system

Abstract

The system according to this embodiment aims to efficiently search the internet for items selected by the user and suggest the most suitable items. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a comparison unit, and a suggestion unit. The reception unit receives information about an item selected by the user. The search unit searches for the item on the internet based on the information received by the reception unit. The comparison unit compares the items based on the information collected by the search unit. The suggestion unit suggests the most suitable item to the user based on the comparison results from the comparison unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process for a user to search for, compare, and find an optimal item on the Internet is complicated and time-consuming.

[0005] The system according to the embodiment aims to efficiently search for an item selected by a user on the Internet and propose an optimal item.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, a comparison unit, and a suggestion unit. The reception unit receives information about items selected by the user. The search unit searches for items on the internet based on the information received by the reception unit. The comparison unit compares items based on the information collected by the search unit. The suggestion unit suggests the most suitable item to the user based on the comparison results from the comparison unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently search the internet for items selected by the user and suggest the most suitable items. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system in which, when a user watches videos or photos on a platform such as a video streaming site in which celebrities or models introduce interior or fashion items, the AI ​​searches for the item on the internet and collects information such as price and quality when the user selects an item they like. Based on the collected information, this system suggests the most suitable item that matches the user's preferences and characteristics. For example, the user selects an item they like in the video or photo. For example, a chair introduced by a celebrity on a video streaming site or clothes worn by a model on a video streaming site. This selection information is input to the AI. Next, the AI ​​searches the internet for the selected item. The AI ​​scrapes the websites of multiple online shops and brands to collect information such as price, quality, and stock status. For example, it collects the price, quality, and stock status of the selected chair from multiple online shops. Based on the collected information, the AI ​​compares the items. Based on price, quality, and the user's preferences and characteristics, it selects the most suitable item. For example, if the user prefers high-quality items, the AI ​​will prioritize suggesting high-quality items. Finally, the AI ​​suggests the most suitable item to the user. The suggested item matches the user's preferences and characteristics, and information such as price and quality is also provided. For example, the AI ​​provides a link to the most suitable online shop for a chair selected by the user. This system allows users to easily find items that suit them without being overwhelmed by a large amount of information. Furthermore, the AI ​​monitors inventory information in real time and displays accurate stock status to the user, providing a smooth purchasing experience. In addition, the AI ​​centralizes and compares information from multiple sales sites and stores, enabling users to make the best choice. This allows the system to search, compare, and suggest the most suitable items based on the user's selected items.

[0029] The system according to this embodiment comprises a reception unit, a search unit, a comparison unit, and a suggestion unit. The reception unit receives information about items selected by the user. For example, when a user selects an item they like from a video or photo, the reception unit receives the selection information. The search unit searches for items on the internet based on the information received by the reception unit. For example, the search unit scrapes multiple online shops and brand websites to collect information such as price, quality, and stock status. For example, the search unit collects the price, quality, and stock status of a selected chair from multiple online shops. The comparison unit compares items based on the information collected by the search unit. For example, the comparison unit selects the optimal item based on price, quality, user preferences, and characteristics. For example, if the user prefers high-quality items, the comparison unit prioritizes suggesting high-quality items. The suggestion unit suggests the optimal item to the user based on the comparison results from the comparison unit. For example, the suggestion unit provides a purchase link to the optimal online shop for the chair selected by the user. This allows the system to search the internet for and compare items based on the user's selection, and then suggest the most suitable item.

[0030] The reception desk receives information about items selected by the user. For example, when a user selects an item they like in a video or photo, the reception desk receives that selection information. Specifically, when a user uses a device such as a smartphone, tablet, or PC to view videos or photos and taps or clicks on an item they like, information about that item is sent to the reception desk. The reception desk receives the image, text information, links, etc., of the selected item and stores them in a database. Furthermore, the reception desk can analyze the user's selection history and preference trends to help with future suggestions. For example, based on information about items the user has previously selected, it can prioritize displaying similar or related items. The reception desk also provides an interface for users to input additional information about the selected items. For example, by entering detailed information such as the color, size, and brand of the selected item, more accurate search results can be obtained. This allows the reception desk to efficiently receive user selection information and smoothly transition to the next search and comparison steps.

[0031] The search unit searches for items on the internet based on information received by the reception unit. For example, the search unit scrapes multiple online shops and brand websites to collect information such as price, quality, and availability. Specifically, the search unit searches internet databases and websites based on keywords and images of items selected by the user, collecting information on related items. The search unit automatically extracts necessary information from online shops and brand websites using web scraping technology. For example, it collects the price, quality, and availability of a selected chair from multiple online shops and stores it in a database. Furthermore, the search unit can improve the accuracy of search results using AI. For example, it can use image recognition technology to search for items similar to the image of the item selected by the user. It can also use natural language processing technology to analyze keywords and text information entered by the user and provide optimal search results. As a result, the search unit can efficiently and accurately search for items on the internet and collect necessary information based on the user's selections.

[0032] The comparison unit compares items based on information collected by the search unit. For example, the comparison unit selects the optimal item based on price, quality, user preferences, and characteristics. Specifically, the comparison unit organizes information from multiple items provided by the search unit and compares them based on criteria such as price, quality, availability, and user ratings. For example, if a user prefers high-quality items, the comparison unit prioritizes high-quality items by placing items with high quality ratings higher in the list. Also, if a user has set a budget, the comparison unit selects the most cost-effective item within that budget. The comparison unit can use AI to analyze user preferences and past selection history to suggest the most suitable items for each individual user. For example, it can learn the characteristics of items previously selected by the user and prioritize displaying similar items. Furthermore, the comparison unit can collect user feedback and continuously improve the accuracy of its suggestions. This allows the comparison unit to select the optimal item that meets the user's needs and provide the most valuable suggestions to the user.

[0033] The suggestion unit proposes the most suitable items to the user based on the comparison results performed by the comparison unit. For example, the suggestion unit provides a purchase link to the best online shop for the chair selected by the user. Specifically, the suggestion unit displays purchase links and detailed information to the user based on the information about the best items provided by the comparison unit. The suggestion unit sends a notification to the user's device, providing the purchase link and detailed information for the selected item. For example, it quickly conveys information to the user using smartphone push notifications, email, or in-app notifications. The suggestion unit can also suggest related items and accessories, taking into account the user's preferences and past selection history. For example, it may suggest related items such as cushions or tables that match the chair selected by the user. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to propose the most valuable items to the user and support their purchasing decision.

