system

Abstract

The system according to this embodiment aims to allow individuals to easily receive accurate and objective advice. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides advice based on the analysis results obtained by the analysis 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for an individual to easily receive accurate and objective advice.

[0005] The system according to the embodiment aims to enable an individual to easily receive accurate and objective advice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment allows individuals to easily receive accurate and objective advice. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​advisory service according to an embodiment of the present invention is a service that uses AI to solve business and personal problems. This AI advisory service is a system that collects data, the AI ​​analyzes that data, and provides advice based on the analysis results. For example, the AI ​​advisory service can be applied in various fields such as medicine, finance, and career counseling. For example, in the medical field, it can provide insights into diseases that may not be detected by a family doctor based on early symptoms. In the financial field, it can provide investment advice based on an individual's risk preference. Advantages of the AI ​​advisory service include higher accuracy, unbiased data processing, and improved results. These services can reduce costs through automated processes. At the start of the service, the AI ​​advisor plays the role of an advisor with specialized knowledge, but in the medium to long term, it learns an individual's decision-making criteria through machine learning, enabling the provision of more personalized advice. Strengths of the AI ​​advisory service include improved accuracy, reduced bias, and improved results. Improved accuracy allows for accurate understanding and evaluation of an individual's financial situation, medical condition, and career aspirations. By reducing bias, we can provide fairer and less biased advice by eliminating individual differences based on attributes such as gender, race, and age. Improved results allow us to propose optimal investment strategies and financial plans, ultimately accelerating individual wealth growth. We can also advise on which specialist to consult for a medical condition and show various paths to career goals. This service does not aim to completely replace human advice, but rather to provide second opinions with a lower barrier to entry. We strive to improve the service by prioritizing customizable data acquisition. This will enable the AI ​​advisory service to efficiently solve business and personal challenges.

[0029] The AI ​​advisory service according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect data such as numerical data, text data, and image data. The data collection unit can collect environmental data using sensors, for example. The data collection unit can also collect publicly available data from the internet. Furthermore, the data collection unit can also receive data input directly from the user. For example, the data collection unit collects text data entered by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using machine learning algorithms, for example. The analysis unit can also analyze text data using natural language processing technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit statistically analyzes the collected numerical data to understand trends. The data provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the data provision unit can propose an optimal investment strategy based on the analysis results. The data provision unit can also provide health management advice based on the analysis results. Furthermore, the data provision unit can provide career counseling based on the analysis results. For example, the service provider proposes a specific action plan to the user based on the analysis results. This allows the AI ​​advisory service according to the embodiment to efficiently collect data, analyze it, and provide advice.

[0030] The data collection unit collects data. The data collection unit can collect various types of data, such as numerical data, text data, and image data. Specifically, numerical data includes environmental data such as temperature, humidity, and pressure obtained from sensors, as well as stock prices, exchange rates, and economic indicators from financial markets. Text data includes survey responses and feedback entered by users, news articles and blog posts from the internet, and comments on social media. Image data includes photos uploaded by users, video footage from surveillance cameras, and satellite imagery. The data collection unit employs various methods to efficiently collect this data. For example, it can collect environmental data in real time using sensors. This allows for a detailed understanding of environmental changes in specific locations and time periods. The data collection unit can also collect publicly available data from the internet. For example, it can automatically obtain necessary data from specific websites using web scraping techniques. Furthermore, the data collection unit can also receive data input directly from users. For example, users can input text data through a dedicated application or web form, which the data collection unit can then acquire in real time. This allows the data collection unit to collect a wide range of data from diverse data sources and integrate it into a system-wide database. The collected data is appropriately organized and stored for analysis by the analysis unit.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze data using machine learning algorithms. Specifically, for collected numerical data, it uses statistical methods such as regression analysis and clustering to understand data trends and patterns. For example, it can analyze stock price data to predict future price fluctuations of specific stocks. For text data, it can analyze the content using natural language processing technology. For example, it can analyze user feedback to extract evaluations and areas for improvement of products and services. Furthermore, for image data, it can analyze the content using image recognition technology. For example, it can analyze surveillance camera footage to detect suspicious activity. By combining these technologies, the analysis unit can analyze collected data from multiple perspectives and obtain more accurate analysis results. In addition, the analysis unit can utilize historical data and external data sources to evaluate long-term trends and risks. For example, it can predict future economic trends and formulate investment strategies based on historical economic data. This allows the analysis unit to quickly and accurately analyze collected data and provide useful information to users.

[0032] The service provider provides advice based on the analysis results obtained by the analysis department. For example, the service provider can propose the optimal investment strategy based on the analysis results. Specifically, it can advise users on when to buy and sell based on stock price fluctuations predicted by the analysis department. The service provider can also provide health management advice based on the analysis results. For example, it can analyze the user's health data and suggest improvements to diet and exercise. Furthermore, the service provider can provide career counseling based on the analysis results. For example, it can analyze the user's skills and experience and suggest appropriate job types and career paths. The service provider uses various means to provide this advice to users in an easy-to-understand manner. For example, it can visually display analysis results and advice through a dedicated application or website. It can also provide important information in a timely manner using email and notification functions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can analyze how users reacted to the advice provided and reflect this in the next advice. In this way, the service provider can always provide users with the best possible advice and improve user satisfaction.

