system

Abstract

We provide the system. [Solution] A means for receiving information including the product name from a user terminal, A means of querying a generative artificial intelligence based on information including the name of the product, A means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Salespersons are required to quickly and accurately confirm the introduction and delivery dates of products, but there are problems that errors are likely to occur and it is time-consuming with the conventional methods. In the current system, it is necessary to manually query the product name and the person in charge confirms the delivery date, and this process is not efficient. Therefore, it is necessary to provide a means for salespersons to immediately obtain the required delivery date information.

Means for Solving the Problems

[0005] This invention is a system that includes means for receiving information including the product name from a user terminal, means for querying a generative artificial intelligence based on the information including the product name, and means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal. As a result, sales representatives can quickly obtain delivery date information from the generative artificial intelligence simply by entering the product name. Furthermore, since the system includes means for generating an error message and returning it to the user terminal if the product name is empty, input errors can also be prevented.

[0006] A "user terminal" is a device used for inputting and outputting information, and includes personal computers, smartphones, tablets, and other similar devices.

[0007] "Merchandise" refers to specific goods or services that are the subject of sales activities.

[0008] "Generative artificial intelligence" refers to a system that uses artificial intelligence technology to perform natural language processing and generate responses based on specified prompts.

[0009] "Means of receiving" refers to the functions or processes for acquiring information transmitted from the user's terminal.

[0010] "Means of making inquiries" refers to functions and processes for asking questions to a generative artificial intelligence based on received information and obtaining the necessary responses.

[0011] "Formatting methods" refer to functions and processes for converting responses received from generative artificial intelligence into a format that is easy for the user to understand.

[0012] "Means of resending" refers to the functions or processes for resending the formatted response information to the user's terminal.

[0013] An "error message" is a message that notifies the user when there are errors in the input information. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system aimed at enabling users to quickly and accurately confirm the delivery date of commercial products. The system's program processing and specific examples will be explained below.

[0036] System-wide configuration

[0037] The system consists of three main components: a user terminal, a server, and a generative artificial intelligence (AI). The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI. The generative AI performs natural language processing based on the requests and generates appropriate responses.

[0038] User terminal operation

[0039] To check the delivery date of a product, the user enters information including the product name from their terminal. The entered information, including the product name, is sent to the server as an HTTP POST request.

[0040] Server operation

[0041] The server analyzes the request received from the user terminal and extracts the product name. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0042] When the server receives a response from the generative artificial intelligence, it formats the response and sends it back to the user's terminal. This allows the user to obtain accurate delivery date information with simple operations.

[0043] How generative artificial intelligence works

[0044] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. Specifically, it receives inquiries that include product names, generates information regarding the delivery date of that product, and sends it back to the server.

[0045] Specific example

[0046] For example, consider a case where a user wants to check the delivery date for the "New X100". The user enters "New X100" into their terminal and sends a request to the server. The server extracts the product name "New X100" from the received request and queries the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative artificial intelligence generates a response such as, "The delivery date for the New X100 is January 15, 2024."

[0047] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal stating, "The delivery date for the new X100 is January 15, 2024."

[0048] This system allows users to quickly and easily check the delivery date of products.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The user sends a request from their device.

[0052] The user enters the product name "New X100" to check the delivery date for the product. The data, including the entered product name, is sent to the server as an HTTP POST request.

[0053] Step 2:

[0054] The server receives the request and parses its contents.

[0055] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It parses the received JSON data and extracts the product name.

[0056] Step 3:

[0057] The server checks for the presence or absence of the product name.

[0058] The server checks if the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user along with a 400 status code.

[0059] Step 4:

[0060] The server queries the generative artificial intelligence.

[0061] If the product name is retrieved correctly, the server will send a query to the generative artificial intelligence based on the product name. Specifically, it will use the generative artificial intelligence's API to send a prompt asking, "When is the delivery date for the new X100?"

[0062] Step 5:

[0063] A generative artificial intelligence generates delivery date information.

[0064] The generative artificial intelligence performs natural language processing based on the received prompt and generates appropriate delivery date information. It sends the response "The delivery date for the new X100 is January 15, 2024" back to the server.

[0065] Step 6:

[0066] The server formats the response.

[0067] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information.

[0068] Step 7:

[0069] The server returns the message to the user's terminal.

[0070] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal that "The delivery date for the new X100 is January 15, 2024."

[0071] Through the steps described above, users can quickly and easily check the delivery date for the products they wish to purchase.

[0072] (Example 1)

[0073] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0074] Conventional systems for confirming product delivery dates often required manual inquiries and verification, demanding significant time and effort. This made it difficult for users to quickly and accurately confirm product delivery dates. Furthermore, the lack of proper error message handling and automated response systems resulted in a poor user experience.

[0075] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0076] In this invention, the server includes means for receiving information including the product name from a user terminal as an HTTP POST request, means for analyzing the information and extracting the product name, means for generating and returning an error message if the product name is empty, means for querying a generative artificial intelligence if the product name is properly obtained, and means for formatting the delivery date information obtained from the generative artificial intelligence and returning it to the user terminal. This makes it possible for users to quickly and accurately confirm the delivery date of the product.

[0077] A "user terminal" is a device used by a user to input and transmit information in order to confirm the delivery date of a product, and can be a computer, smartphone, tablet, or other terminal device.

[0078] "Product name" refers to the name of a product, service, or item, and is used to identify a specific product related to the delivery date that the user wants to check.

[0079] An "HTTP POST request" is a protocol used to send data from a user's terminal to a server, and is primarily used for transferring web data and calling APIs.

[0080] A "server" is a computer system that analyzes requests received from user terminals, performs the necessary processing, and sends the results back to the user terminal.

[0081] "Generative artificial intelligence" refers to artificial intelligence (AI) models that perform natural language processing based on received prompts and generate appropriate responses. Examples include OpenAI's GPT series.

[0082] An "error message" is a message generated by the server and sent back to the user's terminal when the product name entered by the user is inappropriate or empty, informing the user of the nature of the error.

[0083] A "prompt message" is a sentence containing questions or requests in natural language format that is input to a generative artificial intelligence system, and is a sentence that inquires about delivery date information based on the product name.

[0084] Modes for carrying out the invention

[0085] This invention is a system for users to quickly and accurately confirm the delivery date of commercial products. The system mainly consists of three components: a user terminal, a server, and a generative artificial intelligence system.

[0086] User terminal operation

[0087] The user terminal is a device used to input the product name. Users enter the product name using a web browser or mobile application and send that information to the server. For example, if a user enters the product name "New X100" and clicks the "Search" button, the user terminal generates an HTTP POST request based on that information and sends it to a URL (for example, http: / / example.com / api / check-delivery).

[0088] Server operation

[0089] The server parses the HTTP POST request received from the user's terminal. The request body contains the product name in the format {"itemName": "New X100"}. The server parses this request and extracts the product name. If the product name is empty, the server generates an error message "Please enter the product name" and sends it back to the user's terminal.

[0090] If the product name is retrieved correctly, the server queries the generative artificial intelligence. The prompt will be generated as follows: "When is the delivery date for the new X100?" Based on this prompt, the server sends an API request to the generative artificial intelligence. For example, it sends JSON data containing authentication information and the prompt to https: / / api.example.com / v1 / ai / generate.

[0091] How generative artificial intelligence works

[0092] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. For example, it might generate a response such as "The delivery date for the new X100 is January 15, 2024" and send it back to the server. OpenAI's GPT series is one example of a generative artificial intelligence that can be used.

[0093] Return and display of results

[0094] The server formats the response received from the generative artificial intelligence. For example, it converts a message like "The delivery date for the new X100 is January 15, 2024" into a user-friendly format, such as "The delivery date for the new X100 is January 15, 2024." The formatted message is then sent back to the user's terminal as an HTTP response. The user can then view this information on their terminal.

[0095] Specific example

[0096] For example, if a user wants to check the delivery date for the "New X100," they would type "New X100" into the input field of their web browser or mobile application and click the search button. The server receives this and sends a prompt message to the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative AI responds, "The delivery date for the New X100 is January 15, 2024," and the server formats this information and sends it back to the user's terminal. The user can then confirm on the screen that "The delivery date for the New X100 is January 15, 2024."

[0097] Examples of prompt statements include:

[0098] "When will the new X100 be available?"

[0099] "What is the delivery time for the F-series LED displays?"

[0100] "Could you please tell me the schedule for introducing the Y200 commercial printer?"

[0101] In this way, this system helps users to easily and quickly check the delivery date of products.

[0102] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0103] Step 1:

[0104] The user enters the product name. Specifically, the user enters the product name into an input field in a web browser or mobile application and clicks the search button. The entered information (product name) is sent as an HTTP POST request. The input is the product name, and the output is an HTTP POST request.

[0105] Step 2:

[0106] The server receives the request. The server parses the HTTP POST request received from the user's terminal and extracts the product name from the request body. Specifically, it obtains data in the format {"itemName": "New X100"}. The input is the request body of the HTTP POST request, and the output is the extracted product name.

[0107] Step 3:

[0108] The server performs error checking. The server checks whether the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user terminal. For example, it might generate a message such as "Please enter a product name." The input is the extracted product name, and the output is the error message or the product name for the next step.

[0109] Step 4:

[0110] The server queries the generation AI. If the product name is retrieved correctly, the server generates a prompt and sends an API request to the generation AI. For example, it might generate a prompt such as, "What is the delivery date for the new X100?" The input is the correctly retrieved product name, and the output is the generated prompt and the sent API request.

[0111] Step 5:

[0112] The generative AI generates a response. The generative artificial intelligence receives a prompt and generates appropriate delivery date information. Specifically, it generates a response that says, "The delivery date for the new X100 is January 15, 2024." The input is the prompt, and the output is the generated delivery date information response.

[0113] Step 6:

[0114] The server formats the response. The server formats the delivery date information received from the generative artificial intelligence and converts it into a format that is easy for humans to understand. For example, it formats it into a format such as "The delivery date for the new X100 is January 15, 2024." The input is the response from the generative artificial intelligence, and the output is the formatted response message.

[0115] Step 7:

[0116] The server sends a formatted response back to the user terminal. The server sends the formatted message back to the user terminal as an HTTP response. The user terminal receives this message and displays it on the screen. The input is the formatted response message, and the output is the information displayed on the user terminal.

[0117] Step 8:

[0118] The user confirms the results. The user can check delivery date information on their device and take the necessary actions. The input is the information displayed on the user's device, and the output is the user's decision.

[0119] (Application Example 1)

[0120] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0121] Conventional systems made it difficult to instantly check delivery dates and inventory information for product introductions. In particular, product management is complex in logistics centers, requiring rapid and accurate information provision. However, traditional methods required users to manually search for information, which was time-consuming and laborious. Therefore, a system is needed to quickly provide product introduction date and inventory information in order to improve the efficiency of logistics operations.

[0122] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0123] In this invention, the server includes means for receiving information including product names from a user terminal, means for querying a generative artificial intelligence based on the information including product names, means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, and means for generating product delivery date information and inventory information. This makes it possible to quickly and easily obtain product delivery date information and inventory information.

[0124] A "user terminal" is a device used for inputting and outputting information, and includes smartphones, tablets, and personal computers.

[0125] A "product name" is a name or identifier used to identify a specific product.

[0126] "Generative artificial intelligence" is an artificial intelligence technology that performs natural language processing and generates appropriate information based on user inquiries.

[0127] "Inquiry results" refer to information generated by a generative artificial intelligence system based on inquiries sent from the user's terminal.

[0128] "Formatting" refers to the process of converting query results obtained from generative artificial intelligence into a format that is easy for users to understand.

[0129] "Implementation date information" refers to information about when a particular product will be implemented.

[0130] "Inventory information" refers to information about how many units of a particular product are currently in stock.

[0131] An "error message" is a warning or cautionary message displayed to a user when certain conditions are not met.

[0132] This invention provides a system that enables rapid acquisition of delivery date information and inventory information for product introduction at a logistics center. The system consists of a user terminal, a server, and a generative artificial intelligence. When a user enters a product name, the generative artificial intelligence generates information about the product via the server and returns it to the user.

[0133] Hardware and software usage

[0134] The system uses the following hardware and software:

[0135] Hardware:

[0136] User terminal: A device used for inputting and outputting information. Examples include smartphones, tablets, and personal computers.

[0137] Server: Receives requests from user terminals and processes them in cooperation with generative artificial intelligence.

[0138] software:

[0139] Server-side program: Built using Python and Flask.

[0140] Generative Artificial Intelligence: Uses the OpenAI API to perform natural language processing and information generation.

[0141] User terminal application: Developed in ANDROID® (Kotlin), it sends HTTP requests and receives information.

[0142] Data processing and data calculation

[0143] User terminal operation

[0144] On the user terminal, a staff member enters the product name. For example, they might enter "Product 1" on their smartphone. This information is sent to the server as an HTTP POST request. The user terminal uses a library for API communication (e.g., OkHttp).

[0145] Server operation

[0146] The server analyzes the request received from the user's terminal and extracts the product name. If the product name is successfully retrieved, it uses the API key to send a prompt message to OpenAI's generative artificial intelligence. For example, the prompt message might look like this:

[0147] "Please tell me the delivery date and stock status of Product 1."

[0148] The results obtained from the generative artificial intelligence are formatted and sent back to the user's terminal. The server side is built with Python and Flask, and libraries for API communication are also used (e.g., requests).

[0149] How generative artificial intelligence works

[0150] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information and inventory information. For example, it generates responses like the following:

[0151] "The delivery date for Product 1 is February 15, 2024, and we have 50 units in stock."

[0152] The generated information is sent back to the server, which then formats the information and sends it back to the user's terminal.

[0153] Specific example

[0154] For example, consider a scenario where a logistics center staff member wants to check the delivery date and inventory status of a new SKU, "Product 1." The staff member enters "Product 1" into a smartphone application and sends a request to the server. The server generates a prompt, "Please tell me the delivery date and inventory status of Product 1," and sends it to a generative artificial intelligence (AI). The AI ​​generates a response, "The delivery date for Product 1 is February 15, 2024, and the inventory quantity is 50 units," which is then formatted and sent back to the user's terminal. The staff member can then view this information on their smartphone screen.

[0155] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0156] Step 1:

[0157] Enter the product name on the user terminal.

[0158] Input: The user enters the product name (for example, "Product 1").

[0159] Output: HTTP POST request containing the product name.

[0160] Specific operation: Enter the product name into the input field of the smartphone application and press the submit button. At this time, the product name is sent to the server as an HTTP POST request in JSON format.

[0161] Step 2:

[0162] The server receives and analyzes requests from the user's terminal.

[0163] Input: HTTP POST request sent from the user's terminal.

[0164] Output: Product names are extracted.

[0165] Specific operation: A server application using Flask receives a request and parses the data in JSON format. It then extracts the product name from the product name field.

