System

Abstract

An object of a system according to an embodiment is to analyze a purchase history and a search history of a user and propose an optimal product and information.SOLUTION: A system includes a history collection unit, an analysis unit, a proposal unit, and a display unit. The history collection unit collects a purchase history and a search history of a user. The analysis unit analyzes the purchase history and the search history collected by the history collection unit. The proposal unit proposes an optimum product and information to the user on the basis of a result of the analysis by the analysis unit. The display unit displays the product and the information proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that they have not been able to effectively utilize a user's purchase history and search history to suggest optimal products and information.

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

[0006] The system according to the embodiment includes a history collection unit, an analysis unit, a suggestion unit, and a display unit. The history collection unit collects a user's purchase history and search history. The analysis unit analyzes the purchase history and search history collected by the history collection unit. The suggestion unit suggests optimal products and information to the user based on the results of the analysis by the analysis unit. The display unit displays the products and information suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a user's purchase history and search history and suggest optimal products and 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 recommendation system according to an embodiment of the present invention is a system that, while a user is logged in to Yahoo, recommends optimal products and information based on the user's purchase history on Yahoo Shopping, etc. This allows the recommendation system to analyze the user's purchase history and search history and recommend the most relevant products and information to the user.

[0029] A proposal system according to an embodiment includes a history collection unit, an analysis unit, a proposal unit, and a display unit. The history collection unit collects a user's purchase history and search history. For example, the history collection unit stores the user's past purchases and search keywords in a database. The history collection unit can also collect the user's frequently browsed product categories and search keywords. The history collection unit can also collect the user's social media activity and review posting history. The analysis unit analyzes the purchase history and search history collected by the history collection unit. For example, the analysis unit can identify the user's preferences and interests using data mining technology. The analysis unit can also analyze the user's behavioral patterns using statistical analysis techniques. The analysis unit can also analyze the user's emotions during purchases and searches using an emotion estimation function. The proposal unit proposes optimal products and information to the user based on the results of the analysis by the analysis unit. For example, the proposal unit can propose new products that are highly related to products the user has previously purchased. The proposal unit can also select and propose information that matches the user's interests. Furthermore, the suggestion unit can also suggest related services and events based on the user's preferences. The display unit displays the products and information suggested by the suggestion unit. For example, when a user searches on Yahoo, the display unit displays the suggested products and information on a search result page. The display unit can also optimize the display content according to the user's device and browser settings. Furthermore, the display unit can use an emotion estimation function to monitor the user's emotions in real time while viewing the suggested content and dynamically adjust the display content. This allows the suggestion system according to the embodiment to suggest optimal products and information based on the user's purchase history and search history. For example, the user can easily find products and information that match their interests, improving the efficiency of shopping and information gathering.

[0030] The history collection unit can also collect the user's browsing history and data on products added to the cart but not purchased. For example, the history collection unit collects the history of product pages viewed by the user and integrates it with the purchase history and search history. For example, it identifies product categories that the user frequently views and analyzes their preferences in detail. The history collection unit also collects data on products that the user added to the cart but did not purchase and identifies products that the user is highly interested in purchasing. For example, it analyzes the trends of products added to the cart and infers the reasons why the products were not purchased. The history collection unit also collects reviews and ratings of products viewed by the user and uses them for preference analysis. For example, it identifies the preferences of users who frequently view highly rated products and reflects them in suggestions. This enables detailed analysis of users' preferences.

[0031] The history collection unit can also collect the user's social media activity and review posting history. For example, the history collection unit collects the user's social media posting content and activity such as "likes" to identify topics of interest. For example, it analyzes posts about specific brands or products. The history collection unit also collects reviews and ratings posted by the user and integrates them with purchase history and search history. For example, it identifies product categories in which many positive reviews are posted. The history collection unit also collects information on followings and followers on social media to understand the user's interests. For example, it analyzes the preferences of users who follow a specific influencer. This makes it possible to understand the user's overall interests.

[0032] The history collection unit can collect a user's offline purchase history through a smartphone app. The history collection unit collects a user's offline purchase history, for example, through a smartphone app. For example, it provides a receipt scanning function and digitizes purchase data. The history collection unit also integrates the offline purchase history with online purchase history and search history to perform a comprehensive preference analysis. For example, it analyzes trends in products purchased offline. The history collection unit also collects the user's location information through the smartphone app to understand the purchase history at specific stores. For example, it analyzes data on frequently visited stores. This makes it possible to integrate online and offline data.

[0033] The history collection unit can also collect the purchase histories of the user's family and friends. The history collection unit, for example, collects the purchase histories of the user's family and friends and makes gift suggestions. For example, it may suggest products suitable for a family member's birthday. The history collection unit also analyzes the purchase histories of the user's friends to make joint purchase suggestions. For example, it may suggest products related to products purchased by friends. The history collection unit also analyzes the user's preferences in detail based on the purchase histories of family and friends. For example, it may grasp the purchasing trends of the entire family and reflect them in suggestions. This makes it possible to suggest gifts and joint purchases.

[0034] The analysis unit can make suggestions suitable for specific times of the year based on data related to seasons and events in analyzing purchase histories and search histories. The analysis unit, for example, analyzes purchase histories and search histories to extract data related to seasons and events. For example, it identifies products related to the Christmas season. The analysis unit also incorporates data on seasons and events into the analysis to make suggestions suitable for specific times of the year. For example, it suggests presents suitable for birthdays. The analysis unit also analyzes user preferences in detail based on data related to seasons and events. For example, it identifies purchasing trends for products related to specific events. This makes it possible to make suggestions suitable for specific times of the year.