[0033] The analysis unit may include a bias reduction unit that reduces bias based on individual attributes such as gender, race, and age. For example, the analysis unit can preprocess data to reduce gender bias. It can also filter data to reduce race bias. Furthermore, the analysis unit can adjust data weighting to reduce age bias. For example, the analysis unit can apply an algorithm to treat data equally regardless of gender. It can also filter data to treat it equally regardless of race. Furthermore, the analysis unit can adjust weighting to treat data equally regardless of age. This reduces bias and provides fair analysis results. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can reduce bias using an AI model that performs data preprocessing.

[0034] The provisioning unit may include an improvement unit that proposes optimal investment strategies and financial plans. For example, the provisioning unit can perform risk assessments and propose optimal investment strategies. It can also propose methods for constructing portfolios. Furthermore, it can propose income and expenditure plans and asset management plans. For example, the provisioning unit proposes optimal investment strategies based on the user's risk tolerance. It can also propose methods for constructing portfolios based on the user's investment goals. Furthermore, it can propose income and expenditure plans and asset management plans based on the user's income and expenditure situation. This enables the proposal of optimal investment strategies and financial plans. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can propose optimal investment strategies using an AI model for risk assessment.

[0035] The analysis unit may include a learning unit that learns an individual's decision-making framework. The analysis unit can, for example, learn the user's values. It can also learn the user's priorities. Furthermore, it can learn the user's decision criteria. For example, the analysis unit learns values ​​based on the user's past behavioral data. It can also learn priorities based on the user's past choice data. Furthermore, it can learn decision criteria based on the user's past decision data. This allows the system to learn an individual's decision-making framework and provide more personalized advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can learn an individual's decision-making framework using an AI model that learns the user's values.

[0036] The service provider can provide insights into diseases that may not be detected by a primary care physician based on their initial symptoms in the medical field. For example, the service provider can suggest the possibility of a disease based on a list of initial symptoms. It can also evaluate the severity of symptoms and suggest additional tests as needed. Furthermore, it can recommend consultation with a specialist. For example, the service provider can have the user input their initial symptoms and suggest the possibility of a disease based on that list of symptoms. It can also evaluate the severity of symptoms and suggest additional tests as needed. Furthermore, it can recommend consultation with a specialist and guide the user to an appropriate medical institution. This helps in the early detection of diseases in the medical field. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide insights using an AI model that suggests the possibility of a disease based on a list of initial symptoms.

[0037] The service provider can provide investment advice in the financial sector based on an individual's risk preference. For example, the service provider can assess risk tolerance and provide investment advice. It can also provide investment advice based on investment goals. Furthermore, the service provider can propose methods for constructing a portfolio. For example, the service provider can assess a user's risk tolerance and provide investment advice based on it. It can also propose specific investment strategies based on the user's investment goals. Furthermore, the service provider can propose methods for constructing a portfolio based on the user's asset situation. This enables investment advice based on risk preference in the financial sector. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide investment advice using an AI model that assesses risk tolerance and provides investment advice.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has preferred to use in the past. The data collection unit can also select the most efficient collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and suggest improvements to the collection method. For example, the data collection unit can analyze data collection methods that the user has used in the past and select the optimal collection method. The data collection unit can also identify and suggest improvements to the collection method based on the user's past data collection history. This allows for the selection of the optimal collection method based on past data collection 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 data collection history into a generating AI and select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter out unnecessary data according to the user's current situation. Furthermore, the data collection unit can collect highly relevant data based on the user's areas of interest. For example, the data collection unit can prioritize collecting data related to topics that the user is currently interested in. Furthermore, the data collection unit can filter out unnecessary data according to the user's current situation. Furthermore, the data collection unit can collect highly relevant data based on the user's areas of interest. This allows data to be filtered based on the user's current situation 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 the user's current situation and areas of interest into a generating AI and filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can select the optimal data collection method by considering the user's location information. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can select the optimal data collection method by considering the user's location information. This allows for the collection of highly relevant data based on the user's geographical location information. 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 information into a generating AI and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the collection of relevant data based on 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 without AI. For example, the data collection unit can input the user's social media activity into a generating AI and collect relevant data.

[0042] 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 can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to 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 adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specialized medical analysis algorithm to medical data. It can also apply a financial analysis algorithm to financial data. Furthermore, it can apply an analysis algorithm specifically tailored for career counseling to career data. This allows for the application of an appropriate analysis algorithm depending on the data category. 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 data categories into an AI that generates them and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources based on the data collection timing. This allows the analysis priority to be determined based on the data collection timing. 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 data collection timing into a generating AI to determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This allows the order of analysis to be adjusted 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 and adjust the order of analysis.

[0046] The service provider can adjust the level of detail of advice based on the importance of the data when providing advice. For example, the service provider can provide detailed advice based on high-importance data, and simplified advice based on low-importance data. Furthermore, the service provider can optimally allocate resources for advice according to the importance of the data. For example, the service provider can provide detailed advice based on high-importance data, and simplified advice based on low-importance data. Furthermore, the service provider can optimally allocate resources for advice according to the importance of the data. This allows the level of detail of advice to be adjusted according to the importance of the data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the data into a generating AI and adjust the level of detail of the advice.

[0047] The service provider can apply different advice algorithms depending on the data category when providing advice. For example, the service provider can apply a specialized medical advice algorithm to advice based on medical data. It can also apply a financial advice algorithm to advice based on financial data. Furthermore, it can apply an advice algorithm specialized in career counseling to advice based on career data. This allows the service provider to apply an appropriate advice algorithm depending on the data category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data category into a generating AI and apply an appropriate advice algorithm.

