system

Abstract

We provide the system. [Solution] A means to have the user input the data necessary for the price simulation, A terminal means for transmitting the aforementioned data to a server, A means for receiving transmitted data and analyzing said data, A means for generating price suggestions and time optimization results using an artificial intelligence model based on analysis results, Means for transmitting the generated results to a terminal, A means for displaying the results to the user on the 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional fee simulation system, it is difficult to quickly generate an optimal fee proposal or an optimized time result based on user input data, and there is a problem that the results vary depending on the skills and knowledge of the corresponding staff. In addition, there was a risk of losing business opportunities due to delays in proposals to users. An object of the present invention is to solve such problems and provide a system that makes efficient and accurate fee proposals.

Means for Solving the Problems

[0005] The present invention is a system comprising means for allowing a user to input data necessary for a price simulation; terminal means for transmitting the data to a server; means for receiving and analyzing the transmitted data; means for generating price suggestions and time optimization results using an artificial intelligence model based on the analysis results; means for transmitting the generated results to the terminal; and means for displaying the results to the user on the terminal. Furthermore, the system, in which the artificial intelligence model is a natural language processing engine and the terminal means is a means for transmitting data to the server using HTTP requests, automates everything from user data input to the generation and presentation of optimal price suggestions, thereby realizing the provision of a fast and accurate service.

[0006] A "user" is an entity that utilizes the pricing simulation system, inputs the necessary data for the simulation, and receives pricing proposals and time optimization results.

[0007] "Data" refers to information such as product details, quantity, budget, and delivery date provided by the user for the purpose of performing a price simulation.

[0008] A "terminal" refers to a device (e.g., a personal computer, smartphone, tablet, etc.) used by a user to access a system and input data.

[0009] A "server" refers to a computer system that receives data sent by users, analyzes it, uses artificial intelligence models to generate pricing suggestions and time optimization results, and then transmits them to the terminal.

[0010] "Analysis" refers to the process of performing necessary calculations and data processing based on user input to provide pricing suggestions and optimize time.

[0011] An "artificial intelligence model" refers to machine learning algorithms and natural language processing engines used to provide price suggestions and optimize time based on input data.

[0012] "Price proposal" refers to the result of presenting the optimal price based on the user's input data.

[0013] "Time optimization" refers to the process of calculating and presenting the optimal timing and efficiency for making pricing proposals.

[0014] An "HTTP request" is a protocol used by a device to communicate with a server, and refers to the technology used for sending and receiving data.

[0015] "Presentation" refers to the process of displaying the price proposal and time optimization results generated by the server on the user's device in an easy-to-understand format. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be described.

[0019] In the following embodiments, the 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0038] Explanation of the program's processing:

[0039] terminal

[0040] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0041] server

[0042] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses an artificial intelligence model to generate pricing suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4®) to generate requests based on the user's input data, such as the following:

[0043] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0044] The artificial intelligence model generates pricing suggestions and time optimization results based on this request and returns them to the server. The server then analyzes these results and formats them for presentation to the user.

[0045] Specific example

[0046] For example, suppose a user inputs data about product A, such as "order 200 units," "budget 500,000 yen," and "delivery time 2 weeks." The terminal sends this data to the server. The server receives the data and performs analysis.

[0047] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0048] "By setting the unit price of product A at 2,500 yen and shortening the delivery time to one week, we can create an efficient pricing plan."

[0049] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0050] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0051] Thus, using the system of the present invention, it is possible to generate and present a price proposal quickly and accurately based on the user's input data. Since the entire system is automated, variations due to staff differences can be eliminated, and a stable service can be provided.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form provided on the device. After completing the input, the user clicks the "Submit" button.

[0055] Step 2:

[0056] The device receives data entered by the user. This data is structured, for example, in JSON format. The device then sends the received data to the server as the payload of an HTTP request.

[0057] Step 3:

[0058] The server receives data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0059] Step 4:

[0060] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0061] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0062] Step 5:

[0063] An artificial intelligence model receives and processes requests. Based on the user's input data, the model generates price suggestions and time optimization results. For example, it might generate a result such as "Set the unit price of product A to 2500 yen and shorten the delivery time to one week."

[0064] Step 6:

[0065] The server receives the results returned by the artificial intelligence model. It then analyzes the received results and formats them into a user-friendly format.

[0066] Step 7:

[0067] The server sends the formatted result to the terminal. The result is sent as the payload of the HTTP response.

[0068] Step 8:

[0069] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it is presented to the user in the form of "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0070] Step 9:

[0071] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0072] In this way, the system includes a process for quickly and accurately generating and presenting price suggestions and time optimization results to the user based on the user's input data.

[0073] (Example 1)

[0074] 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."

[0075] In today's business environment, complex pricing proposals and schedule optimizations are required. However, performing these tasks manually is not only time-consuming but also increases the risk of human error. Therefore, there is a need for a system that can automatically generate and provide users with fast and accurate proposals. Existing systems struggle to properly analyze user input data and generate optimal pricing proposals and time optimization results based on that data. This complexity increases especially when numerous variables are involved.

[0076] 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.

[0077] In this invention, the server includes means for receiving transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, and means for inputting prompt sentences into the generation AI model. This makes it possible to automatically generate fast and highly accurate price suggestions and time optimization results using advanced data analysis and an AI model.

[0078] A "user" is someone who inputs data to perform a price simulation and receives the results.

[0079] A "terminal" is a device used by a user to input data and send that data to a server; specifically, it refers to electronic devices such as personal computers and smartphones.

[0080] A "server" refers to a device that receives data transmitted from a terminal, performs analysis, and generates price suggestions and time optimization results.

[0081] "Data" refers to the information that users input to perform price simulations, and specifically includes information such as product type, order quantity, budget, and delivery date.

[0082] A "generative AI model" refers to artificial intelligence used to generate price suggestions and time optimization results based on input data, such as a natural language processing engine.

[0083] A "prompt statement" is the text input to a generative AI model, and refers to a sentence that describes a specific analysis request.

[0084] An "HTTP request" refers to a request that uses the web protocol to send data from a terminal to a server.

[0085] "Price proposal" refers to a pricing suggestion provided by the generative AI model based on its analysis results.

[0086] "Time optimization" refers to making adjustments to optimize deadlines and schedules.

[0087] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0088] terminal

[0089] The terminal provides an interface where the user enters the necessary data through an input form for price simulation. The user enters details such as product type, quantity, budget, and delivery date. A specific example of this input is shown below:

[0090] Specific example:

[0091] "Product A", "Quantity Ordered: 200", "Budget: 500,000 yen", "Delivery Time: 2 weeks"

[0092] When a user enters data and clicks the submit button, the device receives that data and sends it to the server using an HTTP request.

[0093] server

[0094] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses a generative AI model to generate price suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate the following prompts based on the user's input data:

[0095] Prompt message:

[0096] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0097] The generative AI model generates pricing suggestions and time optimization results based on this prompt and returns them to the server. For example, it might return a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0098] Presentation of results

[0099] The server receives the results from the generated AI model and formats them in a user-friendly format. It then sends the formatted results back to the terminal as an HTTP response.

[0100] The terminal receives the results from the server and displays them to the user as follows:

[0101] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0102] This system allows users to receive quick and accurate price quotes, thereby improving business efficiency and effectiveness. Furthermore, because the entire system is automated, human error is eliminated, ensuring consistent service delivery.

[0103] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0104] Step 1:

[0105] The user enters the data necessary for the price simulation into the input form on the device.

[0106] Input: Information such as product type, order quantity, budget, and delivery date.

[0107] Operation: Users enter the data necessary for the price simulation using a web form or app interface on their device. For example, they might enter data such as "Product A," "Quantity Ordered: 200," "Budget: 500,000 yen," and "Delivery Time: 2 weeks" into the form.

[0108] Output: The input data is saved to the terminal.

[0109] Step 2:

[0110] When the user clicks the submit button, the device sends the entered data to the server as an HTTP request.

[0111] Input: Data entered by the user into the input form.

[0112] Operation: The terminal converts the input data into JSON format and generates an HTTP request. The generated request is sent to the server via the internet.

[0113] Output: HTTP request from terminal to server (JSON data).

[0114] Step 3:

[0115] The server receives data sent from the terminal and performs data validation and analysis.

[0116] Input: JSON data received from the terminal.

[0117] Operation: The server performs validation to check the format and required fields of the received data. Next, it uses an analysis engine to analyze the content of the data.

[0118] Output: Analyzed data.

[0119] Step 4:

[0120] The server generates prompt messages for the AI ​​model based on the analysis results and inputs them.

[0121] Input: Analyzed data.

[0122] Operation: The server generates prompts for the generated AI model based on the analyzed data. Specifically, it generates prompts such as: "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0123] Output: The generated prompt message.

[0124] Step 5:

[0125] The generative AI model receives a prompt and generates price suggestions and time optimization results.

[0126] Input: The generated prompt message.

[0127] Operation: The generating AI model analyzes the prompt text and generates price suggestions and time optimization results based on the input data. For example, it might generate a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0128] Output: Price proposal and time optimization results.

[0129] Step 6:

[0130] The server receives the generated results and formats them into a user-friendly format.

[0131] Input: Price suggestions and time optimization results returned from a generated AI model.

[0132] Operation: The server receives the results and formats them in a user-friendly format. Specifically, this could include displaying the suggestions in text format or in a table format.

[0133] Output: Formatted result.

[0134] Step 7:

[0135] The server sends the formatted result to the terminal, and the terminal displays the result to the user.

[0136] Input: Formatted result.

[0137] Operation: The server sends the formatted result back to the terminal as an HTTP response. The terminal displays the received result in the user interface. For example, information such as recommended pricing plans and shortened delivery times may be displayed.

[0138] Output: Content displayed to the user.

[0139] In this process, a quick and accurate price quote is automatically generated based on the user's input data.

[0140] (Application Example 1)

[0141] 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."

[0142] Modern food delivery services lack systems that automatically calculate optimal pricing and delivery times, even when users input their preferred food type, quantity, budget, and delivery time. This forces users to manually make the best choices, leading to decreased efficiency and satisfaction. Consequently, there is a need to improve both the user experience and the operational efficiency of delivery service providers.

[0143] 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.

[0144] In this invention, the server includes means for prompting the user to input data necessary for price simulation, terminal means for transmitting the data to the server, means for receiving the transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, means for transmitting the generated results to the terminal, means for displaying the results to the user on the terminal, and means for automatically generating prompt sentences necessary for generating the analysis results. As a result, the user can select the optimal price suggestion and delivery time for food delivery and receive efficient service.

[0145] "User" refers to anyone who uses the price simulation tool.

[0146] "Price simulation" refers to a method where users input data to obtain optimal price suggestions and time optimization results.

[0147] "Data" refers to the information that users enter for price simulations (for example, type of food, quantity, budget, desired delivery time, etc.).

[0148] A "server" refers to a device or system that receives data transmitted from a terminal, performs analysis, and generates results.

[0149] "Terminal means" refers to a device or interface for a user to input data and send it to a server.

[0150] "Analysis means" refers to the means of interpreting data sent by the user, performing necessary analysis, and deriving results for price proposals and time optimization.

[0151] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate pricing suggestions and time optimization results.

[0152] A "prompt statement" refers to a set of instructions necessary for a generative AI model to generate price suggestions and time optimization results.

[0153] "Transmission means" refers to the communication means used to return the generated analysis results to the original terminal.

[0154] "Display means" refers to devices or interfaces used to visually show analysis results to the user.

[0155] A specific embodiment of the food delivery optimization system based on this invention is described below.

[0156] Overall system configuration

[0157] The system consists of terminal means for the user to input data, a server that receives and analyzes the data, communication means that generates the analysis results and sends them back to the user, and means that displays the results to the user.

[0158] Terminal means

[0159] The terminal devices mainly consist of mobile information terminals such as smartphones and tablets. Users input data such as the type of food, quantity, budget, and desired delivery time via the terminal device. The entered data is sent to the server using an HTTP request.

[0160] server

[0161] The server receives data sent from the user. This server uses Flask as its framework and GPT-4, a cognitive processing engine, as its AI model. The received data is analyzed using data science libraries (e.g., NumPy, Pandas). Based on the analysis results, a generative AI model operates to generate optimal price suggestions and delivery time results.

[0162] Generative AI models and prompt sentence generation

[0163] The generation AI model uses prompts based on data analysis. These prompts are automatically generated and are in a format that accurately reflects the analysis results. For example, the following prompts are generated:

[0164] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0165] Sending and displaying results

[0166] The generated optimization results are sent back from the server to the terminal device. The terminal receives the results and displays them visually to the user. Specifically, the optimal price and delivery time are displayed in a suggested format, allowing the user to choose the best food delivery plan based on this information.

[0167] Specific example

[0168] For example, if a user enters "Two pizzas, one salad, budget of 3000 yen, desired delivery time 18:00," this data is sent to the server. Upon receiving the data, the server performs data analysis and generates a prompt message like the one above for the AI ​​model. Based on this prompt message, GPT-4 provides optimization results for pricing and delivery time, sending a suggestion to the user: "We can achieve an efficient pricing plan by setting the total price for two pizzas and one salad to 2800 yen and the delivery time to 17:30." This suggestion is displayed on the terminal, and the user confirms the order accordingly.

[0169] Thus, the system of this invention provides a means for users to efficiently and optimally utilize food delivery services.

[0170] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0171] Step 1:

[0172] The user inputs data using a terminal device.

[0173] The input data includes information such as the type of dish, quantity, budget, and desired delivery time.

[0174] Specifically, users use their smartphones or tablets to enter this information into the application's input form and click the submit button.

[0175] Step 2:

[0176] The terminal sends the entered data to the server.

[0177] The entered data is converted to JSON format and sent to the server using an HTTP request.

[0178] Specifically, the smartphone application parses the data into JSON format and executes an HTTP POST request to the API endpoint.

[0179] Step 3:

[0180] The server analyzes the data it receives.

[0181] The server uses data science libraries (NumPy, Pandas) to analyze incoming data, understand user requests, and generate the necessary prompts.

[0182] Specifically, the JSON data received by the server is converted into a Python object, and then the data is formatted and analyzed using NumPy and Pandas.

[0183] Step 4:

[0184] The server inputs prompt messages into the generated AI model and generates optimization results.

[0185] Based on the analyzed data, the server provides prompt sentences to the generative AI model using GPT-4.

[0186] Specifically, it generates prompt statements like the following:

[0187] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0188] The AI ​​model analyzes this prompt and generates price suggestions and time optimization results.

[0189] Step 5:

[0190] The server formats the generated result and sends it to the terminal.

[0191] The server formats the results obtained from the generated AI model into a user-friendly format, converts them back to JSON format, and sends them back to the terminal.

[0192] Specifically, if the generated result is "Pricing plan: 2800 yen, Delivery time: 17:30", a JSON containing this information is generated and sent back as an HTTP response.

[0193] Step 6:

[0194] The device displays the results it has received to the user.

[0195] The device receives the results sent back from the server and displays the results in the application's UI.