[0035] The analysis unit can analyze purchase history and search history over time to track changes in preferences. For example, the analysis unit analyzes a user's purchase history and search history over time to track changes in preferences. For example, it identifies changes in preferences based on data from the past few years. The analysis unit also analyzes the user's preferences over time to visualize the changes. For example, it identifies product categories in which the user was interested at a specific time. The analysis unit also tracks changes in the user's preferences and reflects them in the content of suggestions. For example, it predicts future preferences based on past data. This makes it possible to track changes in the user's preferences.

[0036] When analyzing purchase history and search history, the analysis unit can compare it with data from other users and refer to the behavior of users with similar preferences. For example, the analysis unit compares purchase history and search history with data from other users to identify users with similar preferences. For example, it groups users who frequently purchase the same products. The analysis unit also refers to the behavior of users with similar preferences and customizes the content of suggestions. For example, it suggests products purchased by users with the same preferences. The analysis unit also analyzes the user's preferences in detail based on data from other users. For example, it refers to the search history of users with the same preferences. This makes it possible to refer to the behavior of users with similar preferences.

[0037] When analyzing purchase history and search history, the analysis unit adds data related to the user's health condition and lifestyle to the analysis, and can suggest health-oriented products and information. For example, the analysis unit collects data related to the user's health condition and adds it to the analysis. For example, health-oriented products are suggested based on data from a fitness tracker. The analysis unit also analyzes data related to the user's lifestyle and reflects this in the suggestions. For example, products suitable for users with active lifestyles are suggested. The analysis unit also analyzes the user's preferences in detail based on the health condition and lifestyle. For example, information suitable for highly health-conscious users is suggested. This makes it possible to suggest health-oriented products and information.

[0038] The suggestion unit can suggest not only related products and information but also related services and events based on the user's preferences. For example, the suggestion unit suggests related services based on the user's preferences. For example, it suggests a subscription service related to a user who has purchased a specific product. The suggestion unit also suggests related events based on the user's preferences. For example, it suggests event information for that brand to a user who has purchased a product from that brand. The suggestion unit also suggests not only products but also related information and services. For example, it suggests information on how to use a specific product to a user who has purchased that product. This makes it possible to suggest related services and events as well.

[0039] The suggestion unit can predict when to repurchase a consumable item based on the user's past purchase history and make suggestions at the appropriate time. The suggestion unit, for example, analyzes the user's past purchase history and predicts when to repurchase the consumable item. For example, it identifies the repurchase timing for a product that is purchased regularly. The suggestion unit also predicts the repurchase timing and makes suggestions at the appropriate time. For example, it sends a reminder before the consumable item runs out. The suggestion unit also makes suggestions appropriate for the user based on the repurchase timing. For example, it suggests related new products to coincide with the repurchase timing of the consumable item. This makes it possible to predict when to repurchase the consumable item and make suggestions at the appropriate time.

[0040] The suggestion unit can make a set suggestion that combines products from different categories based on the user's preferences. The suggestion unit, for example, makes a set suggestion that combines products from different categories based on the user's preferences. For example, it suggests a set of fashion items and accessories. The suggestion unit also makes new suggestions to the user by combining products from different categories. For example, it suggests a set of electronic devices and related accessories. The suggestion unit also analyzes the user's preferences in detail when making a set suggestion. For example, it suggests an optimal combination based on past purchase history. This makes it possible to make a set suggestion that combines products from different categories.

[0041] The suggestion unit can also suggest lifestyle-related information such as travel destinations and restaurants based on the user's preferences. The suggestion unit, for example, suggests travel destinations based on the user's preferences. For example, it suggests travel destinations that the user might be interested in based on past search history. The suggestion unit also suggests restaurants based on the user's preferences. For example, it suggests restaurants that serve dishes that the user likes based on past purchase history. The suggestion unit also analyzes the user's preferences in detail when suggesting lifestyle-related information. For example, it makes optimal suggestions based on past data. This makes it possible to suggest lifestyle-related information as well.

[0042] The display unit can customize the suggestion content based on the user's past behavior and display it on an individual dashboard. The display unit customizes the suggestion content based on, for example, the user's past behavioral data. For example, an individual dashboard is created based on past purchase history or search history. The display unit also displays the suggestion content on an individual dashboard so that the user can easily access it. For example, it displays products and information related to the dashboard. The display unit also customizes the suggestion content based on the user's preferences and displays it on an individual dashboard. For example, it prioritizes displaying products and information that the user is likely to be interested in. This makes it possible to display customized suggestion content on an individual dashboard.

[0043] The display unit can optimize the proposed content according to the settings of the user's device or browser, and adjust the display speed and layout. The display unit, for example, optimizes the display of the proposed content according to the settings of the user's device or browser. For example, it provides a layout suitable for smartphones and tablets. The display unit also optimizes the display speed of the proposed content to allow the user to browse comfortably. For example, it adjusts the loading speed of images and videos. The display unit also adjusts the layout of the proposed content based on the settings of the user's device or browser. For example, it provides different layouts for desktop and mobile. This enables the display to be optimized according to the settings of the user's device or browser.