[0048] The service provider can determine the priority of advice based on the data collection timing when providing advice. For example, the service provider will prioritize advice based on the latest data. Alternatively, the service provider can provide advice based on the latest data while also referring to past data. Furthermore, the service provider can optimally allocate advice resources based on the data collection timing. For example, the service provider will prioritize advice based on the latest data. Alternatively, the service provider can provide advice based on the latest data while also referring to past data. Furthermore, the service provider can optimally allocate advice resources based on the data collection timing. This allows the service provider to determine the priority of advice based on the data collection timing. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data collection timing into a generating AI to determine the priority of advice.

[0049] The service provider can adjust the order of advice based on the relevance of the data when providing advice. For example, the service provider may prioritize advice based on highly relevant data. It may also postpone the provision of advice based on less relevant data. Furthermore, the service provider can optimally allocate the resources for advice based on the relevance of the data. For example, the service provider may prioritize advice based on highly relevant data. It may also postpone the provision of advice based on less relevant data. Furthermore, the service provider can optimally allocate the resources for advice based on the relevance of the data. This allows the order of advice to be adjusted based on the relevance of the data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the data into a generating AI and adjust the order of advice.

[0050] The bias reduction unit can optimize the bias reduction algorithm by referring to past data during bias reduction. For example, the bias reduction unit can analyze past data to identify areas for improvement in the bias reduction algorithm. Furthermore, the bias reduction unit can optimize the bias reduction algorithm based on past data. In addition, the bias reduction unit can improve the accuracy of the bias reduction algorithm by referring to past data. For example, the bias reduction unit can analyze past data to identify areas for improvement in the bias reduction algorithm. Furthermore, the bias reduction unit can optimize the bias reduction algorithm based on past data. In this way, the bias reduction algorithm can be optimized based on past data. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input past data into a generating AI to optimize the bias reduction algorithm.

[0051] The bias reduction unit can weight bias reduction based on the data collection timing. For example, the bias reduction unit can set a higher weight for bias reduction to the most recent data. It can also set a lower weight for bias reduction to historical data. Furthermore, the bias reduction unit can optimally allocate bias reduction resources based on the data collection timing. For example, the bias reduction unit can set a higher weight for bias reduction to the most recent data. It can also set a lower weight for bias reduction to historical data. Furthermore, the bias reduction unit can optimally allocate bias reduction resources based on the data collection timing. This allows for weighting of bias reduction based on the data collection timing. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input the data collection timing into a generating AI and perform bias reduction weighting.

[0052] The improvement unit can optimize the improvement algorithm by referring to past data during the improvement process. For example, the improvement unit can analyze past data to identify areas for improvement in the improvement algorithm. The improvement unit can also optimize the improvement algorithm based on past data. Furthermore, the improvement unit can improve the accuracy of the improvement algorithm by referring to past data. For example, the improvement unit can analyze past data to identify areas for improvement in the improvement algorithm. The improvement unit can also optimize the improvement algorithm based on past data. Furthermore, the improvement unit can improve the accuracy of the improvement algorithm by referring to past data. This allows the improvement algorithm to be optimized based on past data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past data into a generating AI and optimize the improvement algorithm.

[0053] The improvement unit can weight improvements based on the data collection timing. For example, the improvement unit can assign a higher weight to the most recent data and a lower weight to past data. Furthermore, the improvement unit can optimally allocate improvement resources based on the data collection timing. For example, the improvement unit can assign a higher weight to the most recent data and a lower weight to past data. Furthermore, the improvement unit can optimally allocate improvement resources based on the data collection timing. This allows for weighting improvements based on the data collection timing. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the data collection timing into a generating AI and perform weighting of improvements.

[0054] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data to identify areas for improvement in the learning algorithm. The learning unit can also optimize the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data to identify areas for improvement in the learning algorithm. The learning unit can also optimize the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI to optimize the learning algorithm.

[0055] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to past data. Furthermore, the learning unit can optimally allocate resources to the training data based on the data collection timing. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to past data. Furthermore, the learning unit can optimally allocate resources to the training data based on the data collection timing. This allows the learning unit to weight the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection timing into a generating AI and weight the training data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can adjust the analysis method based on the data source. For example, it can perform real-time analysis on data collected from sensors. It can also perform batch processing on data collected from the internet. Furthermore, it can perform interactive analysis on data directly input by users. This allows the analysis method to be adjusted according to the data source. The analysis unit can identify the data source and apply the appropriate analysis method.

[0058] The service provider can analyze the user's past advice history and select the most suitable advice method. For example, it can prioritize suggesting advice methods that the user has previously preferred and accepted. It can also select the most effective advice method based on the user's past advice history. Furthermore, it can analyze the user's past advice history and suggest areas for improvement in the advice methods. This allows for the selection of the most suitable advice method based on past advice history. The service provider can analyze the user's past advice history and select the most suitable advice method.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, it can prioritize the collection of data related to the user's current location. It can also filter highly relevant data based on the user's geographical location. Furthermore, it can select the optimal data collection method considering the user's location. This allows for the collection of highly relevant data based on the user's geographical location. The data collection unit can identify the user's geographical location and prioritize the collection of highly relevant data.

