system

Abstract

The system according to this embodiment aims to securely aggregate scattered personal data and create new value. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that personal data is scattered across various platforms and it is difficult to aggregate and utilize this data.

[0005] The system according to the embodiment aims to securely aggregate scattered personal data and create new value.

Means for Solving the Problems

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

Effects of the Invention

[0007] The system according to this embodiment can securely aggregate scattered personal data and create new value. [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 personal data platform according to an embodiment of the present invention is a system for centrally managing personal data and creating new value by utilizing generative AI. This system begins with the user collecting their data from various platforms and inputting it into the personal data platform. Next, the generative AI analyzes the input data and generates information and suggestions useful to the user. Examples include driving data, purchase history, account information, game information, SNS data, academic performance and educational background information, etc. This platform connects with various services via APIs to collect and analyze data. For example, it collects data from e-commerce sites, banks, car rental companies, universities, SNS, job search sites, etc. This allows users to centrally manage their data and create new value by utilizing generative AI. This mechanism allows users to efficiently manage their data and create new value by utilizing generative AI. For example, it is possible to receive safe driving advice by analyzing driving data, receive product recommendations based on purchase history, or receive optimal learning plans based on academic performance. Furthermore, since data is managed securely, the risk of personal information leakage is reduced. This allows the personal data platform to efficiently manage user data and create new value by leveraging generative AI.

[0029] The personal data platform according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data. The collection unit can collect data such as driving data, purchase history, account information, game information, SNS data, and academic performance / educational background information. For example, the collection unit can collect purchase history from e-commerce sites. The collection unit can also collect account information from banks. The collection unit can also collect driving data from rental car companies. For example, the collection unit can obtain purchase history through the API of e-commerce sites. It can obtain account information through the API of banks. It can obtain driving data through the API of rental car companies. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit analyzes driving data to generate safe driving advice. The analysis unit can also analyze purchase history to suggest recommended products. The analysis unit can also analyze academic performance to suggest an optimal learning plan. For example, the analysis unit takes driving data as input and performs analysis using a generative AI model that outputs safe driving advice. It also takes purchase history as input and performs analysis using a generative AI model that outputs recommended products. It also takes academic performance as input and performs analysis using a generative AI model that outputs an optimal learning plan. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data and provides safe driving advice. The proposal unit can also suggest recommended products based on purchase history. The proposal unit can also suggest an optimal learning plan based on academic performance. For example, the proposal unit provides safe driving advice to the user. It suggests recommended products to the user. It suggests an optimal learning plan to the user. In this way, the personal data platform according to the embodiment can create new value by collecting, analyzing, and making proposals based on user data.

[0030] The data collection unit collects data. For example, the data collection unit can collect data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. Specifically, the data collection unit obtains data from e-commerce sites via APIs to collect purchase history. This allows for accurate understanding of detailed information about products a user has purchased in the past, such as purchase date and time and purchase amount. The data collection unit also uses bank APIs to obtain account balances and transaction history from banks to collect account information. This allows for a detailed understanding of the user's financial situation and the provision of appropriate financial advice. Furthermore, the data collection unit obtains driving history and vehicle condition information from rental car companies via their APIs to collect driving data. This allows for a detailed understanding of the user's driving habits and vehicle usage, and the provision of safe driving advice. In addition, the data collection unit uses social media platform APIs to obtain user posts, friendships, and like history to collect social media data. This allows for an understanding of the user's interests and social relationships, enabling more personalized suggestions. Regarding academic performance and educational background information, by obtaining transcripts and course history from educational institution databases via APIs, it becomes possible to understand the user's learning situation in detail and propose an optimal learning plan. This allows the data collection unit to gather a wide range of data from diverse data sources and centrally manage multifaceted information about the user.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the analysis unit uses a generative AI model that takes driving data as input and outputs safe driving advice in order to analyze driving data and generate safe driving advice. This generative AI model can learn from past driving data and identify dangerous driving behaviors and areas for improvement. For example, it can analyze the frequency of sudden braking and acceleration, and the tendency to exceed the speed limit, and provide specific advice to reduce these behaviors. The analysis unit also uses a generative AI model that takes purchase history as input and outputs recommended products in order to analyze purchase history and suggest recommended products. This generative AI model can learn from the user's past purchase history and the purchasing patterns of other users and predict products that the user may be interested in. For example, it can suggest new products from the same brand or category to users who frequently purchase products from that brand or category. Furthermore, the analysis unit uses a generative AI model that takes academic performance as input and outputs an optimal learning plan in order to analyze academic performance and propose an optimal learning plan. This generative AI model can learn from the user's past performance and learning history and identify the user's weaknesses and areas that need strengthening. For example, if a student's performance is low in a particular subject, the model can suggest supplementary materials and learning methods related to that subject. As a result, the analysis unit can highly analyze the collected data and provide the user with specific and useful advice and suggestions.

[0032] The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data to provide safe driving advice. Specifically, the proposal unit provides users with safe driving advice generated by a generative AI model. This advice includes, for example, driving techniques to avoid sudden braking and acceleration, and specific methods for adhering to speed limits. The proposal unit can also suggest recommended products based on purchase history. Specifically, the proposal unit provides users with a list of recommended products generated by a generative AI model. This list includes products selected based on the user's past purchase history and interests, and can suggest products that the user is likely to become interested in. Furthermore, the proposal unit can also suggest an optimal learning plan based on academic performance. Specifically, the proposal unit provides users with a learning plan generated by a generative AI model. This learning plan includes specific learning methods to overcome the user's weaknesses and supplementary materials for areas that need strengthening. For example, if a user has a low score in a particular subject, the proposal unit can suggest online courses or textbooks related to that subject. In this way, the proposal unit can provide users with specific and practical suggestions to support their lives and learning. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user purchases a suggested product, the purchase history can be analyzed again and reflected in future suggestions. This allows the proposal department to consistently provide users with the most suitable suggestions and increase user satisfaction.

