system

Abstract

An object of a system according to an embodiment is to efficiently propose a product that meets requirements based on an input on a chat.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, an analysis unit, and a proposal unit. The reception unit receives an input in a chat. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the product image of the EC site based on the information analyzed by the analysis unit. The proposal unit proposes a product according to the requirements based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide efficient product suggestions on e-commerce sites based on chat input, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently suggest products that meet requirements based on inputs made via chat. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an analysis section, and a proposal unit. The reception unit receives input via chat. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes product images on the e-commerce site based on the information analyzed by the analysis unit. The proposal unit proposes products that meet requirements based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest products that meet requirements based on inputs made via chat. [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 customer service system according to an embodiment of the present invention uses multimodal AI to provide customer support. The system accepts chat input, analyzes the input information, and analyzes product images on an e-commerce website to suggest products that meet the customer's needs. For example, a customer inputs their desired product via chat. This input can include specific product names, features, and uses. The system then analyzes the input information using multimodal AI. The multimodal AI understands the text information and analyzes product images on the e-commerce website. For example, it collects specific product images from the e-commerce website and analyzes them. Finally, the system uses multimodal AI to suggest products that meet the customer's needs. For example, the system provides detailed product information and a link to purchase the product along with the specific product image. This allows the customer to easily find products that meet their needs. For example, if a customer is looking for clothing in a specific color or design, the multimodal AI can suggest products that meet the customer's needs, saving time and effort. Furthermore, analyzing product images on an e-commerce website enables more accurate suggestions. For example, making suggestions based on detailed product information such as the product's color, design, and material can increase customer satisfaction. Furthermore, this system also has the effect of promoting the use of e-commerce sites. Because customers can easily find products, the frequency of use of e-commerce sites increases, which is expected to lead to increased sales. For example, an increase in purchases on e-commerce sites also means increased profits for site operators.

[0029] The foreign trade service system according to the embodiment includes a reception unit, an analysis unit, an analysis unit, and a proposal unit. The reception unit accepts chat input. The chat input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit provides a chat interface for accepting text input. The reception unit can also provide a microphone interface for accepting voice input. The reception unit can also provide an image upload function for accepting image input. For example, the reception unit displays a chat box for accepting text input and receives text entered by a user. In the case of voice input, the reception unit receives voice through a microphone and converts it into text using voice recognition technology. In the case of image input, the reception unit receives images uploaded by a user and analyzes the content using image analysis technology. The analysis unit analyzes the information accepted by the reception unit. For example, natural language processing, image analysis, data mining, and other techniques are used for the analysis, but are not limited to these examples. For example, the analysis unit uses natural language processing technology to analyze the meaning of text entered by a user. The analysis unit can also analyze the content of images uploaded by a user using image analysis technology. Furthermore, the analysis unit can also analyze the user's past input history using data mining technology. For example, the analysis unit can use natural language processing technology to extract keywords from text entered by the user and analyze their meaning. The analysis unit can use image analysis technology to extract features of images uploaded by the user and analyze their content. The analysis unit can use data mining technology to analyze the user's past input history and identify patterns. The analysis unit can analyze product images on the e-commerce site based on the information analyzed by the analysis unit. For example, techniques such as image recognition algorithms and statistical analysis are used for the analysis, but are not limited to these examples. For example, the analysis unit can analyze product images on the e-commerce site using an image recognition algorithm to identify specific products. The analysis unit can also analyze the features of the product images using statistical analysis technology. The analysis unit can also classify product images using a machine learning algorithm.For example, the analysis unit uses an image recognition algorithm to identify specific products from product images on the e-commerce website. Statistical analysis techniques are used to analyze features of product images, such as color and shape. Machine learning algorithms are used to categorize product images. The suggestion unit suggests products that meet user requirements based on the information analyzed by the analysis unit. For example, technologies such as recommendation systems and filtering algorithms are used for the suggestions, but these examples are not limited to these. For example, the suggestion unit uses a recommendation system to suggest products that meet user requirements. The suggestion unit can also use a filtering algorithm to narrow down products based on the user requirements. Furthermore, the suggestion unit can also use a machine learning algorithm to suggest products that match the user's preferences. For example, the suggestion unit uses a recommendation system to list products that meet the user's requirements. The suggestion unit uses a filtering algorithm to narrow down products based on the user's requirements. The suggestion unit uses a machine learning algorithm to suggest products based on the user's past purchase history and preferences. As a result, the external commerce service system according to the embodiment can efficiently suggest products that meet the user's needs.

[0030] The suggestion unit can provide detailed information about the product. The detailed information includes, for example, product specifications, price, reviews, etc., but is not limited to these examples. The suggestion unit can, for example, provide product specifications of the product. The product specifications include information such as the size, weight, and material of the product. The suggestion unit can also provide price information about the product. The price information includes the selling price of the product, discount information, shipping costs, etc. The suggestion unit can also provide review information about the product. The review information includes ratings and comments from other users. For example, the suggestion unit can display detailed product specifications of the product to allow the user to understand the characteristics of the product. It can provide price information to allow the user to understand the cost of the product. It can display review information to refer to the opinions of other users. This allows the user to check detailed product information.

[0031] The suggestion unit can provide a purchase link. Examples of purchase links include, but are not limited to, URL links and QR codes (registered trademarks). For example, the suggestion unit can provide a URL link to a product purchase page. By clicking the URL link, the user can directly access the product purchase page. The suggestion unit can also provide a QR code to the product purchase page. By scanning the QR code, the user can easily access the product purchase page using a device such as a smartphone. The suggestion unit can also send the purchase link via email or message. For example, the suggestion unit can display a URL link to the product purchase page so that the user can click to access the purchase page. The suggestion unit can display a QR code so that the user can scan it with their smartphone to access the purchase page. The purchase link can be sent via email or message so that the user can access it later. This allows the user to easily purchase the product.

[0032] The analysis unit can collect and analyze product images from the e-commerce site. Examples of collection methods include, but are not limited to, web scraping, API usage, and the like. For example, the analysis unit can automatically collect product images from the e-commerce site using web scraping technology. The analysis unit can also acquire product images using the e-commerce site's API. The analysis unit can also collect product images provided by users. For example, the analysis unit can extract images from product pages on the e-commerce site using web scraping technology. The analysis unit can acquire product images from the e-commerce site using APIs. Product images uploaded by users are collected and used for analysis. For example, techniques such as image recognition algorithms and statistical analysis can be used for analysis, but are not limited to these examples. For example, the analysis unit can analyze the collected product images using an image recognition algorithm to identify specific products. The analysis unit can also analyze the features of the product images using statistical analysis technology. The analysis unit can also classify product images using a machine learning algorithm. For example, the analysis unit can identify specific products from the collected product images using an image recognition algorithm. Statistical analysis techniques are used to analyze the color, shape, and other characteristics of product images. Machine learning algorithms are used to classify product images into categories. This allows for efficient collection and analysis of product images on e-commerce sites.

