system

Abstract

We provide the system. [Solution] The means by which users input product information, A method for automatically generating product descriptions and PR texts in Japanese using a generative AI model, A method for translating the generated product description and PR text into English using a translation API, A method for analyzing product images using an image recognition model to determine export restrictions in various countries, A method for calculating shipping costs to each country and proposing a recommended selling price, A means of automatically generating various legal policies and translating them into Japanese and English, A system that includes a means to automatically generate optimal SEO measures using an SEO optimization model.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When operating an e-commerce (EC) site, especially when conducting cross-border sales, a wide range of operations occur, such as multilingual support for product information, confirmation of export restrictions in each country, shipping cost calculation, preparation of legal policies, and SEO measures etc. These operations require specialized knowledge and are time-consuming, so many domestic companies are currently reluctant to embark on cross-border sales. Solving such problems and enabling Japanese companies to easily conduct cross-border sales is an object of the present invention.

Means for Solving the Problems

[0005] The present invention is a system including the following elements.

[0006] Means for a user to input product information

[0007] A method for automatically generating product descriptions and promotional texts in Japanese using a generative AI model.

[0008] A method for translating the generated product description and PR text into English using a translation API.

[0009] A method for analyzing product images using image recognition models to determine export restrictions in various countries.

[0010] A method for calculating shipping costs to each country and proposing a recommended selling price.

[0011] A means of automatically generating various legal policies and translating them into Japanese and English.

[0012] This system includes a means to automatically generate optimal SEO measures using an SEO optimization model, allowing users to automate necessary tasks based on entered product information and simplify the complex process of cross-border sales. In particular, it includes a means to present product descriptions and promotional texts generated in a user-readable format, as well as a means to notify users of export restriction alerts in various countries, thereby enabling accurate and efficient sales support.

[0013] A "user" is a business or individual that uses this system to input product information and conduct cross-border sales.

[0014] "Product information" refers to information related to the product being sold, including its name, description, price, image, and the countries in which it is sold.

[0015] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to generate text from input information.

[0016] A "product description" is automatically generated text intended to convey the features and appeal of a product.

[0017] A "PR copy" is an automatically generated promotional text intended to advertise a product.

[0018] A "Translation API" is an application programming interface for automatically translating from one language to another.

[0019] An "Image Recognition Model" is an artificial intelligence model that analyzes image data to identify its content.

[0020] "Export Restrictions" refer to matters subject to restrictions based on laws and regulations regarding the export of goods to specific countries.

[0021] "Shipping Fees" are the transportation costs incurred when shipping goods to a specific country.

[0022] "Recommended Selling Price" is the selling price proposed considering shipping fees and costs in each country.

[0023] [[ID=2"]]

[0024] An "SEO Optimization Model" is an artificial intelligence model for automatically performing Search Engine Optimization (SEO) of a website.

[0025] "SEO Measures" are measures to enable a website to be displayed at the top in search engine search results.

[0026] An "Alert" is a notification means for informing users of alerts or warnings.

Brief Explanation of Drawings

[0027] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] ​This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0028] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0029] First, let's explain the terminology used in the following explanation.

[0030] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0031] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0032] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0033] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0034] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0035] [First Embodiment]

[0036] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0037] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0038] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0039] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0040] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0041] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0042] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0043] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0044] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0045] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0046] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0047] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0048] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0049] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0050] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[0051] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[0052] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0053] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0054] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0055] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0056] As described above, the present invention enables users to automate tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures, allowing them to efficiently conduct cross-border sales.

[0057] The following describes the processing flow.

[0058] Step 1:

[0059] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[0060] Step 2:

[0061] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated texts are temporarily saved so that the user can review them later.

[0062] Step 3:

[0063] The server translates the generated product description and PR text into English using a translation API. The translation results are then provided to the user.

[0064] Step 4:

[0065] The server uses an image recognition model to analyze product images uploaded by users. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user is notified.

[0066] Step 5:

[0067] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[0068] Step 6:

[0069] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[0070] Step 7:

[0071] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[0072] Step 8:

[0073] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[0074] (Example 1)

[0075] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0076] Currently, many Japanese companies are attempting to sell their products to overseas markets through e-commerce sites, but they face numerous challenges, including language barriers, export restrictions, shipping cost calculations, legal policy preparation, and SEO measures. These challenges reduce the efficiency of cross-border sales, making it difficult to expand sales. Conventional technologies lack a system that automates these challenges, requiring a great deal of manual work. This invention solves these problems and provides a system that enables companies to conduct cross-border sales efficiently.

[0077] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0078] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into other languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions, means for calculating shipping costs to each country using a shipping information API and proposing a recommended selling price, means for automatically generating legal policies and translating them into other languages, means for automatically generating SEO measures using an SEO optimization model, means for inputting prompt texts, means for saving the documents generated by the generation AI model to a database, and means for notifying the user of alerts. This enables the automation of these processes in a single step, making it possible to conduct cross-border sales efficiently.

[0079] "Means for users to input product information" refers to an interface that allows users to input basic information such as the product name, description, price, and product images using a device.

[0080] "Means for automatically generating product descriptions and promotional texts using a generative AI model" refers to a processing device that uses a generative AI model to automatically generate product descriptions and promotional texts from product information entered by a user.

[0081] "Means for translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of translating product descriptions and PR texts generated by a generative AI model into other languages ​​using a translation API.

[0082] "A means of analyzing product images using an image recognition model to determine export restrictions" refers to an algorithm that uses an image recognition model to analyze product images and makes decisions based on information regarding export restrictions in various countries.

[0083] The "method for calculating shipping costs to various countries and proposing recommended selling prices using a shipping information API" is a system that uses a shipping information API to obtain shipping costs to various countries, and then calculates and proposes a recommended selling price by adding the base product price and service usage fee.

[0084] The "means for automatically generating legal policies and translating them into other languages" refer to a function that automatically generates various legal policies using a generation AI model and then translates them into other languages ​​using a translation API.

[0085] "A method for automatically generating SEO measures using an SEO optimization model" refers to a system that uses an SEO optimization model to automatically generate optimal SEO measures for e-commerce sites.

[0086] "Means for inputting prompt text" refers to input methods for providing product information and generation conditions to the generation AI model.

[0087] "Means for saving documents generated by a generative AI model to a database" refers to a database and its management system for temporarily storing documents generated by a generative AI model.

[0088] "Means of notifying users of alerts" refers to a notification system that alerts users about export restrictions and other important information.

[0089] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0090] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0091] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are temporarily stored in a database for user review. Specific examples of prompts to be input into this generative AI model are as follows:

[0092] Product Description Generation Prompt

[0093] Product name: Japanese-style teacup

[0094] Price: 3000 yen

[0095] Description: A beautiful Japanese-style teacup made of high-quality ceramic. Features a handcrafted pattern.

[0096] Based on this information, please generate a product description for sale within Japan.

[0097] PR statement generation prompt

[0098] Product name: Japanese-style teacup

[0099] Target countries: United States, United Kingdom, Australia

[0100] Please generate advertising copy for this product, tailored to each country.

[0101] The generated product descriptions and promotional texts are translated into English using a translation API (e.g., Google Translate API) and then provided to the user again.

[0102] Furthermore, the server uses an image recognition model (e.g., Google Cloud Vision API) to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is then notified to the user as an alert.

[0103] The server then uses a shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves the shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0104] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0105] Furthermore, the server uses an SEO optimization model (e.g., the Ahrefs API) to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0106] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0107] This invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures.

[0108] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0109] Step 1:

[0110] Users access the e-commerce site using their devices and enter basic information such as the product name, description, price, and product images. They can also specify which countries the product will be sold in.

[0111] input:

[0112] Product name

[0113] Product Description

[0114] Product price

[0115] Product image

[0116] Target countries for sales

[0117] output:

[0118] The entered product information is sent to the server.

[0119] Specific actions:

[0120] The user opens a browser and logs into the e-commerce site's administration panel.

[0121] Enter the product information (product name: Japanese-style teacup, product price: 3000 yen, product description: features a handmade pattern, product image: image file) and select the target countries for sale (USA, UK, Australia).

[0122] Step 2:

[0123] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are then stored in a database.

[0124] input:

[0125] Product information

[0126] output:

[0127] Product description and promotional text in Japanese

[0128] Specific actions:

[0129] The server inputs information such as product name, product description, and product price as prompts into the AI ​​model that generates the product.

[0130] The generative AI model generates product descriptions and promotional texts in Japanese and stores the content in a database.

[0131] Step 3:

[0132] The server uses a translation API (for example, Google Translate API) to translate the generated product descriptions and promotional texts into English. The translated documents are then saved back into the database.

[0133] input:

[0134] Product description and promotional text in Japanese

[0135] output:

[0136] English product description and promotional text

[0137] Specific actions:

[0138] The server retrieves the Japanese product description and PR text generated from the database and sends them to the translation API.

[0139] The translation API translates the document into English and returns the result to the server.

[0140] The server saves the translation results to a database and notifies the user.

[0141] Step 4:

[0142] The server analyzes product images using an image recognition model (e.g., Google Cloud Vision API). Based on export restriction information from various countries, it detects whether specific materials or ingredients are present and determines whether export restrictions apply. If export restrictions exist, the user is notified as an alert.

[0143] input:

[0144] Product image

[0145] output:

[0146] Whether or not there are export restrictions

[0147] Specific actions:

[0148] The server sends product images to an image recognition model and retrieves the analysis results.

[0149] The server compares the analysis results with export restriction databases of various countries.

[0150] If export restrictions are detected, an alert message will be generated and the user will be notified.

[0151] Step 5:

[0152] The server uses a shipping information API (for example, the Shippo API) to obtain shipping costs for each country, calculates a recommended selling price by adding the base product price and service fee, and proposes it to the user.

[0153] input:

[0154] Target countries for sales

[0155] Basic product price

[0156] Service fee

[0157] output:

[0158] Recommended selling price

[0159] Specific actions:

[0160] The server calls the shipping information API to retrieve shipping costs for each target country.

[0161] Based on the acquired shipping information, the server calculates the recommended selling price by adding the base product price and service fee.

[0162] The system presents the calculation results to the user and proposes the optimal selling price.

[0163] Step 6:

[0164] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and then translates them into other languages ​​using a translation API before providing them to users.

[0165] input:

[0166] None (Uses an internally generated AI model)

[0167] output:

[0168] Legal policies in Japanese and English

[0169] Specific actions:

[0170] The server uses a generative AI model to generate standard legal policies.

[0171] The generated policy is sent to the translation API to retrieve the English version.

[0172] Provide users with both the Japanese and English versions and ask them to confirm their choice.

[0173] Step 7:

[0174] The server uses an SEO optimization model (for example, the Ahrefs API) to automatically generate the optimal SEO strategies for the user's e-commerce site. This optimizes the site for search engines.

[0175] input:

[0176] Current status data of e-commerce sites

[0177] output:

[0178] SEO Strategy Proposals

[0179] Specific actions:

[0180] The server sends current data from the user's e-commerce site to an SEO optimization model for analysis.

[0181] Based on the areas for improvement in SEO, we will generate specific action plans.

[0182] The system notifies users of the generated SEO strategies and provides support for their implementation.

[0183] (Application Example 1)

[0184] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0185] Japanese companies need a system that facilitates cross-border sales by efficiently handling complex procedures such as multilingual support, export restriction checks, and shipping cost calculations. However, existing systems struggle to provide all these functions in one place, resulting in a lot of manual work and making efficient operation difficult. Furthermore, an interface that can be easily operated from devices such as smartphones and head-mounted displays is necessary.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0187] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts in natural language using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into multiple languages, means for automatically generating optimal SEO measures using an SEO optimization model, and means for providing each of the aforementioned means as an application that can be installed on a smartphone or head-mounted display. This makes it possible to conduct cross-border sales efficiently and easily.

[0188] "Product information" refers to basic information such as the product name, description, price, and product images.

[0189] A "generative AI model" is an artificial intelligence model that uses natural language processing to automatically generate product descriptions and promotional texts.

[0190] The "Translation API" is an application programming interface for translating generated product descriptions and promotional texts into multiple languages.

[0191] An "image recognition model" is an artificial intelligence model that analyzes product images and identifies specific features or materials.

[0192] "Export restrictions" refer to the prohibition or restriction of exporting certain goods or materials based on the laws and regulations of each country.

[0193] "Shipping cost calculation" is a method for calculating the cost of sending goods to a specific country or region.

[0194] "Recommended selling price" refers to the selling price which includes the base price of the product, shipping costs, and service fees.

[0195] A "legal policy" is a document necessary for compliance with laws and regulations, such as a privacy policy or terms of service.

[0196] An "SEO optimization model" is an artificial intelligence model that optimizes a website to rank higher in search engine results.

[0197] A "smartphone" is a high-functional mobile phone that allows internet access and the use of various applications.

[0198] A "head-mounted display (HMD)" is a display device that provides visual information when worn on the user's head.

[0199] An "application" is a software program that provides specific functions or services.

[0200] This invention is a system that supports the operation of e-commerce sites, and in particular, makes it easier for Japanese companies to conduct cross-border sales. The system of this invention uses a generative AI model to automatically generate product descriptions and promotional texts, and realizes multilingual support, export restriction checks, shipping cost calculations, automatic legal policy generation, SEO optimization, and more. This system starts with a means for the user to input product information and is provided as an application that can be installed on smartphones and head-mounted displays.

[0201] System Configuration

[0202] This system has the following configuration.

[0203] hardware

[0204] Smartphone: A high-functional mobile phone carried by a user.

[0205] Head-mounted display (HMD): A display device that provides visual information.

[0206] Server: As a central management system, it manages and executes various APIs and models, such as generation AI models, translation APIs, image recognition APIs, and shipping cost calculation APIs.

[0207] software

[0208] Generative AI model (GPT-based model): Automatically generates product descriptions and promotional texts.

[0209] Translation API (e.g., Google Translate API): Translates generated product descriptions and promotional texts into multiple languages.

[0210] Image recognition model: Analyzes product images to identify specific materials and features.

[0211] Shipping Cost Calculation API: Calculates shipping costs to various countries.

[0212] Legal Policy Generation Module: Automatically generates privacy policies, terms of service, etc.

[0213] SEO Optimization Model: Automatically generates SEO strategies for your website.

[0214] Processing details

[0215] Users can access the system using their smartphones or HMDs and input product information. For example, if a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the following procedure would be followed.

[0216] Generation using prompt statements

[0217] After the user enters product information (product name, description, price, and product image), the AI ​​model generates a product description and promotional text based on the following prompts.

[0218] Example of a prompt:

[0219] Product name: Japanese-style teacup

[0220] Product Description: This is a traditional Japanese-style teacup.

[0221] Please enter the PR statement to generate:

[0222] The generated product descriptions and promotional texts are automatically translated into English and other languages ​​using a translation API. Next, an image recognition model is used to analyze product images to determine if specific materials are included and to identify export restrictions in each country. This information is then communicated to the user as an alert.

[0223] The shipping cost calculation API calculates shipping costs to each country and suggests a recommended selling price. The calculation includes the base price of the product, shipping costs, and service fees. In addition, a legal policy generation module generates various legal policies and provides them in multiple languages. Finally, an SEO optimization model is used to automatically generate optimal SEO strategies for the website.

[0224] This enables users to conduct cross-border sales efficiently and easily, and to quickly prepare for multilingual support and regulatory compliance. Operation is possible from anywhere using smartphones or HMDs, greatly improving convenience.

[0225] As a concrete example, a user inputs product information for a "Japanese-style teacup," and the AI ​​model generates and translates a promotional text, followed by image recognition. This entire process allows for the rapid preparation of sales to the United States, the United Kingdom, and Australia. A key feature of this invention is that this entire process can be performed seamlessly on a smartphone or HMD (Head-Mounted Display).

[0226] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0227] Step 1:

[0228] Users access the system using a smartphone or head-mounted display (HMD) and enter product information (product name, description, price, and product image). This entered information is sent to the server.

[0229] Input: Product name, product description, price, product image

[0230] Output: Product information is saved on the server.

[0231] Step 2:

[0232] The server uses a generative AI model to generate product descriptions and promotional texts based on product information entered by the user. Prompt text is input into the generative AI model for natural language generation.

[0233] Input: Product name, product description, prompt text

[0234] "Product Name: Japanese-style Teacup\nProduct Description: A traditional Japanese-style teacup.\nPlease enter the promotional text to generate:"

[0235] Output: Generated product description and promotional text

[0236] Step 3:

[0237] The server sends the generated product description and promotional text to a translation API for translation into multiple languages. The translated text is stored on the server.

[0238] Input: Generated product description and PR text

[0239] Output: Translated product description and promotional text (e.g., English)

[0240] Step 4:

[0241] Users can view translated product descriptions and promotional texts on their devices. They can make corrections or additions as needed.

[0242] Input: Translated product description and promotional text

[0243] Output: User-confirmed text

[0244] Step 5:

[0245] The server uses an image recognition model to analyze product images and determine export restrictions in various countries. Based on the analysis results, it generates alerts regarding export restrictions.

[0246] Input: Product image

[0247] Output: Notice regarding export restrictions (e.g., "Export restrictions apply due to the inclusion of certain materials")

[0248] Step 6:

[0249] The server uses a shipping cost calculation API to calculate shipping costs to specified countries and suggests a recommended selling price. The data used includes product price, shipping costs, and service fees.