[0044] The display unit can notify the user of the proposal content via email or social media, and display it on multiple channels. For example, the display unit can notify the user of the proposal content via email, and display it on multiple channels. For example, it can send a list of the proposal content via email. The display unit can also notify the user of the proposal content via social media, and display it on multiple channels. For example, it can notify the user of the proposal content using the messaging function of the social media. The display unit can also display the proposal content on multiple channels, allowing the user to easily access it. For example, it can notify the user of the proposal content via both email and social media. This allows the proposal content to be displayed on multiple channels.

[0045] The display unit can display the suggested content in different formats, such as text, images, or videos, according to the user's preferences. For example, the display unit displays the suggested content in text format according to the user's preferences. For example, product descriptions and reviews are provided in text. The display unit also displays the suggested content in image format according to the user's preferences. For example, product images and related visual content are provided. The display unit also displays the suggested content in video format according to the user's preferences. For example, a video introducing the product or a video showing how to use it is provided. This allows the suggested content to be displayed in different formats according to the user's preferences.

[0046] The suggestion unit can collect user feedback and ratings in addition to the user's new purchase history and search history, and update the suggestions. The suggestion unit, for example, collects the user's new purchase history and search history, and updates the suggestions. For example, the suggestions are adjusted based on data on newly purchased products. The suggestion unit also collects user feedback and ratings, and reflects them in the suggestions. For example, the suggestions are updated based on ratings on suggested products. The suggestion unit also continuously improves the suggestions based on the feedback and ratings. For example, the suggestions are customized by reflecting the user's opinions. This makes it possible to update the suggestions based on the user's feedback and ratings.

[0047] The suggestion unit can track changes in the user's preferences in real time and instantly update the suggestions. For example, the suggestion unit can track changes in the user's preferences in real time and instantly update the suggestions. For example, the suggestion unit can adjust the suggestions based on new search history or purchase history. The suggestion unit can also analyze the user's preferences in real time and reflect them in the suggestions. For example, the suggestion unit can dynamically update the suggestions based on user behavior data. The suggestion unit can also continuously monitor changes in the user's preferences and optimize the suggestions. For example, the suggestion unit can adjust the suggestions based on data collected in real time. This allows changes in the user's preferences to be tracked in real time and the suggestions to be instantly updated.

[0048] The suggestion unit can suggest related new products and trend information based on the user's new purchase history and search history. For example, the suggestion unit suggests related new products based on the user's new purchase history and search history. For example, it suggests products related to a newly purchased product. The suggestion unit also suggests trend information based on the user's new search history. For example, it provides information related to recently searched keywords. The suggestion unit also analyzes the new purchase history and search history to suggest new products and trend information that match the user's preferences. For example, it suggests the latest fashion items and technology products. This makes it possible to suggest related new products and trend information.

[0049] The suggestion unit can update the suggestion content to match seasons and events in accordance with changes in the user's preferences. For example, the suggestion unit analyzes changes in the user's preferences and updates the suggestion content to match seasons and events. For example, it suggests products and information suitable for summer. The suggestion unit also customizes the suggestion content based on data related to seasons and events. For example, it suggests products related to Christmas and Valentine's Day. The suggestion unit also tracks changes in the user's preferences in real time and dynamically updates the suggestion content to match seasons and events. For example, it suggests trendy products for each season. This makes it possible to update the suggestion content to match seasons and events.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The recommendation system can also collect data on the user's health condition and lifestyle and suggest health-oriented products and information. For example, it can suggest health foods and exercise equipment suitable for the user based on data from a fitness tracker. It can also suggest information on outdoor gear and sporting events suitable for active users based on the user's lifestyle. It can also provide information on regular health checks and medical services according to the user's health condition. This makes it possible to make suggestions based on the user's health preferences.

[0052] The recommendation system can also collect the purchase history of the user's family and friends and make suggestions for gifts or joint purchases. For example, it can suggest products suitable for a family member's birthday. It can also analyze the purchase history of the user's friends and make joint purchase suggestions. For example, it can suggest products related to products purchased by friends. Furthermore, it can analyze the user's preferences in detail based on the purchase history of family and friends and reflect them in suggestions for gifts or joint purchases. This makes it possible to make suggestions that take into account the user's relationships with family and friends.

[0053] The proposed system can also collect users' offline purchasing history through a smartphone app and integrate online and offline data. For example, it can provide a receipt scanning function to digitize offline purchase data. It can also collect users' location information and understand their purchasing history at specific stores. For example, it can analyze data on frequently visited stores and make suggestions based on the user's preferences. This enables comprehensive preference analysis that integrates online and offline data.

[0054] The recommendation system can also suggest sets that combine products from different categories based on the user's preferences. For example, it can suggest a set of fashion items and accessories. It can also suggest a set of electronic devices and related accessories. It can also analyze the user's preferences in detail and suggest optimal combinations based on the user's past purchase history. This allows it to make new suggestions to the user.