[0033] The reception unit allows a user to input a specific product name, characteristics, and use. Examples of specific product names, characteristics, and uses include, but are not limited to, keyword input and category selection. The reception unit, for example, provides a text box for receiving keyword input. The user can input a specific product name, characteristics, and use into the text box. The reception unit can also provide a drop-down menu for receiving category selection. The user can select a category from the drop-down menu and input a specific product name, characteristics, and use. The reception unit can also provide a microphone interface for receiving voice input. The user can input a specific product name, characteristics, and use by voice through the microphone. For example, the reception unit displays a text box to allow the user to input a specific product name, characteristics, and use. The reception unit displays a drop-down menu to allow the user to select a category and input. The reception unit provides a microphone interface to allow the user to input a specific product name, characteristics, and use by voice. This allows the user to input a specific product name, characteristics, and use.

[0034] The suggestion unit can identify products that meet the user's requirements. Products that meet the requirements include, but are not limited to, criteria such as filtering conditions and a matching algorithm. For example, the suggestion unit uses filtering conditions to narrow down products that meet the user's requirements. Filtering conditions include product price, color, size, brand, etc. The suggestion unit can also use a matching algorithm to identify products based on the user's requirements. The matching algorithm includes a recommendation system that takes into account product features and user preferences. Furthermore, the suggestion unit can also use a machine learning algorithm to identify products that meet the user's requirements. For example, the suggestion unit uses filtering conditions to list products that meet the user's requirements. The matching algorithm identifies products based on the user's requirements. The machine learning algorithm identifies products based on the user's past purchase history and preferences. This makes it possible to identify products that meet the user's requirements.

[0035] The reception unit can analyze the user's past input history and suggest an optimal input method. The input history includes, for example, past search keywords, input patterns, etc., but is not limited to these examples. For example, the reception unit analyzes past search keywords and automatically displays product names and features frequently entered by the user as candidates. The reception unit can also analyze past input patterns and preferentially suggest input methods (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and suggest product names and features to be used in a specific time period from the past input history. For example, the reception unit analyzes past search keywords and automatically displays product names and features frequently entered by the user as candidates. The reception unit analyzes past input patterns and preferentially suggests input methods used by the user in the past. The reception unit predicts and suggests product names and features to be used in a specific time period from the past input history. This makes it possible to suggest an optimal input method based on the user's past input history.

[0036] The reception unit can filter the input content based on the user's current purchase history and areas of interest at the time of input. Examples of the purchase history and areas of interest include, but are not limited to, a list of purchased products and interest categories. For example, the reception unit can automatically display related product names and features as candidates based on the user's purchase history. The reception unit can also filter the input content based on the user's areas of interest to suggest highly relevant product names and features. Furthermore, the reception unit can combine the user's past purchase history and areas of interest to suggest optimal input content. For example, the reception unit can automatically display related product names and features as candidates based on the user's purchase history. The reception unit can filter the input content based on the user's areas of interest to suggest highly relevant product names and features. The reception unit can combine the user's past purchase history and areas of interest to suggest optimal input content. This makes it possible to provide highly relevant input content based on the user's purchase history and areas of interest.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting information. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs "I want a red dress" by voice, the reception unit prioritizes voice input to quickly input the product name and its characteristics. Furthermore, when a user inputs text, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when a user uploads an image, the reception unit can perform image analysis and automatically input related product names and characteristics. For example, when a user inputs "I want a red dress" by voice, the reception unit prioritizes voice input to quickly input the product name and its characteristics. When a user inputs text, the reception unit provides detailed input options and suggests customizable input methods. When a user uploads an image, the reception unit can provide the optimal input means depending on the user's input method.

[0038] The reception unit can prioritize accepting highly relevant input content in consideration of the user's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the reception unit prioritizes displaying product names and features that are popular in that area. The reception unit can also suggest highly relevant product names and features based on the user's current location. Furthermore, the reception unit can also suggest optimal input content in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes displaying product names and features that are popular in that area. The reception unit suggests highly relevant product names and features based on the user's current location. The reception unit suggests optimal input content in consideration of the user's geographical location information. This makes it possible to provide highly relevant input content based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity at the time of input and suggest related input content. Social media activity includes, for example, posted content, like history, etc., but is not limited to these examples. The reception unit can, for example, suggest product names and features related to places where the user has checked in on social media. The reception unit can also analyze the user's social media posts and suggest related product names and features. Furthermore, the reception unit can also suggest related product names and features by referring to the activities of the user's friends on social media. For example, the reception unit can suggest product names and features related to places where the user has checked in on social media. The reception unit can analyze the user's social media posts and suggest related product names and features. The reception unit can suggest related product names and features by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant input content based on the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The feedback includes, for example, reviews, evaluation comments, etc., but is not limited to these examples. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The input interface is customized based on the user's past feedback. The input procedure is optimized by reflecting the user's past feedback. In this way, the optimal input method can be provided based on the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. The level of importance includes, but is not limited to, criteria such as prioritization and scoring. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the input information. For example, the analysis unit performs a detailed analysis on information with high importance and provides a detailed report. For information with low importance, the analysis unit performs a simplified analysis and provides a concise report. The level of detail of the analysis is dynamically adjusted according to the importance of the input information to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the importance of the input information.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. Categories include, but are not limited to, criteria such as text classification and image classification. The analysis unit selects the optimal analysis algorithm depending on, for example, the category of a product. The analysis unit can also dynamically switch analysis algorithms based on the category of the input information. Furthermore, the analysis unit can apply different analysis algorithms for each category to improve accuracy. For example, the analysis unit selects the optimal analysis algorithm depending on the category of a product and performs analysis. The analysis algorithm is dynamically switched based on the category of the input information to provide optimal analysis results. Different analysis algorithms are applied to each category to improve analysis accuracy. This makes it possible to provide optimal analysis results depending on the category of the input information.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports, analysis logs, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by using the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results to improve accuracy. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis parameters by using the user's past analysis results to provide optimal analysis results. This makes it possible to provide optimal analysis results based on the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. The submission time includes, but is not limited to, criteria such as a timestamp and the order of submission. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone information submitted later. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission time. For example, the analysis unit prioritizes analysis of information submitted earlier and provides results earlier. The analysis unit postpones information submitted later and provides results later. The analysis priority is dynamically adjusted based on the submission time to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the submission time of the input information.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. Relevance includes, but is not limited to, criteria such as co-occurrence networks and correlation analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of highly relevant information and provides results early. The analysis unit postpones analysis of less relevant information and provides results later. The analysis unit dynamically adjusts the order of analysis based on the relevance of the input information and provides optimal analysis results. This makes it possible to provide optimal analysis results according to the relevance of the input information.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, criteria such as survey results and past usage history. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, when the user's level of expertise is high, the analysis unit uses a lot of technical terms to provide detailed analysis results. When the user's level of expertise is low, the analysis unit avoids technical terms and provides concise analysis results. The use of technical terms in the analysis can be dynamically adjusted according to the user's level of expertise to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between product images. Interrelationships include, but are not limited to, criteria such as co-occurrence analysis and relevance scores. For example, the analysis unit analyzes the interrelationships between product images to identify highly related products. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between product images. Furthermore, the analysis unit can apply an optimal analysis algorithm based on the interrelationships between product images. For example, the analysis unit analyzes the interrelationships between product images to identify highly related products. The accuracy of the analysis is improved by taking into account the interrelationships between product images. The optimal analysis algorithm is applied based on the interrelationships between product images to improve the accuracy. This makes it possible to provide optimal analysis results based on the interrelationships between product images.

