system

Abstract

We provide the system. [Solution] A means for collecting user input data, Means for analyzing the aforementioned input data, A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed data, A means for presenting the generated proposals and estimates to the user, A means for collecting feedback from the aforementioned users, Means for improving the system based on the aforementioned feedback, 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 method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] The processes of creating conventional proposals and estimates require a great deal of time and labor by hand, which has caused an increase in the waiting time of users. In addition, there is a problem that the quality of proposals and estimates depends on the experience and ability of the person in charge, and variations are likely to occur. As a result, the satisfaction of users has decreased, and the work efficiency on the service provider side has also deteriorated. The purpose of the present invention is to solve these problems and generate proposals and estimates quickly and accurately, thereby shortening the waiting time of users and improving their satisfaction.

Means for Solving the Problems

[0005] The present invention solves the problem with a system that includes means for collecting user input data, means for analyzing the input data, means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data, means for presenting the generated proposals and estimates to the user, means for collecting feedback from the user, and means for improving the system based on the feedback. This automates the process of creating proposals and estimates, enabling the provision of fast and high-quality services. Furthermore, by continuously improving the system using machine learning algorithms, the accuracy of proposals and estimates can be further improved.

[0006] A "user" refers to an individual or legal entity that uses the system to request proposals and quotes.

[0007] "Input data" refers to information provided by the user, such as requirements, desired conditions, and budget.

[0008] "Means of collection" refers to interfaces and data collection functions used to obtain user input data.

[0009] "Means of analysis" refers to functions that analyze collected input data and extract information necessary for generating optimal proposals and estimates.

[0010] "Generative artificial intelligence" refers to artificial intelligence algorithms that automatically generate proposals and estimates based on collected and analyzed data.

[0011] A "proposal" refers to a series of solutions or designs generated by a generative artificial intelligence system based on user requirements.

[0012] "Estimate" refers to an estimate of the costs and resources required for a proposal.

[0013] "Means of presentation" refers to the functionality for displaying the generated proposals and estimates to the user visually or in other ways.

[0014] "Feedback" refers to the opinions and requests that users provide regarding the proposed solutions and quotes presented.

[0015] "Means of collection" refers to interfaces and data collection functions for obtaining user feedback.

[0016] "Means of improvement" refers to functions that analyze collected feedback and improve the performance of generative artificial intelligence and the overall system. [Brief explanation of the drawing]

[0017] [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]It shows an emotion map to which a plurality of 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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the language used in the following description will be described.

[0020] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0021] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0039] System Overview

[0040] User terminal

[0041] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data and sends it to the server.

[0042] server

[0043] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal suggestions and estimates. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0044] Generative artificial intelligence

[0045] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0046] Program processing

[0047] Collect user input data

[0048] Users enter their requirements and preferences into an input form on their device. For example, if they want a new website design, they would enter the necessary functions, design preferences, budget, etc.

[0049] Send input data

[0050] The terminal converts the collected data into JSON format and sends it to the server. The server immediately begins analyzing the data upon receiving it.

[0051] Data analysis and generation

[0052] The server analyzes the received data and performs the necessary preprocessing. The preprocessed data is then passed to a generative AI, which is instructed to generate optimal proposals and estimates. The generative AI analyzes the data and, based on past training data, creates optimal proposals (e.g., design proposals) and estimates.

[0053] Sending and displaying results

[0054] The server receives the generated design proposals and estimates and sends them to the terminal as a single package. The terminal displays this to the user, who then reviews the contents.

[0055] Gathering feedback

[0056] The user provides feedback on the presented proposals and quotes. The terminal collects this feedback, converts it back into JSON format, and sends it to the server. The server analyzes the feedback and uses it to improve the generative AI algorithms and the logic behind generating proposals.

[0057] Specific example

[0058] 1. User input

[0059] A user (real estate agent) requests a new property listing website. The user enters requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0060] 2. Data transmission and analysis

[0061] The server receives the data sent from the terminal and passes it on to the generative AI. Based on this data, the generative AI analyzes past design approaches and cost structures, and generates the optimal design proposal (e.g., layout proposals for a homepage and property details page) and an estimate (e.g., a total of 950,000 yen).

[0062] 3. Display of results and feedback

[0063] The device displays the generated design proposal and estimate to the user. The user reviews the content and provides feedback, such as, "The design is good, but I'd like to add 3 pages." The device collects this feedback and sends it to the server. The server and AI analyze the feedback and incorporate it into the next proposal.

[0064] Thus, the present invention can quickly respond to user requests and generate high-quality proposals and quotations. This reduces user waiting times and improves satisfaction.

[0065] The following describes the processing flow.

[0066] Step 1:

[0067] The user prepares the input data.

[0068] Users enter project requirements and preferences into an input form on their device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc.

[0069] Step 2:

[0070] The device collects data.

[0071] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). This makes it easier to send to the server.

[0072] Step 3:

[0073] The device sends data to the server.

[0074] The device sends the collected data to the server as an HTTP request. The data transmission includes appropriate authentication information and request headers.

[0075] Step 4:

[0076] The server receives the data.

[0077] The server receives HTTP requests sent from terminals and parses the data. It performs data format validation and preprocessing, and converts the data into a format suitable for generative AI.

[0078] Step 5:

[0079] The server calls a generative AI.

[0080] The server passes the pre-processed data to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[0081] Step 6:

[0082] Generative AI generates proposals and estimates.

[0083] Generative AI automatically generates optimal proposals and estimates based on given data. It uses machine learning models to derive the best solutions based on information learned from past data.

[0084] Step 7:

[0085] The server receives the generated result.

[0086] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The proposal includes design ideas and detailed cost estimates.

[0087] Step 8:

[0088] The server sends the generated results to the terminal.

[0089] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[0090] Step 9:

[0091] The terminal displays the generated result.

[0092] The terminal receives a response from the server and displays the proposal and estimate to the user. The user can review this information and evaluate its contents.

[0093] Step 10:

[0094] Users provide feedback.

[0095] Users provide feedback on the presented proposals and quotes. For example, they may enter requests for design revisions or additional requests.

[0096] Step 11:

[0097] The device collects feedback.

[0098] The device collects user feedback and converts it back into JSON format. The collected feedback is used to create future suggestions.

[0099] Step 12:

[0100] The device sends feedback to the server.

[0101] The device sends the collected feedback to the server. The server analyzes the user feedback and uses it to improve the generative AI and the overall system.

[0102] (Example 1)

[0103] 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."

[0104] Conventional quotation generation systems struggled to respond quickly and accurately to user requests, and especially in projects with complex requirements, providing optimal proposals and quotations was difficult. Furthermore, there was a lack of mechanisms to effectively utilize user feedback and reflect it in system improvements, leaving room for improvement in the quality of final proposals and user satisfaction.

[0105] 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.

[0106] In this invention, the server includes means for collecting user input data, means for converting the collected data into a data format and transmitting it to the server, means for analyzing the input data and performing necessary preprocessing, means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data, means for transmitting the generated proposals and estimates as a single package to the user terminal, means for collecting user feedback and transmitting it back to the server, and means for improving the algorithm and generation logic based on the feedback. This makes it possible to respond quickly and accurately to user requests and provide high-quality proposals and estimates. Furthermore, since the system can be continuously improved by effectively utilizing user feedback, the quality of the final proposals and user satisfaction can be improved.

[0107] A "user" is an individual or organization that uses the system to request the generation of proposals and quotes.

[0108] A "terminal" is a hardware device equipped with an interface for users to provide input data, such as a personal computer or a smartphone.

[0109] A "server" is a centralized computer system that receives data sent from user terminals, analyzes it, and requests data processing from generative artificial intelligence.

[0110] A "data format" is a standardized format used when sending, receiving, or storing data in a computer system, such as the JSON format.

[0111] "Generative artificial intelligence" refers to a program that uses machine learning algorithms to analyze input data and generate optimal suggestions and estimates.

[0112] A "proposal" refers to a solution provided by a generative artificial intelligence system based on the user's requirements and preferences, such as a design proposal.

[0113] An "estimate" is the result of calculations regarding the costs and resources necessary to realize a proposed plan.

[0114] "Feedback" refers to the opinions and requests for changes that users provide regarding the proposed solutions and estimates presented.

[0115] An "algorithm" is a set of computational procedures or methods for solving a specific problem.

[0116] "Generative logic" refers to a set of rules and procedures that generative artificial intelligence uses to generate proposals and estimates.

[0117] "Preprocessing" refers to initial data processing steps to make input data easier to analyze, such as data cleansing and standardization.

[0118] A "package" is a document or file format that aggregates generated proposals and estimates into a single, cohesive set.

[0119] "Analysis" is the process of thoroughly examining collected data and extracting meaningful information.

[0120] This invention is a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0121] System Overview

[0122] User terminal

[0123] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data, converts it to JSON format, and sends it to the server.

[0124] For example, when a real estate agent requests a new property listing website, the user will input specific requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0125] server

[0126] The server receives and analyzes data sent from the user's terminal. It performs preprocessing such as data cleansing and data standardization on the received data, and then passes it on to a generative AI for processing. The generative AI generates proposals and estimates based on the analyzed data. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0127] In this example, the server analyzes data sent by the real estate agent, such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and passes it to a generative AI. Based on this data, the AI ​​generates optimal design proposals (layout proposals for the homepage and property details pages, etc.) and an estimate (totaling 950,000 yen).

[0128] Generative artificial intelligence

[0129] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions, drawing on historical and training data. The generated design proposals and estimates are returned to the server, sent to the user's terminal, and displayed to the user.

[0130] Gathering feedback and improving the system

[0131] user

[0132] Users provide feedback on proposals and quotes presented on their devices. For example, they might offer specific feedback such as, "The design is good, but I'd like to add three more pages."

[0133] terminal

[0134] The device collects this feedback, converts it back to JSON format, and sends it to the server.

[0135] server

[0136] The server analyzes the received feedback and passes it to the generative AI. The generative AI uses this feedback to improve its algorithms and generation logic, and incorporates these improvements into the generation of future proposals and estimates.

[0137] Examples of prompt statements

[0138] Examples of prompt statements for a generative AI model are as follows:

[0139] Prompt message:

[0140] "This is a request from a real estate agent. They need a 10-page property listing website. The budget is under 1 million yen, and the target audience is young people. Based on past design patterns and cost structures, please generate optimal design proposals (website layout, property detail page layouts, etc.) and estimates."

[0141] Thus, the present invention is capable of responding quickly and accurately to user requests, generating high-quality proposals and quotations, and further improving the system by reflecting user feedback, thereby enhancing the quality of the final proposals and user satisfaction.

[0142] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0143] Step 1: The user enters the required information via a web form or application on their device. This input includes specific requirements (e.g., a 10-page website, a budget of under 1 million yen, targeting young people). The device collects this information and converts it into JSON format. The input data is based on the user's requests, and the converted JSON data is output.

[0144] Step 2: The terminal sends the collected JSON data to the server. The server parses the received JSON data. Here, the server confirms receipt of the data and simultaneously checks the data integrity (validity of format and data type). For example, it checks for missing fields or invalid values ​​and performs a cleansing process. As a result of the analysis, the cleansed data is output.

[0145] Step 3: The server passes the pre-processed data to the generative AI and requests the generation of proposals and estimates. The generative AI uses machine learning algorithms to analyze historical data and training data to generate optimal proposals (e.g., web design proposals) and estimates (e.g., total cost of 950,000 yen). This generation process involves analysis and data calculations based on the input data. The generated proposals and estimates are then output.

[0146] Step 4: The generative AI returns the generated design proposals and estimates to the server. The server packages this data together. In this packaging process, the design proposals and estimates are converted to an appropriate format (e.g., HTML or PDF) and output as a single file or document. The packaged data is then output.

[0147] Step 5: The server sends the packaged data to the user's terminal. The terminal receives the transmitted data and displays it to the user. Here, the user can review the displayed design proposals and estimates. The packaged data is displayed on the user's screen.

[0148] Step 6: The user provides feedback on the presented proposal and estimate. Specifically, they enter specific requests and opinions, such as "The design is good, but I would like to add 3 pages." This feedback is collected on the device and converted back into JSON format. The feedback data is then output.

[0149] Step 7: The device sends the collected feedback data to the server. The server analyzes the received feedback. This analysis process examines the content of the feedback in detail and converts it into a data structure for passing to the generative AI. For example, it extracts the number of additional pages and the requirements for design changes. The analyzed feedback data is output.

[0150] Step 8: The server passes the analyzed feedback data to the generative AI and requests it to regenerate the proposals and estimates. The generative AI improves its algorithms and generation logic based on the feedback and generates new proposals and estimates. In this process, the initial input data and feedback data are integrated to create the new generation. The improved proposals and estimates are output.

[0151] Step 9: The generative AI returns the new proposals and estimates it has generated to the server, which then repackages them. The repackaged data is then output.

[0152] Step 10: The server sends the improved, packaged data to the user's terminal. The terminal displays the sent data to the user, who then reviews the improved proposal and estimate. This facilitates the iterative generation of proposals and estimates until the user is satisfied.

[0153] (Application Example 1)

[0154] 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."

[0155] In modern e-commerce, users face the problem of spending a lot of time and effort making the best choice from a wide variety of products. Furthermore, it is difficult to smoothly place customized orders based on user preferences and budgets, and there is a need for quick quotes. Existing systems fail to provide appropriate proposals and quotes to meet user needs, which is a factor in lowering user satisfaction.

[0156] 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.

[0157] In this invention, the server includes means for collecting user input data, means for analyzing the input data, means for a generative artificial intelligence to generate optimal product suggestions and quotes based on the analyzed data, means for presenting the generated product suggestions and quotes to the user, means for collecting feedback from the user, and means for improving the system based on the feedback. This enables the provision of quick and accurate product suggestions and quotes that meet the user's desired conditions, significantly improving the user experience on e-commerce sites.

[0158] A "user" is a buyer who wishes to use the system to receive product suggestions and quotes.

[0159] "Input data" refers to information such as desired conditions and budget that users provide to the system.

[0160] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate optimal product suggestions and quotes.

[0161] "Product suggestions" refer to the optimal product selection provided by generative artificial intelligence based on the user's desired conditions.

[0162] "Estimate" refers to price information for a product calculated by a generative artificial intelligence system.

[0163] "Feedback" refers to opinions and requests regarding suggestions and estimates that users provide to the system.

[0164] A "system" refers to a device or program that includes components that perform a series of processes to generate product suggestions and quotations based on user input data and present them to the user.

[0165] This invention relates to a system that collects user input data, analyzes it, and uses generative artificial intelligence to generate optimal product suggestions and quotes, which are then presented to the user. The system mainly consists of a user terminal, a server, and generative artificial intelligence (AI).

[0166] System Overview

[0167] User terminal

[0168] The user terminal has an interface for users to provide input data. Users use web forms or applications to input information such as desired products, budget, and customization requirements. The terminal collects this input data and sends it to the server.

[0169] server

[0170] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal product suggestions and quotes. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0171] Generative artificial intelligence

[0172] Generative AI generates product suggestions and quotes based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0173] Program details and technologies used

[0174] Hardware and software used

[0175] Hardware: Standard PC terminals, smartphones, cloud servers (general term)

[0176] Software: Flask (Web application framework), generative AI libraries (e.g., custom models based on TENSORFLOW® or PyTorch)

[0177] Data processing and calculation

[0178] The terminal converts the data collected from the user into JSON format and sends it to the server. The server receives this data and performs preprocessing. The preprocessed data is then passed to a generative AI, which analyzes the data and generates optimal product suggestions and quotes. The generated suggestions and quotes are sent back to the user terminal via the server and presented to the user.

[0179] Specific example

[0180] 1. User input

[0181] Users enter their desired purchase criteria on a specific product page of an online shopping site. For example, they might enter, "I would like a blue leather sofa. My budget is under 50,000 yen."

[0182] 2. Data transmission and analysis

[0183] The terminal converts the input data into JSON format and sends it to the server. The server analyzes the received data, performs the necessary preprocessing, and passes the data to the generative AI. The generative AI then generates optimal product suggestions and quotes based on that data.

[0184] 3. Display of results and feedback

[0185] The server sends the generated product proposals and quotes to the terminal as a single package. The user reviews the presented product proposal (for example, "Blue leather sofa (in stock), price 48,000 yen").

[0186] 4. Gathering feedback

[0187] Users provide feedback on the presented product suggestions and quotes. For example, they might enter feedback such as, "The design is good, but I'd like more premium options." The device collects this feedback, converts it back into JSON format, and sends it to the server. The server and AI analyze the feedback and incorporate it into future suggestions.

[0188] In this way, this invention can respond quickly and accurately to the desired conditions of users on e-commerce sites. Furthermore, by having the generative AI learn from the feedback, the overall accuracy of the system and user satisfaction can be improved.

[0189] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0190] Step 1: Collecting user input data

[0191] Users enter their desired purchase conditions, budget, and customization requirements on specific product pages of the online shopping site. For example, they might enter conditions such as, "I would like a blue leather sofa. My budget should be under 50,000 yen."

[0192] Input details: Desired product specifications (color, material, budget, etc.)

[0193] Output data: Input data in JSON format

[0194] Step 2: Submit the input data

[0195] The device converts the collected data into JSON format and sends it to the server. The device sends the data through the API endpoint.

[0196] Input content: Input data in JSON format

[0197] Output data: Data sent to the server

[0198] Step 3: Data reception and analysis

[0199] The server receives the data sent from the terminal and begins analysis. Preprocessing, such as data formatting and normalization, is performed at this stage.

[0200] Input content: JSON format data sent from the device.

[0201] Output data: Preprocessed data

[0202] Step 4: Providing data to generative AI

[0203] The server passes the pre-processed data to the generative AI and instructs it to generate product suggestions and quotes.

[0204] Input content: Preprocessed data

[0205] Output data: Data passed to the generative AI.

[0206] Step 5: AI-powered proposal and quote generation

[0207] Generative AI analyzes data provided by a server to generate optimal product suggestions and quotes. The algorithm is executed based on historical data and training data.

[0208] Input content: Data provided to the generative AI

[0209] Output data: Product proposal and quotation

[0210] Step 6: Submit the results

[0211] The server sends the proposals and estimates received from the generative AI to the user's device. The user can then view this data on their device.

