System

Abstract

An object of a system according to an embodiment is to grasp hobbies and preferences of a user and make an optimal proposal.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data of a user. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it takes time and is troublesome for users to compare and consider what they want to do.

[0005] The system according to the embodiment aims to understand the user's interests and preferences and make optimal suggestions. [Means for solving the problem]

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

[0007] The system according to the embodiment can grasp the user's interests and preferences and make optimal suggestions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A comparison support system according to an embodiment of the present invention collects user data, analyzes it using AI, and makes recommendations. The comparison support system collects data from apps that users regularly use and analyzes it using AI. For example, it collects data from shopping apps and social media accounts frequently used by the user to understand the user's interests. Next, the AI ​​uses the collected data to learn the user's preferences. This allows it to understand what the user is looking for and make optimal recommendations. Furthermore, the AI ​​automates the comparison process. It collects information about the products and services the user desires and creates a comparison table. For example, it compares the specifications and prices of multiple smartphones and presents the best options for the user. This system allows users to make quick decisions without wasting time. By understanding the user's preferences and making optimal recommendations, the AI ​​can reduce the effort required for comparison. For example, if a user is looking for a new restaurant, the AI ​​can suggest the best restaurant based on the user's preferences and past reviews. In this way, utilizing AI streamlines comparison and consideration when users want to do something, allowing them to make optimal choices more quickly. This allows the comparison support system to efficiently collect, analyze, and suggest user data. For example, when a user wants to do something, they can make a quick decision without wasting time. Also, by understanding the user's hobbies and preferences and making optimal suggestions, the time and effort required for comparison and consideration can be reduced.

[0029] A comparison support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user data. The user data includes, but is not limited to, personal information, behavioral history, and purchase history. The collection unit collects, for example, data on the user's shopping app and social networking site usage history, search history, and purchase history. The collection unit can also collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or PC. The collection unit can also collect data with the user's consent. For example, the collection unit collects data only if the user consents. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, a machine learning model, but is not limited to, an analysis of the collected data using the machine learning model to cluster the user's hobbies and preferences. The analysis unit can also anonymize and encrypt the data. For example, the analysis unit anonymizes the collected data to protect privacy. The analysis unit can also estimate the user's emotions. For example, the analysis unit estimates the user's emotions and reflects them in the analysis results. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions may, for example, suggest products or services that are optimal for the user, but are not limited to such examples. For example, the suggestion unit may suggest products or services that are optimal for the user based on the analysis results. The suggestion unit may also set evaluation criteria and scoring for the suggestions. For example, the suggestion unit may set evaluation criteria and scoring for the suggestions to improve the accuracy of the suggestions. This allows the comparison consideration support system according to the embodiment to efficiently collect, analyze, and suggest user data. For example, when a user wants to do something, the user can make a quick decision without wasting time. Furthermore, by understanding the user's hobbies and preferences and making optimal suggestions, the effort of comparison consideration can be reduced.

[0030] The collection unit can collect data on the user's shopping app or SNS usage history, search history, and purchase history. The collection unit, for example, collects the user's shopping app or SNS usage history. For example, the collection unit collects information on products and services viewed by the user. The collection unit can also collect the user's search history. For example, the collection unit collects keywords and phrases searched by the user. The collection unit can also collect the user's purchase history. For example, the collection unit collects information on products and services purchased by the user. By collecting various data on the user, the collection unit can perform more accurate analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's shopping app or SNS usage history, search history, and purchase history into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected data using a machine learning model and cluster the user's hobbies and preferences. The analysis unit, for example, analyzes the collected data using the machine learning model. For example, the analysis unit clusters the user's hobbies and preferences using a clustering algorithm. The analysis unit can also classify the user's hobbies and preferences using a classification algorithm. For example, the analysis unit classifies the user's hobbies and preferences into categories based on the collected data. The analysis unit can also predict the user's hobbies and preferences using a regression algorithm. For example, the analysis unit predicts the user's future hobbies and preferences based on the collected data. In this way, the analysis unit can cluster the user's hobbies and preferences with high accuracy by using the machine learning model. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The suggestion unit can suggest products and services to the user based on the analysis results. The suggestion unit, for example, suggests optimal products and services to the user based on the analysis results. For example, the suggestion unit suggests electronic devices and fashion items based on the user's hobbies and preferences. The suggestion unit can also suggest subscription services based on the user's interests. For example, the suggestion unit suggests subscription services related to a theme in which the user is interested. The suggestion unit can also suggest related products based on the user's past purchase history. For example, the suggestion unit suggests accessories and options related to products the user has previously purchased. In this way, the suggestion unit makes optimal suggestions based on the analysis results, thereby improving user satisfaction. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the analysis results into AI and cause the AI ​​to generate suggestions.

[0033] The comparison support system includes a protection unit that anonymizes or encrypts data. The protection unit, for example, anonymizes data. For example, the protection unit anonymizes a user's personal information to protect privacy. The protection unit can also encrypt data. For example, the protection unit encrypts collected data using an encryption algorithm. The protection unit can also mask data. For example, the protection unit masks the user's personal information to ensure data security. In this way, the protection unit protects the user's privacy by anonymizing or encrypting data. Some or all of the above-described processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit can input collected data to AI and have the AI ​​anonymize or encrypt the data.

[0034] The comparison support system includes a consent unit that obtains user consent. The consent unit, for example, displays a pop-up notification to obtain user consent. For example, the consent unit displays a pop-up notification before data collection, requesting user consent. The consent unit can also send an email notification. For example, the consent unit sends an email to the user, requesting consent for data collection. The consent unit can also display an in-app notification. For example, the consent unit displays a notification in the app, requesting user consent. In this way, the consent unit obtains user consent, thereby improving transparency of data collection. Some or all of the above-described processing in the consent unit may be performed using AI, or may be performed without using AI. For example, the consent unit can cause AI to generate a notification to obtain user consent.

[0035] The comparison support system includes an evaluation unit that performs evaluation criteria and scoring of proposals. The evaluation unit, for example, sets evaluation criteria for proposals. For example, the evaluation unit sets evaluation items for proposals and assigns a score to each item. The evaluation unit can also set a scoring algorithm. For example, the evaluation unit sets a scoring algorithm for proposals to improve the accuracy of proposals. The evaluation unit can also adjust the evaluation criteria based on user feedback. For example, the evaluation unit collects user feedback and reviews the evaluation criteria. As a result, the evaluation unit performs evaluation criteria and scoring of proposals, thereby improving the accuracy of proposals. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can cause AI to perform the evaluation criteria and scoring of proposals.

