System

Abstract

An object of a system according to an embodiment is to provide optimal comparison information based on the status of an individual.SOLUTION: A system includes a personal information collection part, an analysis part, and an information provision part. The personal information collection unit collects personal information of a user. The analysis unit analyzes the personal information collected by the personal information collection unit. The information providing unit provides optimum comparison information to the user on the basis of the information analyzed 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 do not adequately provide optimal comparative information based on an individual's status, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal comparison information based on an individual's status. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal information collection unit, an analysis unit, and an information provision unit. The personal information collection unit collects personal information of a user. The analysis unit analyzes the personal information collected by the personal information collection unit. The information provision unit provides optimal comparison information to the user based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal comparison information based on an individual's status. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The comparison website / app according to the embodiment of the present invention is a system that collects personal information of users, analyzes it using a generation AI, and provides optimal comparison information, thereby enabling the comparison website / app to provide optimal comparison information to users.

[0029] A comparison website / app according to an embodiment includes a personal information collection unit, an analysis unit, and an information provision unit. The personal information collection unit collects personal information of a user. For example, the personal information collection unit may collect information about which credit card the user uses. The personal information collection unit may also collect information about which point programs the user participates in. The personal information collection unit may also collect the user's past purchase history. For example, the personal information collection unit may collect the user's credit card information and identify which credit card the user uses. The personal information collection unit may collect point program information and identify which point programs the user participates in. The personal information collection unit may collect purchase history and identify the user's past purchase history. The analysis unit analyzes the personal information collected by the personal information collection unit. For example, the analysis unit may use a generation AI to analyze the user's personal information. The analysis unit may also use data mining technology to analyze the user's personal information. The analysis unit may also use a machine learning algorithm to analyze the user's personal information. For example, the analysis unit may use a generation AI to analyze the user's credit card information and provide optimal comparison information. The analysis unit uses data mining technology to analyze the user's point program information and provide optimal comparison information. The analysis unit uses a machine learning algorithm to analyze the user's purchase history and provide optimal comparison information. The information provision unit provides the user with optimal comparison information based on the information analyzed by the analysis unit. For example, if the user is a PayPay Card Gold member, the information provision unit provides the user with optimal purchase timing and advantageous campaign information on Yahoo! Shopping. The information provision unit can also provide users in the Rakuten economic zone with optimal purchase timing and advantageous campaign information on Rakuten Market. The information provision unit can also provide optimal comparison information based on the user's personal information. For example, the information provision unit suggests the optimal purchase timing based on the user's credit card information. The information provision unit provides advantageous campaign information based on the user's point program information. The information provision unit provides optimal comparison information based on the user's purchase history.As a result, the comparison site / app according to the embodiment can provide users with optimal comparison information. For example, users can easily understand the best time to purchase and information on advantageous campaigns. Users can enjoy shopping efficiently. Users can compare information on various categories, such as travel reservations and home appliance purchases, on a single platform. This saves users time and effort and allows them to make the best choice.

[0030] The personal information collection unit can collect the user's credit card information, point program information, and past purchase history. The personal information collection unit, for example, collects the user's credit card information. For example, credit card information includes the card number, expiration date, and security code. The personal information collection unit also collects the user's point program information. For example, point program information includes the point balance, point history, and point expiration date. The personal information collection unit also collects the user's past purchase history. For example, purchase history includes the purchase date and time, purchased items, and purchase amount. This makes it possible to collect detailed personal information about the user.

[0031] The personal information collection unit can collect more accurate personal information by analyzing social media activity and identifying interests. The personal information collection unit, for example, analyzes social media activity. For example, social media activity may include analyzing the content of posts, the number of likes, and the content of comments. The personal information collection unit also identifies the user's interests. For example, posts about specific brands or products may be analyzed as interests. The personal information collection unit also identifies the user's interests. For example, accounts followed and groups joined may be analyzed as interests. This makes it possible to collect more accurate personal information based on the user's social media activity.

[0032] The personal information collection unit can collect location information in real time and analyze personal information taking into account regional characteristics. The personal information collection unit, for example, collects location information in real time. For example, location information collected may include GPS data, Wi-Fi location information, and beacon data. The personal information collection unit also analyzes personal information taking into account regional characteristics. For example, regional characteristics may include regional demographics, economic conditions, and cultural background. This makes it possible to analyze personal information based on the user's location information.

