System

Abstract

To provide a system.SOLUTION: A system includes the means for: receiving input information from a user; decomposing the received input information by generative AI to generate attribute data; searching for an article based upon the generated attribute data; displaying an outline of a search result and reviews out together by the generative AI; using a speech recognition system to receive voice input; using a generative AI model to recommend an article based upon the input information from the user; providing a link for purchasing the recommended article; and ordering the article through interaction with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In traditional product purchasing systems, users have to search for detailed information about the product they want, select it, and then order it, which takes time and effort. In addition, checking product reviews requires additional time and effort to read each review. [Means for solving the problem]

[0005] The system of this invention uses generative AI to analyze information input by users and generate attribute data. It then performs a product search based on this attribute data and displays the search results and a summary of reviews compiled by the generative AI. Furthermore, by centralizing product selection and ordering through dialogue with the user, it reduces the burden on the user and provides a new purchasing experience. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g.,

[0012] hard disk), or magnetic tape.

[0013] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0014] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0015] [First embodiment]

[0016] Fig. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. As shown in Fig. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or CC

[0021] It is a small digital camera equipped with an imaging element such as a D (Charge Coupled Device) image sensor.

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0026] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0027] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0028] "Example 1"

[0029] The system of the present invention receives input information from users through, for example, an input form on a website or application, or a voice recognition system. Users input information such as the name, place of origin, manufacturer, and budget of the product they want.

[0030] "Example 2"

[0031] The generative AI breaks down the received input information and generates attribute data, for example, using natural language processing technology. This generative AI analyzes the input information and extracts attribute data such as the product name, place of origin, manufacturer, and budget.

[0032] "Example 3"

[0033] As a means for searching for products based on the generated attribute data, for example, a query is generated and executed against a product database. This product search is performed based on the attribute data to search for products that most closely match the user's needs.

[0034] "Example 4"

[0035] For example, a display area on a website or application can be used to display search results and summaries of reviews compiled by generative AI. This display area displays product information from search results and summaries of related reviews.

[0036] "Example 5"

[0037] Chatbots and voice assistants, for example, are used as a means of ordering products through dialogue. Users can select and order products through dialogue, referring to the displayed product information and review summaries.

[0038] The processing flow of each embodiment will be described below.

[0039] "Example 1"

[0040] Step 1: The user enters information such as the name of the product they want, its origin, manufacturer, and budget into an input form on a website or application.

[0041] Step 2: Input information is sent to the system and received by the generative AI.

[0042] Step 3: The generative AI uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[0043] "Example 2"

[0044] Step 1: Generate a query to the product database based on the attribute data extracted by the generative AI.

[0045] Step 2: Execute the generated query to search the product database for products that best match the user's requirements.

[0046] Step 3: Display the search results in a display area on your website or application.

[0047] "Example 3"

[0048] Step 1: The user selects a product based on the displayed product information.

[0049] Step 2: The user places an order for the selected product via a chatbot or voice assistant.

[0050] Step 3: The system receives the order information and processes the order.

[0051] Example 1

[0052] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0053] In conventional systems, even if a user inputs information about the product they want, it is difficult to process that information appropriately and provide product information that meets the user's needs. In addition, it is often impossible to quickly and accurately return product information generated based on the information entered by the user. This leads to problems such as lower user satisfaction and reduced system usage.

[0054] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0055] In this invention, the server includes means for receiving input information from a user, means for saving the received input information in a database, means for generating a prompt sentence based on the saved information, means for sending the generated prompt sentence to a generative AI model, means for receiving product information generated from the generative AI model, and means for returning the received product information to the user, thereby enabling product information based on a user's request to be provided quickly and accurately.

[0056] "Means for receiving input information from users" refers to the interface for receiving information that users input through a website or application.

[0057] "Means for saving received input information in a database" is a function for storing information received from a user in a database.

[0058] "Means for generating prompt sentences based on stored information" refers to a function that creates instruction sentences (prompt sentences) for the generative AI model based on information stored in the database.

[0059] "Means for sending the generated prompt sentence to the generative AI model" is a function for sending the generated prompt sentence to the generative AI model.

[0060] "Means for receiving product information generated from a generative AI model" refers to a function for receiving product information generated by a generative AI model.

[0061] "Means for returning received product information to the user" is a function for providing product information received from the generative AI model to the user.

[0062] A "generative AI model" is an artificial intelligence model that performs specific tasks based on user input.

[0063] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[0064] MODE FOR CARRYING OUT THE INVENTION

[0065] This invention is a system that receives input information from a user, generates product information based on that information using a generative AI model, and provides the product information to the user. Specific embodiments of this system are described below.

[0066] Hardware and software used

[0067] Hardware

[0068] Server: Use a server machine with a powerful processor and sufficient memory.

[0069] Device: A device such as a PC or smartphone used by a user.

[0070] software

[0071] Database management system: MySQL (registered trademark), PostgreSQL, etc. are used.

[0072] Generative AI model: Uses OpenAI's (registered trademark) GPT-3 (registered trademark), etc.

[0073] System processing flow

[0074] Receiving input from the user

[0075] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget through a form on a website or in an application. For example, they might enter "Japanese apples, from Aomori Prefecture, budget within 1,000 yen."

[0076] Sending and saving input information

[0077] The terminal sends the entered information to the server using the HTTPS protocol, and the server stores the received information in a database. The stored data includes the product name, origin, manufacturer, budget, etc.

[0078] Generate prompt statement

[0079] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0080] Sending prompts to generative AI models

[0081] The server sends the generated prompt to the generative AI model, which uses OpenAI's GPT-3. The prompt is sent via an API.

[0082] Creating and receiving product information

[0083] Based on the received prompt, the generative AI model generates product information that matches the user's request. For example, it generates information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." The server receives the product information generated by the generative AI model.

[0084] Returning product information to the user

[0085] The server returns the product information received from the generative AI model to the user, who can then view this information on a website or application.

[0086] Specific examples

[0087] The specific flow when a user enters the information "Apples from Japan, from Aomori Prefecture, budget within 1000 yen" is shown below.

[0088] 1. The user enters information

[0089] Users enter "Japanese apples, from Aomori Prefecture, budget under 1,000 yen" into the website's input form.

[0090] 2. The device sends the input information to the server

[0091] The terminal sends the entered information to the server using the HTTPS protocol.

[0092] 3. The server saves the input information in a database

[0093] The server stores the received information in a MySQL database. The stored data is "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1000 yen."

[0094] 4. The server generates a prompt

[0095] The server generates a prompt message saying, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0096] 5. The server sends a prompt to the generative AI model

[0097] The server sends the generated prompt text to OpenAI's GPT-3 via an API.

[0098] 6. Generative AI model generates product information

[0099] The generative AI model generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0100] 7. The server returns the generated product information to the user

[0101] The server returns the generated product information to the user, and the user confirms on the website the information: "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0102] In this way, the user can obtain product information that meets his or her needs.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] The user enters information

[0106] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget into an input form on a website or application. For example, they might enter "Apples from Japan, from Aomori Prefecture, budget within 1,000 yen." The entered information is temporarily stored in the device's memory.

[0107] Step 2:

[0108] The device sends the input information to the server

[0109] The terminal sends the information entered by the user to the server using the HTTPS protocol. The input information is sent to the server as JSON format data. For example, it is sent in the format {"Product name": "Apple", "Origin": "Aomori Prefecture", "Budget": "Under 1000 yen"}.

[0110] Step 3:

[0111] The server saves the input information in a database

[0112] The server stores the input information received from the terminal in a database. MySQL or PostgreSQL is used as the database management system. The data stored includes attribute data such as the product name, origin, manufacturer, and budget. For example, a record such as "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1,000 yen" is added to the database.

[0113] Step 4:

[0114] The server generates a prompt

[0115] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen." This prompt is used as an instruction for the generative AI model.

[0116] Step 5:

[0117] The server sends a prompt to the generative AI model

[0118] The server sends the generated prompt to the generative AI model. OpenAI's GPT-3 is used as the generative AI model. The prompt is sent via API. For example, an API request might read, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0119] Step 6:

[0120] Generative AI model generates product information

[0121] The generative AI model generates product information that matches the user's request based on the received prompt. For example, it generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." This generated information is returned to the server.

[0122] Step 7:

[0123] The server returns the generated product information to the user

[0124] The server returns the product information received from the generative AI model to the user. The user can check this information on a website or application. For example, the user might see information on the screen such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0125] In this way, the user can obtain product information that meets his or her needs.

[0126] (Application example 1)

[0127] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0128] On conventional online shopping sites, when users input detailed information such as the name, origin, manufacturer, and budget of the product they want, manual input is cumbersome, and it is difficult to quickly find the right product. Furthermore, even when using voice input, accurate product recommendations are often not provided, which is an issue that does not improve the user experience.

[0129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0130] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing links to purchase the recommended products, and means for ordering the products during the dialogue. This allows the user to easily provide detailed product information using voice input and receive accurate product recommendations from the generative AI model.

[0131] "Means for receiving input information from the user" refers to an interface that allows the user to input information such as the name, place of origin, manufacturer, and budget of the product they want, and includes input forms on websites or applications and voice recognition systems.

[0132] "Means of decomposing using generative AI and generating attribute data" refers to a processing system that uses artificial intelligence technology to analyze input information received from users and convert it into attribute data such as product name, place of origin, manufacturer, and budget.

[0133] The "means for searching for products" is a system for searching for corresponding products in a database or on the Internet based on the generated attribute data.

[0134] "Means for displaying search results and summaries of reviews compiled using generative AI" refers to an interface that uses generative AI to summarize search results and related reviews and display them to users.

[0135] "Means for receiving voice input using a voice recognition system" refers to a system that uses voice recognition technology to convert information input by a user through voice into text data.

[0136] "Means for recommending products based on user-input information using a generative AI model" refers to a system that uses a generative AI model to recommend optimal products based on information entered by the user.

[0137] The "means for providing links to purchase recommended products" refers to an interface that provides links to related online shopping sites so that users can purchase products recommended by the generative AI model.

[0138] The "means for ordering products through dialogue" is a system for centrally selecting and ordering products through dialogue with the user.

[0139] A system for implementing this invention includes means for receiving input information from a user, means for breaking it down using a generative AI to generate attribute data, means for performing a product search based on the generated attribute data, means for displaying a summary of the search results and reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing a link to purchase the recommended product, and means for ordering the product during the dialogue.

[0140] System program processing explanation

[0141] Hardware

[0142] Smartphone (microphone, internet connection)

[0143] software

[0144] Python

[0145] speech_recognition library: Used to convert voice input to text.

[0146] Transformers library: Uses a generative AI model (GPT-3) to recommend products based on user input.

[0147] Data processing and calculation

[0148] 1. Receiving voice input:

[0149] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using the speech recognition system (speech_recognition library).

[0150] 2. Generate attribute data:

[0151] Using a generative AI model (transformers library), the information entered by the user is analyzed and attribute data such as the product name, place of origin, manufacturer, and budget is generated.

[0152] 3. Product recommendation:

[0153] Based on the generated attribute data, a generative AI model is used to recommend optimal products, and the recommended product information is displayed to the user.

[0154] 4. Providing a Purchase Link:

[0155] A link to purchase the recommended product is generated and provided to the user, who can then purchase the product at the relevant online shopping site by clicking on the link.

[0156] 5. Interactive ordering:

[0157] Through interaction with the user, product selection and ordering are performed in one place, allowing users to easily select products and complete orders.

[0158] Specific examples

[0159] When a user speaks, "Japanese apples, within a budget of 1,000 yen," the speech recognition system converts this speech into text. The generative AI model analyzes this text and generates attribute data. The generative AI model then recommends the most suitable product and provides the user with information such as, "The recommended product is Japanese apples, priced at 800 yen. Here is the purchase link."

[0160] Prompt Sentence Examples

[0161] Product information for user: Japanese apples, budget under 1000 yen

[0162] Please tell me what products you recommend.

[0163] In this way, users can easily provide detailed product information using voice input and receive accurate product recommendations from generative AI models.

[0164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0165] Step 1:

[0166] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using a speech recognition system (speech_recognition library). The input is the user's voice data, and the output is text data. In this step, the speech recognition system analyzes the voice data and generates corresponding text data.

[0167] Step 2:

[0168] The server uses a generative AI model (transformers library) to analyze the text data obtained in step 1 and generate attribute data such as product name, place of origin, manufacturer, and budget. The input is text data, and the output is attribute data. In this step, the generative AI model breaks down the text data and extracts each attribute.

[0169] Step 3:

[0170] The server performs a product search based on the generated attribute data. The input is the attribute data and the output is the search results. In this step, the server searches for the relevant products in a database or on the Internet and obtains the search results.

[0171] Step 4:

[0172] The server displays the search results and the review summaries compiled by the generative AI. The input is the search results, and the output is display data including the review summaries. In this step, the generative AI summarizes the reviews related to the search results and generates data to display to the user.

[0173] Step 5:

[0174] The server uses a generative AI model to recommend products based on user input. The input is attribute data, and the output is recommended product information. In this step, the generative AI model analyzes the attribute data and recommends the most suitable products.

[0175] Step 6:

[0176] The server provides a link to purchase the recommended product. The input is the recommended product information, and the output is a purchase link. In this step, the server generates a link to the online shopping site corresponding to the recommended product and provides it to the user.

[0177] Step 7:

[0178] The user places an order through a dialogue. The input is the user's selection information, and the output is order completion information. In this step, the user clicks on the provided link, selects the product on the online shopping site, and completes the order.

[0179] Example 2

[0180] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0181] With conventional systems, it was difficult to efficiently analyze information input by users and extract attribute data. Furthermore, it was not possible to perform product searches based on the extracted attribute data and provide appropriate feedback to users. Furthermore, it was difficult to centrally order products through dialogue with users. This resulted in a poor user experience and reduced system utilization efficiency.

[0182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0183] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and a summary of reviews compiled by the generative AI, means for ordering products through dialogue, means for sending prompts to the generative AI model and extracting attribute data, and means for saving the extracted attribute data and providing feedback to the user. This makes it possible to efficiently analyze input information from a user, extract appropriate attribute data to perform a product search, and provide the user with prompt and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[0184] "User" means an individual or corporation that uses the system to input information and search for and place orders.

[0185] "Input information" refers to information such as the product name, place of origin, manufacturer, and budget that the user provides to the system.

[0186] "Generative AI" is an artificial intelligence technology that analyzes input information and extracts attribute data.

[0187] "Attribute data" refers to data such as the product name, place of origin, manufacturer, and budget that the generative AI extracts from the input information.

[0188] "Product search" is the process in which the system searches for related products based on the generated attribute data.

[0189] A "review summary" is a summary of product ratings and comments compiled by generative AI based on search results.

[0190] "Dialogue" is a communication process in which the user and the system exchange information with each other to select and order products.

[0191] A "prompt sentence" is a sentence that instructs the generative AI to analyze input information and extract attribute data.

[0192] "Feedback" refers to information about analysis results and search results that the system provides to the user.

[0193] This invention is a system that efficiently analyzes information input by users, extracts attribute data, performs product searches, and provides users with quick and accurate feedback. Furthermore, it allows users to select and order products in a unified manner through dialogue with the system.

[0194] Hardware and software used

[0195] Hardware

[0196] server

[0197] User device (PC, smartphone, etc.)

[0198] software

[0199] Generative AI models (e.g., GPT-4 (registered trademark))

[0200] Web browser (user device)

[0201] Data processing and calculation

[0202] Server Processing

[0203] The server sends the input information received from the user to the generative AI model. This generative AI model analyzes the input information using natural language processing technology and extracts attribute data such as the product name, place of origin, manufacturer, and budget. Specifically, the server makes an API request to the generative AI model and uses prompt statements to instruct it to analyze.

[0204] Terminal handling

[0205] The terminal is responsible for sending the information entered by the user to the server. When the user enters information about a product into the input form and presses the send button, the information is sent to the server.

[0206] User Action

[0207] The user enters information such as the product name, place of origin, manufacturer, budget, etc. into the input form on the terminal. For example, they might enter "apples, Aomori Prefecture, sunfruit, budget 500 yen."

[0208] Specific examples

[0209] Prompt Sentence Examples

[0210] "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, Aomori Prefecture, Sunfruit, Budget 500 yen'"

[0211] In this way, the server, terminal, and user each play their respective roles, and the system uses a generative AI model to analyze input information and generate attribute data. This makes it possible to efficiently analyze input information from users, extract appropriate attribute data, perform product searches, and provide users with fast and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[0212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0213] Step 1:

[0214] The user inputs the input information into the terminal.

[0215] The user inputs product information into the input form on the terminal. For example, the user might input "apples, Aomori Prefecture, sunfruit, budget 500 yen." This information includes the product name, place of origin, manufacturer, and budget. When the input information is entered into the terminal, the terminal temporarily stores the information.

[0216] Step 2:

[0217] The terminal sends the input information to the server.

[0218] When the user presses the submit button, the device sends the input information to the server as an HTTP POST request. This request contains all the information the user entered. Specifically, the device converts the input information into JSON format and sends it to the server's API endpoint.

[0219] Step 3:

[0220] The server sends the input information to the generative AI model.

[0221] The server sends the input information received from the device to the generative AI model. Specifically, the server makes an API request to the generative AI model, instructing it to analyze the input information. At this time, the prompt text used is "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, Aomori Prefecture, Sunfruit, Budget 500 yen'." The input is information from the user, and the output is the analysis result from the generative AI model.

[0222] Step 4:

[0223] The generative AI model analyzes the input information and extracts attribute data.

[0224] The generative AI model analyzes the received input information and extracts attribute data. Specifically, the generative AI model uses natural language processing technology to extract the following attribute data from the input information:

[0225] Product Name: Apple

[0226] Origin: Aomori Prefecture

[0227] Manufacturer: Sun Fruits

[0228] Budget: 500 yen

[0229] The input is a prompt from the server, and the output is the extracted attribute data.

[0230] Step 5:

[0231] The server stores the extracted attribute data and provides feedback to the user.

[0232] The server stores the attribute data received from the generative AI model. Specifically, it stores it in a database. The server also provides feedback to the user. For example, it might display to the user, "The following attribute data has been extracted from the input information: Product name: Apple, Origin: Aomori Prefecture, Manufacturer: Sun Fruits, Budget: 500 yen." The input is the attribute data from the generative AI model, and the output is a feedback message to the user.

[0233] (Application example 2)

[0234] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0235] On conventional online shopping sites, when users searched for products, it was difficult to extract appropriate attribute data from the information they entered and provide accurate search results. Furthermore, there was a lack of efficient means to extract detailed attribute data such as the name, origin, manufacturer, and budget of the product the user wanted, so search results often did not meet the user's expectations. Furthermore, there was a need for a system that could display search results and review summaries in a unified manner and allow users to order products interactively.

[0236] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0237] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for interactively ordering products, means for extracting attribute data such as product name, place of origin, manufacturer, and budget from the input information using the generative AI, and means for providing highly accurate search results based on the extracted attribute data. This makes it possible to automatically extract detailed attribute data from the information entered by the user and provide highly accurate search results. Furthermore, search results and review summaries can be displayed in a unified manner, allowing for interactive product ordering.

[0238] The "means for receiving input information from the user" is an interface for incorporating text information entered by the user into the system.

[0239] "Means of breaking down received input information using generative AI and generating attribute data" refers to the process of using generative AI to analyze input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[0240] The "means for searching for products based on the generated attribute data" is a function for searching for related products from a database based on the extracted attribute data.

[0241] "A means of displaying search results and summaries of reviews compiled by generative AI" is a function that displays to users a summary of product reviews compiled by generative AI along with the search results.

[0242] The "means for ordering products interactively" is a function that allows users to select and order products in an integrated manner through an interactive format.

[0243] "Means of using generative AI to extract attribute data such as product name, place of origin, manufacturer, and budget from input information" refers to the process of using generative AI to automatically extract detailed attribute data of a product from information entered by the user.

[0244] "Means for providing highly accurate search results based on extracted attribute data" refers to a function that utilizes extracted attribute data to provide more relevant search results to users.

[0245] The system for implementing this invention receives input information from a user, analyzes it using generative AI to extract attribute data, and performs highly accurate product searches. Specific embodiments of this system are described below.

[0246] System Configuration

[0247] The system consists of the following main components:

[0248] 1. User terminal: The device through which the user provides input information (e.g., a smartphone).

[0249] 2. Server: A central processing unit that runs generative AI models, generates attribute data, and performs product searches.

[0250] 3. Database: Data storage for storing product information and review information.

[0251] Program processing

[0252] The server performs the following steps:

[0253] 1. Receiving input information: Receive text information entered from the user's device. For example, a user might enter, "I'm looking for a new smartphone. My budget is within 50,000 yen, and I'd prefer a Japanese manufacturer."

[0254] 2. Generative AI analysis: The received input information is passed to a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt. An example of a prompt is as follows:

[0255] Please extract the product name, place of origin, manufacturer, and budget from the text below.

[0256] Text: I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer a Japanese brand.

[0257] Attribute data:

[0258] 3. Extracting attribute data: The generative AI model analyzes the prompt and extracts attribute data such as the product name, origin, manufacturer, and budget. For example, the following attribute data is extracted:

[0259] Name: Smartphone

[0260] Origin: Domestic

[0261] Manufacturer: Domestic manufacturer

[0262] Budget: Under 50,000 yen

[0263] 4. Product search: Based on the extracted attribute data, the database is searched for related products. For example, products that meet the criteria of "smartphone," "domestic manufacturer," and "under 50,000 yen" are searched for.