[0250] Input: Product price, shipping costs to each country, service fees

[0251] Output: Suggested selling price (Example: "The suggested selling price for the United States is $25.00")

[0252] Step 7:

[0253] The server uses a legal policy generation module to automatically generate various legal policies (such as privacy policies and terms of service) and translate them into multiple languages.

[0254] Input: Prompts based on a generated AI model

[0255] Output: Legal policy translated into multiple languages

[0256] Step 8:

[0257] The server uses an SEO optimization model to automatically generate SEO strategies for product pages, supporting optimal search engine optimization. The generated SEO strategies are provided to the user, who can review them.

[0258] Input: Product page information

[0259] Output: SEO-optimized webpage content

[0260] Step 9:

[0261] The above functions will be integrated into an application installed on smartphones and HMDs, providing users with access and control from anywhere. Users will be able to seamlessly utilize each function of the system within the application.

[0262] Input: Output results of each processing step

[0263] Output: Integrated application functionality is provided.

[0264] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0265] This invention is a system that supports the operation of e-commerce sites, and in particular, helps Japanese companies to easily conduct cross-border sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it provides a more advanced user experience.

[0266] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0267] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[0268] Furthermore, the server uses an emotion engine to adjust the tone of product descriptions and promotional texts. This emotion engine analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (such as reassurance or excitement).

[0269] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[0270] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0271] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0272] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0273] The emotion engine can further analyze user feedback and incorporate it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine can also detect user stress levels and provide relaxing content as needed.

[0274] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. By using an emotion engine to optimize the tone of the text and incorporating feedback into future generation, more accurate text is produced. Finally, an SEO-optimized website is completed, making cross-border sales easy to achieve.

[0275] As described above, the present invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, SEO measures, and user sentiment management.

[0276] The following describes the processing flow.

[0277] Step 1:

[0278] The user accesses the input form of the e-commerce site using the terminal and enters product information (name, description, price, image). At the same time, the target country where the product will be sold is selected.

[0279] Step 2:

[0280] The server uses the generated AI model to automatically generate a product description text and a PR text in Japanese from the product information entered by the user. The generated product description text and PR text are temporarily saved in a form that can be confirmed by the user.

[0281] Step 3:

[0282] The server translates the generated product description text and PR text into English using a translation API. The translated text is provided to the user.

[0283] Step 4:

[0284] The server uses an emotion engine to adjust the tone of the product description text and PR text. The emotion engine analyzes the emotional tone of the generated text and adjusts it to a tone suitable for the specific emotion of the user.

[0285] Step 5:

[0286] The server analyzes the product image uploaded by the user using an image recognition model. Specific materials and features in the image are detected and verified in light of the export restriction information of each country. If an alert regarding the export restrictions of each country is necessary, the user is notified.

[0287] Step 6:

[0288] The server calculates the shipping fee to each target country using a shipping fee information API. The calculated shipping fee is combined with the basic product price and service usage fee, and a recommended selling price is proposed. Based on this, the user can set an optimal selling price.

[0289] Step 7:

[0290] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[0291] Step 8:

[0292] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[0293] Step 9:

[0294] The emotion engine analyzes user feedback and incorporates it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine also detects user stress levels and provides relaxing content as needed.

[0295] Step 10:

[0296] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[0297] (Example 2)

[0298] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0299] Modern e-commerce sites, especially those involving cross-border sales, face numerous challenges, including multilingual support, export restriction checks, shipping cost calculations, legal compliance, search engine optimization (SEO), and tone adjustments that resonate with user emotions. Manually addressing these challenges is extremely time-consuming, labor-intensive, and prone to errors. Furthermore, reflecting user emotions and feedback requires advanced technology, highlighting the need for a system that efficiently integrates these elements.

[0300] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0301] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using translation means, means for analyzing product images using image recognition technology to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating and translating various regulatory documents into multiple languages, means for automatically generating optimal search engine optimization measures using optimization technology, means for adjusting the emotional tone of product descriptions and promotional texts using sentiment estimation technology, and means for analyzing user feedback and reflecting it in the next generation process. As a result, the user can centrally automate all processes for conducting cross-border sales efficiently and effectively, improving accuracy and efficiency.

[0302] A "user" is a person or organization that accesses the administration screen of an e-commerce site, enters product information, and manages it.

[0303] "Product information" refers to basic information such as the product name, description, price, and product images.

[0304] A "generative AI model" is an artificial intelligence technology that uses natural language processing techniques to automatically generate product descriptions and promotional texts from input data.

[0305] "Translation means" refers to a technology for translating the generated text into multiple languages, such as a translation API.

[0306] "Image recognition technology" is a technology that analyzes product images, extracts specific features from the images, and determines information.

[0307] "Export restrictions" refer to the conditions under which the materials and shapes of specific products are restricted by the regulations of each country.

[0308] "Recommended selling price" refers to the product selling price considering shipping fees and service usage fees to each country.

[0309] "Various regulatory documents" refer to legal documents such as privacy policies and terms of use, which are necessary for compliance with laws and regulations.

[0310] "Optimization technology" refers to a series of technologies and methods for the purpose of search engine optimization (SEO).

[0311] "Emotion estimation technology" is a technology that analyzes the user's emotions from text and feedback and adjusts the tone.

[0312] "Feedback" refers to the evaluations and opinions provided for the content generated by users.

[0313] Modes for Carrying Out the Invention

[0314] The present invention is a system for improving the operation efficiency of an e-commerce (EC) site, particularly a system for supporting cross-border sales. This system combines multiple technologies such as a generative AI model, translation means, image recognition technology, optimization technology, and emotion estimation technology to automate multilingual support, confirmation of export restrictions, shipping fee calculation, compliance with laws and regulations, SEO measures, and response to user emotions.

[0315] First, the user accesses the e-commerce site using their device and enters product information. This product information includes the product name, description, price, and product image. They can also specify the target countries for sales. For example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters the product information and selects the target countries.

[0316] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts from the input product information. A specific example of a generative AI model used here is OpenAI's GPT-4. For example, the prompt input might be, "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." This generative AI model generates detailed product descriptions and promotional texts in Japanese for the specified product.

[0317] The generated Japanese text will be translated into multiple languages ​​using translation tools. Specifically, Google's translation API is likely to be used. The server will translate the generated Japanese description and PR text into English using the translation API and save the results.

[0318] The server also uses image recognition technology to analyze product images and determine export restrictions in various countries. For example, using TensorFlow, it can extract features from product images and compare them with an export restriction list to determine whether the product violates regulations in any country. The results are notified to the user as alerts as needed.

[0319] The server then calls an external shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves shipping costs for different countries, adds the base product price and service fee, and calculates and suggests a recommended selling price. This allows the user to set an accurate selling price.

[0320] In addition, the server automatically generates various regulatory documents (e.g., privacy policies and terms of service) using a generation AI model. These documents are translated into multiple languages ​​using translation tools and provided to users for legal compliance purposes.

[0321] Furthermore, the server uses optimization technology to automatically generate SEO strategies for e-commerce sites. For example, it utilizes a model like Yoast SEO to analyze the entered product information and translated text, generating optimal SEO settings. This helps the user's e-commerce site rank higher in search engine results.

[0322] Finally, the server adjusts the emotional tone of the generated text using emotion estimation technology. This technology analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (e.g., reassurance or excitement). Furthermore, by analyzing user feedback with emotion estimation technology and incorporating it into the next text generation process, it becomes possible to provide higher quality content.

[0323] In this way, the system of the present invention becomes a powerful tool for users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management.

[0324] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0325] Step 1:

[0326] The user accesses the e-commerce site using their device and enters product information. This information includes the product name, description, price, and product image. They also select the target countries for sales. The entered data is sent to the server. The device performs the process of entering product information and target countries for sales and sending them to the server.

[0327] Step 2:

[0328] The server automatically generates product descriptions and promotional texts using a generative AI model based on the received product information. Specifically, the server inputs the product information as a prompt to the generative AI model and retrieves the text generated by the model. For example, the prompt might be "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." The input data consists of product information and the specified target markets, while the output data consists of the automatically generated product descriptions and promotional texts.

[0329] Step 3:

[0330] The server translates the generated product description and promotional text into English using a translation tool. Specifically, the server sends the generated Japanese text to a translation API and retrieves the translated English text. For example, it uses the Google Translate API. The input data is the generated Japanese text, and the output data is the translated English text.

[0331] Step 4:

[0332] The server uses sentiment estimation technology to analyze the emotional tone of the generated text and adjust it to an appropriate tone. Specifically, the server uses a sentiment analysis model to analyze the generated text and adjust the tone to correspond to the user's specific emotion. The input data consists of the generated Japanese and English text, and the output data consists of the adjusted text.

[0333] Step 5:

[0334] The server uses image recognition technology to analyze product images and determine export restrictions in various countries. Specifically, the server uses image recognition technology such as TensorFlow to analyze product images and evaluate whether the materials and shape comply with export restrictions in each country. The input data is the product image, and the output data is the result of the export restriction evaluation.

[0335] Step 6:

[0336] The server uses an external shipping information API to calculate shipping costs for each country and propose a recommended selling price. Specifically, the server calls the shipping information API for each target country to obtain shipping information. Based on the obtained shipping information, it adds it to the base product price to calculate the recommended selling price. The input data is the target country and product price, and the output data is the recommended selling price.

[0337] Step 7:

[0338] The server automatically generates various rule documents using a generative AI model and translates them into multiple languages. Specifically, the server uses the generative AI model to generate documents such as privacy policies and terms of service, and then translates them into English using a translation API. The input data consists of prompts from the generative AI model, and the output data consists of the generated rule documents and their translations.

[0339] Step 8:

[0340] The server automatically generates SEO strategies for e-commerce sites using optimization technology. Specifically, the server uses an SEO optimization model to analyze product information and translated text, and generates optimal SEO settings. The input data consists of product information and translated text, while the output data consists of SEO settings.

[0341] Step 9:

[0342] The server uses sentiment estimation technology to analyze user feedback and incorporate it into the next generation process. Specifically, the server collects user feedback and analyzes it using a sentiment analysis model. Based on the analysis results, it incorporates them into the next text generation. The input data is user feedback, and the output data is the analysis results and how they are applied.

[0343] (Application Example 2)

[0344] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0345] Traditional e-commerce site management systems require operators to individually handle a wide range of tasks, including product information entry, generation of product descriptions and promotional texts, translation, export restriction assessment, shipping cost calculation, legal policy generation, and SEO optimization. This requires significant effort and time from the operator. Furthermore, they often lack consideration for adjusting emotional tone and incorporating user feedback, making it difficult to improve the user experience.

[0346] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and PR texts in Japanese using a generation AI model, means for translating the generated product descriptions and PR texts into other languages ​​using a translation API, means for adjusting the emotional tone of the product descriptions and PR texts using an emotion engine, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into other languages ​​using a translation API, means for automatically generating optimal SEO measures using an SEO optimization model, and an emotion engine that analyzes user feedback and reflects it in the next generation process. This makes it possible to centralize a wide range of tasks in e-commerce site operation and perform them efficiently and quickly. Furthermore, by adjusting the emotional tone of the generated texts and reflecting user feedback, an improvement in the user experience can be expected.

[0347] "A means for users to input product information" refers to an interface for users to input information related to a product, such as its name, description, price, and images.

[0348] "A method for automatically generating product descriptions and promotional texts in Japanese using a generative AI model" refers to a system that uses a machine learning algorithm to automatically generate product descriptions and promotional texts in Japanese based on the input product information.

[0349] "Means of translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of converting generated Japanese text into other languages ​​(such as English) using existing translation software or services.

[0350] "A means of adjusting the emotional tone of product descriptions and PR texts using an emotion engine" refers to a system that analyzes the emotional tone (such as reassurance or excitement) of generated text and adjusts it as needed.

[0351] "A means of analyzing product images using an image recognition model to determine export restrictions in various countries" refers to a system that analyzes product images using machine learning algorithms to determine whether specific materials or designs fall under export restrictions in various countries.

[0352] The "method for calculating shipping costs to each country and suggesting a recommended selling price" is a process that uses an external shipping information API to obtain shipping costs for each country and reflects them in the product pricing.

[0353] "A method for automatically generating various legal policies and translating them into other languages ​​using a translation API" refers to a system that uses a generation AI model to automatically generate legal documents such as privacy policies and terms of service, and then translates them into other languages.

[0354] "A method for automatically generating optimal SEO measures using an SEO optimization model" refers to a system that automatically proposes and implements measures to improve the search engine ranking of e-commerce sites using search engine optimization techniques.

[0355] A "sentiment engine that analyzes user feedback and reflects it in the next generation process" is a system that analyzes user feedback and reflects the results of that analysis in the generation of future product descriptions and promotional texts.

[0356] The "means of notifying users of export restriction alerts from various countries" are systems that quickly inform users when materials or designs subject to export restrictions are detected.

[0357] This invention is a support system for e-commerce site operators to efficiently conduct cross-border sales of products. In particular, it combines a generative AI model and an emotion engine to support multiple languages ​​and improve the user experience. The system of this invention is configured as follows.

[0358] System Program

[0359] User Interface (UI)

[0360] Users utilize a form to input product information from their smartphones or other devices. This form has fields for easily entering product name, description, price, images, and other information.

[0361] Data processing and generation

[0362] The server receives product information entered by the user and automatically generates product descriptions and promotional texts using a generative AI model (e.g., OpenAI's GPT-4). These generated texts are then translated into other languages ​​(e.g., English) using a translation API.

[0363] Emotional tone adjustment

[0364] The generated product descriptions and promotional texts are analyzed by an emotion engine and adjusted to match pre-set emotional tones (such as reassurance or excitement).

[0365] Image recognition and export restriction determination

[0366] Product images are analyzed using image recognition models (e.g., TensorFlow or PyTorch) to determine whether they fall under export restrictions in various countries. The results are then notified to the user as an alert from the server.

[0367] Shipping cost calculation and suggested selling price

[0368] Shipping cost information for each country is obtained via an external shipping cost information API (e.g., EasyPost API). The server calculates the shipping cost to each country based on this information, adds it to the product price, and proposes a recommended selling price.

[0369] Automated generation of legal policies

[0370] Using a generative AI model, legal policy documents such as privacy policies and terms of service are automatically generated, and these are then translated into other languages ​​using a translation API. This allows for the rapid creation of compliance documents.

[0371] SEO measures

[0372] Using an SEO optimization model, the system automatically suggests and implements SEO measures for product pages. This improves the search engine rankings of e-commerce sites.

[0373] Analysis and implementation of user feedback

[0374] The emotion engine analyzes user feedback and incorporates the results into the generation process for future product descriptions and promotional texts. This results in text that more closely matches user expectations.

[0375] Hardware and software

[0376] Hardware: Smartphones, servers, user terminals

[0377] Software: Generative AI models (e.g., GPT-4), image recognition models (e.g., TensorFlow, PyTorch), translation APIs, shipping information APIs (e.g., EasyPost API), SEO optimization models (e.g., Yoast SEO)

[0378] Specific example

[0379] If a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the user enters product information and selects target countries. The server generates and translates product descriptions and promotional texts. Next, it analyzes product images to notify the user of any export restrictions, calculates shipping costs for each country, and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. The tone of the text is optimized using an emotion engine, and feedback is incorporated into future updates.

[0380] Example of a prompt

[0381] Product description generation:

[0382] "We want to sell Japanese-style teacups incorporating traditional Japanese designs. Please generate a product description to match."

[0383] Emotional tone adjustment:

[0384] Please adjust the following sentence to convey a sense of reassurance: "Enrich your daily tea time with high-quality Japanese-style teacups."

[0385] Legal policy generation:

[0386] "Please generate a privacy policy for our e-commerce site in both Japanese and English."

[0387] This system enables e-commerce site operators to conduct cross-border sales efficiently and quickly.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The user inputs product information using a terminal. This product information includes the product name, description, price, and product image. The entered data is sent to the server and used in the next processing step.

[0391] Input: Product name, description, price, and product image

[0392] Output: Product information sent to the server

[0393] Step 2:

[0394] The server uses a generative AI model (e.g., OpenAI's GPT-4) to automatically generate product descriptions and promotional texts in Japanese from product information entered by the user. Specific data such as product names and features are input into the model for the generation process.

[0395] Input: Product Information

[0396] Output: Generated product description and promotional text

[0397] Step 3:

[0398] Next, the server uses a translation API to translate the generated product descriptions and promotional texts into other languages, such as English. The translated texts are temporarily stored on the server and presented to the user in a format they can review.

[0399] Input: Generated product description and PR text (in Japanese)

[0400] Output: Generated product description and PR text (in other languages)

[0401] Step 4:

[0402] The server uses an emotion engine to analyze the emotional tone of the generated product descriptions and promotional texts and adjusts them to match the target emotions of the user.

[0403] Input: Generated product description and PR text (in Japanese and other languages)

[0404] Output: Product description and promotional text with adjusted emotional tone.

[0405] Step 5:

[0406] The server uses an image recognition model (e.g., TensorFlow or PyTorch) to analyze product images and determine export restrictions in each country. This analysis verifies whether the product contains materials or designs subject to export restrictions.