[0196] Specifically, the smartphone application analyzes the received JSON data and visually displays price and time suggestions to the user. Based on this information, the user selects the most suitable food delivery plan.

[0197] 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.

[0198] The system of the present invention allows users to input data necessary for a price simulation and generates price suggestions and time optimization results based on that data. This system consists of an input device (terminal), a server that processes and analyzes the data, an emotion engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0199] Explanation of the program's processing:

[0200] terminal

[0201] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0202] Emotional Engine

[0203] The emotion engine built into the device recognizes the user's emotions based on user input and voice data. For example, it analyzes whether the user is tense or satisfied. After analysis, the emotion data is sent to the server along with simulation data.

[0204] server

[0205] The server receives simulation and emotion data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0206] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0207] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0208] The artificial intelligence model generates price suggestions and time optimization results based on this request.

[0209] Specific example

[0210] For example, suppose a user inputs data about product A, such as "order 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also recognizes from the user's voice that they are nervous. The terminal sends this data and emotion information to the server. The server receives the data and performs analysis.

[0211] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0212] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0213] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0214] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0215] Thus, using the system of the present invention, it is possible to generate and present to the user a rapid and accurate price suggestion based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form on their device. After completing the input, they click the "submit" button.

[0219] Step 2:

[0220] The terminal receives data entered by the user. This data is structured in JSON format. The terminal prepares this data for transmission.

[0221] Step 3:

[0222] The emotion engine analyzes user input and voice data. For example, it analyzes the user's voice tone and input speed to recognize whether the user is nervous or satisfied. The recognized emotion data is sent to the server along with simulation data.

[0223] Step 4:

[0224] The device sends simulation data, including emotional data, to the server. This data is sent to the server as an HTTP request.

[0225] Step 5:

[0226] The server receives data sent from the terminal. The received data includes simulation data and emotion data.

[0227] Step 6:

[0228] The server analyzes the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0229] Step 7:

[0230] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0231] "Based on the provided simulation data and user sentiment data, please output the optimal pricing proposal and time optimization results."

[0232] Step 8:

[0233] An artificial intelligence model receives and processes requests. Based on user input data and sentiment data, the model generates price suggestions and time optimization results. For example, it might generate a result such as, "Set the unit price of product A to 2400 yen and shorten the delivery time to one week to provide reassurance to a stressed user."

[0234] Step 9:

[0235] The server receives the results returned by the artificial intelligence model. It analyzes the received results and formats them into a user-friendly format.

[0236] Step 10:

[0237] The server sends the formatted result to the terminal. The result is sent as an HTTP response.

[0238] Step 11:

[0239] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it displays: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0240] Step 12:

[0241] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0242] Thus, this system can quickly and accurately generate price suggestions and time optimization results based on user input data, and by further considering user sentiment data, it can provide optimal suggestions tailored to individual needs.

[0243] (Example 2)

[0244] 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".

[0245] Conventional pricing simulation systems have the problem of not being able to propose pricing that takes user emotions into account, making it difficult to improve the user experience. Furthermore, there has been no established effective means of generating and providing analysis results that take emotional data into account in real time.

[0246] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for prompting the user to input data necessary for a fee simulation, means for recognizing the data and the user's sentiment data, terminal means for transmitting the data and sentiment data to the server, means for receiving the transmitted data and sentiment data and analyzing the data, means for generating fee proposals and time optimization results using an artificial intelligence model based on the analysis results and sentiment data, means for transmitting the generated results to the terminal, and means for displaying the results to the user on the terminal. This makes it possible to provide personalized fee proposals and time optimization results that take into account the user's sentiment data in real time.

[0247] A "user" is an individual or group that uses the system to perform a price simulation.

[0248] A "price simulation" is the process of calculating the optimal pricing plan for a specific product or service based on user input data.

[0249] "Data" refers to the information that users input to perform a price simulation, including quantity, budget, and delivery date.

[0250] "Emotional data" refers to information that indicates the user's emotional state, including emotions such as tension, satisfaction, and anxiety, extracted from input content and voice data.

[0251] A "terminal" is a hardware and software device used by a user to input data and send it to a server.

[0252] An "emotion engine" is software or an algorithm that analyzes user input and voice data to generate user emotion data.

[0253] A "server" is a computing system that receives data and sentiment data sent by users, and performs analysis and generates price suggestions.

[0254] "Analysis" refers to the process by which a server processes and analyzes the data and sentiment data it receives.

[0255] An "artificial intelligence model" refers to an algorithm or machine learning model used to provide pricing suggestions and optimize time.

[0256] "Pricing suggestions" refer to presenting the most suitable pricing plans for products and services based on user input data and sentiment data.

[0257] "Time optimization" is the process of calculating the optimal delivery date for a product or service based on user input data and sentiment data.

[0258] "Results" refers to pricing suggestions and time optimization information generated by the artificial intelligence model.

[0259] An "HTTP request" is a communication protocol used to send data from a device to a server.

[0260] "Terminal means" refers to the functions installed in a terminal that allow for data input and transmission.

[0261] "Transmission means" refers to the function for sending data from a terminal to a server.

[0262] "Display means" refers to a function on a terminal that presents the results transmitted from the server to the user.

[0263] The system implementing the present invention receives data necessary for a price simulation from the user and generates price suggestions and time optimization results based on that data and sentiment data. This system consists of an input device (terminal), a server that processes and analyzes the data, an sentiment engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0264] First, the user uses the terminal to enter the necessary data into an input form for price simulation. For example, they might enter information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then click the submit button. The terminal has an emotion engine built in to recognize the user's emotions based on the user's input and voice data. The emotion engine analyzes the emotions the user expresses during input, such as tension, satisfaction, and anxiety, and generates emotion data.

[0265] The terminal sends the simulation data entered by the user and the emotion data recognized by the emotion engine to the server. Communication is secure using HTTP requests. The server analyzes the received simulation and emotion data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0266] The server requests analysis from the artificial intelligence model based on the received data and sentiment data. The specific prompt messages are as follows:

[0267] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0268] Based on this prompt, an artificial intelligence model (e.g., a natural language processing engine) generates price suggestions and time optimization results.

[0269] For example, if a user inputs data such as "order 200 units of product A," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also detects tension in the user's voice, the terminal sends this data and emotion information to the server. The server receives the data and performs analysis. Based on the analysis results, the AI ​​model obtains a price suggestion like the following:

[0270] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0271] The server sends this result back to the terminal, which then displays it to the user as follows:

[0272] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0273] As described above, the system of the present invention makes it possible to generate and present rapid and accurate price suggestions to the user based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0274] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0275] Step 1: User data entry

[0276] The user uses the terminal to input data into the input form for fee simulation. Specifically, the user inputs information such as "200 orders", "the budget is 500,000 yen", "the delivery date is 2 weeks", and then clicks the send button. When the input is completed, the terminal temporarily saves this data.

[0277] Input: Simulation data input by the user.

[0278] Output: Temporarily saved simulation data.

[0279] Specific operation: The user uses a browser or a dedicated application to access the input form, inputs the necessary information, and then presses the send button.

[0280] Step 2: Emotion recognition

[0281] While the user is inputting data, the emotion engine built into the terminal recognizes the user's emotion based on the user's input content and voice data. The emotion engine analyzes emotions such as "nervousness", "satisfaction", "uneasiness", etc. from the input data.

[0282] Input: The user's input content and voice data.

[0283] Output: Analyzed emotion data.

[0284] Specific operation: When the user completes the input and leaves a voice comment, the emotion engine acquires the voice data and performs emotion analysis.

[0285] Step 3: Data transmission

[0286] The terminal sends the simulation data input by the user and the emotion data analyzed by the emotion engine to the server together. HTTP requests are used for transmission.

[0287] Input: Simulation data and emotion data.

[0288] Output: Data sent to the server.

[0289] Specific operation: The terminal converts the data into JSON format or similar, creates an HTTP request, and sends the data to the specified endpoint on the server.

[0290] Step 4: Data analysis on the server

[0291] The server receives simulation and emotion data sent from the terminal and begins analysis. The server converts the data into an internal data structure and prepares it to be passed to the artificial intelligence model.

[0292] Input: Simulation data and sentiment data received by the server.

[0293] Output: Data in a format that can be analyzed by an artificial intelligence model.

[0294] Specific operation: The server extracts data from the HTTP request, converts each data item into an internal structure, and passes it to the artificial intelligence model.

[0295] Step 5: Price Proposal & Time Optimization

[0296] The server requests analysis from the artificial intelligence model and generates a price proposal and time optimization results. It also generates prompt messages and sends them to the artificial intelligence model.

[0297] Input: Prompt statement and data.

[0298] Output: Generated pricing proposals and time optimization results.

[0299] Specific operation: The server creates a prompt message, sends it to the artificial intelligence model, and receives the result. Example of a prompt message: "Based on the provided data and user sentiment, output the best price suggestion and time optimization results."

[0300] Step 6: Presentation of Results

[0301] The server returns the generated fee proposal and the result of time optimization to the terminal. The terminal presents the result to the user in an understandable manner.

[0302] Input: Analysis results transmitted from the server.

[0303] Output: Fee proposal and time optimization results displayed to the user.

[0304] Specific operation: The server transmits the analysis results to the terminal, and the terminal updates the screen for displaying the received results to the user and presents the proposed content.

[0305] Through the steps of this process, optimal fee proposals and time optimization results can be quickly generated based on the user's input data and emotional data and provided to the user.

[0306] (Application Example 2)

[0307] 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".

[0308] Conventional fee simulation systems only perform fee proposals and time optimization based on the user's input data and do not consider the user's emotional state. For this reason, there is a problem that personalized proposals for improving the user's psychological satisfaction and sense of security cannot be made. In particular, on e-commerce sites, improving the user experience is important, and a system that takes emotions into account is required.

[0309] 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.

[0310] In this invention, the server includes means for prompting the user to input data necessary for a price simulation, means for receiving and analyzing the said data and the user's sentiment data, and means for generating price suggestions and time optimization results using an artificial intelligence model based on the analysis results. This enables personalized price suggestions and time optimization suggestions that take the user's sentiments into account.

[0311] A "user" refers to an individual or company that uses the system to perform a price simulation.

[0312] "Data required for price simulation" refers to information necessary for price proposals and time optimization, such as product, quantity, budget, and delivery date.

[0313] "Emotional data" refers to data that indicates a user's emotional state, and includes emotional information analyzed from user input, voice, and other sources.

[0314] "Terminal device" refers to a digital device used by a user to input data and transmit that data to a server. Specifically, this includes smartphones and computers.

[0315] A "server" refers to a computer device that receives data transmitted from terminal devices and performs analysis and generates price suggestions.

[0316] "Means of analysis" refers to the process by which the server uses the received data and sentiment data to perform necessary calculations and data transformations.

[0317] An "artificial intelligence model" refers to a sophisticated algorithm or program that generates optimal price and time suggestions based on analyzed data. Specifically, this includes natural language processing engines, among others.

[0318] "Price proposal and time optimization results" refers to the optimal pricing and delivery time proposals generated based on user input data and sentiment data.

[0319] "Means of transmission" refers to the process and technology for sending back the results generated by the server to the terminal.

[0320] "Means of display" refers to interfaces and functions on a terminal that visually present the generated results to the user.

[0321] The system for implementing this invention mainly consists of the following parts: a terminal means for the user to input data, an emotion recognition engine that recognizes the user's emotions, a server that receives and analyzes the data, an artificial intelligence model that makes optimization suggestions for fees and time, and means for presenting the results to the user.

[0322] Terminal means

[0323] The terminal device refers to a digital device such as a smartphone or computer, which provides an interface for the user to input the data necessary for the price simulation (e.g., product, quantity, budget, delivery date, etc.). When the user inputs the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0324] Emotion recognition engine

[0325] The emotion recognition engine utilizes technologies such as Microsoft® Azure® Cognitive Services and Google® Cloud Emotion AI to recognize the user's emotional state from their input or voice. This engine analyzes emotional data, such as whether the user is tense, satisfied, or anxious, and sends the results to the server.

[0326] server

[0327] The server receives price simulation data and sentiment data sent from the terminal. The server converts this data into an internal data structure and prepares it to be passed to an artificial intelligence model (e.g., GPT-4). Specifically, it sends the following prompts to the artificial intelligence model:

[0328] "Based on the provided data and the user's emotions, please generate the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units of product A,' 'budget of 500,000 yen,' and 'delivery date of 2 weeks,' and their emotion is 'anxious,' please provide a suggestion that will increase the user's sense of security."

[0329] The artificial intelligence model analyzes this prompt and generates optimal pricing suggestions and time optimization results. These results are sent from the server to the terminal and displayed to the user.

[0330] Presentation of results

[0331] The terminal receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it may look like this:

[0332] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0333] This system allows users to receive pricing suggestions that take their emotional state into account, resulting in a more satisfying shopping experience.

[0334] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0335] Step 1:

[0336] The user uses a device (smartphone or computer) to input the data necessary for the price simulation. This input data includes product details, quantity, budget, and delivery date. After inputting the data, clicking the submit button will proceed to the next step.

[0337] Input: Data entered by the user, such as product, quantity, budget, and delivery date.

[0338] Output: Holding of input data within the terminal.

[0339] Step 2:

[0340] The terminal device receives the input data and sends it to the server using an HTTP request. The destination URL and parameters are pre-configured.

[0341] Input: Data entered by the user

[0342] Output: Sending data converted into an HTTP request

[0343] Step 3:

[0344] An emotion recognition engine built into the terminal device analyzes the user's input and voice data to generate emotion data. The analyzed emotion data is sent to the server along with the input data.

[0345] Input: User input and audio data

[0346] Output: Sending generated emotion data

[0347] Step 4:

[0348] The server receives input data and sentiment data transmitted from the terminal device. It converts the received data into an internal data structure and prepares it for the next analysis process.

[0349] Input: Input data and sentiment data sent from the device.

[0350] Output: Data converted to an internal data structure

[0351] Step 5:

[0352] The server passes the data to an artificial intelligence model (e.g., GPT-4) and requests analysis to generate price suggestions and time optimization results. Specifically, it sends a prompt message to the AI ​​model such as: "Based on the provided data and the user's sentiment, please output the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units', 'budget 500,000 yen', and 'delivery date 2 weeks' for product A, and their sentiment is 'anxious', please provide suggestions that will increase the user's sense of security."

[0353] Input: Input data and sentiment data converted into an internal data structure, prompt message

[0354] Output: Price proposal and time optimization results

[0355] Step 6:

[0356] The server receives the price proposal and time optimization results obtained from the artificial intelligence model and transmits them to the terminal device.

[0357] Input: Results of price suggestions and time optimization generated by an artificial intelligence model

[0358] Output: Sending results to the terminal device

[0359] Step 7:

[0360] The terminal device receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it might display: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The user will feel more at ease with the shortened delivery time and lower price."

[0361] Input: Rate proposals and time optimization results sent from the server

[0362] Output: Price proposals and time optimization results presented to the user

[0363] 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.