[0212] Input content: Generated product proposal and estimate

[0213] Output data: Proposals and quotes sent to the terminal

[0214] Step 7: Display to the user

[0215] The terminal displays product suggestions and quotes sent from the server to the user, allowing the user to review them.

[0216] Input content: Product proposal and quotation sent to the terminal.

[0217] Output data: Product proposals and quotes presented for user review.

[0218] Step 8: Gathering Feedback

[0219] Users provide feedback on the displayed product suggestions and quotes. For example, they might say, "The design is great, but I'd like to see more premium options."

[0220] Input content: User feedback (text format)

[0221] Output data: Feedback data in JSON format

[0222] Step 9: Submitting Feedback

[0223] The device converts the collected feedback data into JSON format and sends it to the server.

[0224] Input content: Text-formatted feedback

[0225] Output data: Feedback data in JSON format sent to the server

[0226] Step 10: Analysis of Feedback

[0227] The server receives feedback data and performs analysis. The analysis results are used to improve the algorithms of the generative AI.

[0228] Input content: Feedback data sent to the server

[0229] Output data: Improvement data for the AI ​​algorithm

[0230] The above outlines the specific processing steps of the system that realizes the application example. This allows users to obtain product proposals and quotes quickly and accurately, and also contributes to improving the system's accuracy through feedback.

[0231] 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.

[0232] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0233] System Overview

[0234] User terminal

[0235] The user terminal provides an interface for users to input information and has the functionality to collect emotional data. Users input information such as project requirements, preferences, and budget through web forms and applications. Simultaneously, user facial expression and voice data are collected and analyzed by the emotion engine.

[0236] server

[0237] The server receives and analyzes data sent from the user's terminal. Before the data is passed to the generative AI, the emotion engine analyzes the user's emotions, and the results are also provided to the AI. This ensures that proposals and estimates reflect the user's emotions.

[0238] Generative artificial intelligence and emotion engines

[0239] The generative AI generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithms. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[0240] Program processing

[0241] Collect user input data

[0242] Users enter project requirements and preferences into input forms on their devices. Their facial expressions and voice are also collected via the camera and microphone. For example, when requesting a new website design, the user enters the necessary functions, budget, and design preferences, while their facial expressions and voice are simultaneously collected.

[0243] Data analysis and transmission

[0244] The device converts the collected text and emotion data into JSON format and sends it to the server. The server uses an emotion engine to analyze the user's emotions and passes this information to a generative AI.

[0245] Proposal and Estimate Generation

[0246] The server sends emotional data analyzed by the emotion engine, along with other input data, to the generative AI. The generative AI uses this data to generate optimal suggestions and quotes for the user. By using machine learning algorithms and taking the user's emotional state into account, more appropriate suggestions can be provided.

[0247] Sending and displaying results

[0248] The server receives the generated proposals and estimates and sends them to the user's terminal. The terminal displays them to the user, who then reviews the proposals and estimates. These may include design proposals and detailed cost estimates, for example.

[0249] Feedback collection and analysis

[0250] Users provide feedback on the presented proposals and quotes. User input data and re-collected sentiment data are collected by the terminal and sent to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[0251] Specific example

[0252] 1. User input

[0253] A user (for example, a real estate agent) requests a new property listing website. The user inputs requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and their facial expressions and voice are also collected.

[0254] 2. Data transmission and analysis

[0255] The server receives and analyzes the requirements data and emotion data sent from the terminal. The emotion engine analyzes the user's emotions and passes this data to the generative AI.

[0256] 3. Generating proposals

[0257] The generative AI generates optimal design proposals and estimates based on emotional data analyzed by the emotion engine and other requirements data. For example, it generates a "design proposal for the entire site" and an "estimated cost of 950,000 yen."

[0258] 4. Presentation of results and feedback

[0259] The device displays the generated design proposal and estimate to the user. The user provides feedback such as, "The design is good, but I would like a more colorful design," and their facial expression and voice are collected again. The device sends the feedback and emotion data to the server.

[0260] 5. Analysis and implementation of feedback

[0261] The server analyzes feedback and sentiment data and incorporates it into the generative AI. For subsequent proposals, suggestions and estimates that are better adapted to the user's emotions will be generated.

[0262] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] The user prepares the input data.

[0266] The user enters project requirements and preferences into an input form on the device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc. The device simultaneously collects the user's facial expressions and voice data through its camera and microphone.

[0267] Step 2:

[0268] The device collects data.

[0269] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). The collected facial expression data and voice data are also sent to the server at the same time.

[0270] Step 3:

[0271] The device sends data to the server.

[0272] The device sends the collected text and sentiment data to the server as an HTTP request. The data transmission also includes appropriate authentication information and request headers.

[0273] Step 4:

[0274] The server receives the data.

[0275] The server receives HTTP requests sent from terminals and parses the data. This parsing includes data format validation and preprocessing. Sentiment data is then prepared to be sent to the sentiment engine.

[0276] Step 5:

[0277] The server calls the emotion engine.

[0278] The server passes the preprocessed text data and emotion data to the emotion engine. Specifically, it includes API calls and execution of emotion analysis algorithms.

[0279] Step 6:

[0280] The emotion engine analyzes the user's emotion.

[0281] The emotion engine analyzes the given facial expression data, voice data, and text data to identify the user's emotional state. For example, it determines whether the user is nervous or happy. The analysis results are integrated with the text data and sent to the generative AI.

[0282] Step 7:

[0283] The server calls the generative AI.

[0284] The server passes the text data integrated with emotion data to the generative AI. Specifically, it includes API calls and execution of AI algorithms.

[0285] Step 8:

[0286] The generative AI generates proposals and estimates.

[0287] The generative AI automatically generates optimal proposals and estimates based on the given data. It uses a machine learning model to derive an optimal solution based on the information learned from past data. The user's emotional state is also taken into consideration.

[0288] Step 9:

[0289] The server receives the generation result.

[0290] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The formatted data includes design proposals and detailed cost estimates.

[0291] Step 10:

[0292] The server sends the generated results to the terminal.

[0293] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[0294] Step 11:

[0295] The terminal displays the generated result.

[0296] The terminal receives a response from the server and displays proposals and estimates to the user. The user can review this information and evaluate its contents. The displayed proposals may include, for example, design proposals and estimated costs.

[0297] Step 12:

[0298] Users provide feedback.

[0299] Users provide feedback on the presented proposals and quotes. For example, they might enter requests such as, "The design is excellent, but I'd like the colors changed." During this process, the user's facial expressions and voice are also collected again.

[0300] Step 13:

[0301] The device collects feedback.

[0302] The terminal collects the user's feedback and converts it back into JSON format. It then sends the collected feedback and sentiment data to the server.

[0303] Step 14:

[0304] The server receives the feedback.

[0305] The server analyzes the collected feedback and sentiment data and reflects it in the generation of the next proposal and estimate. The generative AI and sentiment engine improve the algorithm based on this feedback information and apply it when creating the next proposal.

[0306] (Example 2)

[0307] Next, 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] In conventional proposal and estimation systems, the main activity is the generation of proposals and estimates based on the user's input data, and it has been difficult to fully reflect the user's emotions and feedback. As a result, proposals and estimates that do not match the user's desires may be generated, leading to a problem of reduced satisfaction. The purpose of this invention is to solve such problems and improve the user's satisfaction by analyzing the user's sentiment data and reflecting it in the generative artificial intelligence.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0310] In this invention, the server includes means for collecting and analyzing user input data and sentiment data; means for a generative artificial intelligence to generate proposals and estimates; means for presenting the generated proposals and estimates to the user; and means for collecting and analyzing user feedback and improving the system. This makes it possible to generate proposals and estimates that take the user's sentiments into account.

[0311] "User input data" refers to information that users enter through the terminal interface, such as project requirements and desired conditions.

[0312] "Emotional data" refers to information that indicates a user's emotional state, obtained from the user's facial expression data and voice data.

[0313] "Means of analysis" refers to algorithms and technologies used to analyze user input data and sentiment data, and to generate necessary proposals and estimates based on that content.

[0314] "Generative artificial intelligence" refers to algorithms and technologies that generate proposals and estimates based on provided data, and utilize machine learning and deep learning techniques.

[0315] "Proposals and estimates" refer to the specific proposals and their estimated costs generated by generative artificial intelligence based on user input data and sentiment data.

[0316] "Feedback" refers to the evaluations, opinions, and additional sentiment data that users provide regarding the proposed ideas and quotes presented.

[0317] "Means of improving the system" refers to methods for analyzing user feedback and sentiment data to improve the quality of future proposals and quotes.

[0318] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0319] User terminal

[0320] The user terminal provides an interface for users to input information. Through web forms and applications, users enter information such as project requirements, preferences, and budget. It also uses cameras and microphones to collect user facial and voice data. This simultaneously collects user emotional data. For example, when requesting a new website design, the user inputs the necessary functions, budget, and design preferences, while their facial expressions and voice are also collected.

[0321] server

[0322] The server is responsible for receiving and analyzing data sent from user terminals. An emotion engine is used for analysis, specifically to analyze the user's emotions. IBM Watson's emotion analysis API is used for this purpose. The results are then provided to a generative AI (for example, OpenAI's GPT-3 model) to generate optimal suggestions and estimates.

[0323] Generative artificial intelligence and emotion engines

[0324] The generative AI generates optimal proposals and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithm. This enables the generation of proposals and quotes that respond to the user's emotions. For example, in response to requirements such as "I want a 10-page website, with a budget of under 1 million yen," the system analyzes the user's facial expressions and voice, such as "I would like a more colorful design," to generate the most suitable proposal.

[0325] Presentation to the user

[0326] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays these to the user, who then reviews the proposals and estimates. These may include, for example, design proposals and detailed cost estimates.

[0327] Feedback collection and analysis

[0328] Users provide feedback on the presented proposals and quotes. This feedback is collected by the terminal as user input data and newly collected sentiment data, and then sent back to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[0329] Specific example

[0330] 1. User input

[0331] User: "I'd like to request a new property listing website. It should be 10 pages long, with a budget of under 1 million yen, and the target audience is young people."

[0332] The user's expression is smiling, and they add in their voice, "I'd prefer a more colorful design."

[0333] 2. Data transmission and analysis

[0334] The device converts the input data and emotion data into JSON format and sends it to the server.

[0335] The server analyzes the data, and the emotion engine analyzes the emotional data, determining, for example, "positive" and "excited."

[0336] 3. Generating proposals

[0337] The server sends the analysis data to the generative AI.

[0338] The generative AI generated a "colorful, youth-oriented design proposal and estimated cost of 950,000 yen."

[0339] 4. Presentation of results and feedback

[0340] The device displays the generated design proposals and estimates to the user.

[0341] A user commented, "The design is good, but I would like a more colorful design."

[0342] 5. Analysis and implementation of feedback

[0343] The server re-analyzes the feedback and sentiment data.

[0344] In the next proposal, the generated proposal will be more tailored to the user's emotions.

[0345] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The user enters project requirements and desired conditions into an input form on their device. Furthermore, the user's facial expressions and voice are collected via the camera and microphone. Input includes text data such as "10-page website" or voice data such as "Make it more colorful." The collected data is converted into JSON format as output.

[0349] Step 2:

[0350] The device converts the collected text and sentiment data into JSON format and sends it to the server. Specifically, it uses an HTTP POST request to send the data. The input is the collected text and sentiment data, and the output is the converted JSON data.

[0351] Step 3:

[0352] The server parses the JSON data received from the terminal. An emotion engine is used for the analysis to determine the user's emotions. For example, the analysis is performed via IBM Watson's emotion analysis API. The input is the received JSON data, and the output is a new dataset containing the emotion analysis results.

[0353] Step 4:

[0354] The server sends emotion data analyzed by the emotion engine and other input data to the generative AI. The generative AI (e.g., OpenAI's GPT-3) generates optimal proposals and estimates based on this data. The input is a dataset containing the analysis results, and the output is the generated proposals and estimates.

[0355] Step 5:

[0356] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays this information to the user. The input is the proposals and estimates obtained from the generative AI, and the output is the information displayed on the user's terminal. For example, the screen might display "Colorful, youth-oriented design proposal, estimated cost 950,000 yen."

[0357] Step 6:

[0358] Users provide feedback on the presented proposals and estimates. Specifically, they input feedback through the terminal interface, and their facial expressions and voice are also collected. The input consists of the user's feedback and associated emotion data, and the output is feedback data converted into JSON format.

[0359] Step 7:

[0360] The terminal converts the feedback and recollected sentiment data into JSON format and sends it to the server. The server re-analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and estimates. The input is feedback and sentiment data, and the output is the re-analyzed dataset.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0363] Conventional proposal systems generate mechanical proposals and quotes without considering the user's emotional state, making it difficult to fully meet the user's essential needs and desires. Furthermore, there was a lack of systems to effectively incorporate user feedback and utilize it in future proposals. As a result, user satisfaction was low, and the accuracy of proposals did not improve easily.

[0364] 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. In this invention, the server includes means for collecting user input data and sentiment data, means for analyzing the input data and sentiment data, and means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data. This makes it possible to generate more appropriate proposals and estimates that take the user's sentiment data into consideration. Furthermore, by analyzing user feedback and sentiment data and reflecting it in the generation of future proposals and estimates, the accuracy of proposals and user satisfaction can be improved.

[0365] "User input data" refers to data that users provide to the system, including information, requests, conditions, and desired budget.

[0366] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions and voice.

[0367] "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates optimal suggestions and estimates based on user input data and emotional data.

[0368] A "proposal" refers to a specific product or service plan proposed by a generative artificial intelligence system in response to a user's request.

[0369] "Estimate" refers to detailed information, including the calculation of price and costs for a proposed item.

[0370] "Feedback" refers to data such as opinions, evaluations, and impressions of proposals and quotes provided by users.

[0371] An "emotion engine" is a dedicated engine that analyzes a user's facial expressions and voice to generate emotional data.

[0372] A "server" is a central computer system that receives and analyzes data from users and generates proposals and estimates using generative artificial intelligence and an emotion engine.

[0373] A "user terminal" is a device used by a user to access a system and input data, and includes smart glasses, smartphones, and other similar devices.

[0374] Bluetooth is a type of short-range wireless communication technology used to send and receive data between a user terminal and a server.

[0375] "Wi-Fi" is a wireless LAN technology, a communication method used to send and receive data between user terminals and servers.

[0376] This invention relates to a system that generates and presents optimal suggestions and quotes using user input data and sentiment data. This system includes a user terminal, a server, generative artificial intelligence, and a sentiment engine.

[0377] System Configuration

[0378] User terminal

[0379] The user terminal refers to devices such as smart glasses or smartphones, which provide an interface for users to input information. This allows users to input information such as project requirements, desired conditions, and budget. Additionally, user facial expression and voice data are collected via cameras and microphones and analyzed by an emotion engine.

[0380] server

[0381] The server receives and analyzes data sent from the user's terminal. The received data is then analyzed by an emotion engine to determine the user's emotions, and this analysis, along with the results, is provided to the generative artificial intelligence. As a result, proposals and estimates reflect the user's emotions.

[0382] Generative artificial intelligence and emotion engines

[0383] The generative artificial intelligence generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative artificial intelligence's algorithm. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[0384] Program processing

[0385] Data collection and transmission

[0386] Users input project requirements, preferences, budget, etc., through smart glasses, and their facial expressions and voice are also collected via camera and microphone. This data is transmitted to a server using Bluetooth or Wi-Fi. The data is converted to JSON format and analyzed on the server.

[0387] Data Analysis

[0388] The server uses an emotion engine to analyze the transmitted data. The results of this analysis are passed to a generative artificial intelligence system, which is used to generate optimal suggestions and estimates.

[0389] Proposal and estimate generation and display

[0390] The server uses the analyzed sentiment data and other input data to generate optimal suggestions and estimates using generative artificial intelligence. The generated suggestions and estimates are displayed on the smart glasses' screen.

[0391] Feedback collection and analysis

[0392] When a user provides feedback on a proposal and quote, their facial expressions and voice are also collected and sent to the server. The server analyzes the feedback and emotional data and incorporates it into future proposals.

[0393] Specific example

[0394] For example, if a user says, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," the smart glasses collect this information, and the emotion engine detects that the user's expression is serious. Based on this information, the generative artificial intelligence generates a suggestion: "X smartphone, 28,000 yen, 24-hour battery life." If the user provides feedback such as, "I'd like more color variations," that feedback and emotion data are sent back to the server and reflected in the next suggestion.

[0395] Example of a prompt

[0396] "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The user wears smart glasses and inputs project requirements, preferences, budget, etc., via voice or text input, while facial expression and voice data are collected via camera and microphone. The input data includes information about the user's desired smartphone features and price range.

[0400] Step 2:

[0401] The device converts the collected user input data, facial expression data, and voice data into JSON format. This data includes information such as, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," as recorded by the user's voice input.

[0402] Step 3:

[0403] The device sends data converted to JSON format to the server via Bluetooth or Wi-Fi. The input data (user requirements and facial / voice data) reaches the server's endpoint.

[0404] Step 4:

[0405] The server analyzes the received JSON data and uses an emotion engine to extract emotion data from the user's facial expressions and voice. This analysis includes emotion information such as "the user has a serious expression."

[0406] Step 5:

[0407] The server inputs sentiment analysis data and user requirements data into a generative artificial intelligence (AI). Based on the input data, the AI ​​generates optimal suggestions and quotes. For example, it might generate suggestions such as "X smartphone, 28,000 yen, 24-hour battery life."

[0408] Step 6:

[0409] The server sends the generated proposals and estimates to the user's terminal (smart glasses). The output data (proposals and estimates) is displayed on the smart glasses' screen.

[0410] Step 7:

[0411] Users review proposals and quotes displayed on their smart glasses and provide feedback via voice or text. For example, they might say, "I'd like to see more color variations."

[0412] Step 8:

[0413] The device collects user feedback, facial expression data, and voice data again, converts them to JSON format, and sends them to the server. It then processes the data again based on the input data and sends it back.

[0414] Step 9:

[0415] The server analyzes user feedback and sentiment data and compares it with previous suggestion data. Using the sentiment engine, it re-evaluates the user's requests and emotions and provides new input data to the generative artificial intelligence.

[0416] Step 10:

[0417] The generative artificial intelligence generates further optimized proposals and estimates based on the analysis results, and displays them again on the user's terminal via the server. This ensures that the next proposal is more in line with the user's emotions and requests.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Second Embodiment]

[0422] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] This invention relates to a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0435] System Overview

[0436] User terminal

[0437] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data and sends it to the server.

