System

Abstract

An object of a system according to an embodiment is to efficiently search for an item selected on a moving image or a photograph and propose an optimal option to a user.SOLUTION: A system includes an analysis unit, a search unit, a collection unit, and a proposal unit. The analysis unit analyzes an item selected by a user on a moving image or a photograph. The search unit searches for an item on the Internet based on the search query generated by the analysis unit. The collection unit collects the information acquired by the search unit. The proposal unit proposes an appropriate option to the user on the basis of the information collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to efficiently search for items seen in videos or photos on the Internet and find the best options.

[0005] The system according to the embodiment aims to efficiently search for selected items in videos and photos and to suggest optimal options to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a search unit, a collection unit, and a suggestion unit. The analysis unit analyzes items selected by a user on videos or photos. The search unit searches for items on the Internet based on a search query generated by the analysis unit. The collection unit collects information acquired by the search unit. The suggestion unit suggests appropriate options to the user based on the information collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for selected items on videos and photos and suggest optimal options to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically analyzes items selected by a user in a video or photo, searches the Internet, collects information, and suggests optimal options. This system allows a user to select an item of interest in a video or photo, and AI analyzes the item, searches for identical or similar items online, collects information such as price, quality, and reviews, and suggests optimal options to the user. For example, when a user selects a chair or bag featured in a video, AI analyzes the item and searches for identical or similar items online. Next, the AI ​​collects search results and obtains information such as price, quality, and reviews for each item. For example, it collects prices and user ratings for the same chair from multiple online shops. Based on the collected information, the AI ​​then suggests optimal options to the user. For example, it may suggest the chair with the best cost performance or the bag with the highest user ratings. This allows users to easily find the optimal item. This system automatically analyzes items selected by a user in a video or photo, searches the Internet, collects information, and suggests optimal options. For example, it eliminates the need for users to compare items from multiple online shops, enabling more efficient shopping. Furthermore, because the AI ​​automatically performs search and comparison, users can easily find items that match their preferences.

[0029] A proposal system according to an embodiment includes an analysis unit, a search unit, a collection unit, and a proposal unit. The analysis unit analyzes an item selected by a user in a video or photo. The analysis unit extracts features of the item using, for example, image recognition technology and generates a search query. The analysis unit can also analyze the features of the selected item using AI and generate a search query. For example, the analysis unit extracts features of the item with high accuracy using deep learning technology. The search unit searches for items on the Internet based on the search query generated by the analysis unit. The search unit searches for items on the Internet using, for example, a search engine. The search unit can also search for items on the Internet based on the generated search query using AI. For example, the search unit optimizes the search query using natural language processing technology to improve search accuracy. The collection unit collects information acquired by the search unit. The collection unit collects information such as the price, quality, and reviews of each item. The collection unit can also collect detailed information about each item from the search results using AI. For example, the collection unit collects information from multiple online shops using web scraping technology. The suggestion unit suggests optimal options to the user based on the information collected by the collection unit. For example, the suggestion unit suggests the most cost-effective item or the item with the highest user rating based on the collected information. The suggestion unit can also use AI to suggest optimal options to the user based on the collected information. For example, the suggestion unit uses a machine learning algorithm to suggest items that match the user's preferences. As a result, the suggestion system according to the embodiment can analyze items selected by the user in videos or photos, search the Internet, collect information, and suggest optimal options.

[0030] The analysis unit can extract item features using image recognition technology and generate a search query. Image recognition technology includes, but is not limited to, deep learning and pattern recognition. The analysis unit can extract item features with high accuracy using, for example, deep learning technology. The analysis unit can also extract item features using pattern recognition technology. The analysis unit can also extract item features using image recognition technology and generate a search query. For example, the analysis unit can use image recognition technology to extract item features such as color, shape, and size, and generate a search query based on the extracted features. In this way, the image recognition technology can accurately extract item features and generate a search query. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input image data into a generation AI, extract item features using image recognition technology, and generate a search query.

[0031] The search unit can search for items on the Internet using the generated search query. Searches on the Internet include, but are not limited to, search engines and search algorithms. For example, the search unit can use a search engine to search for items on the Internet. The search unit can also use AI to search for items on the Internet based on the generated search query. For example, the search unit can use natural language processing technology to optimize the search query and improve search accuracy. The search unit can also use a search algorithm to efficiently search for items on the Internet. For example, the search unit can prioritize displaying highly relevant search results based on the search query. This allows items to be efficiently searched for on the Internet using the generated search query. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the generated search query into AI to search for items on the Internet.

[0032] The collection unit can collect information on the price, quality, and reviews of each item from the search results. Examples of collected information include, but are not limited to, price information, quality information, and review information. For example, the collection unit collects the price of each item from the search results. The collection unit can also collect the quality of each item from the search results. The collection unit can also collect reviews of each item from the search results. For example, the collection unit collects prices and user ratings for the same item from multiple online shops. This allows detailed information on each item to be collected from the search results. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the search results into AI to collect information on the price, quality, and reviews of each item.

[0033] The suggestion unit can suggest appropriate options to the user based on the collected information. The suggested options include, but are not limited to, the user's preferences and past selection history, for example. For example, the suggestion unit can suggest the item with the best cost performance based on the collected information. The suggestion unit can also suggest the item with the highest user rating based on the collected information. The suggestion unit can also suggest an item that matches the user's preferences based on the collected information. For example, the suggestion unit can use a machine learning algorithm to suggest an item that matches the user's preferences. This makes it possible to suggest optimal options to the user based on the collected information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the collected information into AI and suggest appropriate options to the user.

[0034] The analysis unit can analyze background information of videos and photos and extract features based on the scene in which the item is used. Examples of background information include, but are not limited to, location, time, and environment. For example, the analysis unit can analyze furniture and decorations in the background of a video to identify the scene in which the item is used. The analysis unit can also analyze the scenery and environment in the background of a photo to identify the scene in which the item is used. The analysis unit can also analyze how an item is used in a video or photo and extract features based on the scene in which the item is used. For example, the analysis unit can analyze furniture and decorations in the background of a video to identify the scene in which the item is used. By extracting features based on the scene in which the item is used, more accurate analysis results can be obtained. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input background information of a video or photo into a generation AI and extract features based on the scene in which the item is used.

[0035] The analysis unit can analyze the audio information of the video and complement the item's features from the introducer's comments. Audio information includes, but is not limited to, voice recognition and voice analysis. For example, the analysis unit extracts the item's features mentioned by the introducer from the audio in the video. The analysis unit can also analyze the method of using the item mentioned by the introducer from the audio in the video. The analysis unit can also analyze the advantages and disadvantages of the item mentioned by the introducer from the audio in the video and complement the features. For example, the analysis unit uses voice recognition technology to extract the item's features from the audio in the video. This allows the item's features to be complemented from the introducer's comments, thereby obtaining more detailed analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the video's audio data into a generation AI and complement the item's features from the introducer's comments.