[0055] The recommendation system can also suggest lifestyle-related information such as travel destinations and restaurants based on the user's preferences. For example, it can suggest travel destinations that the user might be interested in based on their past search history. It can also suggest restaurants that serve the user's favorite cuisine based on their past purchase history. Furthermore, when suggesting lifestyle-related information, it can analyze the user's preferences in detail and make optimal suggestions. This makes it possible to make suggestions that are tailored to the user's lifestyle.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The history collection unit collects the user's purchase history and search history. For example, the history collection unit stores the user's past purchases and search keywords in a database. The history collection unit can also collect the user's frequently viewed product categories and search keywords, social media activity, and review posting history. Step 2: The analysis unit analyzes the purchase history and search history collected by the history collection unit. For example, it uses data mining technology and statistical analysis methods to identify the user's preferences and behavioral patterns, and analyzes the user's emotions using an emotion estimation function. Step 3: The suggestion unit suggests optimal products and information to the user based on the results of the analysis by the analysis unit. For example, it suggests new products that are highly related to products purchased in the past, information that matches the user's interests, and related services and events. Step 4: The display unit displays the products and information suggested by the suggestion unit. For example, when a user performs a search, the display unit displays the suggested products and information on a search result page, optimizing the display content according to the user's device and browser settings. Furthermore, the display unit uses an emotion estimation function to monitor the user's emotions in real time while viewing the suggested content and dynamically adjust the display content.

[0058] (Example 2) The recommendation system according to an embodiment of the present invention is a system that, while a user is logged in to Yahoo, recommends optimal products and information based on the user's purchase history on Yahoo Shopping, etc. This allows the recommendation system to analyze the user's purchase history and search history and recommend the most relevant products and information to the user.

[0059] A proposal system according to an embodiment includes a history collection unit, an analysis unit, a proposal unit, and a display unit. The history collection unit collects a user's purchase history and search history. For example, the history collection unit stores the user's past purchases and search keywords in a database. The history collection unit can also collect the user's frequently browsed product categories and search keywords. The history collection unit can also collect the user's social media activity and review posting history. The analysis unit analyzes the purchase history and search history collected by the history collection unit. For example, the analysis unit can identify the user's preferences and interests using data mining technology. The analysis unit can also analyze the user's behavioral patterns using statistical analysis techniques. The analysis unit can also analyze the user's emotions during purchases and searches using an emotion estimation function. The proposal unit proposes optimal products and information to the user based on the results of the analysis by the analysis unit. For example, the proposal unit can propose new products that are highly related to products the user has previously purchased. The proposal unit can also select and propose information that matches the user's interests. Furthermore, the suggestion unit can also suggest related services and events based on the user's preferences. The display unit displays the products and information suggested by the suggestion unit. For example, when a user searches on Yahoo, the display unit displays the suggested products and information on a search result page. The display unit can also optimize the display content according to the user's device and browser settings. Furthermore, the display unit can use an emotion estimation function to monitor the user's emotions in real time while viewing the suggested content and dynamically adjust the display content. This allows the suggestion system according to the embodiment to suggest optimal products and information based on the user's purchase history and search history. For example, the user can easily find products and information that match their interests, improving the efficiency of shopping and information gathering.

[0060] The history collection unit can also collect the user's browsing history and data on products added to the cart but not purchased. For example, the history collection unit collects the history of product pages viewed by the user and integrates it with the purchase history and search history. For example, it identifies product categories that the user frequently views and analyzes their preferences in detail. The history collection unit also collects data on products that the user added to the cart but did not purchase and identifies products that the user is highly interested in purchasing. For example, it analyzes the trends of products added to the cart and infers the reasons why the products were not purchased. The history collection unit also collects reviews and ratings of products viewed by the user and uses them for preference analysis. For example, it identifies the preferences of users who frequently view highly rated products and reflects them in suggestions. This enables detailed analysis of users' preferences.

[0061] The history collection unit can also collect the user's social media activity and review posting history. For example, the history collection unit collects the user's social media posting content and activity such as "likes" to identify topics of interest. For example, it analyzes posts about specific brands or products. The history collection unit also collects reviews and ratings posted by the user and integrates them with purchase history and search history. For example, it identifies product categories in which many positive reviews are posted. The history collection unit also collects information on followings and followers on social media to understand the user's interests. For example, it analyzes the preferences of users who follow a specific influencer. This makes it possible to understand the user's overall interests.

[0062] The history collection unit can use the emotion estimation function to collect emotions felt by users when making purchases or searching. The history collection unit, for example, analyzes facial expressions and voices when users purchase products to collect emotion data. For example, it quantifies the joy and satisfaction felt at the time of purchase. The history collection unit also estimates the emotions felt by users when they conduct searches in real time and integrates this with the search history. For example, it analyzes excitement and anticipation felt during searches. The history collection unit also collects the emotions felt by users toward specific products or information based on the emotion estimation data. For example, it identifies products with strong positive emotions and reflects this in recommendations. This makes it possible to analyze data based on user emotions.

[0063] The history collection unit can collect a user's offline purchase history through a smartphone app. The history collection unit collects a user's offline purchase history, for example, through a smartphone app. For example, it provides a receipt scanning function and digitizes purchase data. The history collection unit also integrates the offline purchase history with online purchase history and search history to perform a comprehensive preference analysis. For example, it analyzes trends in products purchased offline. The history collection unit also collects the user's location information through the smartphone app to understand the purchase history at specific stores. For example, it analyzes data on frequently visited stores. This makes it possible to integrate online and offline data.