[0264] 5. Displaying search results and reviews: Along with search results, a summary of product reviews compiled by generative AI is displayed on the user's device, allowing users to see detailed product information and reviews at a glance.

[0265] 6. Interactive ordering: The user selects a product from the search results and completes the order interactively. For example, the user can enter "I would like to purchase this smartphone," and the system will proceed with the order process.

[0266] Hardware and software used

[0267] Hardware: A smartphone (user device) connected to the internet and a server.

[0268] Software: Python, OpenAI API, database management system (e.g., MySQL).

[0269] This system allows users to automatically extract detailed attribute data from the information they enter, resulting in highly accurate search results. It also displays search results and review summaries in a unified manner, and allows users to interactively order products.

[0270] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0271] Step 1:

[0272] The user's device receives the input information. The user enters text information for product search and presses the send button. For example, the user might enter, "I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer one from a Japanese manufacturer." The input information is sent to the server.

[0273] Step 2:

[0274] The server generates a prompt to pass the input information it receives to the generative AI model. Specifically, it creates the following prompt based on the input information:

[0275] Please extract the product name, place of origin, manufacturer, and budget from the text below.

[0276] Text: I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer a Japanese brand.

[0277] Attribute data:

[0278] This prompt is passed to the generative AI model.

[0279] Step 3:

[0280] The server calls a generative AI model, analyzes the prompt, and extracts attribute data. The generative AI model (e.g., OpenAI's GPT-3) analyzes the prompt and extracts attribute data such as the product name, place of origin, manufacturer, and budget. For example, the following attribute data is extracted:

[0281] Name: Smartphone

[0282] Origin: Domestic

[0283] Manufacturer: Domestic manufacturer

[0284] Budget: Under 50,000 yen

[0285] The extracted attribute data is returned to the server.

[0286] Step 4:

[0287] The server performs a product search based on the extracted attribute data. The extracted attribute data is used as a query to search for related products in the database. For example, a search is performed for products that match the criteria "smartphone," "domestic manufacturer," and "under 50,000 yen." The search results are returned to the server.

[0288] Step 5:

[0289] The server generates a review summary compiled from the search results using generative AI. Based on the search results, review information for related products is retrieved from the database and a generative AI model is used to create a review summary. For example, a review summary might be generated that reads, "This smartphone is rated for its high performance and long battery life."

[0290] Step 6:

[0291] The server sends the search results and a summary of the reviews to the user's device, which then displays the search results and a summary of the reviews. The user can then select a product based on the displayed information.

[0292] Step 7:

[0293] The user device interactively orders the product. The user selects the product from the search results and presses the order button. For example, if the user types "I would like to purchase this smartphone," the server proceeds with the order. The order information is saved in the database, and an order confirmation is displayed on the user device.

[0294] Example 3

[0295] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0296] In the previous system, the process of users searching for products, checking reviews, and then placing an order was complicated, resulting in a poor user experience. Furthermore, the accuracy of search results and summaries of reviews were insufficient, making it difficult for users to select the right product. This resulted in lower user satisfaction and reduced frequency of system use.

[0297] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0298] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI model to generate attribute data, means for generating and executing a query to a product database based on the generated attribute data, means for obtaining search results and generating review summaries using the generative AI model, means for displaying the search results and the generated review summaries, and means for selecting and ordering products through an interaction. This allows the user to perform the entire process smoothly, improving the accuracy of search results and review summaries and facilitating appropriate product selection.

[0299] "User" means an individual or corporation that uses the system to search for and place orders.

[0300] "Input information" refers to data provided by the user to the system, including attribute data required for product searches and orders.

[0301] A "generative AI model" is an artificial intelligence model used to analyze user input and generate attribute data or review summaries.

[0302] "Attribute data" is data generated based on information input by the user, and includes information such as the product name, place of origin, manufacturer, and budget.

[0303] A "product database" is a database that stores product information and is used to obtain search results by executing a query.

[0304] A "query" is a search command executed against a product database to retrieve data based on specific conditions.

[0305] "Search results" are a collection of product information obtained when a query is executed against a product database.

[0306] "Review Summary" is a summary of reviews related to search results created using a generative AI model, and is information that users can use to help them select products.

[0307] "Display" means the display area of ​​a website or application that provides a visual summary of search results and generated reviews to users.

[0308] "Interaction tools" are chatbots and voice assistants that allow users to interact with the system to select and order products.

[0309] The present invention provides a system that simplifies the process of users searching for products, checking reviews, and placing orders, thereby improving the user experience. Specific embodiments of this system will be described below.

[0310] First, the user enters attribute data into a search form on a website or application. For example, they enter information such as "price range: under 5,000 yen, brand: ABC, function: waterproof." This input information is sent to the server by the device.

[0311] The server analyzes the received input information using a generative AI model to generate attribute data. This generative AI model may be, for example, OpenAI's GPT-4. Based on the generated attribute data, the server generates a query to the product database. The query is written in SQL or NoSQL format.

[0312] The server then executes the generated query against a product database, such as MySQL or MongoDB, and obtains the search results as a result of the query execution.

[0313] Based on the search results, the server again uses the generative AI model to generate a review summary, which summarizes reviews related to the search results and provides useful information to the user.

[0314] The server sends the search results and a summary of the generated reviews to the device, which then displays this information to the user in the display area of ​​the website or application.

[0315] The user selects and orders products through a chatbot or voice assistant, referring to the displayed product information and review summary. For example, the user inputs "I would like to purchase this product" into the chatbot. The device then sends the user's selection and order details to the server.

[0316] The server processes the received order and generates an order confirmation message, which is then displayed to the user via their terminal, allowing the user to complete the entire process smoothly.

[0317] As an example, the following prompt sentence is used:

[0318] "Search for products with price range: under 5000 yen, brand: ABC, function: waterproof."

[0319] "I would like to purchase this item. Please confirm your order."

[0320] This system allows users to easily search for products, check reviews, and place orders. This is expected to improve the user experience and increase the frequency of system usage. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[0321] Step 1:

[0322] The user enters attribute data.

[0323] Input: The user enters attribute data such as "Price range: under 5,000 yen, Brand: ABC, Function: Waterproof" into a search form on a website or application.

[0324] Output: The entered attribute data is sent to the terminal.

[0325] What happens: A user enters information into a search form and clicks the search button.

[0326] Step 2:

[0327] The terminal transmits the input attribute data to the server.

[0328] Input: Attribute data entered by the user.

[0329] Output: The attribute data is sent to the server in JSON format or similar.

[0330] Specific operation: The terminal converts the attribute data into packets and sends them to the server via the network.

[0331] Step 3:

[0332] The server receives the attribute data, breaks it down using a generative AI model, and generates attribute data.

[0333] Input: Attribute data sent from the terminal.

[0334] Output: Attribute data parsed by the generative AI model.

[0335] Specific operation: The data received by the server is input into the generative AI model, and attribute data such as "price range," "brand," and "function" are extracted.

[0336] Step 4:

[0337] A query to the product database is generated based on the attribute data generated by the server and executed.

[0338] Input: Attribute data generated by a generative AI model.

[0339] Output: A SQL query against the product database.

[0340] Specific operation: The server generates the SQL query "SELECT FROM products WHERE price <= 5000 AND brand = 'ABC' AND features LIKE '%waterproof%'" and executes it against the product database.

[0341] Step 5:

[0342] The server retrieves the search results and generates a summary of the review using a generative AI model.

[0343] Input: Search results retrieved from a product database.

[0344] Output: A summary of the review generated by the generative AI model.

[0345] How it works: The server inputs the search results into a generative AI model, which generates a review summary such as, "Many users rate this product as waterproof and reasonably priced."

[0346] Step 6:

[0347] The server sends the search results and a summary of the generated reviews to the device.

[0348] Input: Search results and a summary of the reviews generated.

[0349] Output: Search results and review summaries are sent to the device in JSON format or similar.

[0350] Specific operation: The server converts search results and review summaries into packets and sends them to the terminal over the network.

[0351] Step 7:

[0352] The device will display a summary of search results and reviews to the user.

[0353] Input: Search results and review summary sent from the server.

[0354] Output: A summary of the search results and reviews is displayed to the user.

[0355] Specific operation: The device analyzes the data received and displays it in the display area of ​​the website or application.

[0356] Step 8:

[0357] Users can select and order products through chatbots or voice assistants based on the displayed product information and review summary.

[0358] Input: A user types into the chatbot, "I would like to purchase this product."

[0359] Output: User selections and order details are sent to the terminal.

[0360] Specific action: The user enters text for the chatbot and clicks the send button.

[0361] Step 9:

[0362] The terminal sends the user's selection and order details to the server.

[0363] Input: User selections and orders.

[0364] Output: Selections and order details are sent to the server in JSON format or similar.

[0365] Specific operation: The terminal converts the selection and order details into packets and sends them to the server over the network.

[0366] Step 10:

[0367] The server processes the received order and generates an order confirmation message.

[0368] Input: Order details sent from the terminal.

[0369] Output: Order confirmation message.

[0370] Specific operation: The server analyzes the order details and generates a confirmation message such as "Your order has been confirmed. We will notify you as soon as it is ready to be shipped."

[0371] Step 11:

[0372] The server sends an order confirmation message to the terminal.

[0373] Input: The generated order confirmation message.

[0374] Output: An order confirmation message is sent to the device, for example in JSON format.

[0375] Specific operation: The server converts the confirmation message into a packet and sends it to the terminal through the network.

[0376] Step 12:

[0377] The terminal displays an order confirmation message to the user.

[0378] Input: The order confirmation message sent by the server.

[0379] Output: Order confirmation message is displayed to the user.

[0380] Specific operation: The device analyzes the confirmation message received and displays it in the display area of ​​the website or application.

[0381] (Application example 3)

[0382] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] On conventional online shopping sites, the process of searching for products, checking reviews, and placing an order was complicated, resulting in a poor user experience. Another issue was that the sheer volume of reviews made it difficult for users to quickly obtain the information they needed. Furthermore, the lack of an interactive ordering process made it difficult for users to smoothly select and order products.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0385] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and review summaries compiled by the generative AI, means for ordering products through dialogue, means for generating review summaries using the generative AI, and means for engaging in dialogue with the user using a chatbot or voice assistant. This enables users to easily search for products, quickly check review summaries, and smoothly order products through dialogue.

[0386] The "means for receiving input information from the user" is an interface for receiving information that the user provides to the system.

[0387] "Means of decomposing using generative AI and generating attribute data" refers to the process of using generative AI to analyze information input from users and generate data on product features and attributes.

[0388] The "means for searching for products based on the generated attribute data" is a function for searching for corresponding products from the product database based on the generated attribute data.

[0389] "Means for displaying search results and summaries of reviews compiled by generative AI" refers to an interface that displays to users information about the searched product and summaries of reviews summarized by generative AI.

[0390] The "means for ordering products interactively" is a function for selecting and ordering products through interaction with the user.

[0391] "Means for generating review summaries using generative AI" refers to a process that uses generative artificial intelligence to summarize multiple reviews and provide them to users in an easy-to-understand format.

[0392] "Means of interacting with users using chatbots and voice assistants" refers to a function that uses chatbots and voice assistants to engage in natural conversations with users and support them in selecting and ordering products.

[0393] As an embodiment of the present invention, we will explain the smart shopping assistant system as an example, which allows users to search for products using a smartphone, check reviews generated by generative AI, and order products through a chatbot or voice assistant.

[0394] The server has a means of receiving input information from the user. Information entered by the user through their smartphone is sent to the server. For example, if a user searches for "red medium-sized shirt," that information is sent to the server.

[0395] Next, the server has a means to break down the received input information using a generative AI and generate attribute data. The generative AI analyzes the user's input information and generates attribute data such as "Color: Red" and "Size: Medium." This generative AI uses OpenAI's GPT-3 and other technologies.

[0396] The server has a means for searching for products based on the generated attribute data. The server searches for corresponding products from the product database based on the generated attribute data. For example, a product matching "red medium-sized shirt" is searched for.

[0397] The server also includes a means for displaying search results and summaries of reviews compiled by the generative AI. The server displays information about the searched product and a summary of the reviews compiled by the generative AI to the user. For example, a review summary such as "This shirt is very comfortable and the color is vibrant" may be displayed.

[0398] Furthermore, the server has a means to order products through a dialogue. The user can select and order products through a chatbot or voice assistant based on the displayed product information and review summary. For example, if the user says, "I'd like to order this shirt," the chatbot will support the ordering process.

[0399] The server also includes a means for generating a review summary using a generative AI. The server uses the generative AI to summarize multiple reviews and provide them to the user in an easy-to-understand format. For example, the server inputs a prompt such as "Please summarize the following review: This shirt is very comfortable and the color is vibrant."

[0400] The server also includes a means for interacting with users using chatbots and voice assistants. The server uses chatbots and voice assistants to have natural conversations with users and support them in selecting and ordering products. For example, it inputs prompt sentences such as "The user wants to order a red medium-sized shirt" into the generative AI.

[0401] In this way, the smart shopping assistant system allows users to easily search for products, quickly check review summaries, and smoothly order products through dialogue.

[0402] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0403] Step 1:

[0404] A user uses a smartphone to provide input for a product search.

[0405] Input: Product information the user is searching for (e.g., "red medium-sized shirt")

[0406] Output: The input information is sent to the server.

[0407] Specific operation: When a user enters product information into a smartphone application and presses the search button, the information is sent to the server.

[0408] Step 2:

[0409] The input information received by the server is broken down using generative AI to generate attribute data.

[0410] Input: Product search information sent by the user

[0411] Output: Generated attribute data (e.g. "Color: Red", "Size: Medium")

[0412] Specific operation: The server uses generative AI (e.g., OpenAI's GPT-3) to analyze the user's input information and generate data on the product's features and attributes.

[0413] Step 3:

[0414] A product search is performed based on the attribute data generated by the server.

[0415] Input: Generated attribute data

[0416] Output: A list of matching products

[0417] Specific operation: The server executes a query against the product database to search for products that match the generated attribute data.

[0418] Step 4:

[0419] The server displays the search results and a summary of reviews compiled using generative AI.

[0420] Input: List of search results

[0421] Output: Summary of product information and reviews

[0422] How it works: The server uses generative AI to summarize product reviews based on search results and display them in a user-friendly format.

[0423] Step 5:

[0424] The user places an order for the product based on the displayed product information and review summary.

[0425] Input: User's ordering intent (e.g., "I would like to order this shirt")

[0426] Output: Order confirmation message

[0427] What it does: When a user places an order for a product through a chatbot or voice assistant, the server accepts the order and sends a confirmation message to the user.

[0428] Step 6:

[0429] The server uses generative AI to generate a summary of the review.

[0430] Input: Product review information

[0431] Output: A condensed review summary

[0432] Specific operation: The server inputs a prompt to the generative AI: "Please summarize the following review: [review content]" and generates a summarized review.

[0433] Step 7:

[0434] The server interacts with the user using a chatbot or voice assistant.

[0435] Input: User interaction (e.g., "I'd like to order this shirt")

[0436] Output: Dialogue response message

[0437] Specific operation: The server uses a chatbot or voice assistant to have natural conversations with the user and assist them in selecting and ordering products.

[0438] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0439] "Example 1"

[0440] In one embodiment of the present invention, an input form on a website or application is used as a means for receiving input information from a user. The user enters information into this input form, such as the name of the product they want, its place of origin, the manufacturer, and their budget. The input form also incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's text and voice input. For example, it can recognize emotions such as "looking forward to it," "concerned," and "angry."

[0441] "Example 2"

[0442] The received input information is sent to the generative AI, which uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget. The generative AI also takes into account emotional information obtained from the emotion engine. For example, if the user expresses the emotion "I'm looking forward to it," the generative AI will search for products that match that emotion.

[0443] "Example 3"

[0444] A query to a product database is generated based on the attribute data and emotional information extracted by the generative AI. The generated query is executed to search the product database for products that most closely match the user's needs. The search results are displayed in a display area on the website or application. "Example 4"

[0445] The user selects a product based on the displayed product information. The selected product is then ordered through a chatbot or voice assistant. The user's emotions are also taken into consideration. For example, if the user expresses anger, the system will expedite the ordering process to calm the user as quickly as possible.

[0446] The processing flow of each embodiment will be described below.

[0447] "Example 1"

[0448] Step 1: The user enters information such as the name of the product they want, its origin, manufacturer, and budget into an input form on a website or application.

[0449] Step 2: The emotion engine is activated to analyze emotions from the user's text and voice input.

[0450] "Example 2"

[0451] Step 1: User input is sent to the generative AI.

[0452] Step 2: The generative AI uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[0453] Step 3: The generative AI also takes into account the emotional information obtained from the emotion engine and searches for products that match that emotion.

[0454] "Example 3"

[0455] Step 1: Generate a query to the product database based on the attribute data and emotional information extracted by the generative AI.

[0456] Step 2: Execute the generated query to search the product database for products that best match the user's requirements.

[0457] Step 3: The search results are displayed in a display area on the website or application.

[0458] "Example 4"

[0459] Step 1: The user selects a product based on the displayed product information.

[0460] Step 2: The user places an order for the selected product through a chatbot or voice assistant.

[0461] Step 3: The user's emotions are also taken into consideration. For example, if the user expresses anger, the system will expedite the ordering process to calm the user down as quickly as possible.

[0462] Example 1

[0463] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0464] Conventional systems simply receive input information from users, but are unable to fully understand their emotions and intentions, making it difficult to make appropriate product suggestions and process orders. Furthermore, the inability to centrally select and order products through dialogue with the user creates a problem of poor user experience.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0466] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information with an emotion analysis engine and generating emotion data, means for integrating the generated emotion data with the input information and generating a prompt sentence to be input to the generative AI model, means for inputting the prompt sentence to the generative AI model and generating a response, means for displaying the generated response to the user, and means for placing a product order during the dialogue. This makes it possible to understand the user's emotions and intentions and to centrally perform appropriate product suggestions and order processing.

[0467] "Means for receiving input information from users" refers to the means by which users use input forms on websites or applications to enter information such as the name, origin, manufacturer, and budget of the desired product.

[0468] An "emotion analysis engine" is an engine that analyzes text and voice data entered by the user and uses natural language processing technology to analyze the user's emotions.

[0469] "Emotion data" is information about the user's emotions analyzed by an emotion analysis engine, and is data that indicates emotions such as "looking forward to it," "troubled," and "angry."

[0470] The "means for generating a prompt sentence to be input to a generative AI model" refers to a means for integrating user input information and emotional data and generating a prompt sentence to be input to a generative AI model.

[0471] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on user input information and emotional data, such as a model that uses natural language generation technology.

[0472] A "means for generating a response" is a means for inputting a prompt sentence into a generative AI model and generating an appropriate response to the user.

[0473] "Means for displaying the generated response to the user" refers to means for displaying the response obtained from the generative AI model to the user, such as on a website or application.

[0474] The "means for placing an order for a product" refers to a means for selecting and ordering a product in a unified manner through dialogue with the user.

[0475] This invention is a system that receives input information from a user, performs emotion analysis based on that information, and generates an appropriate response using a generative AI model. Specific embodiments of this system are described below.

[0476] Receiving input from the user

[0477] A user uses an input form on a website or application to enter information such as the name of the product they want, its place of origin, the manufacturer, and their budget. This input form is built using HTML and JavaScript (registered trademark). For example, a user might enter, "I'm looking for fresh apples from Hokkaido. My budget is 3,000 yen or less."

[0478] Sending input information

[0479] The terminal sends the information entered by the user to the server using the HTTPS protocol. The terminal converts the input form data into JSON format and sends it to the server as an HTTPS request.

[0480] Emotion analysis using an emotion analysis engine

[0481] The server sends the received user input information to a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to analyze the user's emotions. Specifically, it uses services such as Google® Cloud Natural Language API and IBM Watson® Natural Language Understanding. The sentiment analysis engine recognizes emotions such as "looking forward to it," "concerned," and "angry" from the user's input.

[0482] Generate prompt statement

[0483] The server combines the user's input information with the emotion data obtained from the emotion engine to generate a prompt sentence to be input into the generative AI model. For example, it generates a prompt sentence like the one below.

[0484] The user entered, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen." The user also expressed the emotion, "I'm looking forward to it." Based on this information, please suggest the best products for the user.

[0485] Response generation using generative AI models

[0486] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a response. The generative AI model generates a response that suggests the best product for the user based on the prompt. For example, it generates a response like the one below.

[0487] "You're looking for fresh apples from Hokkaido, aren't you? Our recommended product for those under 3,000 yen is Fuji apples from Hokkaido. They're currently on sale at a special price, so be sure to give them a try!"

[0488] Sending and Displaying Responses

[0489] The server sends the response received from the generative AI model to the device, which then displays the response received from the server to the user. The device then inserts the response into an HTML element and displays it to the user.

[0490] In this way, the system makes optimal product recommendations based on the user's input information and emotions. This system makes it possible to understand the user's emotions and intentions, and to make appropriate product recommendations and process orders in an integrated manner.