[0033] The data collection unit can collect data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. For example, the data collection unit can collect driving data. For example, it can collect data such as speed and brake usage frequency from vehicle sensors. The data collection unit can also collect purchase history. For example, it can collect data such as purchase date and time and purchased items from e-commerce sites. The data collection unit can also collect account information. For example, it can collect transaction history and balance information from banks. The data collection unit can also collect game information. For example, it can collect data such as play time and scores from game platforms. The data collection unit can also collect social media data. For example, it can collect data such as post content and the number of likes from social media. The data collection unit can also collect academic performance / educational background information. For example, it can collect data such as report cards and diplomas from schools. By collecting diverse data, it becomes possible to perform more multifaceted analysis and make more informed suggestions. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input driving data acquired from the vehicle's sensors into a generating AI and have the generating AI perform data collection.

[0034] The analysis unit can analyze collected data and generate useful information and suggestions for the user. For example, the analysis unit can analyze collected driving data to generate safe driving advice. For example, the analysis unit can analyze driving data and provide advice such as adhering to speed limits and maintaining an appropriate distance between vehicles. The analysis unit can also analyze collected purchase history and suggest recommended products. For example, the analysis unit can analyze purchase history and suggest products based on past purchase history or popular products. The analysis unit can also analyze collected academic performance and suggest an optimal learning plan. For example, the analysis unit can analyze academic performance and suggest a learning plan including the allocation of study time and the selection of learning materials. In this way, by analyzing the collected data, useful information and suggestions can be provided to the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input collected driving data into a generating AI and have the generating AI generate safe driving advice.

[0035] The suggestion unit can analyze driving data and provide advice on safe driving. For example, the suggestion unit can analyze driving data and provide advice on things like complying with speed limits and maintaining an appropriate distance between vehicles. For example, the suggestion unit can analyze driving data and provide advice on reducing the number of sudden brakes. The suggestion unit can also analyze driving data and provide advice on eco-driving. For example, the suggestion unit can analyze driving data and provide advice on improving fuel efficiency. In this way, by analyzing driving data, it is possible to provide advice on safe driving. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input driving data into a generation AI and have the generation AI generate advice on safe driving.

[0036] The suggestion unit can suggest recommended products based on purchase history. For example, the suggestion unit can analyze purchase history and suggest products based on past purchases. The suggestion unit can also analyze purchase history and suggest popular products. Furthermore, the suggestion unit can analyze purchase history and suggest products based on user interests. For example, the suggestion unit can analyze purchase history and suggest products related to products the user has purchased in the past. This improves the user's purchasing experience by suggesting recommended products based on purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input purchase history into a generative AI and have the generative AI perform the recommended product suggestion.

[0037] The suggestion unit can propose an optimal learning plan based on academic performance. For example, the suggestion unit can analyze academic performance and propose a learning plan that includes the allocation of study time and the selection of learning materials. The suggestion unit can also analyze academic performance and propose a learning plan that strengthens the user's weaknesses. Furthermore, the suggestion unit can analyze academic performance and propose a learning plan that develops the user's strengths. For example, the suggestion unit can analyze academic performance and propose a learning plan that focuses on subjects the user struggles with. By proposing an optimal learning plan based on academic performance, the user's learning efficiency is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input academic performance into a generative AI and have the generative AI propose an optimal learning plan.

[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 can prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, the data collection unit can automatically select relevant data sources based on the types of data the user has collected in the past. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method. This allows the optimal collection method to be selected by analyzing the 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 using AI.

[0039] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is working, the data collection unit can prioritize collecting data related to work. Furthermore, if the user is engrossed in a hobby, the data collection unit can prioritize collecting data related to that hobby. For example, the data collection unit can input the user's activities and areas of interest into a generating AI and have the generating AI perform data filtering. This allows for the collection of highly relevant data by filtering the data based on the user's activities 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 using AI.

[0040] The data collection unit can adjust its collection method while considering the state of the user's device. For example, if the user's device battery level is low, the data collection unit may temporarily stop data collection. The data collection unit may also delay data collection if the user's device is under heavy load. Furthermore, if the user's device is connected to Wi-Fi, the data collection unit can collect a large amount of data. For example, the data collection unit can input the state of the user's device into a generating AI and have the generating AI adjust the collection method. This enables efficient data collection by considering the state of the user's device. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0041] The data collection unit can limit the scope of data collection based on the user's privacy settings. For example, if the user has excluded a specific data source in their privacy settings, the data collection unit will not collect data from that data source. For example, if the user has limited the scope of data collection in their privacy settings, the data collection unit can also collect data only within that scope. Furthermore, if the user has requested anonymization in their privacy settings, the data collection unit can collect only anonymized data. For example, the data collection unit can input the user's privacy settings into a generating AI and have the generating AI perform the limitation of the data collection scope. This makes it possible to collect data while protecting privacy by limiting the scope of data collection based on the user's privacy settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0042] 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, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data related to home. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data. This allows for the priority collection of highly relevant data by considering 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 using AI.

[0043] 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. For example, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, for example, or without using the generating AI.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a driving pattern analysis algorithm to driving data. For example, the analysis unit can also apply a purchasing behavior analysis algorithm to purchase history data. Furthermore, the analysis unit can apply a learning outcome analysis algorithm to academic performance data. For example, the analysis unit can input the data category into the generating AI and have the generating AI execute the application of different analysis algorithms. This makes it possible to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, for example, or without using the generating AI.