[0048] The analysis unit can perform the analysis taking into account attribute information of the product. Attribute information includes, for example, criteria such as color, size, and brand, but is not limited to these examples. The analysis unit performs the analysis taking into account attribute information such as the color and design of the product. The analysis unit can also improve the accuracy of the analysis based on attribute information such as the material and size of the product. Furthermore, the analysis unit can analyze the attribute information of the product and identify highly related products. For example, the analysis unit performs the analysis taking into account attribute information such as the color and design of the product and identifies highly related products. The analysis unit improves the accuracy of the analysis based on attribute information such as the material and size of the product. The attribute information of the product is analyzed and identifies highly related products. This makes it possible to provide optimal analysis results based on the attribute information of the product.

[0049] During analysis, the analysis unit can weight the analysis based on the frequency of submission of products. The frequency of submission includes, but is not limited to, criteria such as the number of submissions and the interval between submissions. For example, the analysis unit prioritizes the analysis of products that are submitted more frequently. The analysis unit can also postpone products that are submitted less frequently. Furthermore, the analysis unit can dynamically adjust the weighting of the analysis based on the frequency of submission. For example, the analysis unit prioritizes the analysis of products that are submitted more frequently and provides results earlier. Products that are submitted less frequently can be postponed and results provided later. The analysis weighting is dynamically adjusted based on the frequency of submission to provide optimal analysis results. This makes it possible to provide optimal analysis results based on the frequency of submission of products.

[0050] The analysis unit can perform the analysis while taking into account the geographical distribution of products. Geographical distribution includes, but is not limited to, criteria such as sales region and delivery area. For example, the analysis unit analyzes the geographical distribution of products to identify highly relevant products. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of products. Furthermore, the analysis unit can apply an optimal analysis algorithm based on the geographical distribution of products. For example, the analysis unit analyzes the geographical distribution of products to identify highly relevant products. The analysis accuracy is improved by taking into account the geographical distribution of products. The optimal analysis algorithm is applied based on the geographical distribution of products to improve the accuracy. This makes it possible to provide optimal analysis results based on the geographical distribution of products.

[0051] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. Examples of related literature include, but are not limited to, academic papers, patent documents, and other standards. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. The analysis unit can also apply an optimal analysis algorithm based on the literature related to the product. Furthermore, the analysis unit can analyze the literature related to the product and identify highly relevant products. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. The analysis unit can apply an optimal analysis algorithm based on the literature related to the product and improve the accuracy. The literature related to the product can be analyzed and highly relevant products can be identified. This makes it possible to provide optimal analysis results based on the literature related to the product.

[0052] The analysis unit may perform the analysis while taking into account the market value of the product. Market value may include, but is not limited to, criteria such as price trends and market share. For example, the analysis unit may analyze the market value of the product and identify highly relevant products. The analysis unit may also improve the accuracy of the analysis by taking into account the market value of the product. Furthermore, the analysis unit may apply an optimal analysis algorithm based on the market value of the product. For example, the analysis unit may analyze the market value of the product and identify highly relevant products. The analysis may improve the accuracy of the analysis by taking into account the market value of the product. The analysis unit may apply an optimal analysis algorithm based on the market value of the product to improve the accuracy. This allows for providing optimal analysis results based on the market value of the product.

[0053] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the product. Criteria for importance include, but are not limited to, sales volume, customer ratings, and the like. For example, the suggestion unit makes a detailed proposal for a product with high importance. The suggestion unit can also make a simplified proposal for a product with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the importance of the product. For example, the suggestion unit makes a detailed proposal for a product with high importance, providing detailed information. For a product with low importance, the suggestion unit makes a simplified proposal, providing concise information. The level of detail of the proposal is dynamically adjusted according to the importance of the product, providing an optimal proposal. This makes it possible to provide an optimal proposal according to the importance of the product.

[0054] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. Examples of categories include, but are not limited to, criteria such as text classification and image classification. The suggestion unit selects an optimal suggestion algorithm depending on, for example, the product category. The suggestion unit can also dynamically switch the suggestion algorithm based on the product category. Furthermore, the suggestion unit can apply different suggestion algorithms for each category to improve accuracy. For example, the suggestion unit selects an optimal suggestion algorithm depending on the product category and makes a suggestion. The suggestion algorithm is dynamically switched based on the product category to provide optimal suggestions. Different suggestion algorithms are applied to each category to improve the accuracy of the suggestions. This makes it possible to provide optimal suggestions depending on the product category.

[0055] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Past suggestion results include, for example, past purchase history, suggestion logs, etc., but are not limited to these examples. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the suggestion parameters by using the user's past suggestion results. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results to improve the accuracy. The suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit adjusts the suggestion parameters by using the user's past suggestion results to provide optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past suggestion results.

[0056] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. The submission time includes, but is not limited to, criteria such as timestamp and submission order. For example, the proposal unit preferentially proposes products that have been submitted earlier. The proposal unit can also postpone products that have been submitted later. Furthermore, the proposal unit can dynamically adjust the priority of the proposal based on the submission time. For example, the proposal unit preferentially proposes products that have been submitted earlier and provides results early. The proposal unit postpones products that have been submitted later and provides results later. The proposal priority is dynamically adjusted based on the submission time to provide the optimal proposal. This makes it possible to provide the optimal proposal according to the submission time of the product.

[0057] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. Relevance includes, but is not limited to, criteria such as co-occurrence networks and correlation analysis. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone products with low relevance. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests highly relevant products and provides results early. The suggestion unit postpones products with low relevance and provides results later. The suggestion order is dynamically adjusted based on the relevance of the products to provide optimal suggestions. This makes it possible to provide optimal suggestions according to the relevance of the products.

[0058] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, criteria such as survey results and past usage history. For example, the suggestion unit uses a lot of technical terms when the user's level of expertise is high. Furthermore, the suggestion unit can avoid technical terms when the user's level of expertise is low. Furthermore, the suggestion unit can dynamically adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, when the user's level of expertise is high, the suggestion unit uses a lot of technical terms and provides a detailed suggestion. When the user's level of expertise is low, the suggestion unit avoids technical terms and provides a concise suggestion. The suggestion unit dynamically adjusts the use of technical terms in the suggestion according to the user's level of expertise to provide an optimal suggestion. This allows the system to provide an optimal suggestion according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also provide related trend information based on the user's input. For example, if a user is looking for a specific fashion item, the reception unit can display the latest fashion trends and styling suggestions related to that item. If a user is looking for a specific gadget, the reception unit can provide the latest technology trends and reviews related to that gadget. Furthermore, if a user is looking for a specific food item, the reception unit can provide recipes and health information related to that food item. This allows the user to not only search for products but also obtain the latest related information.

[0061] The suggestion unit can also suggest bundles of related products based on the user's purchasing history or areas of interest. For example, if a user has previously purchased clothes from a specific brand, the suggestion unit can suggest new items from that brand and related accessories. If a user has purchased books in a specific genre, the suggestion unit can suggest new releases in that genre and related audiobooks. Furthermore, if a user has purchased a specific gadget, the suggestion unit can suggest accessories and upgrade options related to that gadget. This allows users to purchase multiple products at once, improving convenience.

[0062] The reception unit can also provide relevant coupons and discount information based on the user's input. For example, if the user is looking for clothes from a specific brand, the reception unit can display the latest coupons and discount information for that brand. If the user is looking for a specific gadget, the reception unit can provide promotions and sales information related to that gadget. Furthermore, if the user is looking for a specific food item, the reception unit can provide discount coupons and special sale information related to that food item. This allows the user to obtain information on good deals and increases their motivation to purchase.