[0166] Step 3:

[0167] The server makes a query to the generative artificial intelligence.

[0168] Input: Extracted product name (e.g., "Product 1").

[0169] Output: The prompt message sent to the generative artificial intelligence.

[0170] Specific operation: The server uses the OpenAI API to generate the prompt "Please tell me the delivery date and stock status of product 1," and sends it to the generative artificial intelligence.

[0171] Step 4:

[0172] Generative artificial intelligence generates delivery date information and inventory information.

[0173] Input: Prompt message received from the server.

[0174] Output: Delivery date information and inventory information (for example, "The delivery date for product 1 is February 15, 2024, and the inventory quantity is 50 units.").

[0175] Specific operation: Generative artificial intelligence (OpenAI model) analyzes the prompt text and generates delivery date and inventory information for the product.

[0176] Step 5:

[0177] The server formats the information obtained from the generative artificial intelligence and sends it back to the user's terminal.

[0178] Input: Delivery date and inventory information returned by a generative artificial intelligence.

[0179] Output: Formatted delivery date and inventory information.

[0180] Specific operation: The server formats the received information into a format that is easy for the user to understand and sends it back to the user's terminal in JSON format.

[0181] Step 6:

[0182] The user terminal receives and displays the response from the server.

[0183] Input: Formatted delivery date and inventory information sent from the server.

[0184] Output: Delivery date and stock information displayed to the user.

[0185] Specific operation: The smartphone application receives a response from the server and displays the information on the screen. The user confirms the information, "The delivery date for product 1 is February 15, 2024, and the number of units in stock is 50."

[0186] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0187] This invention is a system designed to allow users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's feelings. The system's program processing and specific examples will be explained below.

[0188] System-wide configuration

[0189] The system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0190] User terminal operation

[0191] To check the delivery date of a product, the user enters the product name "New X100" on their terminal. At this time, the emotion engine recognizes the user's emotions based on facial recognition information and text input information. The entered information and emotion data are sent to the server as an HTTP POST request.

[0192] Server operation

[0193] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0194] How generative artificial intelligence works

[0195] Generative artificial intelligence performs natural language processing based on received prompts and user sentiment data to generate appropriate delivery date information. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0196] Server response processing

[0197] The server formats the responses received from the generative artificial intelligence into a format that is easy for the user to understand. It also adjusts the format and content of the responses based on emotional data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[0198] Specific example

[0199] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server.

[0200] The server extracts the product name "New X100" and emotional data from the received request, and sends the emotional data to the generative artificial intelligence along with the prompt, "When is the delivery date for the new X100?".

[0201] The generative artificial intelligence generates the response, "The delivery date for the new X100 is January 15, 2024," and adds the message, "Don't worry, the arrangements are progressing smoothly."

[0202] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal, which reads, "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0203] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0204] The following describes the processing flow.

[0205] Step 1:

[0206] The user sends a request from their device.

[0207] The user enters the product name, "New X100," to confirm the delivery date for the product. As the user enters the name, the terminal analyzes their emotions from facial expressions and text, and sends this data along with the input to the server in an HTTP POST request.

[0208] Step 2:

[0209] The server receives the request and parses its contents.

[0210] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It extracts the product name and sentiment data from the received JSON data.

[0211] Step 3:

[0212] The server checks for the presence or absence of the product name.

[0213] If the product name is empty, the server generates an error message "Product name required" and sends it back to the user with a 400 status code. If the product name is successfully retrieved, the process proceeds to the next step.

[0214] Step 4:

[0215] The server queries the generative artificial intelligence.

[0216] The server passes the extracted product names and sentiment data to the generative artificial intelligence and inquires about delivery dates based on the product names. Specifically, it sends sentiment data along with the prompt, "When is the delivery date for the new X100?"

[0217] Step 5:

[0218] A generative artificial intelligence generates delivery date information.

[0219] The generative artificial intelligence performs natural language processing based on the received prompt and sentiment data. It generates a response such as, "The delivery date for the new X100 is January 15, 2024," and if the user is feeling anxious, it adds a message such as, "Don't worry, the arrangements are progressing smoothly."

[0220] Step 6:

[0221] The server formats the response.

[0222] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information. During this process, the response format and content are adjusted based on sentiment data.

[0223] Step 7:

[0224] The server returns the message to the user's terminal.

[0225] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal the following information: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0226] (Example 2)

[0227] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0228] Conventional product delivery date confirmation systems have problems with users having difficulty obtaining delivery date information quickly and accurately, and in particular, with a lack of information provision that takes user emotions into consideration. As a result, users often feel anxious and worried, which leads to a decrease in customer satisfaction. A system that solves this problem is needed.

[0229] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0230] In this invention, the server includes means for receiving information including product name and sentiment data from a user terminal, means for querying a generative artificial intelligence based on the product name and sentiment data, and means for formatting a message based on the delivery date information and sentiment data obtained from the generative artificial intelligence and sending it back to the user terminal. This makes it possible for the user to quickly and accurately obtain sentiment-sensitive delivery date information.

[0231] A "user terminal" is an electronic device used to input and transmit product names and sentiment data.

[0232] "Product name" refers to the name of a specific product or service, and is information that users enter to confirm delivery dates.

[0233] "Emotional data" refers to data that represents the user's emotional state and is primarily acquired using facial recognition technology.

[0234] A "server" is a central system that processes information received from user terminals and coordinates with generative artificial intelligence.

[0235] "Generative artificial intelligence" refers to an artificial intelligence model that generates appropriate responses based on input prompts and sentiment data.

[0236] "Delivery date information" refers to information regarding the introduction date or delivery date of a specific product.

[0237] A "message" is supplementary text based on emotional data that is added to delivery date information generated by a generative artificial intelligence.

[0238] An "error message" is an error notification message sent from the server to the user's terminal, such as when the product name is empty.

[0239] This invention is a system that aims to allow users to quickly and accurately confirm the delivery date of a product, while taking into account the user's emotions.

[0240] The overall system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0241] Detailed explanation of operation

[0242] 1. User input of product name

[0243] To check the delivery date of a product, the user enters the product name "New X100" into their terminal. The terminal's input screen has a text box, and the process begins when the user enters the product name.

[0244] 2. Capturing emotional data

[0245] The device's camera function is used to capture the user's facial expressions. This data is analyzed through an emotion engine to obtain the user's emotional data. The emotion engine uses facial recognition APIs such as Amazon Rekognition.

[0246] 3. Sending data

[0247] The user terminal sends the entered product name and analyzed sentiment data to the server as an HTTP POST request. The data sent is in JSON format.

[0248] 4. Server-side request analysis

[0249] The server parses the received HTTP POST request and extracts the product name and sentiment data. The Python Flask framework is used for the analysis. If the product name is empty, an error message is generated and sent back to the user's terminal.

[0250] 5. Sending prompts to generative artificial intelligence

[0251] The server sends a prompt to a generative artificial intelligence (for example, OpenAI GPT-3®) based on the product name and sentiment data. The prompt is sent in a format such as, "When is the delivery date for the new X100? Users are impatient."

[0252] 6. Response generation by generative artificial intelligence

[0253] Generative artificial intelligence generates responses based on received prompts and sentiment data. For example, along with the response, "The delivery date for the new X100 is January 15, 2024," the message "Don't worry, arrangements are progressing smoothly" might be added.

[0254] 7. Server-side response formatting

[0255] The server formats the responses received from the generative artificial intelligence. Using the Python Jinja2 library, it transforms them into a user-friendly format. It also adjusts the response format and content based on sentiment data.

[0256] 8. Sending a response

[0257] The server sends a formatted response back to the user's terminal. The response might be in the format of, for example, "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0258] 9. User response confirmation

[0259] The user confirms the response on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0260] Examples of prompt statements

[0261] Send the following prompt to the generative artificial intelligence:

[0262] "When will the new X100 be available? Users are getting impatient."

[0263] This allows users to quickly and accurately obtain emotionally sensitive delivery date information.

[0264] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0265] Step 1:

[0266] The user enters the product name "New X100" into the text box on the device. The device accepts the input and then activates the camera function. The input is saved on the device as text data.

[0267] Step 2:

[0268] The device's camera captures the user's face and sends the image data to the emotion engine. The emotion engine uses facial recognition technology to analyze the user's facial expressions and generate emotion data. This emotion data is output as emotional states, such as "anxiety" or "relief."

[0269] Step 3:

[0270] The terminal combines the entered product name and analyzed sentiment data to generate an HTTP POST request. This request is in JSON format and includes the product name and sentiment data. The request is sent to the server.

[0271] Step 4:

[0272] The server parses the received HTTP POST request and extracts product names and sentiment data from the JSON data. A server-side framework (e.g., Flask in Python) is used for the parsing. The extracted product names and sentiment data are temporarily stored on the server.

[0273] Step 5:

[0274] The server makes a query to the generative artificial intelligence. Specifically, it generates a prompt based on the product name "New X100" and the emotion data "anxiety," and sends it to the generative artificial intelligence. This prompt is in the format of "When is the delivery date for the new X100? The user is anxious."

[0275] Step 6:

[0276] The generative artificial intelligence generates delivery date information and messages based on prompts and sentiment data. For example, it might output a message such as, "The delivery date for the new X100 is January 15, 2024," along with, "Don't worry, arrangements are progressing smoothly." The response data is sent back to the server in JSON format.

[0277] Step 7:

[0278] The server formats the response data received from the generative artificial intelligence. It uses the Python Jinja2 library to convert the data into a user-friendly format. It also adjusts the format and content based on sentiment data. The formatted data might look something like this: "The delivery date for the new X100 is January 15, 2024. Don't worry, arrangements are progressing smoothly."

[0279] Step 8:

[0280] The server sends the formatted response data to the user terminal as an HTTP response. The response is in JSON format and includes the formatted delivery information and message.

[0281] Step 9:

[0282] The user checks the response data received on the terminal. A message saying "The introduction delivery date of the new X100 is January 15, 2024. Please don't worry. The arrangements are proceeding smoothly." is displayed on the terminal screen.

[0283] (Application Example 2)

[0284] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0285] The conventional lead time confirmation system for commercial products only provides uniform information to users and has the problem that it cannot provide flexible responses according to the emotions and situations of users. Therefore, when users feel anxious or uneasy, the user experience may decline in the lead time confirmation process. The problem that this invention aims to solve is to provide a more user-friendly and flexible lead time confirmation system considering the emotions of users.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0287] In this invention, the server includes means for receiving information including the product name from the user terminal, means for analyzing the user's emotion data, means for querying a generative artificial intelligence based on the product name and emotion data, and means for formatting the query result obtained from the generative artificial intelligence, adjusting it according to the emotion data, and sending it back to the user terminal. Thereby, it becomes possible to provide lead time information according to the user's emotion.

[0288] A "user terminal" is a device used by a user to input and output information.

[0289] "Product name" refers to information that indicates the name of a specific product.

[0290] "Emotional data" refers to data that indicates the user's emotional state, analyzed based on the user's facial recognition information and text input information.

[0291] "Generative artificial intelligence" refers to an artificial intelligence model that performs natural language processing and generates appropriate responses based on the content of an inquiry.

[0292] "Query results" refer to response data generated by a generative artificial intelligence system.

[0293] "Adjusting based on emotional data" refers to changing the content and format of the generated response based on the user's emotional state.

[0294] A "server" is a computer system that processes information received from a user terminal and sends an appropriate response back to the user terminal using generative artificial intelligence and emotional data.

[0295] "Delivery date information" refers to information indicating the planned date or period for the introduction or delivery of a specific product.

[0296] "Formatting" refers to converting raw or generated data into a format that is easy for users to understand.

[0297] An "error message" is a message used to inform a user of errors or deficiencies in the information they have entered.

[0298] This invention provides a system that allows users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's emotions. The system consists of four main components: a user terminal, a server, a generative artificial intelligence system, and an emotion engine.

[0299] User terminal

[0300] The user terminal is a device such as a smartphone, tablet, or personal computer, and performs information input and output. The user inputs the name of the merchandise to check the delivery date of the merchandise and provides face recognition information if necessary. The face recognition information and text input information of the user are analyzed by the emotion engine, and emotion data is generated. These information and emotion data are sent to the server as an HTTP POST request.

[0301] Server

[0302] The server analyzes the request received from the user terminal and extracts the merchandise name and emotion data. If the merchandise name is empty, the server generates an error message and returns it to the user terminal. When the merchandise name can be properly obtained, an inquiry is made to the generative artificial intelligence.

[0303] Generative artificial intelligence

[0304] The generative artificial intelligence generates delivery date information based on the received prompt and the user's emotion data. This delivery date information is adjusted according to the emotion data. For example, when the user is feeling anxious, the delivery date information is emphasized and explained, or a specific explanation about the possibility of a delayed delivery date is added.

[0305] Server response processing

[0306] The server formats the query result obtained from the generative artificial intelligence into a form that is easy for the user to understand. Also, based on the emotion data, the format and content of the response are adjusted. For example, if the user appears to be tired, encouraging words can be incorporated into the response text.

[0307] Specific example

[0308] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server. The server extracts the product name "New X100" and emotion data from the received request and sends the emotion data along with the prompt "When is the delivery date for the New X100?" to the generative AI. The generative AI generates a response stating "The delivery date for the New X100 is January 15, 2024," and adds a message such as "Don't worry, the arrangements are progressing smoothly." The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal: "The delivery date for the New X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0309] Example of a prompt

[0310] "When will the new X100 be available? Users are feeling anxious right now."

[0311] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0312] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0313] Step 1:

[0314] The user enters the product name and provides facial recognition information as needed. The user's terminal collects this information and generates input data containing the product name and facial recognition information. The input data is sent to the server as an HTTP POST request.

[0315] Step 2:

[0316] The server analyzes the request received from the user's terminal and extracts the product name and facial recognition information. The server sends the facial recognition information to the emotion engine and obtains emotion data. The emotion engine analyzes the facial recognition information and outputs the user's emotional state (e.g., impatience, anxiety, fatigue, etc.) as data.

[0317] Step 3:

[0318] The server generates an error message if the product name is empty and sends it back to the user's terminal. Generating the error message is a data processing operation that creates error text based on the condition that the product name is empty.

[0319] Step 4:

[0320] The server generates a prompt containing the product name and sentiment data, and queries the generative artificial intelligence. The prompt is in text format, such as, "When is the delivery date for the new X100? The user is currently feeling anxious."

[0321] Step 5:

[0322] Generative artificial intelligence performs natural language processing based on received prompts and sentiment data to generate delivery date information. The generated delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0323] Step 6:

[0324] The server formats the query results (delivery date information and additional messages) obtained from the generative artificial intelligence and sends them back to the user terminal. The formatting process is a data calculation that adjusts the response format and content according to the emotional data. For example, if the user appears tired, words of encouragement will be incorporated into the response.

