system

Abstract

The system according to this embodiment aims to support optimal selection and decision-making by comprehensively analyzing the user's digital information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a suggestion unit. The collection unit collects the user's digital information. The analysis unit analyzes the digital information collected by the collection unit and learns the user's behavior patterns and preferences. The suggestion unit makes suggestions to support the user in making the best choices and decisions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Document

Patent Document

[0007] The system according to this embodiment can comprehensively analyze the user's digital information and support optimal selection and decision-making. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The information integration system according to an embodiment of the present invention integrates digital information such as e-commerce purchase information and website search history, and uses this as training data to constantly support the user in making the most optimal choices, judgments, and actions. Currently, personal information (data) exists in a scattered manner, requiring users to view it separately for each service or app, and it is not linked. The present invention integrates this data and uses AI to support optimal choices and judgments. First, it collects digital information such as the user's e-commerce purchase information and website search history. This includes data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music distribution services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, etc. Next, the AI ​​analyzes the collected digital information and learns the user's behavior patterns and preferences. For example, by analyzing the services the user frequently uses, the products they purchase, and the keywords they search for, the AI ​​understands the user's preferences and behavior patterns. Furthermore, based on the analysis results, the AI ​​makes suggestions to support the user in making the most optimal choices and judgments. For example, if a user is planning a trip, the AI ​​will suggest the optimal travel plan based on their past travel and search history. Similarly, when a user is shopping, the AI ​​will suggest the most suitable products based on their past purchase and search history. In this way, users no longer need to use each service or app individually, and can make optimal choices and decisions based on integrated data. Furthermore, a user journey is created for each service, leading to the revitalization of the economic ecosystem. If a user is planning a trip, the AI ​​will suggest the optimal travel plan based on their past travel and search history. In addition, if a user is using a health management app, the AI ​​will suggest the optimal health management method based on their past health data. This system allows users to centrally manage their information and make optimal choices and decisions. Furthermore, the creation of a user journey for each service leads to the revitalization of the economic ecosystem. Moreover, if a user is planning a trip, the AI's suggestion of the optimal travel plan will increase travel demand, leading to the revitalization of the economic ecosystem.This allows the information integration system to consolidate users' digital information and support them in making optimal choices and decisions.

[0029] The information integration system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects user digital information. The collection unit collects data from, for example, e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music distribution services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information services, news sites, sports information sites, etc. The collection unit can acquire data, for example, through APIs. The collection unit can also collect data using scraping technology. Furthermore, the collection unit can collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or personal computer. The analysis unit analyzes the digital information collected by the collection unit and learns the user's behavior patterns and preferences. The analysis unit can analyze data using, for example, machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes the user's purchase history to understand the user's preferences. The suggestion unit makes suggestions to support the user in making the best choices and decisions based on the analysis results obtained by the analysis unit. For example, if the user is planning a trip, the suggestion unit suggests the best travel plan based on past travel history and search history. Also, when the user is shopping, the suggestion unit suggests the best products based on past purchase history and search history. Furthermore, if the user is using a health management app, the suggestion unit suggests the best health management method based on past health data. In this way, the information integration system according to the embodiment can integrate the user's digital information and support the user in making the best choices and decisions. Some or all of the above-described processes in the collection unit, analysis unit, and suggestion unit may be performed using AI, for example, or without using AI. For example, the collection unit can input data acquired through an API into a generation AI and have the generation AI perform data analysis.The analysis unit inputs the data collected by the collection unit into the generation AI, allowing the generation AI to learn user behavior patterns and preferences. The proposal unit inputs the analysis results obtained by the analysis unit into the generation AI, allowing the generation AI to generate suggestions that support the user in making optimal choices and decisions.

