​​analyzes the collected data to determine the user's emotions and mental state. Based on the determination results, it detects stress and anxiety early and prompts the user to consult a professional when necessary. This mechanism improves the accuracy of emotion analysis and increases the rate of early intervention. It is also expected to reduce medical costs through the prevention of mental health problems and improve the user's quality of life. Furthermore, it is possible to evolve the service by incorporating emotion recognition using the latest AI analysis technology and user feedback. Mental Care Agent targets men and women aged 20 to 60, people in stressful occupations, and companies of all sizes, from small businesses to large corporations, that focus on employee health management. This helps address challenges such as decreased productivity and increased absenteeism due to stress and mental health issues, identify mental health problems, and provide appropriate support. Through this, Mental Care Agent can support users' mental health and, when necessary, encourage them to consult with professionals.

[0070] The mental care agent according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a promotion unit. The collection unit collects the user's daily conversations and texts. The collection unit can collect data in the form of, for example, voice calls, chat messages, and emails. The collection unit can record voice data and save text data. The collection unit can record voice calls and save text messages. The collection unit can also save the contents of emails. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data by methods such as sentiment analysis, keyword extraction, and contextual analysis. The analysis unit performs sentiment analysis to determine the user's emotions. The analysis unit performs keyword extraction to extract important information. The analysis unit performs contextual analysis to understand the meaning of the data. The determination unit determines emotions and mental states based on the data analyzed by the analysis unit. The determination unit can make judgments based on criteria such as emotion scoring and mental state classification. The determination unit performs emotion scoring to quantify the user's emotions. The assessment unit classifies mental states and identifies the user's mental state. Based on the analysis results, the assessment unit determines the user's emotions and mental state. The promotion unit encourages consultation with a professional based on the results determined by the assessment unit. The promotion unit can provide promotion based on criteria such as the method, timing, and content of the notification. The promotion unit sends a notification and encourages the user to consult with a professional. The promotion unit adjusts the timing and sends the notification at an appropriate time. The promotion unit adjusts the content of the notification and provides the user with appropriate information. As a result, the mental care agent according to the embodiment can support the user's mental health by collecting, analyzing, assessing, and promoting the user's daily conversations and texts.

[0071] The data collection unit collects users' daily conversations and texts. The unit can collect data in various formats, such as voice calls, chat messages, and emails. Specifically, in the case of voice calls, the unit records the call content and stores it as audio data. This includes the ability to record all audio from the start to the end of the call in high quality, with the user's permission. In the case of chat messages, the unit retrieves and stores text data in real time from the messaging application used by the user. This includes not only messages sent by the user but also messages received. In the case of emails, the unit accesses the user's email account and periodically scans and stores the content of sent and received emails. This allows for comprehensive collection of the user's entire communication. Furthermore, the unit centrally manages this data and stores it using encryption technology to ensure security. This allows for efficient collection of necessary data while protecting user privacy. With the user's permission, the unit can adjust the frequency and scope of data collection, enabling flexible responses to user needs and circumstances.

[0072] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as sentiment analysis, keyword extraction, and contextual analysis. Specifically, sentiment analysis uses natural language processing technology to determine the user's emotions from text and audio data. For example, in the case of audio data, speech recognition technology is used to convert the audio into text, and the emotions contained in the user's statements are identified by analyzing that text. The sentiment analysis algorithm classifies emotions into categories such as positive, negative, and neutral, and scores the intensity of the emotions. Keyword extraction extracts important keywords from the user's statements and messages to identify the user's concerns and problems. For example, by extracting words and phrases that the user frequently uses and analyzing their frequency of occurrence and context, important information related to the user's mental state can be grasped. Contextual analysis considers the context of the user's statements and messages to understand the meaning of the data. This allows for a more accurate understanding of the user's intentions and emotional changes, rather than just simple keyword extraction. The analysis unit integrates these analysis results to comprehensively evaluate the user's mental state. Furthermore, the analysis unit can utilize past data and statistical information to analyze changes and trends in users' mental states and provide information for long-term mental care.

