System

Abstract

An object of a system according to an embodiment is to analyze a behavior history and a purchase history of a user and provide optimal information.SOLUTION: A system includes an action history collection part, a hobby preference analysis part, and an information provision part. The action history collection unit collects an action history and a purchase history of a user. The hobby and preference analysis unit analyzes the data collected by the action history collection unit and grasps the tendency of the hobby and preference of the user. An information providing part provides optimum information to the user on the basis of the tendency grasped by the hobby taste analysis part.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 technologies have had a problem in that they have not been able to effectively utilize a user's behavioral history and purchase history to provide the user with optimal information.

[0005] The system according to the embodiment aims to analyze a user's behavior history and purchase history and provide optimal information. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral history collection unit, a hobby and preference analysis unit, and an information provision unit. The behavioral history collection unit collects a user's behavioral history and purchase history. The hobby and preference analysis unit analyzes the data collected by the behavioral history collection unit to grasp trends in the user's hobby and preference. The information provision unit provides the user with optimal information based on the trends grasped by the hobby and preference analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's behavior history and purchase history and provide optimal information. [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 concierge service system according to the embodiment of the present invention is a system in which a generation AI analyzes a user's hobbies and preferences based on their behavioral history and purchase history, and provides optimal information. This enables the concierge service system to provide valuable information to users and ensure the sustainability of the service operation.

[0029] A concierge service system according to an embodiment includes a behavioral history collection unit, a hobby / preference analysis unit, and an information provision unit. The behavioral history collection unit collects a user's behavioral history and purchase history. For example, it collects a history of websites the user has visited in the past. The behavioral history collection unit can also collect a history of products purchased by the user. For example, it records the types of products purchased by the user and the purchase dates and times. The behavioral history collection unit can also collect a history of content viewed by the user. For example, it records the types of content frequently viewed by the user. The hobby / preference analysis unit analyzes the data collected by the behavioral history collection unit to understand trends in the user's hobby / preferences. For example, the hobby / preference analysis unit analyzes the product categories frequently purchased by the user. The hobby / preference analysis unit can also analyze the types of content frequently viewed by the user. For example, it analyzes information about new products and events that the user may be interested in. The hobby / preference analysis unit uses a generation AI to analyze the user's hobby / preferences based on the user's behavioral history and purchase history. For example, the generation AI performs analysis based on prompts including the user's behavioral history and purchase history. The information providing unit provides optimal information to the user based on the trends identified by the hobby and preference analysis unit. For example, the information providing unit notifies the user of information about new products and events that are likely to interest the user. The information providing unit can also provide information at a time when the user is most interested. For example, the generation AI generates information based on prompts that include analysis results regarding the user's hobby and preferences. This allows the concierge service system according to the embodiment to provide optimal information based on the user's behavioral history and purchase history. For example, by providing timely information about new products and events that are likely to interest the user, user satisfaction can be increased. Furthermore, advertising revenue and referral fees can be earned to support the operation of the service.

[0030] The behavior history collection unit can collect real-time location information of a user and analyze behavior patterns based on the location information. The behavior history collection unit can collect real-time location information of a user using, for example, GPS data from the user's smartphone. For example, the behavior history collection unit can record location information of stores and event venues visited by the user and analyze behavior patterns based on that data. The behavior history collection unit can also collect real-time location information of a user using Wi-Fi location information. For example, the user's location can be identified based on location information of Wi-Fi access points. The behavior history collection unit can also analyze the user's movement route and identify behavior patterns. For example, it can analyze places frequently visited by the user and movement route patterns. This makes it possible to analyze behavior patterns based on the user's real-time location information. For example, by understanding the frequency and time periods at which a user visits a specific location, it becomes possible to provide more accurate information.

[0031] The behavioral history collection unit collects the user's social media activity history and can analyze the user's interests and preferences from the content of posts or reactions. The behavioral history collection unit, for example, collects the history of posts, comments, and likes from the user's social media account. For example, it analyzes the themes the user frequently posts on and the content that the user responds to to understand the user's interests and preferences. The behavioral history collection unit can also analyze the user's social media activity history using text analysis technology. For example, it can analyze the content of text posted by the user to identify the user's interests and preferences. The behavioral history collection unit can also analyze the user's social media image and video posting history. For example, it can analyze the content of images and videos posted by the user to understand the user's interests and preferences. This makes it possible to analyze the user's interests and preferences based on the user's social media activity history. For example, it can identify content and products that the user is likely to be interested in, enabling more personalized information to be provided.

[0032] The hobby and preference analysis unit can analyze similarities with other users based on their behavioral history and purchase history and refer to the behavioral patterns of similar users. For example, the hobby and preference analysis unit clusters a user's behavioral history and purchase history to identify user groups with similar behavioral patterns. For example, it analyzes the behavioral patterns of a group of users who purchased the same product. The hobby and preference analysis unit can also develop an algorithm that refers to the behavioral patterns of similar users based on the user's behavioral history and purchase history. For example, it predicts products and services that the user is likely to be interested in based on the behavioral patterns of similar users. The hobby and preference analysis unit can also use a generation AI to analyze similarities based on the user's behavioral history and purchase history. For example, the generation AI analyzes similarities based on prompts that include the user's behavioral history and purchase history. This enables more accurate analysis by referring to the behavioral patterns of similar users. For example, it can predict products and services that the user is likely to be interested in, enabling more personalized information to be provided.