[0064] The history collection unit can also collect the purchase histories of the user's family and friends. The history collection unit, for example, collects the purchase histories of the user's family and friends and makes gift suggestions. For example, it may suggest products suitable for a family member's birthday. The history collection unit also analyzes the purchase histories of the user's friends to make joint purchase suggestions. For example, it may suggest products related to products purchased by friends. The history collection unit also analyzes the user's preferences in detail based on the purchase histories of family and friends. For example, it may grasp the purchasing trends of the entire family and reflect them in suggestions. This makes it possible to suggest gifts and joint purchases.

[0065] The history collection unit can use the emotion estimation function to estimate emotions from facial expressions and voices of users while they are browsing products and collect data in real time. For example, the history collection unit uses a camera to analyze facial expressions of users while they are browsing products and collect emotion data. For example, it analyzes expressions of interest and surprise in real time. The history collection unit also uses a microphone to collect voices while the user is browsing products and estimate emotions. For example, it analyzes the tone of an excited voice. The history collection unit also collects emotion estimation data in real time and uses it for analyzing user preferences. For example, it identifies products that evoke strong positive emotions and reflects this in recommendations. This makes it possible to collect data based on users' real-time emotions.

[0066] The analysis unit can make suggestions suitable for specific times of the year based on data related to seasons and events in analyzing purchase histories and search histories. The analysis unit, for example, analyzes purchase histories and search histories to extract data related to seasons and events. For example, it identifies products related to the Christmas season. The analysis unit also incorporates data on seasons and events into the analysis to make suggestions suitable for specific times of the year. For example, it suggests presents suitable for birthdays. The analysis unit also analyzes user preferences in detail based on data related to seasons and events. For example, it identifies purchasing trends for products related to specific events. This makes it possible to make suggestions suitable for specific times of the year.

[0067] The analysis unit can analyze purchase history and search history over time to track changes in preferences. For example, the analysis unit analyzes a user's purchase history and search history over time to track changes in preferences. For example, it identifies changes in preferences based on data from the past few years. The analysis unit also analyzes the user's preferences over time to visualize the changes. For example, it identifies product categories in which the user was interested at a specific time. The analysis unit also tracks changes in the user's preferences and reflects them in the content of suggestions. For example, it predicts future preferences based on past data. This makes it possible to track changes in the user's preferences.

[0068] The analysis unit uses the emotion estimation function to analyze the emotions a user has toward a specific product or information, and can make suggestions based on those emotions. The analysis unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a specific product. For example, it quantifies the joy and satisfaction felt at the time of purchase. The analysis unit also analyzes the emotions a user has toward specific information and reflects them in the content of suggestions. For example, it prioritizes suggestions that evoke strong positive emotions. The analysis unit also analyzes the user's preferences in detail based on the emotion data. For example, it identifies product categories that evoke strong specific emotions and reflects these in suggestions. This makes it possible to make suggestions based on the user's emotions.

[0069] When analyzing purchase history and search history, the analysis unit can compare it with data from other users and refer to the behavior of users with similar preferences. For example, the analysis unit compares purchase history and search history with data from other users to identify users with similar preferences. For example, it groups users who frequently purchase the same products. The analysis unit also refers to the behavior of users with similar preferences and customizes the content of suggestions. For example, it suggests products purchased by users with the same preferences. The analysis unit also analyzes the user's preferences in detail based on data from other users. For example, it refers to the search history of users with the same preferences. This makes it possible to refer to the behavior of users with similar preferences.

[0070] When analyzing purchase history and search history, the analysis unit adds data related to the user's health condition and lifestyle to the analysis, and can suggest health-oriented products and information. For example, the analysis unit collects data related to the user's health condition and adds it to the analysis. For example, health-oriented products are suggested based on data from a fitness tracker. The analysis unit also analyzes data related to the user's lifestyle and reflects this in the suggestions. For example, products suitable for users with active lifestyles are suggested. The analysis unit also analyzes the user's preferences in detail based on the health condition and lifestyle. For example, information suitable for highly health-conscious users is suggested. This makes it possible to suggest health-oriented products and information.

[0071] The analysis unit uses the emotion estimation function to analyze the emotions a user has during a specific time period or day of the week, and can make suggestions suitable for that time period. The analysis unit, for example, uses the emotion estimation function to analyze the emotions a user has during a specific time period. For example, it suggests products suitable for a user who feels relaxed in the evening. The analysis unit also analyzes the emotions a user has on a specific day of the week and reflects this in the suggestions. For example, it suggests information suitable for a user who feels strong positive emotions on weekends. The analysis unit also analyzes the user's preferences in detail based on the emotion data. For example, it identifies products that evoke strong positive emotions during a specific time period and reflects these in the suggestions. This makes it possible to make suggestions suitable for specific time periods or days of the week.

[0072] The suggestion unit can suggest not only related products and information but also related services and events based on the user's preferences. For example, the suggestion unit suggests related services based on the user's preferences. For example, it suggests a subscription service related to a user who has purchased a specific product. The suggestion unit also suggests related events based on the user's preferences. For example, it suggests event information for that brand to a user who has purchased a product from that brand. The suggestion unit also suggests not only products but also related information and services. For example, it suggests information on how to use a specific product to a user who has purchased that product. This makes it possible to suggest related services and events as well.