[0325] Step 7:

[0326] The user checks the delivery date information on their device. The device displays the delivery date information and messages received from the server, providing the user with easy-to-understand information. As a result, the user can obtain information such as, "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0327] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0328] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0329] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0330] [Second Embodiment]

[0331] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0332] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0333] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0334] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0335] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0336] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0337] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0338] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0339] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0340] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0341] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0342] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0343] This invention is a system aimed at enabling users to quickly and accurately confirm the delivery date of commercial products. The system's program processing and specific examples will be explained below.

[0344] System-wide configuration

[0345] The system consists of three main components: a user terminal, a server, and a generative artificial intelligence (AI). The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI. The generative AI performs natural language processing based on the requests and generates appropriate responses.

[0346] User terminal operation

[0347] To check the delivery date of a product, the user enters information including the product name from their terminal. The entered information, including the product name, is sent to the server as an HTTP POST request.

[0348] Server operation

[0349] The server analyzes the request received from the user terminal and extracts the product name. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0350] When the server receives a response from the generative artificial intelligence, it formats the response and sends it back to the user's terminal. This allows the user to obtain accurate delivery date information with simple operations.

[0351] How generative artificial intelligence works

[0352] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. Specifically, it receives inquiries that include product names, generates information regarding the delivery date of that product, and sends it back to the server.

[0353] Specific example

[0354] For example, consider a case where a user wants to check the delivery date for the "New X100". The user enters "New X100" into their terminal and sends a request to the server. The server extracts the product name "New X100" from the received request and queries the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative artificial intelligence generates a response such as, "The delivery date for the New X100 is January 15, 2024."

[0355] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal stating, "The delivery date for the new X100 is January 15, 2024."

[0356] This system allows users to quickly and easily check the delivery date of products.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] The user sends a request from their device.

[0360] The user enters the product name "New X100" to check the delivery date for the product. The data, including the entered product name, is sent to the server as an HTTP POST request.

[0361] Step 2:

[0362] The server receives the request and parses its contents.

[0363] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It parses the received JSON data and extracts the product name.

[0364] Step 3:

[0365] The server checks for the presence or absence of the product name.

[0366] The server checks if the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user along with a 400 status code.

[0367] Step 4:

[0368] The server queries the generative artificial intelligence.

[0369] If the product name is retrieved correctly, the server will send a query to the generative artificial intelligence based on the product name. Specifically, it will use the generative artificial intelligence's API to send a prompt asking, "When is the delivery date for the new X100?"

[0370] Step 5:

[0371] A generative artificial intelligence generates delivery date information.

[0372] The generative artificial intelligence performs natural language processing based on the received prompt and generates appropriate delivery date information. It sends the response "The delivery date for the new X100 is January 15, 2024" back to the server.

[0373] Step 6:

[0374] The server formats the response.

[0375] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information.

[0376] Step 7:

[0377] The server returns the message to the user's terminal.

[0378] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal that "The delivery date for the new X100 is January 15, 2024."

[0379] Through the steps described above, users can quickly and easily check the delivery date for the products they wish to purchase.

[0380] (Example 1)

[0381] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] Conventional systems for confirming product delivery dates often required manual inquiries and verification, demanding significant time and effort. This made it difficult for users to quickly and accurately confirm product delivery dates. Furthermore, the lack of proper error message handling and automated response systems resulted in a poor user experience.

[0383] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0384] In this invention, the server includes means for receiving information including the product name from a user terminal as an HTTP POST request, means for analyzing the information and extracting the product name, means for generating and returning an error message if the product name is empty, means for querying a generative artificial intelligence if the product name is properly obtained, and means for formatting the delivery date information obtained from the generative artificial intelligence and returning it to the user terminal. This makes it possible for users to quickly and accurately confirm the delivery date of the product.

[0385] A "user terminal" is a device used by a user to input and transmit information in order to confirm the delivery date of a product, and can be a computer, smartphone, tablet, or other terminal device.

[0386] "Product name" refers to the name of a product, service, or item, and is used to identify a specific product related to the delivery date that the user wants to check.

[0387] An "HTTP POST request" is a protocol used to send data from a user's terminal to a server, and is primarily used for transferring web data and calling APIs.

[0388] A "server" is a computer system that analyzes requests received from user terminals, performs the necessary processing, and sends the results back to the user terminal.

[0389] "Generative artificial intelligence" refers to artificial intelligence (AI) models that perform natural language processing based on received prompts and generate appropriate responses. Examples include OpenAI's GPT series.

[0390] An "error message" is a message generated by the server and sent back to the user's terminal when the product name entered by the user is inappropriate or empty, informing the user of the nature of the error.

[0391] A "prompt message" is a sentence containing questions or requests in natural language format that is input to a generative artificial intelligence system, and is a sentence that inquires about delivery date information based on the product name.

[0392] Modes for carrying out the invention

[0393] This invention is a system for users to quickly and accurately confirm the delivery date of commercial products. The system mainly consists of three components: a user terminal, a server, and a generative artificial intelligence system.

[0394] User terminal operation

[0395] The user terminal is a device used to input the product name. Users enter the product name using a web browser or mobile application and send that information to the server. For example, if a user enters the product name "New X100" and clicks the "Search" button, the user terminal generates an HTTP POST request based on that information and sends it to a URL (for example, http: / / example.com / api / check-delivery).

[0396] Server operation

[0397] The server parses the HTTP POST request received from the user's terminal. The request body contains the product name in the format {"itemName": "New X100"}. The server parses this request and extracts the product name. If the product name is empty, the server generates an error message "Please enter the product name" and sends it back to the user's terminal.

[0398] If the product name is retrieved correctly, the server queries the generative artificial intelligence. The prompt will be generated as follows: "When is the delivery date for the new X100?" Based on this prompt, the server sends an API request to the generative artificial intelligence. For example, it sends JSON data containing authentication information and the prompt to https: / / api.example.com / v1 / ai / generate.

[0399] How generative artificial intelligence works

[0400] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. For example, it might generate a response such as "The delivery date for the new X100 is January 15, 2024" and send it back to the server. OpenAI's GPT series is one example of a generative artificial intelligence that can be used.

[0401] Return and display of results

[0402] The server formats the response received from the generative artificial intelligence. For example, it converts a message like "The delivery date for the new X100 is January 15, 2024" into a user-friendly format, such as "The delivery date for the new X100 is January 15, 2024." The formatted message is then sent back to the user's terminal as an HTTP response. The user can then view this information on their terminal.

[0403] Specific example

[0404] For example, if a user wants to check the delivery date for the "New X100," they would type "New X100" into the input field of their web browser or mobile application and click the search button. The server receives this and sends a prompt message to the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative AI responds, "The delivery date for the New X100 is January 15, 2024," and the server formats this information and sends it back to the user's terminal. The user can then confirm on the screen that "The delivery date for the New X100 is January 15, 2024."

[0405] Examples of prompt statements include:

[0406] "When will the new X100 be available?"

[0407] "What is the delivery time for the F-series LED displays?"

[0408] "Could you please tell me the schedule for introducing the Y200 commercial printer?"

[0409] In this way, this system helps users to easily and quickly check the delivery date of products.

[0410] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0411] Step 1:

[0412] The user enters the product name. Specifically, the user enters the product name into an input field in a web browser or mobile application and clicks the search button. The entered information (product name) is sent as an HTTP POST request. The input is the product name, and the output is an HTTP POST request.

[0413] Step 2:

[0414] The server receives the request. The server parses the HTTP POST request received from the user's terminal and extracts the product name from the request body. Specifically, it obtains data in the format {"itemName": "New X100"}. The input is the request body of the HTTP POST request, and the output is the extracted product name.

[0415] Step 3:

[0416] The server performs error checking. The server checks whether the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user terminal. For example, it might generate a message such as "Please enter a product name." The input is the extracted product name, and the output is the error message or the product name for the next step.

[0417] Step 4:

[0418] The server queries the generation AI. If the product name is retrieved correctly, the server generates a prompt and sends an API request to the generation AI. For example, it might generate a prompt such as, "What is the delivery date for the new X100?" The input is the correctly retrieved product name, and the output is the generated prompt and the sent API request.

[0419] Step 5:

[0420] The generative AI generates a response. The generative artificial intelligence receives a prompt and generates appropriate delivery date information. Specifically, it generates a response that says, "The delivery date for the new X100 is January 15, 2024." The input is the prompt, and the output is the generated delivery date information response.

[0421] Step 6:

[0422] The server formats the response. The server formats the delivery date information received from the generative artificial intelligence and converts it into a format that is easy for humans to understand. For example, it formats it into a format such as "The delivery date for the new X100 is January 15, 2024." The input is the response from the generative artificial intelligence, and the output is the formatted response message.

[0423] Step 7:

[0424] The server sends a formatted response back to the user terminal. The server sends the formatted message back to the user terminal as an HTTP response. The user terminal receives this message and displays it on the screen. The input is the formatted response message, and the output is the information displayed on the user terminal.

[0425] Step 8:

[0426] The user confirms the results. The user can check delivery date information on their device and take the necessary actions. The input is the information displayed on the user's device, and the output is the user's decision.

[0427] (Application Example 1)

[0428] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0429] Conventional systems made it difficult to instantly check delivery dates and inventory information for product introductions. In particular, product management is complex in logistics centers, requiring rapid and accurate information provision. However, traditional methods required users to manually search for information, which was time-consuming and laborious. Therefore, a system is needed to quickly provide product introduction date and inventory information in order to improve the efficiency of logistics operations.

[0430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0431] In this invention, the server includes means for receiving information including product names from a user terminal, means for querying a generative artificial intelligence based on the information including product names, means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, and means for generating product delivery date information and inventory information. This makes it possible to quickly and easily obtain product delivery date information and inventory information.

[0432] A "user terminal" is a device used for inputting and outputting information, and includes smartphones, tablets, and personal computers.

[0433] A "product name" is a name or identifier used to identify a specific product.

[0434] "Generative artificial intelligence" is an artificial intelligence technology that performs natural language processing and generates appropriate information based on user inquiries.

[0435] "Inquiry results" refer to information generated by a generative artificial intelligence system based on inquiries sent from the user's terminal.

[0436] "Formatting" refers to the process of converting query results obtained from generative artificial intelligence into a format that is easy for users to understand.

[0437] "Implementation date information" refers to information about when a particular product will be implemented.

[0438] "Inventory information" refers to information about how many units of a particular product are currently in stock.

[0439] An "error message" is a warning or cautionary message displayed to a user when certain conditions are not met.

[0440] This invention provides a system that enables rapid acquisition of delivery date information and inventory information for product introduction at a logistics center. The system consists of a user terminal, a server, and a generative artificial intelligence. When a user enters a product name, the generative artificial intelligence generates information about the product via the server and returns it to the user.

[0441] Hardware and software usage

[0442] The system uses the following hardware and software:

[0443] Hardware:

[0444] User terminal: A device used for inputting and outputting information. Examples include smartphones, tablets, and personal computers.

[0445] Server: Receives requests from user terminals and processes them in cooperation with generative artificial intelligence.

[0446] software:

[0447] Server-side program: Built using Python and Flask.

[0448] Generative Artificial Intelligence: Uses the OpenAI API to perform natural language processing and information generation.

[0449] The user terminal application is developed in Android (Kotlin) and sends HTTP requests to receive information.

[0450] Data processing and data calculation

[0451] User terminal operation

[0452] On the user terminal, a staff member enters the product name. For example, they might enter "Product 1" on their smartphone. This information is sent to the server as an HTTP POST request. The user terminal uses a library for API communication (e.g., OkHttp).

[0453] Server operation

[0454] The server analyzes the request received from the user's terminal and extracts the product name. If the product name is successfully retrieved, it uses the API key to send a prompt message to OpenAI's generative artificial intelligence. For example, the prompt message might look like this:

[0455] "Please tell me the delivery date and stock status of Product 1."

[0456] The results obtained from the generative artificial intelligence are formatted and sent back to the user's terminal. The server side is built with Python and Flask, and libraries for API communication are also used (e.g., requests).

[0457] How generative artificial intelligence works

[0458] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information and inventory information. For example, it generates responses like the following:

[0459] "The delivery date for Product 1 is February 15, 2024, and we have 50 units in stock."

[0460] The generated information is sent back to the server, which then formats the information and sends it back to the user's terminal.

[0461] Specific example

[0462] For example, consider a scenario where a logistics center staff member wants to check the delivery date and inventory status of a new SKU, "Product 1." The staff member enters "Product 1" into a smartphone application and sends a request to the server. The server generates a prompt, "Please tell me the delivery date and inventory status of Product 1," and sends it to a generative artificial intelligence (AI). The AI ​​generates a response, "The delivery date for Product 1 is February 15, 2024, and the inventory quantity is 50 units," which is then formatted and sent back to the user's terminal. The staff member can then view this information on their smartphone screen.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] Enter the product name on the user terminal.

[0466] Input: The user enters the product name (for example, "Product 1").

[0467] Output: HTTP POST request containing the product name.

[0468] Specific operation: Enter the product name into the input field of the smartphone application and press the submit button. At this time, the product name is sent to the server as an HTTP POST request in JSON format.

[0469] Step 2:

[0470] The server receives and analyzes requests from the user's terminal.

[0471] Input: HTTP POST request sent from the user's terminal.

[0472] Output: Product names are extracted.

[0473] Specific operation: A server application using Flask receives a request and parses the data in JSON format. It then extracts the product name from the product name field.

[0474] Step 3:

[0475] The server makes a query to the generative artificial intelligence.

[0476] Input: Extracted product name (e.g., "Product 1").

[0477] Output: The prompt message sent to the generative artificial intelligence.

[0478] Specific operation: The server uses the OpenAI API to generate the prompt "Please tell me the delivery date and stock status of product 1," and sends it to the generative artificial intelligence.

[0479] Step 4:

[0480] Generative artificial intelligence generates delivery date information and inventory information.

[0481] Input: Prompt message received from the server.

[0482] Output: Delivery date information and inventory information (for example, "The delivery date for product 1 is February 15, 2024, and the inventory quantity is 50 units.").

[0483] Specific operation: Generative artificial intelligence (OpenAI model) analyzes the prompt text and generates delivery date and inventory information for the product.

[0484] Step 5:

[0485] The server formats the information obtained from the generative artificial intelligence and sends it back to the user's terminal.

[0486] Input: Delivery date and inventory information returned by a generative artificial intelligence.

[0487] Output: Formatted delivery date and inventory information.

[0488] Specific operation: The server formats the received information into a format that is easy for the user to understand and sends it back to the user's terminal in JSON format.

[0489] Step 6:

[0490] The user terminal receives and displays the response from the server.

[0491] Input: Formatted delivery date and inventory information sent from the server.

[0492] Output: Delivery date and stock information displayed to the user.

[0493] Specific operation: The smartphone application receives a response from the server and displays the information on the screen. The user confirms the information, "The delivery date for product 1 is February 15, 2024, and the number of units in stock is 50."