[0073] The judgment unit determines emotions and mental states based on data analyzed by the analysis unit. The judgment unit can make judgments based on criteria such as emotion scoring and mental state classification. Specifically, in emotion scoring, the judgment unit quantifies the user's emotions based on the results of emotion analysis provided by the analysis unit. For example, it assigns high scores to positive emotions and low scores to negative emotions, quantitatively evaluating the intensity of the user's emotions. In mental state classification, the judgment unit classifies the user's mental state into multiple categories based on the user's emotion score and keyword extraction results. For example, it classifies into categories such as stress, depression, anxiety, and stability to identify the user's current mental state. Based on these judgment results, the judgment unit comprehensively evaluates the user's mental state and provides information for taking necessary measures. Furthermore, the judgment unit can utilize past data and statistical information to analyze changes and trends in the user's mental state and provide information for long-term mental care. For example, if the user's mental state has deteriorated over a certain period, measures such as encouraging consultation with a specialist early on can be taken. This allows the judgment unit to accurately understand the user's mental state and provide information to take appropriate measures.

[0074] The Facilitation Unit encourages users to consult with professionals based on the results determined by the Assessment Unit. The Facilitation Unit can promote consultations based on criteria such as the method, timing, and content of notifications. Specifically, it uses methods such as smartphone push notifications, email, SMS, and voice calls to communicate information to users. The timing of notifications is adjusted considering the user's lifestyle and mental state. For example, notifications can be sent during times when the user is relaxed or when it is determined that their stress levels are high, effectively encouraging them to consult with professionals. The content of notifications is customized according to the user's mental state and situation. For example, if a user is feeling stressed, a notification including stress management methods and relaxation suggestions will be sent. Also, if a user is determined to be depressed, a notification strongly recommending consultation with a professional will be sent. Through these notifications, the Facilitation Unit helps users receive professional support at the appropriate time. Furthermore, the Facilitation Unit collects user feedback and evaluates the effectiveness of notifications. For example, it records how users reacted to notifications and uses that data to improve the notification methods and content. This allows the promotion department to provide effective support to users and facilitate improvements in their mental health.

[0075] The analysis unit can analyze emotions from both audio and text. The analysis unit can analyze data in various formats, such as recorded audio, real-time audio, text messages, and emails. The analysis unit analyzes recorded audio to determine the user's emotions. The analysis unit analyzes real-time audio to determine the user's emotions in real time. The analysis unit analyzes text messages to determine the user's emotions. The analysis unit analyzes the content of emails to determine the user's emotions. By analyzing emotions from both audio and text, the accuracy of emotion analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data into a generative AI, which can then analyze the audio data to determine the emotions.

[0076] The evolutionary unit can use evolving algorithms based on machine learning. The evolutionary unit can use algorithms of various types, such as self-learning algorithms and adaptive algorithms. Using a self-learning algorithm, the evolutionary unit learns from data and evolves the algorithm. Using an adaptive algorithm, the evolutionary unit evolves the algorithm while adapting to the environment. By evolving the algorithm, the evolutionary unit improves the accuracy of the system. Thus, using evolving algorithms based on machine learning improves the accuracy of the system. Some or all of the above-described processes in the evolutionary unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evolutionary unit can input data into a generative AI, which can learn from the data and evolve the algorithm.