[0438] server

[0439] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal suggestions and estimates. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0440] Generative artificial intelligence

[0441] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0442] Program processing

[0443] Collect user input data

[0444] Users enter their requirements and preferences into an input form on their device. For example, if they want a new website design, they would enter the necessary functions, design preferences, budget, etc.

[0445] Send input data

[0446] The terminal converts the collected data into JSON format and sends it to the server. The server immediately begins analyzing the data upon receiving it.

[0447] Data analysis and generation

[0448] The server analyzes the received data and performs the necessary preprocessing. The preprocessed data is then passed to a generative AI, which is instructed to generate optimal proposals and estimates. The generative AI analyzes the data and, based on past training data, creates optimal proposals (e.g., design proposals) and estimates.

[0449] Sending and displaying results

[0450] The server receives the generated design proposals and estimates and sends them to the terminal as a single package. The terminal displays this to the user, who then reviews the contents.

[0451] Gathering feedback

[0452] The user provides feedback on the presented proposals and quotes. The terminal collects this feedback, converts it back into JSON format, and sends it to the server. The server analyzes the feedback and uses it to improve the generative AI algorithms and the logic behind generating proposals.

[0453] Specific example

[0454] 1. User input

[0455] A user (real estate agent) requests a new property listing website. The user enters requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0456] 2. Data transmission and analysis

[0457] The server receives the data sent from the terminal and passes it on to the generative AI. Based on this data, the generative AI analyzes past design approaches and cost structures, and generates the optimal design proposal (e.g., layout proposals for a homepage and property details page) and an estimate (e.g., a total of 950,000 yen).

[0458] 3. Display of results and feedback

[0459] The device displays the generated design proposal and estimate to the user. The user reviews the content and provides feedback, such as, "The design is good, but I'd like to add 3 pages." The device collects this feedback and sends it to the server. The server and AI analyze the feedback and incorporate it into the next proposal.

[0460] Thus, the present invention can quickly respond to user requests and generate high-quality proposals and quotations. This reduces user waiting times and improves satisfaction.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user prepares the input data.

[0464] Users enter project requirements and preferences into an input form on their device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc.

[0465] Step 2:

[0466] The device collects data.

[0467] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). This makes it easier to send to the server.

[0468] Step 3:

[0469] The device sends data to the server.

[0470] The device sends the collected data to the server as an HTTP request. The data transmission includes appropriate authentication information and request headers.

[0471] Step 4:

[0472] The server receives the data.

[0473] The server receives HTTP requests sent from terminals and parses the data. It performs data format validation and preprocessing, and converts the data into a format suitable for generative AI.

[0474] Step 5:

[0475] The server calls a generative AI.

[0476] The server passes the pre-processed data to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[0477] Step 6:

[0478] Generative AI generates proposals and estimates.

[0479] Generative AI automatically generates optimal proposals and estimates based on given data. It uses machine learning models to derive the best solutions based on information learned from past data.

[0480] Step 7:

[0481] The server receives the generated result.

[0482] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The proposal includes design ideas and detailed cost estimates.

[0483] Step 8:

[0484] The server sends the generated results to the terminal.

[0485] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[0486] Step 9:

[0487] The terminal displays the generated result.

[0488] The terminal receives a response from the server and displays the proposal and estimate to the user. The user can review this information and evaluate its contents.

[0489] Step 10:

[0490] Users provide feedback.

[0491] Users provide feedback on the presented proposals and quotes. For example, they may enter requests for design revisions or additional requests.

[0492] Step 11:

[0493] The device collects feedback.

[0494] The device collects user feedback and converts it back into JSON format. The collected feedback is used to create future suggestions.

[0495] Step 12:

[0496] The device sends feedback to the server.

[0497] The device sends the collected feedback to the server. The server analyzes the user feedback and uses it to improve the generative AI and the overall system.

[0498] (Example 1)

[0499] 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."

[0500] Conventional quotation generation systems struggled to respond quickly and accurately to user requests, and especially in projects with complex requirements, providing optimal proposals and quotations was difficult. Furthermore, there was a lack of mechanisms to effectively utilize user feedback and reflect it in system improvements, leaving room for improvement in the quality of final proposals and user satisfaction.

[0501] 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.

[0502] In this invention, the server includes means for collecting user input data, means for converting the collected data into a data format and transmitting it to the server, means for analyzing the input data and performing necessary preprocessing, means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data, means for transmitting the generated proposals and estimates as a single package to the user terminal, means for collecting user feedback and transmitting it back to the server, and means for improving the algorithm and generation logic based on the feedback. This makes it possible to respond quickly and accurately to user requests and provide high-quality proposals and estimates. Furthermore, since the system can be continuously improved by effectively utilizing user feedback, the quality of the final proposals and user satisfaction can be improved.

[0503] A "user" is an individual or organization that uses the system to request the generation of proposals and quotes.

[0504] A "terminal" is a hardware device equipped with an interface for users to provide input data, such as a personal computer or a smartphone.

[0505] A "server" is a centralized computer system that receives data sent from user terminals, analyzes it, and requests data processing from generative artificial intelligence.

[0506] A "data format" is a standardized format used when sending, receiving, or storing data in a computer system, such as the JSON format.

[0507] "Generative artificial intelligence" refers to a program that uses machine learning algorithms to analyze input data and generate optimal suggestions and estimates.

[0508] A "proposal" refers to a solution provided by a generative artificial intelligence system based on the user's requirements and preferences, such as a design proposal.

[0509] An "estimate" is the result of calculations regarding the costs and resources necessary to realize a proposed plan.

[0510] "Feedback" refers to the opinions and requests for changes that users provide regarding the proposed solutions and estimates presented.

[0511] An "algorithm" is a set of computational procedures or methods for solving a specific problem.

[0512] "Generative logic" refers to a set of rules and procedures that generative artificial intelligence uses to generate proposals and estimates.

[0513] "Preprocessing" refers to initial data processing steps to make input data easier to analyze, such as data cleansing and standardization.

[0514] A "package" is a document or file format that aggregates generated proposals and estimates into a single, cohesive set.

[0515] "Analysis" is the process of thoroughly examining collected data and extracting meaningful information.

[0516] This invention is a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0517] System Overview

[0518] User terminal

[0519] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data, converts it to JSON format, and sends it to the server.

[0520] For example, when a real estate agent requests a new property listing website, the user will input specific requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0521] server

[0522] The server receives and analyzes data sent from the user's terminal. It performs preprocessing such as data cleansing and data standardization on the received data, and then passes it on to a generative AI for processing. The generative AI generates proposals and estimates based on the analyzed data. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0523] In this example, the server analyzes data sent by the real estate agent, such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and passes it to a generative AI. Based on this data, the AI ​​generates optimal design proposals (layout proposals for the homepage and property details pages, etc.) and an estimate (totaling 950,000 yen).

[0524] Generative artificial intelligence

[0525] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions, drawing on historical and training data. The generated design proposals and estimates are returned to the server, sent to the user's terminal, and displayed to the user.

[0526] Gathering feedback and improving the system

[0527] user

[0528] Users provide feedback on proposals and quotes presented on their devices. For example, they might offer specific feedback such as, "The design is good, but I'd like to add three more pages."

[0529] terminal

[0530] The device collects this feedback, converts it back to JSON format, and sends it to the server.

[0531] server

[0532] The server analyzes the received feedback and passes it to the generative AI. The generative AI uses this feedback to improve its algorithms and generation logic, and incorporates these improvements into the generation of future proposals and estimates.

[0533] Examples of prompt statements

[0534] Examples of prompt statements for a generative AI model are as follows:

[0535] Prompt message:

[0536] "This is a request from a real estate agent. They need a 10-page property listing website. The budget is under 1 million yen, and the target audience is young people. Based on past design patterns and cost structures, please generate optimal design proposals (website layout, property detail page layouts, etc.) and estimates."

[0537] Thus, the present invention is capable of responding quickly and accurately to user requests, generating high-quality proposals and quotations, and further improving the system by reflecting user feedback, thereby enhancing the quality of the final proposals and user satisfaction.

[0538] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0539] Step 1: The user enters the required information via a web form or application on their device. This input includes specific requirements (e.g., a 10-page website, a budget of under 1 million yen, targeting young people). The device collects this information and converts it into JSON format. The input data is based on the user's requests, and the converted JSON data is output.

[0540] Step 2: The terminal sends the collected JSON data to the server. The server parses the received JSON data. Here, the server confirms receipt of the data and simultaneously checks the data integrity (validity of format and data type). For example, it checks for missing fields or invalid values ​​and performs a cleansing process. As a result of the analysis, the cleansed data is output.

[0541] Step 3: The server passes the pre-processed data to the generative AI and requests the generation of proposals and estimates. The generative AI uses machine learning algorithms to analyze historical data and training data to generate optimal proposals (e.g., web design proposals) and estimates (e.g., total cost of 950,000 yen). This generation process involves analysis and data calculations based on the input data. The generated proposals and estimates are then output.

[0542] Step 4: The generative AI returns the generated design proposals and estimates to the server. The server packages this data together. In this packaging process, the design proposals and estimates are converted to an appropriate format (e.g., HTML or PDF) and output as a single file or document. The packaged data is then output.

[0543] Step 5: The server sends the packaged data to the user's terminal. The terminal receives the transmitted data and displays it to the user. Here, the user can review the displayed design proposals and estimates. The packaged data is displayed on the user's screen.

[0544] Step 6: The user provides feedback on the presented proposal and estimate. Specifically, they enter specific requests and opinions, such as "The design is good, but I would like to add 3 pages." This feedback is collected on the device and converted back into JSON format. The feedback data is then output.

[0545] Step 7: The device sends the collected feedback data to the server. The server analyzes the received feedback. This analysis process examines the content of the feedback in detail and converts it into a data structure for passing to the generative AI. For example, it extracts the number of additional pages and the requirements for design changes. The analyzed feedback data is output.

[0546] Step 8: The server passes the analyzed feedback data to the generative AI and requests it to regenerate the proposals and estimates. The generative AI improves its algorithms and generation logic based on the feedback and generates new proposals and estimates. In this process, the initial input data and feedback data are integrated to create the new generation. The improved proposals and estimates are output.

[0547] Step 9: The generative AI returns the new proposals and estimates it has generated to the server, which then repackages them. The repackaged data is then output.

[0548] Step 10: The server sends the improved, packaged data to the user's terminal. The terminal displays the sent data to the user, who then reviews the improved proposal and estimate. This facilitates the iterative generation of proposals and estimates until the user is satisfied.

[0549] (Application Example 1)

[0550] 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."

[0551] In modern e-commerce, users face the problem of spending a lot of time and effort making the best choice from a wide variety of products. Furthermore, it is difficult to smoothly place customized orders based on user preferences and budgets, and there is a need for quick quotes. Existing systems fail to provide appropriate proposals and quotes to meet user needs, which is a factor in lowering user satisfaction.

[0552] 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.

[0553] In this invention, the server includes means for collecting user input data, means for analyzing the input data, means for a generative artificial intelligence to generate optimal product suggestions and quotes based on the analyzed data, means for presenting the generated product suggestions and quotes to the user, means for collecting feedback from the user, and means for improving the system based on the feedback. This enables the provision of quick and accurate product suggestions and quotes that meet the user's desired conditions, significantly improving the user experience on e-commerce sites.

[0554] A "user" is a buyer who wishes to use the system to receive product suggestions and quotes.

[0555] "Input data" refers to information such as desired conditions and budget that users provide to the system.

[0556] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate optimal product suggestions and quotes.

[0557] "Product suggestions" refer to the optimal product selection provided by generative artificial intelligence based on the user's desired conditions.

[0558] "Estimate" refers to price information for a product calculated by a generative artificial intelligence system.

[0559] "Feedback" refers to opinions and requests regarding suggestions and estimates that users provide to the system.

[0560] A "system" refers to a device or program that includes components that perform a series of processes to generate product suggestions and quotations based on user input data and present them to the user.

[0561] This invention relates to a system that collects user input data, analyzes it, and uses generative artificial intelligence to generate optimal product suggestions and quotes, which are then presented to the user. The system mainly consists of a user terminal, a server, and generative artificial intelligence (AI).

[0562] System Overview

[0563] User terminal

[0564] The user terminal has an interface for users to provide input data. Users use web forms or applications to input information such as desired products, budget, and customization requirements. The terminal collects this input data and sends it to the server.

[0565] server

[0566] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal product suggestions and quotes. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0567] Generative artificial intelligence

[0568] Generative AI generates product suggestions and quotes based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0569] Program details and technologies used

[0570] Hardware and software used

[0571] Hardware: Standard PC terminals, smartphones, cloud servers (general term)

[0572] Software: Flask (Web application framework), generative AI libraries (e.g., custom models based on TensorFlow or PyTorch)

[0573] Data processing and calculation

[0574] The terminal converts the data collected from the user into JSON format and sends it to the server. The server receives this data and performs preprocessing. The preprocessed data is then passed to a generative AI, which analyzes the data and generates optimal product suggestions and quotes. The generated suggestions and quotes are sent back to the user terminal via the server and presented to the user.

[0575] Specific example

[0576] 1. User input

[0577] Users enter their desired purchase criteria on a specific product page of an online shopping site. For example, they might enter, "I would like a blue leather sofa. My budget is under 50,000 yen."

[0578] 2. Data transmission and analysis

[0579] The terminal converts the input data into JSON format and sends it to the server. The server analyzes the received data, performs the necessary preprocessing, and passes the data to the generative AI. The generative AI then generates optimal product suggestions and quotes based on that data.

[0580] 3. Display of results and feedback

[0581] The server sends the generated product proposals and quotes to the terminal as a single package. The user reviews the presented product proposal (for example, "Blue leather sofa (in stock), price 48,000 yen").

[0582] 4. Gathering feedback

[0583] Users provide feedback on the presented product suggestions and quotes. For example, they might enter feedback such as, "The design is good, but I'd like more premium options." The device collects this feedback, converts it back into JSON format, and sends it to the server. The server and AI analyze the feedback and incorporate it into future suggestions.

[0584] In this way, this invention can respond quickly and accurately to the desired conditions of users on e-commerce sites. Furthermore, by having the generative AI learn from the feedback, the overall accuracy of the system and user satisfaction can be improved.

[0585] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0586] Step 1: Collecting user input data

[0587] Users enter their desired purchase conditions, budget, and customization requirements on specific product pages of the online shopping site. For example, they might enter conditions such as, "I would like a blue leather sofa. My budget should be under 50,000 yen."

[0588] Input details: Desired product specifications (color, material, budget, etc.)

[0589] Output data: Input data in JSON format

[0590] Step 2: Submit the input data

[0591] The device converts the collected data into JSON format and sends it to the server. The device sends the data through the API endpoint.

[0592] Input content: Input data in JSON format

[0593] Output data: Data sent to the server

[0594] Step 3: Data reception and analysis

[0595] The server receives the data sent from the terminal and begins analysis. Preprocessing, such as data formatting and normalization, is performed at this stage.

[0596] Input content: JSON format data sent from the device.

[0597] Output data: Preprocessed data

[0598] Step 4: Providing data to generative AI

[0599] The server passes the pre-processed data to the generative AI and instructs it to generate product suggestions and quotes.

[0600] Input content: Preprocessed data

[0601] Output data: Data passed to the generative AI.

[0602] Step 5: AI-powered proposal and quote generation

[0603] Generative AI analyzes data provided by a server to generate optimal product suggestions and quotes. The algorithm is executed based on historical data and training data.

[0604] Input content: Data provided to the generative AI

[0605] Output data: Product proposal and quotation

[0606] Step 6: Submit the results

[0607] The server sends the proposals and estimates received from the generative AI to the user's device. The user can then view this data on their device.

[0608] Input content: Generated product proposal and estimate

[0609] Output data: Proposals and quotes sent to the terminal

[0610] Step 7: Display to the user

[0611] The terminal displays product suggestions and quotes sent from the server to the user, allowing the user to review them.

[0612] Input content: Product proposal and quotation sent to the terminal.

[0613] Output data: Product proposals and quotes presented for user review.

[0614] Step 8: Gathering Feedback

[0615] Users provide feedback on the displayed product suggestions and quotes. For example, they might say, "The design is great, but I'd like to see more premium options."

[0616] Input content: User feedback (text format)

[0617] Output data: Feedback data in JSON format

[0618] Step 9: Submitting Feedback

[0619] The device converts the collected feedback data into JSON format and sends it to the server.

[0620] Input content: Text-formatted feedback

[0621] Output data: Feedback data in JSON format sent to the server

[0622] Step 10: Analysis of Feedback

[0623] The server receives feedback data and performs analysis. The analysis results are used to improve the algorithms of the generative AI.

[0624] Input content: Feedback data sent to the server

[0625] Output data: Improvement data for the AI ​​algorithm

[0626] The above outlines the specific processing steps of the system that realizes the application example. This allows users to obtain product proposals and quotes quickly and accurately, and also contributes to improving the system's accuracy through feedback.

[0627] 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.

[0628] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0629] System Overview

[0630] User terminal

[0631] The user terminal provides an interface for users to input information and has the functionality to collect emotional data. Users input information such as project requirements, preferences, and budget through web forms and applications. Simultaneously, user facial expression and voice data are collected and analyzed by the emotion engine.

[0632] server

[0633] The server receives and analyzes data sent from the user's terminal. Before the data is passed to the generative AI, the emotion engine analyzes the user's emotions, and the results are also provided to the AI. This ensures that proposals and estimates reflect the user's emotions.

[0634] Generative artificial intelligence and emotion engines

[0635] The generative AI generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithms. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[0636] Program processing

[0637] Collect user input data

[0638] Users enter project requirements and preferences into input forms on their devices. Their facial expressions and voice are also collected via the camera and microphone. For example, when requesting a new website design, the user enters the necessary functions, budget, and design preferences, while their facial expressions and voice are simultaneously collected.

[0639] Data analysis and transmission

[0640] The device converts the collected text and emotion data into JSON format and sends it to the server. The server uses an emotion engine to analyze the user's emotions and passes this information to a generative AI.

[0641] Proposal and Estimate Generation

[0642] The server sends emotional data analyzed by the emotion engine, along with other input data, to the generative AI. The generative AI uses this data to generate optimal suggestions and quotes for the user. By using machine learning algorithms and taking the user's emotional state into account, more appropriate suggestions can be provided.