[0063] The analysis unit can also provide relevant market trend and competitive information based on the user's input. For example, if a user is looking for a specific gadget, the analysis unit can display information on the market trend and competitive products for that gadget. Also, if a user is looking for a specific fashion item, the analysis unit can provide information on trends for that item and competitive brands. Furthermore, if a user is looking for a specific food item, the analysis unit can provide information on the market trend and competitive products for that food item. This allows users to obtain more information when selecting products.

[0064] The reception unit can also provide information on related communities and forums based on the user's input. For example, if a user is looking for a specific gadget, the reception unit can display user reviews and forum discussions about that gadget. If a user is looking for a specific fashion item, the reception unit can provide styling suggestions and user opinions about that item. Furthermore, if a user is looking for a specific food, the reception unit can provide recipes and health information about that food. This allows the user to refer to the opinions and information of other users.

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

[0066] Step 1: The reception unit accepts chat input. Chat input includes text input, voice input, and image input. For example, the reception unit provides a chat interface for accepting text input, a microphone interface for accepting voice input, and an image upload function for accepting image input. The reception unit receives text entered by the user and converts it into text using voice recognition technology in the case of voice input, and analyzes the content using image analysis technology in the case of image input. Step 2: The analysis unit analyzes the information received by the reception unit. For the analysis, technologies such as natural language processing, image analysis, and data mining are used. For example, the analysis unit may use natural language processing technology to analyze the meaning of the text entered by the user, image analysis technology to analyze the content of the image uploaded by the user, and data mining technology to analyze the user's past input history. Step 3: The analysis unit analyzes the product images on the e-commerce site based on the information analyzed by the analysis unit. The analysis uses techniques such as image recognition algorithms, statistical analysis, and machine learning algorithms. For example, the analysis unit uses image recognition algorithms to analyze product images on the e-commerce site and identify specific products, statistical analysis techniques to analyze the characteristics of the product images, and machine learning algorithms to classify the product images by category. Step 4: The suggestion unit suggests products that meet the user's requirements based on the information analyzed by the analysis unit. The suggestion unit uses technologies such as recommendation systems, filtering algorithms, and machine learning algorithms. For example, the suggestion unit uses a recommendation system to list products that meet the user's requirements, a filtering algorithm to narrow down the products based on the user's requirements, and a machine learning algorithm to suggest products based on the user's past purchase history and preferences.

[0067] (Example 2) A customer service system according to an embodiment of the present invention uses multimodal AI to provide customer support. The system accepts chat input, analyzes the input information, and analyzes product images on an e-commerce website to suggest products that meet the customer's needs. For example, a customer inputs their desired product via chat. This input can include specific product names, features, and uses. The system then analyzes the input information using multimodal AI. The multimodal AI understands the text information and analyzes product images on the e-commerce website. For example, it collects specific product images from the e-commerce website and analyzes them. Finally, the system uses multimodal AI to suggest products that meet the customer's needs. For example, the system provides detailed product information and a link to purchase the product along with the specific product image. This allows the customer to easily find products that meet their needs. For example, if a customer is looking for clothing in a specific color or design, the multimodal AI can suggest products that meet the customer's needs, saving time and effort. Furthermore, analyzing product images on an e-commerce website enables more accurate suggestions. For example, making suggestions based on detailed product information such as the product's color, design, and material can increase customer satisfaction. Furthermore, this system also has the effect of promoting the use of e-commerce sites. Because customers can easily find products, the frequency of use of e-commerce sites increases, which is expected to lead to increased sales. For example, an increase in purchases on e-commerce sites also means increased profits for site operators.

[0068] The foreign trade service system according to the embodiment includes a reception unit, an analysis unit, an analysis unit, and a proposal unit. The reception unit accepts chat input. The chat input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit provides a chat interface for accepting text input. The reception unit can also provide a microphone interface for accepting voice input. The reception unit can also provide an image upload function for accepting image input. For example, the reception unit displays a chat box for accepting text input and receives text entered by a user. In the case of voice input, the reception unit receives voice through a microphone and converts it into text using voice recognition technology. In the case of image input, the reception unit receives images uploaded by a user and analyzes the content using image analysis technology. The analysis unit analyzes the information accepted by the reception unit. For example, natural language processing, image analysis, data mining, and other techniques are used for the analysis, but are not limited to these examples. For example, the analysis unit uses natural language processing technology to analyze the meaning of text entered by a user. The analysis unit can also analyze the content of images uploaded by a user using image analysis technology. Furthermore, the analysis unit can also analyze the user's past input history using data mining technology. For example, the analysis unit can use natural language processing technology to extract keywords from text entered by the user and analyze their meaning. The analysis unit can use image analysis technology to extract features of images uploaded by the user and analyze their content. The analysis unit can use data mining technology to analyze the user's past input history and identify patterns. The analysis unit can analyze product images on the e-commerce site based on the information analyzed by the analysis unit. For example, techniques such as image recognition algorithms and statistical analysis are used for the analysis, but are not limited to these examples. For example, the analysis unit can analyze product images on the e-commerce site using an image recognition algorithm to identify specific products. The analysis unit can also analyze the features of the product images using statistical analysis technology. The analysis unit can also classify product images using a machine learning algorithm.For example, the analysis unit uses an image recognition algorithm to identify specific products from product images on the e-commerce website. Statistical analysis techniques are used to analyze features of product images, such as color and shape. Machine learning algorithms are used to categorize product images. The suggestion unit suggests products that meet user requirements based on the information analyzed by the analysis unit. For example, technologies such as recommendation systems and filtering algorithms are used for the suggestions, but these examples are not limited to these. For example, the suggestion unit uses a recommendation system to suggest products that meet user requirements. The suggestion unit can also use a filtering algorithm to narrow down products based on the user requirements. Furthermore, the suggestion unit can also use a machine learning algorithm to suggest products that match the user's preferences. For example, the suggestion unit uses a recommendation system to list products that meet the user's requirements. The suggestion unit uses a filtering algorithm to narrow down products based on the user's requirements. The suggestion unit uses a machine learning algorithm to suggest products based on the user's past purchase history and preferences. As a result, the external commerce service system according to the embodiment can efficiently suggest products that meet the user's needs.

[0069] The suggestion unit can provide detailed information about the product. The detailed information includes, for example, product specifications, price, reviews, etc., but is not limited to these examples. The suggestion unit can, for example, provide product specifications of the product. The product specifications include information such as the size, weight, and material of the product. The suggestion unit can also provide price information about the product. The price information includes the selling price of the product, discount information, shipping costs, etc. The suggestion unit can also provide review information about the product. The review information includes ratings and comments from other users. For example, the suggestion unit can display detailed product specifications of the product to allow the user to understand the characteristics of the product. It can provide price information to allow the user to understand the cost of the product. It can display review information to refer to the opinions of other users. This allows the user to check detailed product information.

[0070] The suggestion unit can provide a purchase link. Examples of the purchase link include, but are not limited to, a URL link, a QR code, and the like. For example, the suggestion unit can provide a URL link to a product purchase page. By clicking the URL link, the user can directly access the product purchase page. The suggestion unit can also provide a QR code to the product purchase page. By scanning the QR code, the user can easily access the product purchase page using a device such as a smartphone. The suggestion unit can also send the purchase link via email or message. For example, the suggestion unit can display a URL link to a product purchase page so that the user can click to access the purchase page. Or it can display a QR code so that the user can scan it with their smartphone to access the purchase page. The purchase link can be sent via email or message so that the user can access it later. This allows the user to easily purchase the product.