[0077] The Security Department can implement advanced security measures to protect privacy. For example, the Security Department can implement security measures through methods such as encryption technology, access control, and data protection policies. The Security Department uses encryption technology to encrypt and protect data. The Security Department implements access control to restrict access to data. The Security Department develops data protection policies to ensure thorough data protection. This allows for the secure protection of user data through advanced security measures to protect privacy.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of conversation and text collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the collection frequency to collect more detailed data. If the user is relaxed, the data collection unit decreases the collection frequency to reduce the user's burden. If the user is in a hurry, the data collection unit prioritizes collecting data that can be collected in a short time. This allows for more appropriate data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0079] The data collection unit can analyze the user's past conversation history and select the optimal data collection method. For example, the data collection unit may prioritize data collection methods (voice, text, etc.) that the user has frequently used in the past. The data collection unit may select a data collection method for a specific time period from the user's past conversation history. The data collection unit analyzes the user's past conversation history and selects the most effective data collection method. This allows the optimal data collection method to be selected by analyzing the user's past conversation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past conversation history data into a generating AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter conversations and texts based on the user's current lifestyle and areas of interest. For example, the data collection unit prioritizes collecting conversations and texts relevant to the user's current lifestyle. The data collection unit filters relevant conversations and texts based on the user's areas of interest. The data collection unit determines the priority of data to collect based on the user's lifestyle and areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then perform the filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress. If the user is relaxed, the data collection unit will prioritize collecting data related to relaxation. If the user is in a hurry, the data collection unit will prioritize collecting data related to the hurried situation. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of data.

[0082] The data collection unit can prioritize the collection of highly relevant data when collecting conversations or text, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit filters highly relevant data based on the user's geographical location. The data collection unit determines the priority of data to collect, taking into account the user's geographical location. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, which can then prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze the user's social media activity and collect relevant data when collecting conversations and texts. For example, the data collection unit prioritizes collecting relevant conversations and texts based on the user's social media activity. The data collection unit analyzes the user's social media activity and filters the relevant data. The data collection unit determines the priority of the data to be collected based on the user's social media activity. This allows relevant data to be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit uses a simple and easy-to-understand presentation. If the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For data with low importance, the analysis unit performs a simplified analysis. The analysis unit determines the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis based on the importance.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For audio data, it applies a speech recognition algorithm. For image data, it applies an image analysis algorithm. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, which can then apply different analysis algorithms depending on the category.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is in a hurry, the analysis unit will perform a rapid analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit will prioritize the analysis of recently submitted data. The analysis unit will postpone the analysis of older data. The analysis unit will adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI, which can then determine the priority of analysis based on the submission date.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit determines the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then adjust the order of analysis based on the relevance.

[0090] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated user emotions. For example, if the user is stressed, the judgment unit will prioritize criteria related to stress. If the user is relaxed, the judgment unit will prioritize criteria related to relaxation. If the user is in a hurry, the judgment unit will prioritize criteria that allow for quick judgment. By adjusting the judgment criteria based on the user's emotions, more appropriate judgments can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into a generative AI, which can estimate emotions and adjust the judgment criteria.

[0091] The determination unit can improve the accuracy of its determination by considering the interrelationships of the data during the determination process. For example, the determination unit analyzes the interrelationships of the data and makes a determination based on the data with high relevance. The determination unit improves the accuracy of its determination by considering the interrelationships of the data. The determination unit determines the priority of the determination based on the interrelationships of the data. This improves the accuracy of the determination by considering the interrelationships of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without AI. For example, the determination unit can input the interrelationships of the data into a generating AI, and the generating AI can improve the accuracy of the determination by considering the interrelationships.

[0092] The determination unit can make a determination by considering the attribute information of the data submitter. For example, the determination unit may consider the age and gender of the data submitter. The determination unit may consider the occupation and living environment of the data submitter. The determination unit adjusts the criteria for determination based on the attribute information of the data submitter. This makes it possible to make a more appropriate determination by considering the attribute information of the data submitter. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the attribute information of the data submitter into a generating AI, and the generating AI can make a determination by considering the attribute information.

[0093] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is stressed, the judgment unit will prioritize displaying important results. If the user is relaxed, the judgment unit will display detailed results. If the user is in a hurry, the judgment unit will display concise results. By adjusting the order in which the judgment results are displayed based on the user's emotions, more appropriate results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input user emotion data into a generative AI, which can estimate emotions and adjust the order in which the results are displayed.

[0094] The determination unit can make a determination while considering the geographical distribution of the data. For example, the determination unit analyzes the geographical distribution of the data and makes a determination while considering the characteristics of each region. The determination unit adjusts the criteria for determination based on the geographical distribution of the data. The determination unit determines the priority of determination while considering the geographical distribution of the data. This makes it possible to make determinations that reflect the characteristics of each region by considering the geographical distribution of the data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of the data into a generating AI, and the generating AI can make a determination while considering the geographical distribution.