[0036] The analysis unit can improve the accuracy of the analysis results by referring to the user's past selection history. Past selection history includes, but is not limited to, purchase history and browsing history. The analysis unit, for example, corrects the current analysis results based on the characteristics of items previously selected by the user. The analysis unit can also analyze preference trends from the user's past selection history and reflect them in the current analysis results. The analysis unit can also improve the accuracy of the current analysis results based on the user's evaluations of items previously selected by the user. For example, the analysis unit corrects the current analysis results by referring to the user's past selection history. In this way, the accuracy of the analysis results can be improved by referring to the user's past selection history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past selection history into a generation AI to improve the accuracy of the analysis results.

[0037] The analysis unit can analyze metadata of videos and photos to complete the related information of items. Metadata includes, for example, tag information and attribute data, but is not limited to these examples. The analysis unit can, for example, analyze the shooting location and date and time from the video metadata to complete the related information of items. The analysis unit can also analyze camera setting information from the photo metadata to complete the related information of items. The analysis unit can also analyze tag information from the metadata of videos and photos to complete the related information of items. For example, the analysis unit can analyze the shooting location and date and time from the video metadata to complete the related information of items. In this way, by analyzing the metadata, the related information of items can be completed and more detailed information can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the metadata of videos and photos into a generation AI to complete the related information of items.

[0038] The analysis unit can determine the analysis priority based on the playback time and the number of views of the video. The playback time and the number of views include, for example, a range of playback time and a threshold value of the number of views, but are not limited to these examples. For example, the analysis unit prioritizes analysis of videos with long playback times. The analysis unit can also prioritize analysis of videos with many views. The analysis unit can also determine the analysis priority by taking both the playback time and the number of views into consideration. For example, the analysis unit prioritizes analysis of videos with long playback times. This allows important videos to be analyzed preferentially by taking the playback time and the number of views into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the playback time and the number of views of the video into the generation AI and determine the analysis priority.

[0039] The analysis unit can select the optimal analysis method based on the user's device information. Device information includes, but is not limited to, the device type, performance, and usage status. For example, if the user is using a smartphone, the analysis unit can select a lightweight analysis method. Furthermore, if the user is using a tablet, the analysis unit can select a highly accurate analysis method. Furthermore, if the user is using a desktop, the analysis unit can select a detailed analysis method. For example, if the user is using a smartphone, the analysis unit selects a lightweight analysis method. This enables efficient analysis by selecting the optimal analysis method according to the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into a generation AI and select the optimal analysis method.

[0040] The search unit can automatically generate keywords related to the search query to improve search accuracy. Related keywords include, but are not limited to, synonyms, similar words, and related keywords, for example. The search unit can automatically generate synonyms related to the search query to improve search accuracy. The search unit can also automatically generate synonyms related to the search query to improve search accuracy. The search unit can also automatically generate related keywords related to the search query to improve search accuracy. For example, the search unit can automatically generate synonyms related to the search query to improve search accuracy. This allows the automatic generation of related keywords to improve search accuracy. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input a search query to a generation AI and automatically generate related keywords.

[0041] The search unit can collect feedback on the search query and optimize the search algorithm. Examples of the feedback include, but are not limited to, user ratings, click data, and dwell time data. For example, the search unit can collect user click data on search results and optimize the search algorithm. The search unit can also collect user rating data on the search results and optimize the search algorithm. The search unit can also collect user dwell time data on the search results and optimize the search algorithm. For example, the search unit can collect user click data on the search results and optimize the search algorithm. By collecting feedback, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the feedback data into a generation AI to optimize the search algorithm.

[0042] When generating a search query, the search unit can improve accuracy by referring to the user's past search history. Past search history includes, but is not limited to, search query history and click history. For example, the search unit extracts related keywords from the user's past search history to generate a search query. The search unit can also generate a search query by prioritizing frequently searched keywords from the user's past search history. The search unit can also generate a search query by taking into account keywords searched during a specific time period from the user's past search history. For example, the search unit extracts related keywords from the user's past search history to generate a search query. In this way, the accuracy of the search query can be improved by referring to the user's past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past search history into a generation AI to improve the accuracy of the search query.

[0043] The search unit can improve search accuracy by referring to literature related to the search query. Related literature includes, but is not limited to, academic papers, patent documents, and news articles, for example. The search unit can improve search accuracy by referring to academic papers related to the search query. The search unit can also improve search accuracy by referring to patent documents related to the search query. The search unit can also improve search accuracy by referring to news articles related to the search query. For example, the search unit can improve search accuracy by referring to academic papers related to the search query. In this way, by referring to related literature, search accuracy can be improved. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input related literature into a generation AI to improve search accuracy.

[0044] The search unit can provide optimal search results by taking into account the user's geographical location information when generating a search query. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the search unit can prioritize and display search results for nearby stores based on the user's current location. The search unit can also display search results limited to a specific area based on the user's geographical location information. The search unit can also display search results including optimal delivery options based on the user's geographical location information. For example, the search unit prioritizes and displays search results for nearby stores based on the user's current location. This allows optimal search results to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input the user's geographical location information into a generation AI to provide optimal search results.

[0045] When generating a search query, the search unit can analyze the user's social media activity and provide relevant search results. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the search unit can analyze the content of the user's social media posts and provide relevant search results. The search unit can also provide relevant search results by referring to the activities of the user's friends on social media. The search unit can also provide relevant search results based on the user's social media check-in information. For example, the search unit can analyze the content of the user's social media posts and provide relevant search results. In this way, relevant search results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the search unit may be performed, for example, using AI or without AI. For example, the search unit can input the user's social media activity into a generation AI and provide relevant search results.

[0046] The collection unit can evaluate the reliability of information collected from search results and improve the collection accuracy. The reliability of information includes, but is not limited to, the reliability of the information source and the consistency of the information. For example, the collection unit evaluates the reliability of search results and prioritizes the collection of highly rated information. The collection unit can also evaluate the reliability of search results and prioritize the collection of information from official websites. The collection unit can also evaluate the reliability of search results and prioritize the collection of information with many user reviews. For example, the collection unit evaluates the reliability of search results and prioritizes the collection of highly rated information. This allows the collection accuracy to be improved by evaluating the reliability of the information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the search results into a generation AI to evaluate the reliability of the information and improve the collection accuracy.