[0034] The system includes an analysis unit that analyzes user preferences and characteristics. The analysis unit identifies user preferences and characteristics based, for example, on user survey results and past selection history. For instance, the analysis unit can analyze trends in items previously selected by the user to identify user preferences. The analysis unit can also collect information such as the user's age, gender, and occupation to identify user characteristics. This allows the system to suggest more appropriate items by analyzing user preferences and characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user survey results into an AI and have the AI ​​identify user preferences and characteristics.

[0035] The system includes a monitoring unit that monitors inventory information in real time. The monitoring unit, for example, monitors multiple online shops and brand websites and collects inventory information. The monitoring unit can, for example, monitor the inventory status of selected items in real time and provide accurate inventory information to the user. In this way, the system can provide users with accurate inventory status by monitoring inventory information in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input inventory information obtained from online shops and brand websites into the AI ​​and have the AI ​​perform inventory status monitoring.

[0036] The system includes an integration unit that centralizes information from multiple sales sites and stores. The integration unit, for example, centralizes information collected from multiple online shops and brand websites. The integration unit can centralize information such as price, quality, and stock availability and provide it to the user. This allows the system to centralize information from multiple sales sites and stores, enabling the user to make the best choice. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input information obtained from online shops and brand websites into the AI ​​and have the AI ​​perform the information centralization.

[0037] The search unit can scrape multiple online shops and brand websites to collect information such as price, quality, and stock status. The search unit performs scraping using programming languages ​​such as Python or JavaScript (registered trademark). For example, the search unit collects price, quality, and stock status of selected items from multiple online shops. This allows the search unit to collect information such as price, quality, and stock status by scraping multiple online shops and brand websites. Specific scraping methods and techniques can include using libraries such as BeautifulSoup or Selenium. Some or all of the above-described processes in the search unit may be performed using, for example, a generative AI, or without one. For example, the search unit can input information obtained from online shops and brand websites into a generative AI and have the generative AI perform the information collection.

[0038] The suggestion unit can suggest the most suitable items based on the user's preferences and characteristics. For example, the suggestion unit identifies the user's preferences and characteristics based on the user's survey results and past selection history, and then suggests items based on those. For example, if the user prefers high-quality items, the suggestion unit will prioritize suggesting high-quality items. In this way, the suggestion unit can provide items that are suitable for the user by suggesting the most suitable items based on the user's preferences and characteristics. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's survey results into a generative AI and have the generative AI identify the user's preferences and characteristics.

[0039] The reception desk can analyze the user's past selection history and select the optimal reception method. For example, the reception desk can analyze the trends of items the user has selected in the past and prioritize displaying similar items. For example, the reception desk can prioritize providing selection methods that the user has used in the past (tap, swipe, etc.). The reception desk can also predict and suggest items that will be selected at a specific time of day based on the user's past selection history. In this way, the reception desk can select the optimal reception method by analyzing the user's past selection history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past selection history into AI and have the AI ​​select the optimal reception method.

[0040] The reception desk can filter items based on the user's current interests and preferences when they are submitted. For example, the reception desk may prioritize displaying relevant items based on keywords the user has recently searched for. For example, the reception desk may suggest relevant items based on the content of videos or photos the user has recently viewed. The reception desk may also suggest complementary items based on items the user has recently purchased. In this way, the reception desk can provide highly relevant items by filtering based on the user's current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's recent search keywords into the AI ​​and have the AI ​​perform the filtering of relevant items.

[0041] The reception desk can prioritize items that are highly relevant to the user's geographical location when an item is received. For example, if the user is in a specific region, the reception desk will prioritize displaying items that are popular in that region. For example, if the user is traveling, the reception desk will prioritize displaying items that can be used at their travel destination. The reception desk can also prioritize displaying items that can be used at home if the user is at home. In this way, the reception desk can provide highly relevant items by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​perform the process of determining the priority of highly relevant items.

[0042] The reception desk can analyze the user's social media activity when an item is received and accept relevant items. For example, the reception desk can suggest relevant items based on items the user has "liked" on social media. For example, the reception desk can prioritize displaying items introduced by influencers the user follows. The reception desk can also suggest relevant items based on items the user has shared on social media. In this way, the reception desk can provide highly relevant items by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI and have the AI ​​suggest relevant items.

[0043] The search unit can adjust the level of detail in a search based on the importance of the items. For example, the search unit provides detailed information for expensive items. For example, for popular items, the search unit provides information including user reviews and ratings. The search unit can also provide special promotional information for new or limited-edition products. In this way, the search unit can provide more relevant search results by adjusting the level of detail in the search based on the importance of the items. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the importance of the items into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the search.

[0044] The search unit can apply different search algorithms depending on the item category during a search. For example, for fashion items, the search unit applies a search algorithm that takes into account attributes such as color and size. For interior items, the search unit applies a search algorithm that takes into account attributes such as style and material. Furthermore, for electronic devices, the search unit can also apply a search algorithm that takes into account attributes such as specifications and functions. In this way, the search unit can provide more appropriate search results by applying different search algorithms depending on the item category. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the item category into the AI ​​and have the AI ​​execute the application of the search algorithm.

[0045] The search unit can determine search priorities based on when items were submitted. For example, the search unit may prioritize displaying new or limited-edition items. For example, it may prioritize displaying items on sale. The search unit can also prioritize displaying items that the user has searched for in the past. In this way, the search unit can provide more appropriate search results by determining search priorities based on when items were submitted. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the submission dates of items into the AI ​​and have the AI ​​perform the determination of search priorities.

[0046] The search unit can adjust the search order based on the relevance of items during a search. For example, the search unit may prioritize displaying items that match the user's preferences. For example, the search unit may prioritize displaying highly relevant items based on the user's past selection history. The search unit may also prioritize displaying highly relevant items based on the user's current interests. In this way, the search unit can provide more appropriate search results by adjusting the search order based on the relevance of items. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the relevance of items into the AI ​​and have the AI ​​perform the adjustment of the search order.

[0047] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between items. For example, when comparing items of the same brand, the comparison unit considers the brand's reputation and trustworthiness. For example, when comparing items of the same category, the comparison unit considers the popularity and reputation within the category. Furthermore, when comparing items of the same price range, the comparison unit can also consider the balance between price and quality. In this way, the comparison unit can improve the accuracy of the comparison by considering the interrelationships between items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the interrelationships between items into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0048] The comparison unit can perform comparisons while considering the attribute information of the item submitter. For example, the comparison unit may consider whether the submitter is a trustworthy brand or shop. For example, the comparison unit may consider the submitter's past ratings and reviews. The comparison unit may also consider the submitter's region and shipping conditions. In this way, the comparison unit can provide more reliable comparison results by considering the attribute information of the item submitter. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit may input the submitter's attribute information into AI and have the AI ​​perform the comparison.