[0095] The judgment unit can improve the accuracy of its judgment by referring to relevant literature for the data during the judgment process. For example, the judgment unit can refer to relevant literature for the data to improve the accuracy of its judgment. The judgment unit adjusts the criteria for judgment based on the relevant literature for the data. The judgment unit determines the priority of judgment considering the relevant literature for the data. As a result, the accuracy of judgment is improved by referring to relevant literature for the data. Some or all of the above processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input relevant literature for the data into a generating AI, and the generating AI can refer to the relevant literature to improve the accuracy of its judgment.

[0096] The facilitator can estimate the user's emotions and adjust the timing of prompting consultation with a professional based on the estimated emotions. For example, if the user is feeling stressed, the facilitator may prompt consultation with a professional earlier. If the user is relaxed, the facilitator may delay the consultation. If the user is in a hurry, the facilitator may prompt consultation with a professional quickly. By adjusting the timing of consultation based on the user's emotions, it is possible to prompt consultation with a professional at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facilitator may be performed using AI or not using AI. For example, the facilitator can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of the consultation.

[0097] The promotion unit can analyze the user's past consultation history and select the optimal promotion method during the promotion process. For example, the promotion unit selects the optimal promotion method based on the user's past consultation history. The promotion unit analyzes the user's past consultation history and selects an effective promotion method. The promotion unit adjusts the timing of consultations by referring to the user's past consultation history. In this way, the optimal promotion method can be selected by analyzing the user's past consultation history. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input the user's past consultation history data into a generating AI, and the generating AI can select the optimal promotion method.

[0098] The promotion unit can customize the means of promotion based on the user's current living situation during the promotion process. For example, the promotion unit can suggest the most suitable consultation method based on the user's current living situation. The promotion unit can adjust the timing of the consultation according to the user's living situation. The promotion unit can customize the means of consultation based on the user's living situation. In this way, by customizing the means of promotion based on the user's living situation, a more appropriate consultation method can be provided. Some or all of the above processes in the promotion unit may be performed using AI, for example, or without using AI. For example, the promotion unit can input the user's living situation data into a generating AI, and the generating AI can suggest the most suitable consultation method.

[0099] The facilitation unit can estimate the user's emotions and determine facilitation priorities based on the estimated emotions. For example, if the user is stressed, the facilitation unit will prioritize prompting consultation with a professional. If the user is relaxed, the facilitation unit will lower the priority of consultation. If the user is in a hurry, the facilitation unit will prompt consultation quickly. In this way, important consultations can be prioritized by determining facilitation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the facilitation unit may be performed using AI or not using AI. For example, the facilitation unit can input user emotion data into a generative AI, which can estimate emotions and determine facilitation priorities.

[0100] The promotion unit can select the optimal promotion method during promotion, taking into account the user's geographical location information. For example, the promotion unit can suggest the optimal consultation location based on the user's geographical location information. The promotion unit can adjust the timing of the consultation, taking into account the user's geographical location information. The promotion unit can select the means of consultation based on the user's geographical location information. In this way, by taking into account the user's geographical location information, the optimal consultation location and means can be provided. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without using AI. For example, the promotion unit can input the user's geographical location information data into a generating AI, and the generating AI can suggest the optimal consultation location and means.

[0101] The promotion unit can analyze the user's social media activity and propose promotion methods during the promotion process. For example, the promotion unit can propose the most suitable consultation method based on the user's social media activity. The promotion unit analyzes the user's social media activity and proposes effective promotion methods. The promotion unit adjusts the timing of consultations based on the user's social media activity. In this way, by analyzing the user's social media activity, the promotion unit can propose the most suitable consultation method. Some or all of the above processes in the promotion unit may be performed using AI, for example, or not using AI. For example, the promotion unit can input the user's social media activity data into a generating AI, which can then propose the most suitable consultation method.