[0047] The collection unit can automatically classify the categories of collected information, thereby streamlining the organization of information. Information categories include, but are not limited to, product categories and service categories. The collection unit, for example, automatically classifies the collected information into categories such as price, quality, and reviews. The collection unit can also automatically classify the collected information into categories such as brand, store, and region. The collection unit can also automatically classify the collected information into categories such as new arrivals, popularity, and recommendations. For example, the collection unit automatically classifies the collected information into categories such as price, quality, and reviews. This automatic classification of information categories can streamline the organization of information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the collected information into a generation AI to automatically classify the information categories.

[0048] The collection unit can adjust the update frequency of the collected information to provide the latest information. The update frequency of the information includes, but is not limited to, daily updates or weekly updates. For example, the collection unit can increase the update frequency of the collected information to provide the latest price information. The collection unit can also increase the update frequency of the collected information to provide the latest review information. The collection unit can also increase the update frequency of the collected information to provide the latest quality information. For example, the collection unit can increase the update frequency of the collected information to provide the latest price information. In this way, by adjusting the update frequency of the information, the latest information can always be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the update frequency of the collected information to the generation AI to provide the latest information.

[0049] The collection unit can evaluate the relevance of information collected from search results and improve the collection accuracy. The relevance of information includes, but is not limited to, co-occurrence frequency and relevance score. For example, the collection unit evaluates the relevance of search results and prioritizes collection of highly relevant information. The collection unit can also evaluate the relevance of search results and prioritize collection of highly relevant information based on a user's selection history. The collection unit can also evaluate the relevance of search results and prioritize collection of information in the same category. For example, the collection unit evaluates the relevance of search results and prioritizes collection of highly relevant information. By doing so, the collection accuracy can be improved by evaluating the relevance of information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input search results to a generation AI to evaluate the relevance of information and improve the collection accuracy.

[0050] The collection unit can diversify the sources of information collected and improve the reliability of the information. Information sources include, but are not limited to, news sites, blogs, and social networking sites. For example, the collection unit can collect information from multiple online shops to improve reliability. The collection unit can also collect information from user review sites to improve reliability. The collection unit can also collect information from manufacturer official websites to improve reliability. For example, the collection unit can collect information from multiple online shops to improve reliability. By diversifying the information sources, the reliability of the information can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the information sources into a generation AI to improve the reliability of the information.

[0051] The collection unit can standardize the format of the information to be collected and improve the efficiency of organizing the information. Examples of information formats include, but are not limited to, text format and CSV format. The collection unit, for example, converts the collected information into a unified format and improves the efficiency of organizing. The collection unit can also convert the collected information into a unified format for each category and improve the efficiency of organizing. The collection unit can also convert the collected information into a unified format in chronological order and improve the efficiency of organizing. For example, the collection unit converts the collected information into a unified format and improves the efficiency of organizing. This standardizes the format of the information and improves the efficiency of organizing the information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information into a generation AI and standardize the format of the information.

[0052] The suggestion unit can evaluate the reliability of the collected information when making a suggestion and improve the accuracy of the suggestion. The reliability of the information includes, but is not limited to, the reliability of the information source and the consistency of the information. For example, the suggestion unit can evaluate the reliability of the collected information and make a suggestion based on highly reliable information. The suggestion unit can also evaluate the reliability of the collected information and make a suggestion based on information from official websites. The suggestion unit can also evaluate the reliability of the collected information and make a suggestion based on information with many user reviews. For example, the suggestion unit can evaluate the reliability of the collected information and make a suggestion based on highly reliable information. By evaluating the reliability of the information, the accuracy of the suggestion can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the collected information into a generation AI to evaluate the reliability of the information and improve the accuracy of the suggestion.

[0053] When making a suggestion, the suggestion unit can make an optimal suggestion by referring to the user's past selection history. Examples of past selection history include, but are not limited to, purchase history and browsing history. For example, the suggestion unit can analyze preference trends from the user's past selection history and make optimal suggestions. The suggestion unit can also make suggestions based on frequently selected items from the user's past selection history. The suggestion unit can also make suggestions based on items selected during a specific time period from the user's past selection history. For example, the suggestion unit can analyze preference trends from the user's past selection history and make optimal suggestions. This allows optimal suggestions to be made by referring to the user's past selection history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past selection history into a generation AI and make optimal suggestions.

[0054] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current living situation and areas of interest. Examples of living situations and areas of interest include, but are not limited to, occupation, hobbies, and family structure. The suggestion unit, for example, suggests optimal items based on the user's current living situation. The suggestion unit can also suggest related items based on the user's areas of interest. The suggestion unit can also make optimal suggestions by combining the user's current living situation and areas of interest. For example, the suggestion unit suggests optimal items based on the user's current living situation. This enables more appropriate suggestions to be made by customizing the suggestion content based on the user's living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into a generation AI to customize the suggestion content.

[0055] The suggestion unit can evaluate the relevance of the collected information when making a suggestion, thereby improving the accuracy of the suggestion. The relevance of the information includes, but is not limited to, for example, co-occurrence frequency and relevance score. For example, the suggestion unit can evaluate the relevance of the collected information and make a suggestion based on highly relevant information. The suggestion unit can also evaluate the relevance of the collected information and make a suggestion based on highly relevant information based on the user's selection history. The suggestion unit can also evaluate the relevance of the collected information and make a suggestion based on information in the same category. For example, the suggestion unit can evaluate the relevance of the collected information and make a suggestion based on highly relevant information. In this way, the accuracy of the suggestion can be improved by evaluating the relevance of the information. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the collected information into a generation AI, evaluate the relevance of the information, and improve the accuracy of the suggestion.

[0056] When making a suggestion, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the suggestion unit can suggest items from nearby stores based on the user's current location. The suggestion unit can also suggest area-specific items based on the user's geographical location information. The suggestion unit can also suggest items including optimal delivery options based on the user's geographical location information. For example, the suggestion unit can suggest items from nearby stores based on the user's current location. This allows optimal suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI to make optimal suggestions.

[0057] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the suggestion unit can analyze the content of the user's social media posts and suggest related items. The suggestion unit can also suggest related items based on the user's social media check-in information. For example, the suggestion unit can analyze the content of the user's social media posts and suggest related items. In this way, related suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into a generation AI and make related suggestions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The recommendation system can also analyze a user's purchasing history and customize the recommendations based on past purchasing patterns. For example, it can analyze the brands and categories of items a user has previously purchased and suggest new products from similar brands or categories. It can also identify items that a user tends to purchase during a particular season and make recommendations tailored to that season. It can also analyze the characteristics of items that a user has previously rated highly and suggest items with similar characteristics. This allows for more personalized recommendations based on a user's purchasing history.