[0030] The data collection unit collects users' digital information. For example, it collects data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, and sports information sites. The data collection unit can obtain data through APIs. Using APIs allows for efficient collection and real-time updates of data provided by each service. The data collection unit can also collect data using scraping technology. Using scraping technology, necessary information can be automatically extracted from web pages even when APIs are not provided. Furthermore, the data collection unit can collect data directly from users' devices. For example, it can collect data from users' smartphones and personal computers. From smartphones, it can collect location information, app usage history, browsing history, and messaging app data. From personal computers, it can collect browsing history, download history, and data on applications being used. This allows the data collection unit to gather a wide range of data from various devices and services, enabling a detailed understanding of user behavior and preferences. The collected data is stored in a secure database and managed for access by the analysis and proposal units. The frequency and accuracy of data collection are adjusted based on user consent and privacy policies. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes digital information collected by the data collection unit to learn user behavior patterns and preferences. The analysis unit can analyze data using machine learning algorithms. For example, it can use clustering algorithms to group user behavior patterns and identify user groups with common characteristics. It can also use regression analysis to predict user preferences and behavior. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, it can analyze user social media posts and review comments and perform sentiment analysis to understand user emotions and opinions. It can also analyze image data using image recognition technology; for example, it can identify interests and preferences from photos uploaded by users. By combining these technologies, the analysis unit comprehensively analyzes user purchase history, search history, location information, app usage history, etc., to gain a detailed understanding of user preferences and behavior patterns. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, it can predict purchasing trends in specific seasons or events based on past purchase data to formulate future marketing strategies. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department provides suggestions to support users in making optimal choices and decisions based on the analysis results obtained by the Analysis Department. For example, if a user is planning a trip, the Proposal Department will suggest the optimal travel plan based on their past travel and search history. Specifically, it will analyze data such as places the user has visited, accommodations they have stayed at, and modes of transportation they have used, and suggest travel destinations and accommodations that match the user's preferences. The Proposal Department will also suggest optimal products when a user is shopping, based on their past purchase and search history. For example, it will provide information on highly relevant products and new products based on products the user has previously purchased or searched for. Furthermore, if a user is using a health management app, the Proposal Department will suggest the optimal health management method based on their past health data. For example, it will analyze data such as the user's exercise history, meal records, and weight fluctuations, and suggest exercise and meal plans that are suitable for the user. The Proposal Department can utilize notification and reminder functions to provide these suggestions to users at the appropriate time. For example, when planning a trip, it will provide real-time weather and event information for the travel destination, and when shopping, it will notify users of sales and coupon information. Furthermore, in terms of health management, the system provides timely exercise reminders and dietary advice. This allows the recommendation department to support users in making optimal choices and decisions, thereby improving user satisfaction. In addition, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, it can collect user evaluations and comments on suggested travel plans and products and incorporate them into future recommendations. This allows the recommendation department to provide users with more personalized recommendations and improve the overall performance of the system.

[0033] The data collection unit can collect data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services (SNS), music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, and more. The data collection unit can acquire data, for example, through APIs. The data collection unit can acquire purchase history data from e-commerce sites, for example. The data collection unit can also acquire user posting data from SNS. Furthermore, the data collection unit can acquire user playback history data from music streaming services. By collecting diverse digital information, it becomes possible to more accurately understand user behavior patterns and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired through APIs into a generating AI and have the generating AI perform data collection.

[0034] The suggestion unit can propose the optimal travel plan based on the user's past travel and search history when the user is planning a trip. For example, the suggestion unit can propose similar travel plans based on data of destinations and accommodations the user has visited in the past. It can also propose the optimal travel plan based on data of destinations and tourist spots the user has searched for. Furthermore, the suggestion unit can propose customized travel plans according to the user's budget and travel purpose. This allows the system to propose the optimal travel plan based on the user's past travel and search history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's travel history data into a generating AI and have the generating AI propose the optimal travel plan.

[0035] The suggestion unit can suggest the most suitable products to users when they are shopping, based on their past purchase and search history. For example, the suggestion unit can suggest similar products based on data of products and brands that the user has previously purchased. It can also suggest the most suitable products based on data of products and categories that the user has searched for. Furthermore, the suggestion unit can provide customized product suggestions according to the user's budget and preferences. This allows the system to suggest the most suitable products based on the user's past purchase and search history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI perform the task of suggesting the most suitable products.

[0036] The suggestion unit can propose optimal health management methods based on past health data if the user is using a health management app. For example, the suggestion unit can propose optimal meal plans and exercise plans based on the user's past meal and exercise records. Furthermore, the suggestion unit can propose customized health management methods according to the user's health status and goals. In addition, the suggestion unit can analyze the user's health data, predict health risks, and propose preventative measures. This allows the suggestion unit to propose optimal health management methods based on the user's past health data. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI generate suggestions for optimal health management methods.

[0037] The data collection unit can analyze the user's past digital information collection history and select the optimal collection method. For example, the data collection unit can collect digital information during times when the user frequently used the service in the past. The data collection unit can also prioritize collecting digital information from devices and applications that the user has used in the past. Furthermore, the data collection unit can analyze the user's past behavior patterns and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter digital information based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting digital information related to topics the user is currently interested in. The data collection unit can also collect appropriate digital information according to the user's lifestyle (e.g., at work, on vacation, etc.). Furthermore, the data collection unit can filter relevant digital information based on the user's current activities (e.g., exercising, reading, etc.). This allows for the collection of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting digital information. For example, the data collection unit can prioritize the collection of local news and event information related to the user's current location. It can also prioritize the collection of information on nearby shops and services based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of tourist information and transportation information for their travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the information collection.