[0102] The analysis unit can estimate the user's emotions and adjust the voice and text analysis methods based on the estimated emotions. For example, if the user is stressed, the analysis unit will perform a rapid voice and text analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is in a hurry, the analysis unit will perform a concise analysis. This allows for more appropriate analysis by adjusting the voice and text analysis methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the voice and text analysis methods.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio and text during the analysis. For example, the analysis unit performs a detailed analysis on audio and text with high importance. The analysis unit performs a simplified analysis on audio and text with low importance. The analysis unit determines the priority of the analysis based on the importance of the audio and text. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio and text. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the importance of the audio and text into a generating AI, which can then adjust the level of detail of the analysis based on the importance.

[0104] The analysis unit can apply different analysis algorithms depending on the audio and text categories during analysis. For example, the analysis unit applies a natural language processing algorithm to text data, a speech recognition algorithm to audio data, and an image analysis algorithm to image data. By applying different analysis algorithms depending on the audio and text categories, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the audio and text categories into a generating AI, which can then apply different analysis algorithms depending on the category.

[0105] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will perform a short, concise analysis. If the user is relaxed, the analysis unit will perform a detailed analysis. If the user is in a hurry, the analysis unit will perform a rapid analysis. By adjusting the length of the analysis based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the length of the analysis.

[0106] The analysis unit can prioritize analysis based on the submission dates of audio and text. For example, the analysis unit will prioritize analyzing recently submitted audio and text. It will postpone the analysis of older audio and text submissions. The analysis unit adjusts the analysis schedule based on the submission dates. This enables efficient analysis by prioritizing analysis based on the submission dates of audio and text. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the submission dates of audio and text into a generating AI, which can then prioritize analysis based on the submission dates.

[0107] The analysis unit can adjust the order of analysis based on the relevance between audio and text during the analysis process. For example, the analysis unit prioritizes analyzing highly relevant audio and text. It postpones the analysis of less relevant audio and text. The analysis unit determines the order of analysis based on the relevance between audio and text. This allows for efficient analysis by adjusting the order of analysis based on the relevance between audio and text. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance between audio and text into a generating AI, which can then adjust the order of analysis based on the relevance.

[0108] The evolution unit can estimate the user's emotions and select an algorithm to evolve based on the estimated emotions. For example, if the user is stressed, the evolution unit will select an algorithm related to stress. If the user is relaxed, the evolution unit will select an algorithm related to relaxation. If the user is in a hurry, the evolution unit will select an algorithm that evolves quickly. This allows for the use of a more appropriate algorithm by selecting an algorithm that evolves based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evolution unit may be performed using AI, for example, or not using AI. For example, the evolution unit can input user emotion data into a generative AI, which can then estimate the emotion and select an algorithm to evolve.

[0109] The evolutionary unit can optimize the evolutionary algorithm by referring to past training data during evolution. For example, the evolutionary unit optimizes the evolutionary algorithm based on past training data. The evolutionary unit improves the accuracy of the evolutionary algorithm by referring to past training data. The evolutionary unit adjusts the parameters of the evolutionary algorithm based on past training data. This improves the accuracy of the evolutionary algorithm by referring to past training data. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can input past training data into a generating AI, and the generating AI can optimize the evolutionary algorithm by referring to the training data.

[0110] The evolution unit can estimate the user's emotions and adjust the evolution frequency based on the estimated emotions. For example, if the user is stressed, the evolution unit increases the evolution frequency to respond quickly. If the user is relaxed, the evolution unit decreases the evolution frequency to reduce the burden. If the user is in a hurry, the evolution unit adjusts the evolution frequency to respond quickly. This allows for more appropriate evolution frequency by adjusting the evolution frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evolution unit may be performed using AI or not using AI. For example, the evolution unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the evolution frequency.

