system

Abstract

We provide the system. [Solution] Means for collecting internal data and storing it in a database, A means of receiving specific instructions from the user and starting the generation process, A means of generating suggestions based on database information and user instructions using a generative model, A means of displaying the generated suggestions and collecting user feedback, A means of analyzing evaluation feedback and improving the generation process, 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, the method including 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] [[ID=Element]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In large enterprises, there is a problem that it is difficult to effectively utilize huge internal resources to create new businesses. The range of information and resources accessible to each employee is limited, and it is unclear how to combine them to come up with the optimal business ideas. Therefore, the synergy of resources cannot be maximized, and the creation of innovative business ideas is inhibited.

Means for Solving the Problems

[0005] This invention provides a system that effectively generates new business ideas by collecting and storing various internal data and generating proposals based on that data. Specifically, it includes means for extracting relevant data from a database based on instructions received from the user and generating business proposals that meet the user's requirements using a generative model. Furthermore, it collects user evaluations of the generated proposals and improves the generative model based on that feedback, thereby improving the accuracy of subsequent proposals. This enables the efficient use of resources and the creation of innovative business ideas.

[0006] "Internal data" refers to information resources generated or acquired within a company and owned by that company.

[0007] A "database" is a system that stores organized collections of information, making it easy to access and manage.

[0008] A "user" is an individual or organization that uses a system or service to input information and receive the results.

[0009] "Instructions" refer to specific requests or goals that a user gives to the system.

[0010] The "generation process" refers to a series of procedures that create new suggestions based on collected data and user feedback.

[0011] A "generative model" is an algorithm or computational method built to analyze input information and generate a specific output.

[0012] A "proposal" is the output result of a generation process, such as a proposal for a new business or an improvement plan for an existing business.

[0013] "Evaluation" refers to the process of collecting judgments and opinions that users make regarding the generated suggestions.

[0014] "Feedback" refers to information obtained through evaluation and restored to the system for use in subsequent process improvement.

[0015] "Improvement" refers to improvement measures taken to enhance the accuracy and effectiveness of an existing system or model.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one 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.

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

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

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for generating new business ideas by combining resources and data available within a company. The following describes in detail the forms in which this system can be implemented.

[0038] The system according to this invention is implemented through the interaction of a server, a terminal, and a user. The server collects publicly available and useful data within the enterprise and organizes and stores it in a database. The data includes details of past business operations, activity records, and information on available resources.

[0039] Users access the system via a terminal and input specific instructions based on their requirements. This prepares the system to generate business proposals tailored to the company's needs.

[0040] The server analyzes the instructions received from the user and searches for and retrieves relevant data from the database. Next, it uses a generative model to integrate the user's instructions with the information in the database and create the optimal business proposal for the user. The generated proposal is then displayed to the user, and their feedback and evaluations are collected.

[0041] Users review the generated suggestions on their devices, consider them, and provide feedback. This feedback is sent from the device to the server, which analyzes the feedback to improve the generative model. This continuous feedback loop allows the system to gradually improve its accuracy and provide more appropriate business suggestions.

[0042] For example, if a user inputs a request for "proposals for new energy projects that are environmentally friendly," the server will use internal environmental data and information on past energy projects to generate a proposal such as "a localized clean energy plan utilizing renewable energy." The user can then use this as a basis to develop a concrete business plan.

[0043] This invention enables companies with vast amounts of data to make the most of their resources and quickly and efficiently acquire innovative business ideas.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects publicly available data within the company and stores it in a database. This data includes records of past projects, resource information, and research results, all of which are useful for business development.

[0047] Step 2:

[0048] Users use a terminal to input specific instructions based on their business needs into the system. For example, they might input instructions such as "Please provide ideas for a new health management service" via the terminal.

[0049] Step 3:

[0050] The server analyzes the instructions received from the user and extracts relevant keywords and context. Based on this information, it queries the database for relevant data and prepares it for generation.

