system

Abstract

The system according to the embodiment aims to automatically generate an optimized product description simply by inputting a product image and necessary information. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs product images and necessary information. The analysis unit analyzes the information input by the reception unit. The generation unit generates an optimized explanation based on the information analyzed by the analysis unit. The provision unit provides the explanatory material generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that creating product descriptions requires a great deal of effort and does not adequately convey the appeal of the product.

[0005] The system according to the embodiment aims to automatically generate an optimized product description simply by inputting a product image and necessary information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs product images and necessary information. The analysis unit analyzes the information input by the reception unit. The generation unit generates an optimized explanation based on the information analyzed by the analysis unit. The provision unit provides the explanatory material generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate an optimized product description simply by inputting a product image and necessary information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) An automatic product description material generation system according to an embodiment of the present invention uses a generation AI to automatically generate product description materials. A user inputs a product image and necessary information, and the system automatically generates a description optimized for that product category. The generated product description is provided to the user, who can modify it as needed. This effectively communicates the product's appeal and prevents lost opportunities. For example, a user inputs a product image and necessary information, such as the product name, category, price, condition (new, used, etc.), features, and precautions. This information is then input into a generation AI. The generation AI then analyzes the input information and automatically generates a description optimized for that product category. For example, in the case of electronic devices, the AI ​​provides detailed information on specifications, functions, and usage. It also includes important information such as scratches and precautions. The generated product description is provided to the user. The user can review the generated product description and modify it as needed. This effectively communicates the product's appeal and prevents lost opportunities. This system significantly reduces the effort required for individuals to list products on auctions and for businesses to create product description content. In addition, the system can accurately convey the features and key points of a product, maximizing its appeal. This allows the automatic generation of product information materials to effectively communicate the appeal of a product, preventing lost opportunities.

[0029] The system for automatically generating product description materials according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of product images and necessary information from a user. Product images may be in, for example, JPEG or PNG format, but are not limited to these. The necessary information may include, for example, the product name, category, price, condition (new, used, etc.), features, and precautions, but are not limited to these. The reception unit provides an interface for the user to upload product images and input the necessary information. The reception unit can also use a generation AI to convert the information entered by the user into a format that is easy to analyze. The analysis unit uses the generation AI to analyze the information entered by the reception unit. For example, the analysis unit analyzes product images and extracts product features. The analysis unit also analyzes the necessary information and extracts information for generating optimal descriptions based on the product category. For example, the analysis unit uses an image analysis algorithm to analyze product images and extract product features such as color, shape, and texture. The analysis unit also analyzes the necessary information using text analysis technology to extract information such as product specifications, functions, and usage instructions. The generation unit uses a generation AI to generate an optimized explanation based on the information analyzed by the analysis unit. For example, in the case of an electronic device, the generation unit provides detailed explanations of the product's specifications, functions, and usage instructions. The generation unit also includes key points, such as scratches and warnings. For example, the generation unit uses a generation AI to generate text for providing detailed explanations of a product's specifications and functions. The generation unit can also use the generation AI to generate instructions for explaining how to use the product. The provision unit provides the explanatory materials generated by the generation unit to a user. The provision unit can provide the generated explanatory materials in, for example, a PDF format or an HTML format. The provision unit can also provide an interface that allows a user to review the generated explanatory materials and make corrections as necessary. For example, the provision unit can send the generated explanatory materials to a user by email. The provision unit can also provide the generated explanatory materials as a download link. This allows the automatic product description material generation system according to the embodiment to effectively convey the appeal of a product and prevent loss of opportunity.

[0030] The automatic product description material generation system may include an extraction unit that analyzes product images and extracts product features. The extraction unit, for example, analyzes product images and extracts product features. Features include, but are not limited to, the color, shape, and texture of the product. The extraction unit, for example, analyzes product images using an image analysis algorithm and extracts product features. For example, the extraction unit may analyze the color of the product and identify color variations of the product. The extraction unit may also analyze the shape of the product and identify the design of the product. The extraction unit may also analyze the texture of the product and identify the texture of the product. This allows features to be extracted from the product images and reflected in the product materials. Some or all of the above-described processing by the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may input product images to the generation AI and cause the generation AI to extract product features.

[0031] The automatic product information material generation system may include a point generation unit that generates content that highlights key points regarding scratches and precautions. The point generation unit, for example, analyzes scratches and precautions on a product and generates content that highlights the key points. Points include, but are not limited to, surface scratches, internal scratches, handling precautions, and storage methods of the product. The point generation unit, for example, detects scratches on the product using an image analysis algorithm and reflects the details of the scratches in the product information material. For example, the point generation unit may analyze scratches on the surface of the product and identify their location and size. The point generation unit may also analyze internal scratches on the product and identify their impact. The point generation unit may also analyze handling precautions and storage methods of the product and reflect the details in the product information material. For example, the point generation unit may analyze handling precautions for the product and generate specific precautions. The point generation unit may also analyze storage methods for the product and generate appropriate storage methods. This allows the generation of product information that includes important points about the product. Some or all of the above-described processing in the point generation unit may be performed, for example, using a generation AI or without a generation AI. For example, the point generation unit can input data about product scratches and warnings into the generation AI and have the generation AI generate points.

[0032] The product description material automatic generation system may include a providing unit that provides the generated explanatory materials to a user. The providing unit provides the generated explanatory materials to a user, for example. Examples of providing methods include, but are not limited to, sending the generated explanatory materials by email or providing a download link. The providing unit may provide the generated explanatory materials in, for example, a PDF format or an HTML format. For example, the providing unit may send the generated explanatory materials to a user by email. The providing unit may also provide the generated explanatory materials as a download link. The providing unit may also provide an interface that allows a user to review the generated explanatory materials and make corrections as necessary. For example, the providing unit may provide an online editor that allows a user to edit the generated explanatory materials. The providing unit may also provide a printing function that allows a user to print the generated explanatory materials. This allows the generated explanatory materials to be provided to a user, allowing them to review and correct them. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the generated explanatory materials to the generation AI and have the generation AI select a delivery method.

[0033] In the automatic product description material generation system, an analysis unit can generate an appropriate explanation based on a product category. The analysis unit generates the appropriate explanation based on, for example, the product category. Product categories include, but are not limited to, electronic devices, clothing, and food. The analysis unit extracts information for detailed explanations of product specifications, functions, and usage methods based on the product category. For example, in the case of electronic devices, the analysis unit extracts information for detailed explanations of the product specifications, functions, and usage methods. In the case of clothing, the analysis unit can also extract information for detailed explanations of the product's design, materials, size, and so on. In the case of food, the analysis unit can also extract information for detailed explanations of the product's ingredients, nutritional value, storage methods, and so on. This allows for the generation of an explanation optimized for the product category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the information extracted based on the product category into the generation AI and cause the generation AI to generate an appropriate explanation.

[0034] In the automatic product description material generation system, when the generation unit is an electronic device, the generation unit can specifically explain the specifications, functions, and usage instructions. For example, in the case of an electronic device, the generation unit specifically explains the specifications, functions, and usage instructions. Specifications include, but are not limited to, the CPU, memory, and storage capacity. Functions include, but are not limited to, the operation method and usage scenarios. Usage instructions include, but are not limited to, the setup procedure and usage precautions. The generation unit, for example, uses a generation AI to generate text that details the specifications, functions, and usage instructions of the electronic device. For example, the generation unit uses a generation AI to generate text that details the specifications of the electronic device. The generation unit can also use a generation AI to generate text that details the functions of the electronic device. The generation unit can also use a generation AI to generate procedures that details the usage instructions of the electronic device. This allows for the generation of a detailed description of the electronic device. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the specifications, functions, and usage of an electronic device into the generation AI and have the generation AI generate detailed descriptions.