[0033] The analysis unit collects health data and can propose optimal products and services based on the user's health condition. The analysis unit, for example, collects health data. For example, health data collected may include heart rate, blood pressure, exercise volume, and dietary records. The analysis unit also proposes optimal products and services based on the user's health condition. For example, health data may include health checkup results, self-reported data, and data from wearable devices. This makes it possible to propose optimal products and services based on the user's health condition.

[0034] The analysis unit can collect data from home devices and analyze personal information based on the home environment. The analysis unit, for example, collects data from home devices. For example, home devices include smart speakers, smart home appliances, and security cameras. The analysis unit also analyzes personal information based on the home environment. For example, the home environment can be analyzed based on family composition, residence size, interior style, and the like. This makes it possible to analyze personal information based on data from home devices.

[0035] The information providing unit analyzes purchase history and market trends, predicts future price fluctuations, and can suggest the optimal timing for purchase. The information providing unit, for example, analyzes purchase history and market trends. For example, past purchase data can be analyzed as purchase history. The information providing unit also analyzes market trends. For example, sales data, search trends, social media topics, etc. can be analyzed as market trends. The information providing unit also predicts future price fluctuations. For example, price fluctuations can be analyzed as past price data, the balance between supply and demand, seasonal factors, etc. This makes it possible to predict future price fluctuations and suggest the optimal timing for purchase.

[0036] The information providing unit can provide customized comparison information based on hobbies and lifestyles. The information providing unit provides, for example, customized comparison information based on hobbies and lifestyles. For example, comparison information can be provided based on hobbies such as sports, music, movies, and reading. The information providing unit also provides comparison information based on lifestyles. For example, comparison information can be provided based on lifestyles such as diet, exercise habits, and frequency of travel. This makes it possible to provide customized comparison information based on the user's hobbies and lifestyles.

[0037] The information providing unit can provide optimal comparative information based on family structure and life stage. The information providing unit can provide optimal comparative information based on, for example, family structure and life stage. For example, the information providing unit can provide comparative information based on family structure such as the number of family members, age composition, and whether or not the family has pets. The information providing unit can also provide comparative information based on life stage. For example, the information providing unit can provide comparative information based on life stage such as student, working adult, retiree, etc. This makes it possible to provide optimal comparative information based on the user's family structure and life stage.

[0038] The information providing unit can analyze the relevance between different genres and aggregate the most relevant information for the user. The information providing unit, for example, analyzes the relevance between different genres. For example, genres can be analyzed such as music, movies, books, and games. The information providing unit also analyzes the relevance. For example, common themes, user interests, past browsing history, and the like can be analyzed as the relevance. This allows the relevance between different genres to be analyzed and the most relevant information to be aggregated.

[0039] The information providing unit analyzes search history and trends, and can predict and aggregate information on genres whose demand will increase in the future. The information providing unit, for example, analyzes search history and trends. For example, the search history can include search keywords, search date and time, and click history of search results. The information providing unit also analyzes trends. For example, trends can include sales data, search trends, and social media topics. The information providing unit also predicts information on genres whose demand will increase in the future. For example, genres such as music, movies, books, and games can be analyzed. This makes it possible to predict and aggregate information on genres whose demand will increase in the future.

[0040] The information providing unit can aggregate information on genres based on occupations and specialized knowledge, and provide specialized comparative information. The information providing unit aggregates information on genres based on occupations and specialized knowledge, for example. For example, information can be aggregated based on occupations such as doctor, engineer, teacher, artist, etc. The information providing unit also provides information based on specialized knowledge. For example, information can be provided based on qualifications, years of experience, field of expertise, etc. This makes it possible to provide specialized comparative information based on the user's occupation and specialized knowledge.

[0041] The information providing unit can aggregate region-specific information based on travel history and interests, and provide optimal comparison information for each region. The information providing unit aggregates region-specific information based on, for example, travel history and interests. For example, information can be aggregated based on travel history, such as places visited, frequency of travel, and purpose of travel. The information providing unit also provides region-specific information. For example, region-specific information can be provided based on regional tourist information, regional specialty products, regional event information, and the like. This makes it possible to provide region-specific comparison information based on the user's travel history and interests.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit collects the user's health data and can propose optimal products and services based on the user's health condition. For example, health data collected includes heart rate, blood pressure, exercise volume, and dietary records. The analysis unit also proposes optimal products and services based on the user's health condition. For example, the analysis unit can analyze health checkup results, self-reported data, and data from wearable devices. This makes it possible to propose optimal products and services based on the user's health condition.