[0643] Sending and displaying results

[0644] The server receives the generated proposals and estimates and sends them to the user's terminal. The terminal displays them to the user, who then reviews the proposals and estimates. These may include design proposals and detailed cost estimates, for example.

[0645] Feedback collection and analysis

[0646] Users provide feedback on the presented proposals and quotes. User input data and re-collected sentiment data are collected by the terminal and sent to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[0647] Specific example

[0648] 1. User input

[0649] A user (for example, a real estate agent) requests a new property listing website. The user inputs requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and their facial expressions and voice are also collected.

[0650] 2. Data transmission and analysis

[0651] The server receives and analyzes the requirements data and emotion data sent from the terminal. The emotion engine analyzes the user's emotions and passes this data to the generative AI.

[0652] 3. Generating proposals

[0653] The generative AI generates optimal design proposals and estimates based on emotional data analyzed by the emotion engine and other requirements data. For example, it generates a "design proposal for the entire site" and an "estimated cost of 950,000 yen."

[0654] 4. Presentation of results and feedback

[0655] The device displays the generated design proposal and estimate to the user. The user provides feedback such as, "The design is good, but I would like a more colorful design," and their facial expression and voice are collected again. The device sends the feedback and emotion data to the server.

[0656] 5. Analysis and implementation of feedback

[0657] The server analyzes feedback and sentiment data and incorporates it into the generative AI. For subsequent proposals, suggestions and estimates that are better adapted to the user's emotions will be generated.

[0658] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user prepares the input data.

[0662] The user enters project requirements and preferences into an input form on the device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc. The device simultaneously collects the user's facial expressions and voice data through its camera and microphone.

[0663] Step 2:

[0664] The device collects data.

[0665] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). The collected facial expression data and voice data are also sent to the server at the same time.

[0666] Step 3:

[0667] The device sends data to the server.

[0668] The device sends the collected text and sentiment data to the server as an HTTP request. The data transmission also includes appropriate authentication information and request headers.

[0669] Step 4:

[0670] The server receives the data.

[0671] The server receives HTTP requests sent from terminals and parses the data. This parsing includes data format validation and preprocessing. Sentiment data is then prepared to be sent to the sentiment engine.

[0672] Step 5:

[0673] The server invokes the emotion engine.

[0674] The server passes the pre-processed text data and sentiment data to the sentiment engine. Specifically, this involves making API calls and executing sentiment analysis algorithms.

[0675] Step 6:

[0676] The emotion engine analyzes the user's emotions.

[0677] The emotion engine analyzes given facial expression data, voice data, and text data to identify the user's emotional state. For example, it can determine whether the user is nervous or happy. The analysis results are integrated with the text data and sent to the generative AI.

[0678] Step 7:

[0679] The server calls a generative AI.

[0680] The server passes text data, which incorporates emotional data, to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[0681] Step 8:

[0682] Generative AI generates proposals and estimates.

[0683] Generative AI automatically generates optimal suggestions and quotes based on given data. It uses machine learning models to derive the best solutions based on information learned from past data, and also takes the user's emotional state into consideration.

[0684] Step 9:

[0685] The server receives the generated result.

[0686] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The formatted data includes design proposals and detailed cost estimates.

[0687] Step 10:

[0688] The server sends the generated results to the terminal.

[0689] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[0690] Step 11:

[0691] The terminal displays the generated result.

[0692] The terminal receives a response from the server and displays proposals and estimates to the user. The user can review this information and evaluate its contents. The displayed proposals may include, for example, design proposals and estimated costs.

[0693] Step 12:

[0694] Users provide feedback.

[0695] Users provide feedback on the presented proposals and quotes. For example, they might enter requests such as, "The design is excellent, but I'd like the colors changed." During this process, the user's facial expressions and voice are also collected again.

[0696] Step 13:

[0697] The device collects feedback.

[0698] The device collects user feedback and converts it back into JSON format. It then sends the collected feedback and sentiment data to the server.

[0699] Step 14:

[0700] The server receives feedback.

[0701] The server analyzes the collected feedback and sentiment data and incorporates it into the generation of future proposals and estimates. The generative AI and sentiment engine then use this feedback information to improve their algorithms and apply them to the creation of future proposals.

[0702] (Example 2)

[0703] 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".

[0704] Conventional proposal and quotation systems primarily generate proposals and quotations based on user input data, making it difficult to adequately reflect user emotions and feedback. As a result, proposals and quotations that do not match user needs are sometimes generated, leading to decreased satisfaction. This invention aims to solve this problem and improve user satisfaction by analyzing user emotional data and reflecting it in a generative artificial intelligence system.

[0705] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0706] In this invention, the server includes means for collecting and analyzing user input data and sentiment data; means for a generative artificial intelligence to generate proposals and estimates; means for presenting the generated proposals and estimates to the user; and means for collecting and analyzing user feedback and improving the system. This makes it possible to generate proposals and estimates that take the user's sentiments into account.

[0707] "User input data" refers to information that users enter through the terminal interface, such as project requirements and desired conditions.

[0708] "Emotional data" refers to information that indicates a user's emotional state, obtained from the user's facial expression data and voice data.

[0709] "Means of analysis" refers to algorithms and technologies used to analyze user input data and sentiment data, and to generate necessary proposals and estimates based on that content.

[0710] "Generative artificial intelligence" refers to algorithms and technologies that generate proposals and estimates based on provided data, and utilize machine learning and deep learning techniques.

[0711] "Proposals and estimates" refer to the specific proposals and their estimated costs generated by generative artificial intelligence based on user input data and sentiment data.

[0712] "Feedback" refers to the evaluations, opinions, and additional sentiment data that users provide regarding the proposed ideas and quotes presented.

[0713] "Means of improving the system" refers to methods for analyzing user feedback and sentiment data to improve the quality of future proposals and quotes.

[0714] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[0715] User terminal

[0716] The user terminal provides an interface for users to input information. Through web forms and applications, users enter information such as project requirements, preferences, and budget. It also uses cameras and microphones to collect user facial and voice data. This simultaneously collects user emotional data. For example, when requesting a new website design, the user inputs the necessary functions, budget, and design preferences, while their facial expressions and voice are also collected.

[0717] server

[0718] The server is responsible for receiving and analyzing data sent from user terminals. An emotion engine is used for analysis, specifically to analyze the user's emotions. This might involve using IBM Watson's emotion analysis API, for example. The results are then provided to a generative AI (e.g., OpenAI's GPT-3 model) to generate optimal suggestions and estimates.

[0719] Generative artificial intelligence and emotion engines

[0720] The generative AI generates optimal proposals and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithm. This enables the generation of proposals and quotes that respond to the user's emotions. For example, in response to requirements such as "I want a 10-page website, with a budget of under 1 million yen," the system analyzes the user's facial expressions and voice, such as "I would like a more colorful design," to generate the most suitable proposal.

[0721] Presentation to the user

[0722] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays these to the user, who then reviews the proposals and estimates. These may include, for example, design proposals and detailed cost estimates.

[0723] Feedback collection and analysis

[0724] Users provide feedback on the presented proposals and quotes. This feedback is collected by the terminal as user input data and newly collected sentiment data, and then sent back to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[0725] Specific example

[0726] 1. User input

[0727] User: "I'd like to request a new property listing website. It should be 10 pages long, with a budget of under 1 million yen, and the target audience is young people."

[0728] The user's expression is smiling, and they add in their voice, "I'd prefer a more colorful design."

[0729] 2. Data transmission and analysis

[0730] The device converts the input data and emotion data into JSON format and sends it to the server.

[0731] The server analyzes the data, and the emotion engine analyzes the emotional data, determining, for example, "positive" and "excited."

[0732] 3. Generating proposals

[0733] The server sends the analysis data to the generative AI.

[0734] The generative AI generated a "colorful, youth-oriented design proposal and estimated cost of 950,000 yen."

[0735] 4. Presentation of results and feedback

[0736] The device displays the generated design proposals and estimates to the user.

[0737] A user commented, "The design is good, but I would like a more colorful design."

[0738] 5. Analysis and implementation of feedback

[0739] The server re-analyzes the feedback and sentiment data.

[0740] In the next proposal, the generated proposal will be more tailored to the user's emotions.

[0741] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[0742] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0743] Step 1:

[0744] The user enters project requirements and desired conditions into an input form on their device. Furthermore, the user's facial expressions and voice are collected via the camera and microphone. Input includes text data such as "10-page website" or voice data such as "Make it more colorful." The collected data is converted into JSON format as output.

[0745] Step 2:

[0746] The device converts the collected text and sentiment data into JSON format and sends it to the server. Specifically, it uses an HTTP POST request to send the data. The input is the collected text and sentiment data, and the output is the converted JSON data.

[0747] Step 3:

[0748] The server parses the JSON data received from the terminal. An emotion engine is used for the analysis to determine the user's emotions. For example, the analysis is performed via IBM Watson's emotion analysis API. The input is the received JSON data, and the output is a new dataset containing the emotion analysis results.

[0749] Step 4:

[0750] The server sends emotion data analyzed by the emotion engine and other input data to the generative AI. The generative AI (e.g., OpenAI's GPT-3) generates optimal proposals and estimates based on this data. The input is a dataset containing the analysis results, and the output is the generated proposals and estimates.

[0751] Step 5:

[0752] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays this information to the user. The input is the proposals and estimates obtained from the generative AI, and the output is the information displayed on the user's terminal. For example, the screen might display "Colorful, youth-oriented design proposal, estimated cost 950,000 yen."

[0753] Step 6:

[0754] Users provide feedback on the presented proposals and estimates. Specifically, they input feedback through the terminal interface, and their facial expressions and voice are also collected. The input consists of the user's feedback and associated emotion data, and the output is feedback data converted into JSON format.

[0755] Step 7:

[0756] The terminal converts the feedback and recollected sentiment data into JSON format and sends it to the server. The server re-analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and estimates. The input is feedback and sentiment data, and the output is the re-analyzed dataset.

[0757] (Application Example 2)

[0758] 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."

[0759] Conventional proposal systems generate mechanical proposals and quotes without considering the user's emotional state, making it difficult to fully meet the user's essential needs and desires. Furthermore, there was a lack of systems to effectively incorporate user feedback and utilize it in future proposals. As a result, user satisfaction was low, and the accuracy of proposals did not improve easily.

[0760] 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. In this invention, the server includes means for collecting user input data and sentiment data, means for analyzing the input data and sentiment data, and means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data. This makes it possible to generate more appropriate proposals and estimates that take the user's sentiment data into consideration. Furthermore, by analyzing user feedback and sentiment data and reflecting it in the generation of future proposals and estimates, the accuracy of proposals and user satisfaction can be improved.

[0761] "User input data" refers to data that users provide to the system, including information, requests, conditions, and desired budget.

[0762] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions and voice.

[0763] "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates optimal suggestions and estimates based on user input data and emotional data.

[0764] A "proposal" refers to a specific product or service plan proposed by a generative artificial intelligence system in response to a user's request.

[0765] "Estimate" refers to detailed information, including the calculation of price and costs for a proposed item.

[0766] "Feedback" refers to data such as opinions, evaluations, and impressions of proposals and quotes provided by users.

[0767] An "emotion engine" is a dedicated engine that analyzes a user's facial expressions and voice to generate emotional data.

[0768] A "server" is a central computer system that receives and analyzes data from users and generates proposals and estimates using generative artificial intelligence and an emotion engine.

[0769] A "user terminal" is a device used by a user to access a system and input data, and includes smart glasses, smartphones, and other similar devices.

[0770] Bluetooth is a type of short-range wireless communication technology used to send and receive data between a user terminal and a server.

[0771] "Wi-Fi" is a wireless LAN technology, a communication method used to send and receive data between user terminals and servers.

[0772] This invention relates to a system that generates and presents optimal suggestions and quotes using user input data and sentiment data. This system includes a user terminal, a server, generative artificial intelligence, and a sentiment engine.

[0773] System Configuration

[0774] User terminal

[0775] The user terminal refers to devices such as smart glasses or smartphones, which provide an interface for users to input information. This allows users to input information such as project requirements, desired conditions, and budget. Additionally, user facial expression and voice data are collected via cameras and microphones and analyzed by an emotion engine.

[0776] server

[0777] The server receives and analyzes data sent from the user's terminal. The received data is then analyzed by an emotion engine to determine the user's emotions, and this analysis, along with the results, is provided to the generative artificial intelligence. As a result, proposals and estimates reflect the user's emotions.

[0778] Generative artificial intelligence and emotion engines

[0779] The generative artificial intelligence generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative artificial intelligence's algorithm. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[0780] Program processing

[0781] Data collection and transmission

[0782] Users input project requirements, preferences, budget, etc., through smart glasses, and their facial expressions and voice are also collected via camera and microphone. This data is transmitted to a server using Bluetooth or Wi-Fi. The data is converted to JSON format and analyzed on the server.

[0783] Data Analysis

[0784] The server uses an emotion engine to analyze the transmitted data. The results of this analysis are passed to a generative artificial intelligence system, which is used to generate optimal suggestions and estimates.

[0785] Proposal and estimate generation and display

[0786] The server uses the analyzed sentiment data and other input data to generate optimal suggestions and estimates using generative artificial intelligence. The generated suggestions and estimates are displayed on the smart glasses' screen.

[0787] Feedback collection and analysis

[0788] When a user provides feedback on a proposal and quote, their facial expressions and voice are also collected and sent to the server. The server analyzes the feedback and emotional data and incorporates it into future proposals.

[0789] Specific example

[0790] For example, if a user says, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," the smart glasses collect this information, and the emotion engine detects that the user's expression is serious. Based on this information, the generative artificial intelligence generates a suggestion: "X smartphone, 28,000 yen, 24-hour battery life." If the user provides feedback such as, "I'd like more color variations," that feedback and emotion data are sent back to the server and reflected in the next suggestion.

[0791] Example of a prompt

[0792] "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen."

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The user wears smart glasses and inputs project requirements, preferences, budget, etc., via voice or text input, while facial expression and voice data are collected via camera and microphone. The input data includes information about the user's desired smartphone features and price range.

[0796] Step 2:

[0797] The device converts the collected user input data, facial expression data, and voice data into JSON format. This data includes information such as, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," as recorded by the user's voice input.

[0798] Step 3:

[0799] The device sends data converted to JSON format to the server via Bluetooth or Wi-Fi. The input data (user requirements and facial / voice data) reaches the server's endpoint.

[0800] Step 4:

[0801] The server analyzes the received JSON data and uses an emotion engine to extract emotion data from the user's facial expressions and voice. This analysis includes emotion information such as "the user has a serious expression."

[0802] Step 5:

[0803] The server inputs sentiment analysis data and user requirements data into a generative artificial intelligence (AI). Based on the input data, the AI ​​generates optimal suggestions and quotes. For example, it might generate suggestions such as "X smartphone, 28,000 yen, 24-hour battery life."

[0804] Step 6:

[0805] The server sends the generated proposals and estimates to the user's terminal (smart glasses). The output data (proposals and estimates) is displayed on the smart glasses' screen.

[0806] Step 7:

[0807] Users review proposals and quotes displayed on their smart glasses and provide feedback via voice or text. For example, they might say, "I'd like to see more color variations."

[0808] Step 8:

[0809] The device collects user feedback, facial expression data, and voice data again, converts them to JSON format, and sends them to the server. It then processes the data again based on the input data and sends it back.

[0810] Step 9:

[0811] The server analyzes user feedback and sentiment data and compares it with previous suggestion data. Using the sentiment engine, it re-evaluates the user's requests and emotions and provides new input data to the generative artificial intelligence.

[0812] Step 10:

[0813] The generative artificial intelligence generates further optimized proposals and estimates based on the analysis results, and displays them again on the user's terminal via the server. This ensures that the next proposal is more in line with the user's emotions and requests.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] [Third Embodiment]

[0818] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0819] 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.

[0820] 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).

[0821] 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.

[0822] 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.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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".

[0830] This invention relates to a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0831] System Overview

[0832] User terminal

[0833] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data and sends it to the server.

[0834] server

[0835] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal suggestions and estimates. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0836] Generative artificial intelligence

[0837] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0838] Program processing

[0839] Collect user input data

[0840] Users enter their requirements and preferences into an input form on their device. For example, if they want a new website design, they would enter the necessary functions, design preferences, budget, etc.

[0841] Send input data

[0842] The terminal converts the collected data into JSON format and sends it to the server. The server immediately begins analyzing the data upon receiving it.

[0843] Data analysis and generation

[0844] The server analyzes the received data and performs the necessary preprocessing. The preprocessed data is then passed to a generative AI, which is instructed to generate optimal proposals and estimates. The generative AI analyzes the data and, based on past training data, creates optimal proposals (e.g., design proposals) and estimates.

[0845] Sending and displaying results

[0846] The server receives the generated design proposals and estimates and sends them to the terminal as a single package. The terminal displays this to the user, who then reviews the contents.

[0847] Gathering feedback

[0848] The user provides feedback on the presented proposals and quotes. The terminal collects this feedback, converts it back into JSON format, and sends it to the server. The server analyzes the feedback and uses it to improve the generative AI algorithms and the logic behind generating proposals.

[0849] Specific example

[0850] 1. User input

[0851] A user (real estate agent) requests a new property listing website. The user enters requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0852] 2. Data transmission and analysis

[0853] The server receives the data sent from the terminal and passes it on to the generative AI. Based on this data, the generative AI analyzes past design approaches and cost structures, and generates the optimal design proposal (e.g., layout proposals for a homepage and property details page) and an estimate (e.g., a total of 950,000 yen).

[0854] 3. Display of results and feedback

[0855] The device displays the generated design proposal and estimate to the user. The user reviews the content and provides feedback, such as, "The design is good, but I'd like to add 3 pages." The device collects this feedback and sends it to the server. The server and AI analyze the feedback and incorporate it into the next proposal.

[0856] Thus, the present invention can quickly respond to user requests and generate high-quality proposals and quotations. This reduces user waiting times and improves satisfaction.

[0857] The following describes the processing flow.

[0858] Step 1:

[0859] The user prepares the input data.

[0860] Users enter project requirements and preferences into an input form on their device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc.

[0861] Step 2:

[0862] The device collects data.

[0863] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). This makes it easier to send to the server.

[0864] Step 3:

[0865] The device sends data to the server.