[0073] The suggestion unit can predict when to repurchase a consumable item based on the user's past purchase history and make suggestions at the appropriate time. The suggestion unit, for example, analyzes the user's past purchase history and predicts when to repurchase the consumable item. For example, it identifies the repurchase timing for a product that is purchased regularly. The suggestion unit also predicts the repurchase timing and makes suggestions at the appropriate time. For example, it sends a reminder before the consumable item runs out. The suggestion unit also makes suggestions appropriate for the user based on the repurchase timing. For example, it suggests related new products to coincide with the repurchase timing of the consumable item. This makes it possible to predict when to repurchase the consumable item and make suggestions at the appropriate time.

[0074] The suggestion unit can use the emotion estimation function to make suggestions that are likely to cause the user to feel positive emotions toward specific products or information. The suggestion unit, for example, uses the emotion estimation function to analyze the likelihood that the user will feel positive emotions toward a specific product. For example, the suggestion unit customizes the content of the suggestion based on past emotion data. The suggestion unit also analyzes the likelihood that the user will feel positive emotions toward specific information and reflects this in the content of the suggestion. For example, the suggestion unit preferentially suggests information that evokes strong positive emotions. The suggestion unit also analyzes the user's preferences in detail based on the emotion data and makes suggestions that elicit positive emotions. For example, it identifies product categories that evoke strong emotions toward specific products and reflects these in the suggestions. This makes it possible to make suggestions that are likely to cause the user to feel positive emotions.

[0075] The suggestion unit can make a set suggestion that combines products from different categories based on the user's preferences. The suggestion unit, for example, makes a set suggestion that combines products from different categories based on the user's preferences. For example, it suggests a set of fashion items and accessories. The suggestion unit also makes new suggestions to the user by combining products from different categories. For example, it suggests a set of electronic devices and related accessories. The suggestion unit also analyzes the user's preferences in detail when making a set suggestion. For example, it suggests an optimal combination based on past purchase history. This makes it possible to make a set suggestion that combines products from different categories.

[0076] The suggestion unit can also suggest lifestyle-related information such as travel destinations and restaurants based on the user's preferences. The suggestion unit, for example, suggests travel destinations based on the user's preferences. For example, it suggests travel destinations that the user might be interested in based on past search history. The suggestion unit also suggests restaurants based on the user's preferences. For example, it suggests restaurants that serve dishes that the user likes based on past purchase history. The suggestion unit also analyzes the user's preferences in detail when suggesting lifestyle-related information. For example, it makes optimal suggestions based on past data. This makes it possible to suggest lifestyle-related information as well.

[0077] The suggestion unit can use the emotion estimation function to suggest products and information that will evoke positive emotions in the user for a particular season or event. For example, the suggestion unit uses the emotion estimation function to suggest products that will evoke positive emotions in the user for a particular season. For example, it suggests products related to summer. The suggestion unit also suggests information that will evoke positive emotions in the user for a particular event. For example, it suggests information related to Christmas. The suggestion unit also analyzes the user's preferences in detail based on the emotion data and makes suggestions that will elicit positive emotions. For example, it identifies products related to a particular season or event and reflects these in the suggestions. This makes it possible to suggest products and information that will evoke positive emotions in the user for a particular season or event.

[0078] The display unit can customize the suggestion content based on the user's past behavior and display it on an individual dashboard. The display unit customizes the suggestion content based on, for example, the user's past behavioral data. For example, an individual dashboard is created based on past purchase history or search history. The display unit also displays the suggestion content on an individual dashboard so that the user can easily access it. For example, it displays products and information related to the dashboard. The display unit also customizes the suggestion content based on the user's preferences and displays it on an individual dashboard. For example, it prioritizes displaying products and information that the user is likely to be interested in. This makes it possible to display customized suggestion content on an individual dashboard.

[0079] The display unit can optimize the proposed content according to the settings of the user's device or browser, and adjust the display speed and layout. The display unit, for example, optimizes the display of the proposed content according to the settings of the user's device or browser. For example, it provides a layout suitable for smartphones and tablets. The display unit also optimizes the display speed of the proposed content to allow the user to browse comfortably. For example, it adjusts the loading speed of images and videos. The display unit also adjusts the layout of the proposed content based on the settings of the user's device or browser. For example, it provides different layouts for desktop and mobile. This enables the display to be optimized according to the settings of the user's device or browser.

[0080] The display unit can use the emotion estimation function to monitor the emotion of the user while viewing the proposed content in real time and dynamically adjust the displayed content. The display unit, for example, uses the emotion estimation function to monitor the emotion of the user while viewing the proposed content in real time. For example, it collects emotion data using a camera or a microphone. The display unit also dynamically adjusts the display of the proposed content based on the user's emotion data. For example, it preferentially displays products that evoke strong positive emotions. The display unit also collects emotion estimation data in real time and optimizes the display of the proposed content. For example, it adjusts the displayed content according to changes in the user's emotion. This makes it possible to dynamically adjust the displayed content based on the user's emotion.

[0081] The display unit can notify the user of the proposal content via email or social media, and display it on multiple channels. For example, the display unit can notify the user of the proposal content via email, and display it on multiple channels. For example, it can send a list of the proposal content via email. The display unit can also notify the user of the proposal content via social media, and display it on multiple channels. For example, it can notify the user of the proposal content using the messaging function of the social media. The display unit can also display the proposal content on multiple channels, allowing the user to easily access it. For example, it can notify the user of the proposal content via both email and social media. This allows the proposal content to be displayed on multiple channels.