[0049] The comparison unit can perform comparisons while considering the geographical distribution of items. For example, the comparison unit can prioritize comparing items that are close to the user's region. For example, when comparing international items, the comparison unit can consider transportation costs and time. The comparison unit can also consider popularity and ratings in each region when making comparisons. In this way, the comparison unit can provide more appropriate comparison results by considering the geographical distribution of items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the geographical distribution of items into AI and have the AI ​​perform the comparison.

[0050] The comparison unit can improve the accuracy of its comparisons by referring to relevant literature for the items during the comparison process. For example, the comparison unit can compare items by referring to reviews and ratings of the items. For example, the comparison unit can compare items by referring to their technical specifications and patent information. The comparison unit can also compare items by referring to usage examples and user feedback. In this way, the comparison unit can improve the accuracy of its comparisons by referring to relevant literature for the items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant literature for the items into AI and have the AI ​​perform the comparison.

[0051] The suggestion function can adjust the level of detail in its suggestions based on the importance of the items. For example, it might provide detailed information for expensive items. For example, it might provide information including user reviews and ratings for popular items. It might also provide special promotional information for new or limited-edition products. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the items. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function could input the importance of the items into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0052] The suggestion unit can apply different suggestion algorithms depending on the item category when making suggestions. For example, for fashion items, the suggestion unit applies a suggestion algorithm that takes into account attributes such as color and size. For interior items, the suggestion unit applies a suggestion algorithm that takes into account attributes such as style and material. Furthermore, for electronic devices, the suggestion unit can also apply a suggestion algorithm that takes into account attributes such as specifications and functions. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the item category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the item category into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0053] The suggestion department can prioritize suggestions based on when the items are submitted. For example, the suggestion department may prioritize new or limited-edition items. For example, it may prioritize items that are on sale. The suggestion department can also prioritize items that the user has previously searched for. This allows the suggestion department to make more appropriate suggestions by prioritizing suggestions based on when the items are submitted. Some or all of the above processes in the suggestion department may be performed using AI, or not. For example, the suggestion department can input the submission dates of items into the AI ​​and have the AI ​​determine the priority of suggestions.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit can prioritize suggesting items that match the user's preferences. For example, the suggestion unit can prioritize suggesting highly relevant items based on the user's past selection history. The suggestion unit can also prioritize suggesting highly relevant items based on the user's current interests. In this way, the suggestion unit can make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of items into the AI ​​and have the AI ​​perform the adjustment of the suggestion order.

[0055] The analysis unit can select the optimal analysis method by referring to the user's past selection history during analysis. For example, the analysis unit can analyze trends in items the user has previously selected and prioritize displaying similar items. For example, the analysis unit can prioritize providing analysis methods (tap, swipe, etc.) that the user has used in the past. The analysis unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. In this way, the analysis unit can select the optimal analysis method by referring to the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past selection history into AI and have the AI ​​select the optimal analysis method.

[0056] The analysis unit can customize its analysis methods based on the user's current interests and preferences during the analysis process. For example, the analysis unit may prioritize displaying relevant items based on keywords the user has recently searched for. For example, the analysis unit may suggest relevant items based on the content of videos or photos the user has recently viewed. The analysis unit may also suggest complementary items based on items the user has recently purchased. In this way, the analysis unit can provide more appropriate analysis results by customizing its analysis methods based on the user's current interests and preferences. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the user's current interests and preferences into the AI ​​and have the AI ​​perform the customization of the analysis methods.

[0057] The analysis unit can select the optimal analysis method by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit will prioritize displaying items popular in that region. For example, if the user is traveling, the analysis unit will prioritize displaying items available at their travel destination. Furthermore, if the user is at home, the analysis unit can prioritize displaying items available for use at home. In this way, the analysis unit can provide more appropriate analysis results by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​select the optimal analysis method.

[0058] The analysis unit can analyze a user's social media activity and propose analysis methods during the analysis process. For example, the analysis unit can suggest relevant items based on items that the user has "liked" on social media. For example, the analysis unit can prioritize displaying items introduced by influencers that the user follows. The analysis unit can also suggest relevant items based on items that the user has shared on social media. In this way, the analysis unit can provide more appropriate analysis results by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media activity into AI and have the AI ​​perform the task of proposing analysis methods.

[0059] The monitoring unit can select the optimal monitoring method by referring to the user's past selection history during monitoring. For example, the monitoring unit can analyze trends in items the user has previously selected and prioritize displaying similar items. For example, the monitoring unit can prioritize providing monitoring methods (tap, swipe, etc.) that the user has used in the past. The monitoring unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. In this way, the monitoring unit can select the optimal monitoring method by referring to the user's past selection history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past selection history into AI and have the AI ​​select the optimal monitoring method.

[0060] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will prioritize displaying items popular in that region. For example, if the user is traveling, the monitoring unit will prioritize displaying items available at the travel destination. Furthermore, if the user is at home, the monitoring unit can prioritize displaying items available at home. In this way, the monitoring unit can provide more appropriate monitoring results by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal monitoring method.

[0061] The integration unit can select the optimal integration method by referring to the user's past selection history during integration. For example, the integration unit can analyze trends in items previously selected by the user and prioritize displaying similar items. For example, the integration unit can prioritize providing integration methods (tap, swipe, etc.) that the user has used in the past. The integration unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. This allows the integration unit to select the optimal integration method by referring to the user's past selection history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past selection history into AI and have the AI ​​select the optimal integration method.

[0062] The integration unit can select the optimal integration method during integration, taking into account the user's geographical location information. For example, if the user is in a specific region, the integration unit will prioritize displaying items popular in that region. For example, if the user is traveling, the integration unit will prioritize displaying items available at their travel destination. Furthermore, if the user is at home, the integration unit can prioritize displaying items available at home. In this way, the integration unit can provide more appropriate integration results by taking into account the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into AI and have the AI ​​select the optimal integration method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The search engine can analyze a user's past search history and adjust the display order of search results. For example, it can analyze trends in items a user has searched for in the past and prioritize displaying similar items. It can also prioritize displaying items from specific brands or shops if the user prefers them. Furthermore, it can suggest complementary items based on items the user has previously purchased. In this way, the search engine can provide more relevant search results by analyzing the user's past search history.

[0065] The suggestion function can customize its recommendations based on the user's geographical location. For example, if the user is in a specific region, it can suggest popular items in that region. If the user is traveling, it can suggest items they can use at their destination. Furthermore, if the user is at home, it can suggest items they can use at home. This allows the suggestion function to provide more relevant recommendations by considering the user's geographical location.