[0866] The device sends the collected data to the server as an HTTP request. The data transmission includes appropriate authentication information and request headers.

[0867] Step 4:

[0868] The server receives the data.

[0869] The server receives HTTP requests sent from terminals and parses the data. It performs data format validation and preprocessing, and converts the data into a format suitable for generative AI.

[0870] Step 5:

[0871] The server calls a generative AI.

[0872] The server passes the pre-processed data to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[0873] Step 6:

[0874] Generative AI generates proposals and estimates.

[0875] Generative AI automatically generates optimal proposals and estimates based on given data. It uses machine learning models to derive the best solutions based on information learned from past data.

[0876] Step 7:

[0877] The server receives the generated result.

[0878] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The proposal includes design ideas and detailed cost estimates.

[0879] Step 8:

[0880] The server sends the generated results to the terminal.

[0881] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[0882] Step 9:

[0883] The terminal displays the generated result.

[0884] The terminal receives a response from the server and displays the proposal and estimate to the user. The user can review this information and evaluate its contents.

[0885] Step 10:

[0886] Users provide feedback.

[0887] Users provide feedback on the presented proposals and quotes. For example, they may enter requests for design revisions or additional requests.

[0888] Step 11:

[0889] The device collects feedback.

[0890] The device collects user feedback and converts it back into JSON format. The collected feedback is used to create future suggestions.

[0891] Step 12:

[0892] The device sends feedback to the server.

[0893] The device sends the collected feedback to the server. The server analyzes the user feedback and uses it to improve the generative AI and the overall system.

[0894] (Example 1)

[0895] 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."

[0896] Conventional quotation generation systems struggled to respond quickly and accurately to user requests, and especially in projects with complex requirements, providing optimal proposals and quotations was difficult. Furthermore, there was a lack of mechanisms to effectively utilize user feedback and reflect it in system improvements, leaving room for improvement in the quality of final proposals and user satisfaction.

[0897] 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.

[0898] In this invention, the server includes means for collecting user input data, means for converting the collected data into a data format and transmitting it to the server, means for analyzing the input data and performing necessary preprocessing, means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data, means for transmitting the generated proposals and estimates as a single package to the user terminal, means for collecting user feedback and transmitting it back to the server, and means for improving the algorithm and generation logic based on the feedback. This makes it possible to respond quickly and accurately to user requests and provide high-quality proposals and estimates. Furthermore, since the system can be continuously improved by effectively utilizing user feedback, the quality of the final proposals and user satisfaction can be improved.

[0899] A "user" is an individual or organization that uses the system to request the generation of proposals and quotes.

[0900] A "terminal" is a hardware device equipped with an interface for users to provide input data, such as a personal computer or a smartphone.

[0901] A "server" is a centralized computer system that receives data sent from user terminals, analyzes it, and requests data processing from generative artificial intelligence.

[0902] A "data format" is a standardized format used when sending, receiving, or storing data in a computer system, such as the JSON format.

[0903] "Generative artificial intelligence" refers to a program that uses machine learning algorithms to analyze input data and generate optimal suggestions and estimates.

[0904] A "proposal" refers to a solution provided by a generative artificial intelligence system based on the user's requirements and preferences, such as a design proposal.

[0905] An "estimate" is the result of calculations regarding the costs and resources necessary to realize a proposed plan.

[0906] "Feedback" refers to the opinions and requests for changes that users provide regarding the proposed solutions and estimates presented.

[0907] An "algorithm" is a set of computational procedures or methods for solving a specific problem.

[0908] "Generative logic" refers to a set of rules and procedures that generative artificial intelligence uses to generate proposals and estimates.

[0909] "Preprocessing" refers to initial data processing steps to make input data easier to analyze, such as data cleansing and standardization.

[0910] A "package" is a document or file format that aggregates generated proposals and estimates into a single, cohesive set.

[0911] "Analysis" is the process of thoroughly examining collected data and extracting meaningful information.

[0912] This invention is a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[0913] System Overview

[0914] User terminal

[0915] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data, converts it to JSON format, and sends it to the server.

[0916] For example, when a real estate agent requests a new property listing website, the user will input specific requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[0917] server

[0918] The server receives and analyzes data sent from the user's terminal. It performs preprocessing such as data cleansing and data standardization on the received data, and then passes it on to a generative AI for processing. The generative AI generates proposals and estimates based on the analyzed data. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0919] In this example, the server analyzes data sent by the real estate agent, such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and passes it to a generative AI. Based on this data, the AI ​​generates optimal design proposals (layout proposals for the homepage and property details pages, etc.) and an estimate (totaling 950,000 yen).

[0920] Generative artificial intelligence

[0921] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions, drawing on historical and training data. The generated design proposals and estimates are returned to the server, sent to the user's terminal, and displayed to the user.

[0922] Gathering feedback and improving the system

[0923] user

[0924] Users provide feedback on proposals and quotes presented on their devices. For example, they might offer specific feedback such as, "The design is good, but I'd like to add three more pages."

[0925] terminal

[0926] The device collects this feedback, converts it back to JSON format, and sends it to the server.

[0927] server

[0928] The server analyzes the received feedback and passes it to the generative AI. The generative AI uses this feedback to improve its algorithms and generation logic, and incorporates these improvements into the generation of future proposals and estimates.

[0929] Examples of prompt statements

[0930] Examples of prompt statements for a generative AI model are as follows:

[0931] Prompt message:

[0932] "This is a request from a real estate agent. They need a 10-page property listing website. The budget is under 1 million yen, and the target audience is young people. Based on past design patterns and cost structures, please generate optimal design proposals (website layout, property detail page layouts, etc.) and estimates."

[0933] Thus, the present invention is capable of responding quickly and accurately to user requests, generating high-quality proposals and quotations, and further improving the system by reflecting user feedback, thereby enhancing the quality of the final proposals and user satisfaction.

[0934] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0935] Step 1: The user enters the required information via a web form or application on their device. This input includes specific requirements (e.g., a 10-page website, a budget of under 1 million yen, targeting young people). The device collects this information and converts it into JSON format. The input data is based on the user's requests, and the converted JSON data is output.

[0936] Step 2: The terminal sends the collected JSON data to the server. The server parses the received JSON data. Here, the server confirms receipt of the data and simultaneously checks the data integrity (validity of format and data type). For example, it checks for missing fields or invalid values ​​and performs a cleansing process. As a result of the analysis, the cleansed data is output.

[0937] Step 3: The server passes the pre-processed data to the generative AI and requests the generation of proposals and estimates. The generative AI uses machine learning algorithms to analyze historical data and training data to generate optimal proposals (e.g., web design proposals) and estimates (e.g., total cost of 950,000 yen). This generation process involves analysis and data calculations based on the input data. The generated proposals and estimates are then output.

[0938] Step 4: The generative AI returns the generated design proposals and estimates to the server. The server packages this data together. In this packaging process, the design proposals and estimates are converted to an appropriate format (e.g., HTML or PDF) and output as a single file or document. The packaged data is then output.

[0939] Step 5: The server sends the packaged data to the user's terminal. The terminal receives the transmitted data and displays it to the user. Here, the user can review the displayed design proposals and estimates. The packaged data is displayed on the user's screen.

[0940] Step 6: The user provides feedback on the presented proposal and estimate. Specifically, they enter specific requests and opinions, such as "The design is good, but I would like to add 3 pages." This feedback is collected on the device and converted back into JSON format. The feedback data is then output.

[0941] Step 7: The device sends the collected feedback data to the server. The server analyzes the received feedback. This analysis process examines the content of the feedback in detail and converts it into a data structure for passing to the generative AI. For example, it extracts the number of additional pages and the requirements for design changes. The analyzed feedback data is output.

[0942] Step 8: The server passes the analyzed feedback data to the generative AI and requests it to regenerate the proposals and estimates. The generative AI improves its algorithms and generation logic based on the feedback and generates new proposals and estimates. In this process, the initial input data and feedback data are integrated to create the new generation. The improved proposals and estimates are output.

[0943] Step 9: The generative AI returns the new proposals and estimates it has generated to the server, which then repackages them. The repackaged data is then output.

[0944] Step 10: The server sends the improved, packaged data to the user's terminal. The terminal displays the sent data to the user, who then reviews the improved proposal and estimate. This facilitates the iterative generation of proposals and estimates until the user is satisfied.

[0945] (Application Example 1)

[0946] 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."

[0947] In modern e-commerce, users face the problem of spending a lot of time and effort making the best choice from a wide variety of products. Furthermore, it is difficult to smoothly place customized orders based on user preferences and budgets, and there is a need for quick quotes. Existing systems fail to provide appropriate proposals and quotes to meet user needs, which is a factor in lowering user satisfaction.

[0948] 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.

[0949] In this invention, the server includes means for collecting user input data, means for analyzing the input data, means for a generative artificial intelligence to generate optimal product suggestions and quotes based on the analyzed data, means for presenting the generated product suggestions and quotes to the user, means for collecting feedback from the user, and means for improving the system based on the feedback. This enables the provision of quick and accurate product suggestions and quotes that meet the user's desired conditions, significantly improving the user experience on e-commerce sites.

[0950] A "user" is a buyer who wishes to use the system to receive product suggestions and quotes.

[0951] "Input data" refers to information such as desired conditions and budget that users provide to the system.

[0952] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate optimal product suggestions and quotes.

[0953] "Product suggestions" refer to the optimal product selection provided by generative artificial intelligence based on the user's desired conditions.

[0954] "Estimate" refers to price information for a product calculated by a generative artificial intelligence system.

[0955] "Feedback" refers to opinions and requests regarding suggestions and estimates that users provide to the system.

[0956] A "system" refers to a device or program that includes components that perform a series of processes to generate product suggestions and quotations based on user input data and present them to the user.

[0957] This invention relates to a system that collects user input data, analyzes it, and uses generative artificial intelligence to generate optimal product suggestions and quotes, which are then presented to the user. The system mainly consists of a user terminal, a server, and generative artificial intelligence (AI).

[0958] System Overview

[0959] User terminal

[0960] The user terminal has an interface for users to provide input data. Users use web forms or applications to input information such as desired products, budget, and customization requirements. The terminal collects this input data and sends it to the server.

[0961] server

[0962] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal product suggestions and quotes. The generated data is then sent back from the server to the user's terminal and presented to the user.

[0963] Generative artificial intelligence

[0964] Generative AI generates product suggestions and quotes based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[0965] Program details and technologies used

[0966] Hardware and software used

[0967] Hardware: Standard PC terminals, smartphones, cloud servers (general term)

[0968] Software: Flask (Web application framework), generative AI libraries (e.g., custom models based on TensorFlow or PyTorch)

[0969] Data processing and calculation

[0970] The terminal converts the data collected from the user into JSON format and sends it to the server. The server receives this data and performs preprocessing. The preprocessed data is then passed to a generative AI, which analyzes the data and generates optimal product suggestions and quotes. The generated suggestions and quotes are sent back to the user terminal via the server and presented to the user.

[0971] Specific example

[0972] 1. User input

[0973] Users enter their desired purchase criteria on a specific product page of an online shopping site. For example, they might enter, "I would like a blue leather sofa. My budget is under 50,000 yen."

[0974] 2. Data transmission and analysis

[0975] The terminal converts the input data into JSON format and sends it to the server. The server analyzes the received data, performs the necessary preprocessing, and passes the data to the generative AI. The generative AI then generates optimal product suggestions and quotes based on that data.

[0976] 3. Display of results and feedback

[0977] The server sends the generated product proposals and quotes to the terminal as a single package. The user reviews the presented product proposal (for example, "Blue leather sofa (in stock), price 48,000 yen").

[0978] 4. Gathering feedback

[0979] Users provide feedback on the presented product suggestions and quotes. For example, they might enter feedback such as, "The design is good, but I'd like more premium options." The device collects this feedback, converts it back into JSON format, and sends it to the server. The server and AI analyze the feedback and incorporate it into future suggestions.

[0980] In this way, this invention can respond quickly and accurately to the desired conditions of users on e-commerce sites. Furthermore, by having the generative AI learn from the feedback, the overall accuracy of the system and user satisfaction can be improved.

[0981] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0982] Step 1: Collecting user input data

[0983] Users enter their desired purchase conditions, budget, and customization requirements on specific product pages of the online shopping site. For example, they might enter conditions such as, "I would like a blue leather sofa. My budget should be under 50,000 yen."

[0984] Input details: Desired product specifications (color, material, budget, etc.)

[0985] Output data: Input data in JSON format

[0986] Step 2: Submit the input data

[0987] The device converts the collected data into JSON format and sends it to the server. The device sends the data through the API endpoint.

[0988] Input content: Input data in JSON format

[0989] Output data: Data sent to the server

[0990] Step 3: Data reception and analysis

[0991] The server receives the data sent from the terminal and begins analysis. Preprocessing, such as data formatting and normalization, is performed at this stage.

[0992] Input content: JSON format data sent from the device.

[0993] Output data: Preprocessed data

[0994] Step 4: Providing data to generative AI

[0995] The server passes the pre-processed data to the generative AI and instructs it to generate product suggestions and quotes.

[0996] Input content: Preprocessed data

[0997] Output data: Data passed to the generative AI.

[0998] Step 5: AI-powered proposal and quote generation

[0999] Generative AI analyzes data provided by a server to generate optimal product suggestions and quotes. The algorithm is executed based on historical data and training data.

[1000] Input content: Data provided to the generative AI

[1001] Output data: Product proposal and quotation

[1002] Step 6: Submit the results

[1003] The server sends the proposals and estimates received from the generative AI to the user's device. The user can then view this data on their device.

[1004] Input content: Generated product proposal and estimate

[1005] Output data: Proposals and quotes sent to the terminal

[1006] Step 7: Display to the user

[1007] The terminal displays product suggestions and quotes sent from the server to the user, allowing the user to review them.

[1008] Input content: Product proposal and quotation sent to the terminal.

[1009] Output data: Product proposals and quotes presented for user review.

[1010] Step 8: Gathering Feedback

[1011] Users provide feedback on the displayed product suggestions and quotes. For example, they might say, "The design is great, but I'd like to see more premium options."

[1012] Input content: User feedback (text format)

[1013] Output data: Feedback data in JSON format

[1014] Step 9: Submitting Feedback

[1015] The device converts the collected feedback data into JSON format and sends it to the server.

[1016] Input content: Text-formatted feedback

[1017] Output data: Feedback data in JSON format sent to the server

[1018] Step 10: Analysis of Feedback

[1019] The server receives feedback data and performs analysis. The analysis results are used to improve the algorithms of the generative AI.

[1020] Input content: Feedback data sent to the server

[1021] Output data: Improvement data for the AI ​​algorithm

[1022] The above outlines the specific processing steps of the system that realizes the application example. This allows users to obtain product proposals and quotes quickly and accurately, and also contributes to improving the system's accuracy through feedback.

[1023] 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.

[1024] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1025] System Overview

[1026] User terminal

[1027] The user terminal provides an interface for users to input information and has the functionality to collect emotional data. Users input information such as project requirements, preferences, and budget through web forms and applications. Simultaneously, user facial expression and voice data are collected and analyzed by the emotion engine.

[1028] server

[1029] The server receives and analyzes data sent from the user's terminal. Before the data is passed to the generative AI, the emotion engine analyzes the user's emotions, and the results are also provided to the AI. This ensures that proposals and estimates reflect the user's emotions.

[1030] Generative artificial intelligence and emotion engines

[1031] The generative AI generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithms. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[1032] Program processing

[1033] Collect user input data

[1034] Users enter project requirements and preferences into input forms on their devices. Their facial expressions and voice are also collected via the camera and microphone. For example, when requesting a new website design, the user enters the necessary functions, budget, and design preferences, while their facial expressions and voice are simultaneously collected.

[1035] Data analysis and transmission

[1036] The device converts the collected text and emotion data into JSON format and sends it to the server. The server uses an emotion engine to analyze the user's emotions and passes this information to a generative AI.

[1037] Proposal and Estimate Generation

[1038] The server sends emotional data analyzed by the emotion engine, along with other input data, to the generative AI. The generative AI uses this data to generate optimal suggestions and quotes for the user. By using machine learning algorithms and taking the user's emotional state into account, more appropriate suggestions can be provided.

[1039] Sending and displaying results

[1040] The server receives the generated proposals and estimates and sends them to the user's terminal. The terminal displays them to the user, who then reviews the proposals and estimates. These may include design proposals and detailed cost estimates, for example.

[1041] Feedback collection and analysis

[1042] Users provide feedback on the presented proposals and quotes. User input data and re-collected sentiment data are collected by the terminal and sent to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[1043] Specific example

[1044] 1. User input

[1045] A user (for example, a real estate agent) requests a new property listing website. The user inputs requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and their facial expressions and voice are also collected.

[1046] 2. Data transmission and analysis

[1047] The server receives and analyzes the requirements data and emotion data sent from the terminal. The emotion engine analyzes the user's emotions and passes this data to the generative AI.

[1048] 3. Generating proposals

[1049] The generative AI generates optimal design proposals and estimates based on emotional data analyzed by the emotion engine and other requirements data. For example, it generates a "design proposal for the entire site" and an "estimated cost of 950,000 yen."

[1050] 4. Presentation of results and feedback

[1051] The device displays the generated design proposal and estimate to the user. The user provides feedback such as, "The design is good, but I would like a more colorful design," and their facial expression and voice are collected again. The device sends the feedback and emotion data to the server.

[1052] 5. Analysis and implementation of feedback

[1053] The server analyzes feedback and sentiment data and incorporates it into the generative AI. For subsequent proposals, suggestions and estimates that are better adapted to the user's emotions will be generated.

[1054] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[1055] The following describes the processing flow.

[1056] Step 1:

[1057] The user prepares the input data.

[1058] The user enters project requirements and preferences into an input form on the device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc. The device simultaneously collects the user's facial expressions and voice data through its camera and microphone.

[1059] Step 2:

[1060] The device collects data.

[1061] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). The collected facial expression data and voice data are also sent to the server at the same time.

[1062] Step 3:

[1063] The device sends data to the server.

[1064] The device sends the collected text and sentiment data to the server as an HTTP request. The data transmission also includes appropriate authentication information and request headers.

[1065] Step 4:

[1066] The server receives the data.

[1067] The server receives HTTP requests sent from terminals and parses the data. This parsing includes data format validation and preprocessing. Sentiment data is then prepared to be sent to the sentiment engine.