[0035] In the product description material automatic generation system, the reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an appropriate input method. The input history includes, for example, product categories and features previously entered, and input methods used (voice, text, etc.), but is not limited to these examples. The reception unit, for example, automatically displays product categories and features frequently entered by the user in the past as candidates. The reception unit can also prioritize suggesting input methods used by the user in the past. Furthermore, the reception unit can predict and suggest product categories and features to be used in a specific time period based on the user's past input history. For example, the reception unit analyzes data previously entered by the user and prioritizes displaying frequently entered product categories and features. The reception unit can also suggest an optimal input method based on the input methods used by the user in the past. This allows the reception unit to suggest an optimal input method based on the past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0036] In the product information automatic generation system, when a user inputs product images and necessary information, the reception unit can filter the input content based on the user's current areas of interest. For example, when a user inputs product images and necessary information, the reception unit filters the input content based on the user's current areas of interest. Areas of interest include, but are not limited to, past browsing history and survey results. For example, the reception unit prioritizes displaying related input items based on product categories recently searched by the user. The reception unit can also customize the input content based on topics of interest to the user. Furthermore, the reception unit can suggest related input items based on products previously purchased by the user. For example, the reception unit prioritizes displaying related input items based on product categories recently searched by the user. The reception unit can also customize the input content based on topics of interest to the user. This allows the input content to be filtered based on the user's areas of interest, providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering of the input content.

[0037] In the automatic product information generation system, when a user inputs product images and necessary information, the reception unit can prioritize input of highly relevant information based on the user's geographical location information. For example, when inputting product images and necessary information, the reception unit prioritizes input of highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit prioritizes input of region-specific product information based on the user's current location. The reception unit can also suggest input fields for related products taking into account the user's geographical location information. Furthermore, the reception unit can input region-specific precautions and features based on the user's location information. For example, the reception unit prioritizes input of region-specific product information based on the user's current location. The reception unit can also suggest input fields for related products taking into account the user's geographical location information. This allows highly relevant information to be prioritized and region-specific information to be provided. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to input highly relevant information.

[0038] In the product description materials automatic generation system, the reception unit analyzes the user's social media activity and inputs appropriate information when the user inputs product images and necessary information. For example, the reception unit analyzes the user's social media activity and inputs appropriate information when the user inputs product images and necessary information. Social media activity includes, but is not limited to, post content and the number of likes. For example, the reception unit suggests related input fields based on product information shared by the user on social media. The reception unit can also identify product categories of interest from the user's social media activity and customize the input content. Furthermore, the reception unit can suggest input fields based on information about brands and products the user follows on social media. For example, the reception unit suggests related input fields based on product information shared by the user on social media. The reception unit can also identify product categories of interest from the user's social media activity and customize the input content. By analyzing social media activity and inputting relevant information, it is possible to provide information tailored to the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input appropriate information.

[0039] In the automatic product description material generation system, the analysis unit can adjust the level of detail of the analysis based on the importance of the product during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the product. Examples of importance include, but are not limited to, the impact on sales and the level of user interest. For example, the analysis unit may perform a detailed analysis of important products and provide detailed explanations. For general products, the analysis unit may also perform a basic analysis and provide a brief explanation. For products in a specific category, the analysis unit may perform a category-specific analysis and provide detailed explanations. For example, the analysis unit may perform a detailed analysis of important products and provide detailed explanations of the product's specifications, functions, and usage. For general products, the analysis unit may perform a basic analysis and provide a brief explanation of the product's main features and usage. This allows the level of detail of the analysis to be adjusted based on the importance of the product, thereby providing appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] In the automatic product description material generation system, the analysis unit can apply different analysis algorithms depending on the product category during analysis. The analysis unit, for example, applies different analysis algorithms depending on the product category. Categories include, but are not limited to, electronic devices, clothing, and food. For example, in the case of electronic devices, the analysis unit applies an analysis algorithm specialized for specifications and functions. In addition, in the case of fashion items, the analysis unit can also apply an analysis algorithm specialized for design and materials. In addition, in the case of food, the analysis unit can apply an analysis algorithm specialized for ingredients and nutritional value. For example, in the case of electronic devices, the analysis unit applies an algorithm for detailed analysis of the product's specifications and functions. In addition, in the case of fashion items, the analysis unit can also apply an algorithm for detailed analysis of the product's design and materials. This allows the application of an analysis algorithm depending on the product category and provides appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0041] In the automatic product description material generation system, the analysis unit can determine the analysis priority based on the submission date of the product during analysis. The analysis unit determines the analysis priority based on, for example, the submission date of the product. The submission date includes, but is not limited to, the submission deadline and the submission order. For example, the analysis unit prioritizes the analysis of products with an upcoming deadline. The analysis unit can also postpone the analysis of products with a more distant submission date. Furthermore, the analysis unit can automatically adjust the analysis schedule based on the submission date. For example, the analysis unit prioritizes the analysis of products with an upcoming deadline so that the submission deadline can be met. The analysis unit can also postpone the analysis of products with a more distant submission date and prioritize the analysis of other important products. This allows the analysis priority to be determined based on the submission date of the product, thereby achieving efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input product submission date data into the generation AI and have the generation AI determine the analysis priority.

[0042] In the automatic product description material generation system, the analysis unit can adjust the order of analysis based on the relevance of products during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of products. Relevance includes, for example, interrelationships between products and user interest, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly related products. The analysis unit can also postpone analysis of less related products. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly related products and determines the order of analysis taking into account interrelationships between products. The analysis unit can also postpone analysis of less related products and prioritize analysis of other important products. This allows the order of analysis to be adjusted based on the relevance of products, thereby achieving efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0043] The automatic product description material generation system can adjust the level of detail of the description generated by the generation unit based on the importance of the product when generating the product. The generation unit adjusts the level of detail of the description generated based on, for example, the importance of the product. The importance level includes, but is not limited to, the impact on sales and the level of user interest. For example, the generation unit provides detailed descriptions for important products. The generation unit can also provide basic descriptions for general products. The generation unit can also provide category-specific descriptions for products in a specific category. For example, the generation unit can provide detailed descriptions for important products, explaining the product's specifications, functions, and usage in detail. For general products, the generation unit can provide basic descriptions, explaining the main features and usage of the product in a key manner. This allows the level of detail of the description to be adjusted based on the importance of the product, thereby providing an appropriate description. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product importance data into the generation AI and cause the generation AI to adjust the level of detail of the description.