[0066] The search engine can analyze a user's social media activity and reflect relevant items in the search results. For example, it can display related items based on items a user has "liked" on social media. It can also prioritize displaying items recommended by influencers the user follows. Furthermore, it can suggest related items based on items a user has shared on social media. In this way, the search engine can provide more relevant search results by analyzing a user's social media activity.

[0067] The comparison unit can improve the accuracy of its comparisons by referring to relevant literature for each item. For example, it can compare items by referring to reviews and ratings of those items. It can also compare items by referring to their technical specifications and patent information. Furthermore, it can compare items by referring to their use cases and user feedback. In this way, the comparison unit can improve the accuracy of its comparisons by referring to relevant literature for each item.

[0068] The proposal team can prioritize proposals based on when they are submitted. For example, they can prioritize new or limited-edition products. They can also prioritize items that are on sale. Furthermore, they can prioritize items that users have previously searched for. This allows the proposal team to make more appropriate suggestions by prioritizing proposals based on when they are submitted.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives information about the items selected by the user. For example, when a user selects an item they like from a video or photo, the reception desk receives that selection information. Step 2: The search unit searches the internet for items based on the information received by the reception unit. For example, it scrapes multiple online shops and brand websites to collect information such as price, quality, and availability. For instance, it collects the price, quality, and availability of a selected chair from multiple online shops. Step 3: The comparison unit compares items based on the information collected by the search unit. For example, it selects the best item based on price, quality, user preferences, and characteristics. For instance, if a user prefers high-quality items, it will prioritize suggesting high-quality items. Step 4: The suggestion section proposes the most suitable item to the user based on the comparison results from the comparison section. For example, it provides a link to the best online shop to purchase the chair selected by the user.

[0071] (Example of form 2) The system according to an embodiment of the present invention is a system in which, when a user watches videos or photos on a platform such as a video streaming site in which celebrities or models introduce interior or fashion items, the AI ​​searches for the item on the internet and collects information such as price and quality when the user selects an item they like. Based on the collected information, this system suggests the most suitable item that matches the user's preferences and characteristics. For example, the user selects an item they like in the video or photo. For example, a chair introduced by a celebrity on a video streaming site or clothes worn by a model on a video streaming site. This selection information is input to the AI. Next, the AI ​​searches the internet for the selected item. The AI ​​scrapes the websites of multiple online shops and brands to collect information such as price, quality, and stock status. For example, it collects the price, quality, and stock status of the selected chair from multiple online shops. Based on the collected information, the AI ​​compares the items. Based on price, quality, and the user's preferences and characteristics, it selects the most suitable item. For example, if the user prefers high-quality items, the AI ​​will prioritize suggesting high-quality items. Finally, the AI ​​suggests the most suitable item to the user. The suggested item matches the user's preferences and characteristics, and information such as price and quality is also provided. For example, the AI ​​provides a link to the most suitable online shop for a chair selected by the user. This system allows users to easily find items that suit them without being overwhelmed by a large amount of information. Furthermore, the AI ​​monitors inventory information in real time and displays accurate stock status to the user, providing a smooth purchasing experience. In addition, the AI ​​centralizes and compares information from multiple sales sites and stores, enabling users to make the best choice. This allows the system to search, compare, and suggest the most suitable items based on the user's selected items.

[0072] The system according to this embodiment comprises a reception unit, a search unit, a comparison unit, and a suggestion unit. The reception unit receives information about items selected by the user. For example, when a user selects an item they like from a video or photo, the reception unit receives the selection information. The search unit searches for items on the internet based on the information received by the reception unit. For example, the search unit scrapes multiple online shops and brand websites to collect information such as price, quality, and stock status. For example, the search unit collects the price, quality, and stock status of a selected chair from multiple online shops. The comparison unit compares items based on the information collected by the search unit. For example, the comparison unit selects the optimal item based on price, quality, user preferences, and characteristics. For example, if the user prefers high-quality items, the comparison unit prioritizes suggesting high-quality items. The suggestion unit suggests the optimal item to the user based on the comparison results from the comparison unit. For example, the suggestion unit provides a purchase link to the optimal online shop for the chair selected by the user. This allows the system to search the internet for and compare items based on the user's selection, and then suggest the most suitable item.

[0073] The reception desk receives information about items selected by the user. For example, when a user selects an item they like in a video or photo, the reception desk receives that selection information. Specifically, when a user uses a device such as a smartphone, tablet, or PC to view videos or photos and taps or clicks on an item they like, information about that item is sent to the reception desk. The reception desk receives the image, text information, links, etc., of the selected item and stores them in a database. Furthermore, the reception desk can analyze the user's selection history and preference trends to help with future suggestions. For example, based on information about items the user has previously selected, it can prioritize displaying similar or related items. The reception desk also provides an interface for users to input additional information about the selected items. For example, by entering detailed information such as the color, size, and brand of the selected item, more accurate search results can be obtained. This allows the reception desk to efficiently receive user selection information and smoothly transition to the next search and comparison steps.

[0074] The search unit searches for items on the internet based on information received by the reception unit. For example, the search unit scrapes multiple online shops and brand websites to collect information such as price, quality, and availability. Specifically, the search unit searches internet databases and websites based on keywords and images of items selected by the user, collecting information on related items. The search unit automatically extracts necessary information from online shops and brand websites using web scraping technology. For example, it collects the price, quality, and availability of a selected chair from multiple online shops and stores it in a database. Furthermore, the search unit can improve the accuracy of search results using AI. For example, it can use image recognition technology to search for items similar to the image of the item selected by the user. It can also use natural language processing technology to analyze keywords and text information entered by the user and provide optimal search results. As a result, the search unit can efficiently and accurately search for items on the internet and collect necessary information based on the user's selections.

[0075] The comparison unit compares items based on information collected by the search unit. For example, the comparison unit selects the optimal item based on price, quality, user preferences, and characteristics. Specifically, the comparison unit organizes information from multiple items provided by the search unit and compares them based on criteria such as price, quality, availability, and user ratings. For example, if a user prefers high-quality items, the comparison unit prioritizes high-quality items by placing items with high quality ratings higher in the list. Also, if a user has set a budget, the comparison unit selects the most cost-effective item within that budget. The comparison unit can use AI to analyze user preferences and past selection history to suggest the most suitable items for each individual user. For example, it can learn the characteristics of items previously selected by the user and prioritize displaying similar items. Furthermore, the comparison unit can collect user feedback and continuously improve the accuracy of its suggestions. This allows the comparison unit to select the optimal item that meets the user's needs and provide the most valuable suggestions to the user.