[0060] The analysis unit can apply different analysis algorithms depending on the data category. For example, a specialized medical analysis algorithm can be applied to medical data. Similarly, a financial analysis algorithm can be applied to financial data. Furthermore, an analysis algorithm specifically tailored to career counseling can be applied to career data. This allows for the application of the appropriate analysis algorithm according to the data category. The analysis unit can identify the data category and apply the appropriate analysis algorithm.

[0061] The service provider can prioritize advice based on the data collection timing when providing advice. For example, they can prioritize advice based on the latest data. They can also provide advice based on the latest data while referring to past data. Furthermore, they can optimally allocate advice resources based on the data collection timing. This allows them to determine the priority of advice based on the data collection timing. The service provider can identify the data collection timing and determine the priority of advice.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data. The data collection unit can collect data such as numerical data, text data, and image data. The data collection unit can collect environmental data using sensors, and can also collect publicly available data from the internet. It can also receive data input directly from users. For example, it can collect text data entered by a user. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using machine learning algorithms, and can also analyze text data using natural language processing technology. Furthermore, it can analyze image data using image recognition technology. For example, it can statistically analyze the collected numerical data to understand trends. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. Based on the analysis results, the service provider can propose the optimal investment strategy and also provide health management advice. Furthermore, they can also provide career counseling. For example, they can propose a specific action plan to the user based on the analysis results.

[0064] (Example of form 2) The AI ​​advisory service according to an embodiment of the present invention is a service that uses AI to solve business and personal problems. This AI advisory service is a system that collects data, the AI ​​analyzes that data, and provides advice based on the analysis results. For example, the AI ​​advisory service can be applied in various fields such as medicine, finance, and career counseling. For example, in the medical field, it can provide insights into diseases that may not be detected by a family doctor based on early symptoms. In the financial field, it can provide investment advice based on an individual's risk preference. Advantages of the AI ​​advisory service include higher accuracy, unbiased data processing, and improved results. These services can reduce costs through automated processes. At the start of the service, the AI ​​advisor plays the role of an advisor with specialized knowledge, but in the medium to long term, it learns an individual's decision-making criteria through machine learning, enabling the provision of more personalized advice. Strengths of the AI ​​advisory service include improved accuracy, reduced bias, and improved results. Improved accuracy allows for accurate understanding and evaluation of an individual's financial situation, medical condition, and career aspirations. By reducing bias, we can provide fairer and less biased advice by eliminating individual differences based on attributes such as gender, race, and age. Improved results allow us to propose optimal investment strategies and financial plans, ultimately accelerating individual wealth growth. We can also advise on which specialist to consult for a medical condition and show various paths to career goals. This service does not aim to completely replace human advice, but rather to provide second opinions with a lower barrier to entry. We strive to improve the service by prioritizing customizable data acquisition. This will enable the AI ​​advisory service to efficiently solve business and personal challenges.

[0065] The AI ​​advisory service according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect data such as numerical data, text data, and image data. The data collection unit can collect environmental data using sensors, for example. The data collection unit can also collect publicly available data from the internet. Furthermore, the data collection unit can also receive data input directly from the user. For example, the data collection unit collects text data entered by the user. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using machine learning algorithms, for example. The analysis unit can also analyze text data using natural language processing technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit statistically analyzes the collected numerical data to understand trends. The data provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the data provision unit can propose an optimal investment strategy based on the analysis results. The data provision unit can also provide health management advice based on the analysis results. Furthermore, the data provision unit can provide career counseling based on the analysis results. For example, the service provider proposes a specific action plan to the user based on the analysis results. This allows the AI ​​advisory service according to the embodiment to efficiently collect data, analyze it, and provide advice.

[0066] The data collection unit collects data. The data collection unit can collect various types of data, such as numerical data, text data, and image data. Specifically, numerical data includes environmental data such as temperature, humidity, and pressure obtained from sensors, as well as stock prices, exchange rates, and economic indicators from financial markets. Text data includes survey responses and feedback entered by users, news articles and blog posts from the internet, and comments on social media. Image data includes photos uploaded by users, video footage from surveillance cameras, and satellite imagery. The data collection unit employs various methods to efficiently collect this data. For example, it can collect environmental data in real time using sensors. This allows for a detailed understanding of environmental changes in specific locations and time periods. The data collection unit can also collect publicly available data from the internet. For example, it can automatically obtain necessary data from specific websites using web scraping techniques. Furthermore, the data collection unit can also receive data input directly from users. For example, users can input text data through a dedicated application or web form, which the data collection unit can then acquire in real time. This allows the data collection unit to collect a wide range of data from diverse data sources and integrate it into a system-wide database. The collected data is appropriately organized and stored for analysis by the analysis unit.

[0067] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze data using machine learning algorithms. Specifically, for collected numerical data, it uses statistical methods such as regression analysis and clustering to understand data trends and patterns. For example, it can analyze stock price data to predict future price fluctuations of specific stocks. For text data, it can analyze the content using natural language processing technology. For example, it can analyze user feedback to extract evaluations and areas for improvement of products and services. Furthermore, for image data, it can analyze the content using image recognition technology. For example, it can analyze surveillance camera footage to detect suspicious activity. By combining these technologies, the analysis unit can analyze collected data from multiple perspectives and obtain more accurate analysis results. In addition, the analysis unit can utilize historical data and external data sources to evaluate long-term trends and risks. For example, it can predict future economic trends and formulate investment strategies based on historical economic data. This allows the analysis unit to quickly and accurately analyze collected data and provide useful information to users.