[0044] In the automatic product description material generation system, the generation unit can apply different generation algorithms depending on the product category when generating the product description. The generation unit, for example, applies different generation algorithms depending on the product category. Categories include, but are not limited to, electronic devices, clothing, and food. For example, in the case of electronic devices, the generation unit applies a generation algorithm specialized for specifications and functions. In addition, in the case of fashion items, the generation unit can also apply a generation algorithm specialized for design and materials. In addition, in the case of food, the generation unit can apply a generation algorithm specialized for ingredients and nutritional value. For example, in the case of electronic devices, the generation unit applies a generation algorithm for detailed description of the product's specifications and functions. In addition, in the case of fashion items, the generation unit can apply a generation algorithm for detailed description of the product's design and materials. This allows for the application of a generation algorithm depending on the product category to provide an appropriate description. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0045] In the automatic product description material generation system, the generation unit can determine the priority of descriptions to be generated based on the submission date of the product when the product is generated. The generation unit determines the priority of descriptions to be generated based on, for example, the submission date of the product. The submission date includes, but is not limited to, the submission deadline and the submission order. For example, the generation unit prioritizes generating descriptions for products with upcoming deadlines. The generation unit can also postpone descriptions for products with more distant submission dates. Furthermore, the generation unit can automatically adjust the description generation schedule based on the submission date. For example, the generation unit prioritizes generating descriptions for products with upcoming deadlines so that they meet the submission deadline. The generation unit can also postpone descriptions for products with more distant submission dates and prioritize descriptions for other important products. This allows the priority of descriptions to be determined based on the submission date of the product, thereby achieving efficient description generation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input product submission date data into the generation AI and cause the generation AI to determine the priority of descriptions.

[0046] The automatic product description material generation system can adjust the order of descriptions generated by the generation unit based on the relevance of the products when generating the descriptions. The generation unit adjusts the order of descriptions generated based on, for example, the relevance of the products. Relevance includes, for example, interrelationships between products and the user's level of interest, but is not limited to, examples. The generation unit, for example, prioritizes explaining highly related products. The generation unit can also postpone explaining less related products. Furthermore, the generation unit can automatically adjust the order of descriptions based on the relevance of the products. For example, the generation unit prioritizes explaining highly related products and determines the order of descriptions taking into account interrelationships between products. The generation unit can also postpone explaining less related products and prioritize explaining other important products. This allows the order of descriptions to be adjusted based on the relevance of the products, thereby achieving efficient description generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of descriptions.

[0047] In the automatic product description material generation system, the providing unit can select an appropriate display method by referring to the user's past operation history when providing the product description materials. For example, the providing unit selects an appropriate display method by referring to the user's past operation history when providing the product description materials. The operation history includes, but is not limited to, past operation data and frequency analysis. For example, the providing unit can prioritize providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can provide a customizable display method based on the user's past use of the display method. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. This allows the optimal display method to be selected based on the past operation history and provide a display suitable for the user. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's operation history data into the generation AI and cause the generation AI to select an optimal display method.

[0048] In the automatic product description material generation system, the providing unit can select the optimal display method by taking into account the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the data. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. This allows the optimal display method to be selected by taking into account the device information and providing an appropriate display. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0049] In the automatic product description material generation system, the extraction unit can improve the accuracy of extraction based on the interrelationships between products during extraction. The extraction unit improves the accuracy of extraction based on, for example, the interrelationships between products. Interrelationships include, but are not limited to, product associations and co-occurrence relationships. For example, the extraction unit extracts features of related products together to deepen overall understanding. The extraction unit can also prioritize extracting highly related features by taking into account the interrelationships between products. Furthermore, the extraction unit can adjust the feature extraction order based on the interrelationships between products. For example, the extraction unit extracts features of related products together to deepen overall understanding. The extraction unit can also prioritize extracting highly related features by taking into account the interrelationships between products. This improves the accuracy of extraction by taking into account the interrelationships between products, and allows appropriate features to be extracted. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit may input product interrelationship data into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0050] In the automatic product description material generation system, the extraction unit may perform extraction taking into account the geographic distribution of products. The extraction unit performs extraction taking into account, for example, the geographic distribution of products. Examples of geographic distribution include, but are not limited to, regional sales data and geographic information systems (GIS). For example, the extraction unit may prioritize extracting geographically related product features. The extraction unit may also extract region-specific features taking into account the geographic distribution. Furthermore, the extraction unit may adjust the feature extraction order based on the geographic distribution. For example, the extraction unit may prioritize extracting geographically related product features and provide region-specific information. The extraction unit may also extract region-specific features taking into account the geographic distribution. This allows features to be extracted taking into account the geographic distribution and provide region-specific information. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit may input product geographic distribution data into the generation AI and have the generation AI perform extraction.

[0051] In the automatic product information material generation system, the point generation unit can improve the accuracy of point generation based on the interrelationships between products. The point generation unit improves the accuracy of point generation based on, for example, the interrelationships between products. Interrelationships include, but are not limited to, product associations and co-occurrence relationships. For example, the point generation unit generates points for related products together to deepen overall understanding. The point generation unit can also prioritize generating highly related points by taking into account the interrelationships between products. Furthermore, the point generation unit can adjust the point generation order based on the interrelationships between products. For example, the point generation unit generates points for related products together to deepen overall understanding. The point generation unit can also prioritize generating highly related points by taking into account the interrelationships between products. This improves the accuracy of point generation by taking into account the interrelationships between products, thereby generating appropriate points. Some or all of the above-described processing in the point generation unit may be performed using, or without, a generation AI. For example, the point generation unit may input product interrelationship data into the generation AI and cause the generation AI to improve the accuracy of point generation.

[0052] In the automatic product information generation system, the point generation unit may generate points by taking into account the geographical distribution of the product. The point generation unit generates points by taking into account, for example, the geographical distribution of the product. Examples of geographical distribution include, but are not limited to, sales data by region and a geographic information system (GIS). The point generation unit may, for example, prioritize generating points for geographically related products. The point generation unit may also generate region-specific points by taking into account the geographical distribution. Furthermore, the point generation unit may adjust the point generation order based on the geographical distribution. For example, the point generation unit may prioritize generating points for geographically related products and provide region-specific information. The point generation unit may also generate region-specific points by taking into account the geographical distribution. This makes it possible to generate points by taking into account the geographical distribution and provide region-specific information. Some or all of the above-described processing in the point generation unit may be performed using, or without, a generation AI. For example, the point generation unit may input product geographical distribution data into the generation AI and have the generation AI execute the generation.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The automatic product information generation system can analyze a user's past purchase history and automatically generate descriptions of similar products. For example, a description of a new electronic device with similar specifications and functions can be generated based on the history of electronic devices purchased by the user in the past. A description of a new clothing item can also be generated based on the design and materials of previously purchased clothing. Furthermore, a description of a new food item can be generated based on the ingredients and nutritional value of previously purchased food. This makes it possible to provide more personalized product descriptions by utilizing the user's past purchase history.

[0055] The product description material automatic generation system can analyze users' social media activity and generate product descriptions based on trends. For example, it can generate related product descriptions based on products and topics shared by users on social media. It can also generate product descriptions that match trends by referring to the content posted by brands and influencers that users follow. It can also analyze the activity of online communities in which users participate and generate descriptions of products that are popular within those communities. This makes it possible to utilize social media activity to provide product descriptions that match the user's interests.

[0056] The product description material automatic generation system can generate product descriptions that include region-specific information based on the user's geographic location information. For example, if a user lives in a specific region, it can generate descriptions of products that are popular in that region. It can also generate product descriptions that are tailored to events or seasons specific to the region. It can also generate product descriptions based on the region's climate and culture. This makes it possible to provide product descriptions that are specific to the region by utilizing geographic location information.

[0057] The product description material automatic generation system can analyze a user's past search history and automatically generate descriptions of related products. For example, it can generate descriptions of related products based on product categories and keywords that the user has searched for in the past. It can also generate descriptions of related products based on topics that the user frequently searches for. It can also generate descriptions of related products based on reviews and ratings of products that the user has searched for in the past. This makes it possible to utilize past search history to provide product descriptions that match the user's interests.