[0494] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0495] This invention is a system designed to allow users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's feelings. The system's program processing and specific examples will be explained below.

[0496] System-wide configuration

[0497] The system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0498] User terminal operation

[0499] To check the delivery date of a product, the user enters the product name "New X100" on their terminal. At this time, the emotion engine recognizes the user's emotions based on facial recognition information and text input information. The entered information and emotion data are sent to the server as an HTTP POST request.

[0500] Server operation

[0501] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0502] How generative artificial intelligence works

[0503] Generative artificial intelligence performs natural language processing based on received prompts and user sentiment data to generate appropriate delivery date information. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0504] Server response processing

[0505] The server formats the responses received from the generative artificial intelligence into a format that is easy for the user to understand. It also adjusts the format and content of the responses based on emotional data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[0506] Specific example

[0507] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server.

[0508] The server extracts the product name "New X100" and emotional data from the received request, and sends the emotional data to the generative artificial intelligence along with the prompt, "When is the delivery date for the new X100?".

[0509] The generative artificial intelligence generates the response, "The delivery date for the new X100 is January 15, 2024," and adds the message, "Don't worry, the arrangements are progressing smoothly."

[0510] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the following information on their terminal: "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0511] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The user sends a request from their device.

[0515] The user enters the product name, "New X100," to confirm the delivery date for the product. As the user enters the information, the terminal analyzes their emotions from facial expressions and text, and sends this data along with the input to the server in an HTTP POST request.

[0516] Step 2:

[0517] The server receives the request and parses its contents.

[0518] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It extracts the product name and sentiment data from the received JSON data.

[0519] Step 3:

[0520] The server checks for the presence or absence of the product name.

[0521] If the product name is empty, the server generates an error message "Product name required" and sends it back to the user with a 400 status code. If the product name is successfully retrieved, the process proceeds to the next step.

[0522] Step 4:

[0523] The server queries the generative artificial intelligence.

[0524] The server passes the extracted product names and sentiment data to the generative artificial intelligence and inquires about delivery dates based on the product names. Specifically, it sends sentiment data along with the prompt, "When is the delivery date for the new X100?"

[0525] Step 5:

[0526] A generative artificial intelligence generates delivery date information.

[0527] The generative artificial intelligence performs natural language processing based on the received prompt and sentiment data. It generates a response such as, "The delivery date for the new X100 is January 15, 2024," and if the user is feeling anxious, it adds a message such as, "Don't worry, the arrangements are progressing smoothly."

[0528] Step 6:

[0529] The server formats the response.

[0530] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information. During this process, the response format and content are adjusted based on sentiment data.

[0531] Step 7:

[0532] The server returns the message to the user's terminal.

[0533] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal the following information: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0534] (Example 2)

[0535] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0536] Conventional product delivery date confirmation systems have problems with users having difficulty obtaining delivery date information quickly and accurately, and in particular, with a lack of information provision that takes user emotions into consideration. As a result, users often feel anxious and worried, which leads to a decrease in customer satisfaction. A system that solves this problem is needed.

[0537] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0538] In this invention, the server includes means for receiving information including product name and sentiment data from a user terminal, means for querying a generative artificial intelligence based on the product name and sentiment data, and means for formatting a message based on the delivery date information and sentiment data obtained from the generative artificial intelligence and sending it back to the user terminal. This makes it possible for the user to quickly and accurately obtain sentiment-sensitive delivery date information.

[0539] A "user terminal" is an electronic device used to input and transmit product names and sentiment data.

[0540] "Product name" refers to the name of a specific product or service, and is information that users enter to confirm delivery dates.

[0541] "Emotional data" refers to data that represents the user's emotional state and is primarily acquired using facial recognition technology.

[0542] A "server" is a central system that processes information received from user terminals and coordinates with generative artificial intelligence.

[0543] "Generative artificial intelligence" refers to an artificial intelligence model that generates appropriate responses based on input prompts and sentiment data.

[0544] "Delivery date information" refers to information regarding the introduction date or delivery date of a specific product.

[0545] A "message" is supplementary text based on emotional data that is added to delivery date information generated by a generative artificial intelligence.

[0546] An "error message" is an error notification message sent from the server to the user's terminal, such as when the product name is empty.

[0547] This invention is a system that aims to allow users to quickly and accurately confirm the delivery date of a product, while taking into account the user's emotions.

[0548] The overall system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0549] Detailed explanation of operation

[0550] 1. User input of product name

[0551] To check the delivery date of a product, the user enters the product name "New X100" into their terminal. The terminal's input screen has a text box, and the process begins when the user enters the product name.

[0552] 2. Capturing emotional data

[0553] The device's camera function is used to capture the user's facial expressions. This data is analyzed through an emotion engine to obtain the user's emotional data. The emotion engine uses facial recognition APIs such as Amazon Rekognition.

[0554] 3. Sending data

[0555] The user terminal sends the entered product name and analyzed sentiment data to the server as an HTTP POST request. The data sent is in JSON format.

[0556] 4. Server-side request analysis

[0557] The server parses the received HTTP POST request and extracts the product name and sentiment data. The Python Flask framework is used for the analysis. If the product name is empty, an error message is generated and sent back to the user's terminal.

[0558] 5. Sending prompts to generative artificial intelligence

[0559] The server sends prompts to a generative artificial intelligence (e.g., OpenAI GPT-3) based on product names and sentiment data. The prompts are sent in a format such as, "When is the delivery date for the new X100? Users are impatient."

[0560] 6. Response generation by generative artificial intelligence

[0561] Generative artificial intelligence generates responses based on received prompts and sentiment data. For example, along with the response, "The delivery date for the new X100 is January 15, 2024," the message "Don't worry, arrangements are progressing smoothly" might be added.

[0562] 7. Server-side response formatting

[0563] The server formats the responses received from the generative artificial intelligence. Using the Python Jinja2 library, it transforms them into a user-friendly format. It also adjusts the response format and content based on sentiment data.

[0564] 8. Sending a response

[0565] The server sends a formatted response back to the user's terminal. The response might be in the format of, for example, "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0566] 9. User response confirmation

[0567] The user confirms the response on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0568] Examples of prompt statements

[0569] Send the following prompt to the generative artificial intelligence:

[0570] "When will the new X100 be available? Users are getting impatient."

[0571] This allows users to quickly and accurately obtain emotionally sensitive delivery date information.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The user enters the product name "New X100" into the text box on the device. The device accepts the input and then activates the camera function. The input is saved on the device as text data.

[0575] Step 2:

[0576] The device's camera captures the user's face and sends the image data to the emotion engine. The emotion engine uses facial recognition technology to analyze the user's facial expressions and generate emotion data. This emotion data is output as emotional states, such as "anxiety" or "relief."

[0577] Step 3:

[0578] The terminal combines the entered product name and analyzed sentiment data to generate an HTTP POST request. This request is in JSON format and includes the product name and sentiment data. The request is sent to the server.

[0579] Step 4:

[0580] The server parses the received HTTP POST request and extracts product names and sentiment data from the JSON data. A server-side framework (e.g., Flask in Python) is used for the parsing. The extracted product names and sentiment data are temporarily stored on the server.

[0581] Step 5:

[0582] The server makes a query to the generative artificial intelligence. Specifically, it generates a prompt based on the product name "New X100" and the emotion data "anxiety," and sends it to the generative artificial intelligence. This prompt is in the format of "When is the delivery date for the new X100? The user is anxious."

[0583] Step 6:

[0584] The generative artificial intelligence generates delivery date information and messages based on prompts and sentiment data. For example, it might output a message such as, "The delivery date for the new X100 is January 15, 2024," along with, "Don't worry, arrangements are progressing smoothly." The response data is sent back to the server in JSON format.

[0585] Step 7:

[0586] The server formats the response data received from the generative artificial intelligence. It uses the Python Jinja2 library to convert the data into a user-friendly format. It also adjusts the format and content based on sentiment data. The formatted data might look something like this: "The delivery date for the new X100 is January 15, 2024. Don't worry, arrangements are progressing smoothly."

[0587] Step 8:

[0588] The server returns the formatted response data to the user's terminal as an HTTP response. The response is in JSON format and includes formatted delivery date information and a message.

[0589] Step 9:

[0590] The user checks the response data received on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0591] (Application Example 2)

[0592] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0593] Conventional systems for confirming delivery dates for products only provide users with uniform information, lacking the flexibility to respond to users' emotions and circumstances. Therefore, the user experience could be negatively affected during the delivery date confirmation process, especially when users were feeling anxious or stressed. This invention aims to solve the problem of providing a more user-friendly and flexible delivery date confirmation system that takes user emotions into consideration.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0595] In this invention, the server includes means for receiving information including the product name from a user terminal, means for analyzing the user's emotional data, means for querying a generative artificial intelligence based on the product name and emotional data, and means for formatting the query results obtained from the generative artificial intelligence, adjusting them according to the emotional data, and returning them to the user terminal. This makes it possible to provide delivery date information that is tailored to the user's emotions.

[0596] A "user terminal" is a device used by a user to input and output information.

[0597] "Product name" refers to information that indicates the name of a specific product.

[0598] "Emotional data" refers to data that indicates the user's emotional state, analyzed based on the user's facial recognition information and text input information.

[0599] "Generative artificial intelligence" refers to an artificial intelligence model that performs natural language processing and generates appropriate responses based on the content of an inquiry.

[0600] "Query results" refer to response data generated by a generative artificial intelligence system.

[0601] "Adjusting based on emotional data" refers to changing the content and format of the generated response based on the user's emotional state.

[0602] A "server" is a computer system that processes information received from a user terminal and sends an appropriate response back to the user terminal using generative artificial intelligence and emotional data.

[0603] "Delivery date information" refers to information indicating the planned date or period for the introduction or delivery of a specific product.

[0604] "Formatting" refers to converting raw or generated data into a format that is easy for users to understand.

[0605] An "error message" is a message used to inform a user of errors or deficiencies in the information they have entered.

[0606] This invention provides a system that allows users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's emotions. The system consists of four main components: a user terminal, a server, a generative artificial intelligence system, and an emotion engine.

[0607] User terminal

[0608] The user terminal is a device such as a smartphone, tablet, or personal computer, which inputs and outputs information. To check the delivery date of a product, the user enters the product name and, if necessary, provides facial recognition information. The user's facial recognition information and text input information are analyzed by an emotion engine to generate emotion data. This information and emotion data are sent to the server as an HTTP POST request.

[0609] server

[0610] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully retrieved, the server queries the generative artificial intelligence.

[0611] Generative artificial intelligence

[0612] Generative artificial intelligence generates delivery date information based on received prompts and user sentiment data. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized or specific explanations about the possibility of delays may be added.

[0613] Server response processing

[0614] The server formats the query results obtained from the generative artificial intelligence into a user-friendly format. It also adjusts the response format and content based on sentiment data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[0615] Specific example

[0616] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server. The server extracts the product name "New X100" and emotion data from the received request and sends the emotion data along with the prompt "When is the delivery date for the New X100?" to the generative AI. The generative AI generates a response stating "The delivery date for the New X100 is January 15, 2024," and adds a message such as "Don't worry, the arrangements are progressing smoothly." The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal: "The delivery date for the New X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0617] Example of a prompt

[0618] "When will the new X100 be available? Users are feeling anxious right now."

[0619] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The user enters the product name and provides facial recognition information as needed. The user's terminal collects this information and generates input data containing the product name and facial recognition information. The input data is sent to the server as an HTTP POST request.

[0623] Step 2:

[0624] The server analyzes the request received from the user's terminal and extracts the product name and facial recognition information. The server sends the facial recognition information to the emotion engine and obtains emotion data. The emotion engine analyzes the facial recognition information and outputs the user's emotional state (e.g., impatience, anxiety, fatigue, etc.) as data.

[0625] Step 3:

[0626] The server generates an error message if the product name is empty and sends it back to the user's terminal. Generating the error message is a data processing operation that creates error text based on the condition that the product name is empty.

[0627] Step 4:

[0628] The server generates a prompt containing the product name and sentiment data, and queries the generative artificial intelligence. The prompt is in text format, such as, "When is the delivery date for the new X100? The user is currently feeling anxious."

[0629] Step 5:

[0630] Generative artificial intelligence performs natural language processing based on received prompts and sentiment data to generate delivery date information. The generated delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0631] Step 6:

[0632] The server formats the query results (delivery date information and additional messages) obtained from the generative artificial intelligence and sends them back to the user terminal. The formatting process is a data calculation that adjusts the response format and content according to the emotional data. For example, if the user appears tired, words of encouragement will be incorporated into the response.

[0633] Step 7:

[0634] The user checks the delivery date information on their device. The device displays the delivery date information and messages received from the server, providing the user with easy-to-understand information. As a result, the user can obtain information such as, "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0635] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0636] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0637] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0638] [Third Embodiment]

[0639] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0640] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0641] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0642] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0643] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0644] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0645] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0646] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0647] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0648] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0649] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0650] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0651] This invention is a system aimed at enabling users to quickly and accurately confirm the delivery date of commercial products. The system's program processing and specific examples will be explained below.

[0652] System-wide configuration

[0653] The system consists of three main components: a user terminal, a server, and a generative artificial intelligence (AI). The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI. The generative AI performs natural language processing based on the requests and generates appropriate responses.

[0654] User terminal operation

[0655] To check the delivery date of a product, the user enters information including the product name from their terminal. The entered information, including the product name, is sent to the server as an HTTP POST request.

[0656] Server operation

[0657] The server analyzes the request received from the user terminal and extracts the product name. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0658] When the server receives a response from the generative artificial intelligence, it formats the response and sends it back to the user's terminal. This allows the user to obtain accurate delivery date information with simple operations.

[0659] How generative artificial intelligence works

[0660] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. Specifically, it receives inquiries that include product names, generates information regarding the delivery date of that product, and sends it back to the server.

[0661] Specific example

[0662] For example, consider a case where a user wants to check the delivery date for the "New X100". The user enters "New X100" into their terminal and sends a request to the server. The server extracts the product name "New X100" from the received request and queries the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative artificial intelligence generates a response such as, "The delivery date for the New X100 is January 15, 2024."

[0663] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal stating, "The delivery date for the new X100 is January 15, 2024."

[0664] This system allows users to quickly and easily check the delivery date of products.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The user sends a request from their device.

[0668] The user enters the product name "New X100" to check the delivery date for the product. The data, including the entered product name, is sent to the server as an HTTP POST request.

[0669] Step 2:

[0670] The server receives the request and parses its contents.

[0671] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It parses the received JSON data and extracts the product name.

[0672] Step 3:

[0673] The server checks for the presence or absence of the product name.

[0674] The server checks if the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user along with a 400 status code.

[0675] Step 4:

[0676] The server queries the generative artificial intelligence.