[0071] The analysis unit can collect and analyze product images from the e-commerce site. Examples of collection methods include, but are not limited to, web scraping, API usage, and the like. For example, the analysis unit can automatically collect product images from the e-commerce site using web scraping technology. The analysis unit can also acquire product images using the e-commerce site's API. The analysis unit can also collect product images provided by users. For example, the analysis unit can extract images from product pages on the e-commerce site using web scraping technology. The analysis unit can acquire product images from the e-commerce site using APIs. Product images uploaded by users are collected and used for analysis. For example, techniques such as image recognition algorithms and statistical analysis can be used for analysis, but are not limited to these examples. For example, the analysis unit can analyze the collected product images using an image recognition algorithm to identify specific products. The analysis unit can also analyze the features of the product images using statistical analysis technology. The analysis unit can also classify product images using a machine learning algorithm. For example, the analysis unit can identify specific products from the collected product images using an image recognition algorithm. Statistical analysis techniques are used to analyze the color, shape, and other characteristics of product images. Machine learning algorithms are used to classify product images into categories. This allows for efficient collection and analysis of product images on e-commerce sites.

[0072] The reception unit allows a user to input a specific product name, characteristics, and use. Examples of specific product names, characteristics, and uses include, but are not limited to, keyword input and category selection. The reception unit, for example, provides a text box for receiving keyword input. The user can input a specific product name, characteristics, and use into the text box. The reception unit can also provide a drop-down menu for receiving category selection. The user can select a category from the drop-down menu and input a specific product name, characteristics, and use. The reception unit can also provide a microphone interface for receiving voice input. The user can input a specific product name, characteristics, and use by voice through the microphone. For example, the reception unit displays a text box to allow the user to input a specific product name, characteristics, and use. The reception unit displays a drop-down menu to allow the user to select a category and input. The reception unit provides a microphone interface to allow the user to input a specific product name, characteristics, and use by voice. This allows the user to input a specific product name, characteristics, and use.

[0073] The suggestion unit can identify products that meet the user's requirements. Products that meet the requirements include, but are not limited to, criteria such as filtering conditions and a matching algorithm. For example, the suggestion unit uses filtering conditions to narrow down products that meet the user's requirements. Filtering conditions include product price, color, size, brand, etc. The suggestion unit can also use a matching algorithm to identify products based on the user's requirements. The matching algorithm includes a recommendation system that takes into account product features and user preferences. Furthermore, the suggestion unit can also use a machine learning algorithm to identify products that meet the user's requirements. For example, the suggestion unit uses filtering conditions to list products that meet the user's requirements. The matching algorithm identifies products based on the user's requirements. The machine learning algorithm identifies products based on the user's past purchase history and preferences. This makes it possible to identify products that meet the user's requirements.

[0074] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the reception unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The reception unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the reception unit can estimate the emotion from the text input by the user using text analysis technology. For example, the reception unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The reception unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The reception unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the reception unit can adjust the display method of the input interface. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Furthermore, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing the product name and features to be input quickly. For example, if the user is feeling stressed, the reception unit displays a simple interface to minimize input steps. If the user is relaxed, detailed input options are displayed and a customizable input method is suggested. If the user is in a hurry, voice input can be prioritized, allowing the product name and features to be input quickly. This makes it possible to provide an optimal input interface according to the user's emotions.

[0075] The reception unit can analyze the user's past input history and suggest an optimal input method. The input history includes, for example, past search keywords, input patterns, etc., but is not limited to these examples. For example, the reception unit analyzes past search keywords and automatically displays product names and features frequently entered by the user as candidates. The reception unit can also analyze past input patterns and preferentially suggest input methods (voice, text, etc.) used by the user in the past. Furthermore, the reception unit can predict and suggest product names and features to be used in a specific time period from the past input history. For example, the reception unit analyzes past search keywords and automatically displays product names and features frequently entered by the user as candidates. The reception unit analyzes past input patterns and preferentially suggests input methods used by the user in the past. The reception unit predicts and suggests product names and features to be used in a specific time period from the past input history. This makes it possible to suggest an optimal input method based on the user's past input history.

[0076] The reception unit can filter the input content based on the user's current purchase history and areas of interest at the time of input. Examples of the purchase history and areas of interest include, but are not limited to, a list of purchased products and interest categories. For example, the reception unit can automatically display related product names and features as candidates based on the user's purchase history. The reception unit can also filter the input content based on the user's areas of interest to suggest highly relevant product names and features. Furthermore, the reception unit can combine the user's past purchase history and areas of interest to suggest optimal input content. For example, the reception unit can automatically display related product names and features as candidates based on the user's purchase history. The reception unit can filter the input content based on the user's areas of interest to suggest highly relevant product names and features. The reception unit can combine the user's past purchase history and areas of interest to suggest optimal input content. This makes it possible to provide highly relevant input content based on the user's purchase history and areas of interest.

[0077] The reception unit can select the optimal input means depending on the user's input method when inputting information. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs "I want a red dress" by voice, the reception unit prioritizes voice input to quickly input the product name and its characteristics. Furthermore, when a user inputs text, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when a user uploads an image, the reception unit can perform image analysis and automatically input related product names and characteristics. For example, when a user inputs "I want a red dress" by voice, the reception unit prioritizes voice input to quickly input the product name and its characteristics. When a user inputs text, the reception unit provides detailed input options and suggests customizable input methods. When a user uploads an image, the reception unit can provide the optimal input means depending on the user's input method.

[0078] The reception unit can estimate the user's emotion and prioritize input content based on the estimated user emotion. Technologies such as, but not limited to, facial expression recognition, voice analysis, and text analysis can be used to estimate the emotion. For example, the reception unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The reception unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the reception unit can estimate the emotion from the user's input text using text analysis technology. For example, the reception unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The reception unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The reception unit can use text analysis technology to analyze the user's input text and estimate the emotion. Based on the estimated emotion, the reception unit can prioritize the input content. For example, if the user is stressed, simple product names and features can be displayed preferentially. Furthermore, if the user is relaxed, detailed product names and features can be displayed preferentially. Furthermore, if the user is in a hurry, product names and features that can be entered quickly can be displayed preferentially. For example, if the user is feeling stressed, the reception unit will prioritize displaying simple product names and features. If the user is relaxed, the reception unit will prioritize displaying detailed product names and features. If the user is in a hurry, the reception unit will prioritize displaying product names and features that can be input quickly. This makes it possible to provide the optimal priority order of input content according to the user's emotions.

[0079] The reception unit can prioritize accepting highly relevant input content in consideration of the user's geographical location information when inputting information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the reception unit prioritizes displaying product names and features that are popular in that area. The reception unit can also suggest highly relevant product names and features based on the user's current location. Furthermore, the reception unit can also suggest optimal input content in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes displaying product names and features that are popular in that area. The reception unit suggests highly relevant product names and features based on the user's current location. The reception unit suggests optimal input content in consideration of the user's geographical location information. This makes it possible to provide highly relevant input content based on the user's geographical location information.