[0407] Input: Product image

[0408] Output: Alerts regarding export restrictions in various countries

[0409] Step 6:

[0410] The server uses an external shipping cost information API (e.g., EasyPost API) to calculate shipping costs to each country. Based on the shipping cost information, a recommended selling price is suggested, which is added to the base product price.

[0411] Input: Product information, shipping information for each country

[0412] Output: Recommended selling price

[0413] Step 7:

[0414] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generative AI model, and then translates them into other languages ​​using a translation API. The generated legal policy documents are then provided to the user.

[0415] Input: None (automatically generated)

[0416] Output: Various legal policy documents (in Japanese and other languages)

[0417] Step 8:

[0418] Using an SEO optimization model, the server automatically generates SEO strategies for product pages. Optimal keywords and metadata are added, improving search engine rankings.

[0419] Input: Product page information

[0420] Output: Page data with SEO optimization applied.

[0421] Step 9:

[0422] The server analyzes user feedback using an emotion engine and incorporates the results into the process of generating future product descriptions and promotional texts. This feedback is used to improve the accuracy of text generation.

[0423] Input: User Feedback

[0424] Output: Feedback results, to be reflected in the next generation process.

[0425] This process allows users to efficiently and quickly input product information and generate product descriptions and promotional texts with an emotional tone appropriate to the target market. Furthermore, SEO measures and legal policy preparation are automated, making cross-border sales easier.

[0426] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0427] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0429] [Second Embodiment]

[0430] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0431] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0432] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0433] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0434] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0435] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0436] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0437] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0438] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0439] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0440] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0441] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0442] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0443] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0444] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[0445] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[0446] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0447] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0448] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0449] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0450] As described above, the present invention enables users to automate tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures, allowing them to efficiently conduct cross-border sales.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[0454] Step 2:

[0455] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated texts are temporarily saved so that the user can review them later.

[0456] Step 3:

[0457] The server translates the generated product description and PR text into English using a translation API. The translation results are then provided to the user.

[0458] Step 4:

[0459] The server uses an image recognition model to analyze product images uploaded by users. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user is notified.

[0460] Step 5:

[0461] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[0462] Step 6:

[0463] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[0464] Step 7:

[0465] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[0466] Step 8:

[0467] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[0468] (Example 1)

[0469] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0470] Currently, many Japanese companies are attempting to sell their products to overseas markets through e-commerce sites, but they face numerous challenges, including language barriers, export restrictions, shipping cost calculations, legal policy preparation, and SEO measures. These challenges reduce the efficiency of cross-border sales, making it difficult to expand sales. Conventional technologies lack a system that automates these challenges, requiring a great deal of manual work. This invention solves these problems and provides a system that enables companies to conduct cross-border sales efficiently.

[0471] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0472] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into other languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions, means for calculating shipping costs to each country using a shipping information API and proposing a recommended selling price, means for automatically generating legal policies and translating them into other languages, means for automatically generating SEO measures using an SEO optimization model, means for inputting prompt texts, means for saving the documents generated by the generation AI model to a database, and means for notifying the user of alerts. This enables the automation of these processes in a single step, making it possible to conduct cross-border sales efficiently.

[0473] "Means for users to input product information" refers to an interface that allows users to input basic information such as the product name, description, price, and product images using a device.

[0474] "Means for automatically generating product descriptions and promotional texts using a generative AI model" refers to a processing device that uses a generative AI model to automatically generate product descriptions and promotional texts from product information entered by a user.

[0475] "Means for translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of translating product descriptions and PR texts generated by a generative AI model into other languages ​​using a translation API.

[0476] "A means of analyzing product images using an image recognition model to determine export restrictions" refers to an algorithm that uses an image recognition model to analyze product images and makes decisions based on information regarding export restrictions in various countries.

[0477] The "method for calculating shipping costs to various countries and proposing recommended selling prices using a shipping information API" is a system that uses a shipping information API to obtain shipping costs to various countries, and then calculates and proposes a recommended selling price by adding the base product price and service usage fee.

[0478] The "means for automatically generating legal policies and translating them into other languages" refer to a function that automatically generates various legal policies using a generation AI model and then translates them into other languages ​​using a translation API.

[0479] "A method for automatically generating SEO measures using an SEO optimization model" refers to a system that uses an SEO optimization model to automatically generate optimal SEO measures for e-commerce sites.

[0480] "Means for inputting prompt text" refers to input methods for providing product information and generation conditions to the generation AI model.

[0481] "Means for saving documents generated by a generative AI model to a database" refers to a database and its management system for temporarily storing documents generated by a generative AI model.

[0482] "Means of notifying users of alerts" refers to a notification system that alerts users about export restrictions and other important information.

[0483] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0484] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0485] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are temporarily stored in a database for user review. Specific examples of prompts to be input into this generative AI model are as follows:

[0486] Product Description Generation Prompt

[0487] Product name: Japanese-style teacup

[0488] Price: 3000 yen

[0489] Description: A beautiful Japanese-style teacup made of high-quality ceramic. Features a handcrafted pattern.

[0490] Based on this information, please generate a product description for sale within Japan.

[0491] PR statement generation prompt

[0492] Product name: Japanese-style teacup

[0493] Target countries: United States, United Kingdom, Australia

[0494] Please generate advertising copy for this product, tailored to each country.

[0495] The generated product descriptions and promotional texts are translated into English using a translation API (e.g., Google Translate API) and then provided to the user again.

[0496] Furthermore, the server uses an image recognition model (e.g., Google Cloud Vision API) to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is then notified to the user as an alert.

[0497] The server then uses a shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves the shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0498] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0499] Furthermore, the server uses an SEO optimization model (e.g., the Ahrefs API) to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0500] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0501] This invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures.

[0502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0503] Step 1:

[0504] Users access the e-commerce site using their devices and enter basic information such as the product name, description, price, and product images. They can also specify which countries the product will be sold in.

[0505] input:

[0506] Product name

[0507] Product Description

[0508] Product price

[0509] Product image

[0510] Target countries for sales

[0511] output:

[0512] The entered product information is sent to the server.

[0513] Specific actions:

[0514] The user opens a browser and logs into the e-commerce site's administration panel.

[0515] Enter the product information (product name: Japanese-style teacup, product price: 3000 yen, product description: features a handmade pattern, product image: image file) and select the target countries for sale (USA, UK, Australia).

[0516] Step 2:

[0517] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are then stored in a database.

[0518] input:

[0519] Product information

[0520] output:

[0521] Product description and promotional text in Japanese

[0522] Specific actions:

[0523] The server inputs information such as product name, product description, and product price as prompts into the AI ​​model that generates the product.

[0524] The generative AI model generates product descriptions and promotional texts in Japanese and stores the content in a database.

[0525] Step 3:

[0526] The server uses a translation API (for example, Google Translate API) to translate the generated product descriptions and promotional texts into English. The translated documents are then saved back into the database.

[0527] input:

[0528] Product description and promotional text in Japanese

[0529] output:

[0530] English product description and promotional text

[0531] Specific actions:

[0532] The server retrieves the Japanese product description and PR text generated from the database and sends them to the translation API.

[0533] The translation API translates the document into English and returns the result to the server.

[0534] The server saves the translation results to a database and notifies the user.

[0535] Step 4:

[0536] The server analyzes product images using an image recognition model (e.g., Google Cloud Vision API). Based on export restriction information from various countries, it detects whether specific materials or ingredients are present and determines whether export restrictions apply. If export restrictions exist, the user is notified as an alert.

[0537] input:

[0538] Product image

[0539] output:

[0540] Whether or not there are export restrictions

[0541] Specific actions:

[0542] The server sends product images to an image recognition model and retrieves the analysis results.

[0543] The server compares the analysis results with export restriction databases of various countries.

[0544] If export restrictions are detected, an alert message will be generated and the user will be notified.

[0545] Step 5:

[0546] The server uses a shipping information API (for example, the Shippo API) to obtain shipping costs for each country, calculates a recommended selling price by adding the base product price and service fee, and proposes it to the user.

[0547] input:

[0548] Target countries for sales

[0549] Basic product price

[0550] Service fee

[0551] output:

[0552] Recommended selling price

[0553] Specific actions:

[0554] The server calls the shipping information API to retrieve shipping costs for each target country.

[0555] Based on the acquired shipping information, the server calculates the recommended selling price by adding the base product price and service fee.

[0556] The system presents the calculation results to the user and proposes the optimal selling price.

[0557] Step 6:

[0558] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and then translates them into other languages ​​using a translation API before providing them to users.

[0559] input:

[0560] None (Uses an internally generated AI model)

[0561] output:

[0562] Legal policies in Japanese and English

[0563] Specific actions:

[0564] The server uses a generative AI model to generate standard legal policies.

[0565] The generated policy is sent to the translation API to retrieve the English version.

[0566] Provide users with both the Japanese and English versions and ask them to confirm their choice.

[0567] Step 7:

[0568] The server uses an SEO optimization model (for example, the Ahrefs API) to automatically generate the optimal SEO strategies for the user's e-commerce site. This optimizes the site for search engines.

[0569] input:

[0570] Current status data of e-commerce sites

[0571] output:

[0572] SEO Strategy Proposals

[0573] Specific actions:

[0574] The server sends current data from the user's e-commerce site to an SEO optimization model for analysis.

[0575] Based on the areas for improvement in SEO, we will generate specific action plans.

[0576] The system notifies users of the generated SEO strategies and provides support for their implementation.

[0577] (Application Example 1)

[0578] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0579] Japanese companies need a system that facilitates cross-border sales by efficiently handling complex procedures such as multilingual support, export restriction checks, and shipping cost calculations. However, existing systems struggle to provide all these functions in one place, resulting in a lot of manual work and making efficient operation difficult. Furthermore, an interface that can be easily operated from devices such as smartphones and head-mounted displays is necessary.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0581] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts in natural language using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into multiple languages, means for automatically generating optimal SEO measures using an SEO optimization model, and means for providing each of the aforementioned means as an application that can be installed on a smartphone or head-mounted display. This makes it possible to conduct cross-border sales efficiently and easily.

[0582] "Product information" refers to basic information such as the product name, description, price, and product images.

[0583] A "generative AI model" is an artificial intelligence model that uses natural language processing to automatically generate product descriptions and promotional texts.

[0584] The "Translation API" is an application programming interface for translating generated product descriptions and promotional texts into multiple languages.

[0585] An "image recognition model" is an artificial intelligence model that analyzes product images and identifies specific features or materials.

[0586] "Export restrictions" refer to the prohibition or restriction of exporting certain goods or materials based on the laws and regulations of each country.

[0587] "Shipping cost calculation" is a method for calculating the cost of sending goods to a specific country or region.

[0588] "Recommended selling price" refers to the selling price which includes the base price of the product, shipping costs, and service fees.

[0589] A "legal policy" is a document necessary for compliance with laws and regulations, such as a privacy policy or terms of service.

[0590] An "SEO optimization model" is an artificial intelligence model that optimizes a website to rank higher in search engine results.

[0591] A "smartphone" is a high-functional mobile phone that allows internet access and the use of various applications.

[0592] A "head-mounted display (HMD)" is a display device that provides visual information when worn on the user's head.

[0593] An "application" is a software program that provides specific functions or services.

[0594] This invention is a system that supports the operation of e-commerce sites, and in particular, makes it easier for Japanese companies to conduct cross-border sales. The system of this invention uses a generative AI model to automatically generate product descriptions and promotional texts, and realizes multilingual support, export restriction checks, shipping cost calculations, automatic legal policy generation, SEO optimization, and more. This system starts with a means for the user to input product information and is provided as an application that can be installed on smartphones and head-mounted displays.

[0595] System Configuration

[0596] This system has the following configuration.

[0597] hardware

[0598] Smartphone: A high-functional mobile phone carried by a user.

[0599] Head-mounted display (HMD): A display device that provides visual information.

[0600] Server: As a central management system, it manages and executes various APIs and models, such as generation AI models, translation APIs, image recognition APIs, and shipping cost calculation APIs.

[0601] software

[0602] Generative AI model (GPT-based model): Automatically generates product descriptions and promotional texts.

[0603] Translation API (e.g., Google Translate API): Translates generated product descriptions and promotional texts into multiple languages.

[0604] Image recognition model: Analyzes product images to identify specific materials and features.

[0605] Shipping Cost Calculation API: Calculates shipping costs to various countries.

[0606] Legal Policy Generation Module: Automatically generates privacy policies, terms of service, etc.

[0607] SEO Optimization Model: Automatically generates SEO strategies for your website.

[0608] Processing details

[0609] Users can access the system using their smartphones or HMDs and input product information. For example, if a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the following procedure would be followed.

[0610] Generation using prompt statements

[0611] After the user enters product information (product name, description, price, and product image), the AI ​​model generates a product description and promotional text based on the following prompts.

[0612] Example of a prompt:

[0613] Product name: Japanese-style teacup

[0614] Product Description: This is a traditional Japanese-style teacup.

[0615] Please enter the PR statement to generate:

[0616] The generated product descriptions and promotional texts are automatically translated into English and other languages ​​using a translation API. Next, an image recognition model is used to analyze product images to determine if specific materials are included and to identify export restrictions in each country. This information is then communicated to the user as an alert.

[0617] The shipping cost calculation API calculates shipping costs to each country and suggests a recommended selling price. The calculation includes the base price of the product, shipping costs, and service fees. In addition, a legal policy generation module generates various legal policies and provides them in multiple languages. Finally, an SEO optimization model is used to automatically generate optimal SEO strategies for the website.

[0618] This enables users to conduct cross-border sales efficiently and easily, and to quickly prepare for multilingual support and regulatory compliance. Operation is possible from anywhere using smartphones or HMDs, greatly improving convenience.

[0619] As a concrete example, a user inputs product information for a "Japanese-style teacup," and the AI ​​model generates and translates a promotional text, followed by image recognition. This entire process allows for the rapid preparation of sales to the United States, the United Kingdom, and Australia. A key feature of this invention is that this entire process can be performed seamlessly on a smartphone or HMD (Head-Mounted Display).

[0620] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0621] Step 1:

[0622] Users access the system using a smartphone or head-mounted display (HMD) and enter product information (product name, description, price, and product image). This entered information is sent to the server.

[0623] Input: Product name, product description, price, product image

[0624] Output: Product information is saved on the server.

[0625] Step 2:

[0626] The server uses a generative AI model to generate product descriptions and promotional texts based on product information entered by the user. Prompt text is input into the generative AI model for natural language generation.

[0627] Input: Product name, product description, prompt text

[0628] "Product Name: Japanese-style Teacup\nProduct Description: A traditional Japanese-style teacup.\nPlease enter the promotional text to generate:"

[0629] Output: Generated product description and promotional text

[0630] Step 3:

[0631] The server sends the generated product description and promotional text to a translation API for translation into multiple languages. The translated text is stored on the server.

[0632] Input: Generated product description and PR text

[0633] Output: Translated product description and promotional text (e.g., English)

[0634] Step 4:

[0635] Users can view translated product descriptions and promotional texts on their devices. They can make corrections or additions as needed.

[0636] Input: Translated product description and promotional text

[0637] Output: User-confirmed text

[0638] Step 5:

[0639] The server uses an image recognition model to analyze product images and determine export restrictions in various countries. Based on the analysis results, it generates alerts regarding export restrictions.

[0640] Input: Product image

[0641] Output: Notice regarding export restrictions (e.g., "Export restrictions apply due to the inclusion of certain materials")

[0642] Step 6:

[0643] The server uses a shipping cost calculation API to calculate shipping costs to specified countries and suggests a recommended selling price. The data used includes product price, shipping costs, and service fees.

[0644] Input: Product price, shipping costs to each country, service fees

[0645] Output: Suggested selling price (Example: "The suggested selling price for the United States is $25.00")

[0646] Step 7:

[0647] The server uses a legal policy generation module to automatically generate various legal policies (such as privacy policies and terms of service) and translate them into multiple languages.

[0648] Input: Prompts based on a generated AI model

[0649] Output: Legal policy translated into multiple languages

[0650] Step 8:

[0651] The server uses an SEO optimization model to automatically generate SEO strategies for product pages, supporting optimal search engine optimization. The generated SEO strategies are provided to the user, who can review them.

[0652] Input: Product page information

[0653] Output: SEO-optimized webpage content

[0654] Step 9:

[0655] The above functions will be integrated into an application installed on smartphones and HMDs, providing users with access and control from anywhere. Users will be able to seamlessly utilize each function of the system within the application.

[0656] Input: Output results of each processing step

[0657] Output: Integrated application functionality is provided.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] This invention is a system that supports the operation of e-commerce sites, and in particular, helps Japanese companies to easily conduct cross-border sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it provides a more advanced user experience.

[0660] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0661] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[0662] Furthermore, the server uses an emotion engine to adjust the tone of product descriptions and promotional texts. This emotion engine analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (such as reassurance or excitement).

[0663] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[0664] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0665] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0666] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0667] The emotion engine can further analyze user feedback and incorporate it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine can also detect user stress levels and provide relaxing content as needed.

[0668] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. By using an emotion engine to optimize the tone of the text and incorporating feedback into future generation, more accurate text is produced. Finally, an SEO-optimized website is completed, making cross-border sales easy to achieve.