[0111] The evolutionary unit can weight the evolutionary algorithm based on the data submission date during evolution. For example, the evolutionary unit weights the evolutionary algorithm for recently submitted data. The evolutionary unit adjusts the weighting of the evolutionary algorithm for older submitted data. The evolutionary unit adjusts the parameters of the evolutionary algorithm based on the submission date. This allows for more appropriate evolution by weighting the evolutionary algorithm based on the data submission date. Some or all of the above processes in the evolutionary unit may be performed using AI, for example, or without AI. For example, the evolutionary unit can input the data submission date into a generating AI, which can then weight the evolutionary algorithm based on the submission date.

[0112] The security unit can estimate the user's emotions and adjust the strength of security measures based on the estimated emotions. For example, if the user is stressed, the security unit will increase the strength of security measures. If the user is relaxed, the security unit will adjust the strength of security measures. If the user is in a hurry, the security unit will implement security measures quickly. This allows for more appropriate security measures by adjusting the strength of security measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not using AI. For example, the security unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the strength of security measures.

[0113] The security department can select the optimal security measures by referring to past security incidents when implementing security measures. For example, the security department selects the optimal measures based on past security incidents. The security department improves the accuracy of security measures by referring to past security incidents. The security department adjusts the parameters of security measures based on past security incidents. This allows the security department to select the optimal security measures by referring to past security incidents. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input past security incident data into a generating AI, and the generating AI can refer to the incidents and select the optimal measures.

[0114] The security unit can estimate the user's emotions and prioritize security measures based on those emotions. For example, if the user is stressed, the security unit will prioritize security measures. If the user is relaxed, the security unit will adjust the priority of security measures. If the user is in a hurry, the security unit will quickly implement security measures. This allows for more appropriate security measures by prioritizing security measures based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security unit may be performed using AI or not. For example, the security unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of security measures.

[0115] The security department can select the optimal security measures by considering the user's geographical location information. For example, the security department selects the optimal security measures based on the user's geographical location information. The security department determines the priority of security measures by considering the user's geographical location information. The security department adjusts the parameters of security measures based on the user's geographical location information. This allows the security department to select the optimal security measures by considering the user's geographical location information. Some or all of the above processes in the security department may be performed using AI, for example, or without AI. For example, the security department can input the user's geographical location information data into a generating AI, and the generating AI can select the optimal measures by considering the geographical location information.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] A mental care agent can estimate a user's emotions and adjust the content and method of feedback based on those estimates. For example, if a user is stressed, the agent can provide gentle words or music to promote relaxation. If the user is relaxed, the agent can emphasize positive feedback and offer advice to help the user maintain a positive state of mind. If the user is in a hurry, the agent can provide short, concise feedback to avoid wasting the user's time. This allows for more effective support of the user's mental health by providing appropriate feedback tailored to their emotions.

[0118] A mental care agent can estimate a user's emotions and, based on those estimates, propose action plans for the user. For example, if a user is feeling stressed, the agent can suggest specific action plans to help them relax (e.g., deep breathing or a short walk). If a user is relaxed, the agent can suggest action plans to maintain that state (e.g., increasing time for hobbies). If a user is in a hurry, the agent can suggest action plans to help them complete tasks efficiently (e.g., prioritizing tasks). In this way, by providing specific action plans tailored to the user's emotions, the agent can support the user's mental health.

[0119] A mental care agent can estimate a user's emotions and adjust its tone of communication based on those estimates. For example, if a user is stressed, the agent can speak in a calm and reassuring tone. If the user is relaxed, the agent can speak in a friendly and casual tone. If the user is in a hurry, the agent can speak in a quick and efficient tone. This allows the agent to support the user's mental health by providing an appropriate tone of communication that matches their emotions.

[0120] A mental care agent can estimate a user's emotions and adjust the frequency of notifications based on those estimates. For example, if a user is stressed, the agent can send frequent notifications to check on the user's state and provide necessary support. If the user is relaxed, the agent can reduce the frequency of notifications so as not to disturb the user's relaxation. Also, if the user is in a hurry, the agent can send only important notifications to avoid wasting the user's time. In this way, by providing an appropriate notification frequency according to the user's emotions, it is possible to support the user's mental health.