[0045] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the correlation between purchase history and driving data to improve accuracy. The analysis unit can also analyze the correlation between academic performance and social media data to improve accuracy. Furthermore, the analysis unit can analyze the correlation between account information and purchase history to improve accuracy. For example, the analysis unit can input the interrelationships of the data into a generating AI and have the generating AI perform the analysis accuracy improvement. This improves the accuracy of the analysis by considering the interrelationships of the data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0046] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis results based on the submitter's age. The analysis unit can also adjust the analysis results based on the submitter's occupation. Furthermore, the analysis unit can adjust the analysis results based on the submitter's place of residence. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the adjustment of the analysis results. This allows for the provision of more appropriate analysis results by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0047] The analysis unit can evaluate the reliability of the data during analysis and reflect this in the analysis results. For example, the analysis unit can assign higher weights to highly reliable data and reflect this in the analysis results. The analysis unit can also assign lower weights to less reliable data and reflect this in the analysis results. Furthermore, the analysis unit can assign appropriate weights to data with moderate reliability and reflect this in the analysis results. For example, the analysis unit can input the data reliability into the generating AI and have the generating AI perform the task of reflecting this in the analysis results. By evaluating the reliability of the data, it is possible to provide more reliable analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, the generating AI, or without using the generating AI.

[0048] The proposal unit can adjust the level of detail of a proposal based on the importance of the data. For example, the proposal unit can provide a detailed explanation for proposals based on highly important data. For example, the proposal unit can provide a simplified explanation for proposals based on less important data. Furthermore, the proposal unit can provide an explanation with a moderate level of detail for proposals based on moderately important data. For example, the proposal unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the proposal. This allows for more efficient proposals by adjusting the level of detail of the proposal based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0049] The suggestion unit can apply different suggestion algorithms depending on the data category when making suggestions. For example, the suggestion unit can apply an algorithm that provides safe driving advice to driving data. For example, the suggestion unit can also apply a product recommendation algorithm to purchase history data. Furthermore, the suggestion unit can apply an algorithm that proposes an optimal study plan to academic performance data. For example, the suggestion unit can input the data category into a generating AI and have the generating AI apply different suggestion algorithms. This allows for more accurate suggestions by applying different suggestion algorithms depending on the data category. Some or all of the above processing in the suggestion unit may be performed using a generating AI, or it may be performed without using a generating AI.

[0050] The proposal unit can improve the accuracy of its proposals by considering the interrelationships between data. For example, the proposal unit can analyze the correlation between purchase history and driving data to improve accuracy. The proposal unit can also analyze the correlation between academic performance and social media data to improve accuracy. Furthermore, the proposal unit can analyze the correlation between account information and purchase history to improve accuracy. For example, the proposal unit can input the interrelationships of data into a generation AI and have the generation AI perform the improvement of the proposal accuracy. This improves the accuracy of the proposals by considering the interrelationships of data. Some or all of the above processing in the proposal unit may be performed using a generation AI, for example, or without using a generation AI.

[0051] The proposal unit can make proposals while considering the attribute information of the data submitter. For example, the proposal unit can adjust the proposal content based on the submitter's age. The proposal unit can also adjust the proposal content based on the submitter's occupation. Furthermore, the proposal unit can adjust the proposal content based on the submitter's place of residence. For example, the proposal unit can input the submitter's attribute information into a generation AI and have the generation AI perform the adjustment of the proposal content. This allows for the provision of more appropriate proposals by considering the attribute information of the data submitter. Some or all of the above processing in the proposal unit may be performed using a generation AI, for example, or without using a generation AI.

[0052] The proposal unit can evaluate the reliability of the data during the proposal process and reflect this in the proposal results. For example, the proposal unit can assign higher weights to highly reliable data and reflect this in the proposal results. For example, the proposal unit can assign lower weights to less reliable data and reflect this in the proposal results. Furthermore, the proposal unit can assign appropriate weights to data of moderate reliability and reflect this in the proposal results. For example, the proposal unit can input the data reliability into a generating AI and have the generating AI perform the reflection of this into the proposal results. This allows for the provision of more reliable proposal results by evaluating the reliability of the data. Some or all of the above processing in the proposal unit may be performed using a generating AI, for example, or without using a generating AI.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit collects user health data, and the analysis unit analyzes that data to provide health management suggestions. For example, the data collection unit can collect heart rate, steps, and sleep data from the user's smartwatch. It can also collect meal details and calorie intake from the user's food logging app. The analysis unit analyzes the collected health data and can provide the user with exercise advice and suggestions for dietary improvements. For example, the analysis unit can analyze the user's heart rate data and suggest an appropriate exercise intensity. It can also analyze the user's meal data and suggest a nutritionally balanced meal plan. This allows users to understand their own health status and manage their health appropriately.

[0055] The data collection unit collects data on users' hobbies and interests, and the analysis unit analyzes this data to suggest hobby activities. For example, the data collection unit can collect data on users' hobbies and interests from their social media posts and search history. The data collection unit can also collect purchase data on hobby-related products from users' purchase history. The analysis unit analyzes the collected data and can suggest new hobbies and interests to users. For example, the analysis unit can analyze users' social media posts and suggest communities and events with similar hobbies. The analysis unit can also analyze users' purchase history and suggest related hobby items and activities. This allows users to discover new hobbies and interests and lead more fulfilling lives.