[0033] The hobby and preference analysis unit can collect behavioral history and purchase history from different devices and analyze differences in behavior between devices. For example, the hobby and preference analysis unit collects behavioral history and purchase history from different devices used by a user and analyzes behavioral patterns for each device. For example, it can identify differences between behavior on a smartphone and behavior on a PC. The hobby and preference analysis unit can also develop algorithms to analyze differences in behavior between a user's devices. For example, it can analyze the behavior of a user on different devices. The hobby and preference analysis unit can also use a generative AI to analyze differences in behavior between devices based on the user's behavioral history and purchase history. For example, the generative AI can analyze differences in behavior between devices based on prompts including the user's behavioral history and purchase history. This allows data from different devices to be integrated and differences in behavior to be analyzed. For example, by understanding the differences between the behavior a user often performs on a smartphone and the behavior they often perform on a PC, it becomes possible to provide more accurate information.

[0034] The hobby and preference analysis unit can analyze variations in hobby and preference by season and time of day based on the user's behavioral history and purchase history. The hobby and preference analysis unit, for example, analyzes the user's behavioral history and purchase history to identify seasonal variations in hobby and preference. For example, it analyzes products purchased in summer and content viewed in winter. The hobby and preference analysis unit can also analyze variations in hobby and preference by time of day based on the user's behavioral history and purchase history. For example, it analyzes the types of content viewed by the user during a specific time of day. The hobby and preference analysis unit can also use the generation AI to analyze variations in hobby and preference by season and time of day based on the user's behavioral history and purchase history. For example, the generation AI analyzes variations in hobby and preference by season and time of day based on prompts including the user's behavioral history and purchase history. This makes it possible to analyze variations in hobby and preference by season and time of day. For example, by identifying products that the user is interested in in summer and content that the user is interested in in winter, it becomes possible to provide more personalized information.

[0035] When analyzing hobbies and preferences, the hobbies and preferences analysis unit can refer to past trend data and predict future changes in hobbies and preferences. The hobbies and preferences analysis unit, for example, collects past trend data and analyzes changes in the user's hobbies and preferences. For example, it identifies trends based on purchase history and browsing history over the past few years. The hobbies and preferences analysis unit can also develop an algorithm that predicts future changes in hobbies and preferences based on past trend data. For example, it predicts products and services that the user will be interested in next. The hobbies and preferences analysis unit can also predict future changes in hobbies and preferences based on past trend data using a generation AI. For example, the generation AI predicts future changes in hobbies and preferences based on prompts that include past trend data. This makes it possible to predict future changes in hobbies and preferences based on past trend data. For example, it is possible to predict products and services that the user will be interested in next, thereby providing more personalized information.

[0036] The hobby and preference analysis unit can classify the analysis results of the hobby and preference by different age groups and genders to clarify trends for each target demographic. For example, the hobby and preference analysis unit can classify the analysis results of the user's hobby and preference by age group to identify trends for each age group. For example, it can analyze the different hobby and preference profiles for young people and middle-aged and older people. The hobby and preference analysis unit can also classify the analysis results of the user's hobby and preference by gender to identify trends for each gender. For example, it can analyze the different hobby and preference profiles for men and women. The hobby and preference analysis unit can also use a generation AI to classify the analysis results of the user's hobby and preference by age group and gender. For example, the generation AI performs classification based on prompts that include the analysis results of the user's hobby and preference. This can clarify trends in hobby and preference profiles for different age groups and genders. For example, by understanding the types of hobby and preference profiles for specific age groups and genders, it becomes possible to provide information more tailored to the target demographic.

[0037] The hobby and preference analysis unit can compare the analysis results of the hobby and preference between different regions and cultural spheres to reveal regional trends. For example, the hobby and preference analysis unit classifies the analysis results of the user's hobby and preference by region to identify regional trends. For example, it analyzes the different hobby and preference patterns in urban and rural areas. The hobby and preference analysis unit can also classify the analysis results of the user's hobby and preference by cultural sphere to identify trends in each cultural sphere. For example, it analyzes purchasing and browsing trends in different cultural spheres. The hobby and preference analysis unit can also use a generation AI to classify the analysis results of the user's hobby and preference by region or cultural sphere. For example, the generation AI performs classification based on prompts that include the analysis results of the user's hobby and preference. This can reveal the trends in hobby and preference in different regions and cultural spheres. For example, by understanding the types of hobby and preference that exist in specific regions or cultural spheres, it becomes possible to provide more regionally specific information.

[0038] The information provision unit can analyze the user's reactions to information received in the past and provide new information based on the characteristics of the information that generated a favorable response. The information provision unit, for example, analyzes the click rate and viewing time for information received in the past by the user to identify the characteristics of the information that generated a favorable response. For example, it can understand the tendency for information on a particular topic or format to be preferred. The information provision unit can also develop an algorithm that provides new information based on the user's reaction data. For example, it generates new information based on the characteristics of information to which the user responded favorably in the past. The information provision unit can also use a generation AI to provide new information based on the user's reaction data. For example, the generation AI generates information based on prompts that include the user's reaction data. This makes it possible to provide new information based on past reaction data. For example, it becomes possible to provide more personalized information based on the characteristics of information to which the user responded favorably in the past.

[0039] The information providing unit can provide information in different formats based on the user's preferences and identify the most effective format. For example, the information providing unit can provide information in different formats, such as text, images, and videos, based on the user's preferences and analyze the user's reactions. For example, it can identify the format to which the user responds most. The information providing unit can also develop an algorithm to identify the most effective format based on user reaction data. For example, it can analyze the strength of the user's reaction to a specific format. The information providing unit can also use a generation AI to identify the most effective format based on the user reaction data. For example, the generation AI can perform analysis based on prompts that include the user's reaction data. This makes it possible to provide information in different formats and identify the most effective format. For example, if a user responds most to information in text format, preferentially providing that format can improve how the information is received and enable more effective information provision.