[0060] The analysis unit can analyze a user's social media activity and generate search queries based on the user's interests. For example, the analysis unit can analyze the content of posts that the user frequently likes or comments on on social media and search for items related to that content. It can also analyze the content of posts by influencers the user follows and search for similar items. It can also analyze the content of articles and videos the user has shared on social media and search for items related to them. This makes it possible to provide more relevant search results based on the user's social media activity.

[0061] The search unit can provide region-specific search results by taking into account the user's geographic location information. For example, if the user is in a particular city, items related to stores and services in that city can be searched for preferentially. Also, if the user is traveling, items related to the region to which the user is traveling can be searched. Furthermore, if the user is participating in a particular event or festival, items related to that event can be searched for. This allows for more appropriate search results to be provided based on the user's geographic location information.

[0062] The analysis unit can select the optimal analysis method based on the user's device information. For example, if the user is using a smartphone, a lightweight analysis method can be selected. If the user is using a tablet, a highly accurate analysis method can be selected. Furthermore, if the user is using a desktop, a detailed analysis method can be selected. This allows for efficient analysis by selecting the optimal analysis method according to the user's device information.

[0063] The collection unit can evaluate the reliability of information collected from search results and improve the collection accuracy. For example, the reliability of search results can be evaluated and highly rated information can be collected preferentially. Information from official websites can also be collected preferentially. Furthermore, information with many user reviews can also be collected preferentially. In this way, the collection accuracy can be improved by evaluating the reliability of information.

[0064] The suggestion unit can customize the suggestion content based on the user's current living situation and areas of interest. For example, it can suggest optimal items based on the user's occupation, hobbies, and family structure. It can also suggest related items based on the user's areas of interest. Furthermore, it can combine the user's current living situation and areas of interest to make optimal suggestions. This allows the suggestion content to be customized based on the user's living situation and areas of interest, making it possible to make more appropriate suggestions.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes the item selected by the user in the video or photo. The analysis unit uses image recognition technology and AI to extract the item's features and generate a search query. For example, deep learning technology can be used to extract the item's features with high accuracy. Step 2: The search unit searches for items on the Internet based on the search query generated by the analysis unit. The search unit optimizes the search query using search engines, AI, and natural language processing technology to improve search accuracy. Step 3: The collection unit collects the information obtained by the search unit. The collection unit collects information such as the price, quality, and reviews of each item, and uses AI and web scraping technology to collect more detailed information. Step 4: The suggestion unit suggests optimal options to the user based on the information collected by the collection unit. Based on the collected information, the suggestion unit suggests items with the best cost performance and items with the highest user ratings, and uses a machine learning algorithm to suggest items that suit the user's preferences.

[0067] (Example 2) A system according to an embodiment of the present invention automatically analyzes items selected by a user in a video or photo, searches the Internet, collects information, and suggests optimal options. This system allows a user to select an item of interest in a video or photo, and AI analyzes the item, searches for identical or similar items online, collects information such as price, quality, and reviews, and suggests optimal options to the user. For example, when a user selects a chair or bag featured in a video, AI analyzes the item and searches for identical or similar items online. Next, the AI ​​collects search results and obtains information such as price, quality, and reviews for each item. For example, it collects prices and user ratings for the same chair from multiple online shops. Based on the collected information, the AI ​​then suggests optimal options to the user. For example, it may suggest the chair with the best cost performance or the bag with the highest user ratings. This allows users to easily find the optimal item. This system automatically analyzes items selected by a user in a video or photo, searches the Internet, collects information, and suggests optimal options. For example, it eliminates the need for users to compare items from multiple online shops, enabling more efficient shopping. Furthermore, because the AI ​​automatically performs search and comparison, users can easily find items that match their preferences.

[0068] A proposal system according to an embodiment includes an analysis unit, a search unit, a collection unit, and a proposal unit. The analysis unit analyzes an item selected by a user in a video or photo. The analysis unit extracts features of the item using, for example, image recognition technology and generates a search query. The analysis unit can also analyze the features of the selected item using AI and generate a search query. For example, the analysis unit extracts features of the item with high accuracy using deep learning technology. The search unit searches for items on the Internet based on the search query generated by the analysis unit. The search unit searches for items on the Internet using, for example, a search engine. The search unit can also search for items on the Internet based on the generated search query using AI. For example, the search unit optimizes the search query using natural language processing technology to improve search accuracy. The collection unit collects information acquired by the search unit. The collection unit collects information such as the price, quality, and reviews of each item. The collection unit can also collect detailed information about each item from the search results using AI. For example, the collection unit collects information from multiple online shops using web scraping technology. The suggestion unit suggests optimal options to the user based on the information collected by the collection unit. For example, the suggestion unit suggests the most cost-effective item or the item with the highest user rating based on the collected information. The suggestion unit can also use AI to suggest optimal options to the user based on the collected information. For example, the suggestion unit uses a machine learning algorithm to suggest items that match the user's preferences. As a result, the suggestion system according to the embodiment can analyze items selected by the user in videos or photos, search the Internet, collect information, and suggest optimal options.

[0069] The analysis unit can extract item features using image recognition technology and generate a search query. Image recognition technology includes, but is not limited to, deep learning and pattern recognition. The analysis unit can extract item features with high accuracy using, for example, deep learning technology. The analysis unit can also extract item features using pattern recognition technology. The analysis unit can also extract item features using image recognition technology and generate a search query. For example, the analysis unit can use image recognition technology to extract item features such as color, shape, and size, and generate a search query based on the extracted features. In this way, the image recognition technology can accurately extract item features and generate a search query. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input image data into a generation AI, extract item features using image recognition technology, and generate a search query.

[0070] The search unit can search for items on the Internet using the generated search query. Searches on the Internet include, but are not limited to, search engines and search algorithms. For example, the search unit can use a search engine to search for items on the Internet. The search unit can also use AI to search for items on the Internet based on the generated search query. For example, the search unit can use natural language processing technology to optimize the search query and improve search accuracy. The search unit can also use a search algorithm to efficiently search for items on the Internet. For example, the search unit can prioritize displaying highly relevant search results based on the search query. This allows items to be efficiently searched for on the Internet using the generated search query. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the generated search query into AI to search for items on the Internet.

[0071] The collection unit can collect information on the price, quality, and reviews of each item from the search results. Examples of collected information include, but are not limited to, price information, quality information, and review information. For example, the collection unit collects the price of each item from the search results. The collection unit can also collect the quality of each item from the search results. The collection unit can also collect reviews of each item from the search results. For example, the collection unit collects prices and user ratings for the same item from multiple online shops. This allows detailed information on each item to be collected from the search results. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the search results into AI to collect information on the price, quality, and reviews of each item.