[0056] The data collection unit collects user travel data, and the analysis unit analyzes that data to suggest travel plans. For example, the data collection unit can collect travel-related data from the user's past travel history and search history. It can also collect photos and comments of travel destinations from the user's social media posts. The analysis unit analyzes the collected travel data and can suggest new travel destinations and activities to the user. For example, the analysis unit can analyze the user's past travel history and suggest similar travel destinations and activities. It can also analyze the user's search history and suggest travel destinations and tourist spots that might interest them. This allows users to discover new travel destinations and enjoy fulfilling travel experiences.

[0057] The data collection unit collects user fitness data, and the analysis unit analyzes that data to suggest fitness plans. For example, the data collection unit can collect exercise data and heart rate data from the user's smartwatch or fitness tracker. It can also collect exercise history and goal setting data from the user's fitness app. The analysis unit analyzes the collected fitness data and can suggest an effective fitness plan for the user. For example, it can analyze the user's exercise data and suggest appropriate exercise intensity and frequency. It can also analyze the user's goal setting data and suggest a concrete action plan to achieve those goals. This allows users to achieve their fitness goals and live a healthy life.

[0058] The data collection unit collects data related to the user's hobbies, and the analysis unit analyzes this data to suggest hobby activities. For example, the data collection unit can collect data related to hobbies from the user's social media posts and search history. The data collection unit can also collect purchase data of hobby-related products from the user's purchase history. The analysis unit analyzes the collected data and can suggest new hobbies and interests to the user. For example, the analysis unit can analyze the user's social media posts and suggest communities and events with similar hobbies. The analysis unit can also analyze the user's purchase history and suggest related hobby items and activities. This allows users to discover new hobbies and interests and lead more fulfilling lives.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects data. The data collection unit can collect data such as driving data, purchase history, account information, game information, SNS data, and academic performance / educational background information. For example, the data collection unit can collect purchase history from e-commerce sites. The data collection unit can also collect account information from banks. The data collection unit can also collect driving data from rental car companies. For example, the data collection unit can obtain purchase history through the API of e-commerce sites. It can obtain account information through the API of banks. It can obtain driving data through the API of rental car companies. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, generative AI. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can, for example, analyze driving data to generate safe driving advice. The analysis unit can also analyze purchase history to suggest recommended products. The analysis unit can also analyze academic performance to suggest an optimal learning plan. For example, the analysis unit takes driving data as input and performs analysis using a generative AI model that outputs safe driving advice. It takes purchase history as input and performs analysis using a generative AI model that outputs recommended products. It takes academic performance as input and performs analysis using a generative AI model that outputs an optimal learning plan. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data and provides advice on safe driving. The proposal unit can also suggest recommended products based on purchase history. Furthermore, the proposal unit can suggest an optimal learning plan based on academic performance. For example, the proposal unit provides users with advice on safe driving. It suggests recommended products to users. It suggests an optimal learning plan to users.

[0061] (Example of form 2) The personal data platform according to an embodiment of the present invention is a system for centrally managing personal data and creating new value by utilizing generative AI. This system begins with the user collecting their data from various platforms and inputting it into the personal data platform. Next, the generative AI analyzes the input data and generates information and suggestions useful to the user. Examples include driving data, purchase history, account information, game information, SNS data, academic performance and educational background information, etc. This platform connects with various services via APIs to collect and analyze data. For example, it collects data from e-commerce sites, banks, car rental companies, universities, SNS, job search sites, etc. This allows users to centrally manage their data and create new value by utilizing generative AI. This mechanism allows users to efficiently manage their data and create new value by utilizing generative AI. For example, it is possible to receive safe driving advice by analyzing driving data, receive product recommendations based on purchase history, or receive optimal learning plans based on academic performance. Furthermore, since data is managed securely, the risk of personal information leakage is reduced. This allows the personal data platform to efficiently manage user data and create new value by leveraging generative AI.

[0062] The personal data platform according to this embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data. The collection unit can collect data such as driving data, purchase history, account information, game information, SNS data, and academic performance / educational background information. For example, the collection unit can collect purchase history from e-commerce sites. The collection unit can also collect account information from banks. The collection unit can also collect driving data from rental car companies. For example, the collection unit can obtain purchase history through the API of e-commerce sites. It can obtain account information through the API of banks. It can obtain driving data through the API of rental car companies. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit analyzes driving data to generate safe driving advice. The analysis unit can also analyze purchase history to suggest recommended products. The analysis unit can also analyze academic performance to suggest an optimal learning plan. For example, the analysis unit takes driving data as input and performs analysis using a generative AI model that outputs safe driving advice. It also takes purchase history as input and performs analysis using a generative AI model that outputs recommended products. It also takes academic performance as input and performs analysis using a generative AI model that outputs an optimal learning plan. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data and provides safe driving advice. The proposal unit can also suggest recommended products based on purchase history. The proposal unit can also suggest an optimal learning plan based on academic performance. For example, the proposal unit provides safe driving advice to the user. It suggests recommended products to the user. It suggests an optimal learning plan to the user. In this way, the personal data platform according to the embodiment can create new value by collecting, analyzing, and making proposals based on user data.