[0058] The product description material automatic generation system can provide explanatory materials in the optimal display format based on the user's device information. For example, if the user is using a smartphone, the system can provide explanatory materials in a display format that matches the screen size. If the user is using a tablet, the system can provide explanatory materials in a display format optimized for a large screen. Furthermore, if the user is using a desktop, the system can provide explanatory materials in a display format that includes detailed information. In this way, device information can be utilized to provide explanatory materials in the optimal display format.

[0059] The product description material automatic generation system can propose bundles of related products based on the user's past purchase history. For example, it can propose accessories related to electronic devices the user has previously purchased. It can also propose coordinating items to go with clothing items previously purchased. It can also propose recipes and seasonings related to food products previously purchased. This makes it possible to propose bundles of related products by utilizing the user's past purchase history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives the user's input of a product image and necessary information. Product images may be in, but are not limited to, JPEG or PNG format. The necessary information may include the product name, category, price, condition (new, used, etc.), features, and precautions. The reception unit provides an interface for the user to upload the product image and input the necessary information. The reception unit can also use a generation AI to convert the information entered by the user into a format that is easy to analyze. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit analyzes the product image and extracts the product's features. It also analyzes the necessary information and extracts information to generate the optimal description based on the product category. For example, it uses an image analysis algorithm to analyze the product image and extracts features such as the product's color, shape, and texture. It also uses text analysis technology to analyze the necessary information and extract information such as the product's specifications, functions, and usage. Step 3: The generation unit uses a generation AI to generate an optimized explanation based on the information analyzed by the analysis unit. For example, in the case of an electronic device, the generation unit provides a detailed explanation of the product's specifications, functions, and usage. It also includes important information such as scratches and precautions. The generation unit uses a generation AI to generate text that provides a detailed explanation of the product's specifications and functions. It can also generate instructions for explaining how to use the product. Step 4: The providing unit provides the explanatory material generated by the generating unit to the user. The providing unit can provide the generated explanatory material in PDF format or HTML format. The providing unit can also provide an interface that allows the user to check the generated explanatory material and make corrections as necessary. For example, the generated explanatory material can be sent to the user by email. The generated explanatory material can also be provided as a download link.

[0062] (Example 2) An automatic product description material generation system according to an embodiment of the present invention uses a generation AI to automatically generate product description materials. A user inputs a product image and necessary information, and the system automatically generates a description optimized for that product category. The generated product description is provided to the user, who can modify it as needed. This effectively communicates the product's appeal and prevents lost opportunities. For example, a user inputs a product image and necessary information, such as the product name, category, price, condition (new, used, etc.), features, and precautions. This information is then input into a generation AI. The generation AI then analyzes the input information and automatically generates a description optimized for that product category. For example, in the case of electronic devices, the AI ​​provides detailed information on specifications, functions, and usage. It also includes important information such as scratches and precautions. The generated product description is provided to the user. The user can review the generated product description and modify it as needed. This effectively communicates the product's appeal and prevents lost opportunities. This system significantly reduces the effort required for individuals to list products on auctions and for businesses to create product description content. In addition, the system can accurately convey the features and key points of a product, maximizing its appeal. This allows the automatic generation of product information materials to effectively communicate the appeal of a product, preventing lost opportunities.

[0063] The system for automatically generating product description materials according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of product images and necessary information from a user. Product images may be in, for example, JPEG or PNG format, but are not limited to these. The necessary information may include, for example, the product name, category, price, condition (new, used, etc.), features, and precautions, but are not limited to these. The reception unit provides an interface for the user to upload product images and input the necessary information. The reception unit can also use a generation AI to convert the information entered by the user into a format that is easy to analyze. The analysis unit uses the generation AI to analyze the information entered by the reception unit. For example, the analysis unit analyzes product images and extracts product features. The analysis unit also analyzes the necessary information and extracts information for generating optimal descriptions based on the product category. For example, the analysis unit uses an image analysis algorithm to analyze product images and extract product features such as color, shape, and texture. The analysis unit also analyzes the necessary information using text analysis technology to extract information such as product specifications, functions, and usage instructions. The generation unit uses a generation AI to generate an optimized explanation based on the information analyzed by the analysis unit. For example, in the case of an electronic device, the generation unit provides detailed explanations of the product's specifications, functions, and usage instructions. The generation unit also includes key points, such as scratches and warnings. For example, the generation unit uses a generation AI to generate text for providing detailed explanations of a product's specifications and functions. The generation unit can also use the generation AI to generate instructions for explaining how to use the product. The provision unit provides the explanatory materials generated by the generation unit to a user. The provision unit can provide the generated explanatory materials in, for example, a PDF format or an HTML format. The provision unit can also provide an interface that allows a user to review the generated explanatory materials and make corrections as necessary. For example, the provision unit can send the generated explanatory materials to a user by email. The provision unit can also provide the generated explanatory materials as a download link. This allows the automatic product description material generation system according to the embodiment to effectively convey the appeal of a product and prevent loss of opportunity.

[0064] The automatic product description material generation system may include an extraction unit that analyzes product images and extracts product features. The extraction unit, for example, analyzes product images and extracts product features. Features include, but are not limited to, the color, shape, and texture of the product. The extraction unit, for example, analyzes product images using an image analysis algorithm and extracts product features. For example, the extraction unit may analyze the color of the product and identify color variations of the product. The extraction unit may also analyze the shape of the product and identify the design of the product. The extraction unit may also analyze the texture of the product and identify the texture of the product. This allows features to be extracted from the product images and reflected in the product materials. Some or all of the above-described processing by the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit may input product images to the generation AI and cause the generation AI to extract product features.

[0065] The automatic product information material generation system may include a point generation unit that generates content that highlights key points regarding scratches and precautions. The point generation unit, for example, analyzes scratches and precautions on a product and generates content that highlights the key points. Points include, but are not limited to, surface scratches, internal scratches, handling precautions, and storage methods of the product. The point generation unit, for example, detects scratches on the product using an image analysis algorithm and reflects the details of the scratches in the product information material. For example, the point generation unit may analyze scratches on the surface of the product and identify their location and size. The point generation unit may also analyze internal scratches on the product and identify their impact. The point generation unit may also analyze handling precautions and storage methods of the product and reflect the details in the product information material. For example, the point generation unit may analyze handling precautions for the product and generate specific precautions. The point generation unit may also analyze storage methods for the product and generate appropriate storage methods. This allows the generation of product information that includes important points about the product. Some or all of the above-described processing in the point generation unit may be performed, for example, using a generation AI or without a generation AI. For example, the point generation unit can input data about product scratches and warnings into the generation AI and have the generation AI generate points.

[0066] The product description material automatic generation system may include a providing unit that provides the generated explanatory materials to a user. The providing unit provides the generated explanatory materials to a user, for example. Examples of providing methods include, but are not limited to, sending the generated explanatory materials by email or providing a download link. The providing unit may provide the generated explanatory materials in, for example, a PDF format or an HTML format. For example, the providing unit may send the generated explanatory materials to a user by email. The providing unit may also provide the generated explanatory materials as a download link. The providing unit may also provide an interface that allows a user to review the generated explanatory materials and make corrections as necessary. For example, the providing unit may provide an online editor that allows a user to edit the generated explanatory materials. The providing unit may also provide a printing function that allows a user to print the generated explanatory materials. This allows the generated explanatory materials to be provided to a user, allowing them to review and correct them. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the generated explanatory materials to the generation AI and have the generation AI select a delivery method.