[0036] The collection unit can analyze the user's past data collection history and select a collection method. The collection unit, for example, analyzes the user's past data collection history. For example, the collection unit prioritizes data collection from apps that the user frequently used in the past. The collection unit can also prioritize data collection from products that the user has given high ratings to in the past. The collection unit can also prioritize data collection from websites on which the user has spent a lot of time in the past. In this way, the collection unit can select the optimal collection method by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI and have the AI ​​select the collection method.

[0037] The collection unit may perform filtering based on the user's current activity status or areas of interest when collecting data. For example, the collection unit may consider the user's current activity status when collecting data. For example, if the user is currently using a shopping app, the collection unit may prioritize data collection from that app. Also, if the user is currently posting about a specific topic on a social networking site, the collection unit may collect data related to that topic. The collection unit may also collect relevant data based on the content of the website the user is currently browsing. This allows the collection unit to collect more relevant data by filtering data based on the user's current activity status or areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the user's current activity status or areas of interest into AI and have the AI ​​perform data filtering.

[0038] When collecting data, the collection unit can select a collection means according to the user's input method. The collection unit selects a collection means according to, for example, the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uploads images, the collection unit can prioritize collecting image data. This allows the collection unit to select the optimal collection means according to the user's input method, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the collection means.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, takes into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into AI and have the AI ​​collect the data.

[0040] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activity. For example, if the user posts about a specific topic on social media, the collection unit collects data related to that topic. The collection unit can also analyze the activity of accounts the user follows on social media and collect related data. The collection unit can also collect related data based on content shared by the user on social media. In this way, the collection unit can efficiently collect related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activity into AI and have the AI ​​collect the data.

[0041] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback. For example, the collection unit preferentially uses collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. The collection unit can also optimize the collection method based on the user's past feedback. In this way, the collection unit can optimize the collection method by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into AI and have the AI ​​adjust the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform an analysis at an appropriate level of detail on data of medium importance. In this way, the analysis unit adjusts the level of detail of the analysis based on the importance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an evaluation of the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. The analysis unit, for example, selects an analysis algorithm depending on the data category. For example, the analysis unit applies a purchasing pattern analysis algorithm to shopping data. The analysis unit can also apply a sentiment analysis algorithm to SNS data. The analysis unit can also apply a trend analysis algorithm to search history data. In this way, the analysis unit applies an appropriate analysis algorithm depending on the data category, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the data category into AI and have the AI ​​select the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors and improve accuracy based on the user's past analysis results. The analysis unit can also introduce new analysis methods by referring to the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the order of analysis based on the time when the data was collected. The analysis unit determines the order of analysis based on, for example, the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also set a low priority for older data. The analysis unit can also analyze data of moderate recency with a moderate priority. This enables efficient analysis by the analysis unit determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. The analysis unit can also analyze data with moderate relevance in an appropriate order. In this way, the analysis unit adjusts the order of analysis based on the relevance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also provide analysis results that use appropriate technical terms according to the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input information about the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0048] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the product. For example, the suggestion unit makes a detailed suggestion for a product with high importance. The suggestion unit can also make a concise suggestion for a product with low importance. The suggestion unit can also make a suggestion with an appropriate level of detail for a product with medium importance. This enables the suggestion unit to adjust the level of detail of the suggestion based on the importance of the product, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information on the importance of the product to AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0049] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the product category. The suggestion unit selects a suggestion algorithm depending on, for example, the product category. For example, the suggestion unit applies a spec comparison algorithm to electronic devices. The suggestion unit can also apply a trend analysis algorithm to fashion items. The suggestion unit can also apply a review analysis algorithm to restaurants. In this way, the suggestion unit applies an appropriate suggestion algorithm depending on the product category, thereby improving the suggestion accuracy. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the product category into AI and have the AI ​​select the suggestion algorithm.

[0050] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, refers to the user's past proposal results. For example, the suggestion unit optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also correct errors and improve accuracy based on the user's past proposal results. The suggestion unit can also introduce a new proposal method by referring to the user's past proposal results. In this way, the suggestion unit improves the accuracy of the proposal by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past proposal results into AI and cause the AI ​​to improve the accuracy of the proposal.

[0051] When making a proposal, the proposal unit can determine the order of proposals based on the submission date of the products. The proposal unit determines the order of proposals based on, for example, the submission date of the products. For example, the proposal unit prioritizes the most recent products. The proposal unit can also set a low priority for older products. The proposal unit can also propose products of moderate newness with a moderate priority. This enables the proposal unit to determine the priority of proposals based on the submission date of the products, thereby enabling efficient proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information on the submission date of the products into AI and have the AI ​​determine the order of proposals.

[0052] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the products. For example, the suggestion unit prioritizes suggesting products with high relevance. The suggestion unit can also postpone suggesting products with low relevance. The suggestion unit can also suggest products with medium relevance in an appropriate order. This enables efficient suggestions by the suggestion unit adjusting the order of suggestions based on the relevance of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information on the relevance of the products to AI and cause the AI ​​to adjust the order of suggestions.

[0053] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Also, the suggestion unit can make a proposal that uses appropriate technical terminology according to the user's level of expertise. In this way, the suggestion unit can provide a more understandable proposal by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's level of expertise to AI and cause the AI ​​to adjust the use of technical terminology.

[0054] The protection unit can adjust the level of detail of protection based on the importance of the data when protecting data. The protection unit adjusts the level of detail of protection based on, for example, the importance of the data. For example, the protection unit applies strong protection to data of high importance. The protection unit can also apply standard protection to data of low importance. The protection unit can also apply moderate protection to data of medium importance. In this way, the protection unit adjusts the level of detail of protection based on the importance of the data, thereby enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the importance of the data to AI and have the AI ​​adjust the level of detail of protection.

[0055] When protecting data, the protection unit can apply an appropriate protection algorithm depending on the data category. The protection unit, for example, selects a protection algorithm depending on the data category. For example, the protection unit applies a strong encryption algorithm to personal information. The protection unit can also apply a standard encryption algorithm to shopping data. The protection unit can also apply an appropriate encryption algorithm to SNS data. In this way, the protection unit applies an appropriate protection algorithm depending on the data category, thereby improving the accuracy of data protection. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input information on the data category to AI and have the AI ​​select the protection algorithm.

[0056] When protecting data, the protection unit can determine the order of protection based on the time when the data was collected. The protection unit determines the order of protection based on, for example, the time when the data was collected. For example, the protection unit prioritizes protecting the most recent data. The protection unit can also set a low priority for older data. The protection unit can also protect data of medium newness with an appropriate priority. In this way, the protection unit determines the priority of protection based on the time when the data was collected, enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of protection.