[0068] The service provider provides advice based on the analysis results obtained by the analysis department. For example, the service provider can propose the optimal investment strategy based on the analysis results. Specifically, it can advise users on when to buy and sell based on stock price fluctuations predicted by the analysis department. The service provider can also provide health management advice based on the analysis results. For example, it can analyze the user's health data and suggest improvements to diet and exercise. Furthermore, the service provider can provide career counseling based on the analysis results. For example, it can analyze the user's skills and experience and suggest appropriate job types and career paths. The service provider uses various means to provide this advice to users in an easy-to-understand manner. For example, it can visually display analysis results and advice through a dedicated application or website. It can also provide important information in a timely manner using email and notification functions. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can analyze how users reacted to the advice provided and reflect this in the next advice. In this way, the service provider can always provide users with the best possible advice and improve user satisfaction.

[0069] The analysis unit may include a bias reduction unit that reduces bias based on individual attributes such as gender, race, and age. For example, the analysis unit can preprocess data to reduce gender bias. It can also filter data to reduce race bias. Furthermore, the analysis unit can adjust data weighting to reduce age bias. For example, the analysis unit can apply an algorithm to treat data equally regardless of gender. It can also filter data to treat it equally regardless of race. Furthermore, the analysis unit can adjust weighting to treat data equally regardless of age. This reduces bias and provides fair analysis results. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can reduce bias using an AI model that performs data preprocessing.

[0070] The provisioning unit may include an improvement unit that proposes optimal investment strategies and financial plans. For example, the provisioning unit can perform risk assessments and propose optimal investment strategies. It can also propose methods for constructing portfolios. Furthermore, it can propose income and expenditure plans and asset management plans. For example, the provisioning unit proposes optimal investment strategies based on the user's risk tolerance. It can also propose methods for constructing portfolios based on the user's investment goals. Furthermore, it can propose income and expenditure plans and asset management plans based on the user's income and expenditure situation. This enables the proposal of optimal investment strategies and financial plans. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can propose optimal investment strategies using an AI model for risk assessment.

[0071] The analysis unit may include a learning unit that learns an individual's decision-making framework. The analysis unit can, for example, learn the user's values. It can also learn the user's priorities. Furthermore, it can learn the user's decision criteria. For example, the analysis unit learns values ​​based on the user's past behavioral data. It can also learn priorities based on the user's past choice data. Furthermore, it can learn decision criteria based on the user's past decision data. This allows the system to learn an individual's decision-making framework and provide more personalized advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can learn an individual's decision-making framework using an AI model that learns the user's values.

[0072] The service provider can provide insights into diseases that may not be detected by a primary care physician based on their initial symptoms in the medical field. For example, the service provider can suggest the possibility of a disease based on a list of initial symptoms. It can also evaluate the severity of symptoms and suggest additional tests as needed. Furthermore, it can recommend consultation with a specialist. For example, the service provider can have the user input their initial symptoms and suggest the possibility of a disease based on that list of symptoms. It can also evaluate the severity of symptoms and suggest additional tests as needed. Furthermore, it can recommend consultation with a specialist and guide the user to an appropriate medical institution. This helps in the early detection of diseases in the medical field. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide insights using an AI model that suggests the possibility of a disease based on a list of initial symptoms.

[0073] The service provider can provide investment advice in the financial sector based on an individual's risk preference. For example, the service provider can assess risk tolerance and provide investment advice. It can also provide investment advice based on investment goals. Furthermore, the service provider can propose methods for constructing a portfolio. For example, the service provider can assess a user's risk tolerance and provide investment advice based on it. It can also propose specific investment strategies based on the user's investment goals. Furthermore, the service provider can propose methods for constructing a portfolio based on the user's asset situation. This enables investment advice based on risk preference in the financial sector. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide investment advice using an AI model that assesses risk tolerance and provides investment advice.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to collect data quickly. For example, the data collection unit can monitor the user's emotions in real time and adjust the timing of data collection according to changes in emotions. This allows the timing of data collection to be adjusted according to 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 without AI. For example, the data collection unit can input user emotion data into a generative AI and adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has preferred to use in the past. The data collection unit can also select the most efficient collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and suggest improvements to the collection method. For example, the data collection unit can analyze data collection methods that the user has used in the past and select the optimal collection method. The data collection unit can also identify and suggest improvements to the collection method based on the user's past data collection history. This allows for the selection of the optimal collection method based on past data collection 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 data collection history into a generating AI and select the optimal collection method.

[0076] The data collection unit can filter data based on the user's current situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter out unnecessary data according to the user's current situation. Furthermore, the data collection unit can collect highly relevant data based on the user's areas of interest. For example, the data collection unit can prioritize collecting data related to topics that the user is currently interested in. Furthermore, the data collection unit can filter out unnecessary data according to the user's current situation. Furthermore, the data collection unit can collect highly relevant data based on the user's areas of interest. This allows data to be filtered based on the user's current situation 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 the user's current situation and areas of interest into a generating AI and filter the data.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit can monitor the user's emotions in real time and prioritize the data to collect according to changes in emotions. This allows the data to be prioritized according to 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 data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI to determine the priority of the data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can select the optimal data collection method by considering the user's location information. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also filter highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can select the optimal data collection method by considering the user's location information. This allows for the collection of highly relevant data based on the user's geographical location information. 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 information into a generating AI and prioritize the collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. For example, the data collection unit can collect data related to topics of interest from the user's social media activity. The data collection unit can also analyze the content of the user's social media posts and collect highly relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the collection of relevant data based on 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 without AI. For example, the data collection unit can input the user's social media activity into a generating AI and collect relevant data.