[0364] Data generation model 58 is a 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.

[0365] 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.

[0366] [Second Embodiment]

[0367] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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".

[0379] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0380] Explanation of the program's processing:

[0381] terminal

[0382] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0383] server

[0384] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses an artificial intelligence model to generate pricing suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate requests based on the user's input data, such as the following:

[0385] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0386] The artificial intelligence model generates pricing suggestions and time optimization results based on this request and returns them to the server. The server then analyzes these results and formats them for presentation to the user.

[0387] Specific example

[0388] For example, suppose a user inputs data about product A, such as "order 200 units," "budget 500,000 yen," and "delivery time 2 weeks." The terminal sends this data to the server. The server receives the data and performs analysis.

[0389] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0390] "By setting the unit price of product A at 2,500 yen and shortening the delivery time to one week, we can create an efficient pricing plan."

[0391] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0392] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0393] Thus, using the system of the present invention, it is possible to generate and present a price proposal quickly and accurately based on the user's input data. Since the entire system is automated, variations due to staff differences can be eliminated, and a stable service can be provided.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form provided on the device. After completing the input, the user clicks the "Submit" button.

[0397] Step 2:

[0398] The device receives data entered by the user. This data is structured, for example, in JSON format. The device then sends the received data to the server as the payload of an HTTP request.

[0399] Step 3:

[0400] The server receives data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0401] Step 4:

[0402] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0403] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0404] Step 5:

[0405] An artificial intelligence model receives and processes requests. Based on the user's input data, the model generates price suggestions and time optimization results. For example, it might generate a result such as "Set the unit price of product A to 2500 yen and shorten the delivery time to one week."

[0406] Step 6:

[0407] The server receives the results returned by the artificial intelligence model. It then analyzes the received results and formats them into a user-friendly format.

[0408] Step 7:

[0409] The server sends the formatted result to the terminal. The result is sent as the payload of the HTTP response.

[0410] Step 8:

[0411] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it is presented to the user in the form of "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0412] Step 9:

[0413] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0414] In this way, the system includes a process for quickly and accurately generating and presenting price suggestions and time optimization results to the user based on the user's input data.

[0415] (Example 1)

[0416] 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".

[0417] In today's business environment, complex pricing proposals and schedule optimizations are required. However, performing these tasks manually is not only time-consuming but also increases the risk of human error. Therefore, there is a need for a system that can automatically generate and provide users with fast and accurate proposals. Existing systems struggle to properly analyze user input data and generate optimal pricing proposals and time optimization results based on that data. This complexity increases especially when numerous variables are involved.

[0418] 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.

[0419] In this invention, the server includes means for receiving transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, and means for inputting prompt sentences into the generation AI model. This makes it possible to automatically generate fast and highly accurate price suggestions and time optimization results using advanced data analysis and an AI model.

[0420] A "user" is someone who inputs data to perform a price simulation and receives the results.

[0421] A "terminal" is a device used by a user to input data and send that data to a server; specifically, it refers to electronic devices such as personal computers and smartphones.

[0422] A "server" refers to a device that receives data transmitted from a terminal, performs analysis, and generates price suggestions and time optimization results.

[0423] "Data" refers to the information that users input to perform price simulations, and specifically includes information such as product type, order quantity, budget, and delivery date.

[0424] A "generative AI model" refers to artificial intelligence used to generate price suggestions and time optimization results based on input data, such as a natural language processing engine.

[0425] A "prompt statement" is the text input to a generative AI model, and refers to a sentence that describes a specific analysis request.

[0426] An "HTTP request" refers to a request that uses the web protocol to send data from a terminal to a server.

[0427] "Price proposal" refers to a pricing suggestion provided by the generative AI model based on its analysis results.

[0428] "Time optimization" refers to making adjustments to optimize deadlines and schedules.

[0429] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0430] terminal

[0431] The terminal provides an interface where the user enters the necessary data through an input form for price simulation. The user enters details such as product type, quantity, budget, and delivery date. A specific example of this input is shown below:

[0432] Specific example:

[0433] "Product A", "Quantity Ordered: 200", "Budget: 500,000 yen", "Delivery Time: 2 weeks"

[0434] When a user enters data and clicks the submit button, the device receives that data and sends it to the server using an HTTP request.

[0435] server

[0436] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses a generative AI model to generate price suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate the following prompts based on the user's input data:

[0437] Prompt message:

[0438] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0439] The generative AI model generates pricing suggestions and time optimization results based on this prompt and returns them to the server. For example, it might return a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0440] Presentation of results

[0441] The server receives the results from the generated AI model and formats them in a user-friendly format. It then sends the formatted results back to the terminal as an HTTP response.

[0442] The terminal receives the results from the server and displays them to the user as follows:

[0443] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0444] This system allows users to receive quick and accurate price quotes, thereby improving business efficiency and effectiveness. Furthermore, because the entire system is automated, human error is eliminated, ensuring consistent service delivery.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The user enters the data necessary for the price simulation into the input form on the device.

[0448] Input: Information such as product type, order quantity, budget, and delivery date.

[0449] Operation: Users enter the data necessary for the price simulation using a web form or app interface on their device. For example, they might enter data such as "Product A," "Quantity Ordered: 200," "Budget: 500,000 yen," and "Delivery Time: 2 weeks" into the form.

[0450] Output: The input data is saved to the terminal.

[0451] Step 2:

[0452] When the user clicks the submit button, the device sends the entered data to the server as an HTTP request.

[0453] Input: Data entered by the user into the input form.

[0454] Operation: The terminal converts the input data into JSON format and generates an HTTP request. The generated request is sent to the server via the internet.

[0455] Output: HTTP request from terminal to server (JSON data).

[0456] Step 3:

[0457] The server receives data sent from the terminal and performs data validation and analysis.

[0458] Input: JSON data received from the terminal.

[0459] Operation: The server performs validation to check the format and required fields of the received data. Next, it uses an analysis engine to analyze the content of the data.

[0460] Output: Analyzed data.

[0461] Step 4:

[0462] The server generates prompt messages for the AI ​​model based on the analysis results and inputs them.

[0463] Input: Analyzed data.

[0464] Operation: The server generates prompts for the generated AI model based on the analyzed data. Specifically, it generates prompts such as: "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0465] Output: The generated prompt message.

[0466] Step 5:

[0467] The generative AI model receives a prompt and generates price suggestions and time optimization results.

[0468] Input: The generated prompt message.

[0469] Operation: The generating AI model analyzes the prompt text and generates price suggestions and time optimization results based on the input data. For example, it might generate a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0470] Output: Price proposal and time optimization results.

[0471] Step 6:

[0472] The server receives the generated results and formats them into a user-friendly format.

[0473] Input: Price suggestions and time optimization results returned from a generated AI model.

[0474] Operation: The server receives the results and formats them in a user-friendly format. Specifically, this could include displaying the suggestions in text format or in a table format.

[0475] Output: Formatted result.

[0476] Step 7:

[0477] The server sends the formatted result to the terminal, and the terminal displays the result to the user.

[0478] Input: Formatted result.

[0479] Operation: The server sends the formatted result back to the terminal as an HTTP response. The terminal displays the received result in the user interface. For example, information such as recommended pricing plans and shortened delivery times may be displayed.

[0480] Output: Content displayed to the user.

[0481] In this process, a quick and accurate price quote is automatically generated based on the user's input data.

[0482] (Application Example 1)

[0483] 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."

[0484] Modern food delivery services lack systems that automatically calculate optimal pricing and delivery times, even when users input their preferred food type, quantity, budget, and delivery time. This forces users to manually make the best choices, leading to decreased efficiency and satisfaction. Consequently, there is a need to improve both the user experience and the operational efficiency of delivery service providers.

[0485] 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.

[0486] In this invention, the server includes means for prompting the user to input data necessary for price simulation, terminal means for transmitting the data to the server, means for receiving the transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, means for transmitting the generated results to the terminal, means for displaying the results to the user on the terminal, and means for automatically generating prompt sentences necessary for generating the analysis results. As a result, the user can select the optimal price suggestion and delivery time for food delivery and receive efficient service.

[0487] "User" refers to anyone who uses the price simulation tool.

[0488] "Price simulation" refers to a method where users input data to obtain optimal price suggestions and time optimization results.

[0489] "Data" refers to the information that users enter for price simulations (for example, type of food, quantity, budget, desired delivery time, etc.).

[0490] A "server" refers to a device or system that receives data transmitted from a terminal, performs analysis, and generates results.

[0491] "Terminal means" refers to a device or interface for a user to input data and send it to a server.

[0492] "Analysis means" refers to the means of interpreting data sent by the user, performing necessary analysis, and deriving results for price proposals and time optimization.

[0493] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate pricing suggestions and time optimization results.

[0494] A "prompt statement" refers to a set of instructions necessary for a generative AI model to generate price suggestions and time optimization results.

[0495] "Transmission means" refers to the communication means used to return the generated analysis results to the original terminal.

[0496] "Display means" refers to devices or interfaces used to visually show analysis results to the user.

[0497] A specific embodiment of the food delivery optimization system based on this invention is described below.

[0498] Overall system configuration

[0499] The system consists of terminal means for the user to input data, a server that receives and analyzes the data, communication means that generates the analysis results and sends them back to the user, and means that displays the results to the user.

[0500] Terminal means

[0501] The terminal devices mainly consist of mobile information terminals such as smartphones and tablets. Users input data such as the type of food, quantity, budget, and desired delivery time via the terminal device. The entered data is sent to the server using an HTTP request.

[0502] server

[0503] The server receives data sent from the user. This server uses Flask as its framework and GPT-4, a cognitive processing engine, as its AI model. The received data is analyzed using data science libraries (e.g., NumPy, Pandas). Based on the analysis results, a generative AI model operates to generate optimal price suggestions and delivery time results.

[0504] Generative AI models and prompt sentence generation

[0505] The generation AI model uses prompts based on data analysis. These prompts are automatically generated and are in a format that accurately reflects the analysis results. For example, the following prompts are generated:

[0506] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0507] Sending and displaying results

[0508] The generated optimization results are sent back from the server to the terminal device. The terminal receives the results and displays them visually to the user. Specifically, the optimal price and delivery time are displayed in a suggested format, allowing the user to choose the best food delivery plan based on this information.

[0509] Specific example

[0510] For example, if a user enters "Two pizzas, one salad, budget of 3000 yen, desired delivery time 18:00," this data is sent to the server. Upon receiving the data, the server performs data analysis and generates a prompt message like the one above for the AI ​​model. Based on this prompt message, GPT-4 provides optimization results for pricing and delivery time, sending a suggestion to the user: "We can achieve an efficient pricing plan by setting the total price for two pizzas and one salad to 2800 yen and the delivery time to 17:30." This suggestion is displayed on the terminal, and the user confirms the order accordingly.

[0511] Thus, the system of this invention provides a means for users to efficiently and optimally utilize food delivery services.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user inputs data using a terminal device.

[0515] The input data includes information such as the type of dish, quantity, budget, and desired delivery time.

[0516] Specifically, users use their smartphones or tablets to enter this information into the application's input form and click the submit button.

[0517] Step 2:

[0518] The terminal sends the entered data to the server.

[0519] The entered data is converted to JSON format and sent to the server using an HTTP request.

[0520] Specifically, the smartphone application parses the data into JSON format and executes an HTTP POST request to the API endpoint.

[0521] Step 3:

[0522] The server analyzes the data it receives.

[0523] The server uses data science libraries (NumPy, Pandas) to analyze incoming data, understand user requests, and generate the necessary prompts.

[0524] Specifically, the JSON data received by the server is converted into a Python object, and then the data is formatted and analyzed using NumPy and Pandas.

[0525] Step 4:

[0526] The server inputs prompt messages into the generated AI model and generates optimization results.

[0527] Based on the analyzed data, the server provides prompt sentences to the generative AI model using GPT-4.

[0528] Specifically, it generates prompt statements like the following:

[0529] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0530] The AI ​​model analyzes this prompt and generates price suggestions and time optimization results.

[0531] Step 5:

[0532] The server formats the generated result and sends it to the terminal.

[0533] The server formats the results obtained from the generated AI model into a user-friendly format, converts them back to JSON format, and sends them back to the terminal.

[0534] Specifically, if the generated result is "Pricing plan: 2800 yen, Delivery time: 17:30", a JSON containing this information is generated and sent back as an HTTP response.

[0535] Step 6:

[0536] The device displays the results it has received to the user.

[0537] The device receives the results sent back from the server and displays the results in the application's UI.

[0538] Specifically, the smartphone application analyzes the received JSON data and visually displays price and time suggestions to the user. Based on this information, the user selects the most suitable food delivery plan.

[0539] 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.

[0540] The system of the present invention allows users to input data necessary for a price simulation and generates price suggestions and time optimization results based on that data. This system consists of an input device (terminal), a server that processes and analyzes the data, an emotion engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0541] Explanation of the program's processing:

[0542] terminal

[0543] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0544] Emotional Engine

[0545] The emotion engine built into the device recognizes the user's emotions based on user input and voice data. For example, it analyzes whether the user is tense or satisfied. After analysis, the emotion data is sent to the server along with simulation data.

[0546] server

[0547] The server receives simulation and emotion data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0548] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0549] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0550] The artificial intelligence model generates price suggestions and time optimization results based on this request.

[0551] Specific example

[0552] For example, suppose a user inputs data about product A, such as "order 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also recognizes from the user's voice that they are nervous. The terminal sends this data and emotion information to the server. The server receives the data and performs analysis.

[0553] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0554] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0555] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0556] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0557] Thus, using the system of the present invention, it is possible to generate and present to the user a rapid and accurate price suggestion based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form on their device. After completing the input, they click the "submit" button.

[0561] Step 2:

[0562] The terminal receives data entered by the user. This data is structured in JSON format. The terminal prepares this data for transmission.

[0563] Step 3:

[0564] The emotion engine analyzes user input and voice data. For example, it analyzes the user's voice tone and input speed to recognize whether the user is nervous or satisfied. The recognized emotion data is sent to the server along with simulation data.

[0565] Step 4:

[0566] The device sends simulation data, including emotional data, to the server. This data is sent to the server as an HTTP request.

[0567] Step 5:

[0568] The server receives data sent from the terminal. The received data includes simulation data and emotion data.

[0569] Step 6:

[0570] The server analyzes the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0571] Step 7:

[0572] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0573] "Based on the provided simulation data and user sentiment data, please output the optimal pricing proposal and time optimization results."

[0574] Step 8:

[0575] An artificial intelligence model receives and processes requests. Based on user input data and sentiment data, the model generates price suggestions and time optimization results. For example, it might generate a result such as, "Set the unit price of product A to 2400 yen and shorten the delivery time to one week to provide reassurance to a stressed user."

[0576] Step 9:

[0577] The server receives the results returned by the artificial intelligence model. It analyzes the received results and formats them into a user-friendly format.

[0578] Step 10:

[0579] The server sends the formatted result to the terminal. The result is sent as an HTTP response.