[0677] If the product name is retrieved correctly, the server will send a query to the generative artificial intelligence based on the product name. Specifically, it will use the generative artificial intelligence's API to send a prompt asking, "When is the delivery date for the new X100?"

[0678] Step 5:

[0679] A generative artificial intelligence generates delivery date information.

[0680] The generative artificial intelligence performs natural language processing based on the received prompt and generates appropriate delivery date information. It sends the response "The delivery date for the new X100 is January 15, 2024" back to the server.

[0681] Step 6:

[0682] The server formats the response.

[0683] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information.

[0684] Step 7:

[0685] The server returns the message to the user's terminal.

[0686] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal that "The delivery date for the new X100 is January 15, 2024."

[0687] Through the steps described above, users can quickly and easily check the delivery date for the products they wish to purchase.

[0688] (Example 1)

[0689] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0690] Conventional systems for confirming product delivery dates often required manual inquiries and verification, demanding significant time and effort. This made it difficult for users to quickly and accurately confirm product delivery dates. Furthermore, the lack of proper error message handling and automated response systems resulted in a poor user experience.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0692] In this invention, the server includes means for receiving information including the product name from a user terminal as an HTTP POST request, means for analyzing the information and extracting the product name, means for generating and returning an error message if the product name is empty, means for querying a generative artificial intelligence if the product name is properly obtained, and means for formatting the delivery date information obtained from the generative artificial intelligence and returning it to the user terminal. This makes it possible for users to quickly and accurately confirm the delivery date of the product.

[0693] A "user terminal" is a device used by a user to input and transmit information in order to confirm the delivery date of a product, and can be a computer, smartphone, tablet, or other terminal device.

[0694] "Product name" refers to the name of a product, service, or item, and is used to identify a specific product related to the delivery date that the user wants to check.

[0695] An "HTTP POST request" is a protocol used to send data from a user's terminal to a server, and is primarily used for transferring web data and calling APIs.

[0696] A "server" is a computer system that analyzes requests received from user terminals, performs the necessary processing, and sends the results back to the user terminal.

[0697] "Generative artificial intelligence" refers to artificial intelligence (AI) models that perform natural language processing based on received prompts and generate appropriate responses. Examples include OpenAI's GPT series.

[0698] An "error message" is a message generated by the server and sent back to the user's terminal when the product name entered by the user is inappropriate or empty, informing the user of the nature of the error.

[0699] A "prompt message" is a sentence containing questions or requests in natural language format that is input to a generative artificial intelligence system, and is a sentence that inquires about delivery date information based on the product name.

[0700] Modes for carrying out the invention

[0701] This invention is a system for users to quickly and accurately confirm the delivery date of commercial products. The system mainly consists of three components: a user terminal, a server, and a generative artificial intelligence system.

[0702] User terminal operation

[0703] The user terminal is a device used to input the product name. Users enter the product name using a web browser or mobile application and send that information to the server. For example, if a user enters the product name "New X100" and clicks the "Search" button, the user terminal generates an HTTP POST request based on that information and sends it to a URL (for example, http: / / example.com / api / check-delivery).

[0704] Server operation

[0705] The server parses the HTTP POST request received from the user's terminal. The request body contains the product name in the format {"itemName": "New X100"}. The server parses this request and extracts the product name. If the product name is empty, the server generates an error message "Please enter the product name" and sends it back to the user's terminal.

[0706] If the product name is retrieved correctly, the server queries the generative artificial intelligence. The prompt will be generated as follows: "When is the delivery date for the new X100?" Based on this prompt, the server sends an API request to the generative artificial intelligence. For example, it sends JSON data containing authentication information and the prompt to https: / / api.example.com / v1 / ai / generate.

[0707] How generative artificial intelligence works

[0708] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. For example, it might generate a response such as "The delivery date for the new X100 is January 15, 2024" and send it back to the server. OpenAI's GPT series is one example of a generative artificial intelligence that can be used.

[0709] Return and display of results

[0710] The server formats the response received from the generative artificial intelligence. For example, it converts a message like "The delivery date for the new X100 is January 15, 2024" into a user-friendly format, such as "The delivery date for the new X100 is January 15, 2024." The formatted message is then sent back to the user's terminal as an HTTP response. The user can then view this information on their terminal.

[0711] Specific example

[0712] For example, if a user wants to check the delivery date for the "New X100," they would type "New X100" into the input field of their web browser or mobile application and click the search button. The server receives this and sends a prompt message to the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative AI responds, "The delivery date for the New X100 is January 15, 2024," and the server formats this information and sends it back to the user's terminal. The user can then confirm on the screen that "The delivery date for the New X100 is January 15, 2024."

[0713] Examples of prompt statements include:

[0714] "When will the new X100 be available?"

[0715] "What is the delivery time for the F-series LED displays?"

[0716] "Could you please tell me the schedule for introducing the Y200 commercial printer?"

[0717] In this way, this system helps users to easily and quickly check the delivery date of products.

[0718] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0719] Step 1:

[0720] The user enters the product name. Specifically, the user enters the product name into an input field in a web browser or mobile application and clicks the search button. The entered information (product name) is sent as an HTTP POST request. The input is the product name, and the output is an HTTP POST request.

[0721] Step 2:

[0722] The server receives the request. The server parses the HTTP POST request received from the user's terminal and extracts the product name from the request body. Specifically, it obtains data in the format {"itemName": "New X100"}. The input is the request body of the HTTP POST request, and the output is the extracted product name.

[0723] Step 3:

[0724] The server performs error checking. The server checks whether the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user terminal. For example, it might generate a message such as "Please enter a product name." The input is the extracted product name, and the output is the error message or the product name for the next step.

[0725] Step 4:

[0726] The server queries the generation AI. If the product name is retrieved correctly, the server generates a prompt and sends an API request to the generation AI. For example, it might generate a prompt such as, "What is the delivery date for the new X100?" The input is the correctly retrieved product name, and the output is the generated prompt and the sent API request.

[0727] Step 5:

[0728] The generative AI generates a response. The generative artificial intelligence receives a prompt and generates appropriate delivery date information. Specifically, it generates a response that says, "The delivery date for the new X100 is January 15, 2024." The input is the prompt, and the output is the generated delivery date information response.

[0729] Step 6:

[0730] The server formats the response. The server formats the delivery date information received from the generative artificial intelligence and converts it into a format that is easy for humans to understand. For example, it formats it into a format such as "The delivery date for the new X100 is January 15, 2024." The input is the response from the generative artificial intelligence, and the output is the formatted response message.

[0731] Step 7:

[0732] The server sends a formatted response back to the user terminal. The server sends the formatted message back to the user terminal as an HTTP response. The user terminal receives this message and displays it on the screen. The input is the formatted response message, and the output is the information displayed on the user terminal.

[0733] Step 8:

[0734] The user confirms the results. The user can check delivery date information on their device and take the necessary actions. The input is the information displayed on the user's device, and the output is the user's decision.

[0735] (Application Example 1)

[0736] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0737] Conventional systems made it difficult to instantly check delivery dates and inventory information for product introductions. In particular, product management is complex in logistics centers, requiring rapid and accurate information provision. However, traditional methods required users to manually search for information, which was time-consuming and laborious. Therefore, a system is needed to quickly provide product introduction date and inventory information in order to improve the efficiency of logistics operations.

[0738] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0739] In this invention, the server includes means for receiving information including product names from a user terminal, means for querying a generative artificial intelligence based on the information including product names, means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, and means for generating product delivery date information and inventory information. This makes it possible to quickly and easily obtain product delivery date information and inventory information.

[0740] A "user terminal" is a device used for inputting and outputting information, and includes smartphones, tablets, and personal computers.

[0741] A "product name" is a name or identifier used to identify a specific product.

[0742] "Generative artificial intelligence" is an artificial intelligence technology that performs natural language processing and generates appropriate information based on user inquiries.

[0743] "Inquiry results" refer to information generated by a generative artificial intelligence system based on inquiries sent from the user's terminal.

[0744] "Formatting" refers to the process of converting query results obtained from generative artificial intelligence into a format that is easy for users to understand.

[0745] "Implementation date information" refers to information about when a particular product will be implemented.

[0746] "Inventory information" refers to information about how many units of a particular product are currently in stock.

[0747] An "error message" is a warning or cautionary message displayed to a user when certain conditions are not met.

[0748] This invention provides a system that enables rapid acquisition of delivery date information and inventory information for product introduction at a logistics center. The system consists of a user terminal, a server, and a generative artificial intelligence. When a user enters a product name, the generative artificial intelligence generates information about the product via the server and returns it to the user.

[0749] Hardware and software usage

[0750] The system uses the following hardware and software:

[0751] Hardware:

[0752] User terminal: A device used for inputting and outputting information. Examples include smartphones, tablets, and personal computers.

[0753] Server: Receives requests from user terminals and processes them in cooperation with generative artificial intelligence.

[0754] software:

[0755] Server-side program: Built using Python and Flask.

[0756] Generative Artificial Intelligence: Uses the OpenAI API to perform natural language processing and information generation.

[0757] The user terminal application is developed in Android (Kotlin) and sends HTTP requests to receive information.

[0758] Data processing and data calculation

[0759] User terminal operation

[0760] On the user terminal, a staff member enters the product name. For example, they might enter "Product 1" on their smartphone. This information is sent to the server as an HTTP POST request. The user terminal uses a library for API communication (e.g., OkHttp).

[0761] Server operation

[0762] The server analyzes the request received from the user's terminal and extracts the product name. If the product name is successfully retrieved, it uses the API key to send a prompt message to OpenAI's generative artificial intelligence. For example, the prompt message might look like this:

[0763] "Please tell me the delivery date and stock status of Product 1."

[0764] The results obtained from the generative artificial intelligence are formatted and sent back to the user's terminal. The server side is built with Python and Flask, and libraries for API communication are also used (e.g., requests).

[0765] How generative artificial intelligence works

[0766] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information and inventory information. For example, it generates responses like the following:

[0767] "The delivery date for Product 1 is February 15, 2024, and we have 50 units in stock."

[0768] The generated information is sent back to the server, which then formats the information and sends it back to the user's terminal.

[0769] Specific example

[0770] For example, consider a scenario where a logistics center staff member wants to check the delivery date and inventory status of a new SKU, "Product 1." The staff member enters "Product 1" into a smartphone application and sends a request to the server. The server generates a prompt, "Please tell me the delivery date and inventory status of Product 1," and sends it to a generative artificial intelligence (AI). The AI ​​generates a response, "The delivery date for Product 1 is February 15, 2024, and the inventory quantity is 50 units," which is then formatted and sent back to the user's terminal. The staff member can then view this information on their smartphone screen.

[0771] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0772] Step 1:

[0773] Enter the product name on the user terminal.

[0774] Input: The user enters the product name (for example, "Product 1").

[0775] Output: HTTP POST request containing the product name.

[0776] Specific operation: Enter the product name into the input field of the smartphone application and press the submit button. At this time, the product name is sent to the server as an HTTP POST request in JSON format.

[0777] Step 2:

[0778] The server receives and analyzes requests from the user's terminal.

[0779] Input: HTTP POST request sent from the user's terminal.

[0780] Output: Product names are extracted.

[0781] Specific operation: A server application using Flask receives a request and parses the data in JSON format. It then extracts the product name from the product name field.

[0782] Step 3:

[0783] The server makes a query to the generative artificial intelligence.

[0784] Input: Extracted product name (e.g., "Product 1").

[0785] Output: The prompt message sent to the generative artificial intelligence.

[0786] Specific operation: The server uses the OpenAI API to generate the prompt "Please tell me the delivery date and stock status of product 1," and sends it to the generative artificial intelligence.

[0787] Step 4:

[0788] Generative artificial intelligence generates delivery date information and inventory information.

[0789] Input: Prompt message received from the server.

[0790] Output: Delivery date information and inventory information (for example, "The delivery date for product 1 is February 15, 2024, and the inventory quantity is 50 units.").

[0791] Specific operation: Generative artificial intelligence (OpenAI model) analyzes the prompt text and generates delivery date and inventory information for the product.

[0792] Step 5:

[0793] The server formats the information obtained from the generative artificial intelligence and sends it back to the user's terminal.

[0794] Input: Delivery date and inventory information returned by a generative artificial intelligence.

[0795] Output: Formatted delivery date and inventory information.

[0796] Specific operation: The server formats the received information into a format that is easy for the user to understand and sends it back to the user's terminal in JSON format.

[0797] Step 6:

[0798] The user terminal receives and displays the response from the server.

[0799] Input: Formatted delivery date and inventory information sent from the server.

[0800] Output: Delivery date and stock information displayed to the user.

[0801] Specific operation: The smartphone application receives a response from the server and displays the information on the screen. The user confirms the information, "The delivery date for product 1 is February 15, 2024, and the number of units in stock is 50."

[0802] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0803] This invention is a system designed to allow users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's feelings. The system's program processing and specific examples will be explained below.

[0804] System-wide configuration

[0805] The system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0806] User terminal operation

[0807] To check the delivery date of a product, the user enters the product name "New X100" on their terminal. At this time, the emotion engine recognizes the user's emotions based on facial recognition information and text input information. The entered information and emotion data are sent to the server as an HTTP POST request.

[0808] Server operation

[0809] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0810] How generative artificial intelligence works

[0811] Generative artificial intelligence performs natural language processing based on received prompts and user sentiment data to generate appropriate delivery date information. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0812] Server response processing

[0813] The server formats the responses received from the generative artificial intelligence into a format that is easy for the user to understand. It also adjusts the format and content of the responses based on emotional data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[0814] Specific example

[0815] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server.

[0816] The server extracts the product name "New X100" and emotional data from the received request, and sends the emotional data to the generative artificial intelligence along with the prompt, "When is the delivery date for the new X100?".

[0817] The generative artificial intelligence generates the response, "The delivery date for the new X100 is January 15, 2024," and adds the message, "Don't worry, the arrangements are progressing smoothly."

[0818] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the following information on their terminal: "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0819] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0820] The following describes the processing flow.

[0821] Step 1:

[0822] The user sends a request from their device.

[0823] The user enters the product name, "New X100," to confirm the delivery date for the product. As the user enters the information, the terminal analyzes their emotions from facial expressions and text, and sends this data along with the input to the server in an HTTP POST request.

[0824] Step 2:

[0825] The server receives the request and parses its contents.

[0826] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It extracts the product name and sentiment data from the received JSON data.

[0827] Step 3:

[0828] The server checks for the presence or absence of the product name.

[0829] If the product name is empty, the server generates an error message "Product name required" and sends it back to the user with a 400 status code. If the product name is successfully retrieved, the process proceeds to the next step.

[0830] Step 4:

[0831] The server queries the generative artificial intelligence.

[0832] The server passes the extracted product names and sentiment data to the generative artificial intelligence and inquires about delivery dates based on the product names. Specifically, it sends sentiment data along with the prompt, "When is the delivery date for the new X100?"