[0057] The protection unit can adjust the order of protection based on the relevance of the data when protecting the data. The protection unit adjusts the order of protection based on, for example, the relevance of the data. For example, the protection unit prioritizes protection of data with high relevance. The protection unit can also postpone protection of data with low relevance. The protection unit can also protect data with medium relevance in an appropriate order. In this way, the protection unit adjusts the order of protection based on the relevance of the data, thereby enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of protection.

[0058] When obtaining consent, the consent unit can select the consent method by referring to the user's past consent history. The consent unit, for example, refers to the user's past consent history. For example, the consent unit may preferentially use methods to which the user has previously consented. The consent unit can also avoid methods that the user has previously rejected. The consent unit can also suggest a new consent method based on the user's past consent history. In this way, the consent unit can select the optimal consent method by referring to the past consent history. Some or all of the above-mentioned processing in the consent unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent unit can input data on the user's past consent history into AI and have the AI ​​select the consent method.

[0059] The consent unit can adjust the means of consent based on the user's current situation when obtaining consent. The consent unit, for example, adjusts the means of consent based on the user's current situation. For example, the consent unit provides a simple means of consent when the user is on the move. The consent unit can also provide a detailed means of consent when the user is at home. The consent unit can also provide a means for quickly obtaining consent when the user is in a hurry. This allows the consent unit to adjust the means of consent based on the current situation, thereby enabling more appropriate consent to be obtained. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input data on the user's current situation into AI and have the AI ​​adjust the means of consent.

[0060] When obtaining consent, the consent unit can select a consent method based on the user's geographical location information. The consent unit, for example, considers the user's geographical location information. For example, if the user is in a specific area, the consent unit provides a consent method appropriate for that area. Furthermore, if the user is traveling, the consent unit can also provide a consent method appropriate for the travel destination. Furthermore, if the user is at home, the consent unit can also provide a consent method appropriate for the home. In this way, the consent unit can select the optimal consent method by considering the geographical location information. Some or all of the above-mentioned processing in the consent unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent unit can input data on the user's geographical location information into AI and have the AI ​​select the consent method.

[0061] When obtaining consent, the consent unit can analyze the user's social media activity and suggest consent means. The consent unit, for example, analyzes the user's social media activity. For example, if the user posts about a specific topic on social media, the consent unit provides consent means related to that topic. The consent unit can also analyze the activity of accounts the user follows on social media and provide relevant consent means. The consent unit can also provide relevant consent means based on content the user shared on social media. In this way, the consent unit can efficiently suggest relevant consent means by analyzing social media activity. Some or all of the above-mentioned processing in the consent unit may be performed using, or without, AI, for example. For example, the consent unit can input data on the user's social media activity into AI and have the AI ​​execute the suggestion of consent means.

[0062] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the data during evaluation. The evaluation unit adjusts the level of detail of the evaluation based on, for example, the importance of the data. For example, the evaluation unit performs a detailed evaluation for data of high importance. The evaluation unit can also perform a brief evaluation for data of low importance. The evaluation unit can also perform an evaluation with an appropriate level of detail for data of medium importance. This allows the evaluation unit to adjust the level of detail of the evaluation based on the importance of the data, enabling efficient evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the importance of the data to AI and have the AI ​​adjust the level of detail of the evaluation.

[0063] The evaluation unit can apply an appropriate evaluation algorithm depending on the data category during evaluation. The evaluation unit selects an evaluation algorithm depending on the data category, for example. For example, the evaluation unit applies a purchasing pattern evaluation algorithm to shopping data. The evaluation unit can also apply an emotion evaluation algorithm to SNS data. The evaluation unit can also apply a trend evaluation algorithm to search history data. In this way, the evaluation unit applies an appropriate evaluation algorithm depending on the data category, thereby improving the evaluation accuracy. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the data category into AI and have the AI ​​select the evaluation algorithm.

[0064] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on the user's past evaluation results. The evaluation unit, for example, refers to the user's past evaluation results. For example, the evaluation unit optimizes the evaluation algorithm based on the user's past evaluation results. The evaluation unit can also correct errors and improve accuracy based on the user's past evaluation results. The evaluation unit can also introduce a new evaluation method by referring to the user's past evaluation results. In this way, the evaluation unit improves the accuracy of the evaluation by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the user's past evaluation results into AI and have the AI ​​improve the accuracy of the evaluation.

[0065] During evaluation, the evaluation unit can determine the order of evaluation based on the time when the data was collected. The evaluation unit determines the order of evaluation based on, for example, the time when the data was collected. For example, the evaluation unit prioritizes evaluation of the most recent data. The evaluation unit can also set a low priority for older data. The evaluation unit can also evaluate data of moderate newness with a moderate priority. This enables efficient evaluation by the evaluation unit determining the priority of evaluation based on the time when the data was collected. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of evaluation.

[0066] The evaluation unit can adjust the order of evaluation based on the relevance of the data during evaluation. The evaluation unit adjusts the order of evaluation based on, for example, the relevance of the data. For example, the evaluation unit prioritizes evaluation of data with high relevance. The evaluation unit can also postpone evaluation of data with low relevance. The evaluation unit can also evaluate data with moderate relevance in an appropriate order. In this way, the evaluation unit adjusts the order of evaluation based on the relevance of the data, enabling efficient evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of evaluation.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The analysis unit can select an analysis method based on the data collection source. For example, the analysis unit can apply purchase pattern analysis to data collected from shopping apps, and sentiment analysis to data collected from social media. It can also apply trend analysis to search history data. This allows the analysis unit to improve analysis accuracy by selecting the optimal analysis method depending on the data collection source.

[0069] The suggestion unit can analyze the user's past suggestion history and improve the accuracy of suggestions. For example, the suggestion unit can make similar suggestions based on suggestions that the user has accepted in the past. It can also avoid suggestions that the user has rejected in the past. Furthermore, it can introduce new suggestion methods based on the user's past suggestion history. In this way, the suggestion unit can improve the accuracy of suggestions by analyzing the user's past suggestion history.

[0070] The collection unit can determine the priority of data collection based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Also, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the home. In this way, the collection unit can efficiently collect highly relevant data by determining the priority of data collection based on the geographical location information.

[0071] The evaluation unit can select an evaluation method depending on the data category. For example, the evaluation unit can apply a purchasing pattern evaluation to shopping data and an emotion evaluation to social media data. It can also apply a trend evaluation to search history data. This allows the evaluation unit to improve evaluation accuracy by selecting the optimal evaluation method depending on the data category.