[0076] The suggestion unit proposes the most suitable items to the user based on the comparison results performed by the comparison unit. For example, the suggestion unit provides a purchase link to the best online shop for the chair selected by the user. Specifically, the suggestion unit displays purchase links and detailed information to the user based on the information about the best items provided by the comparison unit. The suggestion unit sends a notification to the user's device, providing the purchase link and detailed information for the selected item. For example, it quickly conveys information to the user using smartphone push notifications, email, or in-app notifications. The suggestion unit can also suggest related items and accessories, taking into account the user's preferences and past selection history. For example, it may suggest related items such as cushions or tables that match the chair selected by the user. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the suggestion unit to propose the most valuable items to the user and support their purchasing decision.

[0077] The system includes an analysis unit that analyzes user preferences and characteristics. The analysis unit identifies user preferences and characteristics based, for example, on user survey results and past selection history. For instance, the analysis unit can analyze trends in items previously selected by the user to identify user preferences. The analysis unit can also collect information such as the user's age, gender, and occupation to identify user characteristics. This allows the system to suggest more appropriate items by analyzing user preferences and characteristics. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user survey results into an AI and have the AI ​​identify user preferences and characteristics.

[0078] The system includes a monitoring unit that monitors inventory information in real time. The monitoring unit, for example, monitors multiple online shops and brand websites and collects inventory information. The monitoring unit can, for example, monitor the inventory status of selected items in real time and provide accurate inventory information to the user. In this way, the system can provide users with accurate inventory status by monitoring inventory information in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input inventory information obtained from online shops and brand websites into the AI ​​and have the AI ​​perform inventory status monitoring.

[0079] The system includes an integration unit that centralizes information from multiple sales sites and stores. The integration unit, for example, centralizes information collected from multiple online shops and brand websites. The integration unit can centralize information such as price, quality, and stock availability and provide it to the user. This allows the system to centralize information from multiple sales sites and stores, enabling the user to make the best choice. Some or all of the above-described processes in the integration unit may be performed using AI, or not. For example, the integration unit can input information obtained from online shops and brand websites into the AI ​​and have the AI ​​perform the information centralization.

[0080] The search unit can scrape multiple online shops and brand websites to collect information such as price, quality, and stock status. The search unit performs scraping using programming languages ​​such as Python or JavaScript. For example, the search unit collects price, quality, and stock status of selected items from multiple online shops. This allows the search unit to collect information such as price, quality, and stock status by scraping multiple online shops and brand websites. Specific scraping methods and techniques can include libraries such as BeautifulSoup or Selenium. Some or all of the above-described processes in the search unit may be performed using, for example, a generative AI, or without one. For example, the search unit can input information obtained from online shops and brand websites into a generative AI and have the generative AI perform the information collection.

[0081] The suggestion unit can suggest the most suitable items based on the user's preferences and characteristics. For example, the suggestion unit identifies the user's preferences and characteristics based on the user's survey results and past selection history, and then suggests items based on those. For example, if the user prefers high-quality items, the suggestion unit will prioritize suggesting high-quality items. In this way, the suggestion unit can provide items that are suitable for the user by suggesting the most suitable items based on the user's preferences and characteristics. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input the user's survey results into a generative AI and have the generative AI identify the user's preferences and characteristics.

[0082] The reception unit can estimate the user's emotions and adjust the item selection method based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows the reception unit to make more appropriate selections by adjusting the item selection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0083] The reception desk can analyze the user's past selection history and select the optimal reception method. For example, the reception desk can analyze the trends of items the user has selected in the past and prioritize displaying similar items. For example, the reception desk can prioritize providing selection methods that the user has used in the past (tap, swipe, etc.). The reception desk can also predict and suggest items that will be selected at a specific time of day based on the user's past selection history. In this way, the reception desk can select the optimal reception method by analyzing the user's past selection history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past selection history into AI and have the AI ​​select the optimal reception method.

[0084] The reception desk can filter items based on the user's current interests and preferences when they are submitted. For example, the reception desk may prioritize displaying relevant items based on keywords the user has recently searched for. For example, the reception desk may suggest relevant items based on the content of videos or photos the user has recently viewed. The reception desk may also suggest complementary items based on items the user has recently purchased. In this way, the reception desk can provide highly relevant items by filtering based on the user's current interests and preferences. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's recent search keywords into the AI ​​and have the AI ​​perform the filtering of relevant items.

[0085] The reception desk can estimate the user's emotions and determine the priority of items to be received based on the estimated emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on changes in facial expressions. The reception desk can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the voice and calculate an emotion score. The reception desk can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception desk can calculate an emotion score based on fluctuations in heart rate. As a result, the reception desk can provide more appropriate items by determining the priority of items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0086] The reception desk can prioritize items that are highly relevant to the user's geographical location when an item is received. For example, if the user is in a specific region, the reception desk will prioritize displaying items that are popular in that region. For example, if the user is traveling, the reception desk will prioritize displaying items that can be used at their travel destination. The reception desk can also prioritize displaying items that can be used at home if the user is at home. In this way, the reception desk can provide highly relevant items by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information into the AI ​​and have the AI ​​perform the process of determining the priority of highly relevant items.

[0087] The reception desk can analyze the user's social media activity when an item is received and accept relevant items. For example, the reception desk can suggest relevant items based on items the user has "liked" on social media. For example, the reception desk can prioritize displaying items introduced by influencers the user follows. The reception desk can also suggest relevant items based on items the user has shared on social media. In this way, the reception desk can provide highly relevant items by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI and have the AI ​​suggest relevant items.

[0088] The search unit can estimate the user's emotions and adjust the search results based on those emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on changes in facial expressions. The search unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the search unit can analyze the tone and speed of the voice and calculate an emotion score. The search unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on fluctuations in heart rate. As a result, the search unit can provide more appropriate search results by adjusting the search results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0089] The search unit can adjust the level of detail in a search based on the importance of the items. For example, the search unit provides detailed information for expensive items. For example, for popular items, the search unit provides information including user reviews and ratings. The search unit can also provide special promotional information for new or limited-edition products. In this way, the search unit can provide more relevant search results by adjusting the level of detail in the search based on the importance of the items. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the importance of the items into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the search.

[0090] The search unit can apply different search algorithms depending on the item category during a search. For example, for fashion items, the search unit applies a search algorithm that takes into account attributes such as color and size. For interior items, the search unit applies a search algorithm that takes into account attributes such as style and material. Furthermore, for electronic devices, the search unit can also apply a search algorithm that takes into account attributes such as specifications and functions. In this way, the search unit can provide more appropriate search results by applying different search algorithms depending on the item category. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the item category into the AI ​​and have the AI ​​execute the application of the search algorithm.