[0044] The information providing unit can provide customized comparison information based on the user's hobbies and lifestyle. For example, comparison information can be provided based on hobbies such as sports, music, movies, and reading. The information providing unit can also provide comparison information based on lifestyle. For example, comparison information can be provided based on eating habits, exercise habits, frequency of travel, and the like. This makes it possible to provide customized comparison information based on the user's hobbies and lifestyle.

[0045] The information providing unit can provide optimal comparative information based on family structure and life stage. For example, comparative information can be provided based on family structure such as the number of family members, age composition, and whether or not the user has pets. The information providing unit also provides comparative information based on life stage. For example, comparative information can be provided based on whether the user is a student, a working adult, or a retiree. This makes it possible to provide optimal comparative information based on the user's family structure and life stage.

[0046] The analysis unit can collect data from home devices and analyze personal information based on the home environment. For example, home devices such as smart speakers, smart home appliances, and security cameras can be collected. The analysis unit also analyzes personal information based on the home environment. For example, the analysis unit can analyze family composition, the size of the home, and the interior style. This makes it possible to analyze personal information based on data from home devices.

[0047] The information providing unit can aggregate region-specific information based on travel history and interests, and provide optimal comparison information for each region. For example, information can be aggregated based on travel history, such as places visited, frequency of travel, and purpose of travel. The information providing unit also provides region-specific information. For example, information can be provided based on regional tourist information, regional specialty products, regional event information, and the like. This makes it possible to provide region-specific comparison information based on the user's travel history and interests.

[0048] The information providing unit analyzes search history and trends, and can predict and aggregate information on genres that will see increased demand in the future. For example, search history can be analyzed, such as search keywords, search date and time, and click history of search results. The information providing unit also analyzes trends. For example, it can analyze sales data, search trends, and social media topics. The information providing unit also predicts information on genres that will see increased demand in the future. For example, it can analyze music, movies, books, games, etc. This makes it possible to predict and aggregate information on genres that will see increased demand in the future.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The personal information collection unit collects personal information about the user, such as which credit card the user uses, which point program the user participates in, and past purchase history. Step 2: The analysis unit analyzes the personal information collected by the personal information collection unit. For example, using generative AI, data mining technology, and machine learning algorithms, the analysis unit analyzes the user's credit card information, point program information, and purchase history to provide optimal comparison information. Step 3: The information provider provides the user with optimal comparison information based on the information analyzed by the analyzer. For example, if the user is a PayPay Card Gold member, the information provider provides the user with optimal purchasing timing and advantageous campaign information on Yahoo Shopping, and if the user is in the Rakuten economic zone, the information provider provides the user with optimal purchasing timing and advantageous campaign information on Rakuten Ichiba.

[0051] (Example 2) The comparison website / app according to the embodiment of the present invention is a system that collects personal information of users, analyzes it using a generation AI, and provides optimal comparison information, thereby enabling the comparison website / app to provide optimal comparison information to users.

[0052] A comparison website / app according to an embodiment includes a personal information collection unit, an analysis unit, and an information provision unit. The personal information collection unit collects personal information of a user. For example, the personal information collection unit may collect information about which credit card the user uses. The personal information collection unit may also collect information about which point programs the user participates in. The personal information collection unit may also collect the user's past purchase history. For example, the personal information collection unit may collect the user's credit card information and identify which credit card the user uses. The personal information collection unit may collect point program information and identify which point programs the user participates in. The personal information collection unit may collect purchase history and identify the user's past purchase history. The analysis unit analyzes the personal information collected by the personal information collection unit. For example, the analysis unit may use a generation AI to analyze the user's personal information. The analysis unit may also use data mining technology to analyze the user's personal information. The analysis unit may also use a machine learning algorithm to analyze the user's personal information. For example, the analysis unit may use a generation AI to analyze the user's credit card information and provide optimal comparison information. The analysis unit uses data mining technology to analyze the user's point program information and provide optimal comparison information. The analysis unit uses a machine learning algorithm to analyze the user's purchase history and provide optimal comparison information. The information provision unit provides the user with optimal comparison information based on the information analyzed by the analysis unit. For example, if the user is a PayPay Card Gold member, the information provision unit provides the user with optimal purchase timing and advantageous campaign information on Yahoo! Shopping. The information provision unit can also provide users in the Rakuten economic zone with optimal purchase timing and advantageous campaign information on Rakuten Market. The information provision unit can also provide optimal comparison information based on the user's personal information. For example, the information provision unit suggests the optimal purchase timing based on the user's credit card information. The information provision unit provides advantageous campaign information based on the user's point program information. The information provision unit provides optimal comparison information based on the user's purchase history.As a result, the comparison site / app according to the embodiment can provide users with optimal comparison information. For example, users can easily understand the best time to purchase and information on advantageous campaigns. Users can enjoy shopping efficiently. Users can compare information on various categories, such as travel reservations and home appliance purchases, on a single platform. This saves users time and effort and allows them to make the best choice.