[0063] The data collection unit collects data. For example, the data collection unit can collect data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. Specifically, the data collection unit obtains data from e-commerce sites via APIs to collect purchase history. This allows for accurate understanding of detailed information about products a user has purchased in the past, such as purchase date and time and purchase amount. The data collection unit also uses bank APIs to obtain account balances and transaction history from banks to collect account information. This allows for a detailed understanding of the user's financial situation and the provision of appropriate financial advice. Furthermore, the data collection unit obtains driving history and vehicle condition information from rental car companies via their APIs to collect driving data. This allows for a detailed understanding of the user's driving habits and vehicle usage, and the provision of safe driving advice. In addition, the data collection unit uses social media platform APIs to obtain user posts, friendships, and like history to collect social media data. This allows for an understanding of the user's interests and social relationships, enabling more personalized suggestions. Regarding academic performance and educational background information, by obtaining transcripts and course history from educational institution databases via APIs, it becomes possible to understand the user's learning situation in detail and propose an optimal learning plan. This allows the data collection unit to gather a wide range of data from diverse data sources and centrally manage multifaceted information about the user.

[0064] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, generative AI. Generative AI includes, for example, text generation AI (e.g., LLM) and multimodal generation AI. Specifically, the analysis unit uses a generative AI model that takes driving data as input and outputs safe driving advice in order to analyze driving data and generate safe driving advice. This generative AI model can learn from past driving data and identify dangerous driving behaviors and areas for improvement. For example, it can analyze the frequency of sudden braking and acceleration, and the tendency to exceed the speed limit, and provide specific advice to reduce these behaviors. The analysis unit also uses a generative AI model that takes purchase history as input and outputs recommended products in order to analyze purchase history and suggest recommended products. This generative AI model can learn from the user's past purchase history and the purchasing patterns of other users and predict products that the user may be interested in. For example, it can suggest new products from the same brand or category to users who frequently purchase products from that brand or category. Furthermore, the analysis unit uses a generative AI model that takes academic performance as input and outputs an optimal learning plan in order to analyze academic performance and propose an optimal learning plan. This generative AI model can learn from the user's past performance and learning history and identify the user's weaknesses and areas that need strengthening. For example, if a student's performance is low in a particular subject, the model can suggest supplementary materials and learning methods related to that subject. As a result, the analysis unit can highly analyze the collected data and provide the user with specific and useful advice and suggestions.

[0065] The proposal unit makes suggestions based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data to provide safe driving advice. Specifically, the proposal unit provides users with safe driving advice generated by a generative AI model. This advice includes, for example, driving techniques to avoid sudden braking and acceleration, and specific methods for adhering to speed limits. The proposal unit can also suggest recommended products based on purchase history. Specifically, the proposal unit provides users with a list of recommended products generated by a generative AI model. This list includes products selected based on the user's past purchase history and interests, and can suggest products that the user is likely to become interested in. Furthermore, the proposal unit can also suggest an optimal learning plan based on academic performance. Specifically, the proposal unit provides users with a learning plan generated by a generative AI model. This learning plan includes specific learning methods to overcome the user's weaknesses and supplementary materials for areas that need strengthening. For example, if a user has a low score in a particular subject, the proposal unit can suggest online courses or textbooks related to that subject. In this way, the proposal unit can provide users with specific and practical suggestions to support their lives and learning. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, if a user purchases a suggested product, the purchase history can be analyzed again and reflected in future suggestions. This allows the proposal department to consistently provide users with the most suitable suggestions and increase user satisfaction.

[0066] The data collection unit can collect data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. For example, the data collection unit can collect driving data. For example, it can collect data such as speed and brake usage frequency from vehicle sensors. The data collection unit can also collect purchase history. For example, it can collect data such as purchase date and time and purchased items from e-commerce sites. The data collection unit can also collect account information. For example, it can collect transaction history and balance information from banks. The data collection unit can also collect game information. For example, it can collect data such as play time and scores from game platforms. The data collection unit can also collect social media data. For example, it can collect data such as post content and the number of likes from social media. The data collection unit can also collect academic performance / educational background information. For example, it can collect data such as report cards and diplomas from schools. By collecting diverse data, it becomes possible to perform more multifaceted analysis and make more informed suggestions. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input driving data acquired from the vehicle's sensors into a generating AI and have the generating AI perform data collection.

[0067] The analysis unit can analyze collected data and generate useful information and suggestions for the user. For example, the analysis unit can analyze collected driving data to generate safe driving advice. For example, the analysis unit can analyze driving data and provide advice such as adhering to speed limits and maintaining an appropriate distance between vehicles. The analysis unit can also analyze collected purchase history and suggest recommended products. For example, the analysis unit can analyze purchase history and suggest products based on past purchase history or popular products. The analysis unit can also analyze collected academic performance and suggest an optimal learning plan. For example, the analysis unit can analyze academic performance and suggest a learning plan including the allocation of study time and the selection of learning materials. In this way, by analyzing the collected data, useful information and suggestions can be provided to the user. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input collected driving data into a generating AI and have the generating AI generate safe driving advice.

[0068] The suggestion unit can analyze driving data and provide advice on safe driving. For example, the suggestion unit can analyze driving data and provide advice on things like complying with speed limits and maintaining an appropriate distance between vehicles. For example, the suggestion unit can analyze driving data and provide advice on reducing the number of sudden brakes. The suggestion unit can also analyze driving data and provide advice on eco-driving. For example, the suggestion unit can analyze driving data and provide advice on improving fuel efficiency. In this way, by analyzing driving data, it is possible to provide advice on safe driving. Some or all of the above processing in the suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the suggestion unit can input driving data into a generation AI and have the generation AI generate advice on safe driving.

[0069] The suggestion unit can suggest recommended products based on purchase history. For example, the suggestion unit can analyze purchase history and suggest products based on past purchases. The suggestion unit can also analyze purchase history and suggest popular products. Furthermore, the suggestion unit can analyze purchase history and suggest products based on user interests. For example, the suggestion unit can analyze purchase history and suggest products related to products the user has purchased in the past. This improves the user's purchasing experience by suggesting recommended products based on purchase history. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input purchase history into a generative AI and have the generative AI perform the recommended product suggestion.