[0067] In the automatic product description material generation system, an analysis unit can generate an appropriate explanation based on a product category. The analysis unit generates the appropriate explanation based on, for example, the product category. Product categories include, but are not limited to, electronic devices, clothing, and food. The analysis unit extracts information for detailed explanations of product specifications, functions, and usage methods based on the product category. For example, in the case of electronic devices, the analysis unit extracts information for detailed explanations of the product specifications, functions, and usage methods. In the case of clothing, the analysis unit can also extract information for detailed explanations of the product's design, materials, size, and so on. In the case of food, the analysis unit can also extract information for detailed explanations of the product's ingredients, nutritional value, storage methods, and so on. This allows for the generation of an explanation optimized for the product category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the information extracted based on the product category into the generation AI and cause the generation AI to generate an appropriate explanation.

[0068] In the automatic product description material generation system, when the generation unit is an electronic device, the generation unit can specifically explain the specifications, functions, and usage instructions. For example, in the case of an electronic device, the generation unit specifically explains the specifications, functions, and usage instructions. Specifications include, but are not limited to, the CPU, memory, and storage capacity. Functions include, but are not limited to, the operation method and usage scenarios. Usage instructions include, but are not limited to, the setup procedure and usage precautions. The generation unit, for example, uses a generation AI to generate text that details the specifications, functions, and usage instructions of the electronic device. For example, the generation unit uses a generation AI to generate text that details the specifications of the electronic device. The generation unit can also use a generation AI to generate text that details the functions of the electronic device. The generation unit can also use a generation AI to generate procedures that details the usage instructions of the electronic device. This allows for the generation of a detailed description of the electronic device. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the specifications, functions, and usage of an electronic device into the generation AI and have the generation AI generate detailed descriptions.

[0069] In the automatic product information generation system, a reception unit estimates a user's emotions and adjusts the timing of inputting product images and necessary information based on the estimated user emotions. The reception unit, for example, estimates a user's emotions and adjusts the timing of inputting product images and necessary information based on the estimated user emotions. Emotions include, but are not limited to, stress, relaxation, and hurry. The reception unit estimates a user's emotions using, for example, facial expression recognition technology and adjusts the input timing based on the results. For example, if the user is feeling stressed, the reception unit simplifies the input procedure so that minimal information needs to be input. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input product images and necessary information. This adjusts the input timing according to the user's emotions and reduces stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0070] In the product description material automatic generation system, the reception unit analyzes the user's past input history and suggests an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an appropriate input method. The input history includes, for example, product categories and features previously entered, and input methods used (voice, text, etc.), but is not limited to these examples. The reception unit, for example, automatically displays product categories and features frequently entered by the user in the past as candidates. The reception unit can also prioritize suggesting input methods used by the user in the past. Furthermore, the reception unit can predict and suggest product categories and features to be used in a specific time period based on the user's past input history. For example, the reception unit analyzes data previously entered by the user and prioritizes displaying frequently entered product categories and features. The reception unit can also suggest an optimal input method based on the input methods used by the user in the past. This allows the reception unit to suggest an optimal input method based on the past input history, thereby improving input efficiency. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0071] In the product information automatic generation system, when a user inputs product images and necessary information, the reception unit can filter the input content based on the user's current areas of interest. For example, when a user inputs product images and necessary information, the reception unit filters the input content based on the user's current areas of interest. Areas of interest include, but are not limited to, past browsing history and survey results. For example, the reception unit prioritizes displaying related input items based on product categories recently searched by the user. The reception unit can also customize the input content based on topics of interest to the user. Furthermore, the reception unit can suggest related input items based on products previously purchased by the user. For example, the reception unit prioritizes displaying related input items based on product categories recently searched by the user. The reception unit can also customize the input content based on topics of interest to the user. This allows the input content to be filtered based on the user's areas of interest, providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI and have the generation AI perform filtering of the input content.

[0072] In the automatic product description material generation system, a reception unit can estimate a user's emotions and determine the priority of products to be input based on the estimated user emotions. The reception unit, for example, estimates a user's emotions and determines the priority of products to be input based on the estimated user emotions. Emotions include, but are not limited to, stress, relaxation, and hurry. The reception unit, for example, uses facial expression recognition technology to estimate a user's emotions and determines the priority of product input based on the result. For example, if the user is feeling stressed, the reception unit prioritizes input of important products and postpones other input items. Furthermore, if the user is relaxed, the reception unit can prioritize input of detailed products and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize input of key products to enable quick input. This allows the priority of product input to be determined according to the user's emotions, thereby enabling efficient input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] In the automatic product information generation system, when a user inputs product images and necessary information, the reception unit can prioritize input of highly relevant information based on the user's geographical location information. For example, when inputting product images and necessary information, the reception unit prioritizes input of highly relevant information based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, the reception unit prioritizes input of region-specific product information based on the user's current location. The reception unit can also suggest input fields for related products taking into account the user's geographical location information. Furthermore, the reception unit can input region-specific precautions and features based on the user's location information. For example, the reception unit prioritizes input of region-specific product information based on the user's current location. The reception unit can also suggest input fields for related products taking into account the user's geographical location information. This allows highly relevant information to be prioritized and region-specific information to be provided. Some or all of the above-described processing by the reception unit may be performed, for example, using a generation AI or without a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to input highly relevant information.

[0074] In the product description materials automatic generation system, the reception unit analyzes the user's social media activity and inputs appropriate information when the user inputs product images and necessary information. For example, the reception unit analyzes the user's social media activity and inputs appropriate information when the user inputs product images and necessary information. Social media activity includes, but is not limited to, post content and the number of likes. For example, the reception unit suggests related input fields based on product information shared by the user on social media. The reception unit can also identify product categories of interest from the user's social media activity and customize the input content. Furthermore, the reception unit can suggest input fields based on information about brands and products the user follows on social media. For example, the reception unit suggests related input fields based on product information shared by the user on social media. The reception unit can also identify product categories of interest from the user's social media activity and customize the input content. By analyzing social media activity and inputting relevant information, it is possible to provide information tailored to the user's interests. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to input appropriate information.