[0491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0492] Step 1:

[0493] Users use input forms on websites or applications to enter information such as the name of the product they want, its origin, manufacturer, budget, etc. The input forms are built using HTML and JavaScript.

[0494] Input: A user fills in a form saying, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen."

[0495] Output: Data entered into the input form.

[0496] Step 2:

[0497] The terminal sends the information entered by the user to the server using the HTTPS protocol. The terminal converts the input form data into JSON format and sends it to the server as an HTTPS request.

[0498] Input: Data entered into an input form.

[0499] Output: The data is converted to JSON format and sent to the server.

[0500] Step 3:

[0501] The server then sends the received user input information to a sentiment analysis engine, which then uses natural language processing technology to analyze the user's emotions. Specifically, it uses services such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[0502] Input: User input data in JSON format.

[0503] Output: Sentiment data analyzed by the sentiment analysis engine.

[0504] Step 4:

[0505] The server integrates the user's input information with the emotional data obtained from the emotion engine and generates a prompt sentence to be input into the generative AI model.

[0506] Input: User input and emotional data.

[0507] Output: A prompt to be fed into the generative AI model.

[0508] Specific behavior: The server generates a prompt like this:

[0509] The user entered, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen." The user also expressed the emotion, "I'm looking forward to it." Based on this information, please suggest the best products for the user.

[0510] Step 5:

[0511] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a response. The generative AI model generates a response that suggests the best product for the user based on the prompt.

[0512] Input: The prompt statement.

[0513] Output: The response generated by the generative AI model.

[0514] Specific operation: The server sends a prompt sentence to GPT-3 and receives a response like the following:

[0515] "You're looking for fresh apples from Hokkaido, aren't you? Our recommended product for those under 3,000 yen is Fuji apples from Hokkaido. They're currently on sale at a special price, so be sure to give them a try!"

[0516] Step 6:

[0517] The server sends the response received from the generative AI model to the device, which then displays the response received from the server to the user. The device then inserts the response into an HTML element and displays it to the user.

[0518] Input: The response from the generative AI model.

[0519] Output: The response that is displayed to the user.

[0520] Specific behavior: The response received by the device is inserted into an HTML element and displayed to the user.

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0523] Conventional online shopping sites have the problem that users simply enter information about the product they want, but the site does not provide optimal product suggestions based on the user's emotions and needs. Also, simply searching for products based on the information entered by the user makes it difficult to increase user satisfaction.

[0524] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and a summary of the reviews compiled by the generative AI, means for ordering products through dialogue, means including an emotion engine for analyzing the user's emotions, and means for suggesting optimal products based on the user's input information and emotions. This makes it possible to suggest optimal products based on the user's emotions and needs.

[0525] The "means for receiving input information from the user" is an interface that allows the user to input information such as the name, origin, manufacturer, and budget of the product they want.

[0526] "Generative AI" is an artificial intelligence technology that analyzes input information from users and generates attribute data.

[0527] "Attribute data" is data obtained by breaking down information entered by the user, such as the product name, place of origin, manufacturer, and budget.

[0528] The "means for searching for products" is a function for searching for appropriate products based on the generated attribute data.

[0529] "Means for displaying review summaries" is a function that displays to users a summary of reviews about a product compiled by generative AI.

[0530] The "means for placing an order for a product" is a function for selecting and ordering products in a unified manner through dialogue with the user.

[0531] An "emotion engine" is a technology for analyzing emotions from a user's text or voice input.

[0532] "Means for suggesting optimal products" is a function for suggesting optimal products to users based on the information and emotions input by the user.

[0533] The system for implementing this invention receives input information from a user, analyzes it using generative AI, and proposes optimal products. Specific embodiments are described below.

[0534] System Configuration

[0535] 1. User Device:

[0536] Input form: An interface for users to enter information such as the name of the product they want, its place of origin, manufacturer, budget, etc. It is built using HTML / CSS and JavaScript.

[0537] Speech Recognition System: A system that allows users to input information by voice. It uses the Google Speech-to-Text API.

[0538] 2. Server:

[0539] Generative AI: An artificial intelligence technology that analyzes user input and generates attribute data. It uses TENSORFLOW (registered trademark).

[0540] Emotion Engine: Technology for analyzing emotions from user text and voice input. Uses IBM Watson Tone Analyzer.

[0541] Product search engine: A function for searching for appropriate products based on the generated attribute data.

[0542] Review summary display: A feature that displays a summary of product reviews compiled by generative AI to users.

[0543] Product recommendation engine: A function that suggests the most suitable products based on the user's input information and emotions.

[0544] Processing flow

[0545] 1. User Input:

[0546] Users input information about the product they want through a smartphone app. For example, they can enter "fresh organic vegetables" and set their budget at 3,000 yen.

[0547] In the case of voice input, the user would say, "I want fresh organic vegetables."

[0548] 2. Emotion analysis:

[0549] If a user types "I'm looking forward to it," the emotion engine will interpret it as "positive."

[0550] 3. Product suggestion:

[0551] The server uses AI to suggest the most suitable product based on the user's input information and emotions, for example, a "fresh organic vegetable set."

[0552] Hardware and software used

[0553] Hardware: Smartphones, servers

[0554] Software: Google Speech-to-Text API, IBM Watson Tone Analyzer, TensorFlow, HTML / CSS, JavaScript

[0555] Specific examples

[0556] Input form: The user enters "fresh organic vegetables" and sets the budget to "3,000 yen."

[0557] Voice input: The user says, "I want fresh organic vegetables."

[0558] Sentiment analysis: If a user types "I'm looking forward to it," the sentiment engine interprets it as "positive."

[0559] Product suggestion: AI suggests a "fresh organic vegetable set."

[0560] Prompt Sentence Examples

[0561] The user inputs "fresh organic vegetables" and sets the budget as "3000 yen." The emotion engine analyzes the user's emotion as "looking forward to it." Based on this, please suggest the most suitable products.

[0562] In this way, it becomes possible to propose optimal products according to the user's emotions and needs.

[0563] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0564] Step 1:

[0565] The user device receives input information from the user. The user uses the smartphone app's input form to enter information such as the name of the product they want, its place of origin, manufacturer, and budget. For example, they might enter "fresh organic vegetables" and set their budget at "3,000 yen." The input information is then sent to the server.

[0566] Step 2:

[0567] The user's device uses a speech recognition system to convert voice input into text. If the user says, "I want fresh organic vegetables," the voice data is converted into text using the Google Speech-to-Text API. The converted text data is then sent to the server.

[0568] Step 3:

[0569] The server uses generative AI to analyze the received input information and generate attribute data. The generative AI uses TensorFlow to analyze the product name, place of origin, manufacturer, budget, and other information entered by the user, and generates attribute data. For example, attribute data such as "fresh organic vegetables," "place of origin: Japan," "manufacturer: unknown," and "budget: 3,000 yen" is generated.

[0570] Step 4:

[0571] The server uses an emotion engine to analyze the user's emotions. If the user types "I'm looking forward to it," IBM Watson Tone Analyzer is used to analyze emotions from the text data. The analysis results in the emotional data "positive."

[0572] Step 5:

[0573] The server performs a product search based on the generated attribute data. The product search engine searches the product information in the database and extracts products that match the attribute data. For example, a "fresh organic vegetable set" may be obtained as a search result.

[0574] Step 6:

[0575] The server displays a summary of the reviews compiled by the generative AI. The generative AI analyzes reviews related to the search results and generates summaries. For example, a review summary such as "This organic vegetable set is fresh and delicious" is generated.

[0576] Step 7:

[0577] The server proposes optimal products based on the user's input information and emotions. The product proposal engine selects optimal products based on the generated attribute data and emotion data and proposes them to the user. For example, it proposes a "fresh organic vegetable set."

[0578] Step 8:

[0579] The user's device displays the summary of the suggested products and reviews received from the server. The user can then check the suggested products and consider purchasing them.

[0580] Step 9:

[0581] The user terminal provides an interface for the user to place an order for the product. The user selects the suggested product and completes the order process. The order information is then sent to the server.

[0582] Step 10:

[0583] The server processes the received order information and processes the product shipping. The order information is linked to the logistics system, and the product is shipped.

[0584] Example 2

[0585] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0586] Conventional systems have difficulty properly analyzing information entered by users and extracting attribute data such as product name, origin, and budget. Furthermore, they are unable to take into account the user's emotional information when searching for or ordering products, which makes it difficult to increase user satisfaction.

[0587] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and an overview of the reviews compiled by the generative AI, means for ordering products through dialogue, and means for acquiring emotion information from an emotion engine and reflecting it in the analysis results. This makes it possible to appropriately analyze the user's input information and perform product searches and orders that take emotion information into consideration.

[0588] "User" refers to an individual or corporation that uses the system to input information and search for and place orders.

[0589] "Input information" refers to information such as text data and voice data that a user provides to the system.

[0590] "Generative AI" refers to artificial intelligence technology that analyzes input information and generates attribute data.

[0591] "Attribute data" refers to characteristic information such as product name, place of origin, and budget that generative AI extracts from input information.

[0592] "Product search" refers to the process of searching a database for the product the user is looking for based on the generated attribute data.

[0593] "Review summary" refers to a summary of product ratings and impressions compiled by generative AI.

[0594] An "emotion engine" refers to technology for analyzing and obtaining emotional information from user input.

[0595] "Emotional information" refers to data that indicates the emotional state extracted from the user's input information.

[0596] "Dialogue" refers to the communication process in which a user and a system exchange information.

[0597] "Order" refers to the process for purchasing the product selected by the User.

[0598] This invention is a system that analyzes information input by a user, generates attribute data such as product name, place of origin, budget, etc., and performs product search and ordering taking emotional information into consideration. Specific embodiments of this system are described below.

[0599] The server has a means for receiving input information from users. The user uses a device (such as a smartphone or PC) to input information about the product they are searching for. For example, they might input, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen."

[0600] The device sends the information entered by the user to the server. At this time, the input information is sent to the server in an appropriate format (for example, JSON format). The server then sends the received input information to the generative AI. Since the generative AI is hosted on the cloud, the server accesses the generative AI via the internet.

[0601] The generative AI uses the Python library spaCy and Transformers to analyze the input information. Specifically, it tokenizes the text and understands the meaning of each token. From the analysis results, the generative AI extracts attribute data such as the product name (salmon roe), origin (Hokkaido), and budget (under 5,000 yen). This allows it to specifically understand the user's request.

[0602] Furthermore, generative AI uses emotion engines (such as TextBlob and VADER) to extract emotional information from user input. For example, if the user expresses the emotion "looking forward to it," that emotional information is reflected in the analysis results.

[0603] The server provides appropriate product information to the user based on the analysis results received from the generative AI. For example, it searches for "fresh salmon roe from Hokkaido within a budget of 5,000 yen" and displays the results to the user. The user can then interactively select and order products based on the displayed product information.

[0604] As a concrete example, a user enters into a terminal, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen." The terminal sends this information to a server. The server then sends the received information to a generative AI, which analyzes it using natural language processing technology. The generative AI extracts the product name (salmon roe), place of origin (Hokkaido), and budget (within 5,000 yen), and further analyzes the user's emotions (looking forward to it) using an emotion engine. Based on this information, the server provides the user with appropriate product information.

[0605] An example prompt is:

[0606] User Input: "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen."

[0607] Generative AI output: {"Product name": "Salmon roe", "Origin": "Hokkaido", "Budget": "Under 5,000 yen", "Emotion": "Looking forward to it"}

[0608] In this way, the server analyzes the information input by the user, generates appropriate attribute data, and realizes a system that provides the user with the most suitable product information.

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The user inputs the input information into the terminal.

[0612] The user uses a device (such as a smartphone or computer) to input information about the product they are searching for. For example, they might input, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen." This input information becomes the initial data for the system.

[0613] Step 2:

[0614] The terminal sends the input information to the server.

[0615] The terminal sends the information entered by the user to the server. At this time, the input information is sent to the server in an appropriate format (for example, JSON format). The input information is "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen."

[0616] Step 3:

[0617] The server sends the input information to the generative AI.

[0618] The server sends the received input information to the generative AI. Since the generative AI is hosted on the cloud, the server accesses the generative AI via the internet. The input information is passed directly to the generative AI.

[0619] Step 4:

[0620] Generative AI analyzes input information using natural language processing technology.

[0621] Generative AI uses Python libraries such as spaCy and Transformers to analyze input information. Specifically, it tokenizes sentences and understands the meaning of each token. It analyzes the input information, "Looking for fresh salmon roe from Hokkaido. Budget is under 5,000 yen," and generates tokenized data.

[0622] Step 5:

[0623] Generative AI extracts attribute data such as product name, place of origin, and budget.

[0624] The generative AI extracts attribute data such as the product name (salmon roe), origin (Hokkaido), and budget (under 5,000 yen) from the analysis results. This allows the user's request to be specifically understood. The output data is {"Product name": "salmon roe", "Origin": "Hokkaido", "Budget": "under 5,000 yen"}.

[0625] Step 6:

[0626] The generative AI obtains emotional information from the emotion engine and reflects it in the analysis results.

[0627] The generative AI uses an emotion engine (such as TextBlob or VADER) to obtain emotional information from the user's input. For example, if the user expresses the emotion "I'm looking forward to it," that emotional information is reflected in the analysis results. The output data is {"Product name": "Salmon roe", "Origin": "Hokkaido", "Budget": "Under 5,000 yen", "Emotion": "I'm looking forward to it"}.

[0628] Step 7:

[0629] The server receives the analysis results from the generative AI and provides appropriate product information to the user.

[0630] The server provides the user with appropriate product information based on the analysis results received from the generative AI. For example, it searches for "fresh salmon roe from Hokkaido within a budget of 5,000 yen" and displays the results to the user. The user can interactively select and order products based on the displayed product information. The output data is a list of products as search results.

[0631] (Application example 2)

[0632] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0633] On conventional online shopping sites, when searching for products based on the information entered by the user, it was difficult to take into account the user's emotions and detailed attribute data. As a result, it was not possible to accurately recommend the products that the user really wanted, which could lead to a decrease in satisfaction. In addition, there was also the issue of not being able to centrally select and order products, which resulted in a poor user experience.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0635] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information with a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and an overview of the reviews compiled by the generative AI, means for analyzing the user's emotional information with an emotion engine, means for recommending products taking into account the analyzed emotional information, and means for ordering products through a dialogue. This enables product recommendations that take into account the user's emotions and detailed attribute data, and is expected to improve the user experience.

[0636] "Means for receiving input information from the user" refers to an interface for receiving text or voice information entered by the user.

[0637] "Method of decomposing using generative AI and generating attribute data" refers to the process of using generative AI to analyze input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[0638] The "means for searching for products" is a function that searches for corresponding products from databases and online stores based on the generated attribute data.

[0639] "Means for displaying search results and summaries of reviews compiled by generative AI" refers to an interface that displays to users summaries of product reviews and ratings compiled by generative AI along with search results.

[0640] The "means for analyzing user's emotional information using an emotion engine" is a process of using an emotion engine to analyze emotions from user's input information and extract that information.

[0641] The "means for recommending products taking into consideration analyzed emotional information" is a function that recommends products that match the user's emotions based on analyzed emotional information.

[0642] The "means for ordering products through dialogue" is an interface that allows users to select and order products in a unified manner through dialogue with the user.

[0643] The system for implementing the present invention includes a series of processes for receiving input information from a user, analyzing the information, and recommending products. Specific embodiments are described below.

[0644] Hardware and software used

[0645] Hardware: Smartphone

[0646] software:

[0647] Natural language processing libraries (e.g., spaCy, NLTK)

[0648] Sentiment analysis engine (e.g. IBM Watson Tone Analyzer)

[0649] Generative AI models (e.g., GPT-4)

[0650] Program processing procedure

[0651] 1. Accepting text input:

[0652] A user enters text into a smartphone application.

[0653] Example: "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen."

[0654] 2. Natural Language Processing Analysis:

[0655] The server analyzes the input text using a natural language processing library (e.g., spaCy, NLTK) and extracts attribute data such as the product name, place of origin, manufacturer, and budget.

[0656] Example: Product name: Salmon roe, Origin: Hokkaido, Budget: 5,000 yen

[0657] 3. Emotion analysis:

[0658] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion information.

[0659] Example: Emotion: Enjoyment

[0660] 4. Product recommendation:

[0661] The server uses a generative AI model (e.g., GPT-4) to recommend optimal products based on the extracted attribute data and emotional information.

[0662] Example: "I recommend fresh salmon roe from Hokkaido, priced under 5,000 yen."

[0663] Examples and prompts

[0664] Examples:

[0665] If a user types, "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5,000 yen," the application will display, "Recommendations for fresh salmon roe from Hokkaido within a budget of 5,000 yen."

[0666] Example prompt for a generative AI model:

[0667] User entered text: "I'm looking for fresh Hokkaido salmon roe. My budget is under 5000 yen."

[0668] Extracted attribute data: Product name: salmon roe, origin: Hokkaido, budget: 5,000 yen

[0669] Emotional information: Enjoyment

[0670] Generate product recommendations.

[0671] In this way, we can realize a smartphone application for online shopping sites that recommends optimal products based on the user's input and emotional information. This system makes it possible to recommend products that take into account the user's emotions and detailed attribute data, and is expected to improve the user experience.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] A user enters text into an application on a smartphone.

[0675] Input: Text information entered by the user (e.g. "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen.")

[0676] Output: The entered text information is sent to the server.

[0677] Step 2:

[0678] The server analyzes the received text information using a natural language processing library (e.g., spaCy, NLTK).

[0679] Input: Text information entered by the user

[0680] Data processing: Tokenize text information and extract attribute data such as product name, place of origin, manufacturer, and budget.

[0681] Output: Extracted attribute data (e.g., product name: salmon roe, origin: Hokkaido, budget: 5,000 yen)

[0682] Step 3:

[0683] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion information.

[0684] Input: Text information entered by the user

[0685] Data calculation: Analyze emotions from text information and extract emotional information.

[0686] Output: Extracted emotion information (e.g., emotion: enjoyment)

[0687] Step 4:

[0688] The server uses a generative AI model (e.g., GPT-4) to recommend optimal products based on the extracted attribute data and emotional information.

[0689] Input: Extracted attribute data and emotion information

[0690] Data calculation: Input a prompt sentence into the generative AI model to generate the optimal product.

[0691] Output: Recommended product information (e.g. "We recommend fresh salmon roe from Hokkaido, within a budget of 5,000 yen.")

[0692] Step 5:

[0693] The server displays recommended product information and a summary of reviews compiled by generative AI to the user.

[0694] Input: Recommended product information and review summary

[0695] Output: A summary of product information and reviews displayed on the user's smartphone

[0696] Step 6:

[0697] The user selects and orders products through a dialogue.

[0698] Input: User selection and order information

[0699] Output: The order is confirmed and sent to the server.

[0700] In this way, a system is realized that recommends optimal products based on the user's input information and emotional information, allowing users to easily select and order products.

[0701] Example 3

[0702] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0703] Conventional systems simply use user input information to search for products, but do not take into account the user's emotional information to suggest optimal products or optimize the ordering process. This results in lower user satisfaction and an inability to respond appropriately to users who are in a hurry or in a particular emotional state.

[0704] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0705] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI model to generate attribute data and emotional information, means for generating and executing a query to a product database based on the generated attribute data and emotional information, means for displaying search results and a summary of reviews compiled by the generative AI model, means for selecting and ordering products through dialogue, and means for optimizing the ordering process by taking the user's emotional information into consideration. This enables optimal product suggestions and a speedy ordering process that take the user's emotional information into consideration.

[0706] "User input information" refers to data such as text or voice that a user provides to the system.

[0707] A "generative AI model" is an artificial intelligence model that analyzes user input information and generates attribute data and emotional information.

[0708] "Attribute data" is specific information such as the product name, place of origin, manufacturer, and budget extracted from the information entered by the user.

[0709] "Emotional information" refers to the user's emotional state (e.g., feeling rushed, relaxed, etc.) analyzed from the user's input information.

[0710] A "product database" is a database that stores product information.

[0711] A "query" is a command statement for searching a product database for data based on specific conditions.

[0712] "Search results" are product information obtained from the product database when a query is executed.

[0713] A "review summary" is a summary of product ratings and user opinions compiled by a generative AI model.

[0714] "Dialogue" is communication between a user and a system through text or voice.

[0715] The "order process" is a series of steps a user takes to purchase the product they have selected.

[0716] "Optimization" means taking into account the user's emotional information to efficiently and effectively carry out the ordering process.

[0717] This invention is a system that performs product searches based on user input, displays search results and review summaries, and enables product ordering through dialogue. The system uses a generative AI model to extract user attribute data and emotional information, and generates and executes queries against a product database to search for products that best match the user's needs.

[0718] The server uses a generative AI model (e.g., a general natural language processing model) to analyze the user's input information and generate attribute data and emotional information. Specifically, it receives text or voice data entered by the user on a website or application, analyzes it, and extracts attribute data such as the product name, place of origin, manufacturer, and budget, as well as the user's emotional state (e.g., whether in a hurry or relaxed).

[0719] Next, the server generates a query to the product database based on the extracted attribute data and emotion information. For example, it generates the following SQL query:

[0720] sql

[0721] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[0722] This query is executed to search for the corresponding products in a product database (e.g., a general relational database or NoSQL database).