[0070] The suggestion unit can propose an optimal learning plan based on academic performance. For example, the suggestion unit can analyze academic performance and propose a learning plan that includes the allocation of study time and the selection of learning materials. The suggestion unit can also analyze academic performance and propose a learning plan that strengthens the user's weaknesses. Furthermore, the suggestion unit can analyze academic performance and propose a learning plan that develops the user's strengths. For example, the suggestion unit can analyze academic performance and propose a learning plan that focuses on subjects the user struggles with. By proposing an optimal learning plan based on academic performance, the user's learning efficiency is improved. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input academic performance into a generative AI and have the generative AI propose an optimal learning plan.

[0071] 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 temporarily stop data collection and resume it when the user is relaxed. For example, if the user is concentrating, the data collection unit can prioritize data collection to collect data efficiently. The data collection unit can also delay data collection if the user is tired and resume it after rest. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0072] 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 can prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also analyze the user's past data collection patterns and suggest the optimal collection timing. Furthermore, the data collection unit can automatically select relevant data sources based on the types of data the user has collected in the past. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method. This allows the optimal collection method to be selected by analyzing the 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 using AI.

[0073] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting data related to exercise. For example, if the user is working, the data collection unit can prioritize collecting data related to work. Furthermore, if the user is engrossed in a hobby, the data collection unit can prioritize collecting data related to that hobby. For example, the data collection unit can input the user's activities and areas of interest into a generating AI and have the generating AI perform data filtering. This allows for the collection of highly relevant data by filtering the data based on the user's activities 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 using AI.

[0074] The data collection unit can adjust its collection method while considering the state of the user's device. For example, if the user's device battery level is low, the data collection unit may temporarily stop data collection. The data collection unit may also delay data collection if the user's device is under heavy load. Furthermore, if the user's device is connected to Wi-Fi, the data collection unit can collect a large amount of data. For example, the data collection unit can input the state of the user's device into a generating AI and have the generating AI adjust the collection method. This enables efficient data collection by considering the state of the user's device. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without using AI.

[0075] The data collection unit can limit the scope of data collection based on the user's privacy settings. For example, if the user has excluded a specific data source in their privacy settings, the data collection unit will not collect data from that data source. For example, if the user has limited the scope of data collection in their privacy settings, the data collection unit can also collect data only within that scope. Furthermore, if the user has requested anonymization in their privacy settings, the data collection unit can collect only anonymized data. For example, the data collection unit can input the user's privacy settings into a generating AI and have the generating AI perform the limitation of the data collection scope. This makes it possible to collect data while protecting privacy by limiting the scope of data collection based on the user's privacy settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0076] 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, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of data related to home. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant data. This allows for the priority collection of highly relevant data by considering 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 using AI.

[0077] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, for example, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. In addition, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis 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 processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] 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. For example, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, for example, or without using the generating AI.

[0079] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a driving pattern analysis algorithm to driving data. For example, the analysis unit can also apply a purchasing behavior analysis algorithm to purchase history data. Furthermore, the analysis unit can apply a learning outcome analysis algorithm to academic performance data. For example, the analysis unit can input the data category into the generating AI and have the generating AI execute the application of different analysis algorithms. This makes it possible to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using the generating AI, for example, or without using the generating AI.

[0080] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the correlation between purchase history and driving data to improve accuracy. The analysis unit can also analyze the correlation between academic performance and social media data to improve accuracy. Furthermore, the analysis unit can analyze the correlation between account information and purchase history to improve accuracy. For example, the analysis unit can input the interrelationships of the data into a generating AI and have the generating AI perform the analysis accuracy improvement. This improves the accuracy of the analysis by considering the interrelationships of the data. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0081] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis results based on the submitter's age. The analysis unit can also adjust the analysis results based on the submitter's occupation. Furthermore, the analysis unit can adjust the analysis results based on the submitter's place of residence. For example, the analysis unit can input the submitter's attribute information into a generating AI and have the generating AI perform the adjustment of the analysis results. This allows for the provision of more appropriate analysis results by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0082] The analysis unit can evaluate the reliability of the data during analysis and reflect this in the analysis results. For example, the analysis unit can assign higher weights to highly reliable data and reflect this in the analysis results. The analysis unit can also assign lower weights to less reliable data and reflect this in the analysis results. Furthermore, the analysis unit can assign appropriate weights to data with moderate reliability and reflect this in the analysis results. For example, the analysis unit can input the data reliability into the generating AI and have the generating AI perform the task of reflecting this in the analysis results. By evaluating the reliability of the data, it is possible to provide more reliable analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, the generating AI, or without using the generating AI.

[0083] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 proposed unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposed unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The proposal unit can adjust the level of detail of a proposal based on the importance of the data. For example, the proposal unit can provide a detailed explanation for proposals based on highly important data. For example, the proposal unit can provide a simplified explanation for proposals based on less important data. Furthermore, the proposal unit can provide an explanation with a moderate level of detail for proposals based on moderately important data. For example, the proposal unit can input the importance of the data into the generating AI and have the generating AI adjust the level of detail of the proposal. This allows for more efficient proposals by adjusting the level of detail of the proposal based on the importance of the data. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0085] The suggestion unit can apply different suggestion algorithms depending on the data category when making suggestions. For example, the suggestion unit can apply an algorithm that provides safe driving advice to driving data. For example, the suggestion unit can also apply a product recommendation algorithm to purchase history data. Furthermore, the suggestion unit can apply an algorithm that proposes an optimal study plan to academic performance data. For example, the suggestion unit can input the data category into a generating AI and have the generating AI apply different suggestion algorithms. This allows for more accurate suggestions by applying different suggestion algorithms depending on the data category. Some or all of the above processing in the suggestion unit may be performed using a generating AI, or it may be performed without using a generating AI.