[0669] As described above, the present invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, SEO measures, and user sentiment management.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[0673] Step 2:

[0674] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review.

[0675] Step 3:

[0676] The server translates the generated product description and promotional text into English using a translation API. The translated text is then provided to the user.

[0677] Step 4:

[0678] The server uses an emotion engine to adjust the tone of product descriptions and promotional texts. The emotion engine analyzes the emotional tone of the generated text and adjusts it to match the user's specific emotions.

[0679] Step 5:

[0680] The server uses an image recognition model to analyze product images uploaded by the user. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user will be notified.

[0681] Step 6:

[0682] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[0683] Step 7:

[0684] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[0685] Step 8:

[0686] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[0687] Step 9:

[0688] The emotion engine analyzes user feedback and incorporates it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine also detects user stress levels and provides relaxing content as needed.

[0689] Step 10:

[0690] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[0691] (Example 2)

[0692] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0693] Modern e-commerce sites, especially those involving cross-border sales, face numerous challenges, including multilingual support, export restriction checks, shipping cost calculations, legal compliance, search engine optimization (SEO), and tone adjustments that resonate with user emotions. Manually addressing these challenges is extremely time-consuming, labor-intensive, and prone to errors. Furthermore, reflecting user emotions and feedback requires advanced technology, highlighting the need for a system that efficiently integrates these elements.

[0694] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0695] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using translation means, means for analyzing product images using image recognition technology to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating and translating various regulatory documents into multiple languages, means for automatically generating optimal search engine optimization measures using optimization technology, means for adjusting the emotional tone of product descriptions and promotional texts using sentiment estimation technology, and means for analyzing user feedback and reflecting it in the next generation process. As a result, the user can centrally automate all processes for conducting cross-border sales efficiently and effectively, improving accuracy and efficiency.

[0696] A "user" is a person or organization that accesses the administration screen of an e-commerce site, enters product information, and manages it.

[0697] "Product information" refers to basic information such as the product name, description, price, and product images.

[0698] A "generative AI model" is an artificial intelligence technology that uses natural language processing techniques to automatically generate product descriptions and promotional texts from input data.

[0699] "Translation means" refers to technologies for translating generated text into multiple languages, such as translation APIs.

[0700] "Image recognition technology" is a technology that analyzes product images, extracts specific features from the images, and uses that information to make judgments.

[0701] "Export restrictions" refer to conditions under which the materials or shape of specific goods are constrained by regulations in each country.

[0702] "Recommended selling price" refers to the selling price of a product that includes shipping costs and service fees to each country.

[0703] "Various regulatory documents" refer to legal documents such as privacy policies and terms of service, which are necessary for compliance with laws and regulations.

[0704] "Optimization techniques" refer to a series of techniques and methods aimed at search engine optimization (SEO).

[0705] "Emotion estimation technology" is a technique that analyzes a user's emotions from text and feedback and adjusts the tone accordingly.

[0706] "Feedback" refers to the evaluations and opinions that users provide regarding generated content.

[0707] Modes for carrying out the invention

[0708] This invention is a system for streamlining the operation of e-commerce (EC) sites, and in particular, a system that supports cross-border sales. This system automates multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management by combining multiple technologies such as generative AI models, translation methods, image recognition technology, optimization technology, and sentiment estimation technology.

[0709] First, the user accesses the e-commerce site using their device and enters product information. This product information includes the product name, description, price, and product image. They can also specify the target countries for sales. For example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters the product information and selects the target countries.

[0710] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts from the input product information. A specific example of a generative AI model used here is OpenAI's GPT-4. For example, the prompt input might be, "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." This generative AI model generates detailed product descriptions and promotional texts in Japanese for the specified product.

[0711] The generated Japanese text will be translated into multiple languages ​​using translation tools. Specifically, Google's translation API is likely to be used. The server will translate the generated Japanese description and PR text into English using the translation API and save the results.

[0712] The server also uses image recognition technology to analyze product images and determine export restrictions in various countries. For example, using TensorFlow, it can extract features from product images and compare them with an export restriction list to determine whether the product violates regulations in any country. The results are notified to the user as alerts as needed.

[0713] The server then calls an external shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves shipping costs for different countries, adds the base product price and service fee, and calculates and suggests a recommended selling price. This allows the user to set an accurate selling price.

[0714] In addition, the server automatically generates various regulatory documents (e.g., privacy policies and terms of service) using a generation AI model. These documents are translated into multiple languages ​​using translation tools and provided to users for legal compliance purposes.

[0715] Furthermore, the server uses optimization technology to automatically generate SEO strategies for e-commerce sites. For example, it utilizes a model like Yoast SEO to analyze the entered product information and translated text, generating optimal SEO settings. This helps the user's e-commerce site rank higher in search engine results.

[0716] Finally, the server adjusts the emotional tone of the generated text using emotion estimation technology. This technology analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (e.g., reassurance or excitement). Furthermore, by analyzing user feedback with emotion estimation technology and incorporating it into the next text generation process, it becomes possible to provide higher quality content.

[0717] In this way, the system of the present invention becomes a powerful tool for users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The user accesses the e-commerce site using their device and enters product information. This information includes the product name, description, price, and product image. They also select the target countries for sales. The entered data is sent to the server. The device performs the process of entering product information and target countries for sales and sending them to the server.

[0721] Step 2:

[0722] The server automatically generates product descriptions and promotional texts using a generative AI model based on the received product information. Specifically, the server inputs the product information as a prompt to the generative AI model and retrieves the text generated by the model. For example, the prompt might be "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." The input data consists of product information and the specified target markets, while the output data consists of the automatically generated product descriptions and promotional texts.

[0723] Step 3:

[0724] The server translates the generated product description and promotional text into English using a translation tool. Specifically, the server sends the generated Japanese text to a translation API and retrieves the translated English text. For example, it uses the Google Translate API. The input data is the generated Japanese text, and the output data is the translated English text.

[0725] Step 4:

[0726] The server uses sentiment estimation technology to analyze the emotional tone of the generated text and adjust it to an appropriate tone. Specifically, the server uses a sentiment analysis model to analyze the generated text and adjust the tone to correspond to the user's specific emotion. The input data consists of the generated Japanese and English text, and the output data consists of the adjusted text.

[0727] Step 5:

[0728] The server uses image recognition technology to analyze product images and determine export restrictions in various countries. Specifically, the server uses image recognition technology such as TensorFlow to analyze product images and evaluate whether the materials and shape comply with export restrictions in each country. The input data is the product image, and the output data is the result of the export restriction evaluation.

[0729] Step 6:

[0730] The server uses an external shipping information API to calculate shipping costs for each country and propose a recommended selling price. Specifically, the server calls the shipping information API for each target country to obtain shipping information. Based on the obtained shipping information, it adds it to the base product price to calculate the recommended selling price. The input data is the target country and product price, and the output data is the recommended selling price.

[0731] Step 7:

[0732] The server automatically generates various rule documents using a generative AI model and translates them into multiple languages. Specifically, the server uses the generative AI model to generate documents such as privacy policies and terms of service, and then translates them into English using a translation API. The input data consists of prompts from the generative AI model, and the output data consists of the generated rule documents and their translations.

[0733] Step 8:

[0734] The server automatically generates SEO strategies for e-commerce sites using optimization technology. Specifically, the server uses an SEO optimization model to analyze product information and translated text, and generates optimal SEO settings. The input data consists of product information and translated text, while the output data consists of SEO settings.

[0735] Step 9:

[0736] The server uses sentiment estimation technology to analyze user feedback and incorporate it into the next generation process. Specifically, the server collects user feedback and analyzes it using a sentiment analysis model. Based on the analysis results, it incorporates them into the next text generation. The input data is user feedback, and the output data is the analysis results and how they are applied.

[0737] (Application Example 2)

[0738] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0739] Traditional e-commerce site management systems require operators to individually handle a wide range of tasks, including product information entry, generation of product descriptions and promotional texts, translation, export restriction assessment, shipping cost calculation, legal policy generation, and SEO optimization. This requires significant effort and time from the operator. Furthermore, they often lack consideration for adjusting emotional tone and incorporating user feedback, making it difficult to improve the user experience.

[0740] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and PR texts in Japanese using a generation AI model, means for translating the generated product descriptions and PR texts into other languages ​​using a translation API, means for adjusting the emotional tone of the product descriptions and PR texts using an emotion engine, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into other languages ​​using a translation API, means for automatically generating optimal SEO measures using an SEO optimization model, and an emotion engine that analyzes user feedback and reflects it in the next generation process. This makes it possible to centralize a wide range of tasks in e-commerce site operation and perform them efficiently and quickly. Furthermore, by adjusting the emotional tone of the generated texts and reflecting user feedback, an improvement in the user experience can be expected.

[0741] "A means for users to input product information" refers to an interface for users to input information related to a product, such as its name, description, price, and images.

[0742] "A method for automatically generating product descriptions and promotional texts in Japanese using a generative AI model" refers to a system that uses a machine learning algorithm to automatically generate product descriptions and promotional texts in Japanese based on the input product information.

[0743] "Means of translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of converting generated Japanese text into other languages ​​(such as English) using existing translation software or services.

[0744] "A means of adjusting the emotional tone of product descriptions and PR texts using an emotion engine" refers to a system that analyzes the emotional tone (such as reassurance or excitement) of generated text and adjusts it as needed.

[0745] "A means of analyzing product images using an image recognition model to determine export restrictions in various countries" refers to a system that analyzes product images using machine learning algorithms to determine whether specific materials or designs fall under export restrictions in various countries.

[0746] The "method for calculating shipping costs to each country and suggesting a recommended selling price" is a process that uses an external shipping information API to obtain shipping costs for each country and reflects them in the product pricing.

[0747] "A method for automatically generating various legal policies and translating them into other languages ​​using a translation API" refers to a system that uses a generation AI model to automatically generate legal documents such as privacy policies and terms of service, and then translates them into other languages.

[0748] "A method for automatically generating optimal SEO measures using an SEO optimization model" refers to a system that automatically proposes and implements measures to improve the search engine ranking of e-commerce sites using search engine optimization techniques.

[0749] A "sentiment engine that analyzes user feedback and reflects it in the next generation process" is a system that analyzes user feedback and reflects the results of that analysis in the generation of future product descriptions and promotional texts.

[0750] The "means of notifying users of export restriction alerts from various countries" are systems that quickly inform users when materials or designs subject to export restrictions are detected.

[0751] This invention is a support system for e-commerce site operators to efficiently conduct cross-border sales of products. In particular, it combines a generative AI model and an emotion engine to support multiple languages ​​and improve the user experience. The system of this invention is configured as follows.

[0752] System Program

[0753] User Interface (UI)

[0754] Users utilize a form to input product information from their smartphones or other devices. This form has fields for easily entering product name, description, price, images, and other information.

[0755] Data processing and generation

[0756] The server receives product information entered by the user and automatically generates product descriptions and promotional texts using a generative AI model (e.g., OpenAI's GPT-4). These generated texts are then translated into other languages ​​(e.g., English) using a translation API.

[0757] Emotional tone adjustment

[0758] The generated product descriptions and promotional texts are analyzed by an emotion engine and adjusted to match pre-set emotional tones (such as reassurance or excitement).

[0759] Image recognition and export restriction determination

[0760] Product images are analyzed using image recognition models (e.g., TensorFlow or PyTorch) to determine whether they fall under export restrictions in various countries. The results are then notified to the user as an alert from the server.

[0761] Shipping cost calculation and suggested selling price

[0762] Shipping cost information for each country is obtained via an external shipping cost information API (e.g., EasyPost API). The server calculates the shipping cost to each country based on this information, adds it to the product price, and proposes a recommended selling price.

[0763] Automated generation of legal policies

[0764] Using a generative AI model, legal policy documents such as privacy policies and terms of service are automatically generated, and these are then translated into other languages ​​using a translation API. This allows for the rapid creation of compliance documents.

[0765] SEO measures

[0766] Using an SEO optimization model, the system automatically suggests and implements SEO measures for product pages. This improves the search engine rankings of e-commerce sites.

[0767] Analysis and implementation of user feedback

[0768] The emotion engine analyzes user feedback and incorporates the results into the generation process for future product descriptions and promotional texts. This results in text that more closely matches user expectations.

[0769] Hardware and software

[0770] Hardware: Smartphones, servers, user terminals

[0771] Software: Generative AI models (e.g., GPT-4), image recognition models (e.g., TensorFlow, PyTorch), translation APIs, shipping information APIs (e.g., EasyPost API), SEO optimization models (e.g., Yoast SEO)

[0772] Specific example

[0773] If a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the user enters product information and selects target countries. The server generates and translates product descriptions and promotional texts. Next, it analyzes product images to notify the user of any export restrictions, calculates shipping costs for each country, and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. The tone of the text is optimized using an emotion engine, and feedback is incorporated into future updates.

[0774] Example of a prompt

[0775] Product description generation:

[0776] "We want to sell Japanese-style teacups incorporating traditional Japanese designs. Please generate a product description to match."

[0777] Emotional tone adjustment:

[0778] Please adjust the following sentence to convey a sense of reassurance: "Enrich your daily tea time with high-quality Japanese-style teacups."

[0779] Legal policy generation:

[0780] "Please generate a privacy policy for our e-commerce site in both Japanese and English."

[0781] This system enables e-commerce site operators to conduct cross-border sales efficiently and quickly.

[0782] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0783] Step 1:

[0784] The user inputs product information using a terminal. This product information includes the product name, description, price, and product image. The entered data is sent to the server and used in the next processing step.

[0785] Input: Product name, description, price, and product image

[0786] Output: Product information sent to the server

[0787] Step 2:

[0788] The server uses a generative AI model (e.g., OpenAI's GPT-4) to automatically generate product descriptions and promotional texts in Japanese from product information entered by the user. Specific data such as product names and features are input into the model for the generation process.

[0789] Input: Product Information

[0790] Output: Generated product description and promotional text

[0791] Step 3:

[0792] Next, the server uses a translation API to translate the generated product descriptions and promotional texts into other languages, such as English. The translated texts are temporarily stored on the server and presented to the user in a format they can review.

[0793] Input: Generated product description and PR text (in Japanese)

[0794] Output: Generated product description and PR text (in other languages)

[0795] Step 4:

[0796] The server uses an emotion engine to analyze the emotional tone of the generated product descriptions and promotional texts and adjusts them to match the target emotions of the user.

[0797] Input: Generated product description and PR text (in Japanese and other languages)

[0798] Output: Product description and promotional text with adjusted emotional tone.

[0799] Step 5:

[0800] The server uses an image recognition model (e.g., TensorFlow or PyTorch) to analyze product images and determine export restrictions in each country. This analysis verifies whether the product contains materials or designs subject to export restrictions.

[0801] Input: Product image

[0802] Output: Alerts regarding export restrictions in various countries

[0803] Step 6:

[0804] The server uses an external shipping cost information API (e.g., EasyPost API) to calculate shipping costs to each country. Based on the shipping cost information, a recommended selling price is suggested, which is added to the base product price.

[0805] Input: Product information, shipping information for each country

[0806] Output: Recommended selling price

[0807] Step 7:

[0808] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generative AI model, and then translates them into other languages ​​using a translation API. The generated legal policy documents are then provided to the user.

[0809] Input: None (automatically generated)

[0810] Output: Various legal policy documents (in Japanese and other languages)

[0811] Step 8:

[0812] Using an SEO optimization model, the server automatically generates SEO strategies for product pages. Optimal keywords and metadata are added, improving search engine rankings.

[0813] Input: Product page information

[0814] Output: Page data with SEO optimization applied.

[0815] Step 9:

[0816] The server analyzes user feedback using an emotion engine and incorporates the results into the process of generating future product descriptions and promotional texts. This feedback is used to improve the accuracy of text generation.

[0817] Input: User Feedback

[0818] Output: Feedback results, to be reflected in the next generation process.

[0819] This process allows users to efficiently and quickly input product information and generate product descriptions and promotional texts with an emotional tone appropriate to the target market. Furthermore, SEO measures and legal policy preparation are automated, making cross-border sales easier.

[0820] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0821] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0822] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0823] [Third Embodiment]

[0824] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0825] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0826] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0827] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0828] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0829] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0830] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0831] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0832] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0833] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0834] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0835] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0836] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0837] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0838] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[0839] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[0840] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0841] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0842] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0843] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0844] As described above, the present invention enables users to automate tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures, allowing them to efficiently conduct cross-border sales.

[0845] The following describes the processing flow.

[0846] Step 1:

[0847] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[0848] Step 2:

[0849] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated texts are temporarily saved so that the user can review them later.

[0850] Step 3:

[0851] The server translates the generated product description and PR text into English using a translation API. The translation results are then provided to the user.

[0852] Step 4:

[0853] The server uses an image recognition model to analyze product images uploaded by users. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user is notified.

[0854] Step 5:

[0855] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[0856] Step 6:

[0857] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[0858] Step 7:

[0859] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[0860] Step 8:

[0861] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[0862] (Example 1)

[0863] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0864] Currently, many Japanese companies are attempting to sell their products to overseas markets through e-commerce sites, but they face numerous challenges, including language barriers, export restrictions, shipping cost calculations, legal policy preparation, and SEO measures. These challenges reduce the efficiency of cross-border sales, making it difficult to expand sales. Conventional technologies lack a system that automates these challenges, requiring a great deal of manual work. This invention solves these problems and provides a system that enables companies to conduct cross-border sales efficiently.