[0091] The search unit can estimate the user's emotions and adjust the search length based on the estimated emotions. For example, the search unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on changes in facial expressions. The search unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the search unit can analyze the tone and speed of the voice and calculate an emotion score. The search unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the search unit can calculate an emotion score based on fluctuations in heart rate. As a result, the search unit can provide more appropriate search results by adjusting the search length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0092] The search unit can determine search priorities based on when items were submitted. For example, the search unit may prioritize displaying new or limited-edition items. For example, it may prioritize displaying items on sale. The search unit can also prioritize displaying items that the user has searched for in the past. In this way, the search unit can provide more appropriate search results by determining search priorities based on when items were submitted. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the submission dates of items into the AI ​​and have the AI ​​perform the determination of search priorities.

[0093] The search unit can adjust the search order based on the relevance of items during a search. For example, the search unit may prioritize displaying items that match the user's preferences. For example, the search unit may prioritize displaying highly relevant items based on the user's past selection history. The search unit may also prioritize displaying highly relevant items based on the user's current interests. In this way, the search unit can provide more appropriate search results by adjusting the search order based on the relevance of items. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the relevance of items into the AI ​​and have the AI ​​perform the adjustment of the search order.

[0094] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on the estimated emotions. For example, the comparison unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the comparison unit can calculate an emotion score based on changes in facial expressions. The comparison unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the comparison unit can analyze the tone and speed of the voice and calculate an emotion score. The comparison unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the comparison unit can calculate an emotion score based on fluctuations in heart rate. As a result, the comparison unit can provide more appropriate comparison results by adjusting the comparison criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The comparison unit can improve the accuracy of the comparison by considering the interrelationships between items. For example, when comparing items of the same brand, the comparison unit considers the brand's reputation and trustworthiness. For example, when comparing items of the same category, the comparison unit considers the popularity and reputation within the category. Furthermore, when comparing items of the same price range, the comparison unit can also consider the balance between price and quality. In this way, the comparison unit can improve the accuracy of the comparison by considering the interrelationships between items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the interrelationships between items into AI and have the AI ​​perform the improvement of the comparison accuracy.

[0096] The comparison unit can perform comparisons while considering the attribute information of the item submitter. For example, the comparison unit may consider whether the submitter is a trustworthy brand or shop. For example, the comparison unit may consider the submitter's past ratings and reviews. The comparison unit may also consider the submitter's region and shipping conditions. In this way, the comparison unit can provide more reliable comparison results by considering the attribute information of the item submitter. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit may input the submitter's attribute information into AI and have the AI ​​perform the comparison.

[0097] The comparison unit can estimate the user's emotions and adjust the order in which the comparison results are displayed based on the estimated emotions. For example, the comparison unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the comparison unit can calculate an emotion score based on changes in facial expressions. The comparison unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the comparison unit can analyze the tone and speed of the voice and calculate an emotion score. The comparison unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the comparison unit can calculate an emotion score based on fluctuations in heart rate. As a result, the comparison unit can provide more appropriate comparison results by adjusting the order in which the comparison results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The comparison unit can perform comparisons while considering the geographical distribution of items. For example, the comparison unit can prioritize comparing items that are close to the user's region. For example, when comparing international items, the comparison unit can consider transportation costs and time. The comparison unit can also consider popularity and ratings in each region when making comparisons. In this way, the comparison unit can provide more appropriate comparison results by considering the geographical distribution of items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the geographical distribution of items into AI and have the AI ​​perform the comparison.

[0099] The comparison unit can improve the accuracy of its comparisons by referring to relevant literature for the items during the comparison process. For example, the comparison unit can compare items by referring to reviews and ratings of the items. For example, the comparison unit can compare items by referring to their technical specifications and patent information. The comparison unit can also compare items by referring to usage examples and user feedback. In this way, the comparison unit can improve the accuracy of its comparisons by referring to relevant literature for the items. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input relevant literature for the items into AI and have the AI ​​perform the comparison.

[0100] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. As a result, the suggestion unit can make more appropriate suggestions by adjusting the way it presents its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0101] The suggestion function can adjust the level of detail in its suggestions based on the importance of the items. For example, it might provide detailed information for expensive items. For example, it might provide information including user reviews and ratings for popular items. It might also provide special promotional information for new or limited-edition products. This allows the suggestion function to make more appropriate suggestions by adjusting the level of detail based on the importance of the items. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function could input the importance of the items into the AI ​​and have the AI ​​adjust the level of detail in the suggestions.

[0102] The suggestion unit can apply different suggestion algorithms depending on the item category when making suggestions. For example, for fashion items, the suggestion unit applies a suggestion algorithm that takes into account attributes such as color and size. For interior items, the suggestion unit applies a suggestion algorithm that takes into account attributes such as style and material. Furthermore, for electronic devices, the suggestion unit can also apply a suggestion algorithm that takes into account attributes such as specifications and functions. In this way, the suggestion unit can make more appropriate suggestions by applying different suggestion algorithms depending on the item category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the item category into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expressions. The suggestion unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on fluctuations in heart rate. As a result, the suggestion unit can make more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI, for example, or without AI. For example, the proposed unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0104] The suggestion department can prioritize suggestions based on when the items are submitted. For example, the suggestion department may prioritize new or limited-edition items. For example, it may prioritize items that are on sale. The suggestion department can also prioritize items that the user has previously searched for. This allows the suggestion department to make more appropriate suggestions by prioritizing suggestions based on when the items are submitted. Some or all of the above processes in the suggestion department may be performed using AI, or not. For example, the suggestion department can input the submission dates of items into the AI ​​and have the AI ​​determine the priority of suggestions.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the items when making suggestions. For example, the suggestion unit can prioritize suggesting items that match the user's preferences. For example, the suggestion unit can prioritize suggesting highly relevant items based on the user's past selection history. The suggestion unit can also prioritize suggesting highly relevant items based on the user's current interests. In this way, the suggestion unit can make more appropriate suggestions by adjusting the order of suggestions based on the relevance of the items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of items into the AI ​​and have the AI ​​perform the adjustment of the suggestion order.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0107] The analysis unit can select the optimal analysis method by referring to the user's past selection history during analysis. For example, the analysis unit can analyze trends in items the user has previously selected and prioritize displaying similar items. For example, the analysis unit can prioritize providing analysis methods (tap, swipe, etc.) that the user has used in the past. The analysis unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. In this way, the analysis unit can select the optimal analysis method by referring to the user's past selection history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's past selection history into AI and have the AI ​​select the optimal analysis method.