[0040] The information provision unit can optimize the frequency of information provision based on the user's hobbies and preferences, and avoid providing excessive information. The information provision unit, for example, develops an algorithm that optimizes the frequency of information provision based on the user's hobbies and preferences. For example, it identifies the frequency at which the user is most likely to receive information. The information provision unit can also optimize the frequency of information provision based on user reaction data. For example, it analyzes the reaction the user shows to excessive information provision and sets the optimal frequency. The information provision unit can also optimize the frequency of information provision based on user reaction data using a generation AI. For example, the generation AI performs analysis based on prompts including the user's reaction data and sets the optimal frequency. This makes it possible to optimize the frequency of information provision and avoid excessive information provision. For example, by providing information at a frequency at which the user is most likely to receive information, the way the information is received is improved, and more effective information provision is possible.

[0041] The information providing unit can personalize relevant advertisements based on the user's interests and preferences, thereby improving the click-through rate of advertisements. The information providing unit, for example, develops an algorithm for displaying personalized advertisements based on the user's interests and preferences. For example, advertisements for products and services that are likely to interest the user are preferentially displayed. The information providing unit can also develop a personalization method for improving the click-through rate of advertisements based on user response data. For example, the information providing unit can analyze the characteristics of advertisements that the user has previously clicked and generate new advertisements based on those characteristics. The information providing unit can also personalize advertisements based on the user's interests and preferences using a generation AI. For example, the generation AI generates advertisements based on prompts that include the user's interest and preference data. This can personalize relevant advertisements and improve the click-through rate of advertisements. For example, displaying advertisements for products and services that the user is likely to be interested in increases the effectiveness of the advertisements and improves the click-through rate.

[0042] The information providing unit can predict future purchasing intent based on the user's behavioral history and purchase history, and display advertisements based on the prediction. The information providing unit, for example, develops an algorithm that predicts future purchasing intent based on the user's behavioral history and purchase history. For example, it predicts the product the user is likely to purchase next. The information providing unit can also develop a method for displaying advertisements based on the user's purchasing intent. For example, it displays advertisements related to the product the user is likely to purchase next. The information providing unit can also use a generation AI to predict future purchasing intent based on the user's behavioral history and purchase history. For example, the generation AI predicts purchasing intent based on prompts including the user's behavioral history and purchase history. This makes it possible to predict future purchasing intent and display advertisements based on the prediction. For example, displaying advertisements related to the product the user is likely to purchase next increases the effectiveness of the advertisements and improves purchasing intent.

[0043] The information providing unit can display advertisements in different industries and fields based on the user's hobbies and preferences, thereby discovering new market needs. The information providing unit, for example, develops an algorithm that displays advertisements in different industries and fields based on the user's hobbies and preferences. For example, it displays advertisements in new fields that the user is likely to be interested in. The information providing unit can also develop a method for discovering new market needs based on user response data. For example, it can analyze the user's response to a new field and display advertisements related to that field. The information providing unit can also use a generation AI to display advertisements in different industries and fields based on the user's hobbies and preferences. For example, the generation AI generates advertisements based on prompts that include the user's hobbies and preferences data. This makes it possible to display advertisements in different industries and fields and discover new market needs. For example, displaying advertisements in new fields that the user is likely to be interested in can discover new market needs and increase the effectiveness of advertisements.

[0044] The information providing unit can cross-sell or up-sell related products based on the user's behavioral history or purchase history. The information providing unit, for example, develops an algorithm for cross-selling or up-selling related products based on the user's behavioral history or purchase history. For example, it suggests products related to products purchased by the user. The information providing unit can also develop a method for cross-selling or up-selling based on user response data. For example, it suggests higher-end models or additional features related to products previously purchased by the user. The information providing unit can also use a generation AI to cross-sell or up-sell based on the user's behavioral history or purchase history. For example, the generation AI cross-sells or up-sells based on prompts including the user's behavioral history or purchase history. This makes it possible to cross-sell or up-sell related products. For example, suggesting products related to products purchased by the user can increase purchasing motivation and increase sales.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The concierge service system can estimate a user's health condition and provide health-related information based on the user's behavioral history and purchase history. For example, the behavioral history collection unit collects records of the user's exercise and diet and estimates the user's health condition. The hobby and preference analysis unit can analyze the user's health-related data and grasp health-related trends. For example, it can analyze whether the user is interested in health. The information provision unit can provide health-related information and increase the user's health awareness. For example, it can suggest healthy meals and exercises to the user.

[0047] The concierge service system can estimate a user's learning style based on the user's behavioral history and purchase history, and provide information related to learning. For example, the behavioral history collection unit collects a history of the learning content viewed by the user and the learning materials purchased by the user. Furthermore, the hobby and preference analysis unit can analyze the user's learning style and suggest the optimal learning method. For example, it can analyze whether the user prefers visual learning or auditory learning. The information provision unit can suggest the optimal learning method and materials to the user, thereby improving the learning effect. For example, it can provide the user with visual learning content and auditory learning content.

[0048] The concierge service system can estimate a user's travel preferences based on the user's behavioral history and purchase history, and provide travel-related information. For example, the behavioral history collection unit collects a history of travel destinations visited by the user and travel-related products purchased by the user. Furthermore, the hobby and preference analysis unit can analyze the user's travel preferences and suggest optimal travel destinations and travel plans. For example, it analyzes whether the user prefers nature or urban sightseeing. The information provision unit can suggest optimal travel destinations and travel plans to the user, thereby increasing travel satisfaction. For example, it can suggest to the user travel destinations rich in nature or travel destinations suitable for urban sightseeing.