[0072] The consent unit can optimize the method of obtaining consent by referring to the user's past consent history. For example, the consent unit can preferentially use methods to which the user has previously consented. It can also avoid methods that the user has previously rejected. Furthermore, it can also suggest a new method of obtaining consent based on the user's past consent history. This allows the consent unit to select the optimal method of obtaining consent by referring to the past consent history.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects user data. User data includes, for example, personal information, behavioral history, and purchase history. The collection unit collects data such as the user's shopping app and SNS usage history, search history, and purchase history. The collection unit can also collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or computer. Furthermore, the collection unit can also collect data with the user's consent. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, a machine learning model. The analysis unit analyzes the collected data using the machine learning model and clusters the user's hobbies and preferences. The analysis unit can also anonymize and encrypt the data. Furthermore, the analysis unit estimates the user's emotions and reflects them in the analysis results. Step 3: The suggestion unit makes a suggestion based on the analysis results obtained by the analysis unit. The suggestion may be, for example, a product or service that is optimal for the user. The suggestion unit may suggest a product or service that is optimal for the user based on the analysis results. The suggestion unit may also set evaluation criteria and score the suggestion.

[0075] (Example 2) A comparison support system according to an embodiment of the present invention collects user data, analyzes it using AI, and makes recommendations. The comparison support system collects data from apps that users regularly use and analyzes it using AI. For example, it collects data from shopping apps and social media accounts frequently used by the user to understand the user's interests. Next, the AI ​​uses the collected data to learn the user's preferences. This allows it to understand what the user is looking for and make optimal recommendations. Furthermore, the AI ​​automates the comparison process. It collects information about the products and services the user desires and creates a comparison table. For example, it compares the specifications and prices of multiple smartphones and presents the best options for the user. This system allows users to make quick decisions without wasting time. By understanding the user's preferences and making optimal recommendations, the AI ​​can reduce the effort required for comparison. For example, if a user is looking for a new restaurant, the AI ​​can suggest the best restaurant based on the user's preferences and past reviews. In this way, utilizing AI streamlines comparison and consideration when users want to do something, allowing them to make optimal choices more quickly. This allows the comparison support system to efficiently collect, analyze, and suggest user data. For example, when a user wants to do something, they can make a quick decision without wasting time. Also, by understanding the user's hobbies and preferences and making optimal suggestions, the time and effort required for comparison and consideration can be reduced.

[0076] A comparison support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user data. The user data includes, but is not limited to, personal information, behavioral history, and purchase history. The collection unit collects, for example, data on the user's shopping app and social networking site usage history, search history, and purchase history. The collection unit can also collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or PC. The collection unit can also collect data with the user's consent. For example, the collection unit collects data only if the user consents. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, a machine learning model, but is not limited to, an analysis of the collected data using the machine learning model to cluster the user's hobbies and preferences. The analysis unit can also anonymize and encrypt the data. For example, the analysis unit anonymizes the collected data to protect privacy. The analysis unit can also estimate the user's emotions. For example, the analysis unit estimates the user's emotions and reflects them in the analysis results. The suggestion unit makes suggestions based on the analysis results obtained by the analysis unit. The suggestions may, for example, suggest products or services that are optimal for the user, but are not limited to such examples. For example, the suggestion unit may suggest products or services that are optimal for the user based on the analysis results. The suggestion unit may also set evaluation criteria and scoring for the suggestions. For example, the suggestion unit may set evaluation criteria and scoring for the suggestions to improve the accuracy of the suggestions. This allows the comparison consideration support system according to the embodiment to efficiently collect, analyze, and suggest user data. For example, when a user wants to do something, the user can make a quick decision without wasting time. Furthermore, by understanding the user's hobbies and preferences and making optimal suggestions, the effort of comparison consideration can be reduced.

[0077] The collection unit can collect data on the user's shopping app or SNS usage history, search history, and purchase history. The collection unit, for example, collects the user's shopping app or SNS usage history. For example, the collection unit collects information on products and services viewed by the user. The collection unit can also collect the user's search history. For example, the collection unit collects keywords and phrases searched by the user. The collection unit can also collect the user's purchase history. For example, the collection unit collects information on products and services purchased by the user. By collecting various data on the user, the collection unit can perform more accurate analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's shopping app or SNS usage history, search history, and purchase history into AI and have the AI ​​collect the data.

[0078] The analysis unit can analyze the collected data using a machine learning model and cluster the user's hobbies and preferences. The analysis unit, for example, analyzes the collected data using the machine learning model. For example, the analysis unit clusters the user's hobbies and preferences using a clustering algorithm. The analysis unit can also classify the user's hobbies and preferences using a classification algorithm. For example, the analysis unit classifies the user's hobbies and preferences into categories based on the collected data. The analysis unit can also predict the user's hobbies and preferences using a regression algorithm. For example, the analysis unit predicts the user's future hobbies and preferences based on the collected data. In this way, the analysis unit can cluster the user's hobbies and preferences with high accuracy by using the machine learning model. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0079] The suggestion unit can suggest products and services to the user based on the analysis results. The suggestion unit, for example, suggests optimal products and services to the user based on the analysis results. For example, the suggestion unit suggests electronic devices and fashion items based on the user's hobbies and preferences. The suggestion unit can also suggest subscription services based on the user's interests. For example, the suggestion unit suggests subscription services related to a theme in which the user is interested. The suggestion unit can also suggest related products based on the user's past purchase history. For example, the suggestion unit suggests accessories and options related to products the user has previously purchased. In this way, the suggestion unit makes optimal suggestions based on the analysis results, thereby improving user satisfaction. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the analysis results into AI and cause the AI ​​to generate suggestions.

[0080] The comparison support system includes a protection unit that anonymizes or encrypts data. The protection unit, for example, anonymizes data. For example, the protection unit anonymizes a user's personal information to protect privacy. The protection unit can also encrypt data. For example, the protection unit encrypts collected data using an encryption algorithm. The protection unit can also mask data. For example, the protection unit masks the user's personal information to ensure data security. In this way, the protection unit protects the user's privacy by anonymizing or encrypting data. Some or all of the above-described processing in the protection unit may be performed using AI, or may be performed without using AI. For example, the protection unit can input collected data to AI and have the AI ​​anonymize or encrypt the data.

[0081] The comparison support system includes a consent unit that obtains user consent. The consent unit, for example, displays a pop-up notification to obtain user consent. For example, the consent unit displays a pop-up notification before data collection, requesting user consent. The consent unit can also send an email notification. For example, the consent unit sends an email to the user, requesting consent for data collection. The consent unit can also display an in-app notification. For example, the consent unit displays a notification in the app, requesting user consent. In this way, the consent unit obtains user consent, thereby improving transparency of data collection. Some or all of the above-described processing in the consent unit may be performed using AI, or may be performed without using AI. For example, the consent unit can cause AI to generate a notification to obtain user consent.