[0580] Step 11:

[0581] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it displays: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0582] Step 12:

[0583] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0584] Thus, this system can quickly and accurately generate price suggestions and time optimization results based on user input data, and by further considering user sentiment data, it can provide optimal suggestions tailored to individual needs.

[0585] (Example 2)

[0586] 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".

[0587] Conventional pricing simulation systems have the problem of not being able to propose pricing that takes user emotions into account, making it difficult to improve the user experience. Furthermore, there has been no established effective means of generating and providing analysis results that take emotional data into account in real time.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for prompting the user to input data necessary for a fee simulation, means for recognizing the data and the user's sentiment data, terminal means for transmitting the data and sentiment data to the server, means for receiving the transmitted data and sentiment data and analyzing the data, means for generating fee proposals and time optimization results using an artificial intelligence model based on the analysis results and sentiment data, means for transmitting the generated results to the terminal, and means for displaying the results to the user on the terminal. This makes it possible to provide personalized fee proposals and time optimization results that take into account the user's sentiment data in real time.

[0589] A "user" is an individual or group that uses the system to perform a price simulation.

[0590] A "price simulation" is the process of calculating the optimal pricing plan for a specific product or service based on user input data.

[0591] "Data" refers to the information that users input to perform a price simulation, including quantity, budget, and delivery date.

[0592] "Emotional data" refers to information that indicates the user's emotional state, including emotions such as tension, satisfaction, and anxiety, extracted from input content and voice data.

[0593] A "terminal" is a hardware and software device used by a user to input data and send it to a server.

[0594] An "emotion engine" is software or an algorithm that analyzes user input and voice data to generate user emotion data.

[0595] A "server" is a computing system that receives data and sentiment data sent by users, and performs analysis and generates price suggestions.

[0596] "Analysis" refers to the process by which a server processes and analyzes the data and sentiment data it receives.

[0597] An "artificial intelligence model" refers to an algorithm or machine learning model used to provide pricing suggestions and optimize time.

[0598] "Pricing suggestions" refer to presenting the most suitable pricing plans for products and services based on user input data and sentiment data.

[0599] "Time optimization" is the process of calculating the optimal delivery date for a product or service based on user input data and sentiment data.

[0600] "Results" refers to pricing suggestions and time optimization information generated by the artificial intelligence model.

[0601] An "HTTP request" is a communication protocol used to send data from a device to a server.

[0602] "Terminal means" refers to the functions installed in a terminal that allow for data input and transmission.

[0603] "Transmission means" refers to the function for sending data from a terminal to a server.

[0604] "Display means" refers to a function on a terminal that presents the results transmitted from the server to the user.

[0605] The system implementing the present invention receives data necessary for a price simulation from the user and generates price suggestions and time optimization results based on that data and sentiment data. This system consists of an input device (terminal), a server that processes and analyzes the data, an sentiment engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0606] First, the user uses the terminal to enter the necessary data into an input form for price simulation. For example, they might enter information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then click the submit button. The terminal has an emotion engine built in to recognize the user's emotions based on the user's input and voice data. The emotion engine analyzes the emotions the user expresses during input, such as tension, satisfaction, and anxiety, and generates emotion data.

[0607] The terminal sends the simulation data entered by the user and the emotion data recognized by the emotion engine to the server. Communication is secure using HTTP requests. The server analyzes the received simulation and emotion data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0608] The server requests analysis from the artificial intelligence model based on the received data and sentiment data. The specific prompt messages are as follows:

[0609] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0610] Based on this prompt, an artificial intelligence model (e.g., a natural language processing engine) generates price suggestions and time optimization results.

[0611] For example, if a user inputs data such as "order 200 units of product A," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also detects tension in the user's voice, the terminal sends this data and emotion information to the server. The server receives the data and performs analysis. Based on the analysis results, the AI ​​model obtains a price suggestion like the following:

[0612] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0613] The server sends this result back to the terminal, which then displays it to the user as follows:

[0614] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0615] As described above, the system of the present invention makes it possible to generate and present rapid and accurate price suggestions to the user based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0616] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0617] Step 1: User data entry

[0618] The user uses their device to enter data into an input form for price simulation. Specifically, the user enters information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then clicks the submit button. Once the input is complete, the device temporarily saves this data.

[0619] Input: Simulation data entered by the user.

[0620] Output: Temporarily saved simulation data.

[0621] Specific operation: The user accesses the input form using a browser or dedicated application, enters the required information, and then presses the submit button.

[0622] Step 2: Emotion Recognition

[0623] While the user is entering data, an emotion engine built into the device recognizes the user's emotions based on the user's input and voice data. The emotion engine analyzes emotions such as "tension," "satisfaction," and "anxiety" from the input data.

[0624] Input: User input and audio data.

[0625] Output: Analyzed sentiment data.

[0626] Specific operation: When the user completes input and leaves a voice comment, the emotion engine retrieves the voice data and performs emotion analysis.

[0627] Step 3: Data transmission

[0628] The terminal sends the simulation data entered by the user and the emotion data analyzed by the emotion engine to the server. HTTP requests are used for transmission.

[0629] Input: Simulation data and sentiment data.

[0630] Output: Data sent to the server.

[0631] Specific operation: The terminal converts the data into JSON format or similar, creates an HTTP request, and sends the data to the specified endpoint on the server.

[0632] Step 4: Data analysis on the server

[0633] The server receives simulation and emotion data sent from the terminal and begins analysis. The server converts the data into an internal data structure and prepares it to be passed to the artificial intelligence model.

[0634] Input: Simulation data and sentiment data received by the server.

[0635] Output: Data in a format that can be analyzed by an artificial intelligence model.

[0636] Specific operation: The server extracts data from the HTTP request, converts each data item into an internal structure, and passes it to the artificial intelligence model.

[0637] Step 5: Price Proposal & Time Optimization

[0638] The server requests analysis from the artificial intelligence model and generates a price proposal and time optimization results. It also generates prompt messages and sends them to the artificial intelligence model.

[0639] Input: Prompt statement and data.

[0640] Output: Generated pricing proposals and time optimization results.

[0641] Specific operation: The server creates a prompt message, sends it to the artificial intelligence model, and receives the result. Example of a prompt message: "Based on the provided data and user sentiment, output the best price suggestion and time optimization results."

[0642] Step 6: Presentation of Results

[0643] The server sends the generated price proposal and time optimization results back to the terminal. The terminal then presents the results to the user in an easy-to-understand manner.

[0644] Input: Analysis results sent from the server.

[0645] Output: Price suggestions and time optimization results displayed to the user.

[0646] Specific operation: The server sends the analysis results to the terminal, and the terminal updates the screen to display the received results to the user and presents the suggested content.

[0647] This processing step allows for the rapid generation and provision of optimal pricing suggestions and time optimization results based on user input data and sentiment data.

[0648] (Application Example 2)

[0649] 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."

[0650] Conventional pricing simulation systems merely provide pricing suggestions and time optimization based on user input data, without considering the user's emotional state. This results in a problem where personalized suggestions that enhance user psychological satisfaction and peace of mind are not possible. Especially for e-commerce sites, improving the user experience is crucial, and a system that takes emotions into account is required.

[0651] 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.

[0652] In this invention, the server includes means for prompting the user to input data necessary for a price simulation, means for receiving and analyzing the said data and the user's sentiment data, and means for generating price suggestions and time optimization results using an artificial intelligence model based on the analysis results. This enables personalized price suggestions and time optimization suggestions that take the user's sentiments into account.

[0653] A "user" refers to an individual or company that uses the system to perform a price simulation.

[0654] "Data required for price simulation" refers to information necessary for price proposals and time optimization, such as product, quantity, budget, and delivery date.

[0655] "Emotional data" refers to data that indicates a user's emotional state, and includes emotional information analyzed from user input, voice, and other sources.

[0656] "Terminal device" refers to a digital device used by a user to input data and transmit that data to a server. Specifically, this includes smartphones and computers.

[0657] A "server" refers to a computer device that receives data transmitted from terminal devices and performs analysis and generates price suggestions.

[0658] "Means of analysis" refers to the process by which the server uses the received data and sentiment data to perform necessary calculations and data transformations.

[0659] An "artificial intelligence model" refers to a sophisticated algorithm or program that generates optimal price and time suggestions based on analyzed data. Specifically, this includes natural language processing engines, among others.

[0660] "Price proposal and time optimization results" refers to the optimal pricing and delivery time proposals generated based on user input data and sentiment data.

[0661] "Means of transmission" refers to the process and technology for sending back the results generated by the server to the terminal.

[0662] "Means of display" refers to interfaces and functions on a terminal that visually present the generated results to the user.

[0663] The system for implementing this invention mainly consists of the following parts: a terminal means for the user to input data, an emotion recognition engine that recognizes the user's emotions, a server that receives and analyzes the data, an artificial intelligence model that makes optimization suggestions for fees and time, and means for presenting the results to the user.

[0664] Terminal means

[0665] The terminal device refers to a digital device such as a smartphone or computer, which provides an interface for the user to input the data necessary for the price simulation (e.g., product, quantity, budget, delivery date, etc.). When the user inputs the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0666] Emotion recognition engine

[0667] The emotion recognition engine utilizes technologies such as Microsoft Azure Cognitive Services and Google Cloud Emotion AI to recognize the user's emotional state from their input or voice. This engine analyzes emotional data, such as whether the user is tense, satisfied, or anxious, and sends the results to the server.

[0668] server

[0669] The server receives price simulation data and sentiment data sent from the terminal. The server converts this data into an internal data structure and prepares it to be passed to an artificial intelligence model (e.g., GPT-4). Specifically, it sends the following prompts to the artificial intelligence model:

[0670] "Based on the provided data and the user's emotions, please generate the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units of product A,' 'budget of 500,000 yen,' and 'delivery date of 2 weeks,' and their emotion is 'anxious,' please provide a suggestion that will increase the user's sense of security."

[0671] The artificial intelligence model analyzes this prompt and generates optimal pricing suggestions and time optimization results. These results are sent from the server to the terminal and displayed to the user.

[0672] Presentation of results

[0673] The terminal receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it may look like this:

[0674] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0675] This system allows users to receive pricing suggestions that take their emotional state into account, resulting in a more satisfying shopping experience.

[0676] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0677] Step 1:

[0678] The user uses a device (smartphone or computer) to input the data necessary for the price simulation. This input data includes product details, quantity, budget, and delivery date. After inputting the data, clicking the submit button will proceed to the next step.

[0679] Input: Data entered by the user, such as product, quantity, budget, and delivery date.

[0680] Output: Holding of input data within the terminal.

[0681] Step 2:

[0682] The terminal device receives the input data and sends it to the server using an HTTP request. The destination URL and parameters are pre-configured.

[0683] Input: Data entered by the user

[0684] Output: Sending data converted into an HTTP request

[0685] Step 3:

[0686] An emotion recognition engine built into the terminal device analyzes the user's input and voice data to generate emotion data. The analyzed emotion data is sent to the server along with the input data.

[0687] Input: User input and audio data

[0688] Output: Sending generated emotion data

[0689] Step 4:

[0690] The server receives input data and sentiment data transmitted from the terminal device. It converts the received data into an internal data structure and prepares it for the next analysis process.

[0691] Input: Input data and sentiment data sent from the device.

[0692] Output: Data converted to an internal data structure

[0693] Step 5:

[0694] The server passes the data to an artificial intelligence model (e.g., GPT-4) and requests analysis to generate price suggestions and time optimization results. Specifically, it sends a prompt message to the AI ​​model such as: "Based on the provided data and the user's sentiment, please output the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units', 'budget 500,000 yen', and 'delivery date 2 weeks' for product A, and their sentiment is 'anxious', please provide suggestions that will increase the user's sense of security."

[0695] Input: Input data and sentiment data converted into an internal data structure, prompt message

[0696] Output: Price proposal and time optimization results

[0697] Step 6:

[0698] The server receives the price proposal and time optimization results obtained from the artificial intelligence model and transmits them to the terminal device.

[0699] Input: Results of price suggestions and time optimization generated by an artificial intelligence model

[0700] Output: Sending results to the terminal device

[0701] Step 7:

[0702] The terminal device receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it might display: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The user will feel more at ease with the shortened delivery time and lower price."

[0703] Input: Rate proposals and time optimization results sent from the server

[0704] Output: Price proposals and time optimization results presented to the user

[0705] 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.

[0706] 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.

[0707] 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.

[0708] [Third Embodiment]

[0709] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0710] 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.

[0711] 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).

[0712] 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.

[0713] 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.

[0714] 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).

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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".

[0721] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0722] Explanation of the program's processing:

[0723] terminal

[0724] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0725] server

[0726] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses an artificial intelligence model to generate pricing suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate requests based on the user's input data, such as the following:

[0727] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0728] The artificial intelligence model generates pricing suggestions and time optimization results based on this request and returns them to the server. The server then analyzes these results and formats them for presentation to the user.

[0729] Specific example

[0730] For example, suppose a user inputs data about product A, such as "order 200 units," "budget 500,000 yen," and "delivery time 2 weeks." The terminal sends this data to the server. The server receives the data and performs analysis.

[0731] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0732] "By setting the unit price of product A at 2,500 yen and shortening the delivery time to one week, we can create an efficient pricing plan."

[0733] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0734] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0735] Thus, using the system of the present invention, it is possible to generate and present a price proposal quickly and accurately based on the user's input data. Since the entire system is automated, variations due to staff differences can be eliminated, and a stable service can be provided.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form provided on the device. After completing the input, the user clicks the "Submit" button.

[0739] Step 2:

[0740] The device receives data entered by the user. This data is structured, for example, in JSON format. The device then sends the received data to the server as the payload of an HTTP request.

[0741] Step 3:

[0742] The server receives data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0743] Step 4:

[0744] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0745] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0746] Step 5:

[0747] An artificial intelligence model receives and processes requests. Based on the user's input data, the model generates price suggestions and time optimization results. For example, it might generate a result such as "Set the unit price of product A to 2500 yen and shorten the delivery time to one week."

[0748] Step 6:

[0749] The server receives the results returned by the artificial intelligence model. It then analyzes the received results and formats them into a user-friendly format.

[0750] Step 7:

[0751] The server sends the formatted result to the terminal. The result is sent as the payload of the HTTP response.

[0752] Step 8:

[0753] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it is presented to the user in the form of "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0754] Step 9:

[0755] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0756] In this way, the system includes a process for quickly and accurately generating and presenting price suggestions and time optimization results to the user based on the user's input data.

[0757] (Example 1)

[0758] 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."

[0759] In today's business environment, complex pricing proposals and schedule optimizations are required. However, performing these tasks manually is not only time-consuming but also increases the risk of human error. Therefore, there is a need for a system that can automatically generate and provide users with fast and accurate proposals. Existing systems struggle to properly analyze user input data and generate optimal pricing proposals and time optimization results based on that data. This complexity increases especially when numerous variables are involved.

[0760] 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.