[0865] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0866] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into other languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions, means for calculating shipping costs to each country using a shipping information API and proposing a recommended selling price, means for automatically generating legal policies and translating them into other languages, means for automatically generating SEO measures using an SEO optimization model, means for inputting prompt texts, means for saving the documents generated by the generation AI model to a database, and means for notifying the user of alerts. This enables the automation of these processes in a single step, making it possible to conduct cross-border sales efficiently.

[0867] "Means for users to input product information" refers to an interface that allows users to input basic information such as the product name, description, price, and product images using a device.

[0868] "Means for automatically generating product descriptions and promotional texts using a generative AI model" refers to a processing device that uses a generative AI model to automatically generate product descriptions and promotional texts from product information entered by a user.

[0869] "Means for translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of translating product descriptions and PR texts generated by a generative AI model into other languages ​​using a translation API.

[0870] "A means of analyzing product images using an image recognition model to determine export restrictions" refers to an algorithm that uses an image recognition model to analyze product images and makes decisions based on information regarding export restrictions in various countries.

[0871] The "method for calculating shipping costs to various countries and proposing recommended selling prices using a shipping information API" is a system that uses a shipping information API to obtain shipping costs to various countries, and then calculates and proposes a recommended selling price by adding the base product price and service usage fee.

[0872] The "means for automatically generating legal policies and translating them into other languages" refer to a function that automatically generates various legal policies using a generation AI model and then translates them into other languages ​​using a translation API.

[0873] "A method for automatically generating SEO measures using an SEO optimization model" refers to a system that uses an SEO optimization model to automatically generate optimal SEO measures for e-commerce sites.

[0874] "Means for inputting prompt text" refers to input methods for providing product information and generation conditions to the generation AI model.

[0875] "Means for saving documents generated by a generative AI model to a database" refers to a database and its management system for temporarily storing documents generated by a generative AI model.

[0876] "Means of notifying users of alerts" refers to a notification system that alerts users about export restrictions and other important information.

[0877] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[0878] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[0879] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are temporarily stored in a database for user review. Specific examples of prompts to be input into this generative AI model are as follows:

[0880] Product Description Generation Prompt

[0881] Product name: Japanese-style teacup

[0882] Price: 3000 yen

[0883] Description: A beautiful Japanese-style teacup made of high-quality ceramic. Features a handcrafted pattern.

[0884] Based on this information, please generate a product description for sale within Japan.

[0885] PR statement generation prompt

[0886] Product name: Japanese-style teacup

[0887] Target countries: United States, United Kingdom, Australia

[0888] Please generate advertising copy for this product, tailored to each country.

[0889] The generated product descriptions and promotional texts are translated into English using a translation API (e.g., Google Translate API) and then provided to the user again.

[0890] Furthermore, the server uses an image recognition model (e.g., Google Cloud Vision API) to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is then notified to the user as an alert.

[0891] The server then uses a shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves the shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[0892] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[0893] Furthermore, the server uses an SEO optimization model (e.g., the Ahrefs API) to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[0894] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[0895] This invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures.

[0896] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0897] Step 1:

[0898] Users access the e-commerce site using their devices and enter basic information such as the product name, description, price, and product images. They can also specify which countries the product will be sold in.

[0899] input:

[0900] Product name

[0901] Product Description

[0902] Product price

[0903] Product image

[0904] Target countries for sales

[0905] output:

[0906] The entered product information is sent to the server.

[0907] Specific actions:

[0908] The user opens a browser and logs into the e-commerce site's administration panel.

[0909] Enter the product information (product name: Japanese-style teacup, product price: 3000 yen, product description: features a handmade pattern, product image: image file) and select the target countries for sale (USA, UK, Australia).

[0910] Step 2:

[0911] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are then stored in a database.

[0912] input:

[0913] Product information

[0914] output:

[0915] Product description and promotional text in Japanese

[0916] Specific actions:

[0917] The server inputs information such as product name, product description, and product price as prompts into the AI ​​model that generates the product.

[0918] The generative AI model generates product descriptions and promotional texts in Japanese and stores the content in a database.

[0919] Step 3:

[0920] The server uses a translation API (for example, Google Translate API) to translate the generated product descriptions and promotional texts into English. The translated documents are then saved back into the database.

[0921] input:

[0922] Product description and promotional text in Japanese

[0923] output:

[0924] English product description and promotional text

[0925] Specific actions:

[0926] The server retrieves the Japanese product description and PR text generated from the database and sends them to the translation API.

[0927] The translation API translates the document into English and returns the result to the server.

[0928] The server saves the translation results to a database and notifies the user.

[0929] Step 4:

[0930] The server analyzes product images using an image recognition model (e.g., Google Cloud Vision API). Based on export restriction information from various countries, it detects whether specific materials or ingredients are present and determines whether export restrictions apply. If export restrictions exist, the user is notified as an alert.

[0931] input:

[0932] Product image

[0933] output:

[0934] Whether or not there are export restrictions

[0935] Specific actions:

[0936] The server sends product images to an image recognition model and retrieves the analysis results.

[0937] The server compares the analysis results with export restriction databases of various countries.

[0938] If export restrictions are detected, an alert message will be generated and the user will be notified.

[0939] Step 5:

[0940] The server uses a shipping information API (for example, the Shippo API) to obtain shipping costs for each country, calculates a recommended selling price by adding the base product price and service fee, and proposes it to the user.

[0941] input:

[0942] Target countries for sales

[0943] Basic product price

[0944] Service fee

[0945] output:

[0946] Recommended selling price

[0947] Specific actions:

[0948] The server calls the shipping information API to retrieve shipping costs for each target country.

[0949] Based on the acquired shipping information, the server calculates the recommended selling price by adding the base product price and service fee.

[0950] The system presents the calculation results to the user and proposes the optimal selling price.

[0951] Step 6:

[0952] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and then translates them into other languages ​​using a translation API before providing them to users.

[0953] input:

[0954] None (Uses an internally generated AI model)

[0955] output:

[0956] Legal policies in Japanese and English

[0957] Specific actions:

[0958] The server uses a generative AI model to generate standard legal policies.

[0959] The generated policy is sent to the translation API to retrieve the English version.

[0960] Provide users with both the Japanese and English versions and ask them to confirm their choice.

[0961] Step 7:

[0962] The server uses an SEO optimization model (for example, the Ahrefs API) to automatically generate the optimal SEO strategies for the user's e-commerce site. This optimizes the site for search engines.

[0963] input:

[0964] Current status data of e-commerce sites

[0965] output:

[0966] SEO Strategy Proposals

[0967] Specific actions:

[0968] The server sends current data from the user's e-commerce site to an SEO optimization model for analysis.

[0969] Based on the areas for improvement in SEO, we will generate specific action plans.

[0970] The system notifies users of the generated SEO strategies and provides support for their implementation.

[0971] (Application Example 1)

[0972] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0973] Japanese companies need a system that facilitates cross-border sales by efficiently handling complex procedures such as multilingual support, export restriction checks, and shipping cost calculations. However, existing systems struggle to provide all these functions in one place, resulting in a lot of manual work and making efficient operation difficult. Furthermore, an interface that can be easily operated from devices such as smartphones and head-mounted displays is necessary.

[0974] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0975] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts in natural language using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into multiple languages, means for automatically generating optimal SEO measures using an SEO optimization model, and means for providing each of the aforementioned means as an application that can be installed on a smartphone or head-mounted display. This makes it possible to conduct cross-border sales efficiently and easily.

[0976] "Product information" refers to basic information such as the product name, description, price, and product images.

[0977] A "generative AI model" is an artificial intelligence model that uses natural language processing to automatically generate product descriptions and promotional texts.

[0978] The "Translation API" is an application programming interface for translating generated product descriptions and promotional texts into multiple languages.

[0979] An "image recognition model" is an artificial intelligence model that analyzes product images and identifies specific features or materials.

[0980] "Export restrictions" refer to the prohibition or restriction of exporting certain goods or materials based on the laws and regulations of each country.

[0981] "Shipping cost calculation" is a method for calculating the cost of sending goods to a specific country or region.

[0982] "Recommended selling price" refers to the selling price which includes the base price of the product, shipping costs, and service fees.

[0983] A "legal policy" is a document necessary for compliance with laws and regulations, such as a privacy policy or terms of service.

[0984] An "SEO optimization model" is an artificial intelligence model that optimizes a website to rank higher in search engine results.

[0985] A "smartphone" is a high-functional mobile phone that allows internet access and the use of various applications.

[0986] A "head-mounted display (HMD)" is a display device that provides visual information when worn on the user's head.

[0987] An "application" is a software program that provides specific functions or services.

[0988] This invention is a system that supports the operation of e-commerce sites, and in particular, makes it easier for Japanese companies to conduct cross-border sales. The system of this invention uses a generative AI model to automatically generate product descriptions and promotional texts, and realizes multilingual support, export restriction checks, shipping cost calculations, automatic legal policy generation, SEO optimization, and more. This system starts with a means for the user to input product information and is provided as an application that can be installed on smartphones and head-mounted displays.

[0989] System Configuration

[0990] This system has the following configuration.

[0991] hardware

[0992] Smartphone: A high-functional mobile phone carried by a user.

[0993] Head-mounted display (HMD): A display device that provides visual information.

[0994] Server: As a central management system, it manages and executes various APIs and models, such as generation AI models, translation APIs, image recognition APIs, and shipping cost calculation APIs.

[0995] software

[0996] Generative AI model (GPT-based model): Automatically generates product descriptions and promotional texts.

[0997] Translation API (e.g., Google Translate API): Translates generated product descriptions and promotional texts into multiple languages.

[0998] Image recognition model: Analyzes product images to identify specific materials and features.

[0999] Shipping Cost Calculation API: Calculates shipping costs to various countries.

[1000] Legal Policy Generation Module: Automatically generates privacy policies, terms of service, etc.

[1001] SEO Optimization Model: Automatically generates SEO strategies for your website.

[1002] Processing details

[1003] Users can access the system using their smartphones or HMDs and input product information. For example, if a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the following procedure would be followed.

[1004] Generation using prompt statements

[1005] After the user enters product information (product name, description, price, and product image), the AI ​​model generates a product description and promotional text based on the following prompts.

[1006] Example of a prompt:

[1007] Product name: Japanese-style teacup

[1008] Product Description: This is a traditional Japanese-style teacup.

[1009] Please enter the PR statement to generate:

[1010] The generated product descriptions and promotional texts are automatically translated into English and other languages ​​using a translation API. Next, an image recognition model is used to analyze product images to determine if specific materials are included and to identify export restrictions in each country. This information is then communicated to the user as an alert.

[1011] The shipping cost calculation API calculates shipping costs to each country and suggests a recommended selling price. The calculation includes the base price of the product, shipping costs, and service fees. In addition, a legal policy generation module generates various legal policies and provides them in multiple languages. Finally, an SEO optimization model is used to automatically generate optimal SEO strategies for the website.

[1012] This enables users to conduct cross-border sales efficiently and easily, and to quickly prepare for multilingual support and regulatory compliance. Operation is possible from anywhere using smartphones or HMDs, greatly improving convenience.

[1013] As a concrete example, a user inputs product information for a "Japanese-style teacup," and the AI ​​model generates and translates a promotional text, followed by image recognition. This entire process allows for the rapid preparation of sales to the United States, the United Kingdom, and Australia. A key feature of this invention is that this entire process can be performed seamlessly on a smartphone or HMD (Head-Mounted Display).

[1014] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1015] Step 1:

[1016] Users access the system using a smartphone or head-mounted display (HMD) and enter product information (product name, description, price, and product image). This entered information is sent to the server.

[1017] Input: Product name, product description, price, product image

[1018] Output: Product information is saved on the server.

[1019] Step 2:

[1020] The server uses a generative AI model to generate product descriptions and promotional texts based on product information entered by the user. Prompt text is input into the generative AI model for natural language generation.

[1021] Input: Product name, product description, prompt text

[1022] "Product Name: Japanese-style Teacup\nProduct Description: A traditional Japanese-style teacup.\nPlease enter the promotional text to generate:"

[1023] Output: Generated product description and promotional text

[1024] Step 3:

[1025] The server sends the generated product description and promotional text to a translation API for translation into multiple languages. The translated text is stored on the server.

[1026] Input: Generated product description and PR text

[1027] Output: Translated product description and promotional text (e.g., English)

[1028] Step 4:

[1029] Users can view translated product descriptions and promotional texts on their devices. They can make corrections or additions as needed.

[1030] Input: Translated product description and promotional text

[1031] Output: User-confirmed text

[1032] Step 5:

[1033] The server uses an image recognition model to analyze product images and determine export restrictions in various countries. Based on the analysis results, it generates alerts regarding export restrictions.

[1034] Input: Product image

[1035] Output: Notice regarding export restrictions (e.g., "Export restrictions apply due to the inclusion of certain materials")

[1036] Step 6:

[1037] The server uses a shipping cost calculation API to calculate shipping costs to specified countries and suggests a recommended selling price. The data used includes product price, shipping costs, and service fees.

[1038] Input: Product price, shipping costs to each country, service fees

[1039] Output: Suggested selling price (Example: "The suggested selling price for the United States is $25.00")

[1040] Step 7:

[1041] The server uses a legal policy generation module to automatically generate various legal policies (such as privacy policies and terms of service) and translate them into multiple languages.

[1042] Input: Prompts based on a generated AI model

[1043] Output: Legal policy translated into multiple languages

[1044] Step 8:

[1045] The server uses an SEO optimization model to automatically generate SEO strategies for product pages, supporting optimal search engine optimization. The generated SEO strategies are provided to the user, who can review them.

[1046] Input: Product page information

[1047] Output: SEO-optimized webpage content

[1048] Step 9:

[1049] The above functions will be integrated into an application installed on smartphones and HMDs, providing users with access and control from anywhere. Users will be able to seamlessly utilize each function of the system within the application.

[1050] Input: Output results of each processing step

[1051] Output: Integrated application functionality is provided.

[1052] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1053] This invention is a system that supports the operation of e-commerce sites, and in particular, helps Japanese companies to easily conduct cross-border sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it provides a more advanced user experience.

[1054] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[1055] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[1056] Furthermore, the server uses an emotion engine to adjust the tone of product descriptions and promotional texts. This emotion engine analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (such as reassurance or excitement).

[1057] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[1058] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[1059] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[1060] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[1061] The emotion engine can further analyze user feedback and incorporate it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine can also detect user stress levels and provide relaxing content as needed.

[1062] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. By using an emotion engine to optimize the tone of the text and incorporating feedback into future generation, more accurate text is produced. Finally, an SEO-optimized website is completed, making cross-border sales easy to achieve.

[1063] As described above, the present invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, SEO measures, and user sentiment management.

[1064] The following describes the processing flow.

[1065] Step 1:

[1066] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[1067] Step 2:

[1068] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review.

[1069] Step 3:

[1070] The server translates the generated product description and promotional text into English using a translation API. The translated text is then provided to the user.

[1071] Step 4:

[1072] The server uses an emotion engine to adjust the tone of product descriptions and promotional texts. The emotion engine analyzes the emotional tone of the generated text and adjusts it to match the user's specific emotions.

[1073] Step 5:

[1074] The server uses an image recognition model to analyze product images uploaded by the user. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user will be notified.

[1075] Step 6:

[1076] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[1077] Step 7:

[1078] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[1079] Step 8:

[1080] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[1081] Step 9:

[1082] The emotion engine analyzes user feedback and incorporates it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine also detects user stress levels and provides relaxing content as needed.

[1083] Step 10:

[1084] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[1085] (Example 2)

[1086] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1087] Modern e-commerce sites, especially those involving cross-border sales, face numerous challenges, including multilingual support, export restriction checks, shipping cost calculations, legal compliance, search engine optimization (SEO), and tone adjustments that resonate with user emotions. Manually addressing these challenges is extremely time-consuming, labor-intensive, and prone to errors. Furthermore, reflecting user emotions and feedback requires advanced technology, highlighting the need for a system that efficiently integrates these elements.

[1088] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1089] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using translation means, means for analyzing product images using image recognition technology to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating and translating various regulatory documents into multiple languages, means for automatically generating optimal search engine optimization measures using optimization technology, means for adjusting the emotional tone of product descriptions and promotional texts using sentiment estimation technology, and means for analyzing user feedback and reflecting it in the next generation process. As a result, the user can centrally automate all processes for conducting cross-border sales efficiently and effectively, improving accuracy and efficiency.

[1090] A "user" is a person or organization that accesses the administration screen of an e-commerce site, enters product information, and manages it.

[1091] "Product information" refers to basic information such as the product name, description, price, and product images.

[1092] A "generative AI model" is an artificial intelligence technology that uses natural language processing techniques to automatically generate product descriptions and promotional texts from input data.

[1093] "Translation means" refers to technologies for translating generated text into multiple languages, such as translation APIs.

[1094] "Image recognition technology" is a technology that analyzes product images, extracts specific features from the images, and uses that information to make judgments.