[0723] The device displays search results and a summary of reviews compiled by the generative AI model. The display area includes the product name, price, rating, and review summary. For example, it displays:

[0724] Product name: Smartphone A

[0725] Price: 45,000 yen

[0726] Rating: 4.5

[0727] Review Summary: "Very easy to use. Camera performance is excellent."

[0728] The user selects a product based on the displayed product information and review summary. The selected product can be ordered through a chatbot (e.g., a general conversational agent) or a voice assistant (e.g., a general voice recognition system). For example, the user can input "I would like to order smartphone A" into the chatbot.

[0729] The server optimizes the ordering process by taking into account the user's emotional information. For example, if the user indicates that they are in a hurry, the server will take steps to expedite the ordering process.

[0730] For example, if a user inputs "I'm looking for a new smartphone," the generative AI model extracts the user's attribute data (budget, preferred brand, desired features) and emotional information (hurry, relaxed). The server generates the following query to search for the relevant smartphone in the product database:

[0731] sql

[0732] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[0733] The terminal displays the search results, and the user selects smartphone A based on the displayed information and places an order through the chatbot. If the user is in a hurry, the server will expedite the order process.

[0734] Example prompt sentence:

[0735] If a user is looking for a new smartphone, extract attribute data such as budget, preferred brand, desired features, etc. to generate queries against your product database, and also take into account user sentiment to optimize the ordering process.

[0736] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0737] Step 1:

[0738] The user initiates a product search.

[0739] A user types "looking for a new smartphone" into the search bar on a website or application and clicks the search button.

[0740] Input: User's search query (e.g., "Looking for a new smartphone")

[0741] Output: The user's search query is sent to the server.

[0742] Step 2:

[0743] The server uses the generated AI model to extract the user's attribute data and emotional information.

[0744] The server uses a generative AI model to analyze the user's input information and extract attribute data such as the product name, place of origin, manufacturer, and budget, as well as the user's emotional information.

[0745] Input: User's search query

[0746] Output: Attribute data (e.g., budget, preferred brand, desired features) and emotional information (e.g., rushed, relaxed)

[0747] Step 3:

[0748] The server generates a query to the product database based on the extracted data.

[0749] The server generates a query to the product database based on the extracted attribute data. For example, it generates the following SQL query:

[0750] sql

[0751] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[0752] Input: Attribute data

[0753] Output: A query against the product database

[0754] Step 4:

[0755] The server executes the generated query and retrieves search results from the product database.

[0756] The server executes the generated query and searches for the corresponding product from the product database.

[0757] Input: Query against product database

[0758] Output: Search results (e.g. product name, price, rating, review summary)

[0759] Step 5:

[0760] The device displays search results and a summary of reviews compiled by the generative AI model.

[0761] The device displays product information from search results and a summary of reviews compiled by the generative AI model in a display area on websites and applications.

[0762] Input: Search results

[0763] Output: A summary of the product information and reviews displayed in the display area

[0764] Step 6:

[0765] The user selects a product based on the displayed product information.

[0766] The user refers to the displayed product information and review summary to select the product they wish to purchase.

[0767] Input: Displayed product information and review summary

[0768] Output: The product selected by the user

[0769] Step 7:

[0770] Users place orders through chatbots and voice assistants.

[0771] A user orders a product through a chatbot or voice assistant. For example, the user might say to the chatbot, "I would like to order Smartphone A."

[0772] Input: User's order request

[0773] Output: Start of ordering process

[0774] Step 8:

[0775] The server takes into account the user's emotional information and optimizes the ordering process.

[0776] The server optimizes the ordering process by taking into account the user's emotional information. For example, if the user indicates that they are in a hurry, the server will take steps to expedite the ordering process.

[0777] Input: User's emotional information

[0778] Output: Optimized ordering process

[0779] (Application example 3)

[0780] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0781] On conventional online shopping sites, it was difficult to fully utilize user attribute data and emotional information to optimize product searches. Furthermore, review information in search results was often scattered, leaving users with insufficient information to select the right product. Furthermore, when users placed product orders, the lack of an interactive interface prevented a smooth ordering process. These issues need to be resolved.

[0782] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0783] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for ordering products through dialogue, means for analyzing user emotional information, and means for optimizing the product search by taking the user's emotional information into consideration. This enables optimal product searches using the user's attribute data and emotional information, and by displaying search results and review summaries in a unified manner, it becomes easier for users to select appropriate products. Furthermore, a smooth ordering process can be achieved through an interactive interface.

[0784] "User input information" refers to data such as text or voice that a user provides to the system.

[0785] "Generative AI" is an artificial intelligence technology that analyzes input information from users and generates attribute data and emotional information.

[0786] "Attribute data" is characteristic information extracted from the user's input information, such as the product name, place of origin, manufacturer, and budget.

[0787] "Product search" is the process of finding the most suitable product from the product database based on the generated attribute data.

[0788] The "review summary" is a summary of user ratings and impressions of a product compiled by generative AI.

[0789] "Means for ordering products interactively" refers to a function that allows users to select and order products interactively using a chatbot or voice assistant.

[0790] "Emotion information" is data that indicates the user's emotional state, analyzed from the user's input information.

[0791] "Means to optimize product searches" refers to a function that takes into account the user's emotional information to provide more appropriate products as search results.

[0792] The system for implementing this invention receives user input information, generates attribute data and emotional information using generative AI, performs product searches, displays search results and review summaries, and allows product ordering through an interactive interface. Specific embodiments are described below.

[0793] Hardware and software used

[0794] Hardware: Smartphones, servers

[0795] Software: Python, OpenAI API, EmotionRecognizer library, ChatBot library

[0796] Data processing and calculation

[0797] Receiving and analyzing user input information

[0798] The server receives text and voice input from the user. The received input information is analyzed using generative AI to generate attribute data such as the product name, place of origin, manufacturer, and budget. The EmotionRecognizer library is also used to analyze the user's emotional information.

[0799] Performing a product search

[0800] The server generates a query to the product database based on the generated attribute data and emotion information. This query is generated using the OpenAI API. The generated query is executed to search for the best product.

[0801] View search results and reviews

[0802] The server displays the product information obtained as search results and a summary of the reviews compiled by generative AI on the smartphone's display area, allowing users to view the search results and review summaries in one place.

[0803] Interactive order placement

[0804] Users can select and order products through a chatbot or voice assistant, referring to the displayed product information and review summary. The ChatBot library is used to realize dialogue with the user. The system also takes into account the user's emotional information; for example, if the user indicates that they are feeling stressed, it will prioritize suggestions of products that will help them relax.

[0805] Specific examples

[0806] For example, if a user inputs, "I've been feeling stressed lately, so I want a product that helps me relax," the server will send the following prompt sentence to the generative AI model:

[0807] Example prompt: "Please find the best products based on the user's demographic data: Most of the products purchased in the past are relaxation items, and their emotion: They are stressed."

[0808] The generative AI model uses this prompt to find the best products and displays the search results and a summary of reviews to the user, who can then select the product and complete the order through the chatbot.

[0809] In this way, the system can utilize user attribute data and sentiment information to optimize product searches, display search results and review summaries in a unified manner to help users select the right product, and facilitate a smooth ordering process through a conversational interface.

[0810] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0811] Step 1:

[0812] The server receives input information from the user. The user provides the input information via text or voice using a smartphone. The input information includes the user's wishes and requests. After receiving the input information, the server passes it on to the next processing step.

[0813] Input: User text or voice input

[0814] Output: The input information received

[0815] Step 2:

[0816] The server uses a generative AI to analyze the received input information and generate attribute data and emotional information. The generative AI analyzes the input information and extracts attribute data such as the product name, place of origin, manufacturer, and budget. It also uses the EmotionRecognizer library to analyze the user's emotional information.

[0817] Input: User input information

[0818] Data processing: Extraction of attribute data using generative AI, analysis of emotional information using EmotionRecognizer

[0819] Output: attribute data, emotion information

[0820] Step 3:

[0821] The server generates a query to the product database based on the generated attribute data and emotion information. Using the OpenAI API, it sends a prompt to the generative AI model and generates a query to search for the best product.

[0822] Input: attribute data, emotion information

[0823] Data Computation: Query Generation using OpenAI API

[0824] Output: Product search query

[0825] Step 4:

[0826] The server executes the generated query to search for the most suitable product from the product database, and obtains the product information obtained as a search result.

[0827] Input: Product search query

[0828] Data operations: querying the product database

[0829] Output: Product information from search results

[0830] Step 5:

[0831] The server displays the product information obtained as search results and a summary of the reviews compiled by generative AI on the smartphone's display area, allowing users to view the search results and review summaries in one place.

[0832] Input: Search result product information, review summary

[0833] Data processing: Converting search results and review summaries into a display format

[0834] Output: A summary of search results and reviews displayed in the smartphone display area

[0835] Step 6:

[0836] Users can select and order products through a chatbot or voice assistant, referring to the displayed product information and review summary. The ChatBot library is used to realize dialogue with the user. The system also takes into account the user's emotional information; for example, if the user indicates that they are feeling stressed, it will prioritize suggestions of products that will help them relax.

[0837] Input: User selection information, emotion information

[0838] Data Calculation: Interaction processing using ChatBot library

[0839] Output: User's order information

[0840] Step 7:

[0841] The server receives the user's order information and processes the order, including checking product availability, processing payment, and arranging delivery.

[0842] Input: User's order information

[0843] Data processing: inventory checks, payment processing, delivery arrangements

[0844] Output: Order confirmation information, shipping information

[0845] Through these steps, the system can utilize user attribute data and sentiment information to provide optimal product searches, display search results and review summaries in a unified manner to help users select the right product, and facilitate a smooth ordering process through a conversational interface.

[0846] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0847] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0848] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0849] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0850] [Second embodiment]

[0851] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0852] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0853] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0854] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0855] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0856] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0857] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0858] FIG. 4 shows an example of the main functions of the data processing device 12 and smart glasses 214.

[0859] 4, in the data processing device 12, the specific processing is performed by the processor 28. The storage 32 stores a specific processing program 56.

[0860] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0861] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0862] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0863] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0864] "Example 1"

[0865] The system of the present invention receives input information from users through, for example, an input form on a website or application, or a voice recognition system. Users input information such as the name, place of origin, manufacturer, and budget of the product they want.

[0866] "Example 2"

[0867] The generative AI breaks down the received input information and generates attribute data, for example, using natural language processing technology. This generative AI analyzes the input information and extracts attribute data such as the product name, place of origin, manufacturer, and budget.

[0868] "Example 3"

[0869] As a means for searching for products based on the generated attribute data, for example, a query is generated and executed against a product database. This product search is performed based on the attribute data to search for products that most closely match the user's needs.

[0870] "Example 4"

[0871] For example, a display area on a website or application can be used to display search results and summaries of reviews compiled by generative AI. This display area displays product information from search results and summaries of related reviews.

[0872] "Example 5"

[0873] Chatbots and voice assistants, for example, are used as a means of ordering products through dialogue. Users can select and order products through dialogue, referring to the displayed product information and review summaries.

[0874] The processing flow of each embodiment will be described below.

[0875] "Example 1"

[0876] Step 1: The user enters information such as the name of the product they want, its origin, manufacturer, and budget into an input form on a website or application.

[0877] Step 2: Input information is sent to the system and received by the generative AI.

[0878] Step 3: The generative AI uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[0879] "Example 2"

[0880] Step 1: Generate a query to the product database based on the attribute data extracted by the generative AI.

[0881] Step 2: Execute the generated query to search the product database for products that best match the user's requirements.

[0882] Step 3: Display the search results in a display area on your website or application.

[0883] "Example 3"

[0884] Step 1: The user selects a product based on the displayed product information.

[0885] Step 2: The user places an order for the selected product via a chatbot or voice assistant.

[0886] Step 3: The system receives the order information and processes the order.

[0887] Example 1

[0888] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0889] In conventional systems, even if a user inputs information about the product they want, it is difficult to process that information appropriately and provide product information that meets the user's needs. In addition, it is often impossible to quickly and accurately return product information generated based on the information entered by the user. This leads to problems such as lower user satisfaction and reduced system usage.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0891] In this invention, the server includes means for receiving input information from a user, means for saving the received input information in a database, means for generating a prompt sentence based on the saved information, means for sending the generated prompt sentence to a generative AI model, means for receiving product information generated from the generative AI model, and means for returning the received product information to the user, thereby enabling product information based on a user's request to be provided quickly and accurately.

[0892] "Means for receiving input information from users" refers to the interface for receiving information that users input through a website or application.

[0893] "Means for saving received input information in a database" is a function for storing information received from a user in a database.

[0894] "Means for generating prompt sentences based on stored information" refers to a function that creates instruction sentences (prompt sentences) for the generative AI model based on information stored in the database.

[0895] "Means for sending the generated prompt sentence to the generative AI model" is a function for sending the generated prompt sentence to the generative AI model.

[0896] "Means for receiving product information generated from a generative AI model" refers to a function for receiving product information generated by a generative AI model.

[0897] "Means for returning received product information to the user" is a function for providing product information received from the generative AI model to the user.

[0898] A "generative AI model" is an artificial intelligence model that performs specific tasks based on user input.

[0899] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[0900] MODE FOR CARRYING OUT THE INVENTION

[0901] This invention is a system that receives input information from a user, generates product information based on that information using a generative AI model, and provides the product information to the user. Specific embodiments of this system are described below.

[0902] Hardware and software used

[0903] Hardware

[0904] Server: Use a server machine with a powerful processor and sufficient memory.

[0905] Device: A device such as a PC or smartphone used by a user.

[0906] software

[0907] Database management system: Use MySQL, PostgreSQL, etc.

[0908] Generative AI model: Use OpenAI's GPT-3 or similar.

[0909] System processing flow

[0910] Receiving input from the user

[0911] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget through a form on a website or in an application. For example, they might enter "Japanese apples, from Aomori Prefecture, budget within 1,000 yen."

[0912] Sending and saving input information

[0913] The terminal sends the entered information to the server using the HTTPS protocol, and the server stores the received information in a database. The stored data includes the product name, origin, manufacturer, budget, etc.

[0914] Generate prompt statement

[0915] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0916] Sending prompts to generative AI models

[0917] The server sends the generated prompt to the generative AI model, which uses OpenAI's GPT-3. The prompt is sent via an API.

[0918] Creating and receiving product information

[0919] Based on the received prompt, the generative AI model generates product information that matches the user's request. For example, it generates information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." The server receives the product information generated by the generative AI model.

[0920] Returning product information to the user

[0921] The server returns the product information received from the generative AI model to the user, who can then view this information on a website or application.

[0922] Specific examples

[0923] The specific flow when a user enters the information "Apples from Japan, from Aomori Prefecture, budget within 1000 yen" is shown below.

[0924] 1. The user enters information

[0925] Users enter "Japanese apples, from Aomori Prefecture, budget under 1,000 yen" into the website's input form.

[0926] 2. The device sends the input information to the server

[0927] The terminal sends the entered information to the server using the HTTPS protocol.

[0928] 3. The server saves the input information in a database

[0929] The server stores the received information in a MySQL database. The stored data is "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1000 yen."

[0930] 4. The server generates a prompt

[0931] The server generates a prompt message saying, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0932] 5. The server sends a prompt to the generative AI model

[0933] The server sends the generated prompt text to OpenAI's GPT-3 via an API.

[0934] 6. Generative AI model generates product information

[0935] The generative AI model generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0936] 7. The server returns the generated product information to the user

[0937] The server returns the generated product information to the user, and the user confirms on the website the information: "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0938] In this way, the user can obtain product information that meets his or her needs.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1:

[0941] The user enters information

[0942] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget into an input form on a website or application. For example, they might enter "Apples from Japan, from Aomori Prefecture, budget within 1,000 yen." The entered information is temporarily stored in the device's memory.

[0943] Step 2:

[0944] The device sends the input information to the server

[0945] The terminal sends the information entered by the user to the server using the HTTPS protocol. The input information is sent to the server as JSON format data. For example, it is sent in the format {"Product name": "Apple", "Origin": "Aomori Prefecture", "Budget": "Under 1000 yen"}.

[0946] Step 3:

[0947] The server saves the input information in a database

[0948] The server stores the input information received from the terminal in a database. MySQL or PostgreSQL is used as the database management system. The data stored includes attribute data such as the product name, origin, manufacturer, and budget. For example, a record such as "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1,000 yen" is added to the database.

[0949] Step 4:

[0950] The server generates a prompt

[0951] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen." This prompt is used as an instruction for the generative AI model.

[0952] Step 5:

[0953] The server sends a prompt to the generative AI model

[0954] The server sends the generated prompt to the generative AI model. OpenAI's GPT-3 is used as the generative AI model. The prompt is sent via API. For example, an API request might read, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[0955] Step 6:

[0956] Generative AI model generates product information

[0957] The generative AI model generates product information that matches the user's request based on the received prompt. For example, it generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." This generated information is returned to the server.

[0958] Step 7:

[0959] The server returns the generated product information to the user

[0960] The server returns the product information received from the generative AI model to the user. The user can check this information on a website or application. For example, the user might see information on the screen such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[0961] In this way, the user can obtain product information that meets his or her needs.

[0962] (Application example 1)

[0963] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0964] On conventional online shopping sites, when users input detailed information such as the name, origin, manufacturer, and budget of the product they want, manual input is cumbersome, and it is difficult to quickly find the right product. Furthermore, even when using voice input, accurate product recommendations are often not provided, which is an issue that does not improve the user experience.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0966] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing links to purchase the recommended products, and means for ordering the products during the dialogue. This allows the user to easily provide detailed product information using voice input and receive accurate product recommendations from the generative AI model.

[0967] "Means for receiving input information from the user" refers to an interface that allows the user to input information such as the name, place of origin, manufacturer, and budget of the product they want, and includes input forms on websites or applications and voice recognition systems.

[0968] "Means of decomposing using generative AI and generating attribute data" refers to a processing system that uses artificial intelligence technology to analyze input information received from users and convert it into attribute data such as product name, place of origin, manufacturer, and budget.

[0969] The "means for searching for products" is a system for searching for corresponding products in a database or on the Internet based on the generated attribute data.

[0970] "Means for displaying search results and summaries of reviews compiled using generative AI" refers to an interface that uses generative AI to summarize search results and related reviews and display them to users.

[0971] "Means for receiving voice input using a voice recognition system" refers to a system that uses voice recognition technology to convert information input by a user through voice into text data.

[0972] "Means for recommending products based on user-input information using a generative AI model" refers to a system that uses a generative AI model to recommend optimal products based on information entered by the user.

[0973] The "means for providing links to purchase recommended products" refers to an interface that provides links to related online shopping sites so that users can purchase products recommended by the generative AI model.

[0974] The "means for ordering products through dialogue" is a system for centrally selecting and ordering products through dialogue with the user.

[0975] A system for implementing this invention includes means for receiving input information from a user, means for breaking it down using a generative AI to generate attribute data, means for performing a product search based on the generated attribute data, means for displaying a summary of the search results and reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing a link to purchase the recommended product, and means for ordering the product during the dialogue.

[0976] System program processing explanation

[0977] Hardware

[0978] Smartphone (microphone, internet connection)

[0979] software

[0980] Python

[0981] speech_recognition library: Used to convert voice input to text.

[0982] Transformers library: Uses a generative AI model (GPT-3) to recommend products based on user input.

[0983] Data processing and calculation

[0984] 1. Receiving voice input:

[0985] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using the speech recognition system (speech_recognition library).

[0986] 2. Generate attribute data:

[0987] Using a generative AI model (transformers library), the information entered by the user is analyzed and attribute data such as the product name, place of origin, manufacturer, and budget is generated.

[0988] 3. Product recommendation:

[0989] Based on the generated attribute data, a generative AI model is used to recommend optimal products, and the recommended product information is displayed to the user.

[0990] 4. Providing a Purchase Link:

[0991] A link to purchase the recommended product is generated and provided to the user, who can then purchase the product at the relevant online shopping site by clicking on the link.

[0992] 5. Interactive ordering:

[0993] Through interaction with the user, product selection and ordering are performed in one place, allowing users to easily select products and complete orders.

[0994] Specific examples

[0995] When a user speaks, "Japanese apples, within a budget of 1,000 yen," the speech recognition system converts this speech into text. The generative AI model analyzes this text and generates attribute data. The generative AI model then recommends the most suitable product and provides the user with information such as, "The recommended product is Japanese apples, priced at 800 yen. Here is the purchase link."

[0996] Prompt Sentence Examples

[0997] Product information for user: Japanese apples, budget under 1000 yen

[0998] Please tell me what products you recommend.

[0999] In this way, users can easily provide detailed product information using voice input and receive accurate product recommendations from generative AI models.

[1000] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1001] Step 1:

[1002] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using a speech recognition system (speech_recognition library). The input is the user's voice data, and the output is text data. In this step, the speech recognition system analyzes the voice data and generates corresponding text data.

[1003] Step 2:

[1004] The server uses a generative AI model (transformers library) to analyze the text data obtained in step 1 and generate attribute data such as product name, place of origin, manufacturer, and budget. The input is text data, and the output is attribute data. In this step, the generative AI model breaks down the text data and extracts each attribute.

[1005] Step 3:

[1006] The server performs a product search based on the generated attribute data. The input is the attribute data and the output is the search results. In this step, the server searches for the relevant products in a database or on the Internet and obtains the search results.