[0080] 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 tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can monitor the user's emotions in real time and adjust the presentation of the analysis according to changes in emotions. This allows the presentation of the analysis to be adjusted according to 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, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and adjust the presentation of the analysis.

[0081] 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 can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to 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 adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specialized medical analysis algorithm to medical data. It can also apply a financial analysis algorithm to financial data. Furthermore, it can apply an analysis algorithm specifically tailored for career counseling to career data. This allows for the application of an appropriate analysis algorithm depending on the data category. 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 data categories into an AI that generates them and apply an appropriate analysis algorithm.

[0083] 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 in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, the analysis unit can monitor the user's emotions in real time and adjust the length of the analysis according to changes in emotions. This allows the length of the analysis to be adjusted according to 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, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can optimally allocate analysis resources based on the data collection timing. This allows the analysis priority to be determined based on the data collection timing. 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 data collection timing into a generating AI to determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of the data. This allows the order of analysis to be adjusted 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 and adjust the order of analysis.

[0086] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and easy-to-understand advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise advice. For example, the service provider can monitor the user's emotions in real time and adjust the way advice is presented in response to changes in emotions. This allows the service provider to adjust the way advice is presented according to 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and adjust the way advice is presented.

[0087] The service provider can adjust the level of detail of advice based on the importance of the data when providing advice. For example, the service provider can provide detailed advice based on high-importance data, and simplified advice based on low-importance data. Furthermore, the service provider can optimally allocate resources for advice according to the importance of the data. For example, the service provider can provide detailed advice based on high-importance data, and simplified advice based on low-importance data. Furthermore, the service provider can optimally allocate resources for advice according to the importance of the data. This allows the level of detail of advice to be adjusted according to the importance of the data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the data into a generating AI and adjust the level of detail of the advice.

[0088] The service provider can apply different advice algorithms depending on the data category when providing advice. For example, the service provider can apply a specialized medical advice algorithm to advice based on medical data. It can also apply a financial advice algorithm to advice based on financial data. Furthermore, it can apply an advice algorithm specialized in career counseling to advice based on career data. This allows the service provider to apply an appropriate advice algorithm depending on the data category. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data category into a generating AI and apply an appropriate advice algorithm.

[0089] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is excited, the service provider can provide visually stimulating advice. For example, the service provider can monitor the user's emotions in real time and adjust the length of the advice according to changes in emotions. This allows the length of the advice to be adjusted according to 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and adjust the length of the advice.

[0090] The service provider can determine the priority of advice based on the data collection timing when providing advice. For example, the service provider will prioritize advice based on the latest data. Alternatively, the service provider can provide advice based on the latest data while also referring to past data. Furthermore, the service provider can optimally allocate advice resources based on the data collection timing. For example, the service provider will prioritize advice based on the latest data. Alternatively, the service provider can provide advice based on the latest data while also referring to past data. Furthermore, the service provider can optimally allocate advice resources based on the data collection timing. This allows the service provider to determine the priority of advice based on the data collection timing. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the data collection timing into a generating AI to determine the priority of advice.

[0091] The service provider can adjust the order of advice based on the relevance of the data when providing advice. For example, the service provider may prioritize advice based on highly relevant data. It may also postpone the provision of advice based on less relevant data. Furthermore, the service provider can optimally allocate the resources for advice based on the relevance of the data. For example, the service provider may prioritize advice based on highly relevant data. It may also postpone the provision of advice based on less relevant data. Furthermore, the service provider can optimally allocate the resources for advice based on the relevance of the data. This allows the order of advice to be adjusted based on the relevance of the data. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the data into a generating AI and adjust the order of advice.

[0092] The bias reduction unit can estimate the user's emotions and adjust the bias reduction method based on the estimated user emotions. For example, if the user is stressed, the bias reduction unit can reduce the frequency of bias reduction to alleviate the user's burden. Conversely, if the user is relaxed, the bias reduction unit can increase the frequency of bias reduction to collect more detailed data. Furthermore, if the user is in a hurry, the bias reduction unit can shorten the timing of bias reduction to collect data more quickly. For example, the bias reduction unit can monitor the user's emotions in real time and adjust the bias reduction method according to changes in emotions. This allows the bias reduction method to be adjusted according to 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 bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input user emotion data into the generative AI and adjust the bias reduction method.

[0093] The bias reduction unit can optimize the bias reduction algorithm by referring to past data during bias reduction. For example, the bias reduction unit can analyze past data to identify areas for improvement in the bias reduction algorithm. Furthermore, the bias reduction unit can optimize the bias reduction algorithm based on past data. In addition, the bias reduction unit can improve the accuracy of the bias reduction algorithm by referring to past data. For example, the bias reduction unit can analyze past data to identify areas for improvement in the bias reduction algorithm. Furthermore, the bias reduction unit can optimize the bias reduction algorithm based on past data. In this way, the bias reduction algorithm can be optimized based on past data. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input past data into a generating AI to optimize the bias reduction algorithm.