[1095] "Export restrictions" refer to conditions under which the materials or shape of specific goods are constrained by regulations in each country.

[1096] "Recommended selling price" refers to the selling price of a product that includes shipping costs and service fees to each country.

[1097] "Various regulatory documents" refer to legal documents such as privacy policies and terms of service, which are necessary for compliance with laws and regulations.

[1098] "Optimization techniques" refer to a series of techniques and methods aimed at search engine optimization (SEO).

[1099] "Emotion estimation technology" is a technique that analyzes a user's emotions from text and feedback and adjusts the tone accordingly.

[1100] "Feedback" refers to the evaluations and opinions that users provide regarding generated content.

[1101] Modes for carrying out the invention

[1102] This invention is a system for streamlining the operation of e-commerce (EC) sites, and in particular, a system that supports cross-border sales. This system automates multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management by combining multiple technologies such as generative AI models, translation methods, image recognition technology, optimization technology, and sentiment estimation technology.

[1103] First, the user accesses the e-commerce site using their device and enters product information. This product information includes the product name, description, price, and product image. They can also specify the target countries for sales. For example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters the product information and selects the target countries.

[1104] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts from the input product information. A specific example of a generative AI model used here is OpenAI's GPT-4. For example, the prompt input might be, "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." This generative AI model generates detailed product descriptions and promotional texts in Japanese for the specified product.

[1105] The generated Japanese text will be translated into multiple languages ​​using translation tools. Specifically, Google's translation API is likely to be used. The server will translate the generated Japanese description and PR text into English using the translation API and save the results.

[1106] The server also uses image recognition technology to analyze product images and determine export restrictions in various countries. For example, using TensorFlow, it can extract features from product images and compare them with an export restriction list to determine whether the product violates regulations in any country. The results are notified to the user as alerts as needed.

[1107] The server then calls an external shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves shipping costs for different countries, adds the base product price and service fee, and calculates and suggests a recommended selling price. This allows the user to set an accurate selling price.

[1108] In addition, the server automatically generates various regulatory documents (e.g., privacy policies and terms of service) using a generation AI model. These documents are translated into multiple languages ​​using translation tools and provided to users for legal compliance purposes.

[1109] Furthermore, the server uses optimization technology to automatically generate SEO strategies for e-commerce sites. For example, it utilizes a model like Yoast SEO to analyze the entered product information and translated text, generating optimal SEO settings. This helps the user's e-commerce site rank higher in search engine results.

[1110] Finally, the server adjusts the emotional tone of the generated text using emotion estimation technology. This technology analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (e.g., reassurance or excitement). Furthermore, by analyzing user feedback with emotion estimation technology and incorporating it into the next text generation process, it becomes possible to provide higher quality content.

[1111] In this way, the system of the present invention becomes a powerful tool for users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management.

[1112] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1113] Step 1:

[1114] The user accesses the e-commerce site using their device and enters product information. This information includes the product name, description, price, and product image. They also select the target countries for sales. The entered data is sent to the server. The device performs the process of entering product information and target countries for sales and sending them to the server.

[1115] Step 2:

[1116] The server automatically generates product descriptions and promotional texts using a generative AI model based on the received product information. Specifically, the server inputs the product information as a prompt to the generative AI model and retrieves the text generated by the model. For example, the prompt might be "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." The input data consists of product information and the specified target markets, while the output data consists of the automatically generated product descriptions and promotional texts.

[1117] Step 3:

[1118] The server translates the generated product description and promotional text into English using a translation tool. Specifically, the server sends the generated Japanese text to a translation API and retrieves the translated English text. For example, it uses the Google Translate API. The input data is the generated Japanese text, and the output data is the translated English text.

[1119] Step 4:

[1120] The server uses sentiment estimation technology to analyze the emotional tone of the generated text and adjust it to an appropriate tone. Specifically, the server uses a sentiment analysis model to analyze the generated text and adjust the tone to correspond to the user's specific emotion. The input data consists of the generated Japanese and English text, and the output data consists of the adjusted text.

[1121] Step 5:

[1122] The server uses image recognition technology to analyze product images and determine export restrictions in various countries. Specifically, the server uses image recognition technology such as TensorFlow to analyze product images and evaluate whether the materials and shape comply with export restrictions in each country. The input data is the product image, and the output data is the result of the export restriction evaluation.

[1123] Step 6:

[1124] The server uses an external shipping information API to calculate shipping costs for each country and propose a recommended selling price. Specifically, the server calls the shipping information API for each target country to obtain shipping information. Based on the obtained shipping information, it adds it to the base product price to calculate the recommended selling price. The input data is the target country and product price, and the output data is the recommended selling price.

[1125] Step 7:

[1126] The server automatically generates various rule documents using a generative AI model and translates them into multiple languages. Specifically, the server uses the generative AI model to generate documents such as privacy policies and terms of service, and then translates them into English using a translation API. The input data consists of prompts from the generative AI model, and the output data consists of the generated rule documents and their translations.

[1127] Step 8:

[1128] The server automatically generates SEO strategies for e-commerce sites using optimization technology. Specifically, the server uses an SEO optimization model to analyze product information and translated text, and generates optimal SEO settings. The input data consists of product information and translated text, while the output data consists of SEO settings.

[1129] Step 9:

[1130] The server uses sentiment estimation technology to analyze user feedback and incorporate it into the next generation process. Specifically, the server collects user feedback and analyzes it using a sentiment analysis model. Based on the analysis results, it incorporates them into the next text generation. The input data is user feedback, and the output data is the analysis results and how they are applied.

[1131] (Application Example 2)

[1132] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1133] Traditional e-commerce site management systems require operators to individually handle a wide range of tasks, including product information entry, generation of product descriptions and promotional texts, translation, export restriction assessment, shipping cost calculation, legal policy generation, and SEO optimization. This requires significant effort and time from the operator. Furthermore, they often lack consideration for adjusting emotional tone and incorporating user feedback, making it difficult to improve the user experience.

[1134] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and PR texts in Japanese using a generation AI model, means for translating the generated product descriptions and PR texts into other languages ​​using a translation API, means for adjusting the emotional tone of the product descriptions and PR texts using an emotion engine, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into other languages ​​using a translation API, means for automatically generating optimal SEO measures using an SEO optimization model, and an emotion engine that analyzes user feedback and reflects it in the next generation process. This makes it possible to centralize a wide range of tasks in e-commerce site operation and perform them efficiently and quickly. Furthermore, by adjusting the emotional tone of the generated texts and reflecting user feedback, an improvement in the user experience can be expected.

[1135] "A means for users to input product information" refers to an interface for users to input information related to a product, such as its name, description, price, and images.

[1136] "A method for automatically generating product descriptions and promotional texts in Japanese using a generative AI model" refers to a system that uses a machine learning algorithm to automatically generate product descriptions and promotional texts in Japanese based on the input product information.

[1137] "Means of translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of converting generated Japanese text into other languages ​​(such as English) using existing translation software or services.

[1138] "A means of adjusting the emotional tone of product descriptions and PR texts using an emotion engine" refers to a system that analyzes the emotional tone (such as reassurance or excitement) of generated text and adjusts it as needed.

[1139] "A means of analyzing product images using an image recognition model to determine export restrictions in various countries" refers to a system that analyzes product images using machine learning algorithms to determine whether specific materials or designs fall under export restrictions in various countries.

[1140] The "method for calculating shipping costs to each country and suggesting a recommended selling price" is a process that uses an external shipping information API to obtain shipping costs for each country and reflects them in the product pricing.

[1141] "A method for automatically generating various legal policies and translating them into other languages ​​using a translation API" refers to a system that uses a generation AI model to automatically generate legal documents such as privacy policies and terms of service, and then translates them into other languages.

[1142] "A method for automatically generating optimal SEO measures using an SEO optimization model" refers to a system that automatically proposes and implements measures to improve the search engine ranking of e-commerce sites using search engine optimization techniques.

[1143] A "sentiment engine that analyzes user feedback and reflects it in the next generation process" is a system that analyzes user feedback and reflects the results of that analysis in the generation of future product descriptions and promotional texts.

[1144] The "means of notifying users of export restriction alerts from various countries" are systems that quickly inform users when materials or designs subject to export restrictions are detected.

[1145] This invention is a support system for e-commerce site operators to efficiently conduct cross-border sales of products. In particular, it combines a generative AI model and an emotion engine to support multiple languages ​​and improve the user experience. The system of this invention is configured as follows.

[1146] System Program

[1147] User Interface (UI)

[1148] Users utilize a form to input product information from their smartphones or other devices. This form has fields for easily entering product name, description, price, images, and other information.

[1149] Data processing and generation

[1150] The server receives product information entered by the user and automatically generates product descriptions and promotional texts using a generative AI model (e.g., OpenAI's GPT-4). These generated texts are then translated into other languages ​​(e.g., English) using a translation API.

[1151] Emotional tone adjustment

[1152] The generated product descriptions and promotional texts are analyzed by an emotion engine and adjusted to match pre-set emotional tones (such as reassurance or excitement).

[1153] Image recognition and export restriction determination

[1154] Product images are analyzed using image recognition models (e.g., TensorFlow or PyTorch) to determine whether they fall under export restrictions in various countries. The results are then notified to the user as an alert from the server.

[1155] Shipping cost calculation and suggested selling price

[1156] Shipping cost information for each country is obtained via an external shipping cost information API (e.g., EasyPost API). The server calculates the shipping cost to each country based on this information, adds it to the product price, and proposes a recommended selling price.

[1157] Automated generation of legal policies

[1158] Using a generative AI model, legal policy documents such as privacy policies and terms of service are automatically generated, and these are then translated into other languages ​​using a translation API. This allows for the rapid creation of compliance documents.

[1159] SEO measures

[1160] Using an SEO optimization model, the system automatically suggests and implements SEO measures for product pages. This improves the search engine rankings of e-commerce sites.

[1161] Analysis and implementation of user feedback

[1162] The emotion engine analyzes user feedback and incorporates the results into the generation process for future product descriptions and promotional texts. This results in text that more closely matches user expectations.

[1163] Hardware and software

[1164] Hardware: Smartphones, servers, user terminals

[1165] Software: Generative AI models (e.g., GPT-4), image recognition models (e.g., TensorFlow, PyTorch), translation APIs, shipping information APIs (e.g., EasyPost API), SEO optimization models (e.g., Yoast SEO)

[1166] Specific example

[1167] If a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the user enters product information and selects target countries. The server generates and translates product descriptions and promotional texts. Next, it analyzes product images to notify the user of any export restrictions, calculates shipping costs for each country, and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. The tone of the text is optimized using an emotion engine, and feedback is incorporated into future updates.

[1168] Example of a prompt

[1169] Product description generation:

[1170] "We want to sell Japanese-style teacups incorporating traditional Japanese designs. Please generate a product description to match."

[1171] Emotional tone adjustment:

[1172] Please adjust the following sentence to convey a sense of reassurance: "Enrich your daily tea time with high-quality Japanese-style teacups."

[1173] Legal policy generation:

[1174] "Please generate a privacy policy for our e-commerce site in both Japanese and English."

[1175] This system enables e-commerce site operators to conduct cross-border sales efficiently and quickly.

[1176] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1177] Step 1:

[1178] The user inputs product information using a terminal. This product information includes the product name, description, price, and product image. The entered data is sent to the server and used in the next processing step.

[1179] Input: Product name, description, price, and product image

[1180] Output: Product information sent to the server

[1181] Step 2:

[1182] The server uses a generative AI model (e.g., OpenAI's GPT-4) to automatically generate product descriptions and promotional texts in Japanese from product information entered by the user. Specific data such as product names and features are input into the model for the generation process.

[1183] Input: Product Information

[1184] Output: Generated product description and promotional text

[1185] Step 3:

[1186] Next, the server uses a translation API to translate the generated product descriptions and promotional texts into other languages, such as English. The translated texts are temporarily stored on the server and presented to the user in a format they can review.

[1187] Input: Generated product description and PR text (in Japanese)

[1188] Output: Generated product description and PR text (in other languages)

[1189] Step 4:

[1190] The server uses an emotion engine to analyze the emotional tone of the generated product descriptions and promotional texts and adjusts them to match the target emotions of the user.

[1191] Input: Generated product description and PR text (in Japanese and other languages)

[1192] Output: Product description and promotional text with adjusted emotional tone.

[1193] Step 5:

[1194] The server uses an image recognition model (e.g., TensorFlow or PyTorch) to analyze product images and determine export restrictions in each country. This analysis verifies whether the product contains materials or designs subject to export restrictions.

[1195] Input: Product image

[1196] Output: Alerts regarding export restrictions in various countries

[1197] Step 6:

[1198] The server uses an external shipping cost information API (e.g., EasyPost API) to calculate shipping costs to each country. Based on the shipping cost information, a recommended selling price is suggested, which is added to the base product price.

[1199] Input: Product information, shipping information for each country

[1200] Output: Recommended selling price

[1201] Step 7:

[1202] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generative AI model, and then translates them into other languages ​​using a translation API. The generated legal policy documents are then provided to the user.

[1203] Input: None (automatically generated)

[1204] Output: Various legal policy documents (in Japanese and other languages)

[1205] Step 8:

[1206] Using an SEO optimization model, the server automatically generates SEO strategies for product pages. Optimal keywords and metadata are added, improving search engine rankings.

[1207] Input: Product page information

[1208] Output: Page data with SEO optimization applied.

[1209] Step 9:

[1210] The server analyzes user feedback using an emotion engine and incorporates the results into the process of generating future product descriptions and promotional texts. This feedback is used to improve the accuracy of text generation.

[1211] Input: User Feedback

[1212] Output: Feedback results, to be reflected in the next generation process.

[1213] This process allows users to efficiently and quickly input product information and generate product descriptions and promotional texts with an emotional tone appropriate to the target market. Furthermore, SEO measures and legal policy preparation are automated, making cross-border sales easier.

[1214] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1215] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1217] [Fourth Embodiment]

[1218] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1219] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1221] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1225] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1226] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1229] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1230] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1231] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[1232] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[1233] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[1234] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[1235] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[1236] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[1237] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[1238] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[1239] As described above, the present invention enables users to automate tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures, allowing them to efficiently conduct cross-border sales.

[1240] The following describes the processing flow.

[1241] Step 1:

[1242] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[1243] Step 2:

[1244] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated texts are temporarily saved so that the user can review them later.

[1245] Step 3:

[1246] The server translates the generated product description and PR text into English using a translation API. The translation results are then provided to the user.

[1247] Step 4:

[1248] The server uses an image recognition model to analyze product images uploaded by users. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user is notified.

[1249] Step 5:

[1250] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[1251] Step 6:

[1252] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[1253] Step 7:

[1254] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[1255] Step 8:

[1256] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[1257] (Example 1)

[1258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1259] Currently, many Japanese companies are attempting to sell their products to overseas markets through e-commerce sites, but they face numerous challenges, including language barriers, export restrictions, shipping cost calculations, legal policy preparation, and SEO measures. These challenges reduce the efficiency of cross-border sales, making it difficult to expand sales. Conventional technologies lack a system that automates these challenges, requiring a great deal of manual work. This invention solves these problems and provides a system that enables companies to conduct cross-border sales efficiently.

[1260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1261] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into other languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions, means for calculating shipping costs to each country using a shipping information API and proposing a recommended selling price, means for automatically generating legal policies and translating them into other languages, means for automatically generating SEO measures using an SEO optimization model, means for inputting prompt texts, means for saving the documents generated by the generation AI model to a database, and means for notifying the user of alerts. This enables the automation of these processes in a single step, making it possible to conduct cross-border sales efficiently.

[1262] "Means for users to input product information" refers to an interface that allows users to input basic information such as the product name, description, price, and product images using a device.

[1263] "Means for automatically generating product descriptions and promotional texts using a generative AI model" refers to a processing device that uses a generative AI model to automatically generate product descriptions and promotional texts from product information entered by a user.

[1264] "Means for translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of translating product descriptions and PR texts generated by a generative AI model into other languages ​​using a translation API.

[1265] "A means of analyzing product images using an image recognition model to determine export restrictions" refers to an algorithm that uses an image recognition model to analyze product images and makes decisions based on information regarding export restrictions in various countries.

[1266] The "method for calculating shipping costs to various countries and proposing recommended selling prices using a shipping information API" is a system that uses a shipping information API to obtain shipping costs to various countries, and then calculates and proposes a recommended selling price by adding the base product price and service usage fee.

[1267] The "means for automatically generating legal policies and translating them into other languages" refer to a function that automatically generates various legal policies using a generation AI model and then translates them into other languages ​​using a translation API.

[1268] "A method for automatically generating SEO measures using an SEO optimization model" refers to a system that uses an SEO optimization model to automatically generate optimal SEO measures for e-commerce sites.

[1269] "Means for inputting prompt text" refers to input methods for providing product information and generation conditions to the generation AI model.

[1270] "Means for saving documents generated by a generative AI model to a database" refers to a database and its management system for temporarily storing documents generated by a generative AI model.

[1271] "Means of notifying users of alerts" refers to a notification system that alerts users about export restrictions and other important information.