[0075] In the automatic product description material generation system, an analysis unit can estimate a user's emotions and adjust the analysis presentation method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis presentation method based on the estimated user emotions. Emotions include, but are not limited to, relaxed, rushed, and excited. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions and adjusts the analysis presentation method based on the results. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is rushed. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the user is excited. This allows the analysis presentation method to be adjusted according to the user's emotions and provide appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0076] In the automatic product description material generation system, the analysis unit can adjust the level of detail of the analysis based on the importance of the product during analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the product. Examples of importance include, but are not limited to, the impact on sales and the level of user interest. For example, the analysis unit may perform a detailed analysis of important products and provide detailed explanations. For general products, the analysis unit may also perform a basic analysis and provide a brief explanation. For products in a specific category, the analysis unit may perform a category-specific analysis and provide detailed explanations. For example, the analysis unit may perform a detailed analysis of important products and provide detailed explanations of the product's specifications, functions, and usage. For general products, the analysis unit may perform a basic analysis and provide a brief explanation of the product's main features and usage. This allows the level of detail of the analysis to be adjusted based on the importance of the product, thereby providing appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0077] In the automatic product description material generation system, the analysis unit can apply different analysis algorithms depending on the product category during analysis. The analysis unit, for example, applies different analysis algorithms depending on the product category. Categories include, but are not limited to, electronic devices, clothing, and food. For example, in the case of electronic devices, the analysis unit applies an analysis algorithm specialized for specifications and functions. In addition, in the case of fashion items, the analysis unit can also apply an analysis algorithm specialized for design and materials. In addition, in the case of food, the analysis unit can apply an analysis algorithm specialized for ingredients and nutritional value. For example, in the case of electronic devices, the analysis unit applies an algorithm for detailed analysis of the product's specifications and functions. In addition, in the case of fashion items, the analysis unit can also apply an algorithm for detailed analysis of the product's design and materials. This allows the application of an analysis algorithm depending on the product category and provides appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0078] In the automatic product description material generation system, an analysis unit can estimate a user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotions include, but are not limited to, hurry, relaxation, and excitement. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions and adjusts the length of the analysis based on the results. For example, if the user is hurrying, the analysis unit can provide a short and concise analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. Alternatively, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted according to the user's emotions and provide appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0079] In the automatic product description material generation system, the analysis unit can determine the analysis priority based on the submission date of the product during analysis. The analysis unit determines the analysis priority based on, for example, the submission date of the product. The submission date includes, but is not limited to, the submission deadline and the submission order. For example, the analysis unit prioritizes the analysis of products with an upcoming deadline. The analysis unit can also postpone the analysis of products with a more distant submission date. Furthermore, the analysis unit can automatically adjust the analysis schedule based on the submission date. For example, the analysis unit prioritizes the analysis of products with an upcoming deadline so that the submission deadline can be met. The analysis unit can also postpone the analysis of products with a more distant submission date and prioritize the analysis of other important products. This allows the analysis priority to be determined based on the submission date of the product, thereby achieving efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input product submission date data into the generation AI and have the generation AI determine the analysis priority.

[0080] In the automatic product description material generation system, the analysis unit can adjust the order of analysis based on the relevance of products during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of products. Relevance includes, for example, interrelationships between products and user interest, but is not limited to such examples. The analysis unit, for example, prioritizes analysis of highly related products. The analysis unit can also postpone analysis of less related products. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of products. For example, the analysis unit prioritizes analysis of highly related products and determines the order of analysis taking into account interrelationships between products. The analysis unit can also postpone analysis of less related products and prioritize analysis of other important products. This allows the order of analysis to be adjusted based on the relevance of products, thereby achieving efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input product relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0081] In an automatic product description material generation system, a generation unit can estimate a user's emotion and adjust the expression of the generated explanation based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression of the generated explanation based on the estimated user emotion. Emotions include, but are not limited to, relaxed, rushed, and excited. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotion and adjusts the expression of the explanation based on the result. For example, the generation unit can provide a detailed explanation if the user is relaxed. The generation unit can also provide a concise explanation that focuses on the main points if the user is rushed. The generation unit can also provide an explanation that adds visually stimulating effects if the user is excited. This allows the expression of the explanation to be adjusted according to the user's emotion and provide an appropriate explanation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input user facial expression data into the generation AI and have the generation AI estimate emotions.

[0082] The automatic product description material generation system can adjust the level of detail of the description generated by the generation unit based on the importance of the product when generating the product. The generation unit adjusts the level of detail of the description generated based on, for example, the importance of the product. The importance level includes, but is not limited to, the impact on sales and the level of user interest. For example, the generation unit provides detailed descriptions for important products. The generation unit can also provide basic descriptions for general products. The generation unit can also provide category-specific descriptions for products in a specific category. For example, the generation unit can provide detailed descriptions for important products, explaining the product's specifications, functions, and usage in detail. For general products, the generation unit can provide basic descriptions, explaining the main features and usage of the product in a key manner. This allows the level of detail of the description to be adjusted based on the importance of the product, thereby providing an appropriate description. Some or all of the above-described processing by the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input product importance data into the generation AI and cause the generation AI to adjust the level of detail of the description.

[0083] In the automatic product description material generation system, the generation unit can apply different generation algorithms depending on the product category when generating the product description. The generation unit, for example, applies different generation algorithms depending on the product category. Categories include, but are not limited to, electronic devices, clothing, and food. For example, in the case of electronic devices, the generation unit applies a generation algorithm specialized for specifications and functions. In addition, in the case of fashion items, the generation unit can also apply a generation algorithm specialized for design and materials. In addition, in the case of food, the generation unit can apply a generation algorithm specialized for ingredients and nutritional value. For example, in the case of electronic devices, the generation unit applies a generation algorithm for detailed description of the product's specifications and functions. In addition, in the case of fashion items, the generation unit can apply a generation algorithm for detailed description of the product's design and materials. This allows for the application of a generation algorithm depending on the product category to provide an appropriate description. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product category data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0084] In an automatic product description material generation system, a generation unit can estimate a user's emotion and adjust the length of the generated explanation based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the generated explanation based on the estimated user emotion. Emotions include, but are not limited to, hurry, relaxation, and excitement. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotion and adjusts the length of the explanation based on the result. For example, if the user is hurrying, the generation unit can provide a short, concise explanation. If the user is relaxed, the generation unit can provide a longer explanation with detailed information. If the user is excited, the generation unit can provide an explanation with visually stimulating effects. This allows the length of the explanation to be adjusted according to the user's emotion and provide an appropriate explanation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input user facial expression data into the generation AI and have the generation AI estimate emotions.

[0085] In the automatic product description material generation system, the generation unit can determine the priority of descriptions to be generated based on the submission date of the product when the product is generated. The generation unit determines the priority of descriptions to be generated based on, for example, the submission date of the product. The submission date includes, but is not limited to, the submission deadline and the submission order. For example, the generation unit prioritizes generating descriptions for products with upcoming deadlines. The generation unit can also postpone descriptions for products with more distant submission dates. Furthermore, the generation unit can automatically adjust the description generation schedule based on the submission date. For example, the generation unit prioritizes generating descriptions for products with upcoming deadlines so that they meet the submission deadline. The generation unit can also postpone descriptions for products with more distant submission dates and prioritize descriptions for other important products. This allows the priority of descriptions to be determined based on the submission date of the product, thereby achieving efficient description generation. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input product submission date data into the generation AI and cause the generation AI to determine the priority of descriptions.

[0086] The automatic product description material generation system can adjust the order of descriptions generated by the generation unit based on the relevance of the products when generating the descriptions. The generation unit adjusts the order of descriptions generated based on, for example, the relevance of the products. Relevance includes, for example, interrelationships between products and the user's level of interest, but is not limited to, examples. The generation unit, for example, prioritizes explaining highly related products. The generation unit can also postpone explaining less related products. Furthermore, the generation unit can automatically adjust the order of descriptions based on the relevance of the products. For example, the generation unit prioritizes explaining highly related products and determines the order of descriptions taking into account interrelationships between products. The generation unit can also postpone explaining less related products and prioritize explaining other important products. This allows the order of descriptions to be adjusted based on the relevance of the products, thereby achieving efficient description generation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input product relevance data into the generation AI and cause the generation AI to adjust the order of descriptions.