[0049] The concierge service system can recommend communities related to a user's hobbies and preferences based on the user's behavioral history and purchase history. For example, the behavioral history collection unit collects a history of events the user has attended and hobby-related products purchased by the user. The hobby and preference analysis unit can analyze the user's hobbies and preferences and identify related communities. For example, it can analyze whether the user is interested in a particular hobby. The information provision unit can suggest related communities to the user and enrich the user's hobby activities. For example, it can suggest online communities and events related to the user's hobby to the user.

[0050] The concierge service system can estimate a user's lifestyle based on the user's behavioral history and purchase history, and provide information about the lifestyle. For example, the behavioral history collection unit collects the user's lifestyle habits and the history of lifestyle-related products purchased. Furthermore, the hobby and preference analysis unit can analyze the user's lifestyle and make optimal lifestyle suggestions. For example, it analyzes whether the user is health-conscious or relaxation-conscious. The information provision unit can make optimal lifestyle suggestions to the user and improve their quality of life. For example, it can suggest healthy lifestyle habits and relaxing activities to the user.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The behavioral history collection unit collects the user's behavioral history and purchase history. For example, it records the history of websites the user has visited in the past, the types of products purchased, the date and time of purchase, and the types of content viewed. Step 2: The hobby and preference analysis unit analyzes the data collected by the behavioral history collection unit to understand the user's hobby and preference trends. For example, it analyzes the product categories that the user frequently purchases and the types of content that they frequently view, and uses generation AI to analyze the user's hobby and preference based on the user's behavioral history and purchase history. Step 3: The information provision unit provides the user with the most appropriate information based on the trends identified by the hobby and preference analysis unit. For example, it notifies the user of new products and event information that may interest the user, and provides the information at the time when the user is most interested.

[0053] (Example 2) The concierge service system according to the embodiment of the present invention is a system in which a generation AI analyzes a user's hobbies and preferences based on their behavioral history and purchase history, and provides optimal information. This enables the concierge service system to provide valuable information to users and ensure the sustainability of the service operation.

[0054] A concierge service system according to an embodiment includes a behavioral history collection unit, a hobby / preference analysis unit, and an information provision unit. The behavioral history collection unit collects a user's behavioral history and purchase history. For example, it collects a history of websites the user has visited in the past. The behavioral history collection unit can also collect a history of products purchased by the user. For example, it records the types of products purchased by the user and the purchase dates and times. The behavioral history collection unit can also collect a history of content viewed by the user. For example, it records the types of content frequently viewed by the user. The hobby / preference analysis unit analyzes the data collected by the behavioral history collection unit to understand trends in the user's hobby / preferences. For example, the hobby / preference analysis unit analyzes the product categories frequently purchased by the user. The hobby / preference analysis unit can also analyze the types of content frequently viewed by the user. For example, it analyzes information about new products and events that the user may be interested in. The hobby / preference analysis unit uses a generation AI to analyze the user's hobby / preferences based on the user's behavioral history and purchase history. For example, the generation AI performs analysis based on prompts including the user's behavioral history and purchase history. The information providing unit provides optimal information to the user based on the trends identified by the hobby and preference analysis unit. For example, the information providing unit notifies the user of information about new products and events that are likely to interest the user. The information providing unit can also provide information at a time when the user is most interested. For example, the generation AI generates information based on prompts that include analysis results regarding the user's hobby and preferences. This allows the concierge service system according to the embodiment to provide optimal information based on the user's behavioral history and purchase history. For example, by providing timely information about new products and events that are likely to interest the user, user satisfaction can be increased. Furthermore, advertising revenue and referral fees can be earned to support the operation of the service.

[0055] The behavior history collection unit can collect real-time location information of a user and analyze behavior patterns based on the location information. The behavior history collection unit can collect real-time location information of a user using, for example, GPS data from the user's smartphone. For example, the behavior history collection unit can record location information of stores and event venues visited by the user and analyze behavior patterns based on that data. The behavior history collection unit can also collect real-time location information of a user using Wi-Fi location information. For example, the user's location can be identified based on location information of Wi-Fi access points. The behavior history collection unit can also analyze the user's movement route and identify behavior patterns. For example, it can analyze places frequently visited by the user and movement route patterns. This makes it possible to analyze behavior patterns based on the user's real-time location information. For example, by understanding the frequency and time periods at which a user visits a specific location, it becomes possible to provide more accurate information.

[0056] The behavioral history collection unit collects the user's social media activity history and can analyze the user's interests and preferences from the content of posts or reactions. The behavioral history collection unit, for example, collects the history of posts, comments, and likes from the user's social media account. For example, it analyzes the themes the user frequently posts on and the content that the user responds to to understand the user's interests and preferences. The behavioral history collection unit can also analyze the user's social media activity history using text analysis technology. For example, it can analyze the content of text posted by the user to identify the user's interests and preferences. The behavioral history collection unit can also analyze the user's social media image and video posting history. For example, it can analyze the content of images and videos posted by the user to understand the user's interests and preferences. This makes it possible to analyze the user's interests and preferences based on the user's social media activity history. For example, it can identify content and products that the user is likely to be interested in, enabling more personalized information to be provided.

[0057] The behavioral history collection unit can use the emotion estimation function to estimate the emotion of a user when performing an action or making a purchase, and collect the emotion data along with the behavioral history. For example, the behavioral history collection unit analyzes reviews and comments on a product purchased by a user and estimates the emotion using the emotion estimation function. For example, if a user purchases a product with many positive reviews, the emotion data is added to the behavioral history. The behavioral history collection unit can also capture the user's facial expression when performing an action with a camera and analyze the emotion using an emotion estimation algorithm. For example, the behavioral history collection unit can analyze the user's facial expression when visiting a specific place and add the emotion data to the behavioral history. The behavioral history collection unit can also record the user's voice when performing an action and estimate the emotion using voice analysis technology. For example, the behavioral history collection unit can analyze the voice of a user when participating in a specific event and add the emotion data to the behavioral history. In this way, the user's emotion at the time of the action or purchase can be estimated and collected along with the behavioral history. For example, by understanding the user's emotion regarding a specific action or purchase, it is possible to provide more accurate information.