[0051] Step 4:

[0052] The server uses a generative model to integrate information retrieved from the database with user instructions to generate new business proposals. The generative model considers known patterns and business trends to construct proposals that best meet user needs.

[0053] Step 5:

[0054] The terminal displays the generated business proposal to the user. The user can review the proposal and input their evaluation and feedback on the content into the terminal.

[0055] Step 6:

[0056] The server receives evaluations and feedback submitted by users and uses them as training data for the generative model. This feedback is used to improve the accuracy of the generative model and refine it so that future suggestions are more precise.

[0057] (Example 1)

[0058] 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."

[0059] The goal is to provide a system that efficiently utilizes the vast amount of data and resources held by companies to rapidly generate new business ideas. Furthermore, it aims to implement a mechanism for continuously improving the quality of the generated proposals.

[0060] 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.

[0061] In this invention, the server includes means for collecting data and storing it on a recording medium, means for receiving requests from users and initiating a generation process, and means for generating proposals based on the information on the recording medium and the requests using a generation model. This makes it possible to maximize the potential within a company and provide high-quality business proposals.

[0062] "Means of collecting data and storing it on a recording medium" refers to technologies and methods for acquiring data from various sources both inside and outside a company, structuring it, and storing it on a storage medium.

[0063] "Means for receiving requests from users and initiating the generation process" refers to technologies and methods for receiving information and requests entered by users and triggering the generation process.

[0064] "Means of generating proposals based on information and requests in a recording medium using a generative model" refers to technologies and methods that utilize AI technology and algorithms to combine recorded data with user requests to output new proposals.

[0065] "A means of displaying generated proposals on a visual device and collecting feedback from users" refers to a function or method for displaying generated proposals on a display device and collecting feedback from users.

[0066] "Means for analyzing opinions and improving the generation process" refers to technologies and methods for analyzing feedback collected from users and reflecting it in generation models and techniques to improve the quality of proposals.

[0067] "Means for analyzing the request content and selecting relevant information when retrieving information from a recording medium" refers to technologies and methods for analyzing the user's request content and efficiently selecting and extracting information related to it.

[0068] "Learning methods for adjusting generative models using feedback" refer to learning algorithms and techniques that update and adjust generative models based on user feedback to improve their performance.

[0069] This invention provides a system for efficiently generating new business proposals using a generation AI model that utilizes a wide variety of information obtained from both inside and outside a company.

[0070] The server first collects data using a data management system (e.g., MySQL® or PostgreSQL) to integrate internal company data and information acquired from external sources, and stores it on a recording medium. This data includes past business activities, activity records, resource information, etc.

[0071] Users access the system from their devices via a web browser or dedicated application. They can input business requirements based on their own needs and initiate the generation process. For example, they can provide instructions to the system by entering specific prompts such as, "I need ideas for a new mobile service to be deployed in a specific region."

[0072] The server analyzes the instructions received from the user using natural language processing technology. Based on this analysis, the server efficiently searches for highly relevant data from the storage medium and uses a generative AI model (e.g., GPT-3® or ChatGPT®) to generate the optimal business proposal that matches the user's instructions.

[0073] The generated suggestions are sent from the server to the terminal, where the user can view them in real time. The user reviews the suggestions and provides feedback to the server, including their evaluation and opinions. This feedback is incorporated into the learning process of the generative AI model and used to improve the quality of suggestion generation.

[0074] This process allows the system to make the most of a company's data and quickly deliver high-quality business proposals. By repeating this cycle, the accuracy and relevance of the generated proposals continuously improve, supporting companies in developing businesses that leverage innovative ideas.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server collects data from both within and outside the company. It receives information from various departments and related organizations as input. This data includes historical business records, current resource information, and external market data. The server stores this data in a data management system (e.g., MySQL or PostgreSQL) and saves it in a structured format. At this stage, the data becomes ready for analysis and retrieval.