[0086] The proposal unit can improve the accuracy of its proposals by considering the interrelationships between data. For example, the proposal unit can analyze the correlation between purchase history and driving data to improve accuracy. The proposal unit can also analyze the correlation between academic performance and social media data to improve accuracy. Furthermore, the proposal unit can analyze the correlation between account information and purchase history to improve accuracy. For example, the proposal unit can input the interrelationships of data into a generation AI and have the generation AI perform the improvement of the proposal accuracy. This improves the accuracy of the proposals by considering the interrelationships of data. Some or all of the above processing in the proposal unit may be performed using a generation AI, for example, or without using a generation AI.

[0087] The proposal unit can make proposals while considering the attribute information of the data submitter. For example, the proposal unit can adjust the proposal content based on the submitter's age. The proposal unit can also adjust the proposal content based on the submitter's occupation. Furthermore, the proposal unit can adjust the proposal content based on the submitter's place of residence. For example, the proposal unit can input the submitter's attribute information into a generation AI and have the generation AI perform the adjustment of the proposal content. This allows for the provision of more appropriate proposals by considering the attribute information of the data submitter. Some or all of the above processing in the proposal unit may be performed using a generation AI, for example, or without using a generation AI.

[0088] The proposal unit can evaluate the reliability of the data during the proposal process and reflect this in the proposal results. For example, the proposal unit can assign higher weights to highly reliable data and reflect this in the proposal results. For example, the proposal unit can assign lower weights to less reliable data and reflect this in the proposal results. Furthermore, the proposal unit can assign appropriate weights to data of moderate reliability and reflect this in the proposal results. For example, the proposal unit can input the data reliability into a generating AI and have the generating AI perform the reflection of this into the proposal results. This allows for the provision of more reliable proposal results by evaluating the reliability of the data. Some or all of the above processing in the proposal unit may be performed using a generating AI, for example, or without using a generating AI.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The data collection unit collects user health data, and the analysis unit analyzes that data to provide health management suggestions. For example, the data collection unit can collect heart rate, steps, and sleep data from the user's smartwatch. It can also collect meal details and calorie intake from the user's food logging app. The analysis unit analyzes the collected health data and can provide the user with exercise advice and suggestions for dietary improvements. For example, the analysis unit can analyze the user's heart rate data and suggest an appropriate exercise intensity. It can also analyze the user's meal data and suggest a nutritionally balanced meal plan. This allows users to understand their own health status and manage their health appropriately.

[0091] The analysis unit can estimate the user's emotions and, based on those estimates, suggest stress management strategies. For example, it can analyze the user's facial expression data to estimate their stress level. It can also analyze the user's voice data to detect changes in emotion. Furthermore, it can analyze the user's biometric data (heart rate and skin electrical activity) to assess their stress level. The suggestion unit can suggest relaxation methods and stress-relieving activities based on the estimated stress level. For example, it can suggest deep breathing or meditation techniques to the user. It can also provide relaxing music or nature sounds. This allows the user to manage their stress levels and maintain their physical and mental health.

[0092] The data collection unit collects data on users' hobbies and interests, and the analysis unit analyzes this data to suggest hobby activities. For example, the data collection unit can collect data on users' hobbies and interests from their social media posts and search history. The data collection unit can also collect purchase data on hobby-related products from users' purchase history. The analysis unit analyzes the collected data and can suggest new hobbies and interests to users. For example, the analysis unit can analyze users' social media posts and suggest communities and events with similar hobbies. The analysis unit can also analyze users' purchase history and suggest related hobby items and activities. This allows users to discover new hobbies and interests and lead more fulfilling lives.

[0093] The analysis unit can estimate the user's emotions and suggest entertainment based on those emotions. For example, it can analyze the user's facial expression data to estimate their current emotional state. It can also analyze the user's voice data to detect changes in emotion. Furthermore, it can analyze the user's biometric data (heart rate and skin electrical activity) to evaluate their emotional state. The suggestion unit can suggest entertainment content suitable for the user based on the estimated emotional state. For example, if the user is relaxed, the suggestion unit can suggest relaxing movies or music. If the user is excited, the suggestion unit can suggest action movies or energetic music. This allows users to enjoy entertainment that matches their emotional state.

[0094] The data collection unit collects user travel data, and the analysis unit analyzes that data to suggest travel plans. For example, the data collection unit can collect travel-related data from the user's past travel history and search history. It can also collect photos and comments of travel destinations from the user's social media posts. The analysis unit analyzes the collected travel data and can suggest new travel destinations and activities to the user. For example, the analysis unit can analyze the user's past travel history and suggest similar travel destinations and activities. It can also analyze the user's search history and suggest travel destinations and tourist spots that might interest them. This allows users to discover new travel destinations and enjoy fulfilling travel experiences.

[0095] The analysis unit can estimate the user's emotions and, based on those estimates, make suggestions to improve learning motivation. For example, the analysis unit can analyze the user's facial expression data to estimate changes in learning motivation. It can also analyze the user's voice data to detect changes in emotions regarding learning. Furthermore, the analysis unit can analyze the user's biometric data (heart rate and skin electrical activity) to evaluate learning motivation. The suggestion unit can make suggestions to improve learning efficiency based on the estimated motivation. For example, if the user has lost motivation, the suggestion unit can suggest ways to take breaks or refresh themselves. If the user is highly motivated, the suggestion unit can also suggest challenging tasks or new learning methods. This allows the user to maintain their learning motivation and learn effectively.