[0058] The hobby and preference analysis unit can analyze similarities with other users based on their behavioral history and purchase history and refer to the behavioral patterns of similar users. For example, the hobby and preference analysis unit clusters a user's behavioral history and purchase history to identify user groups with similar behavioral patterns. For example, it analyzes the behavioral patterns of a group of users who purchased the same product. The hobby and preference analysis unit can also develop an algorithm that refers to the behavioral patterns of similar users based on the user's behavioral history and purchase history. For example, it predicts products and services that the user is likely to be interested in based on the behavioral patterns of similar users. The hobby and preference analysis unit can also use a generation AI to analyze similarities based on the user's behavioral history and purchase history. For example, the generation AI analyzes similarities based on prompts that include the user's behavioral history and purchase history. This enables more accurate analysis by referring to the behavioral patterns of similar users. For example, it can predict products and services that the user is likely to be interested in, enabling more personalized information to be provided.

[0059] The hobby and preference analysis unit can collect behavioral history and purchase history from different devices and analyze differences in behavior between devices. For example, the hobby and preference analysis unit collects behavioral history and purchase history from different devices used by a user and analyzes behavioral patterns for each device. For example, it can identify differences between behavior on a smartphone and behavior on a PC. The hobby and preference analysis unit can also develop algorithms to analyze differences in behavior between a user's devices. For example, it can analyze the behavior of a user on different devices. The hobby and preference analysis unit can also use a generative AI to analyze differences in behavior between devices based on the user's behavioral history and purchase history. For example, the generative AI can analyze differences in behavior between devices based on prompts including the user's behavioral history and purchase history. This allows data from different devices to be integrated and differences in behavior to be analyzed. For example, by understanding the differences between the behavior a user often performs on a smartphone and the behavior they often perform on a PC, it becomes possible to provide more accurate information.

[0060] The hobby and preference analysis unit can analyze variations in hobby and preference by season and time of day based on the user's behavioral history and purchase history. The hobby and preference analysis unit, for example, analyzes the user's behavioral history and purchase history to identify seasonal variations in hobby and preference. For example, it analyzes products purchased in summer and content viewed in winter. The hobby and preference analysis unit can also analyze variations in hobby and preference by time of day based on the user's behavioral history and purchase history. For example, it analyzes the types of content viewed by the user during a specific time of day. The hobby and preference analysis unit can also use the generation AI to analyze variations in hobby and preference by season and time of day based on the user's behavioral history and purchase history. For example, the generation AI analyzes variations in hobby and preference by season and time of day based on prompts including the user's behavioral history and purchase history. This makes it possible to analyze variations in hobby and preference by season and time of day. For example, by identifying products that the user is interested in in summer and content that the user is interested in in winter, it becomes possible to provide more personalized information.

[0061] When analyzing hobbies and preferences, the hobbies and preferences analysis unit can refer to past trend data and predict future changes in hobbies and preferences. The hobbies and preferences analysis unit, for example, collects past trend data and analyzes changes in the user's hobbies and preferences. For example, it identifies trends based on purchase history and browsing history over the past few years. The hobbies and preferences analysis unit can also develop an algorithm that predicts future changes in hobbies and preferences based on past trend data. For example, it predicts products and services that the user will be interested in next. The hobbies and preferences analysis unit can also predict future changes in hobbies and preferences based on past trend data using a generation AI. For example, the generation AI predicts future changes in hobbies and preferences based on prompts that include past trend data. This makes it possible to predict future changes in hobbies and preferences based on past trend data. For example, it is possible to predict products and services that the user will be interested in next, thereby providing more personalized information.

[0062] The hobby and preference analysis unit can use the emotion estimation function to analyze a user's emotional response to a hobby or preference and understand trends in the hobby or preference based on the emotion. For example, the hobby and preference analysis unit analyzes the emotion a user shows toward a specific hobby or preference and understands trends in the hobby or preference based on the data. For example, it identifies content for which the user shows positive emotions. The hobby and preference analysis unit can also use the emotion estimation function to develop an algorithm for analyzing a user's emotional response to a hobby or preference. For example, it analyzes the intensity of the emotion a user shows toward a specific hobby or preference. The hobby and preference analysis unit can also use a generation AI to analyze a user's emotional response to a hobby or preference. For example, the generation AI performs analysis based on prompts that include the user's emotional response. This makes it possible to understand trends in the hobby or preference based on the user's emotional response. For example, by understanding how a user feels about a specific hobby or preference, it becomes possible to provide more personalized information.

[0063] The hobby and preference analysis unit can classify the analysis results of the hobby and preference by different age groups and genders to clarify trends for each target demographic. For example, the hobby and preference analysis unit can classify the analysis results of the user's hobby and preference by age group to identify trends for each age group. For example, it can analyze the different hobby and preference profiles for young people and middle-aged and older people. The hobby and preference analysis unit can also classify the analysis results of the user's hobby and preference by gender to identify trends for each gender. For example, it can analyze the different hobby and preference profiles for men and women. The hobby and preference analysis unit can also use a generation AI to classify the analysis results of the user's hobby and preference by age group and gender. For example, the generation AI performs classification based on prompts that include the analysis results of the user's hobby and preference. This can clarify trends in hobby and preference profiles for different age groups and genders. For example, by understanding the types of hobby and preference profiles for specific age groups and genders, it becomes possible to provide information more tailored to the target demographic.