[1272] This invention is a system that supports the operation of e-commerce sites, and in particular, it helps Japanese companies to easily conduct cross-border sales. Specific embodiments for carrying out this invention are described below.

[1273] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[1274] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are temporarily stored in a database for user review. Specific examples of prompts to be input into this generative AI model are as follows:

[1275] Product Description Generation Prompt

[1276] Product name: Japanese-style teacup

[1277] Price: 3000 yen

[1278] Description: A beautiful Japanese-style teacup made of high-quality ceramic. Features a handcrafted pattern.

[1279] Based on this information, please generate a product description for sale within Japan.

[1280] PR statement generation prompt

[1281] Product name: Japanese-style teacup

[1282] Target countries: United States, United Kingdom, Australia

[1283] Please generate advertising copy for this product, tailored to each country.

[1284] The generated product descriptions and promotional texts are translated into English using a translation API (e.g., Google Translate API) and then provided to the user again.

[1285] Furthermore, the server uses an image recognition model (e.g., Google Cloud Vision API) to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is then notified to the user as an alert.

[1286] The server then uses a shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves the shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[1287] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[1288] Furthermore, the server uses an SEO optimization model (e.g., the Ahrefs API) to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[1289] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. Finally, an SEO-optimized website is completed, making cross-border sales easy.

[1290] This invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, and SEO measures.

[1291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1292] Step 1:

[1293] Users access the e-commerce site using their devices and enter basic information such as the product name, description, price, and product images. They can also specify which countries the product will be sold in.

[1294] input:

[1295] Product name

[1296] Product Description

[1297] Product price

[1298] Product image

[1299] Target countries for sales

[1300] output:

[1301] The entered product information is sent to the server.

[1302] Specific actions:

[1303] The user opens a browser and logs into the e-commerce site's administration panel.

[1304] Enter the product information (product name: Japanese-style teacup, product price: 3000 yen, product description: features a handmade pattern, product image: image file) and select the target countries for sale (USA, UK, Australia).

[1305] Step 2:

[1306] The server uses a generative AI model (e.g., OpenAI GPT-4) to automatically generate product descriptions and promotional texts from the input product information. The generated product descriptions and promotional texts are then stored in a database.

[1307] input:

[1308] Product information

[1309] output:

[1310] Product description and promotional text in Japanese

[1311] Specific actions:

[1312] The server inputs information such as product name, product description, and product price as prompts into the AI ​​model that generates the product.

[1313] The generative AI model generates product descriptions and promotional texts in Japanese and stores the content in a database.

[1314] Step 3:

[1315] The server uses a translation API (for example, Google Translate API) to translate the generated product descriptions and promotional texts into English. The translated documents are then saved back into the database.

[1316] input:

[1317] Product description and promotional text in Japanese

[1318] output:

[1319] English product description and promotional text

[1320] Specific actions:

[1321] The server retrieves the Japanese product description and PR text generated from the database and sends them to the translation API.

[1322] The translation API translates the document into English and returns the result to the server.

[1323] The server saves the translation results to a database and notifies the user.

[1324] Step 4:

[1325] The server analyzes product images using an image recognition model (e.g., Google Cloud Vision API). Based on export restriction information from various countries, it detects whether specific materials or ingredients are present and determines whether export restrictions apply. If export restrictions exist, the user is notified as an alert.

[1326] input:

[1327] Product image

[1328] output:

[1329] Whether or not there are export restrictions

[1330] Specific actions:

[1331] The server sends product images to an image recognition model and retrieves the analysis results.

[1332] The server compares the analysis results with export restriction databases of various countries.

[1333] If export restrictions are detected, an alert message will be generated and the user will be notified.

[1334] Step 5:

[1335] The server uses a shipping information API (for example, the Shippo API) to obtain shipping costs for each country, calculates a recommended selling price by adding the base product price and service fee, and proposes it to the user.

[1336] input:

[1337] Target countries for sales

[1338] Basic product price

[1339] Service fee

[1340] output:

[1341] Recommended selling price

[1342] Specific actions:

[1343] The server calls the shipping information API to retrieve shipping costs for each target country.

[1344] Based on the acquired shipping information, the server calculates the recommended selling price by adding the base product price and service fee.

[1345] The system presents the calculation results to the user and proposes the optimal selling price.

[1346] Step 6:

[1347] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and then translates them into other languages ​​using a translation API before providing them to users.

[1348] input:

[1349] None (Uses an internally generated AI model)

[1350] output:

[1351] Legal policies in Japanese and English

[1352] Specific actions:

[1353] The server uses a generative AI model to generate standard legal policies.

[1354] The generated policy is sent to the translation API to retrieve the English version.

[1355] Provide users with both the Japanese and English versions and ask them to confirm their choice.

[1356] Step 7:

[1357] The server uses an SEO optimization model (for example, the Ahrefs API) to automatically generate the optimal SEO strategies for the user's e-commerce site. This optimizes the site for search engines.

[1358] input:

[1359] Current status data of e-commerce sites

[1360] output:

[1361] SEO Strategy Proposals

[1362] Specific actions:

[1363] The server sends current data from the user's e-commerce site to an SEO optimization model for analysis.

[1364] Based on the areas for improvement in SEO, we will generate specific action plans.

[1365] The system notifies users of the generated SEO strategies and provides support for their implementation.

[1366] (Application Example 1)

[1367] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1368] Japanese companies need a system that facilitates cross-border sales by efficiently handling complex procedures such as multilingual support, export restriction checks, and shipping cost calculations. However, existing systems struggle to provide all these functions in one place, resulting in a lot of manual work and making efficient operation difficult. Furthermore, an interface that can be easily operated from devices such as smartphones and head-mounted displays is necessary.

[1369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1370] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts in natural language using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using a translation API, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into multiple languages, means for automatically generating optimal SEO measures using an SEO optimization model, and means for providing each of the aforementioned means as an application that can be installed on a smartphone or head-mounted display. This makes it possible to conduct cross-border sales efficiently and easily.

[1371] "Product information" refers to basic information such as the product name, description, price, and product images.

[1372] A "generative AI model" is an artificial intelligence model that uses natural language processing to automatically generate product descriptions and promotional texts.

[1373] The "Translation API" is an application programming interface for translating generated product descriptions and promotional texts into multiple languages.

[1374] An "image recognition model" is an artificial intelligence model that analyzes product images and identifies specific features or materials.

[1375] "Export restrictions" refer to the prohibition or restriction of exporting certain goods or materials based on the laws and regulations of each country.

[1376] "Shipping cost calculation" is a method for calculating the cost of sending goods to a specific country or region.

[1377] "Recommended selling price" refers to the selling price which includes the base price of the product, shipping costs, and service fees.

[1378] A "legal policy" is a document necessary for compliance with laws and regulations, such as a privacy policy or terms of service.

[1379] An "SEO optimization model" is an artificial intelligence model that optimizes a website to rank higher in search engine results.

[1380] A "smartphone" is a high-functional mobile phone that allows internet access and the use of various applications.

[1381] A "head-mounted display (HMD)" is a display device that provides visual information when worn on the user's head.

[1382] An "application" is a software program that provides specific functions or services.

[1383] This invention is a system that supports the operation of e-commerce sites, and in particular, makes it easier for Japanese companies to conduct cross-border sales. The system of this invention uses a generative AI model to automatically generate product descriptions and promotional texts, and realizes multilingual support, export restriction checks, shipping cost calculations, automatic legal policy generation, SEO optimization, and more. This system starts with a means for the user to input product information and is provided as an application that can be installed on smartphones and head-mounted displays.

[1384] System Configuration

[1385] This system has the following configuration.

[1386] hardware

[1387] Smartphone: A high-functional mobile phone carried by a user.

[1388] Head-mounted display (HMD): A display device that provides visual information.

[1389] Server: As a central management system, it manages and executes various APIs and models, such as generation AI models, translation APIs, image recognition APIs, and shipping cost calculation APIs.

[1390] software

[1391] Generative AI model (GPT-based model): Automatically generates product descriptions and promotional texts.

[1392] Translation API (e.g., Google Translate API): Translates generated product descriptions and promotional texts into multiple languages.

[1393] Image recognition model: Analyzes product images to identify specific materials and features.

[1394] Shipping Cost Calculation API: Calculates shipping costs to various countries.

[1395] Legal Policy Generation Module: Automatically generates privacy policies, terms of service, etc.

[1396] SEO Optimization Model: Automatically generates SEO strategies for your website.

[1397] Processing details

[1398] Users can access the system using their smartphones or HMDs and input product information. For example, if a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the following procedure would be followed.

[1399] Generation using prompt statements

[1400] After the user enters product information (product name, description, price, and product image), the AI ​​model generates a product description and promotional text based on the following prompts.

[1401] Example of a prompt:

[1402] Product name: Japanese-style teacup

[1403] Product Description: This is a traditional Japanese-style teacup.

[1404] Please enter the PR statement to generate:

[1405] The generated product descriptions and promotional texts are automatically translated into English and other languages ​​using a translation API. Next, an image recognition model is used to analyze product images to determine if specific materials are included and to identify export restrictions in each country. This information is then communicated to the user as an alert.

[1406] The shipping cost calculation API calculates shipping costs to each country and suggests a recommended selling price. The calculation includes the base price of the product, shipping costs, and service fees. In addition, a legal policy generation module generates various legal policies and provides them in multiple languages. Finally, an SEO optimization model is used to automatically generate optimal SEO strategies for the website.

[1407] This enables users to conduct cross-border sales efficiently and easily, and to quickly prepare for multilingual support and regulatory compliance. Operation is possible from anywhere using smartphones or HMDs, greatly improving convenience.

[1408] As a concrete example, a user inputs product information for a "Japanese-style teacup," and the AI ​​model generates and translates a promotional text, followed by image recognition. This entire process allows for the rapid preparation of sales to the United States, the United Kingdom, and Australia. A key feature of this invention is that this entire process can be performed seamlessly on a smartphone or HMD (Head-Mounted Display).

[1409] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1410] Step 1:

[1411] Users access the system using a smartphone or head-mounted display (HMD) and enter product information (product name, description, price, and product image). This entered information is sent to the server.

[1412] Input: Product name, product description, price, product image

[1413] Output: Product information is saved on the server.

[1414] Step 2:

[1415] The server uses a generative AI model to generate product descriptions and promotional texts based on product information entered by the user. Prompt text is input into the generative AI model for natural language generation.

[1416] Input: Product name, product description, prompt text

[1417] "Product Name: Japanese-style Teacup\nProduct Description: A traditional Japanese-style teacup.\nPlease enter the promotional text to generate:"

[1418] Output: Generated product description and promotional text

[1419] Step 3:

[1420] The server sends the generated product description and promotional text to a translation API for translation into multiple languages. The translated text is stored on the server.

[1421] Input: Generated product description and PR text

[1422] Output: Translated product description and promotional text (e.g., English)

[1423] Step 4:

[1424] Users can view translated product descriptions and promotional texts on their devices. They can make corrections or additions as needed.

[1425] Input: Translated product description and promotional text

[1426] Output: User-confirmed text

[1427] Step 5:

[1428] The server uses an image recognition model to analyze product images and determine export restrictions in various countries. Based on the analysis results, it generates alerts regarding export restrictions.

[1429] Input: Product image

[1430] Output: Notice regarding export restrictions (e.g., "Export restrictions apply due to the inclusion of certain materials")

[1431] Step 6:

[1432] The server uses a shipping cost calculation API to calculate shipping costs to specified countries and suggests a recommended selling price. The data used includes product price, shipping costs, and service fees.

[1433] Input: Product price, shipping costs to each country, service fees

[1434] Output: Suggested selling price (Example: "The suggested selling price for the United States is $25.00")

[1435] Step 7:

[1436] The server uses a legal policy generation module to automatically generate various legal policies (such as privacy policies and terms of service) and translate them into multiple languages.

[1437] Input: Prompts based on a generated AI model

[1438] Output: Legal policy translated into multiple languages

[1439] Step 8:

[1440] The server uses an SEO optimization model to automatically generate SEO strategies for product pages, supporting optimal search engine optimization. The generated SEO strategies are provided to the user, who can review them.

[1441] Input: Product page information

[1442] Output: SEO-optimized webpage content

[1443] Step 9:

[1444] The above functions will be integrated into an application installed on smartphones and HMDs, providing users with access and control from anywhere. Users will be able to seamlessly utilize each function of the system within the application.

[1445] Input: Output results of each processing step

[1446] Output: Integrated application functionality is provided.

[1447] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1448] This invention is a system that supports the operation of e-commerce sites, and in particular, helps Japanese companies to easily conduct cross-border sales. Furthermore, by combining it with an emotion engine that recognizes user emotions, it provides a more advanced user experience.

[1449] To use this system, users first access the e-commerce site using their terminal and enter product information. This information includes basic details such as the product name, description, price, and product images. Furthermore, they can specify which countries the product will be sold in.

[1450] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts in Japanese from the input product information. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review, and the user can check their contents. The generated texts are also translated into English by the server using a translation API and provided to the user again.

[1451] Furthermore, the server uses an emotion engine to adjust the tone of product descriptions and promotional texts. This emotion engine analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (such as reassurance or excitement).

[1452] Furthermore, the server uses an image recognition model to analyze product images and determine export restrictions in each country. For example, if a product contains a specific material, it checks whether that material is subject to export restrictions. Information regarding export restrictions in each country is notified to the user as an alert.

[1453] The server then uses an external shipping information API to calculate shipping costs to each country. It retrieves shipping costs for each country, adds the base product price and service fee, and proposes a recommended selling price. This allows the user to set an accurate selling price.

[1454] Furthermore, the server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generation AI model, and provides them translated into Japanese and English. This allows users to quickly prepare for legal compliance.

[1455] Furthermore, the server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' e-commerce sites rank higher in search engine results.

[1456] The emotion engine can further analyze user feedback and incorporate it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine can also detect user stress levels and provide relaxing content as needed.

[1457] As a concrete example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters product information and selects target countries. The server generates product descriptions and promotional texts, and translates them into English. Next, it analyzes product images and notifies the user of any export restrictions to the United States. Furthermore, it calculates shipping costs for each country and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. By using an emotion engine to optimize the tone of the text and incorporating feedback into future generation, more accurate text is produced. Finally, an SEO-optimized website is completed, making cross-border sales easy to achieve.

[1458] As described above, the present invention enables users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal policy preparation, SEO measures, and user sentiment management.

[1459] The following describes the processing flow.

[1460] Step 1:

[1461] The user accesses the input form on the e-commerce site using their device and enters product information (name, description, price, image). At the same time, they select the target country for which the product will be sold.

[1462] Step 2:

[1463] The server uses an AI model to automatically generate product descriptions and promotional texts in Japanese from the product information entered by the user. The generated product descriptions and promotional texts are temporarily stored in a format that the user can review.

[1464] Step 3:

[1465] The server translates the generated product description and promotional text into English using a translation API. The translated text is then provided to the user.

[1466] Step 4:

[1467] The server uses an emotion engine to adjust the tone of product descriptions and promotional texts. The emotion engine analyzes the emotional tone of the generated text and adjusts it to match the user's specific emotions.

[1468] Step 5:

[1469] The server uses an image recognition model to analyze product images uploaded by the user. It detects specific materials and features in the images and checks them against export restriction information from various countries. If an alert regarding export restrictions from a country is necessary, the user will be notified.

[1470] Step 6:

[1471] The server uses a shipping information API to calculate shipping costs to each target country. The calculated shipping costs are added to the base product price and service fees to suggest a recommended selling price. The user can then set the optimal selling price based on this suggestion.

[1472] Step 7:

[1473] The server uses an AI model to automatically generate various legal policies, such as privacy policies and terms of service. The generated legal policies are translated into English using a translation API and provided to the user.

[1474] Step 8:

[1475] The server uses an SEO optimization model to automatically generate optimal SEO strategies for e-commerce sites. This helps users' sites rank higher in search engine results.

[1476] Step 9:

[1477] The emotion engine analyzes user feedback and incorporates it into the next generation process. This ensures that product descriptions and promotional texts meet user expectations. The emotion engine also detects user stress levels and provides relaxing content as needed.

[1478] Step 10:

[1479] The user performs a final review and modifies the generated text and alerts as needed. Once all information is verified, the user can begin selling the product.

[1480] (Example 2)

[1481] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1482] Modern e-commerce sites, especially those involving cross-border sales, face numerous challenges, including multilingual support, export restriction checks, shipping cost calculations, legal compliance, search engine optimization (SEO), and tone adjustments that resonate with user emotions. Manually addressing these challenges is extremely time-consuming, labor-intensive, and prone to errors. Furthermore, reflecting user emotions and feedback requires advanced technology, highlighting the need for a system that efficiently integrates these elements.

[1483] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1484] In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and promotional texts using a generation AI model, means for translating the generated product descriptions and promotional texts into multiple languages ​​using translation means, means for analyzing product images using image recognition technology to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating and translating various regulatory documents into multiple languages, means for automatically generating optimal search engine optimization measures using optimization technology, means for adjusting the emotional tone of product descriptions and promotional texts using sentiment estimation technology, and means for analyzing user feedback and reflecting it in the next generation process. As a result, the user can centrally automate all processes for conducting cross-border sales efficiently and effectively, improving accuracy and efficiency.