[0094] The bias reduction unit can estimate the user's emotions and adjust the frequency of bias reduction based on the estimated emotions. For example, if the user is stressed, the bias reduction unit can reduce the frequency of bias reduction to alleviate the user's burden. Conversely, if the user is relaxed, the bias reduction unit can increase the frequency of bias reduction to collect more detailed data. Furthermore, if the user is in a hurry, the bias reduction unit can shorten the timing of bias reduction to collect data more quickly. For example, the bias reduction unit can monitor the user's emotions in real time and adjust the frequency of bias reduction according to changes in emotions. This allows the frequency of bias reduction to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input user emotion data into a generative AI and adjust the frequency of bias reduction.

[0095] The bias reduction unit can weight bias reduction based on the data collection timing. For example, the bias reduction unit can set a higher weight for bias reduction to the most recent data. It can also set a lower weight for bias reduction to historical data. Furthermore, the bias reduction unit can optimally allocate bias reduction resources based on the data collection timing. For example, the bias reduction unit can set a higher weight for bias reduction to the most recent data. It can also set a lower weight for bias reduction to historical data. Furthermore, the bias reduction unit can optimally allocate bias reduction resources based on the data collection timing. This allows for weighting of bias reduction based on the data collection timing. Some or all of the above processing in the bias reduction unit may be performed using AI, for example, or without AI. For example, the bias reduction unit can input the data collection timing into a generating AI and perform bias reduction weighting.

[0096] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated emotions. For example, if the user is tense, the improvement unit can provide a simple and highly visible improvement method. It can also provide a more detailed improvement method if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise improvement method. For example, the improvement unit can monitor the user's emotions in real time and adjust the improvement method according to changes in emotions. This allows the improvement method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into a generative AI and adjust the improvement method.

[0097] The improvement unit can optimize the improvement algorithm by referring to past data during the improvement process. For example, the improvement unit can analyze past data to identify areas for improvement in the improvement algorithm. The improvement unit can also optimize the improvement algorithm based on past data. Furthermore, the improvement unit can improve the accuracy of the improvement algorithm by referring to past data. For example, the improvement unit can analyze past data to identify areas for improvement in the improvement algorithm. The improvement unit can also optimize the improvement algorithm based on past data. Furthermore, the improvement unit can improve the accuracy of the improvement algorithm by referring to past data. This allows the improvement algorithm to be optimized based on past data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past data into a generating AI and optimize the improvement algorithm.

[0098] The improvement unit can estimate the user's emotions and adjust the frequency of improvements based on the estimated emotions. For example, if the user is stressed, the improvement unit can reduce the frequency of improvements to alleviate the user's burden. Conversely, if the user is relaxed, the improvement unit can increase the frequency of improvements and collect more detailed data. Furthermore, if the user is in a hurry, the improvement unit can shorten the timing of improvements and collect data more quickly. For example, the improvement unit can monitor the user's emotions in real time and adjust the frequency of improvements according to changes in emotions. This allows the frequency of improvements to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into the generative AI and adjust the frequency of improvements.

[0099] The improvement unit can weight improvements based on the data collection timing. For example, the improvement unit can assign a higher weight to the most recent data and a lower weight to past data. Furthermore, the improvement unit can optimally allocate improvement resources based on the data collection timing. For example, the improvement unit can assign a higher weight to the most recent data and a lower weight to past data. Furthermore, the improvement unit can optimally allocate improvement resources based on the data collection timing. This allows for weighting improvements based on the data collection timing. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the data collection timing into a generating AI and perform weighting of improvements.

[0100] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is nervous, the learning unit will select simple and highly visual training data. If the user is relaxed, the learning unit can also select detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that gets straight to the point. For example, the learning unit can monitor the user's emotions in real time and select training data according to changes in emotions. This allows for the selection of training data according to 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and select training data.

[0101] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data to identify areas for improvement in the learning algorithm. The learning unit can also optimize the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can analyze past learning data to identify areas for improvement in the learning algorithm. The learning unit can also optimize the learning algorithm based on past learning data. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. This allows the learning algorithm to be optimized based on past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI to optimize the learning algorithm.

[0102] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency to alleviate the user's burden. Conversely, if the user is relaxed, the learning unit can increase the learning frequency to collect more detailed data. Furthermore, if the user is in a hurry, the learning unit can shorten the learning timing to collect data more quickly. For example, the learning unit can monitor the user's emotions in real time and adjust the learning frequency in response to changes in emotions. This allows the learning frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and adjust the learning frequency.

[0103] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to past data. Furthermore, the learning unit can optimally allocate resources to the training data based on the data collection timing. For example, the learning unit can assign a higher weight to the most recent data. It can also assign a lower weight to past data. Furthermore, the learning unit can optimally allocate resources to the training data based on the data collection timing. This allows the learning unit to weight the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the data collection timing into a generating AI and weight the training data.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it will prioritize the analysis of high-priority data. If the user is relaxed, it can perform a more detailed analysis. Furthermore, if the user is in a hurry, it can provide analysis results quickly. This allows the analysis priority to be adjusted according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. The analysis unit can input the user's emotion data into the generative AI and determine the priority of analysis.

[0106] The service provider can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is in a hurry, it can provide concise advice. This allows the advice to be adjusted according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. The service provider can input the user's emotion data into the generative AI and adjust the content of the advice.

[0107] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data can be collected quickly. This allows the data collection method to be adjusted according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. The data collection unit can input the user's emotion data into the generative AI and adjust the data collection method accordingly.

[0108] 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 nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. The analysis unit can input the user's emotion data into the generative AI and adjust the presentation of the analysis.