[0108] The analysis unit can customize its analysis methods based on the user's current interests and preferences during the analysis process. For example, the analysis unit may prioritize displaying relevant items based on keywords the user has recently searched for. For example, the analysis unit may suggest relevant items based on the content of videos or photos the user has recently viewed. The analysis unit may also suggest complementary items based on items the user has recently purchased. In this way, the analysis unit can provide more appropriate analysis results by customizing its analysis methods based on the user's current interests and preferences. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input the user's current interests and preferences into the AI ​​and have the AI ​​perform the customization of the analysis methods.

[0109] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. As a result, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0110] The analysis unit can select the optimal analysis method by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit will prioritize displaying items popular in that region. For example, if the user is traveling, the analysis unit will prioritize displaying items available at their travel destination. Furthermore, if the user is at home, the analysis unit can prioritize displaying items available for use at home. In this way, the analysis unit can provide more appropriate analysis results by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​select the optimal analysis method.

[0111] The analysis unit can analyze a user's social media activity and propose analysis methods during the analysis process. For example, the analysis unit can suggest relevant items based on items that the user has "liked" on social media. For example, the analysis unit can prioritize displaying items introduced by influencers that the user follows. The analysis unit can also suggest relevant items based on items that the user has shared on social media. In this way, the analysis unit can provide more appropriate analysis results by analyzing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's social media activity into AI and have the AI ​​perform the task of proposing analysis methods.

[0112] The monitoring unit can estimate the user's emotions and adjust its monitoring method based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate. As a result, the monitoring unit can provide more appropriate monitoring results by adjusting its monitoring method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0113] The monitoring unit can select the optimal monitoring method by referring to the user's past selection history during monitoring. For example, the monitoring unit can analyze trends in items the user has previously selected and prioritize displaying similar items. For example, the monitoring unit can prioritize providing monitoring methods (tap, swipe, etc.) that the user has used in the past. The monitoring unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. In this way, the monitoring unit can select the optimal monitoring method by referring to the user's past selection history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past selection history into AI and have the AI ​​select the optimal monitoring method.

[0114] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate. As a result, the monitoring unit can provide more appropriate monitoring results by determining monitoring priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0115] The monitoring unit can select the optimal monitoring method while considering the user's geographical location information. For example, if the user is in a specific region, the monitoring unit will prioritize displaying items popular in that region. For example, if the user is traveling, the monitoring unit will prioritize displaying items available at the travel destination. Furthermore, if the user is at home, the monitoring unit can prioritize displaying items available at home. In this way, the monitoring unit can provide more appropriate monitoring results by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal monitoring method.

[0116] The integration unit can estimate the user's emotions and adjust the integration method based on the estimated emotions. For example, the integration unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the integration unit can calculate an emotion score based on changes in facial expressions. The integration unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the integration unit can analyze the tone and speed of the voice and calculate an emotion score. The integration unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the integration unit can calculate an emotion score based on fluctuations in heart rate. As a result, the integration unit can provide more appropriate integration results by adjusting the integration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0117] The integration unit can select the optimal integration method by referring to the user's past selection history during integration. For example, the integration unit can analyze trends in items previously selected by the user and prioritize displaying similar items. For example, the integration unit can prioritize providing integration methods (tap, swipe, etc.) that the user has used in the past. The integration unit can also predict and suggest items that will be selected during a specific time period based on the user's past selection history. This allows the integration unit to select the optimal integration method by referring to the user's past selection history. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past selection history into AI and have the AI ​​select the optimal integration method.

[0118] The integration unit can estimate the user's emotions and determine the integration priority based on the estimated emotions. For example, the integration unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the integration unit can calculate an emotion score based on changes in facial expressions. The integration unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the integration unit can analyze the tone and speed of the voice and calculate an emotion score. The integration unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the integration unit can calculate an emotion score based on fluctuations in heart rate. As a result, the integration unit can provide more appropriate integration results by determining the integration priority based on the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0119] The integration unit can select the optimal integration method during integration, taking into account the user's geographical location information. For example, if the user is in a specific region, the integration unit will prioritize displaying items popular in that region. For example, if the user is traveling, the integration unit will prioritize displaying items available at their travel destination. Furthermore, if the user is at home, the integration unit can prioritize displaying items available at home. In this way, the integration unit can provide more appropriate integration results by taking into account the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location information into AI and have the AI ​​select the optimal integration method.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function may refrain from making suggestions or suggest items that help them relax. If the user is excited, the suggestion function can leverage that excitement to suggest new items. Furthermore, if the user is concentrating, the suggestion function can adjust the timing of suggestions so as not to interrupt their concentration. In this way, the suggestion function can make more appropriate suggestions by adjusting the timing of suggestions based on the user's emotions.

[0122] The search engine can analyze a user's past search history and adjust the display order of search results. For example, it can analyze trends in items a user has searched for in the past and prioritize displaying similar items. It can also prioritize displaying items from specific brands or shops if the user prefers them. Furthermore, it can suggest complementary items based on items the user has previously purchased. In this way, the search engine can provide more relevant search results by analyzing the user's past search history.

[0123] The comparison unit can estimate the user's emotions and adjust the comparison criteria based on those emotions. For example, if the user is feeling anxious, the comparison unit will prioritize displaying reliable items. If the user is excited, the comparison unit can prioritize displaying new or trending items. Furthermore, if the user is relaxed, the comparison unit can focus on price and quality in its comparisons. In this way, the comparison unit can provide more appropriate comparison results by adjusting the comparison criteria based on the user's emotions.

[0124] The suggestion function can customize its recommendations based on the user's geographical location. For example, if the user is in a specific region, it can suggest popular items in that region. If the user is traveling, it can suggest items they can use at their destination. Furthermore, if the user is at home, it can suggest items they can use at home. This allows the suggestion function to provide more relevant recommendations by considering the user's geographical location.

[0125] The search engine can analyze a user's social media activity and reflect relevant items in the search results. For example, it can display related items based on items a user has "liked" on social media. It can also prioritize displaying items recommended by influencers the user follows. Furthermore, it can suggest related items based on items a user has shared on social media. In this way, the search engine can provide more relevant search results by analyzing a user's social media activity.

[0126] The suggestion function can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is stressed, it can suggest items that help them relax. If the user is excited, it can suggest new or trendy items. Furthermore, if the user is relaxed, it can suggest items that focus on price and quality. In this way, the suggestion function can provide more appropriate suggestions by customizing the content of the suggestions based on the user's emotions.