[1068] Step 5:

[1069] The server invokes the emotion engine.

[1070] The server passes the pre-processed text data and sentiment data to the sentiment engine. Specifically, this involves making API calls and executing sentiment analysis algorithms.

[1071] Step 6:

[1072] The emotion engine analyzes the user's emotions.

[1073] The emotion engine analyzes given facial expression data, voice data, and text data to identify the user's emotional state. For example, it can determine whether the user is nervous or happy. The analysis results are integrated with the text data and sent to the generative AI.

[1074] Step 7:

[1075] The server calls a generative AI.

[1076] The server passes text data, which incorporates emotional data, to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[1077] Step 8:

[1078] Generative AI generates proposals and estimates.

[1079] Generative AI automatically generates optimal suggestions and quotes based on given data. It uses machine learning models to derive the best solutions based on information learned from past data, and also takes the user's emotional state into consideration.

[1080] Step 9:

[1081] The server receives the generated result.

[1082] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The formatted data includes design proposals and detailed cost estimates.

[1083] Step 10:

[1084] The server sends the generated results to the terminal.

[1085] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[1086] Step 11:

[1087] The terminal displays the generated result.

[1088] The terminal receives a response from the server and displays proposals and estimates to the user. The user can review this information and evaluate its contents. The displayed proposals may include, for example, design proposals and estimated costs.

[1089] Step 12:

[1090] Users provide feedback.

[1091] Users provide feedback on the presented proposals and quotes. For example, they might enter requests such as, "The design is excellent, but I'd like the colors changed." During this process, the user's facial expressions and voice are also collected again.

[1092] Step 13:

[1093] The device collects feedback.

[1094] The device collects user feedback and converts it back into JSON format. It then sends the collected feedback and sentiment data to the server.

[1095] Step 14:

[1096] The server receives feedback.

[1097] The server analyzes the collected feedback and sentiment data and incorporates it into the generation of future proposals and estimates. The generative AI and sentiment engine then use this feedback information to improve their algorithms and apply them to the creation of future proposals.

[1098] (Example 2)

[1099] 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."

[1100] Conventional proposal and quotation systems primarily generate proposals and quotations based on user input data, making it difficult to adequately reflect user emotions and feedback. As a result, proposals and quotations that do not match user needs are sometimes generated, leading to decreased satisfaction. This invention aims to solve this problem and improve user satisfaction by analyzing user emotional data and reflecting it in a generative artificial intelligence system.

[1101] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1102] In this invention, the server includes means for collecting and analyzing user input data and sentiment data; means for a generative artificial intelligence to generate proposals and estimates; means for presenting the generated proposals and estimates to the user; and means for collecting and analyzing user feedback and improving the system. This makes it possible to generate proposals and estimates that take the user's sentiments into account.

[1103] "User input data" refers to information that users enter through the terminal interface, such as project requirements and desired conditions.

[1104] "Emotional data" refers to information that indicates a user's emotional state, obtained from the user's facial expression data and voice data.

[1105] "Means of analysis" refers to algorithms and technologies used to analyze user input data and sentiment data, and to generate necessary proposals and estimates based on that content.

[1106] "Generative artificial intelligence" refers to algorithms and technologies that generate proposals and estimates based on provided data, and utilize machine learning and deep learning techniques.

[1107] "Proposals and estimates" refer to the specific proposals and their estimated costs generated by generative artificial intelligence based on user input data and sentiment data.

[1108] "Feedback" refers to the evaluations, opinions, and additional sentiment data that users provide regarding the proposed ideas and quotes presented.

[1109] "Means of improving the system" refers to methods for analyzing user feedback and sentiment data to improve the quality of future proposals and quotes.

[1110] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1111] User terminal

[1112] The user terminal provides an interface for users to input information. Through web forms and applications, users enter information such as project requirements, preferences, and budget. It also uses cameras and microphones to collect user facial and voice data. This simultaneously collects user emotional data. For example, when requesting a new website design, the user inputs the necessary functions, budget, and design preferences, while their facial expressions and voice are also collected.

[1113] server

[1114] The server is responsible for receiving and analyzing data sent from user terminals. An emotion engine is used for analysis, specifically to analyze the user's emotions. This might involve using IBM Watson's emotion analysis API, for example. The results are then provided to a generative AI (e.g., OpenAI's GPT-3 model) to generate optimal suggestions and estimates.

[1115] Generative artificial intelligence and emotion engines

[1116] The generative AI generates optimal proposals and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithm. This enables the generation of proposals and quotes that respond to the user's emotions. For example, in response to requirements such as "I want a 10-page website, with a budget of under 1 million yen," the system analyzes the user's facial expressions and voice, such as "I would like a more colorful design," to generate the most suitable proposal.

[1117] Presentation to the user

[1118] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays these to the user, who then reviews the proposals and estimates. These may include, for example, design proposals and detailed cost estimates.

[1119] Feedback collection and analysis

[1120] Users provide feedback on the presented proposals and quotes. This feedback is collected by the terminal as user input data and newly collected sentiment data, and then sent back to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[1121] Specific example

[1122] 1. User input

[1123] User: "I'd like to request a new property listing website. It should be 10 pages long, with a budget of under 1 million yen, and the target audience is young people."

[1124] The user's expression is smiling, and they add in their voice, "I'd prefer a more colorful design."

[1125] 2. Data transmission and analysis

[1126] The device converts the input data and emotion data into JSON format and sends it to the server.

[1127] The server analyzes the data, and the emotion engine analyzes the emotional data, determining, for example, "positive" and "excited."

[1128] 3. Generating proposals

[1129] The server sends the analysis data to the generative AI.

[1130] The generative AI generated a "colorful, youth-oriented design proposal and estimated cost of 950,000 yen."

[1131] 4. Presentation of results and feedback

[1132] The device displays the generated design proposals and estimates to the user.

[1133] A user commented, "The design is good, but I would like a more colorful design."

[1134] 5. Analysis and implementation of feedback

[1135] The server re-analyzes the feedback and sentiment data.

[1136] In the next proposal, the generated proposal will be more tailored to the user's emotions.

[1137] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[1138] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1139] Step 1:

[1140] The user enters project requirements and desired conditions into an input form on their device. Furthermore, the user's facial expressions and voice are collected via the camera and microphone. Input includes text data such as "10-page website" or voice data such as "Make it more colorful." The collected data is converted into JSON format as output.

[1141] Step 2:

[1142] The device converts the collected text and sentiment data into JSON format and sends it to the server. Specifically, it uses an HTTP POST request to send the data. The input is the collected text and sentiment data, and the output is the converted JSON data.

[1143] Step 3:

[1144] The server parses the JSON data received from the terminal. An emotion engine is used for the analysis to determine the user's emotions. For example, the analysis is performed via IBM Watson's emotion analysis API. The input is the received JSON data, and the output is a new dataset containing the emotion analysis results.

[1145] Step 4:

[1146] The server sends emotion data analyzed by the emotion engine and other input data to the generative AI. The generative AI (e.g., OpenAI's GPT-3) generates optimal proposals and estimates based on this data. The input is a dataset containing the analysis results, and the output is the generated proposals and estimates.

[1147] Step 5:

[1148] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays this information to the user. The input is the proposals and estimates obtained from the generative AI, and the output is the information displayed on the user's terminal. For example, the screen might display "Colorful, youth-oriented design proposal, estimated cost 950,000 yen."

[1149] Step 6:

[1150] Users provide feedback on the presented proposals and estimates. Specifically, they input feedback through the terminal interface, and their facial expressions and voice are also collected. The input consists of the user's feedback and associated emotion data, and the output is feedback data converted into JSON format.

[1151] Step 7:

[1152] The terminal converts the feedback and recollected sentiment data into JSON format and sends it to the server. The server re-analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and estimates. The input is feedback and sentiment data, and the output is the re-analyzed dataset.

[1153] (Application Example 2)

[1154] 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."

[1155] Conventional proposal systems generate mechanical proposals and quotes without considering the user's emotional state, making it difficult to fully meet the user's essential needs and desires. Furthermore, there was a lack of systems to effectively incorporate user feedback and utilize it in future proposals. As a result, user satisfaction was low, and the accuracy of proposals did not improve easily.

[1156] 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. In this invention, the server includes means for collecting user input data and sentiment data, means for analyzing the input data and sentiment data, and means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data. This makes it possible to generate more appropriate proposals and estimates that take the user's sentiment data into consideration. Furthermore, by analyzing user feedback and sentiment data and reflecting it in the generation of future proposals and estimates, the accuracy of proposals and user satisfaction can be improved.

[1157] "User input data" refers to data that users provide to the system, including information, requests, conditions, and desired budget.

[1158] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions and voice.

[1159] "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates optimal suggestions and estimates based on user input data and emotional data.

[1160] A "proposal" refers to a specific product or service plan proposed by a generative artificial intelligence system in response to a user's request.

[1161] "Estimate" refers to detailed information, including the calculation of price and costs for a proposed item.

[1162] "Feedback" refers to data such as opinions, evaluations, and impressions of proposals and quotes provided by users.

[1163] An "emotion engine" is a dedicated engine that analyzes a user's facial expressions and voice to generate emotional data.

[1164] A "server" is a central computer system that receives and analyzes data from users and generates proposals and estimates using generative artificial intelligence and an emotion engine.

[1165] A "user terminal" is a device used by a user to access a system and input data, and includes smart glasses, smartphones, and other similar devices.

[1166] Bluetooth is a type of short-range wireless communication technology used to send and receive data between a user terminal and a server.

[1167] "Wi-Fi" is a wireless LAN technology, a communication method used to send and receive data between user terminals and servers.

[1168] This invention relates to a system that generates and presents optimal suggestions and quotes using user input data and sentiment data. This system includes a user terminal, a server, generative artificial intelligence, and a sentiment engine.

[1169] System Configuration

[1170] User terminal

[1171] The user terminal refers to devices such as smart glasses or smartphones, which provide an interface for users to input information. This allows users to input information such as project requirements, desired conditions, and budget. Additionally, user facial expression and voice data are collected via cameras and microphones and analyzed by an emotion engine.

[1172] server

[1173] The server receives and analyzes data sent from the user's terminal. The received data is then analyzed by an emotion engine to determine the user's emotions, and this analysis, along with the results, is provided to the generative artificial intelligence. As a result, proposals and estimates reflect the user's emotions.

[1174] Generative artificial intelligence and emotion engines

[1175] The generative artificial intelligence generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative artificial intelligence's algorithm. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[1176] Program processing

[1177] Data collection and transmission

[1178] Users input project requirements, preferences, budget, etc., through smart glasses, and their facial expressions and voice are also collected via camera and microphone. This data is transmitted to a server using Bluetooth or Wi-Fi. The data is converted to JSON format and analyzed on the server.

[1179] Data Analysis

[1180] The server uses an emotion engine to analyze the transmitted data. The results of this analysis are passed to a generative artificial intelligence system, which is used to generate optimal suggestions and estimates.

[1181] Proposal and estimate generation and display

[1182] The server uses the analyzed sentiment data and other input data to generate optimal suggestions and estimates using generative artificial intelligence. The generated suggestions and estimates are displayed on the smart glasses' screen.

[1183] Feedback collection and analysis

[1184] When a user provides feedback on a proposal and quote, their facial expressions and voice are also collected and sent to the server. The server analyzes the feedback and emotional data and incorporates it into future proposals.

[1185] Specific example

[1186] For example, if a user says, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," the smart glasses collect this information, and the emotion engine detects that the user's expression is serious. Based on this information, the generative artificial intelligence generates a suggestion: "X smartphone, 28,000 yen, 24-hour battery life." If the user provides feedback such as, "I'd like more color variations," that feedback and emotion data are sent back to the server and reflected in the next suggestion.

[1187] Example of a prompt

[1188] "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen."

[1189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1190] Step 1:

[1191] The user wears smart glasses and inputs project requirements, preferences, budget, etc., via voice or text input, while facial expression and voice data are collected via camera and microphone. The input data includes information about the user's desired smartphone features and price range.

[1192] Step 2:

[1193] The device converts the collected user input data, facial expression data, and voice data into JSON format. This data includes information such as, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," as recorded by the user's voice input.

[1194] Step 3:

[1195] The device sends data converted to JSON format to the server via Bluetooth or Wi-Fi. The input data (user requirements and facial / voice data) reaches the server's endpoint.

[1196] Step 4:

[1197] The server analyzes the received JSON data and uses an emotion engine to extract emotion data from the user's facial expressions and voice. This analysis includes emotion information such as "the user has a serious expression."

[1198] Step 5:

[1199] The server inputs sentiment analysis data and user requirements data into a generative artificial intelligence (AI). Based on the input data, the AI ​​generates optimal suggestions and quotes. For example, it might generate suggestions such as "X smartphone, 28,000 yen, 24-hour battery life."

[1200] Step 6:

[1201] The server sends the generated proposals and estimates to the user's terminal (smart glasses). The output data (proposals and estimates) is displayed on the smart glasses' screen.

[1202] Step 7:

[1203] Users review proposals and quotes displayed on their smart glasses and provide feedback via voice or text. For example, they might say, "I'd like to see more color variations."

[1204] Step 8:

[1205] The device collects user feedback, facial expression data, and voice data again, converts them to JSON format, and sends them to the server. It then processes the data again based on the input data and sends it back.

[1206] Step 9:

[1207] The server analyzes user feedback and sentiment data and compares it with previous suggestion data. Using the sentiment engine, it re-evaluates the user's requests and emotions and provides new input data to the generative artificial intelligence.

[1208] Step 10:

[1209] The generative artificial intelligence generates further optimized proposals and estimates based on the analysis results, and displays them again on the user's terminal via the server. This ensures that the next proposal is more in line with the user's emotions and requests.

[1210] 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.

[1211] 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.

[1212] 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.

[1213] [Fourth Embodiment]

[1214] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1215] 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.

[1216] 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).

[1217] 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.

[1218] 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.

[1219] 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).

[1220] 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.

[1221] 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.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] 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.

[1226] 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".

[1227] This invention relates to a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[1228] System Overview

[1229] User terminal

[1230] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data and sends it to the server.

[1231] server

[1232] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal suggestions and estimates. The generated data is then sent back from the server to the user's terminal and presented to the user.

[1233] Generative artificial intelligence

[1234] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[1235] Program processing

[1236] Collect user input data

[1237] Users enter their requirements and preferences into an input form on their device. For example, if they want a new website design, they would enter the necessary functions, design preferences, budget, etc.

[1238] Send input data

[1239] The terminal converts the collected data into JSON format and sends it to the server. The server immediately begins analyzing the data upon receiving it.

[1240] Data analysis and generation

[1241] The server analyzes the received data and performs the necessary preprocessing. The preprocessed data is then passed to a generative AI, which is instructed to generate optimal proposals and estimates. The generative AI analyzes the data and, based on past training data, creates optimal proposals (e.g., design proposals) and estimates.

[1242] Sending and displaying results

[1243] The server receives the generated design proposals and estimates and sends them to the terminal as a single package. The terminal displays this to the user, who then reviews the contents.

[1244] Gathering feedback

[1245] The user provides feedback on the presented proposals and quotes. The terminal collects this feedback, converts it back into JSON format, and sends it to the server. The server analyzes the feedback and uses it to improve the generative AI algorithms and the logic behind generating proposals.

[1246] Specific example

[1247] 1. User input

[1248] A user (real estate agent) requests a new property listing website. The user enters requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[1249] 2. Data transmission and analysis

[1250] The server receives the data sent from the terminal and passes it on to the generative AI. Based on this data, the generative AI analyzes past design approaches and cost structures, and generates the optimal design proposal (e.g., layout proposals for a homepage and property details page) and an estimate (e.g., a total of 950,000 yen).

[1251] 3. Display of results and feedback

[1252] The device displays the generated design proposal and estimate to the user. The user reviews the content and provides feedback, such as, "The design is good, but I'd like to add 3 pages." The device collects this feedback and sends it to the server. The server and AI analyze the feedback and incorporate it into the next proposal.

[1253] Thus, the present invention can quickly respond to user requests and generate high-quality proposals and quotations. This reduces user waiting times and improves satisfaction.

[1254] The following describes the processing flow.

[1255] Step 1:

[1256] The user prepares the input data.

[1257] Users enter project requirements and preferences into an input form on their device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc.

[1258] Step 2:

[1259] The device collects data.

[1260] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). This makes it easier to send to the server.

[1261] Step 3:

[1262] The device sends data to the server.

[1263] The device sends the collected data to the server as an HTTP request. The data transmission includes appropriate authentication information and request headers.

[1264] Step 4:

[1265] The server receives the data.

[1266] The server receives HTTP requests sent from terminals and parses the data. It performs data format validation and preprocessing, and converts the data into a format suitable for generative AI.

[1267] Step 5:

[1268] The server calls a generative AI.

[1269] The server passes the pre-processed data to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[1270] Step 6:

[1271] Generative AI generates proposals and estimates.

[1272] Generative AI automatically generates optimal proposals and estimates based on given data. It uses machine learning models to derive the best solutions based on information learned from past data.

[1273] Step 7:

[1274] The server receives the generated result.

[1275] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The proposal includes design ideas and detailed cost estimates.

[1276] Step 8:

[1277] The server sends the generated results to the terminal.

[1278] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[1279] Step 9:

[1280] The terminal displays the generated result.

[1281] The terminal receives a response from the server and displays the proposal and estimate to the user. The user can review this information and evaluate its contents.

[1282] Step 10:

[1283] Users provide feedback.

[1284] Users provide feedback on the presented proposals and quotes. For example, they may enter requests for design revisions or additional requests.

[1285] Step 11:

[1286] The device collects feedback.

[1287] The device collects user feedback and converts it back into JSON format. The collected feedback is used to create future suggestions.

[1288] Step 12:

[1289] The device sends feedback to the server.

[1290] The device sends the collected feedback to the server. The server analyzes the user feedback and uses it to improve the generative AI and the overall system.

[1291] (Example 1)

[1292] 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".

[1293] Conventional quotation generation systems struggled to respond quickly and accurately to user requests, and especially in projects with complex requirements, providing optimal proposals and quotations was difficult. Furthermore, there was a lack of mechanisms to effectively utilize user feedback and reflect it in system improvements, leaving room for improvement in the quality of final proposals and user satisfaction.

[1294] 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.