[1485] A "user" is a person or organization that accesses the administration screen of an e-commerce site, enters product information, and manages it.

[1486] "Product information" refers to basic information such as the product name, description, price, and product images.

[1487] A "generative AI model" is an artificial intelligence technology that uses natural language processing techniques to automatically generate product descriptions and promotional texts from input data.

[1488] "Translation means" refers to technologies for translating generated text into multiple languages, such as translation APIs.

[1489] "Image recognition technology" is a technology that analyzes product images, extracts specific features from the images, and uses that information to make judgments.

[1490] "Export restrictions" refer to conditions under which the materials or shape of specific goods are constrained by regulations in each country.

[1491] "Recommended selling price" refers to the selling price of a product that includes shipping costs and service fees to each country.

[1492] "Various regulatory documents" refer to legal documents such as privacy policies and terms of service, which are necessary for compliance with laws and regulations.

[1493] "Optimization techniques" refer to a series of techniques and methods aimed at search engine optimization (SEO).

[1494] "Emotion estimation technology" is a technique that analyzes a user's emotions from text and feedback and adjusts the tone accordingly.

[1495] "Feedback" refers to the evaluations and opinions that users provide regarding generated content.

[1496] Modes for carrying out the invention

[1497] This invention is a system for streamlining the operation of e-commerce (EC) sites, and in particular, a system that supports cross-border sales. This system automates multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management by combining multiple technologies such as generative AI models, translation methods, image recognition technology, optimization technology, and sentiment estimation technology.

[1498] First, the user accesses the e-commerce site using their device and enters product information. This product information includes the product name, description, price, and product image. They can also specify the target countries for sales. For example, consider a user who wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia. The user enters the product information and selects the target countries.

[1499] Next, the server uses a generative AI model to automatically generate product descriptions and promotional texts from the input product information. A specific example of a generative AI model used here is OpenAI's GPT-4. For example, the prompt input might be, "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." This generative AI model generates detailed product descriptions and promotional texts in Japanese for the specified product.

[1500] The generated Japanese text will be translated into multiple languages ​​using translation tools. Specifically, Google's translation API is likely to be used. The server will translate the generated Japanese description and PR text into English using the translation API and save the results.

[1501] The server also uses image recognition technology to analyze product images and determine export restrictions in various countries. For example, using TensorFlow, it can extract features from product images and compare them with an export restriction list to determine whether the product violates regulations in any country. The results are notified to the user as alerts as needed.

[1502] The server then calls an external shipping information API (e.g., the Shippo API) to calculate shipping costs to each country. It retrieves shipping costs for different countries, adds the base product price and service fee, and calculates and suggests a recommended selling price. This allows the user to set an accurate selling price.

[1503] In addition, the server automatically generates various regulatory documents (e.g., privacy policies and terms of service) using a generation AI model. These documents are translated into multiple languages ​​using translation tools and provided to users for legal compliance purposes.

[1504] Furthermore, the server uses optimization technology to automatically generate SEO strategies for e-commerce sites. For example, it utilizes a model like Yoast SEO to analyze the entered product information and translated text, generating optimal SEO settings. This helps the user's e-commerce site rank higher in search engine results.

[1505] Finally, the server adjusts the emotional tone of the generated text using emotion estimation technology. This technology analyzes the emotional tone of the generated text and can adjust it to match the user's specific emotions (e.g., reassurance or excitement). Furthermore, by analyzing user feedback with emotion estimation technology and incorporating it into the next text generation process, it becomes possible to provide higher quality content.

[1506] In this way, the system of the present invention becomes a powerful tool for users to efficiently conduct cross-border sales by automating tasks such as multilingual support, export restriction checks, shipping cost calculations, legal compliance, SEO measures, and user sentiment management.

[1507] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1508] Step 1:

[1509] The user accesses the e-commerce site using their device and enters product information. This information includes the product name, description, price, and product image. They also select the target countries for sales. The entered data is sent to the server. The device performs the process of entering product information and target countries for sales and sending them to the server.

[1510] Step 2:

[1511] The server automatically generates product descriptions and promotional texts using a generative AI model based on the received product information. Specifically, the server inputs the product information as a prompt to the generative AI model and retrieves the text generated by the model. For example, the prompt might be "Please create product descriptions and promotional texts for a Japanese-style teacup. The target markets are the United States, the United Kingdom, and Australia." The input data consists of product information and the specified target markets, while the output data consists of the automatically generated product descriptions and promotional texts.

[1512] Step 3:

[1513] The server translates the generated product description and promotional text into English using a translation tool. Specifically, the server sends the generated Japanese text to a translation API and retrieves the translated English text. For example, it uses the Google Translate API. The input data is the generated Japanese text, and the output data is the translated English text.

[1514] Step 4:

[1515] The server uses sentiment estimation technology to analyze the emotional tone of the generated text and adjust it to an appropriate tone. Specifically, the server uses a sentiment analysis model to analyze the generated text and adjust the tone to correspond to the user's specific emotion. The input data consists of the generated Japanese and English text, and the output data consists of the adjusted text.

[1516] Step 5:

[1517] The server uses image recognition technology to analyze product images and determine export restrictions in various countries. Specifically, the server uses image recognition technology such as TensorFlow to analyze product images and evaluate whether the materials and shape comply with export restrictions in each country. The input data is the product image, and the output data is the result of the export restriction evaluation.

[1518] Step 6:

[1519] The server uses an external shipping information API to calculate shipping costs for each country and propose a recommended selling price. Specifically, the server calls the shipping information API for each target country to obtain shipping information. Based on the obtained shipping information, it adds it to the base product price to calculate the recommended selling price. The input data is the target country and product price, and the output data is the recommended selling price.

[1520] Step 7:

[1521] The server automatically generates various rule documents using a generative AI model and translates them into multiple languages. Specifically, the server uses the generative AI model to generate documents such as privacy policies and terms of service, and then translates them into English using a translation API. The input data consists of prompts from the generative AI model, and the output data consists of the generated rule documents and their translations.

[1522] Step 8:

[1523] The server automatically generates SEO strategies for e-commerce sites using optimization technology. Specifically, the server uses an SEO optimization model to analyze product information and translated text, and generates optimal SEO settings. The input data consists of product information and translated text, while the output data consists of SEO settings.

[1524] Step 9:

[1525] The server uses sentiment estimation technology to analyze user feedback and incorporate it into the next generation process. Specifically, the server collects user feedback and analyzes it using a sentiment analysis model. Based on the analysis results, it incorporates them into the next text generation. The input data is user feedback, and the output data is the analysis results and how they are applied.

[1526] (Application Example 2)

[1527] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1528] Traditional e-commerce site management systems require operators to individually handle a wide range of tasks, including product information entry, generation of product descriptions and promotional texts, translation, export restriction assessment, shipping cost calculation, legal policy generation, and SEO optimization. This requires significant effort and time from the operator. Furthermore, they often lack consideration for adjusting emotional tone and incorporating user feedback, making it difficult to improve the user experience.

[1529] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input product information, means for automatically generating product descriptions and PR texts in Japanese using a generation AI model, means for translating the generated product descriptions and PR texts into other languages ​​using a translation API, means for adjusting the emotional tone of the product descriptions and PR texts using an emotion engine, means for analyzing product images using an image recognition model to determine export restrictions in each country, means for calculating shipping costs to each country and proposing a recommended selling price, means for automatically generating various legal policies and translating them into other languages ​​using a translation API, means for automatically generating optimal SEO measures using an SEO optimization model, and an emotion engine that analyzes user feedback and reflects it in the next generation process. This makes it possible to centralize a wide range of tasks in e-commerce site operation and perform them efficiently and quickly. Furthermore, by adjusting the emotional tone of the generated texts and reflecting user feedback, an improvement in the user experience can be expected.

[1530] "A means for users to input product information" refers to an interface for users to input information related to a product, such as its name, description, price, and images.

[1531] "A method for automatically generating product descriptions and promotional texts in Japanese using a generative AI model" refers to a system that uses a machine learning algorithm to automatically generate product descriptions and promotional texts in Japanese based on the input product information.

[1532] "Means of translating generated product descriptions and PR texts into other languages ​​using a translation API" refers to the process of converting generated Japanese text into other languages ​​(such as English) using existing translation software or services.

[1533] "A means of adjusting the emotional tone of product descriptions and PR texts using an emotion engine" refers to a system that analyzes the emotional tone (such as reassurance or excitement) of generated text and adjusts it as needed.

[1534] "A means of analyzing product images using an image recognition model to determine export restrictions in various countries" refers to a system that analyzes product images using machine learning algorithms to determine whether specific materials or designs fall under export restrictions in various countries.

[1535] The "method for calculating shipping costs to each country and suggesting a recommended selling price" is a process that uses an external shipping information API to obtain shipping costs for each country and reflects them in the product pricing.

[1536] "A method for automatically generating various legal policies and translating them into other languages ​​using a translation API" refers to a system that uses a generation AI model to automatically generate legal documents such as privacy policies and terms of service, and then translates them into other languages.

[1537] "A method for automatically generating optimal SEO measures using an SEO optimization model" refers to a system that automatically proposes and implements measures to improve the search engine ranking of e-commerce sites using search engine optimization techniques.

[1538] A "sentiment engine that analyzes user feedback and reflects it in the next generation process" is a system that analyzes user feedback and reflects the results of that analysis in the generation of future product descriptions and promotional texts.

[1539] The "means of notifying users of export restriction alerts from various countries" are systems that quickly inform users when materials or designs subject to export restrictions are detected.

[1540] This invention is a support system for e-commerce site operators to efficiently conduct cross-border sales of products. In particular, it combines a generative AI model and an emotion engine to support multiple languages ​​and improve the user experience. The system of this invention is configured as follows.

[1541] System Program

[1542] User Interface (UI)

[1543] Users utilize a form to input product information from their smartphones or other devices. This form has fields for easily entering product name, description, price, images, and other information.

[1544] Data processing and generation

[1545] The server receives product information entered by the user and automatically generates product descriptions and promotional texts using a generative AI model (e.g., OpenAI's GPT-4). These generated texts are then translated into other languages ​​(e.g., English) using a translation API.

[1546] Emotional tone adjustment

[1547] The generated product descriptions and promotional texts are analyzed by an emotion engine and adjusted to match pre-set emotional tones (such as reassurance or excitement).

[1548] Image recognition and export restriction determination

[1549] Product images are analyzed using image recognition models (e.g., TensorFlow or PyTorch) to determine whether they fall under export restrictions in various countries. The results are then notified to the user as an alert from the server.

[1550] Shipping cost calculation and suggested selling price

[1551] Shipping cost information for each country is obtained via an external shipping cost information API (e.g., EasyPost API). The server calculates the shipping cost to each country based on this information, adds it to the product price, and proposes a recommended selling price.

[1552] Automated generation of legal policies

[1553] Using a generative AI model, legal policy documents such as privacy policies and terms of service are automatically generated, and these are then translated into other languages ​​using a translation API. This allows for the rapid creation of compliance documents.

[1554] SEO measures

[1555] Using an SEO optimization model, the system automatically suggests and implements SEO measures for product pages. This improves the search engine rankings of e-commerce sites.

[1556] Analysis and implementation of user feedback

[1557] The emotion engine analyzes user feedback and incorporates the results into the generation process for future product descriptions and promotional texts. This results in text that more closely matches user expectations.

[1558] Hardware and software

[1559] Hardware: Smartphones, servers, user terminals

[1560] Software: Generative AI models (e.g., GPT-4), image recognition models (e.g., TensorFlow, PyTorch), translation APIs, shipping information APIs (e.g., EasyPost API), SEO optimization models (e.g., Yoast SEO)

[1561] Specific example

[1562] If a user wants to sell "Japanese-style teacups" in the United States, the United Kingdom, and Australia, the user enters product information and selects target countries. The server generates and translates product descriptions and promotional texts. Next, it analyzes product images to notify the user of any export restrictions, calculates shipping costs for each country, and suggests a recommended selling price. Various legal policies are also automatically generated and provided to the user. The tone of the text is optimized using an emotion engine, and feedback is incorporated into future updates.

[1563] Example of a prompt

[1564] Product description generation:

[1565] "We want to sell Japanese-style teacups incorporating traditional Japanese designs. Please generate a product description to match."

[1566] Emotional tone adjustment:

[1567] Please adjust the following sentence to convey a sense of reassurance: "Enrich your daily tea time with high-quality Japanese-style teacups."

[1568] Legal policy generation:

[1569] "Please generate a privacy policy for our e-commerce site in both Japanese and English."

[1570] This system enables e-commerce site operators to conduct cross-border sales efficiently and quickly.

[1571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1572] Step 1:

[1573] The user inputs product information using a terminal. This product information includes the product name, description, price, and product image. The entered data is sent to the server and used in the next processing step.

[1574] Input: Product name, description, price, and product image

[1575] Output: Product information sent to the server

[1576] Step 2:

[1577] The server uses a generative AI model (e.g., OpenAI's GPT-4) to automatically generate product descriptions and promotional texts in Japanese from product information entered by the user. Specific data such as product names and features are input into the model for the generation process.

[1578] Input: Product Information

[1579] Output: Generated product description and promotional text

[1580] Step 3:

[1581] Next, the server uses a translation API to translate the generated product descriptions and promotional texts into other languages, such as English. The translated texts are temporarily stored on the server and presented to the user in a format they can review.

[1582] Input: Generated product description and PR text (in Japanese)

[1583] Output: Generated product description and PR text (in other languages)

[1584] Step 4:

[1585] The server uses an emotion engine to analyze the emotional tone of the generated product descriptions and promotional texts and adjusts them to match the target emotions of the user.

[1586] Input: Generated product description and PR text (in Japanese and other languages)

[1587] Output: Product description and promotional text with adjusted emotional tone.

[1588] Step 5:

[1589] The server uses an image recognition model (e.g., TensorFlow or PyTorch) to analyze product images and determine export restrictions in each country. This analysis verifies whether the product contains materials or designs subject to export restrictions.

[1590] Input: Product image

[1591] Output: Alerts regarding export restrictions in various countries

[1592] Step 6:

[1593] The server uses an external shipping cost information API (e.g., EasyPost API) to calculate shipping costs to each country. Based on the shipping cost information, a recommended selling price is suggested, which is added to the base product price.

[1594] Input: Product information, shipping information for each country

[1595] Output: Recommended selling price

[1596] Step 7:

[1597] The server automatically generates various legal policies (privacy policies, terms of service, etc.) using a generative AI model, and then translates them into other languages ​​using a translation API. The generated legal policy documents are then provided to the user.

[1598] Input: None (automatically generated)

[1599] Output: Various legal policy documents (in Japanese and other languages)

[1600] Step 8:

[1601] Using an SEO optimization model, the server automatically generates SEO strategies for product pages. Optimal keywords and metadata are added, improving search engine rankings.

[1602] Input: Product page information

[1603] Output: Page data with SEO optimization applied.

[1604] Step 9:

[1605] The server analyzes user feedback using an emotion engine and incorporates the results into the process of generating future product descriptions and promotional texts. This feedback is used to improve the accuracy of text generation.

[1606] Input: User Feedback

[1607] Output: Feedback results, to be reflected in the next generation process.

[1608] This process allows users to efficiently and quickly input product information and generate product descriptions and promotional texts with an emotional tone appropriate to the target market. Furthermore, SEO measures and legal policy preparation are automated, making cross-border sales easier.

[1609] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1610] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1611] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1612] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1613] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1614] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1615] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1616] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1617] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1618] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1619] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1620] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1621] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1622] 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.

[1623] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1624] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1625] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1626] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1627] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1628] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1629] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1630] The following is further disclosed regarding the embodiments described above.

[1631] (Claim 1)

[1632] The means by which users input product information,

[1633] A method for automatically generating product descriptions and PR texts in Japanese using a generative AI model,

[1634] A method for translating the generated product description and PR text into English using a translation API,

[1635] A method for analyzing product images using an image recognition model to determine export restrictions in various countries,

[1636] A method for calculating shipping costs to each country and proposing a recommended selling price,

[1637] A means of automatically generating various legal policies and translating them into Japanese and English,

[1638] A system that includes a means to automatically generate optimal SEO measures using an SEO optimization model.

[1639] (Claim 2)

[1640] The system according to claim 1, further comprising means for presenting the generated product description and PR text in a format that can be reviewed by the user.

[1641] (Claim 3)

[1642] The system according to claim 1, further comprising means for notifying export restriction alerts from various countries.

[1643]

[1644] "Example 1"

[1645] (Claim 1)

[1646] The means by which users input product information,

[1647] A means for automatically generating product descriptions and PR texts using a generative AI model, 【164...

Claims

1. The means by which users input product information, A method for automatically generating product descriptions and PR texts in Japanese using a generative AI model, A method for translating the generated product description and PR text into English using a translation API, A method for analyzing product images using an image recognition model to determine export restrictions in various countries, A method for calculating shipping costs to each country and proposing a recommended selling price, A means of automatically generating various legal policies and translating them into Japanese and English, A system that includes a means to automatically generate optimal SEO measures using an SEO optimization model.

2. The system according to claim 1, further comprising means for presenting the generated product description and PR text in a format that can be reviewed by the user.

3. The system according to claim 1, further comprising means for notifying export restriction alerts from various countries.

Images