[0053] The personal information collection unit can collect the user's credit card information, point program information, and past purchase history. The personal information collection unit, for example, collects the user's credit card information. For example, credit card information includes the card number, expiration date, and security code. The personal information collection unit also collects the user's point program information. For example, point program information includes the point balance, point history, and point expiration date. The personal information collection unit also collects the user's past purchase history. For example, purchase history includes the purchase date and time, purchased items, and purchase amount. This makes it possible to collect detailed personal information about the user.

[0054] The analysis unit uses the emotion estimation function to analyze the user's emotion from the purchase history and browsing history, and can collect personal information based on the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the user's purchase history and browsing history. For example, facial expression recognition technology can be used as the emotion estimation function. The analysis unit can also analyze the user's emotion using voice analysis technology. The analysis unit can also analyze the user's emotion using text analysis technology. For example, the analysis unit uses facial expression recognition technology to calculate an emotion score when the user purchases a specific product. The analysis unit uses voice analysis technology to calculate an emotion score when the user browses a specific product. The analysis unit uses text analysis technology to analyze the user's emotion toward a specific product. This makes it possible to collect personal information based on the user's emotion.

[0055] The personal information collection unit can collect more accurate personal information by analyzing social media activity and identifying interests. The personal information collection unit, for example, analyzes social media activity. For example, social media activity may include analyzing the content of posts, the number of likes, and the content of comments. The personal information collection unit also identifies the user's interests. For example, posts about specific brands or products may be analyzed as interests. The personal information collection unit also identifies the user's interests. For example, accounts followed and groups joined may be analyzed as interests. This makes it possible to collect more accurate personal information based on the user's social media activity.

[0056] The personal information collection unit can collect location information in real time and analyze personal information taking into account regional characteristics. The personal information collection unit, for example, collects location information in real time. For example, location information collected may include GPS data, Wi-Fi location information, and beacon data. The personal information collection unit also analyzes personal information taking into account regional characteristics. For example, regional characteristics may include regional demographics, economic conditions, and cultural background. This makes it possible to analyze personal information based on the user's location information.

[0057] The analysis unit collects health data and can propose optimal products and services based on the user's health condition. The analysis unit, for example, collects health data. For example, health data collected may include heart rate, blood pressure, exercise volume, and dietary records. The analysis unit also proposes optimal products and services based on the user's health condition. For example, health data may include health checkup results, self-reported data, and data from wearable devices. This makes it possible to propose optimal products and services based on the user's health condition.

[0058] The analysis unit can collect data from home devices and analyze personal information based on the home environment. The analysis unit, for example, collects data from home devices. For example, home devices include smart speakers, smart home appliances, and security cameras. The analysis unit also analyzes personal information based on the home environment. For example, the home environment can be analyzed based on family composition, residence size, interior style, and the like. This makes it possible to analyze personal information based on data from home devices.

[0059] The analysis unit uses the emotion estimation function to analyze the emotional reaction of the user when reading a specific review, and can collect personal information based on the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when reading a specific review. For example, facial expression recognition technology can be used as the emotion estimation function. The analysis unit can also analyze the user's emotion using voice analysis technology. The analysis unit can also analyze the user's emotion using text analysis technology. For example, the analysis unit uses facial expression recognition technology to calculate an emotion score when the user reads a specific review. The analysis unit uses voice analysis technology to calculate an emotion score when the user reads a specific review. The analysis unit uses text analysis technology to analyze the user's emotion toward a specific review. This makes it possible to collect personal information based on the user's emotional reaction.