[0040] The data collection unit can analyze a user's social media activity and collect relevant information when collecting digital information. For example, the data collection unit can collect relevant digital information based on the content of posts from accounts that the user follows on social media. The data collection unit can also collect relevant digital information based on content that the user "likes" or shares on social media. Furthermore, the data collection unit can collect relevant digital information based on groups and events that the user participates in on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the digital information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important digital information and provide it to the user. Alternatively, the analysis unit can perform a simplified analysis on less important digital information and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the digital information, enabling efficient analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the digital information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of digital information during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase information. It can also apply a search keyword analysis algorithm to search history. Furthermore, it can apply a social network analysis algorithm to social media activity. By applying the appropriate analysis algorithm according to the category of digital information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the timing of digital information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the latest digital information and provides it to the user. The analysis unit can also determine the priority of analysis of past digital information according to its importance. Furthermore, the analysis unit can perform analysis efficiently based on the timing of digital information collection. This enables efficient analysis by determining the priority of analysis based on the timing of digital information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0044] The analysis unit can adjust the order of analysis based on the relationships between digital information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant digital information and provide it to the user. The analysis unit can also perform a simplified analysis on less relevant digital information. Furthermore, the analysis unit can perform analysis efficiently based on the relationships between digital information. This enables efficient analysis by adjusting the order of analysis based on the relationships between digital information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between digital information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The proposal unit can analyze the user's past behavior patterns and select the optimal proposal method when making a proposal. For example, the proposal unit can make proposals based on services and products that the user has previously preferred to use. The proposal unit can also analyze the user's past behavior patterns and select the most effective proposal method. Furthermore, the proposal unit can make customized proposals based on the user's past behavior patterns. This allows the optimal proposal method to be selected by analyzing the user's past behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user behavior pattern data into a generating AI and have the generating AI select the optimal proposal method.

[0046] The suggestion unit can customize the suggestion methods based on the user's current living situation when making suggestions. For example, if the user is at work, the suggestion unit will make work-related suggestions. If the user is on vacation, the suggestion unit can also make suggestions related to relaxation or travel. Furthermore, the suggestion unit can select the most suitable suggestion method based on the user's current living situation. This allows for the selection of the most suitable suggestion method based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the suggestion methods.

[0047] The suggestion unit can select the optimal suggestion method by considering the user's geographical location information when making suggestions. For example, the suggestion unit can suggest local services and shops related to the user's current location. It can also suggest nearby events and activities based on the user's geographical location. Furthermore, if the user is traveling, the suggestion unit can suggest tourist information and transportation information for their travel destination. In this way, the optimal suggestion method can be selected by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the suggestion method.

[0048] The suggestion unit can analyze the user's social media activity and propose methods for making suggestions. For example, the suggestion unit can make relevant suggestions based on the content of posts from accounts the user follows on social media. It can also make relevant suggestions based on content the user "likes" or shares on social media. Furthermore, it can make relevant suggestions based on groups and events the user participates in on social media. This allows for the efficient generation of relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI select the methods for making suggestions.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The information integration system can also collect user digital information by understanding the user's current activity status in real time and collecting information at the appropriate time. For example, if a user is exercising, the data collection unit can delay data collection until the exercise is finished. Similarly, if a user is in a meeting, the data collection unit can refrain from collecting data until the meeting is over. Furthermore, during times when the user is relaxing, the data collection unit can actively collect data and send it to the analysis unit. By adjusting the timing of data collection according to the user's activity status, the system reduces the burden on the user and enables more efficient data collection.

[0051] The information integration system can further analyze users' past behavior patterns and predict future behavior. For example, if a user tends to visit a particular restaurant every weekend, the suggestion system can suggest new menu items or special events at that restaurant. Similarly, if a user tends to plan trips during a specific season, the system can suggest the most suitable travel plan for that season. Furthermore, if a user tends to exercise at a specific time of day, the system can suggest the most suitable exercise plan for that time. This allows for more appropriate suggestions by predicting future behavior based on the user's past behavior patterns.

[0052] The information integration system can further customize its suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can suggest local events and tourist attractions in that area. If the user is traveling, it can suggest tourist information and transportation information for their destination. Furthermore, if the user is at home, it can suggest information about nearby shops and services. This allows for more relevant suggestions based on the user's geographical location.

[0053] The information integration system can further analyze users' social media activity and suggest relevant information. For example, it can suggest relevant products and services based on the content posted by accounts that users follow on social media. It can also suggest relevant events and activities based on content that users "like" or share on social media. Furthermore, it can suggest relevant information based on groups and events that users participate in on social media. This enables more relevant suggestions based on users' social media activity.