[1007] Step 4:

[1008] The server displays the search results and the review summaries compiled by the generative AI. The input is the search results, and the output is display data including the review summaries. In this step, the generative AI summarizes the reviews related to the search results and generates data to display to the user.

[1009] Step 5:

[1010] The server uses a generative AI model to recommend products based on user input. The input is attribute data, and the output is recommended product information. In this step, the generative AI model analyzes the attribute data and recommends the most suitable products.

[1011] Step 6:

[1012] The server provides a link to purchase the recommended product. The input is the recommended product information, and the output is a purchase link. In this step, the server generates a link to the online shopping site corresponding to the recommended product and provides it to the user.

[1013] Step 7:

[1014] The user places an order through a dialogue. The input is the user's selection information, and the output is order completion information. In this step, the user clicks on the provided link, selects the product on the online shopping site, and completes the order.

[1015] Example 2

[1016] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1017] With conventional systems, it was difficult to efficiently analyze information input by users and extract attribute data. Furthermore, it was not possible to perform product searches based on the extracted attribute data and provide appropriate feedback to users. Furthermore, it was difficult to centrally order products through dialogue with users. This resulted in a poor user experience and reduced system utilization efficiency.

[1018] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1019] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and a summary of reviews compiled by the generative AI, means for ordering products through dialogue, means for sending prompts to the generative AI model and extracting attribute data, and means for saving the extracted attribute data and providing feedback to the user. This makes it possible to efficiently analyze input information from a user, extract appropriate attribute data to perform a product search, and provide the user with prompt and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[1020] "User" means an individual or corporation that uses the system to input information and search for and place orders.

[1021] "Input information" refers to information such as the product name, place of origin, manufacturer, and budget that the user provides to the system.

[1022] "Generative AI" is an artificial intelligence technology that analyzes input information and extracts attribute data.

[1023] "Attribute data" refers to data such as the product name, place of origin, manufacturer, and budget that the generative AI extracts from the input information.

[1024] "Product search" is the process in which the system searches for related products based on the generated attribute data.

[1025] A "review summary" is a summary of product ratings and comments compiled by generative AI based on search results.

[1026] "Dialogue" is a communication process in which the user and the system exchange information with each other to select and order products.

[1027] A "prompt sentence" is a sentence that instructs the generative AI to analyze input information and extract attribute data.

[1028] "Feedback" refers to information about analysis results and search results that the system provides to the user.

[1029] This invention is a system that efficiently analyzes information input by users, extracts attribute data, performs product searches, and provides users with quick and accurate feedback. Furthermore, it allows users to select and order products in a unified manner through dialogue with the system.

[1030] Hardware and software used

[1031] Hardware

[1032] server

[1033] User device (PC, smartphone, etc.)

[1034] software

[1035] Generative AI models (e.g., GPT-4)

[1036] Web browser (user device)

[1037] Data processing and calculation

[1038] Server Processing

[1039] The server sends the input information received from the user to the generative AI model. This generative AI model analyzes the input information using natural language processing technology and extracts attribute data such as the product name, place of origin, manufacturer, and budget. Specifically, the server makes an API request to the generative AI model and uses prompt statements to instruct it to analyze.

[1040] Terminal handling

[1041] The terminal is responsible for sending the information entered by the user to the server. When the user enters information about a product into the input form and presses the send button, the information is sent to the server.

[1042] User Action

[1043] The user enters information such as the product name, place of origin, manufacturer, budget, etc. into the input form on the terminal. For example, they might enter "apples, Aomori Prefecture, sunfruit, budget 500 yen."

[1044] Specific examples

[1045] Prompt Sentence Examples

[1046] "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, Aomori Prefecture, Sunfruit, Budget 500 yen'"

[1047] In this way, the server, terminal, and user each play their respective roles, and the system uses a generative AI model to analyze input information and generate attribute data. This makes it possible to efficiently analyze input information from users, extract appropriate attribute data, perform product searches, and provide users with fast and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[1048] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1049] Step 1:

[1050] The user inputs the input information into the terminal.

[1051] The user inputs product information into the input form on the terminal. For example, the user might input "apples, Aomori Prefecture, sunfruit, budget 500 yen." This information includes the product name, place of origin, manufacturer, and budget. When the input information is entered into the terminal, the terminal temporarily stores the information.

[1052] Step 2:

[1053] The terminal sends the input information to the server.

[1054] When the user presses the submit button, the device sends the input information to the server as an HTTP POST request. This request contains all the information the user entered. Specifically, the device converts the input information into JSON format and sends it to the server's API endpoint.

[1055] Step 3:

[1056] The server sends the input information to the generative AI model.

[1057] The server sends the input information received from the device to the generative AI model. Specifically, the server makes an API request to the generative AI model, instructing it to analyze the input information. At this time, the prompt text used is "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, produced in Aomori Prefecture, sunfruit, budget 500 yen'." The input is information from the user, and the output is the analysis result from the generative AI model.

[1058] Step 4:

[1059] The generative AI model analyzes the input information and extracts attribute data.

[1060] The generative AI model analyzes the received input information and extracts attribute data. Specifically, the generative AI model uses natural language processing technology to extract the following attribute data from the input information:

[1061] Product Name: Apple

[1062] Origin: Aomori Prefecture

[1063] Manufacturer: Sun Fruits

[1064] Budget: 500 yen

[1065] The input is a prompt from the server, and the output is the extracted attribute data.

[1066] Step 5:

[1067] The server stores the extracted attribute data and provides feedback to the user.

[1068] The server stores the attribute data received from the generative AI model. Specifically, it stores it in a database. The server also provides feedback to the user. For example, it might display to the user, "The following attribute data has been extracted from the input information: Product name: Apple, Origin: Aomori Prefecture, Manufacturer: Sun Fruits, Budget: 500 yen." The input is the attribute data from the generative AI model, and the output is a feedback message to the user.

[1069] (Application example 2)

[1070] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1071] On conventional online shopping sites, when users searched for products, it was difficult to extract appropriate attribute data from the information they entered and provide accurate search results. Furthermore, there was a lack of efficient means to extract detailed attribute data such as the name, origin, manufacturer, and budget of the product the user wanted, so search results often did not meet the user's expectations. Furthermore, there was a need for a system that could display search results and review summaries in a unified manner and allow users to order products interactively.

[1072] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1073] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for interactively ordering products, means for extracting attribute data such as product name, place of origin, manufacturer, and budget from the input information using the generative AI, and means for providing highly accurate search results based on the extracted attribute data. This makes it possible to automatically extract detailed attribute data from the information entered by the user and provide highly accurate search results. Furthermore, search results and review summaries can be displayed in a unified manner, allowing for interactive product ordering.

[1074] The "means for receiving input information from the user" is an interface for incorporating text information entered by the user into the system.

[1075] "Means of breaking down received input information using generative AI and generating attribute data" refers to the process of using generative AI to analyze input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[1076] The "means for searching for products based on the generated attribute data" is a function for searching for related products from a database based on the extracted attribute data.

[1077] "A means of displaying search results and summaries of reviews compiled by generative AI" is a function that displays to users a summary of product reviews compiled by generative AI along with the search results.

[1078] The "means for ordering products interactively" is a function that allows users to select and order products in an integrated manner through an interactive format.

[1079] "Means of using generative AI to extract attribute data such as product name, place of origin, manufacturer, and budget from input information" refers to the process of using generative AI to automatically extract detailed attribute data of a product from information entered by the user.

[1080] "Means for providing highly accurate search results based on extracted attribute data" refers to a function that utilizes extracted attribute data to provide more relevant search results to users.

[1081] The system for implementing this invention receives input information from a user, analyzes it using generative AI to extract attribute data, and performs highly accurate product searches. Specific embodiments of this system are described below.

[1082] System Configuration

[1083] The system consists of the following main components:

[1084] 1. User terminal: The device through which the user provides input information (e.g., a smartphone).

[1085] 2. Server: A central processing unit that runs generative AI models, generates attribute data, and performs product searches.

[1086] 3. Database: Data storage for storing product information and review information.

[1087] Program processing

[1088] The server performs the following steps:

[1089] 1. Receiving input information: Receive text information entered from the user's device. For example, a user might enter, "I'm looking for a new smartphone. My budget is within 50,000 yen, and I'd prefer a Japanese manufacturer."

[1090] 2. Generative AI analysis: The received input information is passed to a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt. An example of a prompt is as follows:

[1091] Please extract the product name, place of origin, manufacturer, and budget from the text below.

[1092] Text: I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer a Japanese brand.

[1093] Attribute data:

[1094] 3. Extracting attribute data: The generative AI model analyzes the prompt and extracts attribute data such as the product name, origin, manufacturer, and budget. For example, the following attribute data is extracted:

[1095] Name: Smartphone

[1096] Origin: Domestic

[1097] Manufacturer: Domestic manufacturer

[1098] Budget: Under 50,000 yen

[1099] 4. Product search: Based on the extracted attribute data, the database is searched for related products. For example, products that meet the criteria of "smartphone," "domestic manufacturer," and "under 50,000 yen" are searched for.

[1100] 5. Displaying search results and reviews: Along with search results, a summary of product reviews compiled by generative AI is displayed on the user's device, allowing users to see detailed product information and reviews at a glance.

[1101] 6. Interactive ordering: The user selects a product from the search results and completes the order interactively. For example, the user can enter "I would like to purchase this smartphone," and the system will proceed with the order process.

[1102] Hardware and software used

[1103] Hardware: A smartphone (user device) connected to the internet and a server.

[1104] Software: Python, OpenAI API, database management system (e.g., MySQL).

[1105] This system allows users to automatically extract detailed attribute data from the information they enter, resulting in highly accurate search results. It also displays search results and review summaries in a unified manner, and allows users to interactively order products.

[1106] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1107] Step 1:

[1108] The user's device receives the input information. The user enters text information for product search and presses the send button. For example, the user might enter, "I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer one from a Japanese manufacturer." The input information is sent to the server.

[1109] Step 2:

[1110] The server generates a prompt to pass the input information it receives to the generative AI model. Specifically, it creates the following prompt based on the input information:

[1111] Please extract the product name, place of origin, manufacturer, and budget from the text below.

[1112] Text: I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer a Japanese brand.

[1113] Attribute data:

[1114] This prompt is passed to the generative AI model.

[1115] Step 3:

[1116] The server calls a generative AI model, analyzes the prompt, and extracts attribute data. The generative AI model (e.g., OpenAI's GPT-3) analyzes the prompt and extracts attribute data such as the product name, place of origin, manufacturer, and budget. For example, the following attribute data is extracted:

[1117] Name: Smartphone

[1118] Origin: Domestic

[1119] Manufacturer: Domestic manufacturer

[1120] Budget: Under 50,000 yen

[1121] The extracted attribute data is returned to the server.

[1122] Step 4:

[1123] The server performs a product search based on the extracted attribute data. The extracted attribute data is used as a query to search for related products in the database. For example, a search is performed for products that match the criteria "smartphone," "domestic manufacturer," and "under 50,000 yen." The search results are returned to the server.

[1124] Step 5:

[1125] The server generates a review summary compiled from the search results using generative AI. Based on the search results, review information for related products is retrieved from the database and a generative AI model is used to create a review summary. For example, a review summary might be generated that reads, "This smartphone is rated for its high performance and long battery life."

[1126] Step 6:

[1127] The server sends the search results and a summary of the reviews to the user's device, which then displays the search results and a summary of the reviews. The user can then select a product based on the displayed information.

[1128] Step 7:

[1129] The user device interactively places an order. The user selects a product from the search results and presses the order button. For example, if the user types "I would like to purchase this smartphone," the server proceeds with the order. The order information is saved in a database, and an order confirmation is displayed on the user device.

[1130] Example 3

[1131] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1132] In the previous system, the process of users searching for products, checking reviews, and then placing an order was complicated, resulting in a poor user experience. Furthermore, the accuracy of search results and summaries of reviews were insufficient, making it difficult for users to select the right product. This resulted in lower user satisfaction and reduced frequency of system use.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1134] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI model to generate attribute data, means for generating and executing a query to a product database based on the generated attribute data, means for obtaining search results and generating review summaries using the generative AI model, means for displaying the search results and the generated review summaries, and means for selecting and ordering products through an interaction. This allows the user to perform the entire process smoothly, improving the accuracy of search results and review summaries and facilitating appropriate product selection.

[1135] "User" means an individual or corporation that uses the system to search for and place orders.

[1136] "Input information" refers to data provided by the user to the system, including attribute data required for product searches and orders.

[1137] A "generative AI model" is an artificial intelligence model used to analyze user input and generate attribute data or review summaries.

[1138] "Attribute data" is data generated based on information input by the user, and includes information such as the product name, place of origin, manufacturer, and budget.

[1139] A "product database" is a database that stores product information and is used to obtain search results by executing a query.

[1140] A "query" is a search command executed against a product database to retrieve data based on specific conditions.

[1141] "Search results" are a collection of product information obtained when a query is executed against a product database.

[1142] "Review Summary" is a summary of reviews related to search results created using a generative AI model, and is information that users can use to help them select products.

[1143] "Display" means the display area of ​​a website or application that provides a visual summary of search results and generated reviews to users.

[1144] "Interaction tools" are chatbots and voice assistants that allow users to interact with the system to select and order products.

[1145] The present invention provides a system that simplifies the process of users searching for products, checking reviews, and placing orders, thereby improving the user experience. Specific embodiments of this system will be described below.

[1146] First, the user enters attribute data into a search form on a website or application. For example, they enter information such as "price range: under 5,000 yen, brand: ABC, function: waterproof." This input information is sent to the server by the device.

[1147] The server analyzes the received input information using a generative AI model to generate attribute data. This generative AI model may be, for example, OpenAI's GPT-4. Based on the generated attribute data, the server generates a query to the product database. The query is written in SQL or NoSQL format.

[1148] The server then executes the generated query against a product database, such as MySQL or MongoDB, and obtains the search results as a result of the query execution.

[1149] Based on the search results, the server again uses the generative AI model to generate a review summary, which summarizes reviews related to the search results and provides useful information to the user.

[1150] The server sends the search results and a summary of the generated reviews to the device, which then displays this information to the user in the display area of ​​the website or application.

[1151] The user selects and orders products through a chatbot or voice assistant, referring to the displayed product information and review summary. For example, the user inputs "I would like to purchase this product" into the chatbot. The device then sends the user's selection and order details to the server.

[1152] The server processes the received order and generates an order confirmation message, which is then displayed to the user via their terminal, allowing the user to complete the entire process smoothly.

[1153] As an example, the following prompt sentence is used:

[1154] "Search for products with price range: under 5000 yen, brand: ABC, function: waterproof."

[1155] "I would like to purchase this item. Please confirm your order."

[1156] This system allows users to easily search for products, check reviews, and place orders. This is expected to improve the user experience and increase the frequency of system usage. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[1157] Step 1:

[1158] The user enters attribute data.

[1159] Input: The user enters attribute data such as "Price range: under 5,000 yen, Brand: ABC, Function: Waterproof" into a search form on a website or application.

[1160] Output: The entered attribute data is sent to the terminal.

[1161] What happens: A user enters information into a search form and clicks the search button.

[1162] Step 2:

[1163] The terminal transmits the input attribute data to the server.

[1164] Input: Attribute data entered by the user.

[1165] Output: The attribute data is sent to the server in JSON format or similar.

[1166] Specific operation: The terminal converts the attribute data into packets and sends them to the server via the network.

[1167] Step 3:

[1168] The server receives the attribute data, breaks it down using a generative AI model, and generates attribute data.

[1169] Input: Attribute data sent from the terminal.

[1170] Output: Attribute data parsed by the generative AI model.

[1171] Specific operation: The data received by the server is input into the generative AI model, and attribute data such as "price range," "brand," and "function" are extracted.

[1172] Step 4:

[1173] A query to the product database is generated based on the attribute data generated by the server and executed.

[1174] Input: Attribute data generated by a generative AI model.

[1175] Output: A SQL query against the product database.

[1176] Specific operation: The server generates the SQL query "SELECT FROM products WHERE price <= 5000 AND brand = 'ABC' AND features LIKE '%waterproof%'" and executes it against the product database.

[1177] Step 5:

[1178] The server retrieves the search results and generates a summary of the review using a generative AI model.

[1179] Input: Search results retrieved from a product database.

[1180] Output: A summary of the review generated by the generative AI model.

[1181] How it works: The server inputs the search results into a generative AI model, which generates a review summary such as, "Many users rate this product as waterproof and reasonably priced."

[1182] Step 6:

[1183] The server sends the search results and a summary of the generated reviews to the device.

[1184] Input: Search results and a summary of the reviews generated.

[1185] Output: Search results and review summaries are sent to the device in JSON format or similar.

[1186] Specific operation: The server converts search results and review summaries into packets and sends them to the terminal over the network.

[1187] Step 7:

[1188] The device will display a summary of search results and reviews to the user.

[1189] Input: Search results and review summary sent from the server.

[1190] Output: A summary of the search results and reviews is displayed to the user.

[1191] Specific operation: The device analyzes the data received and displays it in the display area of ​​the website or application.

[1192] Step 8:

[1193] Users can select and order products through chatbots or voice assistants based on the displayed product information and review summary.

[1194] Input: A user types into the chatbot, "I would like to purchase this product."

[1195] Output: User selections and order details are sent to the terminal.

[1196] Specific action: The user enters text for the chatbot and clicks the send button.

[1197] Step 9:

[1198] The terminal sends the user's selection and order details to the server.

[1199] Input: User selections and orders.

[1200] Output: Selections and order details are sent to the server in JSON format or similar.

[1201] Specific operation: The terminal converts the selection and order details into packets and sends them to the server over the network.

[1202] Step 10:

[1203] The server processes the received order and generates an order confirmation message.

[1204] Input: Order details sent from the terminal.

[1205] Output: Order confirmation message.

[1206] Specific operation: The server analyzes the order details and generates a confirmation message such as "Your order has been confirmed. We will notify you as soon as it is ready to be shipped."

[1207] Step 11:

[1208] The server sends an order confirmation message to the terminal.

[1209] Input: The generated order confirmation message.

[1210] Output: An order confirmation message is sent to the device, for example in JSON format.

[1211] Specific operation: The server converts the confirmation message into a packet and sends it to the terminal through the network.

[1212] Step 12:

[1213] The terminal displays an order confirmation message to the user.

[1214] Input: The order confirmation message sent by the server.

[1215] Output: Order confirmation message is displayed to the user.

[1216] Specific operation: The device analyzes the confirmation message received and displays it in the display area of ​​the website or application.

[1217] (Application example 3)

[1218] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1219] On conventional online shopping sites, the process of searching for products, checking reviews, and placing an order was complicated, resulting in a poor user experience. Another issue was that the sheer volume of reviews made it difficult for users to quickly obtain the information they needed. Furthermore, the lack of an interactive ordering process made it difficult for users to smoothly select and order products.

[1220] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1221] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and review summaries compiled by the generative AI, means for ordering products through dialogue, means for generating review summaries using the generative AI, and means for engaging in dialogue with the user using a chatbot or voice assistant. This enables users to easily search for products, quickly check review summaries, and smoothly order products through dialogue.

[1222] The "means for receiving input information from the user" is an interface for receiving information that the user provides to the system.

[1223] "Means of decomposing using generative AI and generating attribute data" refers to the process of using generative AI to analyze information input from users and generate data on product features and attributes.

[1224] The "means for searching for products based on the generated attribute data" is a function for searching for corresponding products from the product database based on the generated attribute data.

[1225] "Means for displaying search results and summaries of reviews compiled by generative AI" refers to an interface that displays to users information about the searched product and summaries of reviews summarized by generative AI.

[1226] The "means for ordering products interactively" is a function for selecting and ordering products through interaction with the user.

[1227] "Means for generating review summaries using generative AI" refers to a process that uses generative artificial intelligence to summarize multiple reviews and provide them to users in an easy-to-understand format.

[1228] "Means of interacting with users using chatbots and voice assistants" refers to a function that uses chatbots and voice assistants to engage in natural conversations with users and support them in selecting and ordering products.

[1229] As an embodiment of the present invention, we will explain the smart shopping assistant system as an example, which allows users to search for products using a smartphone, check reviews generated by generative AI, and order products through a chatbot or voice assistant.

[1230] The server has a means of receiving input information from the user. Information entered by the user through their smartphone is sent to the server. For example, if a user searches for "red medium-sized shirt," that information is sent to the server.

[1231] Next, the server has a means to break down the received input information using a generative AI and generate attribute data. The generative AI analyzes the user's input information and generates attribute data such as "Color: Red" and "Size: Medium." This generative AI uses OpenAI's GPT-3 and other technologies.

[1232] The server has a means for searching for products based on the generated attribute data. The server searches for corresponding products from the product database based on the generated attribute data. For example, a product matching "red medium-sized shirt" is searched for.

[1233] The server also includes a means for displaying search results and summaries of reviews compiled by the generative AI. The server displays information about the searched product and a summary of the reviews compiled by the generative AI to the user. For example, a review summary such as "This shirt is very comfortable and the color is vibrant" may be displayed.

[1234] Furthermore, the server has a means to order products through a dialogue. The user can select and order products through a chatbot or voice assistant based on the displayed product information and review summary. For example, if the user says, "I'd like to order this shirt," the chatbot will support the ordering process.