[0833] Step 5:

[0834] A generative artificial intelligence generates delivery date information.

[0835] The generative artificial intelligence performs natural language processing based on the received prompt and sentiment data. It generates a response such as, "The delivery date for the new X100 is January 15, 2024," and if the user is feeling anxious, it adds a message such as, "Don't worry, the arrangements are progressing smoothly."

[0836] Step 6:

[0837] The server formats the response.

[0838] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information. During this process, the response format and content are adjusted based on sentiment data.

[0839] Step 7:

[0840] The server returns the message to the user's terminal.

[0841] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal the following information: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0842] (Example 2)

[0843] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0844] Conventional product delivery date confirmation systems have problems with users having difficulty obtaining delivery date information quickly and accurately, and in particular, with a lack of information provision that takes user emotions into consideration. As a result, users often feel anxious and worried, which leads to a decrease in customer satisfaction. A system that solves this problem is needed.

[0845] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0846] In this invention, the server includes means for receiving information including product name and sentiment data from a user terminal, means for querying a generative artificial intelligence based on the product name and sentiment data, and means for formatting a message based on the delivery date information and sentiment data obtained from the generative artificial intelligence and sending it back to the user terminal. This makes it possible for the user to quickly and accurately obtain sentiment-sensitive delivery date information.

[0847] A "user terminal" is an electronic device used to input and transmit product names and sentiment data.

[0848] "Product name" refers to the name of a specific product or service, and is information that users enter to confirm delivery dates.

[0849] "Emotional data" refers to data that represents the user's emotional state and is primarily acquired using facial recognition technology.

[0850] A "server" is a central system that processes information received from user terminals and coordinates with generative artificial intelligence.

[0851] "Generative artificial intelligence" refers to an artificial intelligence model that generates appropriate responses based on input prompts and sentiment data.

[0852] "Delivery date information" refers to information regarding the introduction date or delivery date of a specific product.

[0853] A "message" is supplementary text based on emotional data that is added to delivery date information generated by a generative artificial intelligence.

[0854] An "error message" is an error notification message sent from the server to the user's terminal, such as when the product name is empty.

[0855] This invention is a system that aims to allow users to quickly and accurately confirm the delivery date of a product, while taking into account the user's emotions.

[0856] The overall system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[0857] Detailed explanation of operation

[0858] 1. User input of product name

[0859] To check the delivery date of a product, the user enters the product name "New X100" into their terminal. The terminal's input screen has a text box, and the process begins when the user enters the product name.

[0860] 2. Capturing emotional data

[0861] The device's camera function is used to capture the user's facial expressions. This data is analyzed through an emotion engine to obtain the user's emotional data. The emotion engine uses facial recognition APIs such as Amazon Rekognition.

[0862] 3. Sending data

[0863] The user terminal sends the entered product name and analyzed sentiment data to the server as an HTTP POST request. The data sent is in JSON format.

[0864] 4. Server-side request analysis

[0865] The server parses the received HTTP POST request and extracts the product name and sentiment data. The Python Flask framework is used for the analysis. If the product name is empty, an error message is generated and sent back to the user's terminal.

[0866] 5. Sending prompts to generative artificial intelligence

[0867] The server sends prompts to a generative artificial intelligence (e.g., OpenAI GPT-3) based on product names and sentiment data. The prompts are sent in a format such as, "When is the delivery date for the new X100? Users are impatient."

[0868] 6. Response generation by generative artificial intelligence

[0869] Generative artificial intelligence generates responses based on received prompts and sentiment data. For example, along with the response, "The delivery date for the new X100 is January 15, 2024," the message "Don't worry, arrangements are progressing smoothly" might be added.

[0870] 7. Server-side response formatting

[0871] The server formats the responses received from the generative artificial intelligence. Using the Python Jinja2 library, it transforms them into a user-friendly format. It also adjusts the response format and content based on sentiment data.

[0872] 8. Sending a response

[0873] The server sends a formatted response back to the user's terminal. The response might be in the format of, for example, "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0874] 9. User response confirmation

[0875] The user confirms the response on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0876] Examples of prompt statements

[0877] Send the following prompt to the generative artificial intelligence:

[0878] "When will the new X100 be available? Users are getting impatient."

[0879] This allows users to quickly and accurately obtain emotionally sensitive delivery date information.

[0880] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0881] Step 1:

[0882] The user enters the product name "New X100" into the text box on the device. The device accepts the input and then activates the camera function. The input is saved on the device as text data.

[0883] Step 2:

[0884] The device's camera captures the user's face and sends the image data to the emotion engine. The emotion engine uses facial recognition technology to analyze the user's facial expressions and generate emotion data. This emotion data is output as emotional states, such as "anxiety" or "relief."

[0885] Step 3:

[0886] The terminal combines the entered product name and analyzed sentiment data to generate an HTTP POST request. This request is in JSON format and includes the product name and sentiment data. The request is sent to the server.

[0887] Step 4:

[0888] The server parses the received HTTP POST request and extracts product names and sentiment data from the JSON data. A server-side framework (e.g., Flask in Python) is used for the parsing. The extracted product names and sentiment data are temporarily stored on the server.

[0889] Step 5:

[0890] The server makes a query to the generative artificial intelligence. Specifically, it generates a prompt based on the product name "New X100" and the emotion data "anxiety," and sends it to the generative artificial intelligence. This prompt is in the format of "When is the delivery date for the new X100? The user is anxious."

[0891] Step 6:

[0892] The generative artificial intelligence generates delivery date information and messages based on prompts and sentiment data. For example, it might output a message such as, "The delivery date for the new X100 is January 15, 2024," along with, "Don't worry, arrangements are progressing smoothly." The response data is sent back to the server in JSON format.

[0893] Step 7:

[0894] The server formats the response data received from the generative artificial intelligence. It uses the Python Jinja2 library to convert the data into a user-friendly format. It also adjusts the format and content based on sentiment data. The formatted data might look something like this: "The delivery date for the new X100 is January 15, 2024. Don't worry, arrangements are progressing smoothly."

[0895] Step 8:

[0896] The server returns the formatted response data to the user's terminal as an HTTP response. The response is in JSON format and includes formatted delivery date information and a message.

[0897] Step 9:

[0898] The user checks the response data received on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[0899] (Application Example 2)

[0900] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0901] Conventional systems for confirming delivery dates for products only provide users with uniform information, lacking the flexibility to respond to users' emotions and circumstances. Therefore, the user experience could be negatively affected during the delivery date confirmation process, especially when users were feeling anxious or stressed. This invention aims to solve the problem of providing a more user-friendly and flexible delivery date confirmation system that takes user emotions into consideration.

[0902] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0903] In this invention, the server includes means for receiving information including the product name from a user terminal, means for analyzing the user's emotional data, means for querying a generative artificial intelligence based on the product name and emotional data, and means for formatting the query results obtained from the generative artificial intelligence, adjusting them according to the emotional data, and returning them to the user terminal. This makes it possible to provide delivery date information that is tailored to the user's emotions.

[0904] A "user terminal" is a device used by a user to input and output information.

[0905] "Product name" refers to information that indicates the name of a specific product.

[0906] "Emotional data" refers to data that indicates the user's emotional state, analyzed based on the user's facial recognition information and text input information.

[0907] "Generative artificial intelligence" refers to an artificial intelligence model that performs natural language processing and generates appropriate responses based on the content of an inquiry.

[0908] "Query results" refer to response data generated by a generative artificial intelligence system.

[0909] "Adjusting based on emotional data" refers to changing the content and format of the generated response based on the user's emotional state.

[0910] A "server" is a computer system that processes information received from a user terminal and sends an appropriate response back to the user terminal using generative artificial intelligence and emotional data.

[0911] "Delivery date information" refers to information indicating the planned date or period for the introduction or delivery of a specific product.

[0912] "Formatting" refers to converting raw or generated data into a format that is easy for users to understand.

[0913] An "error message" is a message used to inform a user of errors or deficiencies in the information they have entered.

[0914] This invention provides a system that allows users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's emotions. The system consists of four main components: a user terminal, a server, a generative artificial intelligence system, and an emotion engine.

[0915] User terminal

[0916] The user terminal is a device such as a smartphone, tablet, or personal computer, which inputs and outputs information. To check the delivery date of a product, the user enters the product name and, if necessary, provides facial recognition information. The user's facial recognition information and text input information are analyzed by an emotion engine to generate emotion data. This information and emotion data are sent to the server as an HTTP POST request.

[0917] server

[0918] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully retrieved, the server queries the generative artificial intelligence.

[0919] Generative artificial intelligence

[0920] Generative artificial intelligence generates delivery date information based on received prompts and user sentiment data. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized or specific explanations about the possibility of delays may be added.

[0921] Server response processing

[0922] The server formats the query results obtained from the generative artificial intelligence into a user-friendly format. It also adjusts the response format and content based on sentiment data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[0923] Specific example

[0924] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server. The server extracts the product name "New X100" and emotion data from the received request and sends the emotion data along with the prompt "When is the delivery date for the New X100?" to the generative AI. The generative AI generates a response stating "The delivery date for the New X100 is January 15, 2024," and adds a message such as "Don't worry, the arrangements are progressing smoothly." The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal: "The delivery date for the New X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0925] Example of a prompt

[0926] "When will the new X100 be available? Users are feeling anxious right now."

[0927] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[0928] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0929] Step 1:

[0930] The user enters the product name and provides facial recognition information as needed. The user's terminal collects this information and generates input data containing the product name and facial recognition information. The input data is sent to the server as an HTTP POST request.

[0931] Step 2:

[0932] The server analyzes the request received from the user's terminal and extracts the product name and facial recognition information. The server sends the facial recognition information to the emotion engine and obtains emotion data. The emotion engine analyzes the facial recognition information and outputs the user's emotional state (e.g., impatience, anxiety, fatigue, etc.) as data.

[0933] Step 3:

[0934] The server generates an error message if the product name is empty and sends it back to the user's terminal. Generating the error message is a data processing operation that creates error text based on the condition that the product name is empty.

[0935] Step 4:

[0936] The server generates a prompt containing the product name and sentiment data, and queries the generative artificial intelligence. The prompt is in text format, such as, "When is the delivery date for the new X100? The user is currently feeling anxious."

[0937] Step 5:

[0938] Generative artificial intelligence performs natural language processing based on received prompts and sentiment data to generate delivery date information. The generated delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[0939] Step 6:

[0940] The server formats the query results (delivery date information and additional messages) obtained from the generative artificial intelligence and sends them back to the user terminal. The formatting process is a data calculation that adjusts the response format and content according to the emotional data. For example, if the user appears tired, words of encouragement will be incorporated into the response.

[0941] Step 7:

[0942] The user checks the delivery date information on their device. The device displays the delivery date information and messages received from the server, providing the user with easy-to-understand information. As a result, the user can obtain information such as, "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[0943] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0944] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0945] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0946] [Fourth Embodiment]

[0947] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0948] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0949] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0950] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0951] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0952] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0953] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0954] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0955] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0956] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0957] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0958] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0959] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0960] This invention is a system aimed at enabling users to quickly and accurately confirm the delivery date of commercial products. The system's program processing and specific examples will be explained below.

[0961] System-wide configuration

[0962] The system consists of three main components: a user terminal, a server, and a generative artificial intelligence (AI). The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI. The generative AI performs natural language processing based on the requests and generates appropriate responses.

[0963] User terminal operation

[0964] To check the delivery date of a product, the user enters information including the product name from their terminal. The entered information, including the product name, is sent to the server as an HTTP POST request.

[0965] Server operation

[0966] The server analyzes the request received from the user terminal and extracts the product name. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[0967] When the server receives a response from the generative artificial intelligence, it formats the response and sends it back to the user's terminal. This allows the user to obtain accurate delivery date information with simple operations.

[0968] How generative artificial intelligence works

[0969] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. Specifically, it receives inquiries that include product names, generates information regarding the delivery date of that product, and sends it back to the server.

[0970] Specific example

[0971] For example, consider a case where a user wants to check the delivery date for the "New X100". The user enters "New X100" into their terminal and sends a request to the server. The server extracts the product name "New X100" from the received request and queries the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative artificial intelligence generates a response such as, "The delivery date for the New X100 is January 15, 2024."

[0972] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal stating, "The delivery date for the new X100 is January 15, 2024."

[0973] This system allows users to quickly and easily check the delivery date of products.

[0974] The following describes the processing flow.

[0975] Step 1:

[0976] The user sends a request from their device.

[0977] The user enters the product name "New X100" to check the delivery date for the product. The data, including the entered product name, is sent to the server as an HTTP POST request.

[0978] Step 2:

[0979] The server receives the request and parses its contents.

[0980] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It parses the received JSON data and extracts the product name.

[0981] Step 3:

[0982] The server checks for the presence or absence of the product name.

[0983] The server checks if the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user along with a 400 status code.

[0984] Step 4:

[0985] The server queries the generative artificial intelligence.

[0986] If the product name is retrieved correctly, the server will send a query to the generative artificial intelligence based on the product name. Specifically, it will use the generative artificial intelligence's API to send a prompt asking, "When is the delivery date for the new X100?"

[0987] Step 5:

[0988] A generative artificial intelligence generates delivery date information.

[0989] The generative artificial intelligence performs natural language processing based on the received prompt and generates appropriate delivery date information. It sends the response "The delivery date for the new X100 is January 15, 2024" back to the server.

[0990] Step 6:

[0991] The server formats the response.

[0992] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information.

[0993] Step 7:

[0994] The server returns the message to the user's terminal.

[0995] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal that "The delivery date for the new X100 is January 15, 2024."

[0996] Through the steps described above, users can quickly and easily check the delivery date for the products they wish to purchase.

[0997] (Example 1)

[0998] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0999] Conventional systems for confirming product delivery dates often required manual inquiries and verification, demanding significant time and effort. This made it difficult for users to quickly and accurately confirm product delivery dates. Furthermore, the lack of proper error message handling and automated response systems resulted in a poor user experience.

[1000] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1001] In this invention, the server includes means for receiving information including the product name from a user terminal as an HTTP POST request, means for analyzing the information and extracting the product name, means for generating and returning an error message if the product name is empty, means for querying a generative artificial intelligence if the product name is properly obtained, and means for formatting the delivery date information obtained from the generative artificial intelligence and returning it to the user terminal. This makes it possible for users to quickly and accurately confirm the delivery date of the product.

[1002] A "user terminal" is a device used by a user to input and transmit information in order to confirm the delivery date of a product, and can be a computer, smartphone, tablet, or other terminal device.

[1003] "Product name" refers to the name of a product, service, or item, and is used to identify a specific product related to the delivery date that the user wants to check.

[1004] An "HTTP POST request" is a protocol used to send data from a user's terminal to a server, and is primarily used for transferring web data and calling APIs.

[1005] A "server" is a computer system that analyzes requests received from user terminals, performs the necessary processing, and sends the results back to the user terminal.