[0054] The information integration system can further analyze a user's past digital information collection history and select the optimal collection timing. For example, it can collect digital information during times when the user frequently used it in the past. It can also prioritize collecting digital information from devices and applications the user has used in the past. Furthermore, it can analyze the user's past behavior patterns and select the most efficient collection timing. In this way, by analyzing the user's past collection history, the optimal collection timing can be selected.

[0055] The information integration system can further customize suggestions based on the user's current lifestyle. For example, if the user is at work, it can offer work-related suggestions. If the user is on vacation, it can offer suggestions related to relaxation and travel. Furthermore, it can select the most appropriate suggestion method based on the user's current lifestyle. This allows for the selection of the most suitable suggestions based on the user's current circumstances.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects the user's digital information. The collection unit collects data from, for example, e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, etc. The collection unit can obtain data through APIs. It can also collect data using scraping techniques. Furthermore, it can collect data directly from the user's device. For example, it can collect data from the user's smartphone or personal computer. Step 2: The analysis unit analyzes the digital information collected by the collection unit to learn user behavior patterns and preferences. The analysis unit can analyze data using machine learning algorithms. It can also analyze text data using natural language processing technology. Furthermore, it can analyze image data using image recognition technology. For example, it can analyze a user's purchase history to understand their preferences. Step 3: The Proposal Department, based on the analysis results obtained by the Analysis Department, makes suggestions to support the user in making the best choices and decisions. If the user is planning a trip, the Proposal Department will suggest the best travel plan based on their past travel and search history. If the user is shopping, the Proposal Department will suggest the best products based on their past purchase and search history. Furthermore, if the user is using a health management app, the Proposal Department will suggest the best health management methods based on their past health data.

[0058] (Example of form 2) The information integration system according to an embodiment of the present invention integrates digital information such as e-commerce purchase information and website search history, and uses this as training data to constantly support the user in making the most optimal choices, judgments, and actions. Currently, personal information (data) exists in a scattered manner, requiring users to view it separately for each service or app, and it is not linked. The present invention integrates this data and uses AI to support optimal choices and judgments. First, it collects digital information such as the user's e-commerce purchase information and website search history. This includes data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music distribution services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, etc. Next, the AI ​​analyzes the collected digital information and learns the user's behavior patterns and preferences. For example, by analyzing the services the user frequently uses, the products they purchase, and the keywords they search for, the AI ​​understands the user's preferences and behavior patterns. Furthermore, based on the analysis results, the AI ​​makes suggestions to support the user in making the most optimal choices and judgments. For example, if a user is planning a trip, the AI ​​will suggest the optimal travel plan based on their past travel and search history. Similarly, when a user is shopping, the AI ​​will suggest the most suitable products based on their past purchase and search history. In this way, users no longer need to use each service or app individually, and can make optimal choices and decisions based on integrated data. Furthermore, a user journey is created for each service, leading to the revitalization of the economic ecosystem. If a user is planning a trip, the AI ​​will suggest the optimal travel plan based on their past travel and search history. In addition, if a user is using a health management app, the AI ​​will suggest the optimal health management method based on their past health data. This system allows users to centrally manage their information and make optimal choices and decisions. Furthermore, the creation of a user journey for each service leads to the revitalization of the economic ecosystem. Moreover, if a user is planning a trip, the AI's suggestion of the optimal travel plan will increase travel demand, leading to the revitalization of the economic ecosystem.This allows the information integration system to consolidate users' digital information and support them in making optimal choices and decisions.

[0059] The information integration system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects user digital information. The collection unit collects data from, for example, e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music distribution services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information services, news sites, sports information sites, etc. The collection unit can acquire data, for example, through APIs. The collection unit can also collect data using scraping technology. Furthermore, the collection unit can collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or personal computer. The analysis unit analyzes the digital information collected by the collection unit and learns the user's behavior patterns and preferences. The analysis unit can analyze data using, for example, machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing technology. Furthermore, the analysis unit can analyze image data using image recognition technology. For example, the analysis unit analyzes the user's purchase history to understand the user's preferences. The suggestion unit makes suggestions to support the user in making the best choices and decisions based on the analysis results obtained by the analysis unit. For example, if the user is planning a trip, the suggestion unit suggests the best travel plan based on past travel history and search history. Also, when the user is shopping, the suggestion unit suggests the best products based on past purchase history and search history. Furthermore, if the user is using a health management app, the suggestion unit suggests the best health management method based on past health data. In this way, the information integration system according to the embodiment can integrate the user's digital information and support the user in making the best choices and decisions. Some or all of the above-described processes in the collection unit, analysis unit, and suggestion unit may be performed using AI, for example, or without using AI. For example, the collection unit can input data acquired through an API into a generation AI and have the generation AI perform data analysis.The analysis unit inputs the data collected by the collection unit into the generation AI, allowing the generation AI to learn user behavior patterns and preferences. The proposal unit inputs the analysis results obtained by the analysis unit into the generation AI, allowing the generation AI to generate suggestions that support the user in making optimal choices and decisions.