[0078] Step 2:

[0079] The user accesses the system using a terminal and enters their business requirements. Here, the system provides specific needs and hints for business ideas as input. For example, the prompt might read, "Please suggest new marketing ideas for eco-friendly services." The terminal then sends this instruction to the server, proceeding to the next step.

[0080] Step 3:

[0081] The server receives instructions from the user and analyzes them using natural language processing techniques. The output of the analysis clarifies keywords and intentions related to the user's needs. Next, the server searches a database based on the analysis results and extracts highly relevant information. This process yields the dataset that best matches the user's instructions.

[0082] Step 4:

[0083] The server uses a generative AI model to integrate user instructions and related data to generate business proposals. The input is the user needs and dataset obtained in the previous step. The generative AI model (e.g., GPT-3 or ChatGPT) outputs optimal suggestions based on the large amount of data and user instructions. These suggestions might take the form of specific business proposals, such as a "product sales strategy using recycled materials."

[0084] Step 5:

[0085] The server sends the generated proposal to the user's terminal. The terminal displays the proposal, allowing the user to review it. The user evaluates the proposal on the terminal and, if appropriate, devises an action plan based on it.

[0086] Step 6:

[0087] Users input their evaluations and feedback on the proposals via their terminals and send them back to the server. The server analyzes this feedback and uses it to improve the generated AI model. It receives the user's feedback as input and adjusts the model's parameters based on it. This feedback process improves the accuracy of subsequent proposal generation, enabling better results.

[0088] (Application Example 1)

[0089] 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."

[0090] There is a problem in that companies and sole proprietors are unable to make the most of the data and resources they possess to effectively generate new business ideas. Furthermore, there is a challenge in formulating business strategies that fully consider local consumer trends.

[0091] 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.

[0092] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from a user and initiating a generation process, means for generating proposals based on database information and user instructions using a generation model, means for analyzing regional information and understanding consumer trends, and means for proposing new business ideas based on consumer trends. This makes it possible to integrate information from both inside and outside the company and quickly generate innovative and needs-based business ideas.

[0093] "Internal data" refers to available information and historical records held by a company or organization.

[0094] A "database" is a system that systematically stores information and allows for quick access and management as needed.

[0095] A "user" is a person or organization that intends to obtain specific information or results by using a system.

[0096] The "generation process" is the process of using data collected based on user instructions to execute a series of steps in order to generate new suggestions or results.

[0097] A "generative model" is an algorithm or machine learning system that generates new proposals or ideas based on input information.

[0098] "Local information" refers to data relating to a specific geographical area, including consumer behavior patterns and market trends.

[0099] "Consumer trends" refer to identifying changes in consumer preferences, behavior, and purchasing patterns in the market.

[0100] A "business idea" is a new business plan or concept that has the potential to provide new value to the existing business or market.

[0101] To implement this invention, it is necessary to construct a system in which a server, a terminal, and a user work in cooperation with each other. In this system, the server plays a crucial role in collecting specific data and efficiently storing it in a database. The server can use software such as MySQL or PostgreSQL as its database system. The terminal functions as an interface for the user to input specific instructions.

[0102] The server receives instructions from the user and uses a natural language processing model as a generative AI model to initiate the generation process, such as OpenAI®'s GPT-3 or its successor. This model integrates the user's input with information in the database to generate new business proposals.

[0103] This new proposal is displayed to and evaluated by the user. The server then analyzes the evaluation information received from the user and further refines the generative model. The system also incorporates data acquisition capabilities via open data sources and APIs on the internet for collecting local information.

[0104] As a concrete example, when a store manager inputs instructions into the system to "come up with a limited-time summer product," one possible prompt message might be "Please propose a new limited-time summer product, taking into account past data of local consumers." Based on this prompt, the system extracts past purchase data from the database and uses an AI model to generate new suggestions.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server collects necessary data from both internal and external sources and stores it in a database. Web scraping tools and APIs are used for data collection, and the collected data is organized and stored in a MySQL database. Input is raw data from various data sources, and output is a structured database.