[0082] The comparison support system includes an evaluation unit that performs evaluation criteria and scoring of proposals. The evaluation unit, for example, sets evaluation criteria for proposals. For example, the evaluation unit sets evaluation items for proposals and assigns a score to each item. The evaluation unit can also set a scoring algorithm. For example, the evaluation unit sets a scoring algorithm for proposals to improve the accuracy of proposals. The evaluation unit can also adjust the evaluation criteria based on user feedback. For example, the evaluation unit collects user feedback and reviews the evaluation criteria. As a result, the evaluation unit performs evaluation criteria and scoring of proposals, thereby improving the accuracy of proposals. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can cause AI to perform the evaluation criteria and scoring of proposals.

[0083] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit estimates the user's emotion using an emotion recognition algorithm. The collection unit also adjusts the timing of data collection based on the estimated user's emotion. For example, if the user is stressed, the collection unit refrains from collecting data and collects it when the user is relaxed. The collection unit can also immediately start data collection and perform real-time analysis when the user is excited. The collection unit can also postpone data collection when the user is tired and collect it after the user has rested. This allows the collection unit to adjust the timing of data collection according to the user's emotion, thereby enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0084] The collection unit can analyze the user's past data collection history and select a collection method. The collection unit, for example, analyzes the user's past data collection history. For example, the collection unit prioritizes data collection from apps that the user frequently used in the past. The collection unit can also prioritize data collection from products that the user has given high ratings to in the past. The collection unit can also prioritize data collection from websites on which the user has spent a lot of time in the past. In this way, the collection unit can select the optimal collection method by analyzing the past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI and have the AI ​​select the collection method.

[0085] The collection unit may perform filtering based on the user's current activity status or areas of interest when collecting data. For example, the collection unit may consider the user's current activity status when collecting data. For example, if the user is currently using a shopping app, the collection unit may prioritize data collection from that app. Also, if the user is currently posting about a specific topic on a social networking site, the collection unit may collect data related to that topic. The collection unit may also collect relevant data based on the content of the website the user is currently browsing. This allows the collection unit to collect more relevant data by filtering data based on the user's current activity status or areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the user's current activity status or areas of interest into AI and have the AI ​​perform data filtering.

[0086] When collecting data, the collection unit can select a collection means according to the user's input method. The collection unit selects a collection means according to, for example, the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Also, if the user uploads images, the collection unit can prioritize collecting image data. This allows the collection unit to select the optimal collection means according to the user's input method, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method into AI and have the AI ​​select the collection means.

[0087] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using an emotion recognition algorithm. The collection unit also determines the priority of data to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit collects immediately required data and sets the priority high. When the user is relaxed, the collection unit can collect detailed data and set the priority low. When the user is stressed, the collection unit can collect only important data and set the priority medium. In this way, the collection unit can prioritize data collection according to the user's emotions, thereby preferentially collecting important data. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0088] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, takes into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking the geographical location information into consideration. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into AI and have the AI ​​collect the data.

[0089] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, analyzes the user's social media activity. For example, if the user posts about a specific topic on social media, the collection unit collects data related to that topic. The collection unit can also analyze the activity of accounts the user follows on social media and collect related data. The collection unit can also collect related data based on content shared by the user on social media. In this way, the collection unit can efficiently collect related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's social media activity into AI and have the AI ​​collect the data.

[0090] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, reflects the user's past feedback. For example, the collection unit preferentially uses collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. The collection unit can also optimize the collection method based on the user's past feedback. In this way, the collection unit can optimize the collection method by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's past feedback into AI and have the AI ​​adjust the collection method.

[0091] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using an emotion recognition algorithm. The analysis unit also adjusts the presentation method of the analysis based on the estimated user's emotion. For example, the analysis unit provides detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually appealing analysis results when the user is excited. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotion and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0092] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform an analysis at an appropriate level of detail on data of medium importance. In this way, the analysis unit adjusts the level of detail of the analysis based on the importance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an evaluation of the importance of the data into AI and have the AI ​​adjust the level of detail of the analysis.

[0093] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the data category. The analysis unit, for example, selects an analysis algorithm depending on the data category. For example, the analysis unit applies a purchasing pattern analysis algorithm to shopping data. The analysis unit can also apply a sentiment analysis algorithm to SNS data. The analysis unit can also apply a trend analysis algorithm to search history data. In this way, the analysis unit applies an appropriate analysis algorithm depending on the data category, thereby improving the analysis accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the data category into AI and have the AI ​​select the analysis algorithm.

[0094] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also correct errors and improve accuracy based on the user's past analysis results. The analysis unit can also introduce new analysis methods by referring to the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0095] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit estimates the user's emotion using an emotion recognition algorithm. The analysis unit also adjusts the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotion and provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0096] During analysis, the analysis unit can determine the order of analysis based on the time when the data was collected. The analysis unit determines the order of analysis based on, for example, the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also set a low priority for older data. The analysis unit can also analyze data of moderate recency with a moderate priority. This enables efficient analysis by the analysis unit determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of analysis.

[0097] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. For example, the analysis unit prioritizes analysis of data with high relevance. The analysis unit can also postpone analysis of data with low relevance. The analysis unit can also analyze data with moderate relevance in an appropriate order. In this way, the analysis unit adjusts the order of analysis based on the relevance of the data, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of analysis.

[0098] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit provides analysis results that use a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also provide analysis results that use appropriate technical terms according to the user's level of expertise. In this way, the analysis unit can provide analysis results that are easier to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input information about the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0099] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit estimates the user's emotion using an emotion recognition algorithm. The suggestion unit also adjusts the way the suggestion is presented based on the estimated user's emotion. For example, the suggestion unit provides detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions that focus on the main points when the user is in a hurry. The suggestion unit can also provide visually appealing suggestions when the user is excited. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way the suggestion is presented based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0100] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the product when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on, for example, the importance of the product. For example, the suggestion unit makes a detailed suggestion for a product with high importance. The suggestion unit can also make a concise suggestion for a product with low importance. The suggestion unit can also make a suggestion with an appropriate level of detail for a product with medium importance. This enables the suggestion unit to adjust the level of detail of the suggestion based on the importance of the product, thereby enabling efficient suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information on the importance of the product to AI and cause the AI ​​to adjust the level of detail of the suggestion.

[0101] When making a suggestion, the suggestion unit can apply an appropriate suggestion algorithm depending on the product category. The suggestion unit selects a suggestion algorithm depending on, for example, the product category. For example, the suggestion unit applies a spec comparison algorithm to electronic devices. The suggestion unit can also apply a trend analysis algorithm to fashion items. The suggestion unit can also apply a review analysis algorithm to restaurants. In this way, the suggestion unit applies an appropriate suggestion algorithm depending on the product category, thereby improving the suggestion accuracy. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input information about the product category into AI and have the AI ​​select the suggestion algorithm.