[1235] The server also includes a means for generating a review summary using a generative AI. The server uses the generative AI to summarize multiple reviews and provide them to the user in an easy-to-understand format. For example, the server inputs a prompt such as "Please summarize the following review: This shirt is very comfortable and the color is vibrant."

[1236] The server also includes a means for interacting with users using chatbots and voice assistants. The server uses chatbots and voice assistants to have natural conversations with users and support them in selecting and ordering products. For example, it inputs prompt sentences such as "The user wants to order a red medium-sized shirt" into the generative AI.

[1237] In this way, the smart shopping assistant system allows users to easily search for products, quickly check review summaries, and smoothly order products through dialogue.

[1238] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1239] Step 1:

[1240] A user uses a smartphone to provide input for a product search.

[1241] Input: Product information the user is searching for (e.g., "red medium-sized shirt")

[1242] Output: The input information is sent to the server.

[1243] Specific operation: When a user enters product information into a smartphone application and presses the search button, the information is sent to the server.

[1244] Step 2:

[1245] The input information received by the server is broken down using generative AI to generate attribute data.

[1246] Input: Product search information sent by the user

[1247] Output: Generated attribute data (e.g. "Color: Red", "Size: Medium")

[1248] Specific operation: The server uses generative AI (e.g., OpenAI's GPT-3) to analyze the user's input information and generate data on the product's features and attributes.

[1249] Step 3:

[1250] A product search is performed based on the attribute data generated by the server.

[1251] Input: Generated attribute data

[1252] Output: A list of matching products

[1253] Specific operation: The server executes a query against the product database to search for products that match the generated attribute data.

[1254] Step 4:

[1255] The server displays the search results and a summary of reviews compiled using generative AI.

[1256] Input: List of search results

[1257] Output: Summary of product information and reviews

[1258] How it works: The server uses generative AI to summarize product reviews based on search results and display them in a user-friendly format.

[1259] Step 5:

[1260] The user places an order for the product based on the displayed product information and review summary.

[1261] Input: User's ordering intent (e.g., "I would like to order this shirt")

[1262] Output: Order confirmation message

[1263] What it does: When a user places an order for a product through a chatbot or voice assistant, the server accepts the order and sends a confirmation message to the user.

[1264] Step 6:

[1265] The server uses generative AI to generate a summary of the review.

[1266] Input: Product review information

[1267] Output: A condensed review summary

[1268] Specific operation: The server inputs a prompt to the generative AI: "Please summarize the following review: [review content]" and generates a summarized review.

[1269] Step 7:

[1270] The server interacts with the user using a chatbot or voice assistant.

[1271] Input: User interaction (e.g., "I'd like to order this shirt")

[1272] Output: Dialogue response message

[1273] Specific operation: The server uses a chatbot or voice assistant to have natural conversations with the user and assist them in selecting and ordering products.

[1274] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1275] "Example 1"

[1276] In one embodiment of the present invention, an input form on a website or application is used as a means for receiving input information from a user. The user enters information into this input form, such as the name of the product they want, its place of origin, the manufacturer, and their budget. The input form also incorporates an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's text and voice input. For example, it can recognize emotions such as "looking forward to it," "concerned," and "angry."

[1277] "Example 2"

[1278] The received input information is sent to the generative AI, which uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget. The generative AI also takes into account emotional information obtained from the emotion engine. For example, if the user expresses the emotion "I'm looking forward to it," the generative AI will search for products that match that emotion.

[1279] "Example 3"

[1280] A query to a product database is generated based on the attribute data and emotional information extracted by the generative AI. The generated query is executed to search the product database for products that most closely match the user's needs. The search results are displayed in a display area on the website or application. "Example 4"

[1281] The user selects a product based on the displayed product information. The selected product is then ordered through a chatbot or voice assistant. The user's emotions are also taken into consideration. For example, if the user expresses anger, the system will expedite the ordering process to calm the user as quickly as possible.

[1282] The processing flow of each embodiment will be described below.

[1283] "Example 1"

[1284] Step 1: The user enters information such as the name of the product they want, its origin, manufacturer, and budget into an input form on a website or application.

[1285] Step 2: The emotion engine is activated to analyze emotions from the user's text and voice input.

[1286] "Example 2"

[1287] Step 1: User input is sent to the generative AI.

[1288] Step 2: The generative AI uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[1289] Step 3: The generative AI also takes into account the emotional information obtained from the emotion engine and searches for products that match that emotion.

[1290] "Example 3"

[1291] Step 1: Generate a query to the product database based on the attribute data and emotional information extracted by the generative AI.

[1292] Step 2: Execute the generated query to search the product database for products that best match the user's requirements.

[1293] Step 3: The search results are displayed in a display area on the website or application.

[1294] "Example 4"

[1295] Step 1: The user selects a product based on the displayed product information.

[1296] Step 2: The user places an order for the selected product through a chatbot or voice assistant.

[1297] Step 3: The user's emotions are also taken into consideration. For example, if the user expresses anger, the system will expedite the ordering process to calm the user down as quickly as possible.

[1298] Example 1

[1299] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1300] Conventional systems simply receive input information from users, but are unable to fully understand their emotions and intentions, making it difficult to make appropriate product suggestions and process orders. Furthermore, the inability to centrally select and order products through dialogue with the user creates a problem of poor user experience.

[1301] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1302] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information with an emotion analysis engine and generating emotion data, means for integrating the generated emotion data with the input information and generating a prompt sentence to be input to the generative AI model, means for inputting the prompt sentence to the generative AI model and generating a response, means for displaying the generated response to the user, and means for placing a product order during the dialogue. This makes it possible to understand the user's emotions and intentions and to centrally perform appropriate product suggestions and order processing.

[1303] "Means for receiving input information from users" refers to the means by which users use input forms on websites or applications to enter information such as the name, origin, manufacturer, and budget of the desired product.

[1304] An "emotion analysis engine" is an engine that analyzes text and voice data entered by the user and uses natural language processing technology to analyze the user's emotions.

[1305] "Emotion data" is information about the user's emotions analyzed by an emotion analysis engine, and is data that indicates emotions such as "looking forward to it," "troubled," and "angry."

[1306] The "means for generating a prompt sentence to be input to a generative AI model" refers to a means for integrating user input information and emotional data and generating a prompt sentence to be input to a generative AI model.

[1307] A "generative AI model" is an artificial intelligence model that generates appropriate responses based on user input information and emotional data, such as a model that uses natural language generation technology.

[1308] A "means for generating a response" is a means for inputting a prompt sentence into a generative AI model and generating an appropriate response to the user.

[1309] "Means for displaying the generated response to the user" refers to means for displaying the response obtained from the generative AI model to the user, such as on a website or application.

[1310] The "means for placing an order for a product" refers to a means for selecting and ordering a product in a unified manner through dialogue with the user.

[1311] This invention is a system that receives input information from a user, performs emotion analysis based on that information, and generates an appropriate response using a generative AI model. Specific embodiments of this system are described below.

[1312] Receiving input from the user

[1313] Users use input forms on websites or applications to enter information such as the name of the product they want, its place of origin, manufacturer, and budget. These input forms are built using HTML and JavaScript. For example, a user might enter, "I'm looking for fresh apples from Hokkaido. My budget is within 3,000 yen."

[1314] Sending input information

[1315] The terminal sends the information entered by the user to the server using the HTTPS protocol. The terminal converts the input form data into JSON format and sends it to the server as an HTTPS request.

[1316] Emotion analysis using an emotion analysis engine

[1317] The server sends the received user input information to a sentiment analysis engine. The sentiment analysis engine uses natural language processing technology to analyze the user's emotions. Specifically, it uses services such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The sentiment analysis engine recognizes emotions such as "looking forward to it," "concerned," and "angry" from the user's input.

[1318] Generate prompt statement

[1319] The server combines the user's input information with the emotion data obtained from the emotion engine to generate a prompt sentence to be input into the generative AI model. For example, it generates a prompt sentence like the one below.

[1320] The user entered, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen." The user also expressed the emotion, "I'm looking forward to it." Based on this information, please suggest the best products for the user.

[1321] Response generation using generative AI models

[1322] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a response. The generative AI model generates a response that suggests the best product for the user based on the prompt. For example, it generates a response like the one below.

[1323] "You're looking for fresh apples from Hokkaido, aren't you? Our recommended product for those under 3,000 yen is Fuji apples from Hokkaido. They're currently on sale at a special price, so be sure to give them a try!"

[1324] Sending and Displaying Responses

[1325] The server sends the response received from the generative AI model to the device, which then displays the response received from the server to the user. The device then inserts the response into an HTML element and displays it to the user.

[1326] In this way, the system makes optimal product recommendations based on the user's input information and emotions. This system makes it possible to understand the user's emotions and intentions, and to make appropriate product recommendations and process orders in an integrated manner.

[1327] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1328] Step 1:

[1329] Users use input forms on websites or applications to enter information such as the name of the product they want, its origin, manufacturer, budget, etc. The input forms are built using HTML and JavaScript.

[1330] Input: A user fills in a form saying, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen."

[1331] Output: Data entered into the input form.

[1332] Step 2:

[1333] The terminal sends the information entered by the user to the server using the HTTPS protocol. The terminal converts the input form data into JSON format and sends it to the server as an HTTPS request.

[1334] Input: Data entered into an input form.

[1335] Output: The data is converted to JSON format and sent to the server.

[1336] Step 3:

[1337] The server then sends the received user input information to a sentiment analysis engine, which then uses natural language processing technology to analyze the user's emotions. Specifically, it uses services such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding.

[1338] Input: User input data in JSON format.

[1339] Output: Sentiment data analyzed by the sentiment analysis engine.

[1340] Step 4:

[1341] The server integrates the user's input information with the emotional data obtained from the emotion engine and generates a prompt sentence to be input into the generative AI model.

[1342] Input: User input and emotional data.

[1343] Output: A prompt to be fed into the generative AI model.

[1344] Specific behavior: The server generates a prompt like this:

[1345] The user entered, "I'm looking for fresh apples from Hokkaido. My budget is under 3000 yen." The user also expressed the emotion, "I'm looking forward to it." Based on this information, please suggest the best products for the user.

[1346] Step 5:

[1347] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-3) to generate a response. The generative AI model generates a response that suggests the best product for the user based on the prompt.

[1348] Input: The prompt statement.

[1349] Output: The response generated by the generative AI model.

[1350] Specific operation: The server sends a prompt sentence to GPT-3 and receives a response like the following:

[1351] "You're looking for fresh apples from Hokkaido, aren't you? Our recommended product for those under 3,000 yen is Fuji apples from Hokkaido. They're currently on sale at a special price, so be sure to give them a try!"

[1352] Step 6:

[1353] The server sends the response received from the generative AI model to the device, which then displays the response received from the server to the user. The device then inserts the response into an HTML element and displays it to the user.

[1354] Input: The response from the generative AI model.

[1355] Output: The response that is displayed to the user.

[1356] Specific behavior: The response received by the device is inserted into an HTML element and displayed to the user.

[1357] (Application example 1)

[1358] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1359] Conventional online shopping sites have the problem that users simply enter information about the product they want, but the site does not provide optimal product suggestions based on the user's emotions and needs. Also, simply searching for products based on the information entered by the user makes it difficult to increase user satisfaction.

[1360] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and a summary of the reviews compiled by the generative AI, means for ordering products through dialogue, means including an emotion engine for analyzing the user's emotions, and means for suggesting optimal products based on the user's input information and emotions. This makes it possible to suggest optimal products based on the user's emotions and needs.

[1361] The "means for receiving input information from the user" is an interface that allows the user to input information such as the name, origin, manufacturer, and budget of the product they want.

[1362] "Generative AI" is an artificial intelligence technology that analyzes input information from users and generates attribute data.

[1363] "Attribute data" is data obtained by breaking down information entered by the user, such as the product name, place of origin, manufacturer, and budget.

[1364] The "means for searching for products" is a function for searching for appropriate products based on the generated attribute data.

[1365] "Means for displaying review summaries" is a function that displays to users a summary of reviews about a product compiled by generative AI.

[1366] The "means for placing an order for a product" is a function for selecting and ordering products in a unified manner through dialogue with the user.

[1367] An "emotion engine" is a technology for analyzing emotions from a user's text or voice input.

[1368] "Means for suggesting optimal products" is a function for suggesting optimal products to users based on the information and emotions input by the user.

[1369] The system for implementing this invention receives input information from a user, analyzes it using generative AI, and proposes optimal products. Specific embodiments are described below.

[1370] System Configuration

[1371] 1. User Device:

[1372] Input form: An interface for users to enter information such as the name of the product they want, its place of origin, manufacturer, budget, etc. It is built using HTML / CSS and JavaScript.

[1373] Speech Recognition System: A system that allows users to input information by voice. It uses the Google Speech-to-Text API.

[1374] 2. Server:

[1375] Generative AI: An artificial intelligence technology that analyzes user input and generates attribute data. It uses TensorFlow.

[1376] Emotion Engine: Technology for analyzing emotions from user text and voice input. Uses IBM Watson Tone Analyzer.

[1377] Product search engine: A function for searching for appropriate products based on the generated attribute data.

[1378] Review summary display: A feature that displays a summary of product reviews compiled by generative AI to users.

[1379] Product recommendation engine: A function that suggests the most suitable products based on the user's input information and emotions.

[1380] Processing flow

[1381] 1. User Input:

[1382] Users input information about the product they want through a smartphone app. For example, they can enter "fresh organic vegetables" and set their budget at 3,000 yen.

[1383] In the case of voice input, the user would say, "I want fresh organic vegetables."

[1384] 2. Emotion analysis:

[1385] If a user types "I'm looking forward to it," the emotion engine will interpret it as "positive."

[1386] 3. Product suggestion:

[1387] The server uses AI to suggest the most suitable product based on the user's input information and emotions, for example, a "fresh organic vegetable set."

[1388] Hardware and software used

[1389] Hardware: Smartphones, servers

[1390] Software: Google Speech-to-Text API, IBM Watson Tone Analyzer, TensorFlow, HTML / CSS, JavaScript

[1391] Specific examples

[1392] Input form: The user enters "fresh organic vegetables" and sets the budget to "3,000 yen."

[1393] Voice input: The user says, "I want fresh organic vegetables."

[1394] Sentiment analysis: If a user types "I'm looking forward to it," the sentiment engine interprets it as "positive."

[1395] Product suggestion: AI suggests a "fresh organic vegetable set."

[1396] Prompt Sentence Examples

[1397] The user inputs "fresh organic vegetables" and sets the budget as "3000 yen." The emotion engine analyzes the user's emotion as "looking forward to it." Based on this, please suggest the most suitable products.

[1398] In this way, it becomes possible to propose optimal products according to the user's emotions and needs.

[1399] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1400] Step 1:

[1401] The user device receives input information from the user. The user uses the smartphone app's input form to enter information such as the name of the product they want, its place of origin, manufacturer, and budget. For example, they might enter "fresh organic vegetables" and set their budget at "3,000 yen." The input information is then sent to the server.

[1402] Step 2:

[1403] The user's device uses a speech recognition system to convert voice input into text. If the user says, "I want fresh organic vegetables," the voice data is converted into text using the Google Speech-to-Text API. The converted text data is then sent to the server.

[1404] Step 3:

[1405] The server uses generative AI to analyze the received input information and generate attribute data. The generative AI uses TensorFlow to analyze the product name, place of origin, manufacturer, budget, and other information entered by the user, and generates attribute data. For example, attribute data such as "fresh organic vegetables," "place of origin: Japan," "manufacturer: unknown," and "budget: 3,000 yen" is generated.

[1406] Step 4:

[1407] The server uses an emotion engine to analyze the user's emotions. If the user types "I'm looking forward to it," IBM Watson Tone Analyzer is used to analyze emotions from the text data. The analysis results in the emotional data "positive."

[1408] Step 5:

[1409] The server performs a product search based on the generated attribute data. The product search engine searches the product information in the database and extracts products that match the attribute data. For example, a "fresh organic vegetable set" can be obtained as a search result.

[1410] Step 6:

[1411] The server displays a summary of the reviews compiled by the generative AI. The generative AI analyzes reviews related to the search results and generates summaries. For example, a review summary such as "This organic vegetable set is fresh and delicious" is generated.

[1412] Step 7:

[1413] The server proposes optimal products based on the user's input information and emotions. The product proposal engine selects optimal products based on the generated attribute data and emotion data and proposes them to the user. For example, it proposes a "set of fresh organic vegetables."

[1414] Step 8:

[1415] The user's device displays the summary of the suggested products and reviews received from the server. The user can then check the suggested products and consider purchasing them.

[1416] Step 9:

[1417] The user terminal provides an interface for the user to place an order for a product. The user selects a suggested product and completes the order process. The order information is then sent to the server.

[1418] Step 10:

[1419] The server processes the received order information and processes the product shipping. The order information is linked to the logistics system, and the product is shipped.

[1420] Example 2

[1421] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1422] Conventional systems have difficulty properly analyzing information entered by users and extracting attribute data such as product name, origin, and budget. Furthermore, they are unable to take into account the user's emotional information when searching for or ordering products, which makes it difficult to increase user satisfaction.

[1423] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and an overview of the reviews compiled by the generative AI, means for ordering products through dialogue, and means for acquiring emotion information from an emotion engine and reflecting it in the analysis results. This makes it possible to appropriately analyze the user's input information and perform product searches and orders that take emotion information into consideration.

[1424] "User" refers to an individual or corporation that uses the system to input information and search for and place orders.

[1425] "Input information" refers to information such as text data and voice data that a user provides to the system.

[1426] "Generative AI" refers to artificial intelligence technology that analyzes input information and generates attribute data.

[1427] "Attribute data" refers to characteristic information such as product name, place of origin, and budget that generative AI extracts from input information.

[1428] "Product search" refers to the process of searching a database for the product the user is looking for based on the generated attribute data.

[1429] "Review summary" refers to a summary of product ratings and impressions compiled by generative AI.

[1430] An "emotion engine" refers to technology for analyzing and obtaining emotional information from user input.

[1431] "Emotional information" refers to data that indicates the emotional state extracted from the user's input information.

[1432] "Dialogue" refers to the communication process in which a user and a system exchange information.

[1433] "Order" refers to the process for purchasing the product selected by the User.

[1434] This invention is a system that analyzes information input by a user, generates attribute data such as product name, place of origin, budget, etc., and performs product search and ordering taking emotional information into consideration. Specific embodiments of this system are described below.

[1435] The server has a means for receiving input information from users. The user uses a device (such as a smartphone or PC) to input information about the product they are searching for. For example, they might input, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen."

[1436] The device sends the information entered by the user to the server. At this time, the input information is sent to the server in an appropriate format (for example, JSON format). The server then sends the received input information to the generative AI. Since the generative AI is hosted on the cloud, the server accesses the generative AI via the internet.

[1437] The generative AI uses the Python library spaCy and Transformers to analyze the input information. Specifically, it tokenizes the text and understands the meaning of each token. From the analysis results, the generative AI extracts attribute data such as the product name (salmon roe), origin (Hokkaido), and budget (under 5,000 yen). This allows it to specifically understand the user's request.

[1438] Furthermore, generative AI uses emotion engines (such as TextBlob and VADER) to extract emotional information from user input. For example, if the user expresses the emotion "looking forward to it," that emotional information is reflected in the analysis results.

[1439] The server provides appropriate product information to the user based on the analysis results received from the generative AI. For example, it searches for "fresh salmon roe from Hokkaido within a budget of 5,000 yen" and displays the results to the user. The user can then interactively select and order products based on the displayed product information.

[1440] As a concrete example, a user enters into a terminal, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen." The terminal sends this information to a server. The server then sends the received information to a generative AI, which analyzes it using natural language processing technology. The generative AI extracts the product name (salmon roe), place of origin (Hokkaido), and budget (within 5,000 yen), and further analyzes the user's emotions (looking forward to it) using an emotion engine. Based on this information, the server provides the user with appropriate product information.

[1441] An example prompt is:

[1442] User Input: "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen."

[1443] Generative AI output: {"Product name": "Salmon roe", "Origin": "Hokkaido", "Budget": "Under 5,000 yen", "Emotion": "Looking forward to it"}

[1444] In this way, the server analyzes the information input by the user, generates appropriate attribute data, and realizes a system that provides the user with the most suitable product information.

[1445] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1446] Step 1:

[1447] The user inputs the input information into the terminal.

[1448] The user uses a device (such as a smartphone or computer) to input information about the product they are searching for. For example, they might input, "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen." This input information becomes the initial data for the system.

[1449] Step 2:

[1450] The terminal sends the input information to the server.

[1451] The terminal sends the information entered by the user to the server. At this time, the input information is sent to the server in an appropriate format (for example, JSON format). The input information is "I'm looking for fresh salmon roe from Hokkaido. My budget is within 5,000 yen."

[1452] Step 3:

[1453] The server sends the input information to the generative AI.

[1454] The server sends the received input information to the generative AI. Since the generative AI is hosted on the cloud, the server accesses the generative AI via the internet. The input information is passed directly to the generative AI.

[1455] Step 4:

[1456] Generative AI analyzes input information using natural language processing technology.

[1457] Generative AI uses Python libraries such as spaCy and Transformers to analyze input information. Specifically, it tokenizes sentences and understands the meaning of each token. It analyzes the input information, "Looking for fresh salmon roe from Hokkaido. Budget is under 5,000 yen," and generates tokenized data.