[1006] "Generative artificial intelligence" refers to artificial intelligence (AI) models that perform natural language processing based on received prompts and generate appropriate responses. Examples include OpenAI's GPT series.

[1007] An "error message" is a message generated by the server and sent back to the user's terminal when the product name entered by the user is inappropriate or empty, informing the user of the nature of the error.

[1008] A "prompt message" is a sentence containing questions or requests in natural language format that is input to a generative artificial intelligence system, and is a sentence that inquires about delivery date information based on the product name.

[1009] Modes for carrying out the invention

[1010] This invention is a system for users to quickly and accurately confirm the delivery date of commercial products. The system mainly consists of three components: a user terminal, a server, and a generative artificial intelligence system.

[1011] User terminal operation

[1012] The user terminal is a device used to input the product name. Users enter the product name using a web browser or mobile application and send that information to the server. For example, if a user enters the product name "New X100" and clicks the "Search" button, the user terminal generates an HTTP POST request based on that information and sends it to a URL (for example, http: / / example.com / api / check-delivery).

[1013] Server operation

[1014] The server parses the HTTP POST request received from the user's terminal. The request body contains the product name in the format {"itemName": "New X100"}. The server parses this request and extracts the product name. If the product name is empty, the server generates an error message "Please enter the product name" and sends it back to the user's terminal.

[1015] If the product name is retrieved correctly, the server queries the generative artificial intelligence. The prompt will be generated as follows: "When is the delivery date for the new X100?" Based on this prompt, the server sends an API request to the generative artificial intelligence. For example, it sends JSON data containing authentication information and the prompt to https: / / api.example.com / v1 / ai / generate.

[1016] How generative artificial intelligence works

[1017] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information. For example, it might generate a response such as "The delivery date for the new X100 is January 15, 2024" and send it back to the server. OpenAI's GPT series is one example of a generative artificial intelligence that can be used.

[1018] Return and display of results

[1019] The server formats the response received from the generative artificial intelligence. For example, it converts a message like "The delivery date for the new X100 is January 15, 2024" into a user-friendly format, such as "The delivery date for the new X100 is January 15, 2024." The formatted message is then sent back to the user's terminal as an HTTP response. The user can then view this information on their terminal.

[1020] Specific example

[1021] For example, if a user wants to check the delivery date for the "New X100," they would type "New X100" into the input field of their web browser or mobile application and click the search button. The server receives this and sends a prompt message to the generative artificial intelligence asking, "When is the delivery date for the New X100?" The generative AI responds, "The delivery date for the New X100 is January 15, 2024," and the server formats this information and sends it back to the user's terminal. The user can then confirm on the screen that "The delivery date for the New X100 is January 15, 2024."

[1022] Examples of prompt statements include:

[1023] "When will the new X100 be available?"

[1024] "What is the delivery time for the F-series LED displays?"

[1025] "Could you please tell me the schedule for introducing the Y200 commercial printer?"

[1026] In this way, this system helps users to easily and quickly check the delivery date of products.

[1027] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1028] Step 1:

[1029] The user enters the product name. Specifically, the user enters the product name into an input field in a web browser or mobile application and clicks the search button. The entered information (product name) is sent as an HTTP POST request. The input is the product name, and the output is an HTTP POST request.

[1030] Step 2:

[1031] The server receives the request. The server parses the HTTP POST request received from the user's terminal and extracts the product name from the request body. Specifically, it obtains data in the format {"itemName": "New X100"}. The input is the request body of the HTTP POST request, and the output is the extracted product name.

[1032] Step 3:

[1033] The server performs error checking. The server checks whether the extracted product name is empty. If the product name is empty, it generates an error message and sends it back to the user terminal. For example, it might generate a message such as "Please enter a product name." The input is the extracted product name, and the output is the error message or the product name for the next step.

[1034] Step 4:

[1035] The server queries the generation AI. If the product name is retrieved correctly, the server generates a prompt and sends an API request to the generation AI. For example, it might generate a prompt such as, "What is the delivery date for the new X100?" The input is the correctly retrieved product name, and the output is the generated prompt and the sent API request.

[1036] Step 5:

[1037] The generative AI generates a response. The generative artificial intelligence receives a prompt and generates appropriate delivery date information. Specifically, it generates a response that says, "The delivery date for the new X100 is January 15, 2024." The input is the prompt, and the output is the generated delivery date information response.

[1038] Step 6:

[1039] The server formats the response. The server formats the delivery date information received from the generative artificial intelligence and converts it into a format that is easy for humans to understand. For example, it formats it into a format such as "The delivery date for the new X100 is January 15, 2024." The input is the response from the generative artificial intelligence, and the output is the formatted response message.

[1040] Step 7:

[1041] The server sends a formatted response back to the user terminal. The server sends the formatted message back to the user terminal as an HTTP response. The user terminal receives this message and displays it on the screen. The input is the formatted response message, and the output is the information displayed on the user terminal.

[1042] Step 8:

[1043] The user confirms the results. The user can check delivery date information on their device and take the necessary actions. The input is the information displayed on the user's device, and the output is the user's decision.

[1044] (Application Example 1)

[1045] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1046] Conventional systems made it difficult to instantly check delivery dates and inventory information for product introductions. In particular, product management is complex in logistics centers, requiring rapid and accurate information provision. However, traditional methods required users to manually search for information, which was time-consuming and laborious. Therefore, a system is needed to quickly provide product introduction date and inventory information in order to improve the efficiency of logistics operations.

[1047] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1048] In this invention, the server includes means for receiving information including product names from a user terminal, means for querying a generative artificial intelligence based on the information including product names, means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, and means for generating product delivery date information and inventory information. This makes it possible to quickly and easily obtain product delivery date information and inventory information.

[1049] A "user terminal" is a device used for inputting and outputting information, and includes smartphones, tablets, and personal computers.

[1050] A "product name" is a name or identifier used to identify a specific product.

[1051] "Generative artificial intelligence" is an artificial intelligence technology that performs natural language processing and generates appropriate information based on user inquiries.

[1052] "Inquiry results" refer to information generated by a generative artificial intelligence system based on inquiries sent from the user's terminal.

[1053] "Formatting" refers to the process of converting query results obtained from generative artificial intelligence into a format that is easy for users to understand.

[1054] "Implementation date information" refers to information about when a particular product will be implemented.

[1055] "Inventory information" refers to information about how many units of a particular product are currently in stock.

[1056] An "error message" is a warning or cautionary message displayed to a user when certain conditions are not met.

[1057] This invention provides a system that enables rapid acquisition of delivery date information and inventory information for product introduction at a logistics center. The system consists of a user terminal, a server, and a generative artificial intelligence. When a user enters a product name, the generative artificial intelligence generates information about the product via the server and returns it to the user.

[1058] Hardware and software usage

[1059] The system uses the following hardware and software:

[1060] Hardware:

[1061] User terminal: A device used for inputting and outputting information. Examples include smartphones, tablets, and personal computers.

[1062] Server: Receives requests from user terminals and processes them in cooperation with generative artificial intelligence.

[1063] software:

[1064] Server-side program: Built using Python and Flask.

[1065] Generative Artificial Intelligence: Uses the OpenAI API to perform natural language processing and information generation.

[1066] The user terminal application is developed in Android (Kotlin) and sends HTTP requests to receive information.

[1067] Data processing and data calculation

[1068] User terminal operation

[1069] On the user terminal, a staff member enters the product name. For example, they might enter "Product 1" on their smartphone. This information is sent to the server as an HTTP POST request. The user terminal uses a library for API communication (e.g., OkHttp).

[1070] Server operation

[1071] The server analyzes the request received from the user's terminal and extracts the product name. If the product name is successfully retrieved, it uses the API key to send a prompt message to OpenAI's generative artificial intelligence. For example, the prompt message might look like this:

[1072] "Please tell me the delivery date and stock status of Product 1."

[1073] The results obtained from the generative artificial intelligence are formatted and sent back to the user's terminal. The server side is built with Python and Flask, and libraries for API communication are also used (e.g., requests).

[1074] How generative artificial intelligence works

[1075] Generative artificial intelligence performs natural language processing based on received prompts to generate appropriate delivery date information and inventory information. For example, it generates responses like the following:

[1076] "The delivery date for Product 1 is February 15, 2024, and we have 50 units in stock."

[1077] The generated information is sent back to the server, which then formats the information and sends it back to the user's terminal.

[1078] Specific example

[1079] For example, consider a scenario where a logistics center staff member wants to check the delivery date and inventory status of a new SKU, "Product 1." The staff member enters "Product 1" into a smartphone application and sends a request to the server. The server generates a prompt, "Please tell me the delivery date and inventory status of Product 1," and sends it to a generative artificial intelligence (AI). The AI ​​generates a response, "The delivery date for Product 1 is February 15, 2024, and the inventory quantity is 50 units," which is then formatted and sent back to the user's terminal. The staff member can then view this information on their smartphone screen.

[1080] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1081] Step 1:

[1082] Enter the product name on the user terminal.

[1083] Input: The user enters the product name (for example, "Product 1").

[1084] Output: HTTP POST request containing the product name.

[1085] Specific operation: Enter the product name into the input field of the smartphone application and press the submit button. At this time, the product name is sent to the server as an HTTP POST request in JSON format.

[1086] Step 2:

[1087] The server receives and analyzes requests from the user's terminal.

[1088] Input: HTTP POST request sent from the user's terminal.

[1089] Output: Product names are extracted.

[1090] Specific operation: A server application using Flask receives a request and parses the data in JSON format. It then extracts the product name from the product name field.

[1091] Step 3:

[1092] The server makes a query to the generative artificial intelligence.

[1093] Input: Extracted product name (e.g., "Product 1").

[1094] Output: The prompt message sent to the generative artificial intelligence.

[1095] Specific operation: The server uses the OpenAI API to generate the prompt "Please tell me the delivery date and stock status of product 1," and sends it to the generative artificial intelligence.

[1096] Step 4:

[1097] Generative artificial intelligence generates delivery date information and inventory information.

[1098] Input: Prompt message received from the server.

[1099] Output: Delivery date information and inventory information (for example, "The delivery date for product 1 is February 15, 2024, and the inventory quantity is 50 units.").

[1100] Specific operation: Generative artificial intelligence (OpenAI model) analyzes the prompt text and generates delivery date and inventory information for the product.

[1101] Step 5:

[1102] The server formats the information obtained from the generative artificial intelligence and sends it back to the user's terminal.

[1103] Input: Delivery date and inventory information returned by a generative artificial intelligence.

[1104] Output: Formatted delivery date and inventory information.

[1105] Specific operation: The server formats the received information into a format that is easy for the user to understand and sends it back to the user's terminal in JSON format.

[1106] Step 6:

[1107] The user terminal receives and displays the response from the server.

[1108] Input: Formatted delivery date and inventory information sent from the server.

[1109] Output: Delivery date and stock information displayed to the user.

[1110] Specific operation: The smartphone application receives a response from the server and displays the information on the screen. The user confirms the information, "The delivery date for product 1 is February 15, 2024, and the number of units in stock is 50."

[1111] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1112] This invention is a system designed to allow users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's feelings. The system's program processing and specific examples will be explained below.

[1113] System-wide configuration

[1114] The system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[1115] User terminal operation

[1116] To check the delivery date of a product, the user enters the product name "New X100" on their terminal. At this time, the emotion engine recognizes the user's emotions based on facial recognition information and text input information. The entered information and emotion data are sent to the server as an HTTP POST request.

[1117] Server operation

[1118] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully obtained, the server queries the generative artificial intelligence based on the product name.

[1119] How generative artificial intelligence works

[1120] Generative artificial intelligence performs natural language processing based on received prompts and user sentiment data to generate appropriate delivery date information. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[1121] Server response processing

[1122] The server formats the responses received from the generative artificial intelligence into a format that is easy for the user to understand. It also adjusts the format and content of the responses based on emotional data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[1123] Specific example

[1124] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server.

[1125] The server extracts the product name "New X100" and emotional data from the received request, and sends the emotional data to the generative artificial intelligence along with the prompt, "When is the delivery date for the new X100?".

[1126] The generative artificial intelligence generates the response, "The delivery date for the new X100 is January 15, 2024," and adds the message, "Don't worry, the arrangements are progressing smoothly."

[1127] The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the following information on their terminal: "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[1128] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[1129] The following describes the processing flow.

[1130] Step 1:

[1131] The user sends a request from their device.

[1132] The user enters the product name, "New X100," to confirm the delivery date for the product. As the user enters the information, the terminal analyzes their emotions from facial expressions and text, and sends this data along with the input to the server in an HTTP POST request.

[1133] Step 2:

[1134] The server receives the request and parses its contents.

[1135] The server uses the Flask web framework to receive POST requests at the / confirm_delivery endpoint. It extracts the product name and sentiment data from the received JSON data.

[1136] Step 3:

[1137] The server checks for the presence or absence of the product name.

[1138] If the product name is empty, the server generates an error message "Product name required" and sends it back to the user with a 400 status code. If the product name is successfully retrieved, the process proceeds to the next step.

[1139] Step 4:

[1140] The server queries the generative artificial intelligence.

[1141] The server passes the extracted product names and sentiment data to the generative artificial intelligence and inquires about delivery dates based on the product names. Specifically, it sends sentiment data along with the prompt, "When is the delivery date for the new X100?"

[1142] Step 5:

[1143] A generative artificial intelligence generates delivery date information.

[1144] The generative artificial intelligence performs natural language processing based on the received prompt and sentiment data. It generates a response such as, "The delivery date for the new X100 is January 15, 2024," and if the user is feeling anxious, it adds a message such as, "Don't worry, the arrangements are progressing smoothly."

[1145] Step 6:

[1146] The server formats the response.

[1147] The server formats the response received from the generative artificial intelligence into a user-friendly format. It modifies and processes the response text as needed, and then formats it as final delivery date information. During this process, the response format and content are adjusted based on sentiment data.

[1148] Step 7:

[1149] The server returns the message to the user's terminal.

[1150] A JSON response containing formatted delivery date information is generated and sent back to the user's terminal. The user then confirms on their terminal the following information: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[1151] (Example 2)

[1152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1153] Conventional product delivery date confirmation systems have problems with users having difficulty obtaining delivery date information quickly and accurately, and in particular, with a lack of information provision that takes user emotions into consideration. As a result, users often feel anxious and worried, which leads to a decrease in customer satisfaction. A system that solves this problem is needed.

[1154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1155] In this invention, the server includes means for receiving information including product name and sentiment data from a user terminal, means for querying a generative artificial intelligence based on the product name and sentiment data, and means for formatting a message based on the delivery date information and sentiment data obtained from the generative artificial intelligence and sending it back to the user terminal. This makes it possible for the user to quickly and accurately obtain sentiment-sensitive delivery date information.