[0102] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, refers to the user's past proposal results. For example, the suggestion unit optimizes the proposal algorithm based on the user's past proposal results. The suggestion unit can also correct errors and improve accuracy based on the user's past proposal results. The suggestion unit can also introduce a new proposal method by referring to the user's past proposal results. In this way, the suggestion unit improves the accuracy of the proposal by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past proposal results into AI and cause the AI ​​to improve the accuracy of the proposal.

[0103] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit estimates the user's emotion using an emotion recognition algorithm. The suggestion unit also adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide a short and to-the-point suggestion when the user is in a hurry. The suggestion unit can also provide a detailed suggestion when the user is relaxed. The suggestion unit can also provide a visually appealing suggestion when the user is excited. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0104] When making a proposal, the proposal unit can determine the order of proposals based on the submission date of the products. The proposal unit determines the order of proposals based on, for example, the submission date of the products. For example, the proposal unit prioritizes the most recent products. The proposal unit can also set a low priority for older products. The proposal unit can also propose products of moderate newness with a moderate priority. This enables the proposal unit to determine the priority of proposals based on the submission date of the products, thereby enabling efficient proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information on the submission date of the products into AI and have the AI ​​determine the order of proposals.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the products when making suggestions. The suggestion unit adjusts the order of suggestions based on, for example, the relevance of the products. For example, the suggestion unit prioritizes suggesting products with high relevance. The suggestion unit can also postpone suggesting products with low relevance. The suggestion unit can also suggest products with medium relevance in an appropriate order. This enables efficient suggestions by the suggestion unit adjusting the order of suggestions based on the relevance of the products. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information on the relevance of the products to AI and cause the AI ​​to adjust the order of suggestions.

[0106] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make a concise and easy-to-understand proposal. Also, the suggestion unit can make a proposal that uses appropriate technical terminology according to the user's level of expertise. In this way, the suggestion unit can provide a more understandable proposal by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information about the user's level of expertise to AI and cause the AI ​​to adjust the use of technical terminology.

[0107] The protection unit can estimate a user's emotion and adjust the method of anonymizing or encrypting data based on the estimated user's emotion. The protection unit, for example, estimates the user's emotion. For example, the protection unit estimates the user's emotion using an emotion recognition algorithm. The protection unit also adjusts the method of anonymizing or encrypting data based on the estimated user's emotion. For example, the protection unit applies strong encryption when the user feels anxious. The protection unit can also apply standard encryption when the user feels relaxed. The protection unit can also apply encryption that can be processed quickly when the user is in a hurry. This allows the protection unit to adjust the method of anonymizing or encrypting data according to the user's emotion, thereby enabling more appropriate data protection. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or without AI. For example, the protection unit can input user emotional data into the generation AI and have the generation AI adjust the method of anonymizing and encrypting the data.

[0108] The protection unit can adjust the level of detail of protection based on the importance of the data when protecting data. The protection unit adjusts the level of detail of protection based on, for example, the importance of the data. For example, the protection unit applies strong protection to data of high importance. The protection unit can also apply standard protection to data of low importance. The protection unit can also apply moderate protection to data of medium importance. In this way, the protection unit adjusts the level of detail of protection based on the importance of the data, thereby enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the importance of the data to AI and have the AI ​​adjust the level of detail of protection.

[0109] When protecting data, the protection unit can apply an appropriate protection algorithm depending on the data category. The protection unit, for example, selects a protection algorithm depending on the data category. For example, the protection unit applies a strong encryption algorithm to personal information. The protection unit can also apply a standard encryption algorithm to shopping data. The protection unit can also apply an appropriate encryption algorithm to SNS data. In this way, the protection unit applies an appropriate protection algorithm depending on the data category, thereby improving the accuracy of data protection. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input information on the data category to AI and have the AI ​​select the protection algorithm.

[0110] The protection unit can estimate a user's emotions and determine the priority of data protection based on the estimated user emotions. The protection unit, for example, estimates the user's emotions. For example, the protection unit estimates the user's emotions using an emotion recognition algorithm. The protection unit also determines the priority of data protection based on the estimated user emotions. For example, the protection unit prioritizes the protection of important data when the user is feeling anxious. The protection unit can also perform standard data protection when the user is relaxed. The protection unit can also perform data protection that can be processed quickly when the user is in a hurry. This enables the protection unit to determine the priority of data protection based on the user's emotions, thereby enabling more appropriate data protection. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or without AI. For example, the protection unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of data protection.

[0111] When protecting data, the protection unit can determine the order of protection based on the time when the data was collected. The protection unit determines the order of protection based on, for example, the time when the data was collected. For example, the protection unit prioritizes protecting the most recent data. The protection unit can also set a low priority for older data. The protection unit can also protect data of medium newness with an appropriate priority. In this way, the protection unit determines the priority of protection based on the time when the data was collected, enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of protection.

[0112] The protection unit can adjust the order of protection based on the relevance of the data when protecting the data. The protection unit adjusts the order of protection based on, for example, the relevance of the data. For example, the protection unit prioritizes protection of data with high relevance. The protection unit can also postpone protection of data with low relevance. The protection unit can also protect data with medium relevance in an appropriate order. In this way, the protection unit adjusts the order of protection based on the relevance of the data, thereby enabling efficient data protection. Some or all of the above-mentioned processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of protection.

[0113] The consent unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user emotions. The consent unit, for example, estimates the user's emotions. For example, the consent unit estimates the user's emotions using an emotion recognition algorithm. The consent unit also adjusts the consent acquisition method based on the estimated user emotions. For example, if the user feels anxious, the consent unit may provide a detailed explanation to acquire consent. If the user feels relaxed, the consent unit may provide a concise explanation to acquire consent. If the user is in a hurry, the consent unit may provide a method for quickly acquiring consent. This allows the consent unit to adjust the consent acquisition method according to the user's emotions, thereby enabling more appropriate consent acquisition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the consent unit may be performed, for example, using AI or without AI. For example, the consent unit can input the user's emotional data into the generation AI and have the generation AI adjust the method of obtaining consent.

[0114] When obtaining consent, the consent unit can select the consent method by referring to the user's past consent history. The consent unit, for example, refers to the user's past consent history. For example, the consent unit may preferentially use methods to which the user has previously consented. The consent unit can also avoid methods that the user has previously rejected. The consent unit can also suggest a new consent method based on the user's past consent history. In this way, the consent unit can select the optimal consent method by referring to the past consent history. Some or all of the above-mentioned processing in the consent unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent unit can input data on the user's past consent history into AI and have the AI ​​select the consent method.