[0060] The information providing unit can utilize the emotion estimation function to provide optimal comparison information based on the emotion. The information providing unit, for example, utilizes the emotion estimation function to provide optimal comparison information based on the user's emotion. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also use voice analysis technology to provide comparison information based on the user's emotion. The information providing unit can also use text analysis technology to provide comparison information based on the user's emotion. For example, the information providing unit can use facial expression recognition technology to preferentially display products that the user feels positive about. The information providing unit can use voice analysis technology to preferentially display products that the user feels positive about. The information providing unit can use text analysis technology to preferentially display products that the user feels positive about. This makes it possible to provide optimal comparison information based on the user's emotion.

[0061] The information providing unit analyzes purchase history and market trends, predicts future price fluctuations, and can suggest the optimal timing for purchase. The information providing unit, for example, analyzes purchase history and market trends. For example, past purchase data can be analyzed as purchase history. The information providing unit also analyzes market trends. For example, sales data, search trends, social media topics, etc. can be analyzed as market trends. The information providing unit also predicts future price fluctuations. For example, price fluctuations can be analyzed as past price data, the balance between supply and demand, seasonal factors, etc. This makes it possible to predict future price fluctuations and suggest the optimal timing for purchase.

[0062] The information providing unit can provide customized comparison information based on hobbies and lifestyles. The information providing unit provides, for example, customized comparison information based on hobbies and lifestyles. For example, comparison information can be provided based on hobbies such as sports, music, movies, and reading. The information providing unit also provides comparison information based on lifestyles. For example, comparison information can be provided based on lifestyles such as diet, exercise habits, and frequency of travel. This makes it possible to provide customized comparison information based on the user's hobbies and lifestyles.

[0063] The information providing unit can provide optimal comparative information based on family structure and life stage. The information providing unit can provide optimal comparative information based on, for example, family structure and life stage. For example, the information providing unit can provide comparative information based on family structure such as the number of family members, age composition, and whether or not the family has pets. The information providing unit can also provide comparative information based on life stage. For example, the information providing unit can provide comparative information based on life stage such as student, working adult, retiree, etc. This makes it possible to provide optimal comparative information based on the user's family structure and life stage.

[0064] The information providing unit uses the emotion estimation function to analyze the emotional response of the user when viewing specific comparison information, and can customize the comparison information based on the emotion. The information providing unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing specific comparison information. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also analyze the emotional response of the user using voice analysis technology. The information providing unit can also analyze the emotional response of the user using text analysis technology. For example, the information providing unit uses facial expression recognition technology to calculate an emotion score when the user views the specific comparison information. The information providing unit uses voice analysis technology to calculate an emotion score when the user views the specific comparison information. The information providing unit uses text analysis technology to analyze the emotion the user has toward the specific comparison information. This makes it possible to customize the comparison information based on the user's emotional response.

[0065] The information providing unit can utilize the emotion estimation function to aggregate information of all genres based on emotions. The information providing unit, for example, utilizes the emotion estimation function to aggregate information of all genres based on the user's emotions. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also aggregate information based on the user's emotions using voice analysis technology. The information providing unit can also aggregate information based on the user's emotions using text analysis technology. For example, the information providing unit uses facial expression recognition technology to prioritize aggregate genres for which the user has positive emotions. The information providing unit uses voice analysis technology to prioritize aggregate genres for which the user has positive emotions. The information providing unit uses text analysis technology to prioritize aggregate genres for which the user has positive emotions. This makes it possible to aggregate information of all genres based on the user's emotions.

[0066] The information providing unit can analyze the relevance between different genres and aggregate the most relevant information for the user. The information providing unit, for example, analyzes the relevance between different genres. For example, genres can be analyzed such as music, movies, books, and games. The information providing unit also analyzes the relevance. For example, common themes, user interests, past browsing history, and the like can be analyzed as the relevance. This allows the relevance between different genres to be analyzed and the most relevant information to be aggregated.

[0067] The information providing unit analyzes search history and trends, and can predict and aggregate information on genres whose demand will increase in the future. The information providing unit, for example, analyzes search history and trends. For example, the search history can include search keywords, search date and time, and click history of search results. The information providing unit also analyzes trends. For example, trends can include sales data, search trends, and social media topics. The information providing unit also predicts information on genres whose demand will increase in the future. For example, genres such as music, movies, books, and games can be analyzed. This makes it possible to predict and aggregate information on genres whose demand will increase in the future.