[0121] A mental care agent can estimate a user's emotions and, based on those estimates, suggest activities for the user. For example, if a user is stressed, the agent can suggest activities to help them relax (such as yoga or meditation). If a user is relaxed, the agent can suggest activities to maintain that state (such as increasing time for hobbies). If a user is in a hurry, the agent can suggest activities to help them complete tasks efficiently (such as prioritizing tasks). In this way, by providing appropriate activities tailored to the user's emotions, the agent can support the user's mental health.

[0122] A mental health agent can analyze a user's past mental health data to understand their mental health trends. For example, it can identify periods and situations in the user's past that tended to cause stress and suggest preventative measures based on that information. It can also identify periods and situations in the user's past that made them feel relaxed and provide advice on how to relax based on that information. Furthermore, it can identify periods and situations in the user's past that made them feel rushed and provide advice on how to complete tasks efficiently based on that information. In this way, it is possible to provide more effective support by utilizing the user's past mental health data.

[0123] Mental care agents can collect users' lifestyle data and identify factors that influence their mental health. For example, they can analyze users' sleep patterns, diet, and exercise habits to assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's lifestyle data. Furthermore, they can continuously monitor the user's lifestyle data and update the advice as needed. This allows for more effective mental health support by leveraging the user's lifestyle data.

[0124] Mental health agents can analyze users' social network data to identify relationships that influence their mental health. For example, they can analyze who users frequently communicate with and the nature of those relationships, and assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's social network data. Furthermore, they can continuously monitor the user's social network data and update advice as needed. This allows for more effective mental health support by leveraging the user's social network data.

[0125] Mental care agents can leverage users' geographical location information to identify environmental factors that influence their mental health. For example, they can analyze places users frequently visit and the environment of those places (e.g., noise levels and congestion) to assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's geographical location information. Furthermore, they can continuously monitor the user's geographical location information and update advice as needed. This allows for more effective mental health support by utilizing the user's geographical location information.

[0126] Mental care agents can collect users' health data and identify physical factors that affect their mental health. For example, they can monitor vital signs such as heart rate, blood pressure, and body temperature and assess their impact on mental health. They can also provide specific advice to improve mental health based on the user's health data. Furthermore, they can continuously monitor the user's health data and update the advice as needed. This allows them to leverage the user's health data to provide more effective mental health support.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The collection unit collects the user's daily conversations and texts. The collection unit can collect data in various formats, such as voice calls, chat messages, and emails. The collection unit can record voice data and save text data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using methods such as sentiment analysis, keyword extraction, and contextual analysis. The analysis unit performs sentiment analysis to determine the user's emotions. The analysis unit performs keyword extraction to extract important information. The analysis unit performs contextual analysis to understand the meaning of the data. Step 3: The judgment unit determines the emotions and mental state based on the data analyzed by the analysis unit. The judgment unit can make judgments based on criteria such as emotion scoring and mental state classification. The judgment unit scores emotions and quantifies the user's emotions. The judgment unit classifies mental states and identifies the user's mental state. Step 4: The Facilitation Unit prompts the user to consult with an expert based on the results determined by the Judgment Unit. The Facilitation Unit can facilitate consultation based on criteria such as the method, timing, and content of the notification. The Facilitation Unit issues a notification and prompts the user to consult with an expert. The Facilitation Unit adjusts the timing and issues the notification at an appropriate time. The Facilitation Unit adjusts the content of the notification and provides the user with appropriate information.

[0129] 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.

[0130] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, promotion unit, analysis unit, evolution unit, and security unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's daily conversations and texts using the microphone 38B and touch panel 38A of the smart device 14. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The determination unit determines the emotions and mental state based on the data analyzed by the analysis unit. The promotion unit prompts consultation with a specialist based on the results determined by the determination unit. The analysis unit analyzes emotions from both voice and text. The evolution unit uses an evolving algorithm based on machine learning. The security unit implements advanced security measures to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0138] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] 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.