[0082] The display unit can display the suggested content in different formats, such as text, images, or videos, according to the user's preferences. For example, the display unit displays the suggested content in text format according to the user's preferences. For example, product descriptions and reviews are provided in text. The display unit also displays the suggested content in image format according to the user's preferences. For example, product images and related visual content are provided. The display unit also displays the suggested content in video format according to the user's preferences. For example, a video introducing the product or a video showing how to use it is provided. This allows the suggested content to be displayed in different formats according to the user's preferences.

[0083] The display unit can use the emotion estimation function to personalize the display content based on the emotion the user feels when viewing the proposed content. The display unit, for example, uses the emotion estimation function to analyze the emotion the user feels when viewing the proposed content and personalize the display content. For example, products with strong positive emotions are preferentially displayed. The display unit also customizes the display of the proposed content based on the user's emotion data. For example, products with high emotion scores are prominently displayed. The display unit also collects emotion estimation data in real time and dynamically adjusts the display of the proposed content. For example, the display content is personalized according to changes in the user's emotion. This makes it possible to personalize the display content based on the user's emotion.

[0084] The suggestion unit can collect user feedback and ratings in addition to the user's new purchase history and search history, and update the suggestions. The suggestion unit, for example, collects the user's new purchase history and search history, and updates the suggestions. For example, the suggestions are adjusted based on data on newly purchased products. The suggestion unit also collects user feedback and ratings, and reflects them in the suggestions. For example, the suggestions are updated based on ratings on suggested products. The suggestion unit also continuously improves the suggestions based on the feedback and ratings. For example, the suggestions are customized by reflecting the user's opinions. This makes it possible to update the suggestions based on the user's feedback and ratings.

[0085] The suggestion unit can track changes in the user's preferences in real time and instantly update the suggestions. For example, the suggestion unit can track changes in the user's preferences in real time and instantly update the suggestions. For example, the suggestion unit can adjust the suggestions based on new search history or purchase history. The suggestion unit can also analyze the user's preferences in real time and reflect them in the suggestions. For example, the suggestion unit can dynamically update the suggestions based on user behavior data. The suggestion unit can also continuously monitor changes in the user's preferences and optimize the suggestions. For example, the suggestion unit can adjust the suggestions based on data collected in real time. This allows changes in the user's preferences to be tracked in real time and the suggestions to be instantly updated.

[0086] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward new proposed content and update the content based on the emotion. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user feels toward new proposed content. For example, it quantifies positive emotion toward the proposed content. The suggestion unit also updates the proposed content based on the user's emotion data. For example, it preferentially displays proposed content with a strong positive emotion. The suggestion unit also collects emotion estimation data in real time and uses it to update the proposed content. For example, it adjusts the proposed content according to changes in the user's emotion. This makes it possible to update the proposed content based on the user's emotion.

[0087] The suggestion unit can suggest related new products and trend information based on the user's new purchase history and search history. For example, the suggestion unit suggests related new products based on the user's new purchase history and search history. For example, it suggests products related to a newly purchased product. The suggestion unit also suggests trend information based on the user's new search history. For example, it provides information related to recently searched keywords. The suggestion unit also analyzes the new purchase history and search history to suggest new products and trend information that match the user's preferences. For example, it suggests the latest fashion items and technology products. This makes it possible to suggest related new products and trend information.

[0088] The suggestion unit can update the suggestion content to match seasons and events in accordance with changes in the user's preferences. For example, the suggestion unit analyzes changes in the user's preferences and updates the suggestion content to match seasons and events. For example, it suggests products and information suitable for summer. The suggestion unit also customizes the suggestion content based on data related to seasons and events. For example, it suggests products related to Christmas and Valentine's Day. The suggestion unit also tracks changes in the user's preferences in real time and dynamically updates the suggestion content to match seasons and events. For example, it suggests trendy products for each season. This makes it possible to update the suggestion content to match seasons and events.

[0089] The suggestion unit can use the emotion estimation function to monitor the user's emotions toward new suggested content in real time and dynamically update the suggested content. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotions toward new suggested content in real time. For example, it analyzes the user's facial expressions and voice to collect emotion data. The suggestion unit also dynamically updates the suggested content based on the user's emotion data. For example, it preferentially displays suggested content with a strong positive emotion. The suggestion unit also collects emotion estimation data in real time and uses it to update the suggested content. For example, it adjusts the suggested content according to changes in the user's emotion. This makes it possible to dynamically update the suggested content based on the user's emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The recommendation system can also collect data on the user's health condition and lifestyle and suggest health-oriented products and information. For example, it can suggest health foods and exercise equipment suitable for the user based on data from a fitness tracker. It can also suggest information on outdoor gear and sporting events suitable for active users based on the user's lifestyle. It can also provide information on regular health checks and medical services according to the user's health condition. This makes it possible to make suggestions based on the user's health preferences.

[0092] The recommendation system can also collect the purchase history of the user's family and friends and make suggestions for gifts or joint purchases. For example, it can suggest products suitable for a family member's birthday. It can also analyze the purchase history of the user's friends and make joint purchase suggestions. For example, it can suggest products related to products purchased by friends. Furthermore, it can analyze the user's preferences in detail based on the purchase history of family and friends and reflect them in suggestions for gifts or joint purchases. This makes it possible to make suggestions that take into account the user's relationships with family and friends.