[0068] The information providing unit can aggregate information on genres based on occupations and specialized knowledge, and provide specialized comparative information. The information providing unit aggregates information on genres based on occupations and specialized knowledge, for example. For example, information can be aggregated based on occupations such as doctor, engineer, teacher, artist, etc. The information providing unit also provides information based on specialized knowledge. For example, information can be provided based on qualifications, years of experience, field of expertise, etc. This makes it possible to provide specialized comparative information based on the user's occupation and specialized knowledge.

[0069] The information providing unit can aggregate region-specific information based on travel history and interests, and provide optimal comparison information for each region. The information providing unit aggregates region-specific information based on, for example, travel history and interests. For example, information can be aggregated based on travel history, such as places visited, frequency of travel, and purpose of travel. The information providing unit also provides region-specific information. For example, region-specific information can be provided based on regional tourist information, regional specialty products, regional event information, and the like. This makes it possible to provide region-specific comparison information based on the user's travel history and interests.

[0070] The information providing unit uses the emotion estimation function to analyze the emotional response of the user when viewing information of a specific genre, and can customize the genre information based on the emotion. The information providing unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing information of a specific genre. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also analyze the user's emotional response using voice analysis technology. The information providing unit can also analyze the user's emotional response using text analysis technology. For example, the information providing unit uses facial expression recognition technology to calculate an emotion score when the user views information of a specific genre. The information providing unit uses voice analysis technology to calculate an emotion score when the user views information of a specific genre. The information providing unit uses text analysis technology to analyze the user's emotion regarding information of a specific genre. This makes it possible to customize the genre information based on the user's emotional response.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The analysis unit collects the user's health data and can propose optimal products and services based on the user's health condition. For example, health data collected includes heart rate, blood pressure, exercise volume, and dietary records. The analysis unit also proposes optimal products and services based on the user's health condition. For example, the analysis unit can analyze health checkup results, self-reported data, and data from wearable devices. This makes it possible to propose optimal products and services based on the user's health condition.

[0073] The information providing unit can provide customized comparison information based on the user's hobbies and lifestyle. For example, comparison information can be provided based on hobbies such as sports, music, movies, and reading. The information providing unit can also provide comparison information based on lifestyle. For example, comparison information can be provided based on eating habits, exercise habits, frequency of travel, and the like. This makes it possible to provide customized comparison information based on the user's hobbies and lifestyle.

[0074] The analysis unit can use an emotion estimation function to analyze the emotional reaction of a user when reading a specific review and collect personal information based on the emotion. For example, facial expression recognition technology can be used as the emotion estimation function. The analysis unit can also analyze the user's emotion using voice analysis technology. The analysis unit can also analyze the user's emotion using text analysis technology. For example, the analysis unit can use facial expression recognition technology to calculate an emotion score when a user reads a specific review. The analysis unit can use voice analysis technology to calculate an emotion score when a user reads a specific review. The analysis unit can use text analysis technology to analyze the user's emotion toward a specific review. This makes it possible to collect personal information based on the user's emotional reaction.

[0075] The information providing unit can provide optimal comparative information based on family structure and life stage. For example, comparative information can be provided based on family structure such as the number of family members, age composition, and whether or not the user has pets. The information providing unit also provides comparative information based on life stage. For example, comparative information can be provided based on whether the user is a student, a working adult, or a retiree. This makes it possible to provide optimal comparative information based on the user's family structure and life stage.

[0076] The information providing unit can utilize the emotion estimation function to provide optimal comparison information based on the emotion. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also use voice analysis technology to provide comparison information based on the user's emotion. The information providing unit can also use text analysis technology to provide comparison information based on the user's emotion. For example, the information providing unit can use facial expression recognition technology to preferentially display products that the user feels positive about. The information providing unit can use voice analysis technology to preferentially display products that the user feels positive about. The information providing unit can use text analysis technology to preferentially display products that the user feels positive about. This makes it possible to provide optimal comparison information based on the user's emotion.