[0064] The hobby and preference analysis unit can compare the analysis results of the hobby and preference between different regions and cultural spheres to reveal regional trends. For example, the hobby and preference analysis unit classifies the analysis results of the user's hobby and preference by region to identify regional trends. For example, it analyzes the different hobby and preference patterns in urban and rural areas. The hobby and preference analysis unit can also classify the analysis results of the user's hobby and preference by cultural sphere to identify trends in each cultural sphere. For example, it analyzes purchasing and browsing trends in different cultural spheres. The hobby and preference analysis unit can also use a generation AI to classify the analysis results of the user's hobby and preference by region or cultural sphere. For example, the generation AI performs classification based on prompts that include the analysis results of the user's hobby and preference. This can reveal the trends in hobby and preference in different regions and cultural spheres. For example, by understanding the types of hobby and preference that exist in specific regions or cultural spheres, it becomes possible to provide more regionally specific information.

[0065] The hobby and preference analysis unit can use the emotion estimation function to monitor the user's emotional reactions to the hobby and preference in real time and update the analysis results based on that data. The hobby and preference analysis unit, for example, uses the emotion estimation function to monitor the user's emotional reactions to the hobby and preference in real time. For example, it analyzes the emotions a user feels when viewing specific content. The hobby and preference analysis unit can also develop an algorithm that updates the analysis results based on the user's emotional reactions. For example, it analyzes changes in the emotions a user shows toward specific hobby and preference in real time. The hobby and preference analysis unit can also use the generation AI to update the analysis results based on the user's emotional reactions. For example, the generation AI performs analysis based on prompts that include the user's emotional reactions and updates the results. This allows the user's emotional reactions to be monitored in real time and the analysis results to be updated. For example, by understanding the user's emotions toward specific hobby and preference in real time, it becomes possible to provide more accurate information.

[0066] The information provision unit can analyze the user's reactions to information received in the past and provide new information based on the characteristics of the information that generated a favorable response. The information provision unit, for example, analyzes the click rate and viewing time for information received in the past by the user to identify the characteristics of the information that generated a favorable response. For example, it can understand the tendency for information on a particular topic or format to be preferred. The information provision unit can also develop an algorithm that provides new information based on the user's reaction data. For example, it generates new information based on the characteristics of information to which the user responded favorably in the past. The information provision unit can also use a generation AI to provide new information based on the user's reaction data. For example, the generation AI generates information based on prompts that include the user's reaction data. This makes it possible to provide new information based on past reaction data. For example, it becomes possible to provide more personalized information based on the characteristics of information to which the user responded favorably in the past.

[0067] The information provision unit can use the emotion estimation function to estimate the emotion a user has when receiving information and provide information that elicits positive emotions. The information provision unit, for example, analyzes the user's facial expression and voice when receiving information to estimate the emotion. For example, a camera captures the user's facial expression when viewing information and provides information based on that emotion data. The information provision unit can also develop an algorithm to provide information that elicits positive emotions based on the emotion a user has when receiving information. For example, it can identify characteristics of information that indicate positive emotions in the user and generate new information based on those characteristics. The information provision unit can also use a generation AI to provide information based on the user's emotion data. For example, the generation AI generates information based on prompts that include the user's emotion data. This makes it possible to estimate the user's emotion and provide information that elicits positive emotions. For example, if a user has positive emotions when receiving information, the way they receive information can be improved, enabling more effective information provision.

[0068] The information providing unit can provide information in different formats based on the user's preferences and identify the most effective format. For example, the information providing unit can provide information in different formats, such as text, images, and videos, based on the user's preferences and analyze the user's reactions. For example, it can identify the format to which the user responds most. The information providing unit can also develop an algorithm to identify the most effective format based on user reaction data. For example, it can analyze the strength of the user's reaction to a specific format. The information providing unit can also use a generation AI to identify the most effective format based on the user reaction data. For example, the generation AI can perform analysis based on prompts that include the user's reaction data. This makes it possible to provide information in different formats and identify the most effective format. For example, if a user responds most to information in text format, preferentially providing that format can improve how the information is received and enable more effective information provision.

[0069] The information provision unit can optimize the frequency of information provision based on the user's hobbies and preferences, and avoid providing excessive information. The information provision unit, for example, develops an algorithm that optimizes the frequency of information provision based on the user's hobbies and preferences. For example, it identifies the frequency at which the user is most likely to receive information. The information provision unit can also optimize the frequency of information provision based on user reaction data. For example, it analyzes the reaction the user shows to excessive information provision and sets the optimal frequency. The information provision unit can also optimize the frequency of information provision based on user reaction data using a generation AI. For example, the generation AI performs analysis based on prompts including the user's reaction data and sets the optimal frequency. This makes it possible to optimize the frequency of information provision and avoid excessive information provision. For example, by providing information at a frequency at which the user is most likely to receive information, the way the information is received is improved, and more effective information provision is possible.

[0070] The information provision unit can use the emotion estimation function to monitor the emotion of the user when receiving information in real time and provide information according to the emotion. For example, the information provision unit can use the emotion estimation function to monitor the emotion of the user when receiving information in real time. For example, it can prioritize providing information that indicates a positive emotion to the user. The information provision unit can also develop an algorithm to provide information according to the emotion based on the user's emotion data. For example, it can identify characteristics of information that indicate a specific emotion to the user and generate new information based on those characteristics. The information provision unit can also use a generation AI to provide information based on the user's emotion data. For example, the generation AI can generate information based on prompts that include the user's emotion data. This makes it possible to monitor the user's emotion in real time and provide information according to the emotion. For example, if a user has positive emotions when receiving information, the way they receive information will improve, and more effective information can be provided.