[0115] The consent unit can adjust the means of consent based on the user's current situation when obtaining consent. The consent unit, for example, adjusts the means of consent based on the user's current situation. For example, the consent unit provides a simple means of consent when the user is on the move. The consent unit can also provide a detailed means of consent when the user is at home. The consent unit can also provide a means for quickly obtaining consent when the user is in a hurry. This allows the consent unit to adjust the means of consent based on the current situation, thereby enabling more appropriate consent to be obtained. Some or all of the above-mentioned processing in the consent unit may be performed using AI, for example, or may be performed without using AI. For example, the consent unit can input data on the user's current situation into AI and have the AI ​​adjust the means of consent.

[0116] The consent unit can estimate the user's emotions and determine the priority of consents based on the estimated user's emotions. The consent unit, for example, estimates the user's emotions. For example, the consent unit estimates the user's emotions using an emotion recognition algorithm. The consent unit also determines the priority of consents based on the estimated user's emotions. For example, the consent unit prioritizes important consents when the user is feeling anxious. The consent unit can also obtain standard consents when the user is relaxed. The consent unit can also provide a method for quickly obtaining consents when the user is in a hurry. This enables the consent unit to determine the priority of consents according to the user's emotions, thereby enabling more appropriate consent acquisition. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the consent unit may be performed using, for example, AI, or without AI. For example, the consent unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of consent.

[0117] When obtaining consent, the consent unit can select a consent method based on the user's geographical location information. The consent unit, for example, considers the user's geographical location information. For example, if the user is in a specific area, the consent unit provides a consent method appropriate for that area. Furthermore, if the user is traveling, the consent unit can also provide a consent method appropriate for the travel destination. Furthermore, if the user is at home, the consent unit can also provide a consent method appropriate for the home. In this way, the consent unit can select the optimal consent method by considering the geographical location information. Some or all of the above-mentioned processing in the consent unit may be performed using, for example, AI, or may be performed without using AI. For example, the consent unit can input data on the user's geographical location information into AI and have the AI ​​select the consent method.

[0118] When obtaining consent, the consent unit can analyze the user's social media activity and suggest consent means. The consent unit, for example, analyzes the user's social media activity. For example, if the user posts about a specific topic on social media, the consent unit provides consent means related to that topic. The consent unit can also analyze the activity of accounts the user follows on social media and provide relevant consent means. The consent unit can also provide relevant consent means based on content the user shared on social media. In this way, the consent unit can efficiently suggest relevant consent means by analyzing social media activity. Some or all of the above-mentioned processing in the consent unit may be performed using, or without, AI, for example. For example, the consent unit can input data on the user's social media activity into AI and have the AI ​​execute the suggestion of consent means.

[0119] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria and scoring method based on the estimated user's emotion. The evaluation unit, for example, estimates the user's emotion. For example, the evaluation unit estimates the user's emotion using an emotion recognition algorithm. The evaluation unit also adjusts the evaluation criteria and scoring method based on the estimated user's emotion. For example, the evaluation unit provides detailed evaluation criteria when the user is relaxed. The evaluation unit can also provide concise evaluation criteria when the user is in a hurry. The evaluation unit can also provide visually appealing evaluation criteria when the user is excited. This allows the evaluation unit to adjust the evaluation criteria and scoring method according to the user's emotion, enabling more appropriate evaluation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input user emotion data into the generation AI and have the generation AI adjust the evaluation criteria and scoring method.

[0120] The evaluation unit can adjust the level of detail of the evaluation based on the importance of the data during evaluation. The evaluation unit adjusts the level of detail of the evaluation based on, for example, the importance of the data. For example, the evaluation unit performs a detailed evaluation for data of high importance. The evaluation unit can also perform a brief evaluation for data of low importance. The evaluation unit can also perform an evaluation with an appropriate level of detail for data of medium importance. This allows the evaluation unit to adjust the level of detail of the evaluation based on the importance of the data, enabling efficient evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the importance of the data to AI and have the AI ​​adjust the level of detail of the evaluation.

[0121] The evaluation unit can apply an appropriate evaluation algorithm depending on the data category during evaluation. The evaluation unit selects an evaluation algorithm depending on the data category, for example. For example, the evaluation unit applies a purchasing pattern evaluation algorithm to shopping data. The evaluation unit can also apply an emotion evaluation algorithm to SNS data. The evaluation unit can also apply a trend evaluation algorithm to search history data. In this way, the evaluation unit applies an appropriate evaluation algorithm depending on the data category, thereby improving the evaluation accuracy. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the data category into AI and have the AI ​​select the evaluation algorithm.

[0122] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on the user's past evaluation results. The evaluation unit, for example, refers to the user's past evaluation results. For example, the evaluation unit optimizes the evaluation algorithm based on the user's past evaluation results. The evaluation unit can also correct errors and improve accuracy based on the user's past evaluation results. The evaluation unit can also introduce a new evaluation method by referring to the user's past evaluation results. In this way, the evaluation unit improves the accuracy of the evaluation by referring to the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the user's past evaluation results into AI and have the AI ​​improve the accuracy of the evaluation.

[0123] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions. For example, the evaluation unit estimates the user's emotions using an emotion recognition algorithm. The evaluation unit also determines the priority of evaluations based on the estimated user's emotions. For example, the evaluation unit can prioritize detailed evaluations when the user is relaxed. The evaluation unit can also prioritize concise evaluations when the user is in a hurry. The evaluation unit can also prioritize visually appealing evaluations when the user is excited. This allows the evaluation unit to determine the priority of evaluations according to the user's emotions, thereby enabling more appropriate evaluations. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the evaluation.

[0124] During evaluation, the evaluation unit can determine the order of evaluation based on the time when the data was collected. The evaluation unit determines the order of evaluation based on, for example, the time when the data was collected. For example, the evaluation unit prioritizes evaluation of the most recent data. The evaluation unit can also set a low priority for older data. The evaluation unit can also evaluate data of moderate newness with a moderate priority. This enables efficient evaluation by the evaluation unit determining the priority of evaluation based on the time when the data was collected. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the time when the data was collected into AI and have the AI ​​determine the order of evaluation.