[0108] Step 2:

[0109] The user logs into the system using a terminal and inputs specific business needs. This input consists of the conditions and requirements for the business proposal the user desires. This information forms part of the prompts for the generating AI model. As output, prompt sentences based on the input are generated and sent to the server.

[0110] Step 3:

[0111] The server receives a prompt from the user and extracts data from the database related to the conditions entered in the second step. This involves using SQL queries to search the database and filtering and retrieve relevant information. The input is the user prompt, and the output is the relevant dataset.

[0112] Step 4:

[0113] The server sends prompt messages to the generative AI model, which then generates business proposals based on information extracted from the database. This process involves the generative AI model analyzing data and creating new business ideas. The input consists of prompt messages and a dataset, while the output is a new business proposal.

[0114] Step 5:

[0115] The generated business proposal is sent from the server to the user's terminal and displayed there. The user reviews the results and enters an evaluation based on the validity and satisfaction level of the proposal. The input is the user's evaluation, and the output is feedback information.

[0116] Step 6:

[0117] The server collects evaluation feedback from users and analyzes it as an evaluation of the generative model. This analysis is used to refine the generative AI model and improve the accuracy of its suggestions. The input is the user's evaluation result, and the output is the adjustment data for the generation process. Actual operation includes retraining the model using the evaluation data.

[0118] 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.

[0119] This invention is a system that recognizes user emotions and adjusts and optimizes business proposals based on those emotions. The following describes a specific form for implementing this system.

[0120] This system consists of a server, terminals, and an emotion engine. The server collects publicly available data from within the company and stores it in a database. This provides the foundational data for generating business proposals. The data includes past project information, resource information, and research results.

[0121] The user sends specific instructions to the system via their device, tailored to their business objectives. During this process, the emotion engine analyzes the user's emotional state in real time. The emotion engine analyzes the user's tone of voice, facial expressions, and input style to identify the appropriate emotion for the situation.

[0122] The server generates optimal suggestions based on emotional data received from the emotion engine, along with acquired data and user instructions. The generative model also considers emotional information and adjusts the content and expression of the suggestions. In this process, efforts are made to provide suggestions that the user will find interesting and accept.

[0123] The generated suggestions are displayed on the terminal. Users can review the suggestions and provide feedback on the spot. The server collects the feedback and records it as evaluation data. Furthermore, it records changes in emotions through the emotion engine and uses this data to improve the generation process. This is reflected in subsequent suggestion generation, improving the system's accuracy and the flexibility of user response.

[0124] For example, if a user instructs the system to "provide ideas for new support services that will improve the customer experience," the emotion engine will analyze the user's expectations and interests. It will then generate suggestions that the user is interested in, such as "a personalized support system utilizing an AI chatbot." This allows the user to receive appropriate business suggestions that take their emotional state into consideration.

[0125] This invention aims to provide optimal advisory services that better meet user needs by incorporating emotional information into the business proposal generation process.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The server collects publicly available data from within the company and stores it in a database. This data includes business details, available resources, and historical performance information, forming the basis for business proposals.

[0129] Step 2:

[0130] Users input specific instructions into the system via their terminal. For example, they might specify business needs such as, "Please propose a new energy efficiency improvement plan."

[0131] Step 3:

[0132] The emotion engine analyzes the user's emotional state at the time of input. It examines the user's voice intonation, input speed, and past emotional history to infer what kinds of suggestions the user might be interested in.

[0133] Step 4:

[0134] The server takes sentiment data into account and queries the database for information related to the user's instructions. It selects the most relevant data and prepares it as input for the generative model.

[0135] Step 5:

[0136] The generative model generates business proposals tailored to the user based on emotional information and data obtained through queries. The emotional information is used to adjust the tone of expression, resulting in content that resonates with the user's emotions.

[0137] Step 6:

[0138] The device displays the generated suggestions to the user. The user can review the suggestions and provide feedback on their satisfaction level, requests for additional information, etc.