[0077] The analysis unit can collect data from home devices and analyze personal information based on the home environment. For example, home devices such as smart speakers, smart home appliances, and security cameras can be collected. The analysis unit also analyzes personal information based on the home environment. For example, the analysis unit can analyze family composition, the size of the home, and the interior style. This makes it possible to analyze personal information based on data from home devices.

[0078] The information providing unit can aggregate region-specific information based on travel history and interests, and provide optimal comparison information for each region. For example, information can be aggregated based on travel history, such as places visited, frequency of travel, and purpose of travel. The information providing unit also provides region-specific information. For example, information can be provided based on regional tourist information, regional specialty products, regional event information, and the like. This makes it possible to provide region-specific comparison information based on the user's travel history and interests.

[0079] The information providing unit can use the emotion estimation function to analyze the emotional response of a user when viewing information of a specific genre, and customize the genre information based on the emotion. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also analyze the user's emotional response using voice analysis technology. The information providing unit can also analyze the user's emotional response using text analysis technology. For example, the information providing unit can use facial expression recognition technology to calculate an emotion score when a user views information of a specific genre. The information providing unit can use voice analysis technology to calculate an emotion score when a user views information of a specific genre. The information providing unit can use text analysis technology to analyze the user's emotion regarding information of a specific genre. This makes it possible to customize the genre information based on the user's emotional response.

[0080] The information providing unit analyzes search history and trends, and can predict and aggregate information on genres that will see increased demand in the future. For example, search history can be analyzed, such as search keywords, search date and time, and click history of search results. The information providing unit also analyzes trends. For example, it can analyze sales data, search trends, and social media topics. The information providing unit also predicts information on genres that will see increased demand in the future. For example, it can analyze music, movies, books, games, etc. This makes it possible to predict and aggregate information on genres that will see increased demand in the future.

[0081] The information providing unit can use the emotion estimation function to analyze the emotional response of the user when viewing specific comparison information, and customize the comparison information based on the emotion. For example, facial expression recognition technology can be used as the emotion estimation function. The information providing unit can also analyze the emotional response of the user using voice analysis technology. The information providing unit can also analyze the emotional response of the user using text analysis technology. For example, the information providing unit can use facial expression recognition technology to calculate an emotion score when the user views the specific comparison information. The information providing unit can use voice analysis technology to calculate an emotion score when the user views the specific comparison information. The information providing unit can use text analysis technology to analyze the emotion the user has toward the specific comparison information. This makes it possible to customize the comparison information based on the user's emotional response.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The personal information collection unit collects personal information about the user, such as which credit card the user uses, which point program the user participates in, and past purchase history. Step 2: The analysis unit analyzes the personal information collected by the personal information collection unit. For example, using generative AI, data mining technology, and machine learning algorithms, the analysis unit analyzes the user's credit card information, point program information, and purchase history to provide optimal comparison information. Step 3: The information provider provides the user with optimal comparison information based on the information analyzed by the analyzer. For example, if the user is a PayPay Card Gold member, the information provider provides the user with optimal purchasing timing and advantageous campaign information on Yahoo Shopping, and if the user is in the Rakuten economic zone, the information provider provides the user with optimal purchasing timing and advantageous campaign information on Rakuten Ichiba.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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, to avoid confusion and 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.

[0150] 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. [Explanation of symbols]

[0151] 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 personal information collection unit that collects personal information of users; an analysis unit that analyzes the personal information collected by the personal information collection unit; an information providing unit that provides optimal comparison information to a user based on the information analyzed by the analysis unit; A system characterized by:

2. The analysis unit Analyzing user emotions from purchase and browsing history and collecting personal information based on emotions 2. The system of claim 1.

3. The personal information collection unit Collect location information in real time and analyze personal information taking into account the characteristics of each region 2. The system of claim 1.

4. The analysis unit Collecting user health data and recommending optimal products or services based on the user's health status 2. The system of claim 1.

5. The information providing unit Utilizing emotion estimation function to provide optimal comparison information based on emotions 2. The system of claim 1.

6. The information providing unit Analyzing the user's emotional response when viewing the specific comparative information and customizing the comparative information based on the emotional response.

2. The system of claim 1.

7. The information providing unit Utilizing emotion estimation functionality to aggregate emotion-based information across all genres 2. The system of claim 1.

8. The information providing unit Analyzing the emotional response of a user when viewing the information in a particular genre, and customizing the information in the genre based on the emotional response.

2. The system of claim 1.

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