[0761] In this invention, the server includes means for receiving transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, and means for inputting prompt sentences into the generation AI model. This makes it possible to automatically generate fast and highly accurate price suggestions and time optimization results using advanced data analysis and an AI model.

[0762] A "user" is someone who inputs data to perform a price simulation and receives the results.

[0763] A "terminal" is a device used by a user to input data and send that data to a server; specifically, it refers to electronic devices such as personal computers and smartphones.

[0764] A "server" refers to a device that receives data transmitted from a terminal, performs analysis, and generates price suggestions and time optimization results.

[0765] "Data" refers to the information that users input to perform price simulations, and specifically includes information such as product type, order quantity, budget, and delivery date.

[0766] A "generative AI model" refers to artificial intelligence used to generate price suggestions and time optimization results based on input data, such as a natural language processing engine.

[0767] A "prompt statement" is the text input to a generative AI model, and refers to a sentence that describes a specific analysis request.

[0768] An "HTTP request" refers to a request that uses the web protocol to send data from a terminal to a server.

[0769] "Price proposal" refers to a pricing suggestion provided by the generative AI model based on its analysis results.

[0770] "Time optimization" refers to making adjustments to optimize deadlines and schedules.

[0771] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[0772] terminal

[0773] The terminal provides an interface where the user enters the necessary data through an input form for price simulation. The user enters details such as product type, quantity, budget, and delivery date. A specific example of this input is shown below:

[0774] Specific example:

[0775] "Product A", "Quantity Ordered: 200", "Budget: 500,000 yen", "Delivery Time: 2 weeks"

[0776] When a user enters data and clicks the submit button, the device receives that data and sends it to the server using an HTTP request.

[0777] server

[0778] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses a generative AI model to generate price suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate the following prompts based on the user's input data:

[0779] Prompt message:

[0780] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0781] The generative AI model generates pricing suggestions and time optimization results based on this prompt and returns them to the server. For example, it might return a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0782] Presentation of results

[0783] The server receives the results from the generated AI model and formats them in a user-friendly format. It then sends the formatted results back to the terminal as an HTTP response.

[0784] The terminal receives the results from the server and displays them to the user as follows:

[0785] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[0786] This system allows users to receive quick and accurate price quotes, thereby improving business efficiency and effectiveness. Furthermore, because the entire system is automated, human error is eliminated, ensuring consistent service delivery.

[0787] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0788] Step 1:

[0789] The user enters the data necessary for the price simulation into the input form on the device.

[0790] Input: Information such as product type, order quantity, budget, and delivery date.

[0791] Operation: Users enter the data necessary for the price simulation using a web form or app interface on their device. For example, they might enter data such as "Product A," "Quantity Ordered: 200," "Budget: 500,000 yen," and "Delivery Time: 2 weeks" into the form.

[0792] Output: The input data is saved to the terminal.

[0793] Step 2:

[0794] When the user clicks the submit button, the device sends the entered data to the server as an HTTP request.

[0795] Input: Data entered by the user into the input form.

[0796] Operation: The terminal converts the input data into JSON format and generates an HTTP request. The generated request is sent to the server via the internet.

[0797] Output: HTTP request from terminal to server (JSON data).

[0798] Step 3:

[0799] The server receives data sent from the terminal and performs data validation and analysis.

[0800] Input: JSON data received from the terminal.

[0801] Operation: The server performs validation to check the format and required fields of the received data. Next, it uses an analysis engine to analyze the content of the data.

[0802] Output: Analyzed data.

[0803] Step 4:

[0804] The server generates prompt messages for the AI ​​model based on the analysis results and inputs them.

[0805] Input: Analyzed data.

[0806] Operation: The server generates prompts for the generated AI model based on the analyzed data. Specifically, it generates prompts such as: "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[0807] Output: The generated prompt message.

[0808] Step 5:

[0809] The generative AI model receives a prompt and generates price suggestions and time optimization results.

[0810] Input: The generated prompt message.

[0811] Operation: The generating AI model analyzes the prompt text and generates price suggestions and time optimization results based on the input data. For example, it might generate a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[0812] Output: Price proposal and time optimization results.

[0813] Step 6:

[0814] The server receives the generated results and formats them into a user-friendly format.

[0815] Input: Price suggestions and time optimization results returned from a generated AI model.

[0816] Operation: The server receives the results and formats them in a user-friendly format. Specifically, this could include displaying the suggestions in text format or in a table format.

[0817] Output: Formatted result.

[0818] Step 7:

[0819] The server sends the formatted result to the terminal, and the terminal displays the result to the user.

[0820] Input: Formatted result.

[0821] Operation: The server sends the formatted result back to the terminal as an HTTP response. The terminal displays the received result in the user interface. For example, information such as recommended pricing plans and shortened delivery times may be displayed.

[0822] Output: Content displayed to the user.

[0823] In this process, a quick and accurate price quote is automatically generated based on the user's input data.

[0824] (Application Example 1)

[0825] 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."

[0826] Modern food delivery services lack systems that automatically calculate optimal pricing and delivery times, even when users input their preferred food type, quantity, budget, and delivery time. This forces users to manually make the best choices, leading to decreased efficiency and satisfaction. Consequently, there is a need to improve both the user experience and the operational efficiency of delivery service providers.

[0827] 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.

[0828] In this invention, the server includes means for prompting the user to input data necessary for price simulation, terminal means for transmitting the data to the server, means for receiving the transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, means for transmitting the generated results to the terminal, means for displaying the results to the user on the terminal, and means for automatically generating prompt sentences necessary for generating the analysis results. As a result, the user can select the optimal price suggestion and delivery time for food delivery and receive efficient service.

[0829] "User" refers to anyone who uses the price simulation tool.

[0830] "Price simulation" refers to a method where users input data to obtain optimal price suggestions and time optimization results.

[0831] "Data" refers to the information that users enter for price simulations (for example, type of food, quantity, budget, desired delivery time, etc.).

[0832] A "server" refers to a device or system that receives data transmitted from a terminal, performs analysis, and generates results.

[0833] "Terminal means" refers to a device or interface for a user to input data and send it to a server.

[0834] "Analysis means" refers to the means of interpreting data sent by the user, performing necessary analysis, and deriving results for price proposals and time optimization.

[0835] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate pricing suggestions and time optimization results.

[0836] A "prompt statement" refers to a set of instructions necessary for a generative AI model to generate price suggestions and time optimization results.

[0837] "Transmission means" refers to the communication means used to return the generated analysis results to the original terminal.

[0838] "Display means" refers to devices or interfaces used to visually show analysis results to the user.

[0839] A specific embodiment of the food delivery optimization system based on this invention is described below.

[0840] Overall system configuration

[0841] The system consists of terminal means for the user to input data, a server that receives and analyzes the data, communication means that generates the analysis results and sends them back to the user, and means that displays the results to the user.

[0842] Terminal means

[0843] The terminal devices mainly consist of mobile information terminals such as smartphones and tablets. Users input data such as the type of food, quantity, budget, and desired delivery time via the terminal device. The entered data is sent to the server using an HTTP request.

[0844] server

[0845] The server receives data sent from the user. This server uses Flask as its framework and GPT-4, a cognitive processing engine, as its AI model. The received data is analyzed using data science libraries (e.g., NumPy, Pandas). Based on the analysis results, a generative AI model operates to generate optimal price suggestions and delivery time results.

[0846] Generative AI models and prompt sentence generation

[0847] The generation AI model uses prompts based on data analysis. These prompts are automatically generated and are in a format that accurately reflects the analysis results. For example, the following prompts are generated:

[0848] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0849] Sending and displaying results

[0850] The generated optimization results are sent back from the server to the terminal device. The terminal receives the results and displays them visually to the user. Specifically, the optimal price and delivery time are displayed in a suggested format, allowing the user to choose the best food delivery plan based on this information.

[0851] Specific example

[0852] For example, if a user enters "Two pizzas, one salad, budget of 3000 yen, desired delivery time 18:00," this data is sent to the server. Upon receiving the data, the server performs data analysis and generates a prompt message like the one above for the AI ​​model. Based on this prompt message, GPT-4 provides optimization results for pricing and delivery time, sending a suggestion to the user: "We can achieve an efficient pricing plan by setting the total price for two pizzas and one salad to 2800 yen and the delivery time to 17:30." This suggestion is displayed on the terminal, and the user confirms the order accordingly.

[0853] Thus, the system of this invention provides a means for users to efficiently and optimally utilize food delivery services.

[0854] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0855] Step 1:

[0856] The user inputs data using a terminal device.

[0857] The input data includes information such as the type of dish, quantity, budget, and desired delivery time.

[0858] Specifically, users use their smartphones or tablets to enter this information into the application's input form and click the submit button.

[0859] Step 2:

[0860] The terminal sends the entered data to the server.

[0861] The entered data is converted to JSON format and sent to the server using an HTTP request.

[0862] Specifically, the smartphone application parses the data into JSON format and executes an HTTP POST request to the API endpoint.

[0863] Step 3:

[0864] The server analyzes the data it receives.

[0865] The server uses data science libraries (NumPy, Pandas) to analyze incoming data, understand user requests, and generate the necessary prompts.

[0866] Specifically, the JSON data received by the server is converted into a Python object, and then the data is formatted and analyzed using NumPy and Pandas.

[0867] Step 4:

[0868] The server inputs prompt messages into the generated AI model and generates optimization results.

[0869] Based on the analyzed data, the server provides prompt sentences to the generative AI model using GPT-4.

[0870] Specifically, it generates prompt statements like the following:

[0871] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[0872] The AI ​​model analyzes this prompt and generates price suggestions and time optimization results.

[0873] Step 5:

[0874] The server formats the generated result and sends it to the terminal.

[0875] The server formats the results obtained from the generated AI model into a user-friendly format, converts them back to JSON format, and sends them back to the terminal.

[0876] Specifically, if the generated result is "Pricing plan: 2800 yen, Delivery time: 17:30", a JSON containing this information is generated and sent back as an HTTP response.

[0877] Step 6:

[0878] The device displays the results it has received to the user.

[0879] The device receives the results sent back from the server and displays the results in the application's UI.

[0880] Specifically, the smartphone application analyzes the received JSON data and visually displays price and time suggestions to the user. Based on this information, the user selects the most suitable food delivery plan.

[0881] 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.

[0882] The system of the present invention allows users to input data necessary for a price simulation and generates price suggestions and time optimization results based on that data. This system consists of an input device (terminal), a server that processes and analyzes the data, an emotion engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0883] Explanation of the program's processing:

[0884] terminal

[0885] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[0886] Emotional Engine

[0887] The emotion engine built into the device recognizes the user's emotions based on user input and voice data. For example, it analyzes whether the user is tense or satisfied. After analysis, the emotion data is sent to the server along with simulation data.

[0888] server

[0889] The server receives simulation and emotion data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0890] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0891] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0892] The artificial intelligence model generates price suggestions and time optimization results based on this request.

[0893] Specific example

[0894] For example, suppose a user inputs data about product A, such as "order 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also recognizes from the user's voice that they are nervous. The terminal sends this data and emotion information to the server. The server receives the data and performs analysis.

[0895] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[0896] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0897] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[0898] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0899] Thus, using the system of the present invention, it is possible to generate and present to the user a rapid and accurate price suggestion based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0900] The following describes the processing flow.

[0901] Step 1:

[0902] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form on their device. After completing the input, they click the "submit" button.

[0903] Step 2:

[0904] The terminal receives data entered by the user. This data is structured in JSON format. The terminal prepares this data for transmission.

[0905] Step 3:

[0906] The emotion engine analyzes user input and voice data. For example, it analyzes the user's voice tone and input speed to recognize whether the user is nervous or satisfied. The recognized emotion data is sent to the server along with simulation data.

[0907] Step 4:

[0908] The device sends simulation data, including emotional data, to the server. This data is sent to the server as an HTTP request.

[0909] Step 5:

[0910] The server receives data sent from the terminal. The received data includes simulation data and emotion data.

[0911] Step 6:

[0912] The server analyzes the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0913] Step 7:

[0914] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[0915] "Based on the provided simulation data and user sentiment data, please output the optimal pricing proposal and time optimization results."

[0916] Step 8:

[0917] An artificial intelligence model receives and processes requests. Based on user input data and sentiment data, the model generates price suggestions and time optimization results. For example, it might generate a result such as, "Set the unit price of product A to 2400 yen and shorten the delivery time to one week to provide reassurance to a stressed user."

[0918] Step 9:

[0919] The server receives the results returned by the artificial intelligence model. It analyzes the received results and formats them into a user-friendly format.

[0920] Step 10:

[0921] The server sends the formatted result to the terminal. The result is sent as an HTTP response.

[0922] Step 11:

[0923] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it displays: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0924] Step 12:

[0925] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[0926] Thus, this system can quickly and accurately generate price suggestions and time optimization results based on user input data, and by further considering user sentiment data, it can provide optimal suggestions tailored to individual needs.

[0927] (Example 2)

[0928] 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."

[0929] Conventional pricing simulation systems have the problem of not being able to propose pricing that takes user emotions into account, making it difficult to improve the user experience. Furthermore, there has been no established effective means of generating and providing analysis results that take emotional data into account in real time.

[0930] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for prompting the user to input data necessary for a fee simulation, means for recognizing the data and the user's sentiment data, terminal means for transmitting the data and sentiment data to the server, means for receiving the transmitted data and sentiment data and analyzing the data, means for generating fee proposals and time optimization results using an artificial intelligence model based on the analysis results and sentiment data, means for transmitting the generated results to the terminal, and means for displaying the results to the user on the terminal. This makes it possible to provide personalized fee proposals and time optimization results that take into account the user's sentiment data in real time.

[0931] A "user" is an individual or group that uses the system to perform a price simulation.

[0932] A "price simulation" is the process of calculating the optimal pricing plan for a specific product or service based on user input data.

[0933] "Data" refers to the information that users input to perform a price simulation, including quantity, budget, and delivery date.

[0934] "Emotional data" refers to information that indicates the user's emotional state, including emotions such as tension, satisfaction, and anxiety, extracted from input content and voice data.

[0935] A "terminal" is a hardware and software device used by a user to input data and send it to a server.

[0936] An "emotion engine" is software or an algorithm that analyzes user input and voice data to generate user emotion data.

[0937] A "server" is a computing system that receives data and sentiment data sent by users, and performs analysis and generates price suggestions.

[0938] "Analysis" refers to the process by which a server processes and analyzes the data and sentiment data it receives.

[0939] An "artificial intelligence model" refers to an algorithm or machine learning model used to provide pricing suggestions and optimize time.

[0940] "Pricing suggestions" refer to presenting the most suitable pricing plans for products and services based on user input data and sentiment data.

[0941] "Time optimization" is the process of calculating the optimal delivery date for a product or service based on user input data and sentiment data.

[0942] "Results" refers to pricing suggestions and time optimization information generated by the artificial intelligence model.

[0943] An "HTTP request" is a communication protocol used to send data from a device to a server.

[0944] "Terminal means" refers to the functions installed in a terminal that allow for data input and transmission.