[0060] The data collection unit collects users' digital information. For example, it collects data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, and sports information sites. The data collection unit can obtain data through APIs. Using APIs allows for efficient collection and real-time updates of data provided by each service. The data collection unit can also collect data using scraping technology. Using scraping technology, necessary information can be automatically extracted from web pages even when APIs are not provided. Furthermore, the data collection unit can collect data directly from users' devices. For example, it can collect data from users' smartphones and personal computers. From smartphones, it can collect location information, app usage history, browsing history, and messaging app data. From personal computers, it can collect browsing history, download history, and data on applications being used. This allows the data collection unit to gather a wide range of data from various devices and services, enabling a detailed understanding of user behavior and preferences. The collected data is stored in a secure database and managed for access by the analysis and proposal units. The frequency and accuracy of data collection are adjusted based on user consent and privacy policies. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes digital information collected by the data collection unit to learn user behavior patterns and preferences. The analysis unit can analyze data using machine learning algorithms. For example, it can use clustering algorithms to group user behavior patterns and identify user groups with common characteristics. It can also use regression analysis to predict user preferences and behavior. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, it can analyze user social media posts and review comments and perform sentiment analysis to understand user emotions and opinions. It can also analyze image data using image recognition technology; for example, it can identify interests and preferences from photos uploaded by users. By combining these technologies, the analysis unit comprehensively analyzes user purchase history, search history, location information, app usage history, etc., to gain a detailed understanding of user preferences and behavior patterns. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, it can predict purchasing trends in specific seasons or events based on past purchase data to formulate future marketing strategies. Furthermore, by using anomaly detection algorithms, it is possible to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The Proposal Department provides suggestions to support users in making optimal choices and decisions based on the analysis results obtained by the Analysis Department. For example, if a user is planning a trip, the Proposal Department will suggest the optimal travel plan based on their past travel and search history. Specifically, it will analyze data such as places the user has visited, accommodations they have stayed at, and modes of transportation they have used, and suggest travel destinations and accommodations that match the user's preferences. The Proposal Department will also suggest optimal products when a user is shopping, based on their past purchase and search history. For example, it will provide information on highly relevant products and new products based on products the user has previously purchased or searched for. Furthermore, if a user is using a health management app, the Proposal Department will suggest the optimal health management method based on their past health data. For example, it will analyze data such as the user's exercise history, meal records, and weight fluctuations, and suggest exercise and meal plans that are suitable for the user. The Proposal Department can utilize notification and reminder functions to provide these suggestions to users at the appropriate time. For example, when planning a trip, it will provide real-time weather and event information for the travel destination, and when shopping, it will notify users of sales and coupon information. Furthermore, in terms of health management, the system provides timely exercise reminders and dietary advice. This allows the recommendation department to support users in making optimal choices and decisions, thereby improving user satisfaction. In addition, the recommendation department can collect user feedback and continuously improve the accuracy and effectiveness of its recommendations. For example, it can collect user evaluations and comments on suggested travel plans and products and incorporate them into future recommendations. This allows the recommendation department to provide users with more personalized recommendations and improve the overall performance of the system.

[0063] The data collection unit can collect data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services (SNS), music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, and more. The data collection unit can acquire data, for example, through APIs. The data collection unit can acquire purchase history data from e-commerce sites, for example. The data collection unit can also acquire user posting data from SNS. Furthermore, the data collection unit can acquire user playback history data from music streaming services. By collecting diverse digital information, it becomes possible to more accurately understand user behavior patterns and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired through APIs into a generating AI and have the generating AI perform data collection.

[0064] The suggestion unit can propose the optimal travel plan based on the user's past travel and search history when the user is planning a trip. For example, the suggestion unit can propose similar travel plans based on data of destinations and accommodations the user has visited in the past. It can also propose the optimal travel plan based on data of destinations and tourist spots the user has searched for. Furthermore, the suggestion unit can propose customized travel plans according to the user's budget and travel purpose. This allows the system to propose the optimal travel plan based on the user's past travel and search history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's travel history data into a generating AI and have the generating AI propose the optimal travel plan.