[0087] In the automatic product description material generation system, a providing unit estimates a user's emotion and adjusts the display method of the provided explanatory material based on the estimated user emotion. For example, the providing unit estimates the user's emotion and adjusts the display method of the provided explanatory material based on the estimated user emotion. Emotions include, but are not limited to, nervous, relaxed, and rushed. For example, the providing unit estimates the user's emotion using facial expression recognition technology and adjusts the display method based on the result. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, if the user is rushed, the providing unit can provide a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotion and appropriate explanatory material to be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0088] In the automatic product description material generation system, the providing unit can select an appropriate display method by referring to the user's past operation history when providing the product description materials. For example, the providing unit selects an appropriate display method by referring to the user's past operation history when providing the product description materials. The operation history includes, but is not limited to, past operation data and frequency analysis. For example, the providing unit can prioritize providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the providing unit can provide a customizable display method based on the user's past use of the display method. For example, the providing unit prioritizes providing a display method that the user has previously used. The providing unit can also suggest an optimal display method based on the user's past operation history. This allows the optimal display method to be selected based on the past operation history and provide a display suitable for the user. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's operation history data into the generation AI and cause the generation AI to select an optimal display method.

[0089] In the automatic product description material generation system, a providing unit estimates a user's emotions and adjusts the operation procedures of the provided explanatory materials based on the estimated user emotions. For example, the providing unit estimates the user's emotions and adjusts the operation procedures of the provided explanatory materials based on the estimated user emotions. Emotions include, but are not limited to, nervous, relaxed, and hurried. For example, the providing unit estimates the user's emotions using facial expression recognition technology and adjusts the operation procedures based on the results. For example, the providing unit provides simple and intuitive operation procedures when the user is nervous. Furthermore, the providing unit can provide detailed operation procedures when the user is relaxed. Furthermore, the providing unit can provide quick operation procedures when the user is hurried. This allows the operation procedures to be adjusted according to the user's emotions and provide appropriate operation procedures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion.

[0090] In the automatic product description material generation system, the providing unit can select the optimal display method by taking into account the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into account the user's device information when providing the data. Device information includes, but is not limited to, the device type, screen size, and OS. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is concise and highly visible. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a larger screen. This allows the optimal display method to be selected by taking into account the device information and providing an appropriate display. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0091] In the automatic product description material generation system, an extraction unit can estimate a user's emotion and determine the priority of product features to be extracted based on the estimated user emotion. The extraction unit, for example, estimates the user's emotion and determines the priority of product features to be extracted based on the estimated user emotion. Emotions include, but are not limited to, stress, relaxation, and hurry. The extraction unit, for example, uses facial expression recognition technology to estimate the user's emotion and determines the priority of product features based on the result. For example, the extraction unit can prioritize extracting important features when the user is stressed. The extraction unit can also prioritize extracting detailed features when the user is relaxed. The extraction unit can also prioritize extracting key features when the user is in a hurry. This allows the priority of product features to be determined according to the user's emotion, thereby achieving efficient feature extraction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI. For example, the extraction unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0092] In the automatic product description material generation system, the extraction unit can improve the accuracy of extraction based on the interrelationships between products during extraction. The extraction unit improves the accuracy of extraction based on, for example, the interrelationships between products. Interrelationships include, but are not limited to, product associations and co-occurrence relationships. For example, the extraction unit extracts features of related products together to deepen overall understanding. The extraction unit can also prioritize extracting highly related features by taking into account the interrelationships between products. Furthermore, the extraction unit can adjust the feature extraction order based on the interrelationships between products. For example, the extraction unit extracts features of related products together to deepen overall understanding. The extraction unit can also prioritize extracting highly related features by taking into account the interrelationships between products. This improves the accuracy of extraction by taking into account the interrelationships between products, and allows appropriate features to be extracted. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit may input product interrelationship data into the generation AI and cause the generation AI to improve the accuracy of extraction.

[0093] In the automatic product description material generation system, an extraction unit estimates a user's emotion and adjusts the display method of the extracted product features based on the estimated user emotion. The extraction unit, for example, estimates a user's emotion and adjusts the display method of the extracted product features based on the estimated user emotion. Emotions include, but are not limited to, nervous, relaxed, and rushed. The extraction unit estimates the user's emotion using, for example, facial expression recognition technology and adjusts the display method based on the result. For example, if the user is nervous, the extraction unit provides a simple, highly visible display method. If the user is relaxed, the extraction unit can provide a display method that includes detailed information. If the user is rushed, the extraction unit can provide a display method that focuses on the main points. This allows the display method of the features to be adjusted according to the user's emotion and provide an appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI. For example, the extraction unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0094] In the automatic product description material generation system, the extraction unit may perform extraction taking into account the geographic distribution of products. The extraction unit performs extraction taking into account, for example, the geographic distribution of products. Examples of geographic distribution include, but are not limited to, regional sales data and geographic information systems (GIS). For example, the extraction unit may prioritize extracting geographically related product features. The extraction unit may also extract region-specific features taking into account the geographic distribution. Furthermore, the extraction unit may adjust the feature extraction order based on the geographic distribution. For example, the extraction unit may prioritize extracting geographically related product features and provide region-specific information. The extraction unit may also extract region-specific features taking into account the geographic distribution. This allows features to be extracted taking into account the geographic distribution and provide region-specific information. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit may input product geographic distribution data into the generation AI and have the generation AI perform extraction.

[0095] In the automatic product description material generation system, a point generation unit can estimate a user's emotions and determine the priority of points to be generated based on the estimated user emotions. The point generation unit, for example, estimates the user's emotions and determines the priority of points to be generated based on the estimated user emotions. Emotions include, but are not limited to, stress, relaxation, and hurry. The point generation unit, for example, uses facial expression recognition technology to estimate the user's emotions and determines the priority of points based on the result. For example, if the user is feeling stressed, the point generation unit can prioritize generating important points. Also, if the user is relaxed, the point generation unit can prioritize generating detailed points. Also, if the user is in a hurry, the point generation unit can prioritize generating main points. This allows the priority of points to be determined according to the user's emotions, thereby achieving efficient point generation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the point generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the point generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0096] In the automatic product information material generation system, the point generation unit can improve the accuracy of point generation based on the interrelationships between products. The point generation unit improves the accuracy of point generation based on, for example, the interrelationships between products. Interrelationships include, but are not limited to, product associations and co-occurrence relationships. For example, the point generation unit generates points for related products together to deepen overall understanding. The point generation unit can also prioritize generating highly related points by taking into account the interrelationships between products. Furthermore, the point generation unit can adjust the point generation order based on the interrelationships between products. For example, the point generation unit generates points for related products together to deepen overall understanding. The point generation unit can also prioritize generating highly related points by taking into account the interrelationships between products. This improves the accuracy of point generation by taking into account the interrelationships between products, thereby generating appropriate points. Some or all of the above-described processing in the point generation unit may be performed using, or without, a generation AI. For example, the point generation unit may input product interrelationship data into the generation AI and cause the generation AI to improve the accuracy of point generation.