[0139] Step 7:

[0140] The server collects feedback and emotion change data sent from the terminal and uses it as training material for the generative model. Based on the feedback, the model is improved to further enhance the accuracy of future suggestions.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In recent years, there has been a growing need to respond quickly to the diversifying needs of consumers. However, conventional business proposal systems have struggled to generate proposals that take into account the emotional state of the user, making it difficult to quickly present the most suitable proposal to the user. A solution to this problem is needed.

[0144] 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.

[0145] In this invention, the server includes means for collecting internal and external information and storing data, means for using an emotion analysis device to analyze the user's emotional state, and means for generating suggestions based on information, user instructions, and emotional state using a generative model. This makes it possible to quickly and effectively generate and present optimal suggestions that are tailored to the user's emotions.

[0146] "Internal and external information" refers to information that includes publicly available data from within a company and related data obtained from external sources.

[0147] "Means of accumulating data" refers to the function of organizing collected data and saving it to storage so that it can be centrally managed.

[0148] "User's emotional state" refers to the psychological and emotional state analyzed from the user's voice tone, facial expressions, input style, etc.

[0149] An "emotion analysis device" refers to a component that uses technologies such as voice analysis and image analysis to identify the user's emotions.

[0150] A "generative model" refers to a system that uses algorithms and artificial intelligence technologies to provide optimal suggestions to users, based on accumulated data and emotional information.

[0151] "Means for generating proposals based on information, user instructions, and emotional state" refers to a function that manages the process of generating optimal business proposals based on the results of the user's emotional analysis.

[0152] Specific embodiments for carrying out this invention are shown below.

[0153] This system is based on a server, terminals, and emotion analysis devices.

[0154] The server first collects internal and external information and stores it in a database. This information includes publicly available internal company data and related information obtained from external sources. The collected data is centrally managed and functions as the basis for generating proposals.

[0155] The user sends specific instructions aligned with business objectives to the system via a terminal. At this time, an emotion analysis device installed in the terminal is activated to analyze the user's emotional state. This utilizes voice analysis software and image analysis hardware, among other things. For example, the terminal's camera can capture the user's facial expressions, or the microphone can analyze the tone of their voice.

[0156] The server uses a generative model to generate optimal suggestions based on information stored in the database, as well as user instructions and emotional states. The generative model is enhanced by machine learning algorithms, and the suggestions are adjusted to match the user's expectations.

[0157] The generated suggestions are displayed on the device, and the user reviews them. The user can send feedback on the suggestions via the device. For example, feedback can include comments such as "This suggestion is very helpful" or "Please explain the suggestion in more detail."

[0158] For example, if a user inputs "I would like you to suggest a new marketing strategy," the emotion analysis device detects feelings of anticipation and excitement. As a result, the server generates a suggestion such as "an interactive advertising campaign using AR technology" and displays it on the device.

[0159] An example of a prompt might be, "Please propose a new marketing strategy. We are particularly interested in proposals that utilize the latest technologies." By incorporating emotional information in this way into the proposal generation process and providing business proposals that meet the user's needs, we aim to realize more effective advisory services.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The server collects internal and external information and stores it in a database. It uses publicly available information within the company and data obtained from external sources as input. Specifically, it retrieves data from each information source using APIs, checks the data's integrity, and then stores it in the database. This process prepares the basic data necessary for subsequent proposal generation. The output is a well-organized and saved database.

[0163] Step 2:

[0164] The user sends specific instructions to the system via a terminal. This input includes the user entering requests and prompts tailored to business objectives. Specifically, the user types text using a keyboard on the user interface and presses the send button. At this time, the sentiment analysis device is simultaneously activated. The output is the instruction information sent by the user.

[0165] Step 3:

[0166] An emotion analysis device identifies the user's emotional state. It collects the user's voice, facial expressions, and input style as input. Specifically, it records audio using the device's microphone, acquires video using the camera, and analyzes the speed and frequency of text input. Based on this data, an emotion recognition algorithm is executed to generate the user's emotional data. The output is emotional data based on the analyzed emotional state.