[0065] The suggestion unit can suggest the most suitable products to users when they are shopping, based on their past purchase and search history. For example, the suggestion unit can suggest similar products based on data of products and brands that the user has previously purchased. It can also suggest the most suitable products based on data of products and categories that the user has searched for. Furthermore, the suggestion unit can provide customized product suggestions according to the user's budget and preferences. This allows the system to suggest the most suitable products based on the user's past purchase and search history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's purchase history data into a generating AI and have the generating AI perform the task of suggesting the most suitable products.

[0066] The suggestion unit can propose optimal health management methods based on past health data if the user is using a health management app. For example, the suggestion unit can propose optimal meal plans and exercise plans based on the user's past meal and exercise records. Furthermore, the suggestion unit can propose customized health management methods according to the user's health status and goals. In addition, the suggestion unit can analyze the user's health data, predict health risks, and propose preventative measures. This allows the suggestion unit to propose optimal health management methods based on the user's past health data. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health data into a generating AI and have the generating AI generate suggestions for optimal health management methods.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of digital information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection and collect data when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect digital information in real time and immediately send it for analysis. Additionally, if the user is tired, the data collection unit can adjust the timing of collection and collect data while the user is resting. This allows for more appropriate information collection by adjusting the timing of digital information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0068] The data collection unit can analyze the user's past digital information collection history and select the optimal collection method. For example, the data collection unit can collect digital information during times when the user frequently used the service in the past. The data collection unit can also prioritize collecting digital information from devices and applications that the user has used in the past. Furthermore, the data collection unit can analyze the user's past behavior patterns and select the most efficient collection method. This allows the optimal collection method to be selected by analyzing the user's past collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter digital information based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting digital information related to topics the user is currently interested in. The data collection unit can also collect appropriate digital information according to the user's lifestyle (e.g., at work, on vacation, etc.). Furthermore, the data collection unit can filter relevant digital information based on the user's current activities (e.g., exercising, reading, etc.). This allows for the collection of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of digital information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting entertainment-related digital information. It may also prioritize collecting digital information related to relaxation and stress relief if the user is stressed. Furthermore, if the user is excited, the data collection unit may prioritize collecting digital information related to activities and events. This allows for the collection of more relevant information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0071] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting digital information. For example, the data collection unit can prioritize the collection of local news and event information related to the user's current location. It can also prioritize the collection of information on nearby shops and services based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of tourist information and transportation information for their travel destination. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the information collection.

[0072] The data collection unit can analyze a user's social media activity and collect relevant information when collecting digital information. For example, the data collection unit can collect relevant digital information based on the content of posts from accounts that the user follows on social media. The data collection unit can also collect relevant digital information based on content that the user "likes" or shares on social media. Furthermore, the data collection unit can collect relevant digital information based on groups and events that the user participates in on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the information collection.

[0073] 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 to help the user understand them more deeply. If the user is stressed, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the user is agitated, the analysis unit can provide analysis results using visually appealing graphs or charts. This allows for 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, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the digital information during the analysis. For example, the analysis unit can perform a detailed analysis on highly important digital information and provide it to the user. Alternatively, the analysis unit can perform a simplified analysis on less important digital information and provide it to the user. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the digital information, enabling efficient analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the digital information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of digital information during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase information. It can also apply a search keyword analysis algorithm to search history. Furthermore, it can apply a social network analysis algorithm to social media activity. By applying the appropriate analysis algorithm according to the category of digital information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on the timing of digital information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the latest digital information and provides it to the user. The analysis unit can also determine the priority of analysis of past digital information according to its importance. Furthermore, the analysis unit can perform analysis efficiently based on the timing of digital information collection. This enables efficient analysis by determining the priority of analysis based on the timing of digital information collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input digital information collection timing data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0078] The analysis unit can adjust the order of analysis based on the relationships between digital information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant digital information and provide it to the user. The analysis unit can also perform a simplified analysis on less relevant digital information. Furthermore, the analysis unit can perform analysis efficiently based on the relationships between digital information. This enables efficient analysis by adjusting the order of analysis based on the relationships between digital information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between digital information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0079] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions to help the user understand them better. If the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. Furthermore, if the user is excited, the suggestion unit can use visually appealing graphs and charts to present suggestions. By adjusting the presentation of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0080] The proposal unit can analyze the user's past behavior patterns and select the optimal proposal method when making a proposal. For example, the proposal unit can make proposals based on services and products that the user has previously preferred to use. The proposal unit can also analyze the user's past behavior patterns and select the most effective proposal method. Furthermore, the proposal unit can make customized proposals based on the user's past behavior patterns. This allows the optimal proposal method to be selected by analyzing the user's past behavior patterns. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user behavior pattern data into a generating AI and have the generating AI select the optimal proposal method.