[0945] "Transmission means" refers to the function for sending data from a terminal to a server.

[0946] "Display means" refers to a function on a terminal that presents the results transmitted from the server to the user.

[0947] The system implementing the present invention receives data necessary for a price simulation from the user and generates price suggestions and time optimization results based on that data and sentiment data. This system consists of an input device (terminal), a server that processes and analyzes the data, an sentiment engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[0948] First, the user uses the terminal to enter the necessary data into an input form for price simulation. For example, they might enter information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then click the submit button. The terminal has an emotion engine built in to recognize the user's emotions based on the user's input and voice data. The emotion engine analyzes the emotions the user expresses during input, such as tension, satisfaction, and anxiety, and generates emotion data.

[0949] The terminal sends the simulation data entered by the user and the emotion data recognized by the emotion engine to the server. Communication is secure using HTTP requests. The server analyzes the received simulation and emotion data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[0950] The server requests analysis from the artificial intelligence model based on the received data and sentiment data. The specific prompt messages are as follows:

[0951] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[0952] Based on this prompt, an artificial intelligence model (e.g., a natural language processing engine) generates price suggestions and time optimization results.

[0953] For example, if a user inputs data such as "order 200 units of product A," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also detects tension in the user's voice, the terminal sends this data and emotion information to the server. The server receives the data and performs analysis. Based on the analysis results, the AI ​​model obtains a price suggestion like the following:

[0954] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[0955] The server sends this result back to the terminal, which then displays it to the user as follows:

[0956] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[0957] As described above, the system of the present invention makes it possible to generate and present rapid and accurate price suggestions to the user based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[0958] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0959] Step 1: User data entry

[0960] The user uses their device to enter data into an input form for price simulation. Specifically, the user enters information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then clicks the submit button. Once the input is complete, the device temporarily saves this data.

[0961] Input: Simulation data entered by the user.

[0962] Output: Temporarily saved simulation data.

[0963] Specific operation: The user accesses the input form using a browser or dedicated application, enters the required information, and then presses the submit button.

[0964] Step 2: Emotion Recognition

[0965] While the user is entering data, an emotion engine built into the device recognizes the user's emotions based on the user's input and voice data. The emotion engine analyzes emotions such as "tension," "satisfaction," and "anxiety" from the input data.

[0966] Input: User input and audio data.

[0967] Output: Analyzed sentiment data.

[0968] Specific operation: When the user completes input and leaves a voice comment, the emotion engine retrieves the voice data and performs emotion analysis.

[0969] Step 3: Data transmission

[0970] The terminal sends the simulation data entered by the user and the emotion data analyzed by the emotion engine to the server. HTTP requests are used for transmission.

[0971] Input: Simulation data and sentiment data.

[0972] Output: Data sent to the server.

[0973] Specific operation: The terminal converts the data into JSON format or similar, creates an HTTP request, and sends the data to the specified endpoint on the server.

[0974] Step 4: Data analysis on the server

[0975] The server receives simulation and emotion data sent from the terminal and begins analysis. The server converts the data into an internal data structure and prepares it to be passed to the artificial intelligence model.

[0976] Input: Simulation data and sentiment data received by the server.

[0977] Output: Data in a format that can be analyzed by an artificial intelligence model.

[0978] Specific operation: The server extracts data from the HTTP request, converts each data item into an internal structure, and passes it to the artificial intelligence model.

[0979] Step 5: Price Proposal & Time Optimization

[0980] The server requests analysis from the artificial intelligence model and generates a price proposal and time optimization results. It also generates prompt messages and sends them to the artificial intelligence model.

[0981] Input: Prompt statement and data.

[0982] Output: Generated pricing proposals and time optimization results.

[0983] Specific operation: The server creates a prompt message, sends it to the artificial intelligence model, and receives the result. Example of a prompt message: "Based on the provided data and user sentiment, output the best price suggestion and time optimization results."

[0984] Step 6: Presentation of Results

[0985] The server sends the generated price proposal and time optimization results back to the terminal. The terminal then presents the results to the user in an easy-to-understand manner.

[0986] Input: Analysis results sent from the server.

[0987] Output: Price suggestions and time optimization results displayed to the user.

[0988] Specific operation: The server sends the analysis results to the terminal, and the terminal updates the screen to display the received results to the user and presents the suggested content.

[0989] This processing step allows for the rapid generation and provision of optimal pricing suggestions and time optimization results based on user input data and sentiment data.

[0990] (Application Example 2)

[0991] 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."

[0992] Conventional pricing simulation systems merely provide pricing suggestions and time optimization based on user input data, without considering the user's emotional state. This results in a problem where personalized suggestions that enhance user psychological satisfaction and peace of mind are not possible. Especially for e-commerce sites, improving the user experience is crucial, and a system that takes emotions into account is required.

[0993] 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.

[0994] In this invention, the server includes means for prompting the user to input data necessary for a price simulation, means for receiving and analyzing the said data and the user's sentiment data, and means for generating price suggestions and time optimization results using an artificial intelligence model based on the analysis results. This enables personalized price suggestions and time optimization suggestions that take the user's sentiments into account.

[0995] A "user" refers to an individual or company that uses the system to perform a price simulation.

[0996] "Data required for price simulation" refers to information necessary for price proposals and time optimization, such as product, quantity, budget, and delivery date.

[0997] "Emotional data" refers to data that indicates a user's emotional state, and includes emotional information analyzed from user input, voice, and other sources.

[0998] "Terminal device" refers to a digital device used by a user to input data and transmit that data to a server. Specifically, this includes smartphones and computers.

[0999] A "server" refers to a computer device that receives data transmitted from terminal devices and performs analysis and generates price suggestions.

[1000] "Means of analysis" refers to the process by which the server uses the received data and sentiment data to perform necessary calculations and data transformations.

[1001] An "artificial intelligence model" refers to a sophisticated algorithm or program that generates optimal price and time suggestions based on analyzed data. Specifically, this includes natural language processing engines, among others.

[1002] "Price proposal and time optimization results" refers to the optimal pricing and delivery time proposals generated based on user input data and sentiment data.

[1003] "Means of transmission" refers to the process and technology for sending back the results generated by the server to the terminal.

[1004] "Means of display" refers to interfaces and functions on a terminal that visually present the generated results to the user.

[1005] The system for implementing this invention mainly consists of the following parts: a terminal means for the user to input data, an emotion recognition engine that recognizes the user's emotions, a server that receives and analyzes the data, an artificial intelligence model that makes optimization suggestions for fees and time, and means for presenting the results to the user.

[1006] Terminal means

[1007] The terminal device refers to a digital device such as a smartphone or computer, which provides an interface for the user to input the data necessary for the price simulation (e.g., product, quantity, budget, delivery date, etc.). When the user inputs the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[1008] Emotion recognition engine

[1009] The emotion recognition engine utilizes technologies such as Microsoft Azure Cognitive Services and Google Cloud Emotion AI to recognize the user's emotional state from their input or voice. This engine analyzes emotional data, such as whether the user is tense, satisfied, or anxious, and sends the results to the server.

[1010] server

[1011] The server receives price simulation data and sentiment data sent from the terminal. The server converts this data into an internal data structure and prepares it to be passed to an artificial intelligence model (e.g., GPT-4). Specifically, it sends the following prompts to the artificial intelligence model:

[1012] "Based on the provided data and the user's emotions, please generate the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units of product A,' 'budget of 500,000 yen,' and 'delivery date of 2 weeks,' and their emotion is 'anxious,' please provide a suggestion that will increase the user's sense of security."

[1013] The artificial intelligence model analyzes this prompt and generates optimal pricing suggestions and time optimization results. These results are sent from the server to the terminal and displayed to the user.

[1014] Presentation of results

[1015] The terminal receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it may look like this:

[1016] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[1017] This system allows users to receive pricing suggestions that take their emotional state into account, resulting in a more satisfying shopping experience.

[1018] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1019] Step 1:

[1020] The user uses a device (smartphone or computer) to input the data necessary for the price simulation. This input data includes product details, quantity, budget, and delivery date. After inputting the data, clicking the submit button will proceed to the next step.

[1021] Input: Data entered by the user, such as product, quantity, budget, and delivery date.

[1022] Output: Holding of input data within the terminal.

[1023] Step 2:

[1024] The terminal device receives the input data and sends it to the server using an HTTP request. The destination URL and parameters are pre-configured.

[1025] Input: Data entered by the user

[1026] Output: Sending data converted into an HTTP request

[1027] Step 3:

[1028] An emotion recognition engine built into the terminal device analyzes the user's input and voice data to generate emotion data. The analyzed emotion data is sent to the server along with the input data.

[1029] Input: User input and audio data

[1030] Output: Sending generated emotion data

[1031] Step 4:

[1032] The server receives input data and sentiment data transmitted from the terminal device. It converts the received data into an internal data structure and prepares it for the next analysis process.

[1033] Input: Input data and sentiment data sent from the device.

[1034] Output: Data converted to an internal data structure

[1035] Step 5:

[1036] The server passes the data to an artificial intelligence model (e.g., GPT-4) and requests analysis to generate price suggestions and time optimization results. Specifically, it sends a prompt message to the AI ​​model such as: "Based on the provided data and the user's sentiment, please output the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units', 'budget 500,000 yen', and 'delivery date 2 weeks' for product A, and their sentiment is 'anxious', please provide suggestions that will increase the user's sense of security."

[1037] Input: Input data and sentiment data converted into an internal data structure, prompt message

[1038] Output: Price proposal and time optimization results

[1039] Step 6:

[1040] The server receives the price proposal and time optimization results obtained from the artificial intelligence model and transmits them to the terminal device.

[1041] Input: Results of price suggestions and time optimization generated by an artificial intelligence model

[1042] Output: Sending results to the terminal device

[1043] Step 7:

[1044] The terminal device receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it might display: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The user will feel more at ease with the shortened delivery time and lower price."

[1045] Input: Rate proposals and time optimization results sent from the server

[1046] Output: Price proposals and time optimization results presented to the user

[1047] 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.

[1048] 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.

[1049] 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.

[1050] [Fourth Embodiment]

[1051] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1052] 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.

[1053] 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).

[1054] 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.

[1055] 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.

[1056] 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).

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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".

[1064] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[1065] Explanation of the program's processing:

[1066] terminal

[1067] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[1068] server

[1069] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses an artificial intelligence model to generate pricing suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate requests based on the user's input data, such as the following:

[1070] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[1071] The artificial intelligence model generates pricing suggestions and time optimization results based on this request and returns them to the server. The server then analyzes these results and formats them for presentation to the user.

[1072] Specific example

[1073] For example, suppose a user inputs data about product A, such as "order 200 units," "budget 500,000 yen," and "delivery time 2 weeks." The terminal sends this data to the server. The server receives the data and performs analysis.

[1074] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[1075] "By setting the unit price of product A at 2,500 yen and shortening the delivery time to one week, we can create an efficient pricing plan."

[1076] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[1077] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[1078] Thus, using the system of the present invention, it is possible to generate and present a price proposal quickly and accurately based on the user's input data. Since the entire system is automated, variations due to staff differences can be eliminated, and a stable service can be provided.

[1079] The following describes the processing flow.

[1080] Step 1:

[1081] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form provided on the device. After completing the input, the user clicks the "Submit" button.

[1082] Step 2:

[1083] The device receives data entered by the user. This data is structured, for example, in JSON format. The device then sends the received data to the server as the payload of an HTTP request.

[1084] Step 3:

[1085] The server receives data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[1086] Step 4:

[1087] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[1088] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[1089] Step 5:

[1090] An artificial intelligence model receives and processes requests. Based on the user's input data, the model generates price suggestions and time optimization results. For example, it might generate a result such as "Set the unit price of product A to 2500 yen and shorten the delivery time to one week."

[1091] Step 6:

[1092] The server receives the results returned by the artificial intelligence model. It then analyzes the received results and formats them into a user-friendly format.

[1093] Step 7:

[1094] The server sends the formatted result to the terminal. The result is sent as the payload of the HTTP response.

[1095] Step 8:

[1096] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it is presented to the user in the form of "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[1097] Step 9:

[1098] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[1099] In this way, the system includes a process for quickly and accurately generating and presenting price suggestions and time optimization results to the user based on the user's input data.

[1100] (Example 1)

[1101] 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".

[1102] In today's business environment, complex pricing proposals and schedule optimizations are required. However, performing these tasks manually is not only time-consuming but also increases the risk of human error. Therefore, there is a need for a system that can automatically generate and provide users with fast and accurate proposals. Existing systems struggle to properly analyze user input data and generate optimal pricing proposals and time optimization results based on that data. This complexity increases especially when numerous variables are involved.

[1103] 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.

[1104] In this invention, the server includes means for receiving transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, and means for inputting prompt sentences into the generation AI model. This makes it possible to automatically generate fast and highly accurate price suggestions and time optimization results using advanced data analysis and an AI model.

[1105] A "user" is someone who inputs data to perform a price simulation and receives the results.

[1106] A "terminal" is a device used by a user to input data and send that data to a server; specifically, it refers to electronic devices such as personal computers and smartphones.

[1107] A "server" refers to a device that receives data transmitted from a terminal, performs analysis, and generates price suggestions and time optimization results.

[1108] "Data" refers to the information that users input to perform price simulations, and specifically includes information such as product type, order quantity, budget, and delivery date.

[1109] A "generative AI model" refers to artificial intelligence used to generate price suggestions and time optimization results based on input data, such as a natural language processing engine.

[1110] A "prompt statement" is the text input to a generative AI model, and refers to a sentence that describes a specific analysis request.

[1111] An "HTTP request" refers to a request that uses the web protocol to send data from a terminal to a server.

[1112] "Price proposal" refers to a pricing suggestion provided by the generative AI model based on its analysis results.

[1113] "Time optimization" refers to making adjustments to optimize deadlines and schedules.

[1114] The system of the present invention allows the user to input data necessary for a price simulation, and automatically generates optimal price suggestions and time optimization results based on that data. This system consists of a user input device (terminal), a server that processes and analyzes the data, and means for presenting the suggested results to the user.

[1115] terminal

[1116] The terminal provides an interface where the user enters the necessary data through an input form for price simulation. The user enters details such as product type, quantity, budget, and delivery date. A specific example of this input is shown below:

[1117] Specific example:

[1118] "Product A", "Quantity Ordered: 200", "Budget: 500,000 yen", "Delivery Time: 2 weeks"

[1119] When a user enters data and clicks the submit button, the device receives that data and sends it to the server using an HTTP request.

[1120] server

[1121] The server receives data sent from the terminal. Upon receiving the data, the server analyzes it and uses a generative AI model to generate price suggestions and time optimization results. Specifically, the server uses a natural language processing engine (e.g., GPT-4) to generate the following prompts based on the user's input data:

[1122] Prompt message:

[1123] "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[1124] The generative AI model generates pricing suggestions and time optimization results based on this prompt and returns them to the server. For example, it might return a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[1125] Presentation of results

[1126] The server receives the results from the generated AI model and formats them in a user-friendly format. It then sends the formatted results back to the terminal as an HTTP response.