[0093] The proposed system can also collect users' offline purchasing history through a smartphone app and integrate online and offline data. For example, it can provide a receipt scanning function to digitize offline purchase data. It can also collect users' location information and understand their purchasing history at specific stores. For example, it can analyze data on frequently visited stores and make suggestions based on the user's preferences. This enables comprehensive preference analysis that integrates online and offline data.

[0094] The recommendation system can also suggest sets that combine products from different categories based on the user's preferences. For example, it can suggest a set of fashion items and accessories. It can also suggest a set of electronic devices and related accessories. It can also analyze the user's preferences in detail and suggest optimal combinations based on the user's past purchase history. This allows it to make new suggestions to the user.

[0095] The recommendation system can also suggest lifestyle-related information such as travel destinations and restaurants based on the user's preferences. For example, it can suggest travel destinations that the user might be interested in based on their past search history. It can also suggest restaurants that serve the user's favorite cuisine based on their past purchase history. Furthermore, when suggesting lifestyle-related information, it can analyze the user's preferences in detail and make optimal suggestions. This makes it possible to make suggestions that are tailored to the user's lifestyle.

[0096] The recommendation system can estimate the user's emotions and suggest products and information that evoke positive feelings about specific seasons or events. For example, it can suggest products related to summer. It can also suggest information related to Christmas. Furthermore, it can analyze the user's preferences in detail based on the emotion data, identify products related to specific seasons or events, and reflect these in the suggestions. This makes it possible to suggest products and information that evoke positive feelings about the user.

[0097] The recommendation system can estimate a user's emotions and make suggestions suited to specific times of the day or day of the week. For example, it can suggest products suited to users who feel relaxed in the evening. It can also suggest information suited to users who feel more positive on weekends. Furthermore, it can analyze user preferences in detail based on the emotion data, identify products that evoke more positive emotions during specific times of the day or on specific days of the week, and reflect these in its recommendations. This makes it possible to make suggestions suited to specific times of the day or day of the week.

[0098] The suggestion system can estimate the user's emotions, monitor their feelings toward new suggestions in real time, and dynamically update the suggestions. For example, it can analyze the user's facial expressions and voice to collect emotional data. It can also prioritize suggestions that evoke positive emotions based on the user's emotional data. Furthermore, it can collect emotion estimation data in real time and adjust the suggestions according to changes in the user's emotions. This allows the suggestion system to dynamically update the suggestions based on the user's emotions.

[0099] The recommendation system can estimate a user's emotions and make suggestions that are likely to evoke positive feelings toward specific products or information. For example, it can customize the content of suggestions based on past emotion data. It can also analyze the likelihood that a user will evoke positive feelings toward specific information and reflect this in the content of suggestions. Furthermore, it can analyze the user's preferences in detail based on the emotion data and make suggestions that elicit positive emotions. This makes it possible to make suggestions that are likely to evoke positive feelings toward the user.

[0100] The suggestion system can estimate the user's emotions, analyze the emotions felt toward new suggestions, and update the suggestions based on those emotions. For example, it can quantify the positive emotions felt toward the suggestions. It can also prioritize suggestions that evoke strong positive emotions based on the user's emotional data. Furthermore, it can collect emotion estimation data in real time and adjust the suggestions according to changes in the user's emotions. This allows the suggestions to be updated based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The history collection unit collects the user's purchase history and search history. For example, the history collection unit stores the user's past purchases and search keywords in a database. The history collection unit can also collect the user's frequently viewed product categories and search keywords, social media activity, and review posting history. Step 2: The analysis unit analyzes the purchase history and search history collected by the history collection unit. For example, it uses data mining technology and statistical analysis methods to identify the user's preferences and behavioral patterns, and analyzes the user's emotions using an emotion estimation function. Step 3: The suggestion unit suggests optimal products and information to the user based on the results of the analysis by the analysis unit. For example, it suggests new products that are highly related to products purchased in the past, information that matches the user's interests, and related services and events. Step 4: The display unit displays the products and information suggested by the suggestion unit. For example, when a user performs a search, the display unit displays the suggested products and information on a search result page, optimizing the display content according to the user's device and browser settings. Furthermore, the display unit uses an emotion estimation function to monitor the user's emotions in real time while viewing the suggested content and dynamically adjust the display content.

[0103] 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.

[0104] 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.

[0105] 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.

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

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0119] 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.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 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 history collection unit that collects a user's purchase history and search history; an analysis unit that analyzes the purchase history and the search history collected by the history collection unit; a suggestion unit that suggests optimal products and information to the user based on the results of the analysis by the analysis unit; a display unit that displays the product and the information suggested by the suggestion unit. A system characterized by:

2. The history collection unit Collect the emotions the user had when making a purchase or searching 2. The system of claim 1.

3. The analysis unit Analyze the user's feelings about specific products or information and make suggestions based on those feelings.

2. The system of claim 1.

4. The proposal unit Providing suggestions that are likely to cause the user to have positive feelings about specific products or information 2. The system of claim 1.

5. The display is The user's emotions are monitored in real time while viewing the suggestions, and the displayed content is dynamically adjusted.

2. The system of claim 1.

6. The history collection unit Collect the user's offline purchase history through a smartphone app 2. The system of claim 1.

7. The analysis unit Analysis of purchase and search history to provide suggestions appropriate for specific times based on seasonal and event-related data 2. The system of claim 1.

8. The proposal unit Suggesting relevant products and information as well as related services and events based on the user's preferences 2. The system of claim 1.

Images