[0125] The evaluation unit can adjust the order of evaluation based on the relevance of the data during evaluation. The evaluation unit adjusts the order of evaluation based on, for example, the relevance of the data. For example, the evaluation unit prioritizes evaluation of data with high relevance. The evaluation unit can also postpone evaluation of data with low relevance. The evaluation unit can also evaluate data with moderate relevance in an appropriate order. In this way, the evaluation unit adjusts the order of evaluation based on the relevance of the data, enabling efficient evaluation. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input information on the relevance of the data to AI and have the AI ​​adjust the order of evaluation. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, protection unit, consent unit, and evaluation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect user data using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and makes optimal suggestions to the user based on the analysis results. The protection unit anonymizes and encrypts data by the specific processing unit 290 of the data processing device 12. The consent unit displays a notification for obtaining user consent by the control unit 46A of the smart device 14. The evaluation unit uses the specific processing unit 290 of the data processing device 12 to determine evaluation criteria and score the suggestions. The collection unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, protection unit, consent unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect user data using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and makes optimal suggestions to the user based on the analysis results. The protection unit anonymizes and encrypts data by the specific processing unit 290 of the data processing device 12. The consent unit displays a notification for obtaining user consent by the control unit 46A of the smart glasses 214. The evaluation unit determines evaluation criteria and scoring for the suggestions by the specific processing unit 290 of the data processing device 12. The collection unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, suggestion unit, protection unit, consent unit, and evaluation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect user data using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and makes optimal suggestions to the user based on the analysis results. The protection unit anonymizes and encrypts data using the specific processing unit 290 of the data processing device 12. The consent unit displays a notification for obtaining user consent using the control unit 46A of the headset type terminal 314. The evaluation unit uses the specific processing unit 290 of the data processing device 12 to set evaluation criteria and score the suggestions. The collection unit estimates the user's emotions using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the timing of data collection using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, protection unit, consent unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect user data using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and makes optimal suggestions to the user based on the analysis results. The protection unit anonymizes and encrypts data by the specific processing unit 290 of the data processing device 12. The consent unit displays a notification for obtaining user consent by the control unit 46A of the robot 414. The evaluation unit uses the specific processing unit 290 of the data processing device 12 to set evaluation criteria and score the suggestions. The collection unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of data collection using the specific processing unit 290 of the data processing device 12.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of important data and postpone detailed analysis. Also, if the user is relaxed, detailed analysis can be prioritized. Furthermore, if the user is excited, visually appealing analysis results can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the priority of analysis according to the user's emotions.

[0128] The suggestion unit can estimate the user's emotion and adjust the timing of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Also, the suggestion unit can provide concise suggestions when the user is in a hurry. Furthermore, the suggestion unit can provide visually appealing suggestions when the user is excited. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the timing of the suggestion according to the user's emotion.

[0129] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed and increase the frequency of data collection when the user is relaxed. Also, if the user is excited, data can be collected in real time. This allows the collection unit to adjust the frequency of data collection according to the user's emotions, thereby enabling more appropriate data collection.

[0130] The evaluation unit can estimate the user's emotion and adjust the evaluation feedback method based on the estimated user's emotion. For example, the evaluation unit can provide detailed feedback when the user is relaxed. Also, the evaluation unit can provide concise feedback when the user is in a hurry. Furthermore, the evaluation unit can provide visually appealing feedback when the user is excited. In this way, the evaluation unit can provide more appropriate feedback by adjusting the evaluation feedback method according to the user's emotion.

[0131] The protection unit can estimate the user's emotions and adjust the data protection method based on the estimated user's emotions. For example, the protection unit can apply strong encryption if the user feels anxious. Alternatively, the protection unit can apply standard encryption if the user feels relaxed. Furthermore, the protection unit can apply encryption that can be processed quickly if the user is in a hurry. In this way, the protection unit can adjust the data protection method according to the user's emotions, thereby enabling more appropriate data protection.

[0132] The analysis unit can select an analysis method based on the data collection source. For example, the analysis unit can apply purchase pattern analysis to data collected from shopping apps, and sentiment analysis to data collected from social media. It can also apply trend analysis to search history data. This allows the analysis unit to improve analysis accuracy by selecting the optimal analysis method depending on the data collection source.

[0133] The suggestion unit can analyze the user's past suggestion history and improve the accuracy of suggestions. For example, the suggestion unit can make similar suggestions based on suggestions that the user has accepted in the past. It can also avoid suggestions that the user has rejected in the past. Furthermore, it can introduce new suggestion methods based on the user's past suggestion history. In this way, the suggestion unit can improve the accuracy of suggestions by analyzing the user's past suggestion history.

[0134] The collection unit can determine the priority of data collection based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Also, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the home. In this way, the collection unit can efficiently collect highly relevant data by determining the priority of data collection based on the geographical location information.

[0135] The evaluation unit can select an evaluation method depending on the data category. For example, the evaluation unit can apply a purchasing pattern evaluation to shopping data and an emotion evaluation to social media data. It can also apply a trend evaluation to search history data. This allows the evaluation unit to improve evaluation accuracy by selecting the optimal evaluation method depending on the data category.

[0136] The consent unit can optimize the method of obtaining consent by referring to the user's past consent history. For example, the consent unit can preferentially use methods to which the user has previously consented. It can also avoid methods that the user has previously rejected. Furthermore, it can also suggest a new method of obtaining consent based on the user's past consent history. This allows the consent unit to select the optimal method of obtaining consent by referring to the past consent history.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The collection unit collects user data. User data includes, for example, personal information, behavioral history, and purchase history. The collection unit collects data such as the user's shopping app and SNS usage history, search history, and purchase history. The collection unit can also collect data directly from the user's device. For example, the collection unit collects data from the user's smartphone or computer. Furthermore, the collection unit can also collect data with the user's consent. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, a machine learning model. The analysis unit analyzes the collected data using the machine learning model and clusters the user's hobbies and preferences. The analysis unit can also anonymize and encrypt the data. Furthermore, the analysis unit estimates the user's emotions and reflects them in the analysis results. Step 3: The suggestion unit makes a suggestion based on the analysis results obtained by the analysis unit. The suggestion may be, for example, a product or service that is optimal for the user. The suggestion unit may suggest a product or service that is optimal for the user based on the analysis results. The suggestion unit may also set evaluation criteria and score the suggestion.

[0139] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 7, a 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.

[0177] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0183] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0184] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0185] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0187] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0188] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0189] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0191] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0192] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0194] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0195] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0196] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0200] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0201] 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.

[0202] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0203] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0204] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0205] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0207] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0208] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0209] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0210] [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data on users' shopping app and SNS usage history, search history, and purchase history 2. The system of claim 1.

3. The analysis unit The collected data is analyzed using a machine learning model to cluster users' interests and preferences.

2. The system of claim 1.

4. The proposal unit Based on the analysis results, we suggest products and services to users.

2. The system of claim 1.

5. Includes protection for anonymizing or encrypting data 2. The system of claim 1.

6. Equipped with a consent section to obtain user consent 2. The system of claim 1.

7. Equipped with an evaluation department that sets evaluation criteria and scores for proposals 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Analyze the user's past data collection history and select the collection method 2. The system of claim 1.

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