[0071] The information providing unit can personalize relevant advertisements based on the user's interests and preferences, thereby improving the click-through rate of advertisements. The information providing unit, for example, develops an algorithm for displaying personalized advertisements based on the user's interests and preferences. For example, advertisements for products and services that are likely to interest the user are preferentially displayed. The information providing unit can also develop a personalization method for improving the click-through rate of advertisements based on user response data. For example, the information providing unit can analyze the characteristics of advertisements that the user has previously clicked and generate new advertisements based on those characteristics. The information providing unit can also personalize advertisements based on the user's interests and preferences using a generation AI. For example, the generation AI generates advertisements based on prompts that include the user's interest and preference data. This can personalize relevant advertisements and improve the click-through rate of advertisements. For example, displaying advertisements for products and services that the user is likely to be interested in increases the effectiveness of the advertisements and improves the click-through rate.

[0072] The information providing unit can predict future purchasing intent based on the user's behavioral history and purchase history, and display advertisements based on the prediction. The information providing unit, for example, develops an algorithm that predicts future purchasing intent based on the user's behavioral history and purchase history. For example, it predicts the product the user is likely to purchase next. The information providing unit can also develop a method for displaying advertisements based on the user's purchasing intent. For example, it displays advertisements related to the product the user is likely to purchase next. The information providing unit can also use a generation AI to predict future purchasing intent based on the user's behavioral history and purchase history. For example, the generation AI predicts purchasing intent based on prompts including the user's behavioral history and purchase history. This makes it possible to predict future purchasing intent and display advertisements based on the prediction. For example, displaying advertisements related to the product the user is likely to purchase next increases the effectiveness of the advertisements and improves purchasing intent.

[0073] The information providing unit can display advertisements in different industries and fields based on the user's hobbies and preferences, thereby discovering new market needs. The information providing unit, for example, develops an algorithm that displays advertisements in different industries and fields based on the user's hobbies and preferences. For example, it displays advertisements in new fields that the user is likely to be interested in. The information providing unit can also develop a method for discovering new market needs based on user response data. For example, it can analyze the user's response to a new field and display advertisements related to that field. The information providing unit can also use a generation AI to display advertisements in different industries and fields based on the user's hobbies and preferences. For example, the generation AI generates advertisements based on prompts that include the user's hobbies and preferences data. This makes it possible to display advertisements in different industries and fields and discover new market needs. For example, displaying advertisements in new fields that the user is likely to be interested in can discover new market needs and increase the effectiveness of advertisements.

[0074] The information providing unit can cross-sell or up-sell related products based on the user's behavioral history or purchase history. The information providing unit, for example, develops an algorithm for cross-selling or up-selling related products based on the user's behavioral history or purchase history. For example, it suggests products related to products purchased by the user. The information providing unit can also develop a method for cross-selling or up-selling based on user response data. For example, it suggests higher-end models or additional features related to products previously purchased by the user. The information providing unit can also use a generation AI to cross-sell or up-sell based on the user's behavioral history or purchase history. For example, the generation AI cross-sells or up-sells based on prompts including the user's behavioral history or purchase history. This makes it possible to cross-sell or up-sell related products. For example, suggesting products related to products purchased by the user can increase purchasing motivation and increase sales.

[0075] The information providing unit can use the emotion estimation function to monitor the emotions of users when they view an advertisement in real time and display advertisements that correspond to those emotions. The information providing unit, for example, uses the emotion estimation function to monitor the emotions of users when they view an advertisement in real time. For example, advertisements that show positive emotions to the user are preferentially displayed. The information providing unit can also develop a method for displaying advertisements that correspond to emotions based on the user's emotion data. For example, the information providing unit can identify characteristics of advertisements that show specific emotions to the user and generate new advertisements based on those characteristics. The information providing unit can also use a generation AI to display advertisements based on the user's emotion data. For example, the generation AI generates advertisements based on prompts that include the user's emotion data. This makes it possible to monitor the user's emotions in real time and display advertisements that correspond to those emotions. For example, if a user feels positive emotions when viewing an advertisement, the effectiveness of the advertisement will increase and the click-through rate will improve.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The concierge service system can estimate a user's health condition and provide health-related information based on the user's behavioral history and purchase history. For example, the behavioral history collection unit collects records of the user's exercise and diet and estimates the user's health condition. The hobby and preference analysis unit can analyze the user's health-related data and grasp health-related trends. For example, it can analyze whether the user is interested in health. The information provision unit can provide health-related information and increase the user's health awareness. For example, it can suggest healthy meals and exercises to the user.

[0078] The concierge service system can estimate a user's learning style based on the user's behavioral history and purchase history, and provide information related to learning. For example, the behavioral history collection unit collects a history of the learning content viewed by the user and the learning materials purchased by the user. Furthermore, the hobby and preference analysis unit can analyze the user's learning style and suggest the optimal learning method. For example, it can analyze whether the user prefers visual learning or auditory learning. The information provision unit can suggest the optimal learning method and materials to the user, thereby improving the learning effect. For example, it can provide the user with visual learning content and auditory learning content.

[0079] The concierge service system can estimate a user's travel preferences based on the user's behavioral history and purchase history, and provide travel-related information. For example, the behavioral history collection unit collects a history of travel destinations visited by the user and travel-related products purchased by the user. Furthermore, the hobby and preference analysis unit can analyze the user's travel preferences and suggest optimal travel destinations and travel plans. For example, it analyzes whether the user prefers nature or urban sightseeing. The information provision unit can suggest optimal travel destinations and travel plans to the user, thereby increasing travel satisfaction. For example, it can suggest to the user travel destinations rich in nature or travel destinations suitable for urban sightseeing.