[0167] Step 4:

[0168] The server uses a generated AI model to process information from a database and user instructions and sentiment data. Inputs include database information, user instructions, and sentiment data. Specifically, a machine learning model analyzes this data and generates a list of potential suggestions. The model considers the emotional state and selects the most appropriate suggestion. The output is an optimized business proposal.

[0169] Step 5:

[0170] The terminal displays the generated business proposal. It receives optimal proposals sent from the server as input. Specifically, it performs a function to display text and images on the terminal display to visualize the proposal content. The user can review this proposal. The output is the display status of the proposal presented to the user.

[0171] Step 6:

[0172] Users provide feedback on suggestions and record their emotional changes. The system receives comments and ratings from users as input. Specifically, users enter text into a feedback form and submit it. At this point, the sentiment analysis system restarts to track emotional changes before and after viewing the suggestion. The output consists of collected feedback information and updated sentiment data.

[0173] Step 7:

[0174] The server uses feedback information and sentiment data to train the system. As input, it collects user feedback and data on changes in sentiment. Specifically, this involves analyzing this data and adjusting the parameters of the generative AI model. At this stage, the system's accuracy is improved, which is useful for the next proposal generation process. The output is the updated model state.

[0175] (Application Example 2)

[0176] 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 device 14 will be referred to as the "terminal."

[0177] In the field of mail-order sales, there is a challenge in optimizing the user experience and purchase intent due to a lack of product suggestions based on user emotions. Specifically, there is a need to respond quickly to changes in user interests and emotions and propose personalized products and promotions.

[0178] 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.

[0179] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from the user and initiating a generation process, and means for analyzing the user's visual and auditory data and recognizing their emotions. This enables appropriate product suggestions and promotional displays that correspond to the user's emotional state.

[0180] "Internal data" refers to data collected and stored within a company, such as past project information and resource information.

[0181] A "generative model" is an algorithm that generates optimal suggestions based on acquired information, user instructions, and sentiment data.

[0182] "Visual data" refers to image information such as facial expressions and movements obtained from the user.

[0183] "Voice data" refers to sound information such as voice tone and speaking style obtained from the user.

[0184] "Means of recognizing emotions" refers to technologies that analyze a user's visual and auditory data to identify their current emotional state.

[0185] "Means of generating proposals" refers to a method of generating proposals using a generative model based on database information and user instructions.

[0186] "Evaluation feedback" refers to information that includes user evaluations and opinions on the generated suggestions.

[0187] "Means of improving the generation process" refers to methods of analyzing evaluation feedback and adjusting the generation model so that future proposals better meet user needs.

[0188] To realize this invention, it is necessary to configure a system that uses a server, a terminal, and an emotion recognition engine. The server collects internal data from within the company and stores it in a database. This data consists of past project information and resource information, and is used as basic data for generating suggestions using a generative AI model.

[0189] The terminal functions as an interface for users to send specific business instructions to the system. The terminal is equipped with a camera to capture the user's facial expressions and a microphone to pick up audio, thereby acquiring visual and audio data in real time. An emotion recognition engine analyzes this data to identify the user's emotions. Existing software such as OpenCV and Google® Cloud Speech-to-Text API are used for the analysis.

[0190] The server generates suggestions using a generative AI model based on sentiment data and user instructions. An algorithm operates to extract relevant information from the database during this process. The generated suggestions are displayed on the terminal, and the user has the option to review them and provide feedback. This feedback is sent to the server and used to improve the generation process.

[0191] As a concrete example, when a user searches for new headphones, the emotion recognition engine detects from the user's facial expressions that they are showing a high level of interest. Based on this information, the server extracts appropriate product information from the database and presents it to the user in order to suggest related accessories and promotions.