[1295] In this invention, the server includes means for collecting user input data, means for converting the collected data into a data format and transmitting it to the server, means for analyzing the input data and performing necessary preprocessing, means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data, means for transmitting the generated proposals and estimates as a single package to the user terminal, means for collecting user feedback and transmitting it back to the server, and means for improving the algorithm and generation logic based on the feedback. This makes it possible to respond quickly and accurately to user requests and provide high-quality proposals and estimates. Furthermore, since the system can be continuously improved by effectively utilizing user feedback, the quality of the final proposals and user satisfaction can be improved.

[1296] A "user" is an individual or organization that uses the system to request the generation of proposals and quotes.

[1297] A "terminal" is a hardware device equipped with an interface for users to provide input data, such as a personal computer or a smartphone.

[1298] A "server" is a centralized computer system that receives data sent from user terminals, analyzes it, and requests data processing from generative artificial intelligence.

[1299] A "data format" is a standardized format used when sending, receiving, or storing data in a computer system, such as the JSON format.

[1300] "Generative artificial intelligence" refers to a program that uses machine learning algorithms to analyze input data and generate optimal suggestions and estimates.

[1301] A "proposal" refers to a solution provided by a generative artificial intelligence system based on the user's requirements and preferences, such as a design proposal.

[1302] An "estimate" is the result of calculations regarding the costs and resources necessary to realize a proposed plan.

[1303] "Feedback" refers to the opinions and requests for changes that users provide regarding the proposed solutions and estimates presented.

[1304] An "algorithm" is a set of computational procedures or methods for solving a specific problem.

[1305] "Generative logic" refers to a set of rules and procedures that generative artificial intelligence uses to generate proposals and estimates.

[1306] "Preprocessing" refers to initial data processing steps to make input data easier to analyze, such as data cleansing and standardization.

[1307] A "package" is a document or file format that aggregates generated proposals and estimates into a single, cohesive set.

[1308] "Analysis" is the process of thoroughly examining collected data and extracting meaningful information.

[1309] This invention is a system that utilizes generative artificial intelligence (AI) based on user requests to generate and present optimal proposals and estimates. The system mainly consists of a user terminal, a server, and a generative AI.

[1310] System Overview

[1311] User terminal

[1312] The user terminal has an interface for users to provide input data. Users enter necessary information such as requirements, preferences, and budget using web forms or applications. The terminal collects this input data, converts it to JSON format, and sends it to the server.

[1313] For example, when a real estate agent requests a new property listing website, the user will input specific requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people."

[1314] server

[1315] The server receives and analyzes data sent from the user's terminal. It performs preprocessing such as data cleansing and data standardization on the received data, and then passes it on to a generative AI for processing. The generative AI generates proposals and estimates based on the analyzed data. The generated data is then sent back from the server to the user's terminal and presented to the user.

[1316] In this example, the server analyzes data sent by the real estate agent, such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and passes it to a generative AI. Based on this data, the AI ​​generates optimal design proposals (layout proposals for the homepage and property details pages, etc.) and an estimate (totaling 950,000 yen).

[1317] Generative artificial intelligence

[1318] Generative AI generates proposals and estimates based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions, drawing on historical and training data. The generated design proposals and estimates are returned to the server, sent to the user's terminal, and displayed to the user.

[1319] Gathering feedback and improving the system

[1320] user

[1321] Users provide feedback on proposals and quotes presented on their devices. For example, they might offer specific feedback such as, "The design is good, but I'd like to add three more pages."

[1322] terminal

[1323] The device collects this feedback, converts it back to JSON format, and sends it to the server.

[1324] server

[1325] The server analyzes the received feedback and passes it to the generative AI. The generative AI uses this feedback to improve its algorithms and generation logic, and incorporates these improvements into the generation of future proposals and estimates.

[1326] Examples of prompt statements

[1327] Examples of prompt statements for a generative AI model are as follows:

[1328] Prompt message:

[1329] "This is a request from a real estate agent. They need a 10-page property listing website. The budget is under 1 million yen, and the target audience is young people. Based on past design patterns and cost structures, please generate optimal design proposals (website layout, property detail page layouts, etc.) and estimates."

[1330] Thus, the present invention is capable of responding quickly and accurately to user requests, generating high-quality proposals and quotations, and further improving the system by reflecting user feedback, thereby enhancing the quality of the final proposals and user satisfaction.

[1331] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1332] Step 1: The user enters the required information via a web form or application on their device. This input includes specific requirements (e.g., a 10-page website, a budget of under 1 million yen, targeting young people). The device collects this information and converts it into JSON format. The input data is based on the user's requests, and the converted JSON data is output.

[1333] Step 2: The terminal sends the collected JSON data to the server. The server parses the received JSON data. Here, the server confirms receipt of the data and simultaneously checks the data integrity (validity of format and data type). For example, it checks for missing fields or invalid values ​​and performs a cleansing process. As a result of the analysis, the cleansed data is output.

[1334] Step 3: The server passes the pre-processed data to the generative AI and requests the generation of proposals and estimates. The generative AI uses machine learning algorithms to analyze historical data and training data to generate optimal proposals (e.g., web design proposals) and estimates (e.g., total cost of 950,000 yen). This generation process involves analysis and data calculations based on the input data. The generated proposals and estimates are then output.

[1335] Step 4: The generative AI returns the generated design proposals and estimates to the server. The server packages this data together. In this packaging process, the design proposals and estimates are converted to an appropriate format (e.g., HTML or PDF) and output as a single file or document. The packaged data is then output.

[1336] Step 5: The server sends the packaged data to the user's terminal. The terminal receives the transmitted data and displays it to the user. Here, the user can review the displayed design proposals and estimates. The packaged data is displayed on the user's screen.

[1337] Step 6: The user provides feedback on the presented proposal and estimate. Specifically, they enter specific requests and opinions, such as "The design is good, but I would like to add 3 pages." This feedback is collected on the device and converted back into JSON format. The feedback data is then output.

[1338] Step 7: The device sends the collected feedback data to the server. The server analyzes the received feedback. This analysis process examines the content of the feedback in detail and converts it into a data structure for passing to the generative AI. For example, it extracts the number of additional pages and the requirements for design changes. The analyzed feedback data is output.

[1339] Step 8: The server passes the analyzed feedback data to the generative AI and requests it to regenerate the proposals and estimates. The generative AI improves its algorithms and generation logic based on the feedback and generates new proposals and estimates. In this process, the initial input data and feedback data are integrated to create the new generation. The improved proposals and estimates are output.

[1340] Step 9: The generative AI returns the new proposals and estimates it has generated to the server, which then repackages them. The repackaged data is then output.

[1341] Step 10: The server sends the improved, packaged data to the user's terminal. The terminal displays the sent data to the user, who then reviews the improved proposal and estimate. This facilitates the iterative generation of proposals and estimates until the user is satisfied.

[1342] (Application Example 1)

[1343] 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".

[1344] In modern e-commerce, users face the problem of spending a lot of time and effort making the best choice from a wide variety of products. Furthermore, it is difficult to smoothly place customized orders based on user preferences and budgets, and there is a need for quick quotes. Existing systems fail to provide appropriate proposals and quotes to meet user needs, which is a factor in lowering user satisfaction.

[1345] 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.

[1346] In this invention, the server includes means for collecting user input data, means for analyzing the input data, means for a generative artificial intelligence to generate optimal product suggestions and quotes based on the analyzed data, means for presenting the generated product suggestions and quotes to the user, means for collecting feedback from the user, and means for improving the system based on the feedback. This enables the provision of quick and accurate product suggestions and quotes that meet the user's desired conditions, significantly improving the user experience on e-commerce sites.

[1347] A "user" is a buyer who wishes to use the system to receive product suggestions and quotes.

[1348] "Input data" refers to information such as desired conditions and budget that users provide to the system.

[1349] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate optimal product suggestions and quotes.

[1350] "Product suggestions" refer to the optimal product selection provided by generative artificial intelligence based on the user's desired conditions.

[1351] "Estimate" refers to price information for a product calculated by a generative artificial intelligence system.

[1352] "Feedback" refers to opinions and requests regarding suggestions and estimates that users provide to the system.

[1353] A "system" refers to a device or program that includes components that perform a series of processes to generate product suggestions and quotations based on user input data and present them to the user.

[1354] This invention relates to a system that collects user input data, analyzes it, and uses generative artificial intelligence to generate optimal product suggestions and quotes, which are then presented to the user. The system mainly consists of a user terminal, a server, and generative artificial intelligence (AI).

[1355] System Overview

[1356] User terminal

[1357] The user terminal has an interface for users to provide input data. Users use web forms or applications to input information such as desired products, budget, and customization requirements. The terminal collects this input data and sends it to the server.

[1358] server

[1359] The server receives and analyzes data sent from the user's terminal. It then passes this data to a generative AI to generate optimal product suggestions and quotes. The generated data is then sent back from the server to the user's terminal and presented to the user.

[1360] Generative artificial intelligence

[1361] Generative AI generates product suggestions and quotes based on data provided by the server. The AI ​​uses machine learning algorithms to generate optimal solutions by referring to historical data and training data.

[1362] Program details and technologies used

[1363] Hardware and software used

[1364] Hardware: Standard PC terminals, smartphones, cloud servers (general term)

[1365] Software: Flask (Web application framework), generative AI libraries (e.g., custom models based on TensorFlow or PyTorch)

[1366] Data processing and calculation

[1367] The terminal converts the data collected from the user into JSON format and sends it to the server. The server receives this data and performs preprocessing. The preprocessed data is then passed to a generative AI, which analyzes the data and generates optimal product suggestions and quotes. The generated suggestions and quotes are sent back to the user terminal via the server and presented to the user.

[1368] Specific example

[1369] 1. User input

[1370] Users enter their desired purchase criteria on a specific product page of an online shopping site. For example, they might enter, "I would like a blue leather sofa. My budget is under 50,000 yen."

[1371] 2. Data transmission and analysis

[1372] The terminal converts the input data into JSON format and sends it to the server. The server analyzes the received data, performs the necessary preprocessing, and passes the data to the generative AI. The generative AI then generates optimal product suggestions and quotes based on that data.

[1373] 3. Display of results and feedback

[1374] The server sends the generated product proposals and quotes to the terminal as a single package. The user reviews the presented product proposal (for example, "Blue leather sofa (in stock), price 48,000 yen").

[1375] 4. Gathering feedback

[1376] Users provide feedback on the presented product suggestions and quotes. For example, they might enter feedback such as, "The design is good, but I'd like more premium options." The device collects this feedback, converts it back into JSON format, and sends it to the server. The server and AI analyze the feedback and incorporate it into future suggestions.

[1377] In this way, this invention can respond quickly and accurately to the desired conditions of users on e-commerce sites. Furthermore, by having the generative AI learn from the feedback, the overall accuracy of the system and user satisfaction can be improved.

[1378] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1379] Step 1: Collecting user input data

[1380] Users enter their desired purchase conditions, budget, and customization requirements on specific product pages of the online shopping site. For example, they might enter conditions such as, "I would like a blue leather sofa. My budget should be under 50,000 yen."

[1381] Input details: Desired product specifications (color, material, budget, etc.)

[1382] Output data: Input data in JSON format

[1383] Step 2: Submit the input data

[1384] The device converts the collected data into JSON format and sends it to the server. The device sends the data through the API endpoint.

[1385] Input content: Input data in JSON format

[1386] Output data: Data sent to the server

[1387] Step 3: Data reception and analysis

[1388] The server receives the data sent from the terminal and begins analysis. Preprocessing, such as data formatting and normalization, is performed at this stage.

[1389] Input content: JSON format data sent from the device.

[1390] Output data: Preprocessed data

[1391] Step 4: Providing data to generative AI

[1392] The server passes the pre-processed data to the generative AI and instructs it to generate product suggestions and quotes.

[1393] Input content: Preprocessed data

[1394] Output data: Data passed to the generative AI.

[1395] Step 5: AI-powered proposal and quote generation

[1396] Generative AI analyzes data provided by a server to generate optimal product suggestions and quotes. The algorithm is executed based on historical data and training data.

[1397] Input content: Data provided to the generative AI

[1398] Output data: Product proposal and quotation

[1399] Step 6: Submit the results

[1400] The server sends the proposals and estimates received from the generative AI to the user's device. The user can then view this data on their device.

[1401] Input content: Generated product proposal and estimate

[1402] Output data: Proposals and quotes sent to the terminal

[1403] Step 7: Display to the user

[1404] The terminal displays product suggestions and quotes sent from the server to the user, allowing the user to review them.

[1405] Input content: Product proposal and quotation sent to the terminal.

[1406] Output data: Product proposals and quotes presented for user review.

[1407] Step 8: Gathering Feedback

[1408] Users provide feedback on the displayed product suggestions and quotes. For example, they might say, "The design is great, but I'd like to see more premium options."

[1409] Input content: User feedback (text format)

[1410] Output data: Feedback data in JSON format

[1411] Step 9: Submitting Feedback

[1412] The device converts the collected feedback data into JSON format and sends it to the server.

[1413] Input content: Text-formatted feedback

[1414] Output data: Feedback data in JSON format sent to the server

[1415] Step 10: Analysis of Feedback

[1416] The server receives feedback data and performs analysis. The analysis results are used to improve the algorithms of the generative AI.

[1417] Input content: Feedback data sent to the server

[1418] Output data: Improvement data for the AI ​​algorithm

[1419] The above outlines the specific processing steps of the system that realizes the application example. This allows users to obtain product proposals and quotes quickly and accurately, and also contributes to improving the system's accuracy through feedback.

[1420] 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.

[1421] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1422] System Overview

[1423] User terminal

[1424] The user terminal provides an interface for users to input information and has the functionality to collect emotional data. Users input information such as project requirements, preferences, and budget through web forms and applications. Simultaneously, user facial expression and voice data are collected and analyzed by the emotion engine.

[1425] server

[1426] The server receives and analyzes data sent from the user's terminal. Before the data is passed to the generative AI, the emotion engine analyzes the user's emotions, and the results are also provided to the AI. This ensures that proposals and estimates reflect the user's emotions.

[1427] Generative artificial intelligence and emotion engines

[1428] The generative AI generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithms. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[1429] Program processing

[1430] Collect user input data

[1431] Users enter project requirements and preferences into input forms on their devices. Their facial expressions and voice are also collected via the camera and microphone. For example, when requesting a new website design, the user enters the necessary functions, budget, and design preferences, while their facial expressions and voice are simultaneously collected.

[1432] Data analysis and transmission

[1433] The device converts the collected text and emotion data into JSON format and sends it to the server. The server uses an emotion engine to analyze the user's emotions and passes this information to a generative AI.

[1434] Proposal and Estimate Generation

[1435] The server sends emotional data analyzed by the emotion engine, along with other input data, to the generative AI. The generative AI uses this data to generate optimal suggestions and quotes for the user. By using machine learning algorithms and taking the user's emotional state into account, more appropriate suggestions can be provided.

[1436] Sending and displaying results

[1437] The server receives the generated proposals and estimates and sends them to the user's terminal. The terminal displays them to the user, who then reviews the proposals and estimates. These may include design proposals and detailed cost estimates, for example.

[1438] Feedback collection and analysis

[1439] Users provide feedback on the presented proposals and quotes. User input data and re-collected sentiment data are collected by the terminal and sent to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[1440] Specific example

[1441] 1. User input

[1442] A user (for example, a real estate agent) requests a new property listing website. The user inputs requirements such as "a 10-page website, a budget of under 1 million yen, and a target audience of young people," and their facial expressions and voice are also collected.

[1443] 2. Data transmission and analysis

[1444] The server receives and analyzes the requirements data and emotion data sent from the terminal. The emotion engine analyzes the user's emotions and passes this data to the generative AI.

[1445] 3. Generating proposals

[1446] The generative AI generates optimal design proposals and estimates based on emotional data analyzed by the emotion engine and other requirements data. For example, it generates a "design proposal for the entire site" and an "estimated cost of 950,000 yen."

[1447] 4. Presentation of results and feedback

[1448] The device displays the generated design proposal and estimate to the user. The user provides feedback such as, "The design is good, but I would like a more colorful design," and their facial expression and voice are collected again. The device sends the feedback and emotion data to the server.

[1449] 5. Analysis and implementation of feedback

[1450] The server analyzes feedback and sentiment data and incorporates it into the generative AI. For subsequent proposals, suggestions and estimates that are better adapted to the user's emotions will be generated.

[1451] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[1452] The following describes the processing flow.

[1453] Step 1:

[1454] The user prepares the input data.

[1455] The user enters project requirements and preferences into an input form on the device. For example, if requesting website design, the user would enter the number of pages, desired design style, budget, etc. The device simultaneously collects the user's facial expressions and voice data through its camera and microphone.

[1456] Step 2:

[1457] The device collects data.

[1458] The terminal collects data entered by the user into input forms and formats the data as needed (for example, converting it to JSON format). The collected facial expression data and voice data are also sent to the server at the same time.

[1459] Step 3:

[1460] The device sends data to the server.

[1461] The device sends the collected text and sentiment data to the server as an HTTP request. The data transmission also includes appropriate authentication information and request headers.

[1462] Step 4:

[1463] The server receives the data.

[1464] The server receives HTTP requests sent from terminals and parses the data. This parsing includes data format validation and preprocessing. Sentiment data is then prepared to be sent to the sentiment engine.

[1465] Step 5:

[1466] The server invokes the emotion engine.

[1467] The server passes the pre-processed text data and sentiment data to the sentiment engine. Specifically, this involves making API calls and executing sentiment analysis algorithms.

[1468] Step 6:

[1469] The emotion engine analyzes the user's emotions.

[1470] The emotion engine analyzes given facial expression data, voice data, and text data to identify the user's emotional state. For example, it can determine whether the user is nervous or happy. The analysis results are integrated with the text data and sent to the generative AI.

[1471] Step 7:

[1472] The server calls a generative AI.

[1473] The server passes text data, which incorporates emotional data, to the generative AI. Specifically, this involves API calls and the execution of AI algorithms.

[1474] Step 8:

[1475] Generative AI generates proposals and estimates.

[1476] Generative AI automatically generates optimal suggestions and quotes based on given data. It uses machine learning models to derive the best solutions based on information learned from past data, and also takes the user's emotional state into consideration.

[1477] Step 9:

[1478] The server receives the generated result.

[1479] The server receives the proposal and estimate data returned by the generative AI and formats it into the appropriate format. The formatted data includes design proposals and detailed cost estimates.