[0081] The suggestion unit can customize the suggestion methods based on the user's current living situation when making suggestions. For example, if the user is at work, the suggestion unit will make work-related suggestions. If the user is on vacation, the suggestion unit can also make suggestions related to relaxation or travel. Furthermore, the suggestion unit can select the most suitable suggestion method based on the user's current living situation. This allows for the selection of the most suitable suggestion method based on the user's current living situation. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the suggestion methods.

[0082] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will prioritize entertainment-related suggestions. If the user is stressed, the suggestion unit can also prioritize suggestions related to relaxation and stress relief. Furthermore, if the user is excited, the suggestion unit can also prioritize suggestions related to activities and events. This allows for more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0083] The suggestion unit can select the optimal suggestion method by considering the user's geographical location information when making suggestions. For example, the suggestion unit can suggest local services and shops related to the user's current location. It can also suggest nearby events and activities based on the user's geographical location. Furthermore, if the user is traveling, the suggestion unit can suggest tourist information and transportation information for their travel destination. In this way, the optimal suggestion method can be selected by considering the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the suggestion method.

[0084] The suggestion unit can analyze the user's social media activity and propose methods for making suggestions. For example, the suggestion unit can make relevant suggestions based on the content of posts from accounts the user follows on social media. It can also make relevant suggestions based on content the user "likes" or shares on social media. Furthermore, it can make relevant suggestions based on groups and events the user participates in on social media. This allows for the efficient generation of relevant suggestions by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI select the methods for making suggestions.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The information integration system can also collect user digital information by understanding the user's current activity status in real time and collecting information at the appropriate time. For example, if a user is exercising, the data collection unit can delay data collection until the exercise is finished. Similarly, if a user is in a meeting, the data collection unit can refrain from collecting data until the meeting is over. Furthermore, during times when the user is relaxing, the data collection unit can actively collect data and send it to the analysis unit. By adjusting the timing of data collection according to the user's activity status, the system reduces the burden on the user and enables more efficient data collection.

[0087] The information integration system can further estimate the user's emotions and customize suggestions based on those emotions. For example, if the user is stressed, the suggestion system can offer suggestions related to relaxation and stress relief. If the user is excited, the suggestion system can offer suggestions related to activities and events. Furthermore, if the user is relaxed, the suggestion system can offer suggestions related to entertainment and hobbies. By customizing suggestions according to the user's emotions, more appropriate suggestions can be made.

[0088] The information integration system can further analyze users' past behavior patterns and predict future behavior. For example, if a user tends to visit a particular restaurant every weekend, the suggestion system can suggest new menu items or special events at that restaurant. Similarly, if a user tends to plan trips during a specific season, the system can suggest the most suitable travel plan for that season. Furthermore, if a user tends to exercise at a specific time of day, the system can suggest the most suitable exercise plan for that time. This allows for more appropriate suggestions by predicting future behavior based on the user's past behavior patterns.

[0089] The information integration system can further customize its suggestions by taking into account the user's geographical location. For example, if the user is in a specific region, it can suggest local events and tourist attractions in that area. If the user is traveling, it can suggest tourist information and transportation information for their destination. Furthermore, if the user is at home, it can suggest information about nearby shops and services. This allows for more relevant suggestions based on the user's geographical location.

[0090] The information integration system can further analyze users' social media activity and suggest relevant information. For example, it can suggest relevant products and services based on the content posted by accounts that users follow on social media. It can also suggest relevant events and activities based on content that users "like" or share on social media. Furthermore, it can suggest relevant information based on groups and events that users participate in on social media. This enables more relevant suggestions based on users' social media activity.

[0091] The information integration system can further estimate the user's emotions and adjust how digital information is collected based on those emotions. For example, if the user is stressed, the collection unit can prioritize collecting information related to stress relief. If the user is relaxed, the collection unit can prioritize collecting information related to entertainment and hobbies. Furthermore, if the user is excited, the collection unit can prioritize collecting information related to activities and events. This allows for more appropriate information collection by adjusting the information collection method according to the user's emotions.

[0092] The information integration system can further analyze a user's past digital information collection history and select the optimal collection timing. For example, it can collect digital information during times when the user frequently used it in the past. It can also prioritize collecting digital information from devices and applications the user has used in the past. Furthermore, it can analyze the user's past behavior patterns and select the most efficient collection timing. In this way, by analyzing the user's past collection history, the optimal collection timing can be selected.

[0093] The information integration system can further estimate the user's emotions and adjust the presentation of the analysis results based on those estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results to allow the user to understand them more deeply. If the user is stressed, it can provide concise and to-the-point analysis results. Furthermore, if the user is excited, it can present the analysis results using visually appealing graphs and charts. By adjusting the presentation of the analysis results according to the user's emotions, it can provide more appropriate results.