[0192] Examples of prompt statements include the following:

[0193] "We analyze the user's facial expressions and voice to generate product recommendations based on their level of interest. Example: 'If the user shows interest in product X, please tell us how to provide related item Y and promotional information.'"

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server collects internal data from within the company and stores it in a database. Inputs include past project and resource information, which, through storage in the database, build an information base usable for subsequent proposal generation. Outputs are the foundational data necessary for proposal generation.

[0197] Step 2:

[0198] The user sends specific instructions to the system via a terminal. The input is the user's text or voice instructions, which the terminal passes to the emotion recognition engine. The output is user instruction data for analysis.

[0199] Step 3:

[0200] The device uses a camera and microphone to acquire the user's visual and auditory data in real time. The input consists of the user's facial expressions and voice, and the data is processed using OpenCV or the Google Cloud Speech-to-Text API. The output is emotion data used by the emotion recognition engine.

[0201] Step 4:

[0202] The server receives emotional data analyzed by the emotion recognition engine and user instructions, and generates suggestions using a generative AI model. Inputs include database information, user instructions, and emotional data. Output is the optimal business proposal presented to the user.

[0203] Step 5:

[0204] The generated proposals are displayed on the terminal, and the user reviews them and provides evaluation feedback. The input is the presented proposal, and the output is the evaluation feedback data sent to the server.

[0205] Step 6:

[0206] The server analyzes user feedback and adjusts the generative model. The input is feedback data, used for learning to optimize the generative process. The output is the improved proposal generation process.

[0207] 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.

[0208] 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 (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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system for generating new business ideas by combining resources and data available within a company. The following describes in detail the forms in which this system can be implemented.

[0224] The system according to this invention is implemented through the interaction of a server, a terminal, and a user. The server collects publicly available and useful data within the enterprise and organizes and stores it in a database. The data includes details of past business operations, activity records, and information on available resources.

[0225] Users access the system via a terminal and input specific instructions based on their requirements. This prepares the system to generate business proposals tailored to the company's needs.

[0226] The server analyzes the instructions received from the user and searches for and retrieves relevant data from the database. Next, it uses a generative model to integrate the user's instructions with the information in the database and create the optimal business proposal for the user. The generated proposal is then displayed to the user, and their feedback and evaluations are collected.

[0227] Users review the generated suggestions on their devices, consider them, and provide feedback. This feedback is sent from the device to the server, which analyzes the feedback to improve the generative model. This continuous feedback loop allows the system to gradually improve its accuracy and provide more appropriate business suggestions.

[0228] For example, if a user inputs a request for "proposals for new energy projects that are environmentally friendly," the server will use internal environmental data and information on past energy projects to generate a proposal such as "a localized clean energy plan utilizing renewable energy." The user can then use this as a basis to develop a concrete business plan.

[0229] This invention enables companies with vast amounts of data to make the most of their resources and quickly and efficiently acquire innovative business ideas.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server collects publicly available data within the company and stores it in a database. This data includes records of past projects, resource information, and research results, all of which are useful for business development.

[0233] Step 2:

[0234] Users use a terminal to input specific instructions based on their business needs into the system. For example, they might input instructions such as "Please provide ideas for a new health management service" via the terminal.

[0235] Step 3:

[0236] The server analyzes the instructions received from the user and extracts relevant keywords and context. Based on this information, it queries the database for relevant data and prepares it for generation.

[0237] Step 4:

[0238] The server uses a generative model to integrate information retrieved from the database with user instructions to generate new business proposals. The generative model considers known patterns and business trends to construct proposals that best meet user needs.

[0239] Step 5:

[0240] The terminal displays the generated business proposal to the user. The user can review the proposal and input their evaluation and feedback on the content into the terminal.

[0241] Step 6:

[0242] The server receives evaluations and feedback submitted by users and uses them as training data for the generative model. This feedback is used to improve the accuracy of the generative model and refine it so that future suggestions are more precise.

[0243] (Example 1)

[0244] 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."