[1127] The terminal receives the results from the server and displays them to the user as follows:

[1128] "Recommended pricing plan: Set the price of product A to 2500 yen and shorten the delivery time to one week."

[1129] This system allows users to receive quick and accurate price quotes, thereby improving business efficiency and effectiveness. Furthermore, because the entire system is automated, human error is eliminated, ensuring consistent service delivery.

[1130] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1131] Step 1:

[1132] The user enters the data necessary for the price simulation into the input form on the device.

[1133] Input: Information such as product type, order quantity, budget, and delivery date.

[1134] Operation: Users enter the data necessary for the price simulation using a web form or app interface on their device. For example, they might enter data such as "Product A," "Quantity Ordered: 200," "Budget: 500,000 yen," and "Delivery Time: 2 weeks" into the form.

[1135] Output: The input data is saved to the terminal.

[1136] Step 2:

[1137] When the user clicks the submit button, the device sends the entered data to the server as an HTTP request.

[1138] Input: Data entered by the user into the input form.

[1139] Operation: The terminal converts the input data into JSON format and generates an HTTP request. The generated request is sent to the server via the internet.

[1140] Output: HTTP request from terminal to server (JSON data).

[1141] Step 3:

[1142] The server receives data sent from the terminal and performs data validation and analysis.

[1143] Input: JSON data received from the terminal.

[1144] Operation: The server performs validation to check the format and required fields of the received data. Next, it uses an analysis engine to analyze the content of the data.

[1145] Output: Analyzed data.

[1146] Step 4:

[1147] The server generates prompt messages for the AI ​​model based on the analysis results and inputs them.

[1148] Input: Analyzed data.

[1149] Operation: The server generates prompts for the generated AI model based on the analyzed data. Specifically, it generates prompts such as: "Based on the provided data, please output the optimal pricing proposal and time optimization results."

[1150] Output: The generated prompt message.

[1151] Step 5:

[1152] The generative AI model receives a prompt and generates price suggestions and time optimization results.

[1153] Input: The generated prompt message.

[1154] Operation: The generating AI model analyzes the prompt text and generates price suggestions and time optimization results based on the input data. For example, it might generate a result such as, "By setting the unit price of product A to 2500 yen and shortening the delivery time to one week, we can achieve an efficient pricing plan."

[1155] Output: Price proposal and time optimization results.

[1156] Step 6:

[1157] The server receives the generated results and formats them into a user-friendly format.

[1158] Input: Price suggestions and time optimization results returned from a generated AI model.

[1159] Operation: The server receives the results and formats them in a user-friendly format. Specifically, this could include displaying the suggestions in text format or in a table format.

[1160] Output: Formatted result.

[1161] Step 7:

[1162] The server sends the formatted result to the terminal, and the terminal displays the result to the user.

[1163] Input: Formatted result.

[1164] Operation: The server sends the formatted result back to the terminal as an HTTP response. The terminal displays the received result in the user interface. For example, information such as recommended pricing plans and shortened delivery times may be displayed.

[1165] Output: Content displayed to the user.

[1166] In this process, a quick and accurate price quote is automatically generated based on the user's input data.

[1167] (Application Example 1)

[1168] 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".

[1169] Modern food delivery services lack systems that automatically calculate optimal pricing and delivery times, even when users input their preferred food type, quantity, budget, and delivery time. This forces users to manually make the best choices, leading to decreased efficiency and satisfaction. Consequently, there is a need to improve both the user experience and the operational efficiency of delivery service providers.

[1170] 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.

[1171] In this invention, the server includes means for prompting the user to input data necessary for price simulation, terminal means for transmitting the data to the server, means for receiving the transmitted data and analyzing the data, means for generating price suggestions and time optimization results using a generation AI model based on the analysis results, means for transmitting the generated results to the terminal, means for displaying the results to the user on the terminal, and means for automatically generating prompt sentences necessary for generating the analysis results. As a result, the user can select the optimal price suggestion and delivery time for food delivery and receive efficient service.

[1172] "User" refers to anyone who uses the price simulation tool.

[1173] "Price simulation" refers to a method where users input data to obtain optimal price suggestions and time optimization results.

[1174] "Data" refers to the information that users enter for price simulations (for example, type of food, quantity, budget, desired delivery time, etc.).

[1175] A "server" refers to a device or system that receives data transmitted from a terminal, performs analysis, and generates results.

[1176] "Terminal means" refers to a device or interface for a user to input data and send it to a server.

[1177] "Analysis means" refers to the means of interpreting data sent by the user, performing necessary analysis, and deriving results for price proposals and time optimization.

[1178] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate pricing suggestions and time optimization results.

[1179] A "prompt statement" refers to a set of instructions necessary for a generative AI model to generate price suggestions and time optimization results.

[1180] "Transmission means" refers to the communication means used to return the generated analysis results to the original terminal.

[1181] "Display means" refers to devices or interfaces used to visually show analysis results to the user.

[1182] A specific embodiment of the food delivery optimization system based on this invention is described below.

[1183] Overall system configuration

[1184] The system consists of terminal means for the user to input data, a server that receives and analyzes the data, communication means that generates the analysis results and sends them back to the user, and means that displays the results to the user.

[1185] Terminal means

[1186] The terminal devices mainly consist of mobile information terminals such as smartphones and tablets. Users input data such as the type of food, quantity, budget, and desired delivery time via the terminal device. The entered data is sent to the server using an HTTP request.

[1187] server

[1188] The server receives data sent from the user. This server uses Flask as its framework and GPT-4, a cognitive processing engine, as its AI model. The received data is analyzed using data science libraries (e.g., NumPy, Pandas). Based on the analysis results, a generative AI model operates to generate optimal price suggestions and delivery time results.

[1189] Generative AI models and prompt sentence generation

[1190] The generation AI model uses prompts based on data analysis. These prompts are automatically generated and are in a format that accurately reflects the analysis results. For example, the following prompts are generated:

[1191] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[1192] Sending and displaying results

[1193] The generated optimization results are sent back from the server to the terminal device. The terminal receives the results and displays them visually to the user. Specifically, the optimal price and delivery time are displayed in a suggested format, allowing the user to choose the best food delivery plan based on this information.

[1194] Specific example

[1195] For example, if a user enters "Two pizzas, one salad, budget of 3000 yen, desired delivery time 18:00," this data is sent to the server. Upon receiving the data, the server performs data analysis and generates a prompt message like the one above for the AI ​​model. Based on this prompt message, GPT-4 provides optimization results for pricing and delivery time, sending a suggestion to the user: "We can achieve an efficient pricing plan by setting the total price for two pizzas and one salad to 2800 yen and the delivery time to 17:30." This suggestion is displayed on the terminal, and the user confirms the order accordingly.

[1196] Thus, the system of this invention provides a means for users to efficiently and optimally utilize food delivery services.

[1197] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1198] Step 1:

[1199] The user inputs data using a terminal device.

[1200] The input data includes information such as the type of dish, quantity, budget, and desired delivery time.

[1201] Specifically, users use their smartphones or tablets to enter this information into the application's input form and click the submit button.

[1202] Step 2:

[1203] The terminal sends the entered data to the server.

[1204] The entered data is converted to JSON format and sent to the server using an HTTP request.

[1205] Specifically, the smartphone application parses the data into JSON format and executes an HTTP POST request to the API endpoint.

[1206] Step 3:

[1207] The server analyzes the data it receives.

[1208] The server uses data science libraries (NumPy, Pandas) to analyze incoming data, understand user requests, and generate the necessary prompts.

[1209] Specifically, the JSON data received by the server is converted into a Python object, and then the data is formatted and analyzed using NumPy and Pandas.

[1210] Step 4:

[1211] The server inputs prompt messages into the generated AI model and generates optimization results.

[1212] Based on the analyzed data, the server provides prompt sentences to the generative AI model using GPT-4.

[1213] Specifically, it generates prompt statements like the following:

[1214] "Based on the following data, please output the optimal price suggestion and delivery time optimization results: Food: 2 pizzas, 1 salad, budget: 3000 yen, desired delivery time: 18:00"

[1215] The AI ​​model analyzes this prompt and generates price suggestions and time optimization results.

[1216] Step 5:

[1217] The server formats the generated result and sends it to the terminal.

[1218] The server formats the results obtained from the generated AI model into a user-friendly format, converts them back to JSON format, and sends them back to the terminal.

[1219] Specifically, if the generated result is "Pricing plan: 2800 yen, Delivery time: 17:30", a JSON containing this information is generated and sent back as an HTTP response.

[1220] Step 6:

[1221] The device displays the results it has received to the user.

[1222] The device receives the results sent back from the server and displays the results in the application's UI.

[1223] Specifically, the smartphone application analyzes the received JSON data and visually displays price and time suggestions to the user. Based on this information, the user selects the most suitable food delivery plan.

[1224] 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.

[1225] The system of the present invention allows users to input data necessary for a price simulation and generates price suggestions and time optimization results based on that data. This system consists of an input device (terminal), a server that processes and analyzes the data, an emotion engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[1226] Explanation of the program's processing:

[1227] terminal

[1228] The terminal provides an interface for users to enter data into an input form for a price simulation. Once the user enters the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[1229] Emotional Engine

[1230] The emotion engine built into the device recognizes the user's emotions based on user input and voice data. For example, it analyzes whether the user is tense or satisfied. After analysis, the emotion data is sent to the server along with simulation data.

[1231] server

[1232] The server receives simulation and emotion data sent from the terminal. The server initiates the necessary processes to analyze the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[1233] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[1234] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[1235] The artificial intelligence model generates price suggestions and time optimization results based on this request.

[1236] Specific example

[1237] For example, suppose a user inputs data about product A, such as "order 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also recognizes from the user's voice that they are nervous. The terminal sends this data and emotion information to the server. The server receives the data and performs analysis.

[1238] Based on the analysis results, the server obtains the following suggestions from the artificial intelligence model:

[1239] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[1240] The server returns the retrieved results to the terminal, and the terminal displays them to the user as follows:

[1241] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[1242] Thus, using the system of the present invention, it is possible to generate and present to the user a rapid and accurate price suggestion based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[1243] The following describes the processing flow.

[1244] Step 1:

[1245] The user enters the data necessary for the price simulation. The user enters information such as "product name," "quantity," "budget," and "delivery date" into the input form on their device. After completing the input, they click the "submit" button.

[1246] Step 2:

[1247] The terminal receives data entered by the user. This data is structured in JSON format. The terminal prepares this data for transmission.

[1248] Step 3:

[1249] The emotion engine analyzes user input and voice data. For example, it analyzes the user's voice tone and input speed to recognize whether the user is nervous or satisfied. The recognized emotion data is sent to the server along with simulation data.

[1250] Step 4:

[1251] The device sends simulation data, including emotional data, to the server. This data is sent to the server as an HTTP request.

[1252] Step 5:

[1253] The server receives data sent from the terminal. The received data includes simulation data and emotion data.

[1254] Step 6:

[1255] The server analyzes the received data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[1256] Step 7:

[1257] The server requests analysis from the artificial intelligence model. Specifically, it sends the following request to the artificial intelligence model (for example, a natural language processing engine):

[1258] "Based on the provided simulation data and user sentiment data, please output the optimal pricing proposal and time optimization results."

[1259] Step 8:

[1260] An artificial intelligence model receives and processes requests. Based on user input data and sentiment data, the model generates price suggestions and time optimization results. For example, it might generate a result such as, "Set the unit price of product A to 2400 yen and shorten the delivery time to one week to provide reassurance to a stressed user."

[1261] Step 9:

[1262] The server receives the results returned by the artificial intelligence model. It analyzes the received results and formats them into a user-friendly format.

[1263] Step 10:

[1264] The server sends the formatted result to the terminal. The result is sent as an HTTP response.

[1265] Step 11:

[1266] The terminal receives the results sent from the server. The terminal displays the received results on the user interface. Specifically, it displays: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[1267] Step 12:

[1268] The user reviews the proposed pricing and time optimization results presented on their device. Based on this information, the user decides on their next action (e.g., whether to accept the proposal or request further adjustments).

[1269] Thus, this system can quickly and accurately generate price suggestions and time optimization results based on user input data, and by further considering user sentiment data, it can provide optimal suggestions tailored to individual needs.

[1270] (Example 2)

[1271] 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".

[1272] Conventional pricing simulation systems have the problem of not being able to propose pricing that takes user emotions into account, making it difficult to improve the user experience. Furthermore, there has been no established effective means of generating and providing analysis results that take emotional data into account in real time.

[1273] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for prompting the user to input data necessary for a fee simulation, means for recognizing the data and the user's sentiment data, terminal means for transmitting the data and sentiment data to the server, means for receiving the transmitted data and sentiment data and analyzing the data, means for generating fee proposals and time optimization results using an artificial intelligence model based on the analysis results and sentiment data, means for transmitting the generated results to the terminal, and means for displaying the results to the user on the terminal. This makes it possible to provide personalized fee proposals and time optimization results that take into account the user's sentiment data in real time.

[1274] A "user" is an individual or group that uses the system to perform a price simulation.

[1275] A "price simulation" is the process of calculating the optimal pricing plan for a specific product or service based on user input data.

[1276] "Data" refers to the information that users input to perform a price simulation, including quantity, budget, and delivery date.

[1277] "Emotional data" refers to information that indicates the user's emotional state, including emotions such as tension, satisfaction, and anxiety, extracted from input content and voice data.

[1278] A "terminal" is a hardware and software device used by a user to input data and send it to a server.

[1279] An "emotion engine" is software or an algorithm that analyzes user input and voice data to generate user emotion data.

[1280] A "server" is a computing system that receives data and sentiment data sent by users, and performs analysis and generates price suggestions.

[1281] "Analysis" refers to the process by which a server processes and analyzes the data and sentiment data it receives.

[1282] An "artificial intelligence model" refers to an algorithm or machine learning model used to provide pricing suggestions and optimize time.

[1283] "Pricing suggestions" refer to presenting the most suitable pricing plans for products and services based on user input data and sentiment data.

[1284] "Time optimization" is the process of calculating the optimal delivery date for a product or service based on user input data and sentiment data.

[1285] "Results" refers to pricing suggestions and time optimization information generated by the artificial intelligence model.

[1286] An "HTTP request" is a communication protocol used to send data from a device to a server.

[1287] "Terminal means" refers to the functions installed in a terminal that allow for data input and transmission.

[1288] "Transmission means" refers to the function for sending data from a terminal to a server.

[1289] "Display means" refers to a function on a terminal that presents the results transmitted from the server to the user.

[1290] The system implementing the present invention receives data necessary for a price simulation from the user and generates price suggestions and time optimization results based on that data and sentiment data. This system consists of an input device (terminal), a server that processes and analyzes the data, an sentiment engine that recognizes the user's emotions, and means for presenting the suggestion results to the user.