[0140] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, promotion unit, analysis unit, evolution unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's daily conversations and texts using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12. The determination unit determines the emotions and mental state based on the data analyzed by the analysis unit. The promotion unit prompts consultation with a specialist based on the results determined by the determination unit. The analysis unit analyzes emotions from both voice and text. The evolution unit uses an evolving algorithm based on machine learning. The security unit implements advanced security measures to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0154] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] 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.

[0156] 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.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, promotion unit, analysis unit, evolution unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's daily conversations and texts using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12. The determination unit determines the emotions and mental state based on the data analyzed by the analysis unit. The promotion unit prompts consultation with a specialist based on the results determined by the determination unit. The analysis unit analyzes emotions from both voice and text. The evolution unit uses an evolving algorithm based on machine learning. The security unit implements advanced security measures to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0168] 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.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0170] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] 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.

[0172] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] 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.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, promotion unit, analysis unit, evolution unit, and security unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects the user's daily conversations and texts using the microphone 238 and camera 42 of the robot 414. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12. The determination unit determines the emotions and mental state based on the data analyzed by the analysis unit. The promotion unit prompts consultation with a specialist based on the results determined by the determination unit. The analysis unit analyzes emotions from both voice and text. The evolution unit uses an evolving algorithm based on machine learning. The security unit implements advanced security measures to protect privacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] 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.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] 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.

[0185] 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.

[0186] 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, and motorcycles, 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 based, for example, 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.

[0187] 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."

[0188] 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.

[0189] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] 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 other things 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.

[0199] 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.

[0200] (Note 1) A collection unit that collects users' daily conversations and texts, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines emotions and mental state based on the data analyzed by the aforementioned analysis unit, The system includes a promotion unit that prompts consultation with an expert based on the result determined by the determination unit. A system characterized by the following features. (Note 2) It features an analysis unit that analyzes emotions from both voice and text. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an evolutionary section that uses evolving algorithms based on machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has a security department that implements advanced security measures to protect privacy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation and text collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past conversation history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting conversations and texts, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting conversations and texts, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting conversations and texts, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the representation of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, When making a judgment, the accuracy of the judgment is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a decision, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, When making a decision, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned promotion unit is It estimates the user's emotions and adjusts the timing of prompting consultation with an expert based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned promotion unit is During the promotion process, the system analyzes the user's past consultation history to select the most suitable promotion method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned promotion unit is During promotion, the promotion methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned promotion unit is It estimates user sentiment and determines promotional priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned promotion unit is During promotion, the optimal promotion method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned promotion unit is During the promotion phase, we analyze users' social media activity and propose promotional strategies. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is It estimates the user's emotions and adjusts the voice and text analysis methods based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of audio and text. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on whether the data is in audio or text format. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit is During the analysis, we prioritize the analysis based on the timing of audio and text submissions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relationship between audio and text. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned evolutionary section is The system estimates user emotions and selects an algorithm that evolves based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned evolutionary section is During evolution, the evolutionary algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned evolutionary section is It estimates the user's emotions and adjusts the evolution frequency based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned evolutionary section is During evolution, the evolutionary algorithm is weighted based on the timing of data submission. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned security unit is It estimates user sentiment and adjusts the strength of security measures based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned security unit is When implementing security measures, refer to past security incidents to select the most appropriate countermeasures. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned security unit is It estimates user sentiment and prioritizes security measures based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned security unit is When implementing security measures, the optimal solution should be selected by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects users' daily conversations and texts, An analysis unit analyzes the data collected by the aforementioned collection unit, A determination unit that determines emotions and mental state based on the data analyzed by the aforementioned analysis unit, The system includes a promotion unit that prompts consultation with an expert based on the result determined by the determination unit. A system characterized by the following features.

2. It features an analysis unit that analyzes emotions from both voice and text. The system according to feature 1.

3. It features an evolutionary section that uses evolving algorithms based on machine learning. The system according to feature 1.

4. It has a security department that implements advanced security measures to protect privacy. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of conversation and text collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past conversation history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting conversations and texts, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting conversations and texts, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is When collecting conversations and texts, analyze users' social media activity and collect relevant data. The system according to feature 1.

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