[1458] Step 5:

[1459] Generative AI extracts attribute data such as product name, place of origin, and budget.

[1460] The generative AI extracts attribute data such as the product name (salmon roe), origin (Hokkaido), and budget (under 5,000 yen) from the analysis results. This allows the user's request to be specifically understood. The output data is {"Product name": "salmon roe", "Origin": "Hokkaido", "Budget": "under 5,000 yen"}.

[1461] Step 6:

[1462] The generative AI obtains emotional information from the emotion engine and reflects it in the analysis results.

[1463] The generative AI uses an emotion engine (such as TextBlob or VADER) to obtain emotional information from the user's input. For example, if the user expresses the emotion "I'm looking forward to it," that emotional information is reflected in the analysis results. The output data is {"Product name": "Salmon roe", "Origin": "Hokkaido", "Budget": "Under 5,000 yen", "Emotion": "I'm looking forward to it"}.

[1464] Step 7:

[1465] The server receives the analysis results from the generative AI and provides appropriate product information to the user.

[1466] The server provides the user with appropriate product information based on the analysis results received from the generative AI. For example, it searches for "fresh salmon roe from Hokkaido within a budget of 5,000 yen" and displays the results to the user. The user can interactively select and order products based on the displayed product information. The output data is a list of products as search results.

[1467] (Application example 2)

[1468] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1469] On conventional online shopping sites, when searching for products based on the information entered by the user, it was difficult to take into account the user's emotions and detailed attribute data. As a result, it was not possible to accurately recommend the products that the user really wanted, which could lead to a decrease in satisfaction. In addition, there was also the issue of not being able to centrally select and order products, which resulted in a poor user experience.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1471] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information with a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and an overview of the reviews compiled by the generative AI, means for analyzing the user's emotional information with an emotion engine, means for recommending products taking into account the analyzed emotional information, and means for ordering products through a dialogue. This enables product recommendations that take into account the user's emotions and detailed attribute data, and is expected to improve the user experience.

[1472] "Means for receiving input information from the user" refers to an interface for receiving text or voice information entered by the user.

[1473] "Method of decomposing using generative AI and generating attribute data" refers to the process of using generative AI to analyze input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[1474] The "means for searching for products" is a function that searches for corresponding products from databases and online stores based on the generated attribute data.

[1475] "Means for displaying search results and summaries of reviews compiled by generative AI" refers to an interface that displays to users summaries of product reviews and ratings compiled by generative AI along with search results.

[1476] The "means for analyzing user's emotional information using an emotion engine" is a process of using an emotion engine to analyze emotions from user's input information and extract that information.

[1477] The "means for recommending products taking into consideration analyzed emotional information" is a function that recommends products that match the user's emotions based on analyzed emotional information.

[1478] The "means for ordering products through dialogue" is an interface that allows users to select and order products in a unified manner through dialogue with the user.

[1479] The system for implementing the present invention includes a series of processes for receiving input information from a user, analyzing the information, and recommending products. Specific embodiments are described below.

[1480] Hardware and software used

[1481] Hardware: Smartphone

[1482] software:

[1483] Natural language processing libraries (e.g., spaCy, NLTK)

[1484] Sentiment analysis engine (e.g. IBM Watson Tone Analyzer)

[1485] Generative AI models (e.g., GPT-4)

[1486] Program processing procedure

[1487] 1. Accepting text input:

[1488] A user enters text into a smartphone application.

[1489] Example: "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen."

[1490] 2. Natural Language Processing Analysis:

[1491] The server analyzes the input text using a natural language processing library (e.g., spaCy, NLTK) and extracts attribute data such as the product name, place of origin, manufacturer, and budget.

[1492] Example: Product name: Salmon roe, Origin: Hokkaido, Budget: 5,000 yen

[1493] 3. Emotion analysis:

[1494] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion information.

[1495] Example: Emotion: Enjoyment

[1496] 4. Product recommendation:

[1497] The server uses a generative AI model (e.g., GPT-4) to recommend optimal products based on the extracted attribute data and emotional information.

[1498] Example: "I recommend fresh salmon roe from Hokkaido, priced under 5,000 yen."

[1499] Examples and prompts

[1500] Examples:

[1501] If a user types, "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5,000 yen," the application will display, "Recommendations for fresh salmon roe from Hokkaido within a budget of 5,000 yen."

[1502] Example prompt for a generative AI model:

[1503] User entered text: "I'm looking for fresh Hokkaido salmon roe. My budget is under 5000 yen."

[1504] Extracted attribute data: Product name: salmon roe, origin: Hokkaido, budget: 5,000 yen

[1505] Emotional information: Enjoyment

[1506] Generate product recommendations.

[1507] In this way, we can realize a smartphone application for online shopping sites that recommends optimal products based on the user's input and emotional information. This system makes it possible to recommend products that take into account the user's emotions and detailed attribute data, and is expected to improve the user experience.

[1508] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1509] Step 1:

[1510] A user enters text into an application on a smartphone.

[1511] Input: Text information entered by the user (e.g. "I'm looking for fresh salmon roe from Hokkaido. My budget is under 5000 yen.")

[1512] Output: The entered text information is sent to the server.

[1513] Step 2:

[1514] The server analyzes the received text information using a natural language processing library (e.g., spaCy, NLTK).

[1515] Input: Text information entered by the user

[1516] Data processing: Tokenize text information and extract attribute data such as product name, place of origin, manufacturer, and budget.

[1517] Output: Extracted attribute data (e.g., product name: salmon roe, origin: Hokkaido, budget: 5,000 yen)

[1518] Step 3:

[1519] The server uses an emotion analysis engine (e.g., IBM Watson Tone Analyzer) to analyze the user's emotion information.

[1520] Input: Text information entered by the user

[1521] Data calculation: Analyze emotions from text information and extract emotional information.

[1522] Output: Extracted emotion information (e.g., emotion: enjoyment)

[1523] Step 4:

[1524] The server uses a generative AI model (e.g., GPT-4) to recommend optimal products based on the extracted attribute data and emotional information.

[1525] Input: Extracted attribute data and emotion information

[1526] Data calculation: Input a prompt sentence into the generative AI model to generate the optimal product.

[1527] Output: Recommended product information (e.g. "We recommend fresh salmon roe from Hokkaido, within a budget of 5,000 yen.")

[1528] Step 5:

[1529] The server displays recommended product information and a summary of reviews compiled by generative AI to the user.

[1530] Input: Recommended product information and review summary

[1531] Output: A summary of product information and reviews displayed on the user's smartphone

[1532] Step 6:

[1533] The user selects and orders products through a dialogue.

[1534] Input: User selection and order information

[1535] Output: The order is confirmed and sent to the server.

[1536] In this way, a system is realized that recommends optimal products based on the user's input information and emotional information, allowing users to easily select and order products.

[1537] Example 3

[1538] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1539] Conventional systems simply use user input information to search for products, but do not take into account the user's emotional information to suggest optimal products or optimize the ordering process. This results in lower user satisfaction and an inability to respond appropriately to users who are in a hurry or in a particular emotional state.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1541] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI model to generate attribute data and emotional information, means for generating and executing a query to a product database based on the generated attribute data and emotional information, means for displaying search results and a summary of reviews compiled by the generative AI model, means for selecting and ordering products through dialogue, and means for optimizing the ordering process by taking the user's emotional information into consideration. This enables optimal product suggestions and a speedy ordering process that take the user's emotional information into consideration.

[1542] "User input information" refers to data such as text or voice that a user provides to the system.

[1543] A "generative AI model" is an artificial intelligence model that analyzes user input information and generates attribute data and emotional information.

[1544] "Attribute data" is specific information such as the product name, place of origin, manufacturer, and budget extracted from the information entered by the user.

[1545] "Emotional information" refers to the user's emotional state (e.g., feeling rushed, relaxed, etc.) analyzed from the user's input information.

[1546] A "product database" is a database that stores product information.

[1547] A "query" is a command statement for searching a product database for data based on specific conditions.

[1548] "Search results" are product information obtained from the product database when a query is executed.

[1549] A "review summary" is a summary of product ratings and user opinions compiled by a generative AI model.

[1550] "Dialogue" is communication between a user and a system through text or voice.

[1551] The "order process" is a series of steps a user takes to purchase the product they have selected.

[1552] "Optimization" means taking into account the user's emotional information to efficiently and effectively carry out the ordering process.

[1553] This invention is a system that performs product searches based on user input, displays search results and review summaries, and enables product ordering through dialogue. The system uses a generative AI model to extract user attribute data and emotional information, and generates and executes queries against a product database to search for products that best match the user's needs.

[1554] The server uses a generative AI model (e.g., a general natural language processing model) to analyze the user's input information and generate attribute data and emotional information. Specifically, it receives text or voice data entered by the user on a website or application, analyzes it, and extracts attribute data such as the product name, place of origin, manufacturer, and budget, as well as the user's emotional state (e.g., whether in a hurry or relaxed).

[1555] Next, the server generates a query to the product database based on the extracted attribute data and emotion information. For example, it generates the following SQL query:

[1556] sql

[1557] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[1558] This query is executed to search for the corresponding products in a product database (e.g., a general relational database or NoSQL database).

[1559] The device displays search results and a summary of reviews compiled by the generative AI model. The display area includes the product name, price, rating, and review summary. For example, it displays:

[1560] Product name: Smartphone A

[1561] Price: 45,000 yen

[1562] Rating: 4.5

[1563] Review Summary: "Very easy to use. Camera performance is excellent."

[1564] The user selects a product based on the displayed product information and review summary. The selected product can be ordered through a chatbot (e.g., a general conversational agent) or a voice assistant (e.g., a general voice recognition system). For example, the user can input "I would like to order smartphone A" into the chatbot.

[1565] The server optimizes the ordering process by taking into account the user's emotional information. For example, if the user indicates that they are in a hurry, the server will take steps to expedite the ordering process.

[1566] For example, if a user inputs "I'm looking for a new smartphone," the generative AI model extracts the user's attribute data (budget, preferred brand, desired features) and emotional information (hurry, relaxed). The server generates the following query to search for the relevant smartphone in the product database:

[1567] sql

[1568] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[1569] The terminal displays the search results, and the user selects smartphone A based on the displayed information and places an order through the chatbot. If the user is in a hurry, the server will expedite the order process.

[1570] Example prompt sentence:

[1571] If a user is looking for a new smartphone, extract attribute data such as budget, preferred brand, desired features, etc. to generate queries against your product database, and also take into account user sentiment to optimize the ordering process.

[1572] The above is an embodiment of the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1573] Step 1:

[1574] The user initiates a product search.

[1575] A user types "looking for a new smartphone" into the search bar on a website or application and clicks the search button.

[1576] Input: User's search query (e.g., "Looking for a new smartphone")

[1577] Output: The user's search query is sent to the server.

[1578] Step 2:

[1579] The server uses the generated AI model to extract the user's attribute data and emotional information.

[1580] The server uses a generative AI model to analyze the user's input information and extract attribute data such as the product name, place of origin, manufacturer, and budget, as well as the user's emotional information.

[1581] Input: User's search query

[1582] Output: Attribute data (e.g., budget, preferred brand, desired features) and emotional information (e.g., rushed, relaxed)

[1583] Step 3:

[1584] The server generates a query to the product database based on the extracted data.

[1585] The server generates a query to the product database based on the extracted attribute data. For example, it generates the following SQL query:

[1586] sql

[1587] SELECT FROM products WHERE category = 'smartphone' AND price <= 50000 AND brand IN ('Apple', 'Samsung') AND features LIKE '%5G%'

[1588] Input: Attribute data

[1589] Output: A query against the product database

[1590] Step 4:

[1591] The server executes the generated query and retrieves search results from the product database.

[1592] The server executes the generated query and searches for the corresponding product from the product database.

[1593] Input: Query against product database

[1594] Output: Search results (e.g. product name, price, rating, review summary)

[1595] Step 5:

[1596] The device displays search results and a summary of reviews compiled by the generative AI model.

[1597] The device displays product information from search results and a summary of reviews compiled by the generative AI model in a display area on websites and applications.

[1598] Input: Search results

[1599] Output: A summary of the product information and reviews displayed in the display area

[1600] Step 6:

[1601] The user selects a product based on the displayed product information.

[1602] The user refers to the displayed product information and review summary to select the product they wish to purchase.

[1603] Input: Displayed product information and review summary

[1604] Output: The product selected by the user

[1605] Step 7:

[1606] Users place orders through chatbots and voice assistants.

[1607] A user orders a product through a chatbot or voice assistant. For example, the user might say to the chatbot, "I would like to order Smartphone A."

[1608] Input: User's order request

[1609] Output: Start of ordering process

[1610] Step 8:

[1611] The server takes into account the user's emotional information and optimizes the ordering process.

[1612] The server optimizes the ordering process by taking into account the user's emotional information. For example, if the user indicates that they are in a hurry, the server will take steps to expedite the ordering process.

[1613] Input: User's emotional information

[1614] Output: Optimized ordering process

[1615] (Application example 3)

[1616] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1617] On conventional online shopping sites, it was difficult to fully utilize user attribute data and emotional information to optimize product searches. Furthermore, review information in search results was often scattered, leaving users with insufficient information to select the right product. Furthermore, when users placed product orders, the lack of an interactive interface prevented a smooth ordering process. These issues need to be resolved.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1619] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for ordering products through dialogue, means for analyzing user emotional information, and means for optimizing the product search by taking the user's emotional information into consideration. This enables optimal product searches using the user's attribute data and emotional information, and by displaying search results and review summaries in a unified manner, it becomes easier for users to select appropriate products. Furthermore, a smooth ordering process can be achieved through an interactive interface.

[1620] "User input information" refers to data such as text or voice that a user provides to the system.

[1621] "Generative AI" is an artificial intelligence technology that analyzes input information from users and generates attribute data and emotional information.

[1622] "Attribute data" is characteristic information extracted from the user's input information, such as the product name, place of origin, manufacturer, and budget.

[1623] "Product search" is the process of finding the most suitable product from the product database based on the generated attribute data.

[1624] The "review summary" is a summary of user ratings and impressions of a product compiled by generative AI.

[1625] "Means for ordering products interactively" refers to a function that allows users to select and order products interactively using a chatbot or voice assistant.

[1626] "Emotion information" is data that indicates the user's emotional state, analyzed from the user's input information.

[1627] "Means to optimize product searches" refers to a function that takes into account the user's emotional information to provide more appropriate products as search results.

[1628] The system for implementing this invention receives user input information, generates attribute data and emotional information using generative AI, performs product searches, displays search results and review summaries, and allows product ordering through an interactive interface. Specific embodiments are described below.

[1629] Hardware and software used

[1630] Hardware: Smartphones, servers

[1631] Software: Python, OpenAI API, EmotionRecognizer library, ChatBot library

[1632] Data processing and calculation

[1633] Receiving and analyzing user input information

[1634] The server receives text and voice input from the user. The received input information is analyzed using generative AI to generate attribute data such as the product name, place of origin, manufacturer, and budget. The EmotionRecognizer library is also used to analyze the user's emotional information.

[1635] Performing a product search

[1636] The server generates a query to the product database based on the generated attribute data and emotion information. This query is generated using the OpenAI API. The generated query is executed to search for the best product.

[1637] View search results and reviews

[1638] The server displays the product information obtained as search results and a summary of the reviews compiled by generative AI on the smartphone's display area, allowing users to view the search results and review summaries in one place.

[1639] Interactive order placement

[1640] Users can select and order products through a chatbot or voice assistant, referring to the displayed product information and review summary. The ChatBot library is used to realize dialogue with the user. The system also takes into account the user's emotional information; for example, if the user indicates that they are feeling stressed, it will prioritize suggestions of products that will help them relax.

[1641] Specific examples

[1642] For example, if a user inputs, "I've been feeling stressed lately, so I want a product that helps me relax," the server will send the following prompt sentence to the generative AI model:

[1643] Example prompt: "Please find the best products based on the user's demographic data: Most of the products purchased in the past are relaxation items, and their emotion: They are stressed."

[1644] The generative AI model uses this prompt to find the best products and displays the search results and a summary of reviews to the user, who can then select the product and complete the order through the chatbot.

[1645] In this way, the system can utilize user attribute data and sentiment information to optimize product searches, display search results and review summaries in a unified manner to help users select the right product, and facilitate a smooth ordering process through a conversational interface.

[1646] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1647] Step 1:

[1648] The server receives input information from the user. The user provides the input information via text or voice using a smartphone. The input information includes the user's wishes and requests. After receiving the input information, the server passes it on to the next processing step.

[1649] Input: User text or voice input

[1650] Output: The input information received

[1651] Step 2:

[1652] The server uses a generative AI to analyze the received input information and generate attribute data and emotional information. The generative AI analyzes the input information and extracts attribute data such as the product name, place of origin, manufacturer, and budget. It also uses the EmotionRecognizer library to analyze the user's emotional information.

[1653] Input: User input information

[1654] Data processing: Extraction of attribute data using generative AI, analysis of emotional information using EmotionRecognizer

[1655] Output: attribute data, emotion information

[1656] Step 3:

[1657] The server generates a query to the product database based on the generated attribute data and emotion information. Using the OpenAI API, it sends a prompt to the generative AI model and generates a query to search for the best product.

[1658] Input: attribute data, emotion information

[1659] Data Computation: Query Generation using OpenAI API

[1660] Output: Product search query

[1661] Step 4:

[1662] The server executes the generated query to search for the most suitable product from the product database, and obtains the product information obtained as a search result.

[1663] Input: Product search query

[1664] Data operations: querying the product database

[1665] Output: Product information from search results

[1666] Step 5:

[1667] The server displays the product information obtained as search results and a summary of the reviews compiled by generative AI on the smartphone's display area, allowing users to view the search results and review summaries in one place.

[1668] Input: Search result product information, review summary

[1669] Data processing: Converting search results and review summaries into a display format

[1670] Output: A summary of search results and reviews displayed in the smartphone display area

[1671] Step 6:

[1672] Users can select and order products through a chatbot or voice assistant, referring to the displayed product information and review summary. The ChatBot library is used to realize dialogue with the user. The system also takes into account the user's emotional information; for example, if the user indicates that they are feeling stressed, it will prioritize suggestions of products that will help them relax.

[1673] Input: User selection information, emotion information

[1674] Data Calculation: Interaction processing using ChatBot library

[1675] Output: User's order information

[1676] Step 7:

[1677] The server receives the user's order information and processes the order, including checking product availability, processing payment, and arranging delivery.

[1678] Input: User's order information

[1679] Data processing: inventory checks, payment processing, delivery arrangements

[1680] Output: Order confirmation information, shipping information

[1681] Through these steps, the system can utilize user attribute data and sentiment information to provide optimal product searches, display search results and review summaries in a unified manner to help users select the right product, and facilitate a smooth ordering process through a conversational interface.

[1682] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1683] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1684] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1685] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1686] [Third embodiment]

[1687] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1688] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1689] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1690] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1691] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1692] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1693] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1694] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1695] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1696] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1697] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1698] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1699] "Example 1"

[1700] The system of the present invention receives input information from users through, for example, an input form on a website or application, or a voice recognition system. Users input information such as the name, place of origin, manufacturer, and budget of the product they want.

[1701] "Example 2"

[1702] The generative AI breaks down the received input information and generates attribute data, for example, using natural language processing technology. This generative AI analyzes the input information and extracts attribute data such as the product name, place of origin, manufacturer, and budget.

[1703] "Example 3"

[1704] As a means for searching for products based on the generated attribute data, for example, a query is generated and executed against a product database. This product search is performed based on the attribute data to search for products that most closely match the user's needs.

[1705] "Example 4"

[1706] For example, a display area on a website or application can be used to display search results and summaries of reviews compiled by generative AI. This display area displays product information from search results and summaries of related reviews.

[1707] "Example 5"

[1708] Chatbots and voice assistants, for example, are used as a means of ordering products through dialogue. Users can select and order products through dialogue, referring to the displayed product information and review summaries.

[1709] The processing flow of each embodiment will be described below.

[1710] "Example 1"

[1711] Step 1: The user enters information such as the name of the product they want, its origin, manufacturer, and budget into an input form on a website or application.

[1712] Step 2: Input information is sent to the system and received by the generative AI.

[1713] Step 3: The generative AI uses natural language processing technology to analyze the input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[1714] "Example 2"

[1715] Step 1: Generate a query to the product database based on the attribute data extracted by the generative AI.

[1716] Step 2: Execute the generated query to search the product database for products that best match the user's requirements.

[1717] Step 3: Display the search results in a display area on your website or application.

[1718] "Example 3"

[1719] Step 1: The user selects a product based on the displayed product information.

[1720] Step 2: The user places an order for the selected product via a chatbot or voice assistant.

[1721] Step 3: The system receives the order information and processes the order.

[1722] Example 1

[1723] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1724] In conventional systems, even if a user inputs information about the product they want, it is difficult to process that information appropriately and provide product information that meets the user's needs. In addition, it is often impossible to quickly and accurately return product information generated based on the information entered by the user. This leads to problems such as lower user satisfaction and reduced system usage.