[0080] The reception unit can analyze the user's social media activity at the time of input and suggest related input content. Social media activity includes, for example, posted content, like history, etc., but is not limited to these examples. The reception unit can, for example, suggest product names and features related to places where the user has checked in on social media. The reception unit can also analyze the user's social media posts and suggest related product names and features. Furthermore, the reception unit can also suggest related product names and features by referring to the activities of the user's friends on social media. For example, the reception unit can suggest product names and features related to places where the user has checked in on social media. The reception unit can analyze the user's social media posts and suggest related product names and features. The reception unit can suggest related product names and features by referring to the activities of the user's friends on social media. This makes it possible to provide highly relevant input content based on the user's social media activity.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The feedback includes, for example, reviews, evaluation comments, etc., but is not limited to these examples. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The input interface is customized based on the user's past feedback. The input procedure is optimized by reflecting the user's past feedback. In this way, the optimal input method can be provided based on the user's past feedback.

[0082] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the analysis unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the analysis unit can estimate the emotion from the text input by the user using text analysis technology. For example, the analysis unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the analysis unit can adjust the presentation method of the analysis. For example, if the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result can be provided. Furthermore, if the user is excited, an analysis result with visually stimulating effects can be provided. For example, if the user is relaxed, the analysis unit displays detailed analysis results. If the user is in a hurry, the analysis unit displays analysis results that focus on the main points. If the user is excited, the analysis unit displays analysis results with visually stimulating effects. This makes it possible to provide optimal analysis results according to the user's emotions.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. The level of importance includes, but is not limited to, criteria such as prioritization and scoring. For example, the analysis unit performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the input information. For example, the analysis unit performs a detailed analysis on information with high importance and provides a detailed report. For information with low importance, the analysis unit performs a simplified analysis and provides a concise report. The level of detail of the analysis is dynamically adjusted according to the importance of the input information to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the importance of the input information.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. Categories include, but are not limited to, criteria such as text classification and image classification. The analysis unit selects the optimal analysis algorithm depending on, for example, the category of a product. The analysis unit can also dynamically switch analysis algorithms based on the category of the input information. Furthermore, the analysis unit can apply different analysis algorithms for each category to improve accuracy. For example, the analysis unit selects the optimal analysis algorithm depending on the category of a product and performs analysis. The analysis algorithm is dynamically switched based on the category of the input information to provide optimal analysis results. Different analysis algorithms are applied to each category to improve analysis accuracy. This makes it possible to provide optimal analysis results depending on the category of the input information.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports, analysis logs, etc. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the analysis parameters by using the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results to improve accuracy. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit adjusts the analysis parameters by using the user's past analysis results to provide optimal analysis results. This makes it possible to provide optimal analysis results based on the user's past analysis results.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the analysis unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the analysis unit can estimate the emotion from the user's input text using text analysis technology. For example, the analysis unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can use text analysis technology to analyze the user's input text and estimate the emotion. Based on the estimated emotion, the analysis unit adjusts the length of the analysis. For example, if the user is in a hurry, a short and concise analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is excited, an analysis result with visually stimulating effects can be provided. For example, if the user is in a hurry, the analysis unit displays a short, to-the-point analysis result. If the user is relaxed, the analysis unit displays a detailed analysis result. If the user is excited, the analysis unit displays an analysis result with a visually stimulating effect. This allows the analysis unit to provide the optimal analysis result according to the user's emotions.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input information. The submission time includes, but is not limited to, criteria such as a timestamp and the order of submission. For example, the analysis unit prioritizes analysis of information submitted earlier. The analysis unit can also postpone information submitted later. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the submission time. For example, the analysis unit prioritizes analysis of information submitted earlier and provides results earlier. The analysis unit postpones information submitted later and provides results later. The analysis priority is dynamically adjusted based on the submission time to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the submission time of the input information.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. Relevance includes, but is not limited to, criteria such as co-occurrence networks and correlation analysis. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the input information. For example, the analysis unit prioritizes analysis of highly relevant information and provides results early. The analysis unit postpones analysis of less relevant information and provides results later. The analysis unit dynamically adjusts the order of analysis based on the relevance of the input information and provides optimal analysis results. This makes it possible to provide optimal analysis results according to the relevance of the input information.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, criteria such as survey results and past usage history. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. For example, when the user's level of expertise is high, the analysis unit uses a lot of technical terms to provide detailed analysis results. When the user's level of expertise is low, the analysis unit avoids technical terms and provides concise analysis results. The use of technical terms in the analysis can be dynamically adjusted according to the user's level of expertise to provide optimal analysis results. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0090] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the analysis unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the analysis unit can estimate the emotion from the text input by the user using text analysis technology. For example, the analysis unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the analysis unit can adjust the analysis criteria. For example, if the user is relaxed, detailed analysis criteria can be applied. If the user is in a hurry, simplified analysis criteria can be applied. Furthermore, if the user is excited, analysis criteria with visually stimulating effects can be applied. For example, if the user is relaxed, the analysis unit applies detailed analysis criteria to provide detailed analysis results. If the user is in a hurry, the analysis unit applies simplified analysis criteria to provide concise analysis results. If the user is excited, the analysis unit applies analysis criteria with visually stimulating effects to provide visually appealing analysis results. This makes it possible to provide optimal analysis criteria according to the user's emotions.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between product images. Interrelationships include, but are not limited to, criteria such as co-occurrence analysis and relevance scores. For example, the analysis unit analyzes the interrelationships between product images to identify highly related products. The analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between product images. Furthermore, the analysis unit can apply an optimal analysis algorithm based on the interrelationships between product images. For example, the analysis unit analyzes the interrelationships between product images to identify highly related products. The accuracy of the analysis is improved by taking into account the interrelationships between product images. The optimal analysis algorithm is applied based on the interrelationships between product images to improve the accuracy. This makes it possible to provide optimal analysis results based on the interrelationships between product images.

[0092] The analysis unit can perform the analysis taking into account attribute information of the product. Attribute information includes, for example, criteria such as color, size, and brand, but is not limited to these examples. The analysis unit performs the analysis taking into account attribute information such as the color and design of the product. The analysis unit can also improve the accuracy of the analysis based on attribute information such as the material and size of the product. Furthermore, the analysis unit can analyze the attribute information of the product and identify highly related products. For example, the analysis unit performs the analysis taking into account attribute information such as the color and design of the product and identifies highly related products. The analysis unit improves the accuracy of the analysis based on attribute information such as the material and size of the product. The attribute information of the product is analyzed and identifies highly related products. This makes it possible to provide optimal analysis results based on the attribute information of the product.

[0093] During analysis, the analysis unit can weight the analysis based on the frequency of submission of products. The frequency of submission includes, but is not limited to, criteria such as the number of submissions and the interval between submissions. For example, the analysis unit prioritizes the analysis of products that are submitted more frequently. The analysis unit can also postpone products that are submitted less frequently. Furthermore, the analysis unit can dynamically adjust the weighting of the analysis based on the frequency of submission. For example, the analysis unit prioritizes the analysis of products that are submitted more frequently and provides results earlier. Products that are submitted less frequently can be postponed and results provided later. The analysis weighting is dynamically adjusted based on the frequency of submission to provide optimal analysis results. This makes it possible to provide optimal analysis results based on the frequency of submission of products.