[0072] The suggestion unit can suggest appropriate options to the user based on the collected information. The suggested options include, but are not limited to, the user's preferences and past selection history, for example. For example, the suggestion unit can suggest the item with the best cost performance based on the collected information. The suggestion unit can also suggest the item with the highest user rating based on the collected information. The suggestion unit can also suggest an item that matches the user's preferences based on the collected information. For example, the suggestion unit can use a machine learning algorithm to suggest an item that matches the user's preferences. This makes it possible to suggest optimal options to the user based on the collected information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the collected information into AI and suggest appropriate options to the user.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit increases the analysis accuracy to extract detailed features. Alternatively, if the user is relaxed, the analysis unit can maintain normal analysis accuracy and extract standard features. Alternatively, if the user is in a hurry, the analysis unit can lower the analysis accuracy to quickly extract features. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, the analysis unit can analyze the user's text data and estimate the emotion. For example, the analysis unit calculates an emotion score based on changes in the user's facial expression. This allows for adjusting the analysis accuracy according to the user's emotions to obtain more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and adjust the accuracy of the analysis based on the emotion.

[0074] The analysis unit can analyze background information of videos and photos and extract features based on the scene in which the item is used. Examples of background information include, but are not limited to, location, time, and environment. For example, the analysis unit can analyze furniture and decorations in the background of a video to identify the scene in which the item is used. The analysis unit can also analyze the scenery and environment in the background of a photo to identify the scene in which the item is used. The analysis unit can also analyze how an item is used in a video or photo and extract features based on the scene in which the item is used. For example, the analysis unit can analyze furniture and decorations in the background of a video to identify the scene in which the item is used. By extracting features based on the scene in which the item is used, more accurate analysis results can be obtained. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input background information of a video or photo into a generation AI and extract features based on the scene in which the item is used.

[0075] The analysis unit can analyze the audio information of the video and complement the item's features from the introducer's comments. Audio information includes, but is not limited to, voice recognition and voice analysis. For example, the analysis unit extracts the item's features mentioned by the introducer from the audio in the video. The analysis unit can also analyze the method of using the item mentioned by the introducer from the audio in the video. The analysis unit can also analyze the advantages and disadvantages of the item mentioned by the introducer from the audio in the video and complement the features. For example, the analysis unit uses voice recognition technology to extract the item's features from the audio in the video. This allows the item's features to be complemented from the introducer's comments, thereby obtaining more detailed analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the video's audio data into a generation AI and complement the item's features from the introducer's comments.

[0076] The analysis unit can improve the accuracy of the analysis results by referring to the user's past selection history. Past selection history includes, but is not limited to, purchase history and browsing history. The analysis unit, for example, corrects the current analysis results based on the characteristics of items previously selected by the user. The analysis unit can also analyze preference trends from the user's past selection history and reflect them in the current analysis results. The analysis unit can also improve the accuracy of the current analysis results based on the user's evaluations of items previously selected by the user. For example, the analysis unit corrects the current analysis results by referring to the user's past selection history. In this way, the accuracy of the analysis results can be improved by referring to the user's past selection history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past selection history into a generation AI to improve the accuracy of the analysis results.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Examples of display methods for the analysis results include, but are not limited to, graph display and list display. For example, if the user is excited, the analysis unit can display detailed analysis results. Furthermore, if the user is relaxed, the analysis unit can display standard analysis results. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can analyze the user's text data and estimate the emotion. For example, the analysis unit can calculate an emotion score based on changes in the user's facial expression. This allows the display method of the analysis results to be adjusted according to the user's emotions, thereby enabling the optimal display for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and adjust the display method of the analysis results based on the emotion.

[0078] The analysis unit can analyze metadata of videos and photos to complete the related information of items. Metadata includes, for example, tag information and attribute data, but is not limited to these examples. The analysis unit can, for example, analyze the shooting location and date and time from the video metadata to complete the related information of items. The analysis unit can also analyze camera setting information from the photo metadata to complete the related information of items. The analysis unit can also analyze tag information from the metadata of videos and photos to complete the related information of items. For example, the analysis unit can analyze the shooting location and date and time from the video metadata to complete the related information of items. In this way, by analyzing the metadata, the related information of items can be completed and more detailed information can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the metadata of videos and photos into a generation AI to complete the related information of items.

[0079] The analysis unit can determine the analysis priority based on the playback time and the number of views of the video. The playback time and the number of views include, for example, a range of playback time and a threshold value of the number of views, but are not limited to these examples. For example, the analysis unit prioritizes analysis of videos with long playback times. The analysis unit can also prioritize analysis of videos with many views. The analysis unit can also determine the analysis priority by taking both the playback time and the number of views into consideration. For example, the analysis unit prioritizes analysis of videos with long playback times. This allows important videos to be analyzed preferentially by taking the playback time and the number of views into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the playback time and the number of views of the video into the generation AI and determine the analysis priority.

[0080] The analysis unit can select the optimal analysis method based on the user's device information. Device information includes, but is not limited to, the device type, performance, and usage status. For example, if the user is using a smartphone, the analysis unit can select a lightweight analysis method. Furthermore, if the user is using a tablet, the analysis unit can select a highly accurate analysis method. Furthermore, if the user is using a desktop, the analysis unit can select a detailed analysis method. For example, if the user is using a smartphone, the analysis unit selects a lightweight analysis method. This enables efficient analysis by selecting the optimal analysis method according to the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's device information into a generation AI and select the optimal analysis method.

[0081] The search unit can estimate a user's emotions and adjust a search query generation method based on the estimated user emotions. Examples of search query generation methods include, but are not limited to, keyword selection methods and query structure. For example, if the user is excited, the search unit generates a detailed search query. Furthermore, if the user is relaxed, the search unit can generate a standard search query. Furthermore, if the user is in a hurry, the search unit can generate a concise search query. For example, the search unit can capture a user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, the search unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the search unit can analyze the user's text data and estimate the user's emotions. For example, the search unit can calculate an emotion score based on changes in the user's facial expression. By adjusting the search query generation method according to the user's emotions, more appropriate search results can be obtained. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input user emotion data into the generation AI and adjust the search query generation method based on the emotion.

[0082] The search unit can automatically generate keywords related to the search query to improve search accuracy. Related keywords include, but are not limited to, synonyms, similar words, and related keywords, for example. The search unit can automatically generate synonyms related to the search query to improve search accuracy. The search unit can also automatically generate synonyms related to the search query to improve search accuracy. The search unit can also automatically generate related keywords related to the search query to improve search accuracy. For example, the search unit can automatically generate synonyms related to the search query to improve search accuracy. This allows the automatic generation of related keywords to improve search accuracy. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input a search query to a generation AI and automatically generate related keywords.