[1156] A "user terminal" is an electronic device used to input and transmit product names and sentiment data.

[1157] "Product name" refers to the name of a specific product or service, and is information that users enter to confirm delivery dates.

[1158] "Emotional data" refers to data that represents the user's emotional state and is primarily acquired using facial recognition technology.

[1159] A "server" is a central system that processes information received from user terminals and coordinates with generative artificial intelligence.

[1160] "Generative artificial intelligence" refers to an artificial intelligence model that generates appropriate responses based on input prompts and sentiment data.

[1161] "Delivery date information" refers to information regarding the introduction date or delivery date of a specific product.

[1162] A "message" is supplementary text based on emotional data that is added to delivery date information generated by a generative artificial intelligence.

[1163] An "error message" is an error notification message sent from the server to the user's terminal, such as when the product name is empty.

[1164] This invention is a system that aims to allow users to quickly and accurately confirm the delivery date of a product, while taking into account the user's emotions.

[1165] The overall system consists of four main components: a user terminal, a server, a generative artificial intelligence (AI), and an emotion engine. The user terminal is a device that inputs and outputs information, while the server receives requests from the user and processes them in cooperation with the generative AI and the emotion engine. The generative AI performs natural language processing based on the requests, and the emotion engine analyzes the user's emotions.

[1166] Detailed explanation of operation

[1167] 1. User input of product name

[1168] To check the delivery date of a product, the user enters the product name "New X100" into their terminal. The terminal's input screen has a text box, and the process begins when the user enters the product name.

[1169] 2. Capturing emotional data

[1170] The device's camera function is used to capture the user's facial expressions. This data is analyzed through an emotion engine to obtain the user's emotional data. The emotion engine uses facial recognition APIs such as Amazon Rekognition.

[1171] 3. Sending data

[1172] The user terminal sends the entered product name and analyzed sentiment data to the server as an HTTP POST request. The data sent is in JSON format.

[1173] 4. Server-side request analysis

[1174] The server parses the received HTTP POST request and extracts the product name and sentiment data. The Python Flask framework is used for the analysis. If the product name is empty, an error message is generated and sent back to the user's terminal.

[1175] 5. Sending prompts to generative artificial intelligence

[1176] The server sends prompts to a generative artificial intelligence (e.g., OpenAI GPT-3) based on product names and sentiment data. The prompts are sent in a format such as, "When is the delivery date for the new X100? Users are impatient."

[1177] 6. Response generation by generative artificial intelligence

[1178] Generative artificial intelligence generates responses based on received prompts and sentiment data. For example, along with the response, "The delivery date for the new X100 is January 15, 2024," the message "Don't worry, arrangements are progressing smoothly" might be added.

[1179] 7. Server-side response formatting

[1180] The server formats the responses received from the generative artificial intelligence. Using the Python Jinja2 library, it transforms them into a user-friendly format. It also adjusts the response format and content based on sentiment data.

[1181] 8. Sending a response

[1182] The server sends a formatted response back to the user's terminal. The response might be in the format of, for example, "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[1183] 9. User response confirmation

[1184] The user confirms the response on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[1185] Examples of prompt statements

[1186] Send the following prompt to the generative artificial intelligence:

[1187] "When will the new X100 be available? Users are getting impatient."

[1188] This allows users to quickly and accurately obtain emotionally sensitive delivery date information.

[1189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1190] Step 1:

[1191] The user enters the product name "New X100" into the text box on the device. The device accepts the input and then activates the camera function. The input is saved on the device as text data.

[1192] Step 2:

[1193] The device's camera captures the user's face and sends the image data to the emotion engine. The emotion engine uses facial recognition technology to analyze the user's facial expressions and generate emotion data. This emotion data is output as emotional states, such as "anxiety" or "relief."

[1194] Step 3:

[1195] The terminal combines the entered product name and analyzed sentiment data to generate an HTTP POST request. This request is in JSON format and includes the product name and sentiment data. The request is sent to the server.

[1196] Step 4:

[1197] The server parses the received HTTP POST request and extracts product names and sentiment data from the JSON data. A server-side framework (e.g., Flask in Python) is used for the parsing. The extracted product names and sentiment data are temporarily stored on the server.

[1198] Step 5:

[1199] The server makes a query to the generative artificial intelligence. Specifically, it generates a prompt based on the product name "New X100" and the emotion data "anxiety," and sends it to the generative artificial intelligence. This prompt is in the format of "When is the delivery date for the new X100? The user is anxious."

[1200] Step 6:

[1201] The generative artificial intelligence generates delivery date information and messages based on prompts and sentiment data. For example, it might output a message such as, "The delivery date for the new X100 is January 15, 2024," along with, "Don't worry, arrangements are progressing smoothly." The response data is sent back to the server in JSON format.

[1202] Step 7:

[1203] The server formats the response data received from the generative artificial intelligence. It uses the Python Jinja2 library to convert the data into a user-friendly format. It also adjusts the format and content based on sentiment data. The formatted data might look something like this: "The delivery date for the new X100 is January 15, 2024. Don't worry, arrangements are progressing smoothly."

[1204] Step 8:

[1205] The server returns the formatted response data to the user's terminal as an HTTP response. The response is in JSON format and includes formatted delivery date information and a message.

[1206] Step 9:

[1207] The user checks the response data received on their device. The device screen displays the message: "The delivery date for the new X100 is January 15, 2024. Please don't worry, the arrangements are progressing smoothly."

[1208] (Application Example 2)

[1209] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1210] Conventional systems for confirming delivery dates for products only provide users with uniform information, lacking the flexibility to respond to users' emotions and circumstances. Therefore, the user experience could be negatively affected during the delivery date confirmation process, especially when users were feeling anxious or stressed. This invention aims to solve the problem of providing a more user-friendly and flexible delivery date confirmation system that takes user emotions into consideration.

[1211] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1212] In this invention, the server includes means for receiving information including the product name from a user terminal, means for analyzing the user's emotional data, means for querying a generative artificial intelligence based on the product name and emotional data, and means for formatting the query results obtained from the generative artificial intelligence, adjusting them according to the emotional data, and returning them to the user terminal. This makes it possible to provide delivery date information that is tailored to the user's emotions.

[1213] A "user terminal" is a device used by a user to input and output information.

[1214] "Product name" refers to information that indicates the name of a specific product.

[1215] "Emotional data" refers to data that indicates the user's emotional state, analyzed based on the user's facial recognition information and text input information.

[1216] "Generative artificial intelligence" refers to an artificial intelligence model that performs natural language processing and generates appropriate responses based on the content of an inquiry.

[1217] "Query results" refer to response data generated by a generative artificial intelligence system.

[1218] "Adjusting based on emotional data" refers to changing the content and format of the generated response based on the user's emotional state.

[1219] A "server" is a computer system that processes information received from a user terminal and sends an appropriate response back to the user terminal using generative artificial intelligence and emotional data.

[1220] "Delivery date information" refers to information indicating the planned date or period for the introduction or delivery of a specific product.

[1221] "Formatting" refers to converting raw or generated data into a format that is easy for users to understand.

[1222] An "error message" is a message used to inform a user of errors or deficiencies in the information they have entered.

[1223] This invention provides a system that allows users to quickly and accurately confirm the delivery date of a product, while also taking into account the user's emotions. The system consists of four main components: a user terminal, a server, a generative artificial intelligence system, and an emotion engine.

[1224] User terminal

[1225] The user terminal is a device such as a smartphone, tablet, or personal computer, which inputs and outputs information. To check the delivery date of a product, the user enters the product name and, if necessary, provides facial recognition information. The user's facial recognition information and text input information are analyzed by an emotion engine to generate emotion data. This information and emotion data are sent to the server as an HTTP POST request.

[1226] server

[1227] The server analyzes the request received from the user terminal and extracts the product name and sentiment data. If the product name is empty, the server generates an error message and sends it back to the user terminal. If the product name is successfully retrieved, the server queries the generative artificial intelligence.

[1228] Generative artificial intelligence

[1229] Generative artificial intelligence generates delivery date information based on received prompts and user sentiment data. This delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized or specific explanations about the possibility of delays may be added.

[1230] Server response processing

[1231] The server formats the query results obtained from the generative artificial intelligence into a user-friendly format. It also adjusts the response format and content based on sentiment data. For example, if the user appears tired, encouraging words can be incorporated into the response.

[1232] Specific example

[1233] For example, consider a case where a user wants to check the delivery date for the "New X100." The user enters "New X100" into their terminal, and the emotion engine analyzes the user's facial recognition information, determining that the user is feeling anxious. This information, along with the product name, is sent to the server. The server extracts the product name "New X100" and emotion data from the received request and sends the emotion data along with the prompt "When is the delivery date for the New X100?" to the generative AI. The generative AI generates a response stating "The delivery date for the New X100 is January 15, 2024," and adds a message such as "Don't worry, the arrangements are progressing smoothly." The server receives this response, formats it, and sends it back to the user's terminal. The user can then see the information on their terminal: "The delivery date for the New X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[1234] Example of a prompt

[1235] "When will the new X100 be available? Users are feeling anxious right now."

[1236] This system allows users to quickly and easily check the delivery date of products, and also provides more user-friendly support through an emotional engine.

[1237] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1238] Step 1:

[1239] The user enters the product name and provides facial recognition information as needed. The user's terminal collects this information and generates input data containing the product name and facial recognition information. The input data is sent to the server as an HTTP POST request.

[1240] Step 2:

[1241] The server analyzes the request received from the user's terminal and extracts the product name and facial recognition information. The server sends the facial recognition information to the emotion engine and obtains emotion data. The emotion engine analyzes the facial recognition information and outputs the user's emotional state (e.g., impatience, anxiety, fatigue, etc.) as data.

[1242] Step 3:

[1243] The server generates an error message if the product name is empty and sends it back to the user's terminal. Generating the error message is a data processing operation that creates error text based on the condition that the product name is empty.

[1244] Step 4:

[1245] The server generates a prompt containing the product name and sentiment data, and queries the generative artificial intelligence. The prompt is in text format, such as, "When is the delivery date for the new X100? The user is currently feeling anxious."

[1246] Step 5:

[1247] Generative artificial intelligence performs natural language processing based on received prompts and sentiment data to generate delivery date information. The generated delivery date information is adjusted according to the sentiment data. For example, if the user is feeling anxious, the delivery date information may be emphasized, or specific explanations about the possibility of delays may be added.

[1248] Step 6:

[1249] The server formats the query results (delivery date information and additional messages) obtained from the generative artificial intelligence and sends them back to the user terminal. The formatting process is a data calculation that adjusts the response format and content according to the emotional data. For example, if the user appears tired, words of encouragement will be incorporated into the response.

[1250] Step 7:

[1251] The user checks the delivery date information on their device. The device displays the delivery date information and messages received from the server, providing the user with easy-to-understand information. As a result, the user can obtain information such as, "The delivery date for the new X100 is January 15, 2024. Don't worry, the arrangements are progressing smoothly."

[1252] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1253] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1254] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1255] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1256] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1257] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1258] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1259] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1260] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1261] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1262] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1263] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1264] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1265] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1266] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1267] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1268] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1269] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1270] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1271] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1272] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1273] The following is further disclosed regarding the embodiments described above.

[1274] (Claim 1)

[1275] A means of receiving information including the product name from the user terminal,

[1276] A means of querying a generative artificial intelligence based on information including the name of the product,

[1277] A means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal,

[1278] A system that includes this.

[1279] (Claim 2)

[1280] The system according to claim 1, wherein a generative artificial intelligence generates delivery date information.

[1281] (Claim 3)

[1282] The system according to claim 1, further comprising means for generating an error message and returning it to the user terminal if the product name is empty.

[1283] "Example 1"

[1284] (Claim 1)

[1285] A means of receiving information including the product name from the user terminal,

[1286] A means of sending information including the product name to the server as an HTTP POST request,

[1287] A server analyzes the information and extracts the product name,

[1288] A method for generating an error message and sending it back to the user's terminal when the product name is empty,

[1289] A means of querying a generative artificial intelligence when the product name is properly obtained,

[1290] A means of formatting delivery date information obtained from a generative artificial intelligence and returning it to the user terminal,

[1291] A system that includes this.

[1292] (Claim 2)

[1293] The system according to claim 1, wherein a generative artificial intelligence generates information regarding the delivery date of a product.

[1294] (Claim 3)

[1295] The system according to claim 1, further comprising means for a user to enter a product name using a web browser or mobile application and transmit the information.

[1296] "Application Example 1"

[1297] (Claim 1)

[1298] A means for receiving information including product names from a user terminal,

[1299] A means for querying a generative artificial intelligence based on information including the product name,

[1300] A means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal,

[1301] A means for generating delivery date information and inventory information related to products,

[1302] A system that includes this.

[1303] (Claim 2)

[1304] The system according to claim 1, wherein a generative artificial intelligence generates delivery date information.

[1305] (Claim 3)

[1306] The system according to claim 1, wherein the generative artificial intelligence also generates inventory information.

[1307] (Claim 4)

[1308] The system according to claim 1, further comprising means for generating an error message and returning it to the user terminal if the product name is empty.

[1309] "Example 2 of combining an emotion engine"

[1310] (Claim 1)

[1311] A means for receiving information including product name and sentiment data from a user terminal,

[1312] A means for querying a generative artificial intelligence based on the product name and sentiment data,

[1313] A means for formatting a message based on delivery date information and emotion data obtained from a generative artificial intelligence and sending it back to the user terminal,

[1314] A system that includes this.

[1315] (Claim 2)

[1316] The system according to claim 1, wherein a generative artificial intelligence generates delivery date information and adds a message based on emotional data to it.

[1317] (Claim 3)

[1318] The system according to claim 1, further comprising means for generating an error message and returning it to the user terminal if the product name is empty.

[1319] "Application example 2 when combining with an emotional engine"

[1320] (Claim 1)

[1321] A means of receiving information including the product name from the user terminal,

[1322] Methods for analyzing user sentiment data,

[1323] A means for querying a generative artificial intelligence based on the product name and sentiment data,

[1324] A means for formatting the query results obtained from the generative artificial intelligence, adjusting them according to emotion data, and returning them to the user terminal,

[1325] A system that includes this.

[1326] (Claim 2)

[1327] The system according to claim 1, wherein a generative artificial intelligence generates delivery date information and adjusts the delivery date information based on the user's emotional data.

[1328] (Claim 3)

[1329] The system according to claim 1, further comprising means for generating an error message and returning it to the user terminal if the product name is empty. [Explanation of Symbols]

[1330] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information including the product name from the user terminal, A means of querying a generative artificial intelligence based on information including the name of the product, A means for formatting the query results obtained from the generative artificial intelligence and returning them to the user terminal, A system that includes this.

2. The system according to claim 1, wherein a generative artificial intelligence generates delivery date information.

3. The system according to claim 1, further comprising means for generating an error message and returning it to the user terminal if the product name is empty.

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