[0127] The comparison unit can improve the accuracy of its comparisons by referring to relevant literature for each item. For example, it can compare items by referring to reviews and ratings of those items. It can also compare items by referring to their technical specifications and patent information. Furthermore, it can compare items by referring to their use cases and user feedback. In this way, the comparison unit can improve the accuracy of its comparisons by referring to relevant literature for each item.

[0128] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, it can use a simple and easy-to-read display method. If the user is excited, it can use a colorful and visually appealing display method. Furthermore, if the user is relaxed, it can use a display method that includes detailed information. In this way, the search engine can provide more relevant search results by adjusting how search results are displayed based on the user's emotions.

[0129] The proposal team can prioritize proposals based on when they are submitted. For example, they can prioritize new or limited-edition products. They can also prioritize items that are on sale. Furthermore, they can prioritize items that users have previously searched for. This allows the proposal team to make more appropriate suggestions by prioritizing proposals based on when they are submitted.

[0130] The comparison unit can estimate the user's emotions and adjust the display order of the comparison results based on those emotions. For example, if the user is stressed, it can adopt a simple and easy-to-read display order. If the user is excited, it can adopt a colorful and visually appealing display order. Furthermore, if the user is relaxed, it can adopt a display order that includes detailed information. In this way, the comparison unit can provide more appropriate comparison results by adjusting the display order of the comparison results based on the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk receives information about the items selected by the user. For example, when a user selects an item they like from a video or photo, the reception desk receives that selection information. Step 2: The search unit searches the internet for items based on the information received by the reception unit. For example, it scrapes multiple online shops and brand websites to collect information such as price, quality, and availability. For instance, it collects the price, quality, and availability of a selected chair from multiple online shops. Step 3: The comparison unit compares items based on the information collected by the search unit. For example, it selects the best item based on price, quality, user preferences, and characteristics. For instance, if a user prefers high-quality items, it will prioritize suggesting high-quality items. Step 4: The suggestion section proposes the most suitable item to the user based on the comparison results from the comparison section. For example, it provides a link to the best online shop to purchase the chair selected by the user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0135] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, search unit, comparison unit, suggestion unit, analysis unit, monitoring unit, and integration unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information on items selected by the user. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and searches for items on the internet. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and compares items based on the collected information. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests the most suitable items to the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and characteristics. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and monitors inventory information in real time. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and centralizes information from multiple sales sites and stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, search unit, comparison unit, suggestion unit, analysis unit, monitoring unit, and integration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information on items selected by the user. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and searches for items on the internet. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and compares items based on the collected information. The suggestion unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and suggests the most suitable items to the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the user's preferences and characteristics. The monitoring unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and monitors inventory information in real time. The integration unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and centralizes information from multiple sales sites and stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the reception unit, search unit, comparison unit, suggestion unit, analysis unit, monitoring unit, and integration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information on items selected by the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for items on the internet. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares items based on the collected information. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests the most suitable items to the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and characteristics. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors inventory information in real time. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centralizes information from multiple sales sites and stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0173] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0178] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0179] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0180] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the reception unit, search unit, comparison unit, suggestion unit, analysis unit, monitoring unit, and integration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information on items selected by the user. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for items on the internet. The comparison unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and compares items based on the collected information. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests the most suitable items to the user. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's preferences and characteristics. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors inventory information in real time. The integration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and centralizes information from multiple sales sites and stores. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0188] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0189] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0190] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0194] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0195] 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.

[0196] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0197] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0198] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0199] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0201] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0202] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0203] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0204] (Note 1) A reception desk that receives information about the items selected by the user, A search unit that searches for items on the internet based on the information received by the reception unit, A comparison unit compares items based on the information collected by the aforementioned search unit, The system includes a suggestion unit that proposes the most suitable item to the user based on the results of the comparison performed by the comparison unit. A system characterized by the following features. (Note 2) It includes an analysis unit that analyzes user preferences and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a monitoring unit that monitors inventory information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) It has an integrated unit that centralizes information from multiple sales sites and stores. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, We scrape multiple online shops and brand websites to collect information such as price, quality, and stock availability. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We suggest the most suitable items based on the user's preferences and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how items are selected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past selection history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving items, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of items to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting items, the system prioritizes accepting items that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When an item is submitted, the system analyzes the user's social media activity and accepts relevant items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, It estimates the user's sentiment and adjusts the search results based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, adjust the search level based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, different search algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, It estimates the user's sentiment and adjusts the search length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When searching, prioritize your search based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned search unit, When searching, the search order is adjusted based on the relevance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 19) The comparison unit is, It estimates the user's emotions and adjusts the comparison criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The comparison unit is, When making comparisons, consider the interrelationships between items to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 21) The comparison unit is, When making comparisons, the attribute information of the item submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The comparison unit is, It estimates the user's sentiment and adjusts the order in which comparison results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The comparison unit is, When making comparisons, the geographical distribution of items should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The comparison unit is, When making comparisons, refer to relevant literature for each item to improve the accuracy of the comparison. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit is During analysis, the system selects the optimal analysis method by referring to the user's past selection history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit is During analysis, the analysis methods are customized based on the user's current interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned analysis unit is During analysis, the optimal analysis method is selected by considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned analysis unit is During the analysis, we will analyze users' social media activity and propose analytical methods. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, the system selects the optimal monitoring method by referring to the user's past selection history. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned integration unit is It estimates the user's emotions and adjusts the integration method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned integration unit is During integration, the system selects the optimal integration method by referring to the user's past selection history. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned integration unit is During integration, the optimal integration method is selected, taking into account the user's geographical location information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information about the items selected by the user, A search unit that searches for items on the internet based on the information received by the reception unit, A comparison unit compares items based on the information collected by the aforementioned search unit, The system includes a suggestion unit that proposes the most suitable item to the user based on the results of the comparison performed by the comparison unit. A system characterized by the following features.

2. It includes an analysis unit that analyzes user preferences and characteristics. The system according to feature 1.

3. It is equipped with a monitoring unit that monitors inventory information in real time. The system according to feature 1.

4. It has an integrated unit that centralizes information from multiple sales sites and stores. The system according to feature 1.

5. The aforementioned search unit, We scrape multiple online shops and brand websites to collect information such as price, quality, and stock availability. The system according to feature 1.

6. The aforementioned proposal section is, We suggest the most suitable items based on the user's preferences and characteristics. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how items are selected based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past selection history to select the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving items, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

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