[0094] The analysis unit can estimate the user's emotion and adjust the order in which the analysis results are displayed based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the analysis unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The analysis unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the analysis unit can estimate the emotion from the text input by the user using text analysis technology. For example, the analysis unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The analysis unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the analysis unit can adjust the order in which the analysis results are displayed. For example, if the user is relaxed, detailed analysis results can be displayed preferentially. If the user is in a hurry, analysis results that focus on the main points can be displayed preferentially. If the user is excited, analysis results with visually stimulating effects can be displayed preferentially. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit will prioritize displaying analysis results that focus on the main points. If the user is excited, the analysis unit will prioritize displaying analysis results with visually stimulating effects. This makes it possible to provide optimal analysis results according to the user's emotions.

[0095] The analysis unit can perform the analysis while taking into account the geographical distribution of products. Geographical distribution includes, but is not limited to, criteria such as sales region and delivery area. For example, the analysis unit analyzes the geographical distribution of products to identify highly relevant products. The analysis unit can also improve the accuracy of the analysis by taking into account the geographical distribution of products. Furthermore, the analysis unit can apply an optimal analysis algorithm based on the geographical distribution of products. For example, the analysis unit analyzes the geographical distribution of products to identify highly relevant products. The analysis accuracy is improved by taking into account the geographical distribution of products. The optimal analysis algorithm is applied based on the geographical distribution of products to improve the accuracy. This makes it possible to provide optimal analysis results based on the geographical distribution of products.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. Examples of related literature include, but are not limited to, academic papers, patent documents, and other standards. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. The analysis unit can also apply an optimal analysis algorithm based on the literature related to the product. Furthermore, the analysis unit can analyze the literature related to the product and identify highly relevant products. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the product. The analysis unit can apply an optimal analysis algorithm based on the literature related to the product and improve the accuracy. The literature related to the product can be analyzed and highly relevant products can be identified. This makes it possible to provide optimal analysis results based on the literature related to the product.

[0097] The analysis unit may perform the analysis while taking into account the market value of the product. Market value may include, but is not limited to, criteria such as price trends and market share. For example, the analysis unit may analyze the market value of the product and identify highly relevant products. The analysis unit may also improve the accuracy of the analysis by taking into account the market value of the product. Furthermore, the analysis unit may apply an optimal analysis algorithm based on the market value of the product. For example, the analysis unit may analyze the market value of the product and identify highly relevant products. The analysis may improve the accuracy of the analysis by taking into account the market value of the product. The analysis unit may apply an optimal analysis algorithm based on the market value of the product to improve the accuracy. This allows for providing optimal analysis results based on the market value of the product.

[0098] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the suggestion unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The suggestion unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the suggestion unit can estimate the emotion from the text input by the user using text analysis technology. For example, the suggestion unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The suggestion unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the suggestion unit can adjust the way the suggestion is presented. For example, if the user is relaxed, detailed suggestions can be made. If the user is in a hurry, suggestions that focus on the main points can be made. Furthermore, if the user is excited, suggestions can be made with visually stimulating effects. For example, if the user is relaxed, the suggestion unit makes detailed suggestions. If the user is in a hurry, the suggestion unit makes suggestions that focus on the main points. If the user is excited, the suggestion unit makes suggestions that add visually stimulating effects. In this way, it is possible to provide optimal suggestions according to the user's emotions.

[0099] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the product. Criteria for importance include, but are not limited to, sales volume, customer ratings, and the like. For example, the suggestion unit makes a detailed proposal for a product with high importance. The suggestion unit can also make a simplified proposal for a product with low importance. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the importance of the product. For example, the suggestion unit makes a detailed proposal for a product with high importance, providing detailed information. For a product with low importance, the suggestion unit makes a simplified proposal, providing concise information. The level of detail of the proposal is dynamically adjusted according to the importance of the product, providing an optimal proposal. This makes it possible to provide an optimal proposal according to the importance of the product.

[0100] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the product category. Examples of categories include, but are not limited to, criteria such as text classification and image classification. The suggestion unit selects an optimal suggestion algorithm depending on, for example, the product category. The suggestion unit can also dynamically switch the suggestion algorithm based on the product category. Furthermore, the suggestion unit can apply different suggestion algorithms for each category to improve accuracy. For example, the suggestion unit selects an optimal suggestion algorithm depending on the product category and makes a suggestion. The suggestion algorithm is dynamically switched based on the product category to provide optimal suggestions. Different suggestion algorithms are applied to each category to improve the accuracy of the suggestions. This makes it possible to provide optimal suggestions depending on the product category.

[0101] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. Past suggestion results include, for example, past purchase history, suggestion logs, etc., but are not limited to these examples. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the suggestion parameters by using the user's past suggestion results. For example, the suggestion unit optimizes the suggestion algorithm based on the user's past suggestion results to improve the accuracy. The suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit adjusts the suggestion parameters by using the user's past suggestion results to provide optimal suggestions. This makes it possible to provide optimal suggestions based on the user's past suggestion results.

[0102] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. To estimate the emotion, technologies such as facial expression recognition, voice analysis, and text analysis are used, but are not limited to these examples. For example, the suggestion unit can estimate the emotion from the user's facial expression using facial expression recognition technology. The suggestion unit can also estimate the emotion from the tone and speed of the user's voice using voice analysis technology. Furthermore, the suggestion unit can estimate the emotion from the text input by the user using text analysis technology. For example, the suggestion unit can use facial expression recognition technology to capture the user's facial expression with a camera and estimate the emotion. The suggestion unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit can use text analysis technology to analyze the text input by the user and estimate the emotion. Based on the estimated emotion, the suggestion unit adjusts the length of the suggestion. For example, if the user is in a hurry, the suggestion unit can make a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can make a detailed suggestion. If the user is excited, the suggestion unit can make a suggestion with a visually stimulating effect. For example, if the user is in a hurry, the suggestion unit will provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit will provide a detailed suggestion. If the user is excited, the suggestion unit will provide a suggestion with a visually stimulating effect. This allows the suggestion unit to provide the most appropriate suggestion according to the user's emotions.

[0103] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the product. The submission time includes, but is not limited to, criteria such as timestamp and submission order. For example, the proposal unit preferentially proposes products that have been submitted earlier. The proposal unit can also postpone products that have been submitted later. Furthermore, the proposal unit can dynamically adjust the priority of the proposal based on the submission time. For example, the proposal unit preferentially proposes products that have been submitted earlier and provides results early. The proposal unit postpones products that have been submitted later and provides results later. The proposal priority is dynamically adjusted based on the submission time to provide the optimal proposal. This makes it possible to provide the optimal proposal according to the submission time of the product.

[0104] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the products. Relevance includes, but is not limited to, criteria such as co-occurrence networks and correlation analysis. For example, the suggestion unit preferentially suggests highly relevant products. The suggestion unit can also postpone products with low relevance. Furthermore, the suggestion unit can dynamically adjust the order of suggestions based on the relevance of the products. For example, the suggestion unit preferentially suggests highly relevant products and provides results early. The suggestion unit postpones products with low relevance and provides results later. The suggestion order is dynamically adjusted based on the relevance of the products to provide optimal suggestions. This makes it possible to provide optimal suggestions according to the relevance of the products.