[1725] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1726] In this invention, the server includes means for receiving input information from a user, means for saving the received input information in a database, means for generating a prompt sentence based on the saved information, means for sending the generated prompt sentence to a generative AI model, means for receiving product information generated from the generative AI model, and means for returning the received product information to the user, thereby enabling product information based on a user's request to be provided quickly and accurately.

[1727] "Means for receiving input information from users" refers to the interface for receiving information that users input through a website or application.

[1728] "Means for saving received input information in a database" is a function for storing information received from a user in a database.

[1729] "Means for generating prompt sentences based on stored information" refers to a function that creates instruction sentences (prompt sentences) for the generative AI model based on information stored in the database.

[1730] "Means for sending the generated prompt sentence to the generative AI model" is a function for sending the generated prompt sentence to the generative AI model.

[1731] "Means for receiving product information generated from a generative AI model" refers to a function for receiving product information generated by a generative AI model.

[1732] "Means for returning received product information to the user" is a function for providing product information received from the generative AI model to the user.

[1733] A "generative AI model" is an artificial intelligence model that performs specific tasks based on user input.

[1734] A "prompt" is an instruction given to a generative AI model to perform a specific task.

[1735] MODE FOR CARRYING OUT THE INVENTION

[1736] This invention is a system that receives input information from a user, generates product information based on that information using a generative AI model, and provides the product information to the user. Specific embodiments of this system are described below.

[1737] Hardware and software used

[1738] Hardware

[1739] Server: Use a server machine with a powerful processor and sufficient memory.

[1740] Device: A device such as a PC or smartphone used by a user.

[1741] software

[1742] Database management system: Use MySQL, PostgreSQL, etc.

[1743] Generative AI model: Use OpenAI's GPT-3 or similar.

[1744] System processing flow

[1745] Receiving input from the user

[1746] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget through a form on a website or in an application. For example, they might enter "Japanese apples, from Aomori Prefecture, budget within 1,000 yen."

[1747] Sending and saving input information

[1748] The terminal sends the entered information to the server using the HTTPS protocol, and the server stores the received information in a database. The stored data includes the product name, origin, manufacturer, budget, etc.

[1749] Generate prompt statement

[1750] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[1751] Sending prompts to generative AI models

[1752] The server sends the generated prompt to the generative AI model, which uses OpenAI's GPT-3. The prompt is sent via an API.

[1753] Creating and receiving product information

[1754] Based on the received prompt, the generative AI model generates product information that matches the user's request. For example, it generates information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." The server receives the product information generated by the generative AI model.

[1755] Returning product information to the user

[1756] The server returns the product information received from the generative AI model to the user, who can then view this information on a website or application.

[1757] Specific examples

[1758] The specific flow when a user enters the information "Apples from Japan, from Aomori Prefecture, budget within 1000 yen" is shown below.

[1759] 1. The user enters information

[1760] Users enter "Japanese apples, from Aomori Prefecture, budget under 1,000 yen" into the website's input form.

[1761] 2. The device sends the input information to the server

[1762] The terminal sends the entered information to the server using the HTTPS protocol.

[1763] 3. The server saves the input information in a database

[1764] The server stores the received information in a MySQL database. The stored data is "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1000 yen."

[1765] 4. The server generates a prompt

[1766] The server generates a prompt message saying, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[1767] 5. The server sends a prompt to the generative AI model

[1768] The server sends the generated prompt text to OpenAI's GPT-3 via an API.

[1769] 6. Generative AI model generates product information

[1770] The generative AI model generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[1771] 7. The server returns the generated product information to the user

[1772] The server returns the generated product information to the user, and the user confirms on the website the information: "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[1773] In this way, the user can obtain product information that meets his or her needs.

[1774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1775] Step 1:

[1776] The user enters information

[1777] Users enter information such as the name of the product they want, its place of origin, manufacturer, and budget into an input form on a website or application. For example, they might enter "Apples from Japan, from Aomori Prefecture, budget within 1,000 yen." The entered information is temporarily stored in the device's memory.

[1778] Step 2:

[1779] The device sends the input information to the server

[1780] The terminal sends the information entered by the user to the server using the HTTPS protocol. The input information is sent to the server as JSON format data. For example, it is sent in the format {"Product name": "Apple", "Origin": "Aomori Prefecture", "Budget": "Under 1000 yen"}.

[1781] Step 3:

[1782] The server saves the input information in a database

[1783] The server stores the input information received from the terminal in a database. MySQL or PostgreSQL is used as the database management system. The data stored includes attribute data such as the product name, origin, manufacturer, and budget. For example, a record such as "Product name: Apple, Origin: Aomori Prefecture, Budget: Under 1,000 yen" is added to the database.

[1784] Step 4:

[1785] The server generates a prompt

[1786] The server generates a prompt for the generative AI model based on the information stored in the database. For example, it generates a prompt such as, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen." This prompt is used as an instruction for the generative AI model.

[1787] Step 5:

[1788] The server sends a prompt to the generative AI model

[1789] The server sends the generated prompt to the generative AI model. OpenAI's GPT-3 is used as the generative AI model. The prompt is sent via API. For example, an API request might read, "Please provide information about apples from Japan, produced in Aomori Prefecture, within a budget of 1,000 yen."

[1790] Step 6:

[1791] Generative AI model generates product information

[1792] The generative AI model generates product information that matches the user's request based on the received prompt. For example, it generates product information such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm." This generated information is returned to the server.

[1793] Step 7:

[1794] The server returns the generated product information to the user

[1795] The server returns the product information received from the generative AI model to the user. The user can check this information on a website or application. For example, the user might see information on the screen such as "Apples from Aomori Prefecture, price 800 yen, sold by XX Farm."

[1796] In this way, the user can obtain product information that meets his or her needs.

[1797] (Application example 1)

[1798] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1799] On conventional online shopping sites, when users input detailed information such as the name, origin, manufacturer, and budget of the product they want, manual input is cumbersome, and it is difficult to quickly find the right product. Furthermore, even when using voice input, accurate product recommendations are often not provided, which is an issue that does not improve the user experience.

[1800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1801] In this invention, the server includes means for receiving input information from a user, means for breaking down the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing links to purchase the recommended products, and means for ordering the products during the dialogue. This allows the user to easily provide detailed product information using voice input and receive accurate product recommendations from the generative AI model.

[1802] "Means for receiving input information from the user" refers to an interface that allows the user to input information such as the name, place of origin, manufacturer, and budget of the product they want, and includes input forms on websites or applications and voice recognition systems.

[1803] "Means of decomposing using generative AI and generating attribute data" refers to a processing system that uses artificial intelligence technology to analyze input information received from users and convert it into attribute data such as product name, place of origin, manufacturer, and budget.

[1804] The "means for searching for products" is a system for searching for corresponding products in a database or on the Internet based on the generated attribute data.

[1805] "Means for displaying search results and summaries of reviews compiled using generative AI" refers to an interface that uses generative AI to summarize search results and related reviews and display them to users.

[1806] "Means for receiving voice input using a voice recognition system" refers to a system that uses voice recognition technology to convert information input by a user through voice into text data.

[1807] "Means for recommending products based on user-input information using a generative AI model" refers to a system that uses a generative AI model to recommend optimal products based on information entered by the user.

[1808] The "means for providing links to purchase recommended products" refers to an interface that provides links to related online shopping sites so that users can purchase products recommended by the generative AI model.

[1809] The "means for ordering products through dialogue" is a system for centrally selecting and ordering products through dialogue with the user.

[1810] A system for implementing this invention includes means for receiving input information from a user, means for breaking it down using a generative AI to generate attribute data, means for performing a product search based on the generated attribute data, means for displaying a summary of the search results and reviews compiled by the generative AI, means for receiving voice input using a voice recognition system, means for recommending products based on the user's input information using a generative AI model, means for providing a link to purchase the recommended product, and means for ordering the product during the dialogue.

[1811] System program processing explanation

[1812] Hardware

[1813] Smartphone (microphone, internet connection)

[1814] software

[1815] Python

[1816] speech_recognition library: Used to convert voice input to text.

[1817] Transformers library: Uses a generative AI model (GPT-3) to recommend products based on user input.

[1818] Data processing and calculation

[1819] 1. Receiving voice input:

[1820] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using the speech recognition system (speech_recognition library).

[1821] 2. Generate attribute data:

[1822] Using a generative AI model (transformers library), the information entered by the user is analyzed and attribute data such as the product name, place of origin, manufacturer, and budget is generated.

[1823] 3. Product recommendation:

[1824] Based on the generated attribute data, a generative AI model is used to recommend optimal products, and the recommended product information is displayed to the user.

[1825] 4. Providing a Purchase Link:

[1826] A link to purchase the recommended product is generated and provided to the user, who can then purchase the product at the relevant online shopping site by clicking on the link.

[1827] 5. Interactive ordering:

[1828] Through interaction with the user, product selection and ordering are performed in one place, allowing users to easily select products and complete orders.

[1829] Specific examples

[1830] When a user speaks, "Japanese apples, within a budget of 1,000 yen," the speech recognition system converts this speech into text. The generative AI model analyzes this text and generates attribute data. The generative AI model then recommends the most suitable product and provides the user with information such as, "The recommended product is Japanese apples, priced at 800 yen. Here is the purchase link."

[1831] Prompt Sentence Examples

[1832] Product information for user: Japanese apples, budget under 1000 yen

[1833] Please tell me what products you recommend.

[1834] In this way, users can easily provide detailed product information using voice input and receive accurate product recommendations from generative AI models.

[1835] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1836] Step 1:

[1837] The user inputs voice through the smartphone's microphone. The voice data is converted into text data using a speech recognition system (speech_recognition library). The input is the user's voice data, and the output is text data. In this step, the speech recognition system analyzes the voice data and generates corresponding text data.

[1838] Step 2:

[1839] The server uses a generative AI model (transformers library) to analyze the text data obtained in step 1 and generate attribute data such as product name, place of origin, manufacturer, and budget. The input is text data, and the output is attribute data. In this step, the generative AI model breaks down the text data and extracts each attribute.

[1840] Step 3:

[1841] The server performs a product search based on the generated attribute data. The input is the attribute data and the output is the search results. In this step, the server searches for the relevant products in a database or on the Internet and obtains the search results.

[1842] Step 4:

[1843] The server displays the search results and the review summaries compiled by the generative AI. The input is the search results, and the output is display data including the review summaries. In this step, the generative AI summarizes the reviews related to the search results and generates data to display to the user.

[1844] Step 5:

[1845] The server uses a generative AI model to recommend products based on user input. The input is attribute data, and the output is recommended product information. In this step, the generative AI model analyzes the attribute data and recommends the most suitable products.

[1846] Step 6:

[1847] The server provides a link to purchase the recommended product. The input is the recommended product information, and the output is a purchase link. In this step, the server generates a link to the online shopping site corresponding to the recommended product and provides it to the user.

[1848] Step 7:

[1849] The user places an order through a dialogue. The input is the user's selection information, and the output is order completion information. In this step, the user clicks on the provided link, selects the product on the online shopping site, and completes the order.

[1850] Example 2

[1851] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1852] With conventional systems, it was difficult to efficiently analyze information input by users and extract attribute data. Furthermore, it was not possible to perform product searches based on the extracted attribute data and provide appropriate feedback to users. Furthermore, it was difficult to centrally order products through dialogue with users. This resulted in a poor user experience and reduced system utilization efficiency.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1854] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying the search results and a summary of reviews compiled by the generative AI, means for ordering products through dialogue, means for sending prompts to the generative AI model and extracting attribute data, and means for saving the extracted attribute data and providing feedback to the user. This makes it possible to efficiently analyze input information from a user, extract appropriate attribute data to perform a product search, and provide the user with prompt and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[1855] "User" means an individual or corporation that uses the system to input information and search for and place orders.

[1856] "Input information" refers to information such as the product name, place of origin, manufacturer, and budget that the user provides to the system.

[1857] "Generative AI" is an artificial intelligence technology that analyzes input information and extracts attribute data.

[1858] "Attribute data" refers to data such as the product name, place of origin, manufacturer, and budget that the generative AI extracts from the input information.

[1859] "Product search" is the process in which the system searches for related products based on the generated attribute data.

[1860] A "review summary" is a summary of product ratings and comments compiled by generative AI based on search results.

[1861] "Dialogue" is a communication process in which the user and the system exchange information with each other to select and order products.

[1862] A "prompt sentence" is a sentence that instructs the generative AI to analyze input information and extract attribute data.

[1863] "Feedback" refers to information about analysis results and search results that the system provides to the user.

[1864] This invention is a system that efficiently analyzes information input by users, extracts attribute data, performs product searches, and provides users with quick and accurate feedback. Furthermore, it allows users to select and order products in a unified manner through dialogue with the system.

[1865] Hardware and software used

[1866] Hardware

[1867] server

[1868] User device (PC, smartphone, etc.)

[1869] software

[1870] Generative AI models (e.g., GPT-4)

[1871] Web browser (user device)

[1872] Data processing and calculation

[1873] Server Processing

[1874] The server sends the input information received from the user to the generative AI model. This generative AI model analyzes the input information using natural language processing technology and extracts attribute data such as the product name, place of origin, manufacturer, and budget. Specifically, the server makes an API request to the generative AI model and uses prompt statements to instruct it to analyze.

[1875] Terminal handling

[1876] The terminal is responsible for sending the information entered by the user to the server. When the user enters information about a product into the input form and presses the send button, the information is sent to the server.

[1877] User Action

[1878] The user enters information such as the product name, place of origin, manufacturer, budget, etc. into the input form on the terminal. For example, they might enter "apples, Aomori Prefecture, sunfruit, budget 500 yen."

[1879] Specific examples

[1880] Prompt Sentence Examples

[1881] "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, Aomori Prefecture, Sunfruit, Budget 500 yen'"

[1882] In this way, the server, terminal, and user each play their respective roles, and the system uses a generative AI model to analyze input information and generate attribute data. This makes it possible to efficiently analyze input information from users, extract appropriate attribute data, perform product searches, and provide users with fast and accurate feedback. Furthermore, product selection and ordering can be performed centrally through dialogue with the user, improving the user experience and system utilization efficiency.

[1883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1884] Step 1:

[1885] The user inputs the input information into the terminal.

[1886] The user inputs product information into the input form on the terminal. For example, the user might input "apples, Aomori Prefecture, sunfruit, budget 500 yen." This information includes the product name, place of origin, manufacturer, and budget. When the input information is entered into the terminal, the terminal temporarily stores the information.

[1887] Step 2:

[1888] The terminal sends the input information to the server.

[1889] When the user presses the submit button, the device sends the input information to the server as an HTTP POST request. This request contains all the information the user entered. Specifically, the device converts the input information into JSON format and sends it to the server's API endpoint.

[1890] Step 3:

[1891] The server sends the input information to the generative AI model.

[1892] The server sends the input information received from the device to the generative AI model. Specifically, the server makes an API request to the generative AI model, instructing it to analyze the input information. At this time, the prompt text used is "Analyze the following information and extract the product name, origin, manufacturer, and budget. Input information: 'Apples, produced in Aomori Prefecture, sunfruit, budget 500 yen'." The input is information from the user, and the output is the analysis result from the generative AI model.

[1893] Step 4:

[1894] The generative AI model analyzes the input information and extracts attribute data.

[1895] The generative AI model analyzes the received input information and extracts attribute data. Specifically, the generative AI model uses natural language processing technology to extract the following attribute data from the input information:

[1896] Product Name: Apple

[1897] Origin: Aomori Prefecture

[1898] Manufacturer: Sun Fruits

[1899] Budget: 500 yen

[1900] The input is a prompt from the server, and the output is the extracted attribute data.

[1901] Step 5:

[1902] The server stores the extracted attribute data and provides feedback to the user.

[1903] The server stores the attribute data received from the generative AI model. Specifically, it stores it in a database. The server also provides feedback to the user. For example, it might display to the user, "The following attribute data has been extracted from the input information: Product name: Apple, Origin: Aomori Prefecture, Manufacturer: Sun Fruits, Budget: 500 yen." The input is the attribute data from the generative AI model, and the output is a feedback message to the user.

[1904] (Application example 2)

[1905] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1906] On conventional online shopping sites, when users searched for products, it was difficult to extract appropriate attribute data from the information they entered and provide accurate search results. Furthermore, there was a lack of efficient means to extract detailed attribute data such as the name, origin, manufacturer, and budget of the product the user wanted, so search results often did not meet the user's expectations. Furthermore, there was a need for a system that could display search results and review summaries in a unified manner and allow users to order products interactively.

[1907] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1908] In this invention, the server includes means for receiving input information from a user, means for analyzing the received input information using a generative AI and generating attribute data, means for performing a product search based on the generated attribute data, means for displaying search results and summaries of reviews compiled by the generative AI, means for interactively ordering products, means for extracting attribute data such as product name, place of origin, manufacturer, and budget from the input information using the generative AI, and means for providing highly accurate search results based on the extracted attribute data. This makes it possible to automatically extract detailed attribute data from the information entered by the user and provide highly accurate search results. Furthermore, search results and review summaries can be displayed in a unified manner, allowing for interactive product ordering.

[1909] The "means for receiving input information from the user" is an interface for incorporating text information entered by the user into the system.

[1910] "Means of breaking down received input information using generative AI and generating attribute data" refers to the process of using generative AI to analyze input information and extract attribute data such as the product name, place of origin, manufacturer, and budget.

[1911] The "means for searching for products based on the generated attribute data" is a function for searching for related products from a database based on the extracted attribute data.

[1912] "A means of displaying search results and summaries of reviews compiled by generative AI" is a function that displays to users a summary of product reviews compiled by generative AI along with the search results.

[1913] The "means for ordering products interactively" is a function that allows users to select and order products in an integrated manner through an interactive format.

[1914] "Means of using generative AI to extract attribute data such as product name, place of origin, manufacturer, and budget from input information" refers to the process of using generative AI to automatically extract detailed attribute data of a product from information entered by the user.

[1915] "Means for providing highly accurate search results based on extracted attribute data" refers to a function that utilizes extracted attribute data to provide more relevant search results to users.

[1916] The system for implementing this invention receives input information from a user, analyzes it using generative AI to extract attribute data, and performs highly accurate product searches. Specific embodiments of this system are described below.

[1917] System Configuration

[1918] The system consists of the following main components:

[1919] 1. User terminal: The device through which the user provides input information (e.g., a smartphone).

[1920] 2. Server: A central processing unit that runs generative AI models, generates attribute data, and performs product searches.

[1921] 3. Database: Data storage for storing product information and review information.

[1922] Program processing

[1923] The server performs the following steps:

[1924] 1. Receiving input information: Receive text information entered from the user's device. For example, a user might enter, "I'm looking for a new smartphone. My budget is within 50,000 yen, and I'd prefer a Japanese manufacturer."

[1925] 2. Generative AI analysis: The received input information is passed to a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt. An example of a prompt is as follows:

[1926] Please extract the product name, place of origin, manufacturer, and budget from the text below.

[1927] Text: I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer a Japanese brand.

[1928] Attribute data:

[1929] 3. Extracting attribute data: The generative AI model analyzes the prompt and extracts attribute data such as the product name, origin, manufacturer, and budget. For example, the following attribute data is extracted:

[1930] Name: Smartphone

[1931] Origin: Domestic

[1932] Manufacturer: Domestic manufacturer

[1933] Budget: Under 50,000 yen

[1934] 4. Product search: Based on the extracted attribute data, the database is searched for related products. For example, products that meet the criteria of "smartphone," "domestic manufacturer," and "under 50,000 yen" are searched for.

[1935] 5. Displaying search results and reviews: Along with search results, a summary of product reviews compiled by generative AI is displayed on the user's device, allowing users to see detailed product information and reviews at a glance.

[1936] 6. Interactive ordering: The user selects a product from the search results and completes the order interactively. For example, the user can enter "I would like to purchase this smartphone," and the system will proceed with the order process.

[1937] Hardware and software used

[1938] Hardware: A smartphone (user device) connected to the internet and a server.

[1939] Software: Python, OpenAI API, database management system (e.g., MySQL).

[1940] This system allows users to automatically extract detailed attribute data from the information they enter, resulting in highly accurate search results. It also displays search results and review summaries in a unified manner, and allows users to interactively order products.

[1941] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1942] Step 1:

[1943] The user's device receives the input information. The user enters text information for product search and presses the send button. For example, the user might enter, "I'm looking for a new smartphone. My budget is under 50,000 yen, and I'd prefer one from a Japanese manufacturer." The...

Claims

1. A means for receiving input information from a user; A means for decomposing the received input information using a generative AI and generating attribute data; A means for performing a product search based on the generated attribute data; A means for displaying the search results and a summary of reviews compiled by generative AI; and means for receiving speech input using a speech recognition system; A means for recommending products based on user input information using a generative AI model; a means for providing a link to purchase the recommended product; means for effecting an order for a product in interaction with a user; A system including:

2. A means for receiving input information from a user; A means for decomposing the received input information using a generative AI and generating attribute data; A means for performing a product search based on the generated attribute data; A means for displaying the search results and a summary of reviews compiled by generative AI; and means for effecting an order for a product in interaction with a user; A means for sending a prompt sentence to the generative AI model and extracting attribute data; A means for storing the extracted attribute data and providing feedback to the user; and A system including:

3. The attribute information includes at least one of the name, place of origin, manufacturer, and budget of the product. A system according to claim 1 or claim 2.

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