[0083] The search unit can collect feedback on the search query and optimize the search algorithm. Examples of the feedback include, but are not limited to, user ratings, click data, and dwell time data. For example, the search unit can collect user click data on search results and optimize the search algorithm. The search unit can also collect user rating data on the search results and optimize the search algorithm. The search unit can also collect user dwell time data on the search results and optimize the search algorithm. For example, the search unit can collect user click data on the search results and optimize the search algorithm. By collecting feedback, the search algorithm can be optimized and search accuracy can be improved. Some or all of the above-described processing in the search unit can be performed using, for example, AI, or can be performed without using AI. For example, the search unit can input the feedback data into a generation AI to optimize the search algorithm.

[0084] When generating a search query, the search unit can improve accuracy by referring to the user's past search history. Past search history includes, but is not limited to, search query history and click history. For example, the search unit extracts related keywords from the user's past search history to generate a search query. The search unit can also generate a search query by prioritizing frequently searched keywords from the user's past search history. The search unit can also generate a search query by taking into account keywords searched during a specific time period from the user's past search history. For example, the search unit extracts related keywords from the user's past search history to generate a search query. In this way, the accuracy of the search query can be improved by referring to the user's past search history. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the user's past search history into a generation AI to improve the accuracy of the search query.

[0085] The search unit can estimate a user's emotions and adjust the display method of search results based on the estimated user emotions. Examples of display methods for search results include, but are not limited to, a list display or a grid display. For example, if the user is excited, the search unit can display detailed search results. Furthermore, if the user is relaxed, the search unit can display standard search results. Furthermore, if the user is in a hurry, the search unit can display concise search results. For example, the search unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the search unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the search unit can analyze the user's text data and estimate the emotion. For example, the search unit can calculate an emotion score based on changes in the user's facial expression. This allows the display method of search results to be optimized for the user by adjusting it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit may input user emotion data into the generation AI and adjust the display method of search results based on the emotion.

[0086] The search unit can improve search accuracy by referring to literature related to the search query. Related literature includes, but is not limited to, academic papers, patent documents, and news articles, for example. The search unit can improve search accuracy by referring to academic papers related to the search query. The search unit can also improve search accuracy by referring to patent documents related to the search query. The search unit can also improve search accuracy by referring to news articles related to the search query. For example, the search unit can improve search accuracy by referring to academic papers related to the search query. In this way, by referring to related literature, search accuracy can be improved. Some or all of the above-described processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input related literature into a generation AI to improve search accuracy.

[0087] The search unit can provide optimal search results by taking into account the user's geographical location information when generating a search query. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the search unit can prioritize and display search results for nearby stores based on the user's current location. The search unit can also display search results limited to a specific area based on the user's geographical location information. The search unit can also display search results including optimal delivery options based on the user's geographical location information. For example, the search unit prioritizes and displays search results for nearby stores based on the user's current location. This allows optimal search results to be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the search unit can be performed using, or without, AI. For example, the search unit can input the user's geographical location information into a generation AI to provide optimal search results.

[0088] When generating a search query, the search unit can analyze the user's social media activity and provide relevant search results. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the search unit can analyze the content of the user's social media posts and provide relevant search results. The search unit can also provide relevant search results by referring to the activities of the user's friends on social media. The search unit can also provide relevant search results based on the user's social media check-in information. For example, the search unit can analyze the content of the user's social media posts and provide relevant search results. In this way, relevant search results can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the search unit may be performed, for example, using AI or without AI. For example, the search unit can input the user's social media activity into a generation AI and provide relevant search results.

[0089] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. Information priorities include, but are not limited to, the importance and urgency of the information. For example, if the user is excited, the collection unit can prioritize collecting detailed information. Furthermore, if the user is relaxed, the collection unit can prioritize collecting standard information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting concise information. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the user's text data and estimate the emotion. For example, the collection unit can calculate an emotion score based on changes in the user's facial expression. This allows for more appropriate information to be collected by prioritizing the information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and determine the priority of information to be collected based on the emotion.

[0090] The collection unit can evaluate the reliability of information collected from search results and improve the collection accuracy. The reliability of information includes, but is not limited to, the reliability of the information source and the consistency of the information. For example, the collection unit evaluates the reliability of search results and prioritizes the collection of highly rated information. The collection unit can also evaluate the reliability of search results and prioritize the collection of information from official websites. The collection unit can also evaluate the reliability of search results and prioritize the collection of information with many user reviews. For example, the collection unit evaluates the reliability of search results and prioritizes the collection of highly rated information. This allows the collection accuracy to be improved by evaluating the reliability of the information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the search results into a generation AI to evaluate the reliability of the information and improve the collection accuracy.

[0091] The collection unit can automatically classify the categories of collected information, thereby streamlining the organization of information. Information categories include, but are not limited to, product categories and service categories. The collection unit, for example, automatically classifies the collected information into categories such as price, quality, and reviews. The collection unit can also automatically classify the collected information into categories such as brand, store, and region. The collection unit can also automatically classify the collected information into categories such as new arrivals, popularity, and recommendations. For example, the collection unit automatically classifies the collected information into categories such as price, quality, and reviews. This automatic classification of information categories can streamline the organization of information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the collected information into a generation AI to automatically classify the information categories.

[0092] The collection unit can adjust the update frequency of the collected information to provide the latest information. The update frequency of the information includes, but is not limited to, daily updates or weekly updates. For example, the collection unit can increase the update frequency of the collected information to provide the latest price information. The collection unit can also increase the update frequency of the collected information to provide the latest review information. The collection unit can also increase the update frequency of the collected information to provide the latest quality information. For example, the collection unit can increase the update frequency of the collected information to provide the latest price information. In this way, by adjusting the update frequency of the information, the latest information can always be provided. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the update frequency of the collected information to the generation AI to provide the latest information.

[0093] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. Information display methods include, but are not limited to, list display and card display. For example, the collection unit can display detailed information when the user is excited. The collection unit can also display standard information when the user is relaxed. The collection unit can also display concise information when the user is in a hurry. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. The collection unit can also analyze the user's text data and estimate the emotion. For example, the collection unit can calculate an emotion score based on changes in the user's facial expression. This allows the display method of information to be optimized for the user by adjusting it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and adjust the way information is displayed based on the emotion.

[0094] The collection unit can evaluate the relevance of information collected from search results and improve the collection accuracy. The relevance of information includes, but is not limited to, co-occurrence frequency and relevance score. For example, the collection unit evaluates the relevance of search results and prioritizes collection of highly relevant information. The collection unit can also evaluate the relevance of search results and prioritize collection of highly relevant information based on a user's selection history. The collection unit can also evaluate the relevance of search results and prioritize collection of information in the same category. For example, the collection unit evaluates the relevance of search results and prioritizes collection of highly relevant information. By doing so, the collection accuracy can be improved by evaluating the relevance of information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input search results to a generation AI to evaluate the relevance of information and improve the collection accuracy.