[0096] The data collection unit collects user fitness data, and the analysis unit analyzes that data to suggest fitness plans. For example, the data collection unit can collect exercise data and heart rate data from the user's smartwatch or fitness tracker. It can also collect exercise history and goal setting data from the user's fitness app. The analysis unit analyzes the collected fitness data and can suggest an effective fitness plan for the user. For example, it can analyze the user's exercise data and suggest appropriate exercise intensity and frequency. It can also analyze the user's goal setting data and suggest a concrete action plan to achieve those goals. This allows users to achieve their fitness goals and live a healthy life.

[0097] The analysis unit can estimate the user's emotions and suggest communication based on those estimates. For example, it can analyze the user's facial expression data to estimate their current emotional state. It can also analyze the user's voice data to detect changes in emotion. Furthermore, it can analyze the user's biometric data (heart rate and skin electrical activity) to evaluate their emotional state. The suggestion unit can suggest appropriate communication methods based on the estimated emotional state. For example, if the user is relaxed, the suggestion unit can suggest relaxed conversations with friends or family. If the user is stressed, the suggestion unit can suggest communication methods to relieve stress. This allows users to communicate in a way that suits their emotional state and maintain their mental health.

[0098] The data collection unit collects data related to the user's hobbies, and the analysis unit analyzes this data to suggest hobby activities. For example, the data collection unit can collect data related to hobbies from the user's social media posts and search history. The data collection unit can also collect purchase data of hobby-related products from the user's purchase history. The analysis unit analyzes the collected data and can suggest new hobbies and interests to the user. For example, the analysis unit can analyze the user's social media posts and suggest communities and events with similar hobbies. The analysis unit can also analyze the user's purchase history and suggest related hobby items and activities. This allows users to discover new hobbies and interests and lead more fulfilling lives.

[0099] The analysis unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the analysis unit can analyze the user's facial expression data to estimate their current emotional state. It can also analyze the user's voice data to detect changes in emotion. Furthermore, the analysis unit can analyze the user's biometric data (heart rate and skin electrical activity) to evaluate their emotional state. The suggestion unit can suggest a feedback method appropriate to the user based on the estimated emotional state. For example, if the user is relaxed, the suggestion unit can provide detailed feedback. If the user is in a hurry, the suggestion unit can provide concise feedback that gets straight to the point. This allows the user to receive feedback that matches their emotional state and take effective action.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The data collection unit collects data. The data collection unit can collect data such as driving data, purchase history, account information, game information, SNS data, and academic performance / educational background information. For example, the data collection unit can collect purchase history from e-commerce sites. The data collection unit can also collect account information from banks. The data collection unit can also collect driving data from rental car companies. For example, the data collection unit can obtain purchase history through the API of e-commerce sites. It can obtain account information through the API of banks. It can obtain driving data through the API of rental car companies. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, generative AI. Generative AI can be, for example, text generation AI (e.g., LLM) or multimodal generation AI. The analysis unit can, for example, analyze driving data to generate safe driving advice. The analysis unit can also analyze purchase history to suggest recommended products. The analysis unit can also analyze academic performance to suggest an optimal learning plan. For example, the analysis unit takes driving data as input and performs analysis using a generative AI model that outputs safe driving advice. It takes purchase history as input and performs analysis using a generative AI model that outputs recommended products. It takes academic performance as input and performs analysis using a generative AI model that outputs an optimal learning plan. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit analyzes driving data and provides advice on safe driving. The proposal unit can also suggest recommended products based on purchase history. Furthermore, the proposal unit can suggest an optimal learning plan based on academic performance. For example, the proposal unit provides users with advice on safe driving. It suggests recommended products to users. It suggests an optimal learning plan to users.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data from e-commerce sites, banks, car rental companies, etc., via the communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The proposal unit is implemented in the control unit 46A of the smart device 14 and proposes safe driving advice, recommended products, and an optimal learning plan 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.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from e-commerce sites, banks, car rental companies, etc., via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The proposal unit is implemented in the control unit 46A of the smart glasses 214 and proposes safe driving advice, recommended products, and an optimal learning plan 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.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data from e-commerce sites, banks, car rental companies, etc., via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The proposal unit is implemented in the control unit 46A of the headset terminal 314 and proposes safe driving advice, recommended products, and an optimal learning plan 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.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from e-commerce sites, banks, car rental companies, etc., via the communication I / F 44 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using generated AI. The proposal unit is implemented, for example, by the control unit 46A of the robot 414, and proposes safe driving advice, recommended products, and an optimal learning plan 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to generate useful information and suggestions for the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We analyze driving data and provide advice on safe driving. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Recommended products based on your purchase history have been updated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal learning plan based on your academic performance. 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 activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, the collection method is adjusted to take into account the state of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the scope of data collection is limited based on the user's privacy settings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, 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 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, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the reliability of the data is evaluated and reflected in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, consider the interrelationships between data to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, take into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, evaluate the reliability of the data and reflect this in the proposal results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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, A proposal unit makes a proposal based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as driving data, purchase history, account information, game information, social media data, and academic performance / educational background information. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to generate useful information and suggestions for the user. The system according to feature 1.

4. The aforementioned proposal section is, We analyze driving data and provide advice on safe driving. The system according to feature 1.

5. The aforementioned proposal section is, Recommended products based on your purchase history have been updated. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal learning plan based on your academic performance. 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.

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