[0080] The concierge service system can recommend communities related to a user's hobbies and preferences based on the user's behavioral history and purchase history. For example, the behavioral history collection unit collects a history of events the user has attended and hobby-related products purchased by the user. The hobby and preference analysis unit can analyze the user's hobbies and preferences and identify related communities. For example, it can analyze whether the user is interested in a particular hobby. The information provision unit can suggest related communities to the user and enrich the user's hobby activities. For example, it can suggest online communities and events related to the user's hobby to the user.

[0081] The concierge service system can estimate a user's lifestyle based on the user's behavioral history and purchase history, and provide information about the lifestyle. For example, the behavioral history collection unit collects the user's lifestyle habits and the history of lifestyle-related products purchased. Furthermore, the hobby and preference analysis unit can analyze the user's lifestyle and make optimal lifestyle suggestions. For example, it analyzes whether the user is health-conscious or relaxation-conscious. The information provision unit can make optimal lifestyle suggestions to the user and improve their quality of life. For example, it can suggest healthy lifestyle habits and relaxing activities to the user.

[0082] The concierge service system can estimate a user's emotions based on the user's behavioral history and purchase history, and provide music that matches the emotions. For example, the behavioral history collection unit collects a history of music listened to by the user and music-related products purchased by the user. The hobby and preference analysis unit can analyze the user's emotions and identify music that matches the emotions. For example, it analyzes the music that the user listens to when they want to relax and the music that the user listens to when they want to cheer up. The information provision unit can suggest music that matches the user's emotions and improve their mood. For example, it can suggest relaxing music or uplifting music to the user.

[0083] The concierge service system can estimate a user's emotions based on the user's behavioral history and purchase history, and provide movies and dramas that match the emotions. For example, the behavioral history collection unit collects a history of movies and dramas that the user has watched. Furthermore, the hobby and preference analysis unit can analyze the user's emotions and identify movies and dramas that match the emotions. For example, it analyzes the movies that the user watches when they want to be moved, or the dramas that the user watches when they want to laugh. The information provision unit can suggest movies and dramas that match the user's emotions, improving the entertainment experience. For example, it can suggest movies that will move the user or dramas that will make the user laugh.

[0084] The concierge service system can estimate a user's emotions based on the user's behavioral history and purchase history, and provide a reading list that matches the emotions. For example, the behavioral history collection unit collects a history of books the user has read and purchased. The hobby and preference analysis unit can analyze the user's emotions and identify a reading list that matches the emotions. For example, it analyzes books that the user reads when they want to relax and books that the user reads when they are seeking stimulation. The information provision unit can suggest a reading list that matches the user's emotions, improving the reading experience. For example, it can suggest relaxing books and stimulating books to the user.

[0085] The concierge service system can estimate a user's emotions based on the user's behavioral history and purchase history, and provide recipes that correspond to the emotions. For example, the behavioral history collection unit collects a history of dishes made by the user and ingredients purchased by the user. Furthermore, the hobby and preference analysis unit can analyze the user's emotions and identify recipes that correspond to the emotions. For example, it analyzes dishes that the user makes when they want to relax and dishes that they make when they want to cheer up. The information provision unit can suggest recipes to the user that correspond to the emotions, improving the cooking experience. For example, it can suggest dishes that will relax or cheer up the user.

[0086] The concierge service system can estimate a user's emotions based on the user's behavioral history and purchase history, and provide a fitness plan that matches the emotions. For example, the behavioral history collection unit collects a history of the user's exercises and fitness-related products purchased. The hobby and preference analysis unit can analyze the user's emotions and identify a fitness plan that matches the emotions. For example, it analyzes the exercises the user does when they want to relax and the exercises they do when they want to cheer up. The information provision unit can suggest a fitness plan to the user that matches the emotions, improving the exercise experience. For example, it can suggest relaxing exercises and energizing exercises to the user.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The behavioral history collection unit collects the user's behavioral history and purchase history. For example, it records the history of websites the user has visited in the past, the types of products purchased, the date and time of purchase, and the types of content viewed. Step 2: The hobby and preference analysis unit analyzes the data collected by the behavioral history collection unit to understand the user's hobby and preference trends. For example, it analyzes the product categories that the user frequently purchases and the types of content that they frequently view, and uses generation AI to analyze the user's hobby and preference based on the user's behavioral history and purchase history. Step 3: The information provision unit provides the user with the most appropriate information based on the trends identified by the hobby and preference analysis unit. For example, it notifies the user of new products and event information that may interest the user, and provides the information at the time when the user is most interested.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] 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]

[0156] 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 behavior history collection unit that collects user behavior history and purchase history; a hobby and preference analysis unit that analyzes the data collected by the behavior history collection unit and grasps the user's hobby and preference trends; an information providing unit that provides optimal information to a user based on the tendency grasped by the hobby and preference analysis unit. A system characterized by:

2. The behavioral history collection unit Collecting real-time location information of users and analyzing behavioral patterns based on the location information 2. The system of claim 1.

3. The hobby and preference analysis unit Based on the behavioral history and purchase history, the user's similarity with other users is analyzed, and the behavioral patterns of similar users are used as reference.

2. The system of claim 1.

4. The hobby and preference analysis unit Analyze users' emotional responses to their hobbies and preferences to understand their emotional trends.

2. The system of claim 1.

5. The information providing unit Estimates the emotions users feel when receiving information and provides information that elicits positive emotions 2. The system of claim 1.

6. The behavioral history collection unit Estimate the emotions of users when they take actions or make purchases, and collect that emotional data along with their behavioral history.

2. The system of claim 1.

7. The hobby and preference analysis unit Monitor users' emotional responses to their preferences in real time and update the analysis results based on that data.

2. The system of claim 1.

8. The information providing unit Monitor users' emotions in real time when receiving information and provide information according to their emotions.

2. The system of claim 1.

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