[0109] The service provider can estimate the user's emotions and adjust the length of the advice based on those emotions. For example, if the user is in a hurry, it can provide short, concise advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is excited, it can provide visually stimulating advice. This allows the length of the advice to be adjusted according to the user's emotions. Emotion estimation is performed using an emotion engine or generative AI. The service provider can input the user's emotion data into the generative AI and adjust the length of the advice.

[0110] The analysis unit can adjust the analysis method based on the data source. For example, it can perform real-time analysis on data collected from sensors. It can also perform batch processing on data collected from the internet. Furthermore, it can perform interactive analysis on data directly input by users. This allows the analysis method to be adjusted according to the data source. The analysis unit can identify the data source and apply the appropriate analysis method.

[0111] The service provider can analyze the user's past advice history and select the most suitable advice method. For example, it can prioritize suggesting advice methods that the user has previously preferred and accepted. It can also select the most effective advice method based on the user's past advice history. Furthermore, it can analyze the user's past advice history and suggest areas for improvement in the advice methods. This allows for the selection of the most suitable advice method based on past advice history. The service provider can analyze the user's past advice history and select the most suitable advice method.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, it can prioritize the collection of data related to the user's current location. It can also filter highly relevant data based on the user's geographical location. Furthermore, it can select the optimal data collection method considering the user's location. This allows for the collection of highly relevant data based on the user's geographical location. The data collection unit can identify the user's geographical location and prioritize the collection of highly relevant data.

[0113] The analysis unit can apply different analysis algorithms depending on the data category. For example, a specialized medical analysis algorithm can be applied to medical data. Similarly, a financial analysis algorithm can be applied to financial data. Furthermore, an analysis algorithm specifically tailored to career counseling can be applied to career data. This allows for the application of the appropriate analysis algorithm according to the data category. The analysis unit can identify the data category and apply the appropriate analysis algorithm.

[0114] The service provider can prioritize advice based on the data collection timing when providing advice. For example, they can prioritize advice based on the latest data. They can also provide advice based on the latest data while referring to past data. Furthermore, they can optimally allocate advice resources based on the data collection timing. This allows them to determine the priority of advice based on the data collection timing. The service provider can identify the data collection timing and determine the priority of advice.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects data. The data collection unit can collect data such as numerical data, text data, and image data. The data collection unit can collect environmental data using sensors, and can also collect publicly available data from the internet. It can also receive data input directly from users. For example, it can collect text data entered by a user. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using machine learning algorithms, and can also analyze text data using natural language processing technology. Furthermore, it can analyze image data using image recognition technology. For example, it can statistically analyze the collected numerical data to understand trends. Step 3: The service provider provides advice based on the analysis results obtained by the analysis unit. Based on the analysis results, the service provider can propose the optimal investment strategy and also provide health management advice. Furthermore, they can also provide career counseling. For example, they can propose a specific action plan to the user based on the analysis results.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented using the data collection functions of the smart device 14's sensors and the internet. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms and natural language processing techniques. The data provision unit is implemented in the control unit 46A of the smart device 14 and provides advice to the user based on the analysis results. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented using the sensors of the smart glasses 214 and the data collection function from the internet. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms and natural language processing techniques. The data provision unit is implemented in the control unit 46A of the smart glasses 214 and provides advice to the user based on the analysis results. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented using the sensors of the headset terminal 314 and the data collection function from the internet. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms and natural language processing techniques. The data provision unit is implemented in the control unit 46A of the headset terminal 314 and provides advice to the user based on the analysis results. 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.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented using the robot 414's sensors and data collection functions from the internet. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the data using machine learning algorithms and natural language processing techniques. The data provision unit is implemented in the control unit 46A of the robot 414, and provides advice to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides advice based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a bias reduction unit that reduces bias based on an individual's attributes such as gender, race, and age. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We have an improvement department that proposes optimal investment strategies and financial plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It includes a learning section for developing an individual's decision-making framework. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, In the medical field, it provides insights into diseases that may not be detected by a primary care physician based on their initial symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing investment advice based on individual risk preferences in the financial sector. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) 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 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, adjust the level of detail of the advice based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The bias reduction unit is We estimate the user's emotions and adjust the bias reduction method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The bias reduction unit is When reducing bias, the bias reduction algorithm is optimized by referring to historical data. The system described in Appendix 2, characterized by the features described herein. (Note 27) The bias reduction unit is The system estimates the user's emotions and adjusts the frequency of bias reduction based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The bias reduction unit is When reducing bias, weight the bias reduction based on when the data was collected. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned improvement unit is, When making improvements, the improvement algorithm is optimized by referring to past data. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned improvement unit is, It estimates the user's emotions and adjusts the frequency of improvements based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned improvement unit is, When making improvements, weight the improvements based on when the data was collected. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0189] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a provisioning unit that provides advice based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a bias reduction unit that reduces bias based on an individual's attributes such as gender, race, and age. The system according to feature 1.

3. The aforementioned supply unit is, We have an improvement department that proposes optimal investment strategies and financial plans. The system according to feature 1.

4. The aforementioned analysis unit, It includes a learning section for developing an individual's decision-making framework. The system according to feature 1.

5. The aforementioned supply unit is, In the medical field, it provides insights into diseases that may not be detected by a primary care physician based on their initial symptoms. The system according to feature 1.

6. The aforementioned supply unit is, Providing investment advice based on individual risk preferences in the financial sector. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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