[0094] The information integration system can further customize suggestions based on the user's current lifestyle. For example, if the user is at work, it can offer work-related suggestions. If the user is on vacation, it can offer suggestions related to relaxation and travel. Furthermore, it can select the most appropriate suggestion method based on the user's current lifestyle. This allows for the selection of the most suitable suggestions based on the user's current circumstances.

[0095] The information integration system can further estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, entertainment-related suggestions will be prioritized. If the user is stressed, suggestions related to relaxation and stress relief will be prioritized. Furthermore, if the user is excited, suggestions related to activities and events will be prioritized. By prioritizing suggestions according to the user's emotions, more appropriate suggestions can be made.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The collection unit collects the user's digital information. The collection unit collects data from, for example, e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social networking services, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information apps, news sites, sports information sites, etc. The collection unit can obtain data through APIs. It can also collect data using scraping techniques. Furthermore, it can collect data directly from the user's device. For example, it can collect data from the user's smartphone or personal computer. Step 2: The analysis unit analyzes the digital information collected by the collection unit to learn user behavior patterns and preferences. The analysis unit can analyze data using machine learning algorithms. It can also analyze text data using natural language processing technology. Furthermore, it can analyze image data using image recognition technology. For example, it can analyze a user's purchase history to understand their preferences. Step 3: The Proposal Department, based on the analysis results obtained by the Analysis Department, makes suggestions to support the user in making the best choices and decisions. If the user is planning a trip, the Proposal Department will suggest the best travel plan based on their past travel and search history. If the user is shopping, the Proposal Department will suggest the best products based on their past purchase and search history. Furthermore, if the user is using a health management app, the Proposal Department will suggest the best health management methods based on their past health data.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 via the communication I / F 44 of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's behavior patterns and preferences. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to support the user in making optimal choices and decisions based on the analysis results. Some or all of the data collection unit, analysis unit, and proposal unit may be implemented in the control unit 46A of the smart device 14. 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.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion 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 via the communication I / F 44 of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's behavior patterns and preferences. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to support the user in making optimal choices and decisions based on the analysis results. Some or all of the data collection unit, analysis unit, and suggestion unit may be implemented in the control unit 46A of the smart glasses 214. 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.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and suggestion 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 via the communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's behavior patterns and preferences. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to support the user in making optimal choices and decisions based on the analysis results. Some or all of the data collection unit, analysis unit, and suggestion unit may be implemented in the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 robot 414 and the data processing unit 12. For example, the data collection unit collects data via the communication I / F 44 of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to learn the user's behavior patterns and preferences. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to support the user in making optimal choices and decisions based on the analysis results. Some or all of the data collection unit, analysis unit, and proposal unit may be implemented in the control unit 46A of the robot 414, for example. 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A collection unit that collects users' digital information, An analysis unit analyzes the digital information collected by the aforementioned collection unit and learns the user's behavior patterns and preferences. The system includes a proposal unit that provides suggestions to support the user in making optimal choices and decisions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social media, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information services, news sites, sports information sites, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, When a user is planning a trip, the system will suggest the best travel plan based on their past travel and search history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, When users make a purchase, the system suggests the most suitable products based on their past purchase and search history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, If a user is using a health management app, it will suggest the most suitable health management methods based on past health data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of digital information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past digital information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting digital information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of digital information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting digital information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting digital information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the digital information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of digital information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the digital information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the digital information. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned proposal section is, When making a proposal, we analyze the user's past behavior patterns to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, customize the suggestion method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, the optimal proposal method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest methods for making the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects users' digital information, An analysis unit analyzes the digital information collected by the aforementioned collection unit and learns the user's behavior patterns and preferences. The system includes a proposal unit that provides suggestions to support the user in making optimal choices and decisions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from e-commerce sites, auction sites, travel booking sites, hometown tax donation sites, fashion e-commerce sites, insurance services, search engines, social media, music streaming services, food delivery services, health management apps, securities trading services, map apps, car navigation systems, transit information services, news sites, sports information sites, and more. The system according to feature 1.

3. The aforementioned proposal section is, When a user is planning a trip, the system will suggest the best travel plan based on their past travel and search history. The system according to feature 1.

4. The aforementioned proposal section is, When users make a purchase, the system suggests the most suitable products based on their past purchase and search history. The system according to feature 1.

5. The aforementioned proposal section is, If a user is using a health management app, it will suggest the most suitable health management methods based on past health data. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze the user's past digital information gathering history and select the optimal collection method. The system according to feature 1.

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

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of digital information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting digital information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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