[0097] In the automatic product description material generation system, a point generation unit estimates a user's emotions and adjusts the display method of the generated points based on the estimated user emotions. The point generation unit, for example, estimates the user's emotions and adjusts the display method of the generated points based on the estimated user emotions. Emotions include, but are not limited to, nervous, relaxed, and rushed. The point generation unit estimates the user's emotions using, for example, facial expression recognition technology and adjusts the display method based on the results. For example, if the user is nervous, the point generation unit provides a simple, highly visible display method. If the user is relaxed, the point generation unit can also provide a display method that includes detailed information. If the user is rushed, the point generation unit can also provide a display method that focuses on the main points. This allows the display method of the points to be adjusted according to the user's emotions and provide an appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the point generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the point generation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0098] In the automatic product information generation system, the point generation unit may generate points by taking into account the geographical distribution of the product. The point generation unit generates points by taking into account, for example, the geographical distribution of the product. Examples of geographical distribution include, but are not limited to, sales data by region and a geographic information system (GIS). The point generation unit may, for example, prioritize generating points for geographically related products. The point generation unit may also generate region-specific points by taking into account the geographical distribution. Furthermore, the point generation unit may adjust the point generation order based on the geographical distribution. For example, the point generation unit may prioritize generating points for geographically related products and provide region-specific information. The point generation unit may also generate region-specific points by taking into account the geographical distribution. This makes it possible to generate points by taking into account the geographical distribution and provide region-specific information. Some or all of the above-described processing in the point generation unit may be performed using, or without, a generation AI. For example, the point generation unit may input product geographical distribution data into the generation AI and have the generation AI execute the generation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the smart device 14 and provides an interface for the user to input product images and necessary information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized explanation based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated explanatory material to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the smart glasses 214 and provides an interface for the user to input product images and necessary information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized explanation based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated explanatory material to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the headset type terminal 314 and provides an interface for the user to input product images and necessary information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized explanation based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated explanatory material to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 control unit 46A of the robot 414 and provides an interface for the user to input product images and necessary information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimized explanation based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated explanatory material to the user.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The automatic product information generation system can analyze a user's past purchase history and automatically generate descriptions of similar products. For example, a description of a new electronic device with similar specifications and functions can be generated based on the history of electronic devices purchased by the user in the past. A description of a new clothing item can also be generated based on the design and materials of previously purchased clothing. Furthermore, a description of a new food item can be generated based on the ingredients and nutritional value of previously purchased food. This makes it possible to provide more personalized product descriptions by utilizing the user's past purchase history.

[0101] The automatic product description material generation system can adjust the tone and style of the product description material based on the user's current mood. For example, if the user is relaxed, the product description material can be generated in a casual and friendly tone. If the user is in a hurry, the product description material can be generated in a concise and to-the-point tone. Furthermore, if the user is excited, the product description material can be generated in a style that adds visually stimulating effects. This makes it possible to provide the user with the optimal product description material according to their mood.

[0102] The product description material automatic generation system can analyze users' social media activity and generate product descriptions based on trends. For example, it can generate related product descriptions based on products and topics shared by users on social media. It can also generate product descriptions that match trends by referring to the content posted by brands and influencers that users follow. It can also analyze the activity of online communities in which users participate and generate descriptions of products that are popular within those communities. This makes it possible to utilize social media activity to provide product descriptions that match the user's interests.

[0103] The product description material automatic generation system can generate product descriptions that include region-specific information based on the user's geographic location information. For example, if a user lives in a specific region, it can generate descriptions of products that are popular in that region. It can also generate product descriptions that are tailored to events or seasons specific to the region. It can also generate product descriptions based on the region's climate and culture. This makes it possible to provide product descriptions that are specific to the region by utilizing geographic location information.

[0104] The automatic product material generation system can estimate the user's emotions and customize the content of the product materials based on the estimated emotions. For example, if the user is feeling stressed, it can emphasize descriptions of products with a relaxing effect. If the user is excited, it can emphasize descriptions of products suitable for an active lifestyle. Furthermore, if the user is relaxed, it can provide detailed descriptions of products with a relaxing effect. This makes it possible to provide optimal product descriptions according to the user's emotions.

[0105] The product description material automatic generation system can analyze a user's past search history and automatically generate descriptions of related products. For example, it can generate descriptions of related products based on product categories and keywords that the user has searched for in the past. It can also generate descriptions of related products based on topics that the user frequently searches for. It can also generate descriptions of related products based on reviews and ratings of products that the user has searched for in the past. This makes it possible to utilize past search history to provide product descriptions that match the user's interests.

[0106] The automatic product explanation material generation system can estimate the user's emotions and adjust the layout of the explanation materials based on the estimated emotions. For example, if the user is nervous, a simple, highly visible layout can be provided. If the user is relaxed, a layout including detailed information can be provided. Furthermore, if the user is in a hurry, a layout that focuses on the main points can be provided. In this way, it is possible to provide the optimal layout according to the user's emotions.

[0107] The product description material automatic generation system can provide explanatory materials in the optimal display format based on the user's device information. For example, if the user is using a smartphone, the system can provide explanatory materials in a display format that matches the screen size. If the user is using a tablet, the system can provide explanatory materials in a display format optimized for a large screen. Furthermore, if the user is using a desktop, the system can provide explanatory materials in a display format that includes detailed information. In this way, device information can be utilized to provide explanatory materials in the optimal display format.

[0108] The automatic product description material generation system can estimate the user's emotions and adjust the colors of the materials based on the estimated emotions. For example, if the user is relaxed, the system can provide materials with calming colors. If the user is excited, the system can provide materials with vibrant colors. Furthermore, if the user is stressed, the system can provide materials with colors that have a relaxing effect. This makes it possible to provide materials with optimal colors according to the user's emotions.

[0109] The product description material automatic generation system can propose bundles of related products based on the user's past purchase history. For example, it can propose accessories related to electronic devices the user has previously purchased. It can also propose coordinating items to go with clothing items previously purchased. It can also propose recipes and seasonings related to food products previously purchased. This makes it possible to propose bundles of related products by utilizing the user's past purchase history.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit receives the user's input of a product image and necessary information. Product images may be in, but are not limited to, JPEG or PNG format. The necessary information may include the product name, category, price, condition (new, used, etc.), features, and precautions. The reception unit provides an interface for the user to upload the product image and input the necessary information. The reception unit can also use a generation AI to convert the information entered by the user into a format that is easy to analyze. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit. The analysis unit analyzes the product image and extracts the product's features. It also analyzes the necessary information and extracts information to generate the optimal description based on the product category. For example, it uses an image analysis algorithm to analyze the product image and extracts features such as the product's color, shape, and texture. It also uses text analysis technology to analyze the necessary information and extract information such as the product's specifications, functions, and usage. Step 3: The generation unit uses a generation AI to generate an optimized explanation based on the information analyzed by the analysis unit. For example, in the case of an electronic device, the generation unit provides a detailed explanation of the product's specifications, functions, and usage. It also includes important information such as scratches and precautions. The generation unit uses a generation AI to generate text that provides a detailed explanation of the product's specifications and functions. It can also generate instructions for explaining how to use the product. Step 4: The providing unit provides the explanatory material generated by the generating unit to the user. The providing unit can provide the generated explanatory material in PDF format or HTML format. The providing unit can also provide an interface that allows the user to check the generated explanatory material and make corrections as necessary. For example, the generated explanatory material can be sent to the user by email. The generated explanatory material can also be provided as a download link.

[0112] 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.

[0113] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] 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.

[0183] [Explanation of symbols]

[0184] 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 desk where product images and necessary information can be entered; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates an optimized explanation based on the information analyzed by the analysis unit; a providing unit that provides the explanatory material generated by the generating unit; Equipped with A system characterized by:

2. Equipped with an extraction unit that analyzes product images and extracts product features 2. The system of claim 1.

3. It has a point generation unit that generates content that highlights the key points of defects and precautions.

2. The system of claim 1.

4. The providing unit Provide the generated explanatory materials to the user 2. The system of claim 1.

5. The analysis unit Generate appropriate descriptions based on product categories 2. The system of claim 1.

6. The generation unit For electronic devices, provide specific explanations of the specifications, functions, and how to use them.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting product images and necessary information 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 appropriate input methods 2. The system of claim 1.

9. The reception unit When viewing product images or entering required information, filter input based on the user's current interests 2. The system of claim 1.

Images