[1480] Step 10:

[1481] The server sends the generated results to the terminal.

[1482] The server sends the formatted data to the terminal as an HTTP response. It verifies the data's integrity and ensures that it is displayed correctly on the user's terminal.

[1483] Step 11:

[1484] The terminal displays the generated result.

[1485] The terminal receives a response from the server and displays proposals and estimates to the user. The user can review this information and evaluate its contents. The displayed proposals may include, for example, design proposals and estimated costs.

[1486] Step 12:

[1487] Users provide feedback.

[1488] Users provide feedback on the presented proposals and quotes. For example, they might enter requests such as, "The design is excellent, but I'd like the colors changed." During this process, the user's facial expressions and voice are also collected again.

[1489] Step 13:

[1490] The device collects feedback.

[1491] The device collects user feedback and converts it back into JSON format. It then sends the collected feedback and sentiment data to the server.

[1492] Step 14:

[1493] The server receives feedback.

[1494] The server analyzes the collected feedback and sentiment data and incorporates it into the generation of future proposals and estimates. The generative AI and sentiment engine then use this feedback information to improve their algorithms and apply them to the creation of future proposals.

[1495] (Example 2)

[1496] 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".

[1497] Conventional proposal and quotation systems primarily generate proposals and quotations based on user input data, making it difficult to adequately reflect user emotions and feedback. As a result, proposals and quotations that do not match user needs are sometimes generated, leading to decreased satisfaction. This invention aims to solve this problem and improve user satisfaction by analyzing user emotional data and reflecting it in a generative artificial intelligence system.

[1498] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1499] In this invention, the server includes means for collecting and analyzing user input data and sentiment data; means for a generative artificial intelligence to generate proposals and estimates; means for presenting the generated proposals and estimates to the user; and means for collecting and analyzing user feedback and improving the system. This makes it possible to generate proposals and estimates that take the user's sentiments into account.

[1500] "User input data" refers to information that users enter through the terminal interface, such as project requirements and desired conditions.

[1501] "Emotional data" refers to information that indicates a user's emotional state, obtained from the user's facial expression data and voice data.

[1502] "Means of analysis" refers to algorithms and technologies used to analyze user input data and sentiment data, and to generate necessary proposals and estimates based on that content.

[1503] "Generative artificial intelligence" refers to algorithms and technologies that generate proposals and estimates based on provided data, and utilize machine learning and deep learning techniques.

[1504] "Proposals and estimates" refer to the specific proposals and their estimated costs generated by generative artificial intelligence based on user input data and sentiment data.

[1505] "Feedback" refers to the evaluations, opinions, and additional sentiment data that users provide regarding the proposed ideas and quotes presented.

[1506] "Means of improving the system" refers to methods for analyzing user feedback and sentiment data to improve the quality of future proposals and quotes.

[1507] This invention relates to a system that generates and presents optimal suggestions and estimates by combining generative artificial intelligence (AI) and an emotion engine based on user input data and feedback. This system consists of a user terminal, a server, a generative AI, and an emotion engine.

[1508] User terminal

[1509] The user terminal provides an interface for users to input information. Through web forms and applications, users enter information such as project requirements, preferences, and budget. It also uses cameras and microphones to collect user facial and voice data. This simultaneously collects user emotional data. For example, when requesting a new website design, the user inputs the necessary functions, budget, and design preferences, while their facial expressions and voice are also collected.

[1510] server

[1511] The server is responsible for receiving and analyzing data sent from user terminals. An emotion engine is used for analysis, specifically to analyze the user's emotions. This might involve using IBM Watson's emotion analysis API, for example. The results are then provided to a generative AI (e.g., OpenAI's GPT-3 model) to generate optimal suggestions and estimates.

[1512] Generative artificial intelligence and emotion engines

[1513] The generative AI generates optimal proposals and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative AI's algorithm. This enables the generation of proposals and quotes that respond to the user's emotions. For example, in response to requirements such as "I want a 10-page website, with a budget of under 1 million yen," the system analyzes the user's facial expressions and voice, such as "I would like a more colorful design," to generate the most suitable proposal.

[1514] Presentation to the user

[1515] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays these to the user, who then reviews the proposals and estimates. These may include, for example, design proposals and detailed cost estimates.

[1516] Feedback collection and analysis

[1517] Users provide feedback on the presented proposals and quotes. This feedback is collected by the terminal as user input data and newly collected sentiment data, and then sent back to the server. The server analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and quotes.

[1518] Specific example

[1519] 1. User input

[1520] User: "I'd like to request a new property listing website. It should be 10 pages long, with a budget of under 1 million yen, and the target audience is young people."

[1521] The user's expression is smiling, and they add in their voice, "I'd prefer a more colorful design."

[1522] 2. Data transmission and analysis

[1523] The device converts the input data and emotion data into JSON format and sends it to the server.

[1524] The server analyzes the data, and the emotion engine analyzes the emotional data, determining, for example, "positive" and "excited."

[1525] 3. Generating proposals

[1526] The server sends the analysis data to the generative AI.

[1527] The generative AI generated a "colorful, youth-oriented design proposal and estimated cost of 950,000 yen."

[1528] 4. Presentation of results and feedback

[1529] The device displays the generated design proposals and estimates to the user.

[1530] A user commented, "The design is good, but I would like a more colorful design."

[1531] 5. Analysis and implementation of feedback

[1532] The server re-analyzes the feedback and sentiment data.

[1533] In the next proposal, the generated proposal will be more tailored to the user's emotions.

[1534] By combining this with an emotion engine, the present invention can generate proposals and estimates that take into account the user's emotional state, thereby further improving user satisfaction.

[1535] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1536] Step 1:

[1537] The user enters project requirements and desired conditions into an input form on their device. Furthermore, the user's facial expressions and voice are collected via the camera and microphone. Input includes text data such as "10-page website" or voice data such as "Make it more colorful." The collected data is converted into JSON format as output.

[1538] Step 2:

[1539] The device converts the collected text and sentiment data into JSON format and sends it to the server. Specifically, it uses an HTTP POST request to send the data. The input is the collected text and sentiment data, and the output is the converted JSON data.

[1540] Step 3:

[1541] The server parses the JSON data received from the terminal. An emotion engine is used for the analysis to determine the user's emotions. For example, the analysis is performed via IBM Watson's emotion analysis API. The input is the received JSON data, and the output is a new dataset containing the emotion analysis results.

[1542] Step 4:

[1543] The server sends emotion data analyzed by the emotion engine and other input data to the generative AI. The generative AI (e.g., OpenAI's GPT-3) generates optimal proposals and estimates based on this data. The input is a dataset containing the analysis results, and the output is the generated proposals and estimates.

[1544] Step 5:

[1545] The server sends the generated proposals and estimates to the user's terminal. The user's terminal displays this information to the user. The input is the proposals and estimates obtained from the generative AI, and the output is the information displayed on the user's terminal. For example, the screen might display "Colorful, youth-oriented design proposal, estimated cost 950,000 yen."

[1546] Step 6:

[1547] Users provide feedback on the presented proposals and estimates. Specifically, they input feedback through the terminal interface, and their facial expressions and voice are also collected. The input consists of the user's feedback and associated emotion data, and the output is feedback data converted into JSON format.

[1548] Step 7:

[1549] The terminal converts the feedback and recollected sentiment data into JSON format and sends it to the server. The server re-analyzes this feedback using a sentiment engine and incorporates it into the generation of future proposals and estimates. The input is feedback and sentiment data, and the output is the re-analyzed dataset.

[1550] (Application Example 2)

[1551] 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".

[1552] Conventional proposal systems generate mechanical proposals and quotes without considering the user's emotional state, making it difficult to fully meet the user's essential needs and desires. Furthermore, there was a lack of systems to effectively incorporate user feedback and utilize it in future proposals. As a result, user satisfaction was low, and the accuracy of proposals did not improve easily.

[1553] 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. In this invention, the server includes means for collecting user input data and sentiment data, means for analyzing the input data and sentiment data, and means for a generative artificial intelligence to generate proposals and estimates based on the analyzed data. This makes it possible to generate more appropriate proposals and estimates that take the user's sentiment data into consideration. Furthermore, by analyzing user feedback and sentiment data and reflecting it in the generation of future proposals and estimates, the accuracy of proposals and user satisfaction can be improved.

[1554] "User input data" refers to data that users provide to the system, including information, requests, conditions, and desired budget.

[1555] "Emotional data" refers to data that indicates a user's emotional state, obtained by analyzing their facial expressions and voice.

[1556] "Generative artificial intelligence" refers to artificial intelligence that has an algorithm that automatically generates optimal suggestions and estimates based on user input data and emotional data.

[1557] A "proposal" refers to a specific product or service plan proposed by a generative artificial intelligence system in response to a user's request.

[1558] "Estimate" refers to detailed information, including the calculation of price and costs for a proposed item.

[1559] "Feedback" refers to data such as opinions, evaluations, and impressions of proposals and quotes provided by users.

[1560] An "emotion engine" is a dedicated engine that analyzes a user's facial expressions and voice to generate emotional data.

[1561] A "server" is a central computer system that receives and analyzes data from users and generates proposals and estimates using generative artificial intelligence and an emotion engine.

[1562] A "user terminal" is a device used by a user to access a system and input data, and includes smart glasses, smartphones, and other similar devices.

[1563] Bluetooth is a type of short-range wireless communication technology used to send and receive data between a user terminal and a server.

[1564] "Wi-Fi" is a wireless LAN technology, a communication method used to send and receive data between user terminals and servers.

[1565] This invention relates to a system that generates and presents optimal suggestions and quotes using user input data and sentiment data. This system includes a user terminal, a server, generative artificial intelligence, and a sentiment engine.

[1566] System Configuration

[1567] User terminal

[1568] The user terminal refers to devices such as smart glasses or smartphones, which provide an interface for users to input information. This allows users to input information such as project requirements, desired conditions, and budget. Additionally, user facial expression and voice data are collected via cameras and microphones and analyzed by an emotion engine.

[1569] server

[1570] The server receives and analyzes data sent from the user's terminal. The received data is then analyzed by an emotion engine to determine the user's emotions, and this analysis, along with the results, is provided to the generative artificial intelligence. As a result, proposals and estimates reflect the user's emotions.

[1571] Generative artificial intelligence and emotion engines

[1572] The generative artificial intelligence generates optimal suggestions and quotes based on data provided by the server. The emotion engine analyzes user input data, feedback, and collected emotion data, and incorporates this into the generative artificial intelligence's algorithm. This enables the generation of suggestions and quotes that are sensitive to the user's emotions.

[1573] Program processing

[1574] Data collection and transmission

[1575] Users input project requirements, preferences, budget, etc., through smart glasses, and their facial expressions and voice are also collected via camera and microphone. This data is transmitted to a server using Bluetooth or Wi-Fi. The data is converted to JSON format and analyzed on the server.

[1576] Data Analysis

[1577] The server uses an emotion engine to analyze the transmitted data. The results of this analysis are passed to a generative artificial intelligence system, which is used to generate optimal suggestions and estimates.

[1578] Proposal and estimate generation and display

[1579] The server uses the analyzed sentiment data and other input data to generate optimal suggestions and estimates using generative artificial intelligence. The generated suggestions and estimates are displayed on the smart glasses' screen.

[1580] Feedback collection and analysis

[1581] When a user provides feedback on a proposal and quote, their facial expressions and voice are also collected and sent to the server. The server analyzes the feedback and emotional data and incorporates it into future proposals.

[1582] Specific example

[1583] For example, if a user says, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," the smart glasses collect this information, and the emotion engine detects that the user's expression is serious. Based on this information, the generative artificial intelligence generates a suggestion: "X smartphone, 28,000 yen, 24-hour battery life." If the user provides feedback such as, "I'd like more color variations," that feedback and emotion data are sent back to the server and reflected in the next suggestion.

[1584] Example of a prompt

[1585] "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen."

[1586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1587] Step 1:

[1588] The user wears smart glasses and inputs project requirements, preferences, budget, etc., via voice or text input, while facial expression and voice data are collected via camera and microphone. The input data includes information about the user's desired smartphone features and price range.

[1589] Step 2:

[1590] The device converts the collected user input data, facial expression data, and voice data into JSON format. This data includes information such as, "I'm looking for a smartphone with a long battery life and a budget of under 30,000 yen," as recorded by the user's voice input.

[1591] Step 3:

[1592] The device sends data converted to JSON format to the server via Bluetooth or Wi-Fi. The input data (user requirements and facial / voice data) reaches the server's endpoint.

[1593] Step 4:

[1594] The server analyzes the received JSON data and uses an emotion engine to extract emotion data from the user's facial expressions and voice. This analysis includes emotion information such as "the user has a serious expression."

[1595] Step 5:

[1596] The server inputs sentiment analysis data and user requirements data into a generative artificial intelligence (AI). Based on the input data, the AI ​​generates optimal suggestions and quotes. For example, it might generate suggestions such as "X smartphone, 28,000 yen, 24-hour battery life."

[1597] Step 6:

[1598] The server sends the generated proposals and estimates to the user's terminal (smart glasses). The output data (proposals and estimates) is displayed on the smart glasses' screen.

[1599] Step 7:

[1600] Users review proposals and quotes displayed on their smart glasses and provide feedback via voice or text. For example, they might say, "I'd like to see more color variations."

[1601] Step 8:

[1602] The device collects user feedback, facial expression data, and voice data again, converts them to JSON format, and sends them to the server. It then processes the data again based on the input data and sends it back.

[1603] Step 9:

[1604] The server analyzes user feedback and sentiment data and compares it with previous suggestion data. Using the sentiment engine, it re-evaluates the user's requests and emotions and provides new input data to the generative artificial intelligence.

[1605] Step 10:

[1606] The generative artificial intelligence generates further optimized proposals and estimates based on the analysis results, and displays them again on the user's terminal via the server. This ensures that the next proposal is more in line with the user's emotions and requests.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] 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."

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] The following is further disclosed regarding the embodiments described above.

[1629] (Claim 1)

[1630] Means for collecting user input data,

[1631] Means for analyzing the aforementioned input data,

[1632] A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed data,

[1633] A means for presenting the generated proposals and estimates to the user,

[1634] A means for collecting feedback from the aforementioned users,

[1635] Means for improving the system based on the aforementioned feedback,

[1636] A system that includes this.

[1637] (Claim 2)

[1638] The system according to claim 1, characterized in that the generative artificial intelligence generates optimal proposals and estimates using a machine learning algorithm.

[1639] (Claim 3)

[1640] The system according to claim 1, characterized in that it analyzes the feedback from the user and reflects it in the generation of the next proposal and estimate.

[1641] "Example 1"

[1642] (Claim 1)

[1643] Means for collecting user input data,

[1644] A means for converting the collected data into a data format and sending it to a server,

[1645] Means for analyzing the aforementioned input data and performing necessary preprocessing,

[1646] A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed data,

[1647] A means for sending the generated proposal and estimate as a single package to the user terminal,

[1648] A means for collecting user feedback and sending it back to the server,

[1649] A means for improving the algorithm and generation logic based on the aforementioned feedback,

[1650] A system that includes this.

[1651] (Claim 2)

[1652] The system according to claim 1, characterized in that the generative artificial intelligence generates optimal proposals and estimates using a machine learning algorithm.

[1653] (Claim 3)

[1654] The system according to claim 1, characterized in that it analyzes the feedback from the user and reflects it in the generation of the next proposal and estimate.

[1655] "Application Example 1"

[1656] (Claim 1)

[1657] Means for collecting user input data,

[1658] Means for analyzing the aforementioned input data,

[1659] A means by which a generative artificial intelligence generates optimal product suggestions and estimates based on the analyzed data,

[1660] A means for presenting the generated product proposal and estimate to the user,

[1661] A means for collecting feedback from the aforementioned users,

[1662] Means for improving the system based on the aforementioned feedback,

[1663] A system that includes this.

[1664] (Claim 2)

[1665] The system according to claim 1, characterized in that the generative artificial intelligence generates optimal product suggestions and estimates using a machine learning algorithm.

[1666] (Claim 3)

[1667] The system according to claim 1, characterized in that it analyzes the feedback from the user and reflects it in the generation of the next proposal and estimate.

[1668] "Example 2 of combining an emotion engine"

[1669] (Claim 1)

[1670] Means for collecting user input data,

[1671] Means for analyzing the aforementioned input data and user sentiment data,

[1672] A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed emotional data and user input data,

[1673] A means for presenting the generated proposals and estimates to the user,

[1674] A means for collecting feedback from the aforementioned users,

[1675] Means for improving the system based on the aforementioned feedback and emotional data,

[1676] A system that includes this.

[1677] (Claim 2)

[1678] The system according to claim 1, characterized in that the generative artificial intelligence generates optimal proposals and estimates using a machine learning algorithm.

[1679] (Claim 3)

[1680] The system according to claim 1, characterized in that it incorporates user feedback and analyzed sentiment data into the generation of the next proposal and estimate.

[1681] "Application example 2 when combining with an emotional engine"

[1682] (Claim 1)

[1683] Means for collecting user input data and sentiment data,

[1684] Means for analyzing the aforementioned input data and sentiment data,

[1685] A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed data,

[1686] A means for presenting the generated proposals and estimates to the user,

[1687] A means for collecting feedback from the aforementioned users,

[1688] Means for improving the system based on the aforementioned feedback and emotional data,

[1689] A system that includes this.

[1690] (Claim 2)

[1691] The system according to claim 1, characterized in that the generative artificial intelligence generates optimal proposals and estimates using machine learning algorithms and sentiment analysis data.

[1692] (Claim 3)

[1693] The system according to claim 1, characterized in that it analyzes user feedback and sentiment data and reflects them in the generation of future proposals and estimates. [Explanation of Symbols]

[1694] 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. Means for collecting user input data, Means for analyzing the aforementioned input data, A means by which a generative artificial intelligence generates proposals and estimates based on the analyzed data, A means for presenting the generated proposals and estimates to the user, A means for collecting feedback from the aforementioned users, Means for improving the system based on the aforementioned feedback, A system that includes this.

2. The system according to claim 1, characterized in that the generative artificial intelligence generates optimal proposals and estimates using a machine learning algorithm.

3. The system according to claim 1, characterized in that it analyzes the feedback from the user and reflects it in the generation of the next proposal and estimate.

Images