[0095] The collection unit can diversify the sources of information collected and improve the reliability of the information. Information sources include, but are not limited to, news sites, blogs, and social networking sites. For example, the collection unit can collect information from multiple online shops to improve reliability. The collection unit can also collect information from user review sites to improve reliability. The collection unit can also collect information from manufacturer official websites to improve reliability. For example, the collection unit can collect information from multiple online shops to improve reliability. By diversifying the information sources, the reliability of the information can be improved. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the information sources into a generation AI to improve the reliability of the information.

[0096] The collection unit can standardize the format of the information to be collected and improve the efficiency of organizing the information. Examples of information formats include, but are not limited to, text format and CSV format. The collection unit, for example, converts the collected information into a unified format and improves the efficiency of organizing. The collection unit can also convert the collected information into a unified format for each category and improve the efficiency of organizing. The collection unit can also convert the collected information into a unified format in chronological order and improve the efficiency of organizing. For example, the collection unit converts the collected information into a unified format and improves the efficiency of organizing. This standardizes the format of the information and improves the efficiency of organizing the information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the collected information into a generation AI and standardize the format of the information.

[0097] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. Examples of ways to present the suggestion include, but are not limited to, text suggestions and visual suggestions. For example, if the user is excited, the suggestion unit can provide detailed suggestions. For example, if the user is relaxed, the suggestion unit can provide standard suggestions. For example, if the user is in a hurry, the suggestion unit can provide concise suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the user's text data and estimate the emotion. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expression. This allows the suggestion to be tailored to the user's emotion, thereby providing optimal suggestions for the user. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into the generation AI and adjust the way the suggestion is presented based on the emotion.

[0098] The suggestion unit can evaluate the reliability of the collected information when making a suggestion and improve the accuracy of the suggestion. The reliability of the information includes, but is not limited to, the reliability of the information source and the consistency of the information. For example, the suggestion unit can evaluate the reliability of the collected information and make a suggestion based on highly reliable information. The suggestion unit can also evaluate the reliability of the collected information and make a suggestion based on information from official websites. The suggestion unit can also evaluate the reliability of the collected information and make a suggestion based on information with many user reviews. For example, the suggestion unit can evaluate the reliability of the collected information and make a suggestion based on highly reliable information. By evaluating the reliability of the information, the accuracy of the suggestion can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the collected information into a generation AI to evaluate the reliability of the information and improve the accuracy of the suggestion.

[0099] When making a suggestion, the suggestion unit can make an optimal suggestion by referring to the user's past selection history. Examples of past selection history include, but are not limited to, purchase history and browsing history. For example, the suggestion unit can analyze preference trends from the user's past selection history and make optimal suggestions. The suggestion unit can also make suggestions based on frequently selected items from the user's past selection history. The suggestion unit can also make suggestions based on items selected during a specific time period from the user's past selection history. For example, the suggestion unit can analyze preference trends from the user's past selection history and make optimal suggestions. This allows optimal suggestions to be made by referring to the user's past selection history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past selection history into a generation AI and make optimal suggestions.

[0100] When making a suggestion, the suggestion unit can customize the suggestion content based on the user's current living situation and areas of interest. Examples of living situations and areas of interest include, but are not limited to, occupation, hobbies, and family structure. The suggestion unit, for example, suggests optimal items based on the user's current living situation. The suggestion unit can also suggest related items based on the user's areas of interest. The suggestion unit can also make optimal suggestions by combining the user's current living situation and areas of interest. For example, the suggestion unit suggests optimal items based on the user's current living situation. This enables more appropriate suggestions to be made by customizing the suggestion content based on the user's living situation and areas of interest. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into a generation AI to customize the suggestion content.

[0101] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The priority of suggestions can include, but is not limited to, the importance and urgency of the suggestions. For example, if the user is excited, the suggestion unit can prioritize detailed suggestions. If the user is relaxed, the suggestion unit can prioritize standard suggestions. If the user is in a hurry, the suggestion unit can prioritize concise suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. The suggestion unit can also analyze the user's text data and estimate the emotion. For example, the suggestion unit can calculate an emotion score based on changes in the user's facial expression. This enables more appropriate suggestions to be made by prioritizing suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into the generation AI and determine the priority of suggestions based on the emotion.

[0102] The suggestion unit can evaluate the relevance of the collected information when making a suggestion, thereby improving the accuracy of the suggestion. The relevance of the information includes, but is not limited to, for example, co-occurrence frequency and relevance score. For example, the suggestion unit can evaluate the relevance of the collected information and make a suggestion based on highly relevant information. The suggestion unit can also evaluate the relevance of the collected information and make a suggestion based on highly relevant information based on the user's selection history. The suggestion unit can also evaluate the relevance of the collected information and make a suggestion based on information in the same category. For example, the suggestion unit can evaluate the relevance of the collected information and make a suggestion based on highly relevant information. In this way, the accuracy of the suggestion can be improved by evaluating the relevance of the information. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input the collected information into a generation AI, evaluate the relevance of the information, and improve the accuracy of the suggestion.

[0103] When making a suggestion, the suggestion unit can make optimal suggestions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. For example, the suggestion unit can suggest items from nearby stores based on the user's current location. The suggestion unit can also suggest area-specific items based on the user's geographical location information. The suggestion unit can also suggest items including optimal delivery options based on the user's geographical location information. For example, the suggestion unit can suggest items from nearby stores based on the user's current location. This allows optimal suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI to make optimal suggestions.