[0105] When making a suggestion, the suggestion unit can adjust the use of technical terms in the suggestion according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, criteria such as survey results and past usage history. For example, the suggestion unit uses a lot of technical terms when the user's level of expertise is high. Furthermore, the suggestion unit can avoid technical terms when the user's level of expertise is low. Furthermore, the suggestion unit can dynamically adjust the use of technical terms in the suggestion according to the user's level of expertise. For example, when the user's level of expertise is high, the suggestion unit uses a lot of technical terms and provides a detailed suggestion. When the user's level of expertise is low, the suggestion unit avoids technical terms and provides a concise suggestion. The suggestion unit dynamically adjusts the use of technical terms in the suggestion according to the user's level of expertise to provide an optimal suggestion. This allows the system to provide an optimal suggestion according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, analysis section, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides a chat interface. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and image analysis. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes product images on an e-commerce site. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products that meet customer requirements. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, analysis section, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 or the camera 42 of the smart glasses 214 and provides a chat interface. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and image analysis. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes product images on an e-commerce site. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products that meet requirements. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, analysis 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 reception unit is realized by the microphone 238 and the camera 42 of the headset-type terminal 314 and provides a chat interface. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and image analysis. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes product images on an e-commerce site. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products that meet requirements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, analysis section, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and the camera 42 of the robot 414 and provides a chat interface. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and image analysis. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes product images on an e-commerce site. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests products that meet requirements.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also provide related trend information based on the user's input. For example, if a user is looking for a specific fashion item, the reception unit can display the latest fashion trends and styling suggestions related to that item. If a user is looking for a specific gadget, the reception unit can provide the latest technology trends and reviews related to that gadget. Furthermore, if a user is looking for a specific food item, the reception unit can provide recipes and health information related to that food item. This allows the user to not only search for products but also obtain the latest related information.

[0108] The suggestion unit can also estimate the user's emotions and adjust the presentation method of the suggested products based on the estimated emotions. For example, if the user is excited, the suggestion unit can introduce products using visually appealing animations and effects. If the user is relaxed, the suggestion unit can introduce products using calm colors and simple designs. Furthermore, if the user is stressed, the suggestion unit can introduce products using a simple and intuitive interface, reducing the burden on the user. This makes it possible to provide optimal product presentations according to the user's emotions.

[0109] The suggestion unit can also suggest bundles of related products based on the user's purchasing history or areas of interest. For example, if a user has previously purchased clothes from a specific brand, the suggestion unit can suggest new items from that brand and related accessories. If a user has purchased books in a specific genre, the suggestion unit can suggest new releases in that genre and related audiobooks. Furthermore, if a user has purchased a specific gadget, the suggestion unit can suggest accessories and upgrade options related to that gadget. This allows users to purchase multiple products at once, improving convenience.

[0110] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis unit can display the analysis results using visually stimulating graphs and charts. If the user is relaxed, the analysis unit can provide a report with a simple and calm design. Furthermore, if the user is feeling stressed, the analysis unit can provide a concise report that focuses on the main points, reducing the burden on the user. This makes it possible to provide an optimal display method of the analysis results according to the user's emotions.

[0111] The reception unit can also provide relevant coupons and discount information based on the user's input. For example, if the user is looking for clothes from a specific brand, the reception unit can display the latest coupons and discount information for that brand. If the user is looking for a specific gadget, the reception unit can provide promotions and sales information related to that gadget. Furthermore, if the user is looking for a specific food item, the reception unit can provide discount coupons and special sale information related to that food item. This allows the user to obtain information on good deals and increases their motivation to purchase.

[0112] The suggestion unit can also estimate the user's emotions and adjust the order of suggested products based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can prioritize displaying the most relevant products. If the user is relaxed, the suggestion unit can sequentially display products containing detailed information. Furthermore, if the user is excited, the suggestion unit can prioritize displaying visually appealing products. This makes it possible to provide an optimal order of suggested products according to the user's emotions.

[0113] The analysis unit can also provide relevant market trend and competitive information based on the user's input. For example, if a user is looking for a specific gadget, the analysis unit can display information on the market trend and competitive products for that gadget. Also, if a user is looking for a specific fashion item, the analysis unit can provide information on trends for that item and competitive brands. Furthermore, if a user is looking for a specific food item, the analysis unit can provide information on the market trend and competitive products for that food item. This allows users to obtain more information when selecting products.

[0114] The suggestion unit can also estimate the user's emotions and adjust the suggested product description based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide a detailed and thorough description. If the user is in a hurry, the suggestion unit can provide a concise and to-the-point description. Furthermore, if the user is excited, the suggestion unit can provide a description with visually appealing effects. This makes it possible to provide the optimal product description according to the user's emotions.

[0115] The reception unit can also provide information on related communities and forums based on the user's input. For example, if a user is looking for a specific gadget, the reception unit can display user reviews and forum discussions about that gadget. If a user is looking for a specific fashion item, the reception unit can provide styling suggestions and user opinions about that item. Furthermore, if a user is looking for a specific food, the reception unit can provide recipes and health information about that food. This allows the user to refer to the opinions and information of other users.

[0116] The suggestion unit can also estimate the user's emotions and adjust the price range of the suggested products based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can suggest products in a wide price range. Also, if the user is in a hurry, the suggestion unit can preferentially suggest affordable products. Furthermore, if the user is excited, the suggestion unit can suggest expensive and attractive products. In this way, it is possible to suggest products in the optimal price range according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit accepts chat input. Chat input includes text input, voice input, and image input. For example, the reception unit provides a chat interface for accepting text input, a microphone interface for accepting voice input, and an image upload function for accepting image input. The reception unit receives text entered by the user and converts it into text using voice recognition technology in the case of voice input, and analyzes the content using image analysis technology in the case of image input. Step 2: The analysis unit analyzes the information received by the reception unit. For the analysis, technologies such as natural language processing, image analysis, and data mining are used. For example, the analysis unit may use natural language processing technology to analyze the meaning of the text entered by the user, image analysis technology to analyze the content of the image uploaded by the user, and data mining technology to analyze the user's past input history. Step 3: The analysis unit analyzes the product images on the e-commerce site based on the information analyzed by the analysis unit. The analysis uses techniques such as image recognition algorithms, statistical analysis, and machine learning algorithms. For example, the analysis unit uses image recognition algorithms to analyze product images on the e-commerce site and identify specific products, statistical analysis techniques to analyze the characteristics of the product images, and machine learning algorithms to classify the product images by category. Step 4: The suggestion unit suggests products that meet the user's requirements based on the information analyzed by the analysis unit. The suggestion unit uses technologies such as recommendation systems, filtering algorithms, and machine learning algorithms. For example, the suggestion unit uses a recommendation system to list products that meet the user's requirements, a filtering algorithm to narrow down the products based on the user's requirements, and a machine learning algorithm to suggest products based on the user's past purchase history and preferences.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0133] 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives chat input; an analysis unit that analyzes the information received by the reception unit; an analysis unit that analyzes product images on an EC site based on the information analyzed by the analysis unit; a proposal unit that proposes products that meet requirements based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The proposal unit Provide detailed product information 2. The system of claim 1.

3. The proposal unit Provide a purchase link 2. The system of claim 1.

4. The analysis unit Collect and analyze product images from e-commerce sites 2. The system of claim 1.

5. The reception unit Enter the specific product name, features, and use 2. The system of claim 1.

6. The proposal unit Identify products that meet your requirements 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

Images