[1291] First, the user uses the terminal to enter the necessary data into an input form for price simulation. For example, they might enter information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then click the submit button. The terminal has an emotion engine built in to recognize the user's emotions based on the user's input and voice data. The emotion engine analyzes the emotions the user expresses during input, such as tension, satisfaction, and anxiety, and generates emotion data.

[1292] The terminal sends the simulation data entered by the user and the emotion data recognized by the emotion engine to the server. Communication is secure using HTTP requests. The server analyzes the received simulation and emotion data. The data is converted into an internal data structure and prepared to be passed to the artificial intelligence model.

[1293] The server requests analysis from the artificial intelligence model based on the received data and sentiment data. The specific prompt messages are as follows:

[1294] "Based on the provided data and user sentiment, please output the optimal pricing proposal and time optimization results."

[1295] Based on this prompt, an artificial intelligence model (e.g., a natural language processing engine) generates price suggestions and time optimization results.

[1296] For example, if a user inputs data such as "order 200 units of product A," "budget of 500,000 yen," and "delivery time of 2 weeks," and the emotion engine also detects tension in the user's voice, the terminal sends this data and emotion information to the server. The server receives the data and performs analysis. Based on the analysis results, the AI ​​model obtains a price suggestion like the following:

[1297] "By setting the unit price of product A at 2400 yen and shortening the delivery time to one week, we can provide reassurance to anxious users."

[1298] The server sends this result back to the terminal, which then displays it to the user as follows:

[1299] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[1300] As described above, the system of the present invention makes it possible to generate and present rapid and accurate price suggestions to the user based on the user's input data and emotional data. By considering emotional data, more personalized suggestions become possible, thereby improving the user experience.

[1301] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1302] Step 1: User data entry

[1303] The user uses their device to enter data into an input form for price simulation. Specifically, the user enters information such as "order of 200 units," "budget of 500,000 yen," and "delivery time of 2 weeks," and then clicks the submit button. Once the input is complete, the device temporarily saves this data.

[1304] Input: Simulation data entered by the user.

[1305] Output: Temporarily saved simulation data.

[1306] Specific operation: The user accesses the input form using a browser or dedicated application, enters the required information, and then presses the submit button.

[1307] Step 2: Emotion Recognition

[1308] While the user is entering data, an emotion engine built into the device recognizes the user's emotions based on the user's input and voice data. The emotion engine analyzes emotions such as "tension," "satisfaction," and "anxiety" from the input data.

[1309] Input: User input and audio data.

[1310] Output: Analyzed sentiment data.

[1311] Specific operation: When the user completes input and leaves a voice comment, the emotion engine retrieves the voice data and performs emotion analysis.

[1312] Step 3: Data transmission

[1313] The terminal sends the simulation data entered by the user and the emotion data analyzed by the emotion engine to the server. HTTP requests are used for transmission.

[1314] Input: Simulation data and sentiment data.

[1315] Output: Data sent to the server.

[1316] Specific operation: The terminal converts the data into JSON format or similar, creates an HTTP request, and sends the data to the specified endpoint on the server.

[1317] Step 4: Data analysis on the server

[1318] The server receives simulation and emotion data sent from the terminal and begins analysis. The server converts the data into an internal data structure and prepares it to be passed to the artificial intelligence model.

[1319] Input: Simulation data and sentiment data received by the server.

[1320] Output: Data in a format that can be analyzed by an artificial intelligence model.

[1321] Specific operation: The server extracts data from the HTTP request, converts each data item into an internal structure, and passes it to the artificial intelligence model.

[1322] Step 5: Price Proposal & Time Optimization

[1323] The server requests analysis from the artificial intelligence model and generates a price proposal and time optimization results. It also generates prompt messages and sends them to the artificial intelligence model.

[1324] Input: Prompt statement and data.

[1325] Output: Generated pricing proposals and time optimization results.

[1326] Specific operation: The server creates a prompt message, sends it to the artificial intelligence model, and receives the result. Example of a prompt message: "Based on the provided data and user sentiment, output the best price suggestion and time optimization results."

[1327] Step 6: Presentation of Results

[1328] The server sends the generated price proposal and time optimization results back to the terminal. The terminal then presents the results to the user in an easy-to-understand manner.

[1329] Input: Analysis results sent from the server.

[1330] Output: Price suggestions and time optimization results displayed to the user.

[1331] Specific operation: The server sends the analysis results to the terminal, and the terminal updates the screen to display the received results to the user and presents the suggested content.

[1332] This processing step allows for the rapid generation and provision of optimal pricing suggestions and time optimization results based on user input data and sentiment data.

[1333] (Application Example 2)

[1334] 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".

[1335] Conventional pricing simulation systems merely provide pricing suggestions and time optimization based on user input data, without considering the user's emotional state. This results in a problem where personalized suggestions that enhance user psychological satisfaction and peace of mind are not possible. Especially for e-commerce sites, improving the user experience is crucial, and a system that takes emotions into account is required.

[1336] 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.

[1337] In this invention, the server includes means for prompting the user to input data necessary for a price simulation, means for receiving and analyzing the said data and the user's sentiment data, and means for generating price suggestions and time optimization results using an artificial intelligence model based on the analysis results. This enables personalized price suggestions and time optimization suggestions that take the user's sentiments into account.

[1338] A "user" refers to an individual or company that uses the system to perform a price simulation.

[1339] "Data required for price simulation" refers to information necessary for price proposals and time optimization, such as product, quantity, budget, and delivery date.

[1340] "Emotional data" refers to data that indicates a user's emotional state, and includes emotional information analyzed from user input, voice, and other sources.

[1341] "Terminal device" refers to a digital device used by a user to input data and transmit that data to a server. Specifically, this includes smartphones and computers.

[1342] A "server" refers to a computer device that receives data transmitted from terminal devices and performs analysis and generates price suggestions.

[1343] "Means of analysis" refers to the process by which the server uses the received data and sentiment data to perform necessary calculations and data transformations.

[1344] An "artificial intelligence model" refers to a sophisticated algorithm or program that generates optimal price and time suggestions based on analyzed data. Specifically, this includes natural language processing engines, among others.

[1345] "Price proposal and time optimization results" refers to the optimal pricing and delivery time proposals generated based on user input data and sentiment data.

[1346] "Means of transmission" refers to the process and technology for sending back the results generated by the server to the terminal.

[1347] "Means of display" refers to interfaces and functions on a terminal that visually present the generated results to the user.

[1348] The system for implementing this invention mainly consists of the following parts: a terminal means for the user to input data, an emotion recognition engine that recognizes the user's emotions, a server that receives and analyzes the data, an artificial intelligence model that makes optimization suggestions for fees and time, and means for presenting the results to the user.

[1349] Terminal means

[1350] The terminal device refers to a digital device such as a smartphone or computer, which provides an interface for the user to input the data necessary for the price simulation (e.g., product, quantity, budget, delivery date, etc.). When the user inputs the data and clicks the submit button, the terminal receives the data and sends it to the server using an HTTP request.

[1351] Emotion recognition engine

[1352] The emotion recognition engine utilizes technologies such as Microsoft Azure Cognitive Services and Google Cloud Emotion AI to recognize the user's emotional state from their input or voice. This engine analyzes emotional data, such as whether the user is tense, satisfied, or anxious, and sends the results to the server.

[1353] server

[1354] The server receives price simulation data and sentiment data sent from the terminal. The server converts this data into an internal data structure and prepares it to be passed to an artificial intelligence model (e.g., GPT-4). Specifically, it sends the following prompts to the artificial intelligence model:

[1355] "Based on the provided data and the user's emotions, please generate the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units of product A,' 'budget of 500,000 yen,' and 'delivery date of 2 weeks,' and their emotion is 'anxious,' please provide a suggestion that will increase the user's sense of security."

[1356] The artificial intelligence model analyzes this prompt and generates optimal pricing suggestions and time optimization results. These results are sent from the server to the terminal and displayed to the user.

[1357] Presentation of results

[1358] The terminal receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it may look like this:

[1359] "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The shortened delivery time and lower price will give users peace of mind."

[1360] This system allows users to receive pricing suggestions that take their emotional state into account, resulting in a more satisfying shopping experience.

[1361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1362] Step 1:

[1363] The user uses a device (smartphone or computer) to input the data necessary for the price simulation. This input data includes product details, quantity, budget, and delivery date. After inputting the data, clicking the submit button will proceed to the next step.

[1364] Input: Data entered by the user, such as product, quantity, budget, and delivery date.

[1365] Output: Holding of input data within the terminal.

[1366] Step 2:

[1367] The terminal device receives the input data and sends it to the server using an HTTP request. The destination URL and parameters are pre-configured.

[1368] Input: Data entered by the user

[1369] Output: Sending data converted into an HTTP request

[1370] Step 3:

[1371] An emotion recognition engine built into the terminal device analyzes the user's input and voice data to generate emotion data. The analyzed emotion data is sent to the server along with the input data.

[1372] Input: User input and audio data

[1373] Output: Sending generated emotion data

[1374] Step 4:

[1375] The server receives input data and sentiment data transmitted from the terminal device. It converts the received data into an internal data structure and prepares it for the next analysis process.

[1376] Input: Input data and sentiment data sent from the device.

[1377] Output: Data converted to an internal data structure

[1378] Step 5:

[1379] The server passes the data to an artificial intelligence model (e.g., GPT-4) and requests analysis to generate price suggestions and time optimization results. Specifically, it sends a prompt message to the AI ​​model such as: "Based on the provided data and the user's sentiment, please output the optimal price suggestion and delivery date. For example, if the user inputs data such as 'order 200 units', 'budget 500,000 yen', and 'delivery date 2 weeks' for product A, and their sentiment is 'anxious', please provide suggestions that will increase the user's sense of security."

[1380] Input: Input data and sentiment data converted into an internal data structure, prompt message

[1381] Output: Price proposal and time optimization results

[1382] Step 6:

[1383] The server receives the price proposal and time optimization results obtained from the artificial intelligence model and transmits them to the terminal device.

[1384] Input: Results of price suggestions and time optimization generated by an artificial intelligence model

[1385] Output: Sending results to the terminal device

[1386] Step 7:

[1387] The terminal device receives the price proposal and time optimization results sent from the server and provides an interface to visually present them to the user. For example, it might display: "Recommended pricing plan: Set the price of product A to 2400 yen and shorten the delivery time to one week. Reason for recommendation: The user will feel more at ease with the shortened delivery time and lower price."

[1388] Input: Rate proposals and time optimization results sent from the server

[1389] Output: Price proposals and time optimization results presented to the user

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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."

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1411] The following is further disclosed regarding the embodiments described above.

[1412] (Claim 1)

[1413] A means of having users input the data necessary for the price simulation,

[1414] A terminal means for transmitting the aforementioned data to a server,

[1415] A means for receiving transmitted data and analyzing said data,

[1416] A means for generating price suggestions and time optimization results using an artificial intelligence model based on analysis results,

[1417] Means for transmitting the generated results to a terminal,

[1418] A means for displaying the results to the user on the terminal,

[1419] A system that includes this.

[1420] (Claim 2)

[1421] The system according to claim 1, wherein the artificial intelligence model is a natural language processing engine.

[1422] (Claim 3)

[1423] The system according to claim 1, wherein the terminal means is a means for sending data to a server using an HTTP request.

[1424] "Example 1"

[1425] (Claim 1)

[1426] A means of having users input the data necessary for the price simulation,

[1427] A terminal means for transmitting the aforementioned data to a server,

[1428] A means for receiving transmitted data and analyzing said data,

[1429] A means for generating price suggestions and time optimization results using a generated AI model based on the analysis results,

[1430] A means of inputting prompt sentences into an AI model,

[1431] Means for transmitting the generated results to a terminal,

[1432] A means for displaying the results to the user on the terminal,

[1433] A system that includes this.

[1434] (Claim 2)

[1435] The system according to claim 1, wherein the generating AI model is a natural language processing engine.

[1436] (Claim 3)

[1437] The system according to claim 1, wherein the terminal means is a means for sending data to a server using an HTTP request.

[1438] "Application Example 1"

[1439] (Claim 1)

[1440] A means of having users input the data necessary for the price simulation,

[1441] A terminal means for transmitting the aforementioned data to a server,

[1442] A means for receiving transmitted data and analyzing said data,

[1443] A means for generating price suggestions and time optimization results using a generated AI model based on the analysis results,

[1444] Means for transmitting the generated results to a terminal,

[1445] A means for displaying the results to the user on the terminal,

[1446] A means for automatically generating prompt sentences necessary for generating the aforementioned analysis results,

[1447] A system that includes this.

[1448] (Claim 2)

[1449] The system according to claim 1, wherein the generating AI model is a natural language processing engine.

[1450] (Claim 3)

[1451] The system according to claim 1, wherein the terminal means is a means for sending data to a server using an HTTP request.

[1452] "Example 2 of combining an emotion engine"

[1453] (Claim 1)

[1454] A means of having users input the data necessary for the price simulation,

[1455] Means for recognizing the aforementioned data and user sentiment data,

[1456] A terminal means for transmitting the aforementioned data and emotional data to a server,

[1457] A means for receiving transmitted data and sentiment data, and for analyzing said data,

[1458] A means for generating price suggestions and time optimization results using an artificial intelligence model based on analysis results and sentiment data,

[1459] Means for transmitting the generated results to a terminal,

[1460] A means for displaying the results to the user on the terminal,

[1461] A system that includes this.

[1462] (Claim 2)

[1463] The system according to claim 1, wherein the artificial intelligence model is a natural language processing engine that generates optimal price suggestions and time optimization results based on the analysis results and sentiment data.

[1464] (Claim 3)

[1465] The system according to claim 1, wherein the terminal means is a means for transmitting data and sentiment data to a server using an HTTP request.

[1466] "Application example 2 when combining with an emotional engine"

[1467] (Claim 1)

[1468] A means of having users input the data necessary for the price simulation,

[1469] A terminal means for transmitting the aforementioned data and user sentiment data to a server,

[1470] A means for receiving transmitted data and sentiment data, and for analyzing said data and sentiment data,

[1471] A means for generating price suggestions and time optimization results using an artificial intelligence model based on analysis results,

[1472] Means for transmitting the generated results to a terminal,

[1473] A means for displaying the results to the user on the terminal,

[1474] A system that includes this.

[1475] (Claim 2)

[1476] The system according to claim 1, wherein the artificial intelligence model is a natural language processing engine.

[1477] (Claim 3)

[1478] The system according to claim 1, wherein the terminal means is a means for sending data to a server using an HTTP request. [Explanation of Symbols]

[1479] 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 having users input the data necessary for the price simulation, A terminal means for transmitting the aforementioned data to a server, A means for receiving transmitted data and analyzing said data, A means for generating price suggestions and time optimization results using an artificial intelligence model based on analysis results, Means for transmitting the generated results to a terminal, A means for displaying the results to the user on the terminal, A system that includes this.

2. The system according to claim 1, wherein the artificial intelligence model is a natural language processing engine.

3. The system according to claim 1, wherein the terminal means is a means for sending data to a server using an HTTP request.

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