[0104] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to these examples. For example, the suggestion unit can analyze the content of the user's social media posts and suggest related items. The suggestion unit can also suggest related items based on the user's social media check-in information. For example, the suggestion unit can analyze the content of the user's social media posts and suggest related items. In this way, related suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity into a generation AI and make related suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, search unit, collection unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit can photograph an item selected by the user using the camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 can extract features of the item using image recognition technology. The search unit, for example, searches for items on the Internet based on a search query generated by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects information such as price, quality, and reviews of each item from the search results by the specific processing unit 290 of the data processing device 12. The suggestion unit, for example, suggests optimal options to the user based on the information collected by the specific processing unit 290 of the data processing device 12. This allows the user to find the optimal item without any effort. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described analysis unit, search unit, collection unit, and suggestion unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit can photograph an item selected by the user using the camera 42 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 can extract features of the item using image recognition technology. The search unit, for example, searches for items on the Internet based on a search query generated by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects information such as price, quality, and reviews of each item from the search results by the specific processing unit 290 of the data processing device 12. The suggestion unit, for example, suggests optimal options to the user based on the information collected by the specific processing unit 290 of the data processing device 12. This allows the user to find the optimal item without any hassle. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described analysis unit, search unit, collection unit, and suggestion unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit can photograph an item selected by the user using the camera 42 of the headset-type terminal 314, and the specific processing unit 290 of the data processing device 12 can extract features of the item using image recognition technology. The search unit, for example, searches for items on the Internet based on a search query generated by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects information such as price, quality, and reviews of each item from the search results by the specific processing unit 290 of the data processing device 12. The suggestion unit, for example, suggests optimal options to the user based on the information collected by the specific processing unit 290 of the data processing device 12. This allows the user to find the optimal item without any hassle. === Hard Collateral 1-4 === Each of the multiple elements, including the above-described analysis unit, search unit, collection unit, and suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit can use the camera 42 of the robot 414 to photograph an item selected by the user, and the specific processing unit 290 of the data processing device 12 can extract features of the item using image recognition technology. The search unit, for example, searches for items on the Internet based on a search query generated by the specific processing unit 290 of the data processing device 12. The collection unit, for example, collects information such as price, quality, and reviews of each item from the search results by the specific processing unit 290 of the data processing device 12. The suggestion unit, for example, suggests optimal options to the user based on the information collected by the specific processing unit 290 of the data processing device 12. This allows the user to find the optimal item without any hassle.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The recommendation system can also analyze a user's purchasing history and customize the recommendations based on past purchasing patterns. For example, it can analyze the brands and categories of items a user has previously purchased and suggest new products from similar brands or categories. It can also identify items that a user tends to purchase during a particular season and make recommendations tailored to that season. It can also analyze the characteristics of items that a user has previously rated highly and suggest items with similar characteristics. This allows for more personalized recommendations based on a user's purchasing history.

[0107] The analysis unit can analyze a user's social media activity and generate search queries based on the user's interests. For example, the analysis unit can analyze the content of posts that the user frequently likes or comments on on social media and search for items related to that content. It can also analyze the content of posts by influencers the user follows and search for similar items. It can also analyze the content of articles and videos the user has shared on social media and search for items related to them. This makes it possible to provide more relevant search results based on the user's social media activity.

[0108] The search unit can provide region-specific search results by taking into account the user's geographic location information. For example, if the user is in a particular city, items related to stores and services in that city can be searched for preferentially. Also, if the user is traveling, items related to the region to which the user is traveling can be searched. Furthermore, if the user is participating in a particular event or festival, items related to that event can be searched for. This allows for more appropriate search results to be provided based on the user's geographic location information.

[0109] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, if the user is excited, detailed information can be collected with priority. Also, if the user is relaxed, standard information can be collected with priority. Furthermore, if the user is in a hurry, concise information can be collected with priority. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. In this way, more appropriate information can be collected by determining the priority of information to be collected according to the user's emotions.

[0110] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is excited, detailed suggestions can be made. If the user is relaxed, standard suggestions can be made. Furthermore, if the user is in a hurry, brief suggestions can be made. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. This allows the way suggestions are expressed to be adjusted according to the user's emotions, making it possible to provide the most suitable suggestions for the user.

[0111] The analysis unit can select the optimal analysis method based on the user's device information. For example, if the user is using a smartphone, a lightweight analysis method can be selected. If the user is using a tablet, a highly accurate analysis method can be selected. Furthermore, if the user is using a desktop, a detailed analysis method can be selected. This allows for efficient analysis by selecting the optimal analysis method according to the user's device information.

[0112] The search unit can estimate the user's emotions and adjust the search query generation method based on the estimated emotions. For example, if the user is excited, a detailed search query can be generated. If the user is relaxed, a standard search query can be generated. Furthermore, if the user is in a hurry, a concise search query can be generated. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. This allows the search query generation method to be adjusted according to the user's emotions, resulting in more appropriate search results.

[0113] The collection unit can evaluate the reliability of information collected from search results and improve the collection accuracy. For example, the reliability of search results can be evaluated and highly rated information can be collected preferentially. Information from official websites can also be collected preferentially. Furthermore, information with many user reviews can also be collected preferentially. In this way, the collection accuracy can be improved by evaluating the reliability of information.

[0114] The suggestion unit can customize the suggestion content based on the user's current living situation and areas of interest. For example, it can suggest optimal items based on the user's occupation, hobbies, and family structure. It can also suggest related items based on the user's areas of interest. Furthermore, it can combine the user's current living situation and areas of interest to make optimal suggestions. This allows the suggestion content to be customized based on the user's living situation and areas of interest, making it possible to make more appropriate suggestions.

[0115] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated emotions. For example, if the user is excited, detailed suggestions can be prioritized. Also, if the user is relaxed, standard suggestions can be prioritized. Furthermore, if the user is in a hurry, concise suggestions can be prioritized. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using an emotion estimation algorithm. This allows more appropriate suggestions to be made by prioritizing suggestions according to the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The analysis unit analyzes the item selected by the user in the video or photo. The analysis unit uses image recognition technology and AI to extract the item's features and generate a search query. For example, deep learning technology can be used to extract the item's features with high accuracy. Step 2: The search unit searches for items on the Internet based on the search query generated by the analysis unit. The search unit optimizes the search query using search engines, AI, and natural language processing technology to improve search accuracy. Step 3: The collection unit collects the information obtained by the search unit. The collection unit collects information such as the price, quality, and reviews of each item, and uses AI and web scraping technology to collect more detailed information. Step 4: The suggestion unit suggests optimal options to the user based on the information collected by the collection unit. Based on the collected information, the suggestion unit suggests items with the best cost performance and items with the highest user ratings, and uses a machine learning algorithm to suggest items that suit the user's preferences.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes items selected by a user on a video or photo; a search unit that searches for items on the Internet based on the search query generated by the analysis unit; a collection unit that collects the information acquired by the search unit; a suggestion unit that suggests appropriate options to a user based on the information collected by the collection unit. A system characterized by:

2. The analysis unit Extracting item features using image recognition technology and generating search queries The system of claim 1 .

3. The search unit Searching for items on the Internet using the generated search query The system of claim 1 .

4. The collecting unit Collect price, quality, and review information for each item from the search results The system of claim 1 .

5. The proposal unit Suggest appropriate options to users based on collected information The system of claim 1 .

6. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

7. The analysis unit Analyzes background information from videos and photos and extracts features based on the item's usage scenario The system of claim 1 .

8. The analysis unit Analyzes audio information from videos and complements item features from the introducer's comments The system of claim 1 .

Images