[0245] The goal is to provide a system that efficiently utilizes the vast amount of data and resources held by companies to rapidly generate new business ideas. Furthermore, it aims to implement a mechanism for continuously improving the quality of the generated proposals.

[0246] 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.

[0247] In this invention, the server includes means for collecting data and storing it on a recording medium, means for receiving requests from users and initiating a generation process, and means for generating proposals based on the information on the recording medium and the requests using a generation model. This makes it possible to maximize the potential within a company and provide high-quality business proposals.

[0248] "Means of collecting data and storing it on a recording medium" refers to technologies and methods for acquiring data from various sources both inside and outside a company, structuring it, and storing it on a storage medium.

[0249] "Means for receiving requests from users and initiating the generation process" refers to technologies and methods for receiving information and requests entered by users and triggering the generation process.

[0250] "Means of generating proposals based on information and requests in a recording medium using a generative model" refers to technologies and methods that utilize AI technology and algorithms to combine recorded data with user requests to output new proposals.

[0251] "A means of displaying generated proposals on a visual device and collecting feedback from users" refers to a function or method for displaying generated proposals on a display device and collecting feedback from users.

[0252] "Means for analyzing opinions and improving the generation process" refers to technologies and methods for analyzing feedback collected from users and reflecting it in generation models and techniques to improve the quality of proposals.

[0253] "Means for analyzing the request content and selecting relevant information when retrieving information from a recording medium" refers to technologies and methods for analyzing the user's request content and efficiently selecting and extracting information related to it.

[0254] "Learning methods for adjusting generative models using feedback" refer to learning algorithms and techniques that update and adjust generative models based on user feedback to improve their performance.

[0255] This invention provides a system for efficiently generating new business proposals using a generation AI model that utilizes a wide variety of information obtained from both inside and outside a company.

[0256] The server first collects data using a data management system (e.g., MySQL or PostgreSQL) to integrate internal company data and information acquired from external sources, and stores it on a storage medium. This data includes past business activities, activity records, resource information, and more.

[0257] Users access the system from their devices via a web browser or dedicated application. They can input business requirements based on their own needs and initiate the generation process. For example, they can provide instructions to the system by entering specific prompts such as, "I need ideas for a new mobile service to be deployed in a specific region."

[0258] The server analyzes the instructions received from the user using natural language processing technology. Based on this analysis, the server efficiently searches for highly relevant data from the storage medium and uses a generative AI model (e.g., GPT-3 or ChatGPT) to generate the optimal business proposal that matches the user's instructions.

[0259] The generated suggestions are sent from the server to the terminal, where the user can view them in real time. The user reviews the suggestions and provides feedback to the server, including their evaluation and opinions. This feedback is incorporated into the learning process of the generative AI model and used to improve the quality of suggestion generation.

[0260] This process allows the system to make the most of a company's data and quickly deliver high-quality business proposals. By repeating this cycle, the accuracy and relevance of the generated proposals continuously improve, supporting companies in developing businesses that leverage innovative ideas.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server collects data from both within and outside the company. It receives information from various departments and related organizations as input. This data includes historical business records, current resource information, and external market data. The server stores this data in a data management system (e.g., MySQL or PostgreSQL) and saves it in a structured format. At this stage, the data becomes ready for analysis and retrieval.

[0264] Step 2:

[0265] The user accesses the system using a terminal and enters their business requirements. Here, the system provides specific needs and hints for business ideas as input. For example, the prompt might read, "Please suggest new marketing ideas for eco-friendly services." The terminal then sends this instruction to the server, proceeding to the next step.

[0266] Step 3:

[0267] The server receives instructions from the user and analyzes them using natural language processing techniques. The output of the analysis clarifies keywords and intentions related to the user's needs. Next, the server searches a database based on the analysis results and extracts highly relevant information. This process yields the dataset that best matches the user's instructions.

[0268] Step 4:

[0269] The server uses a generative AI model to integrate user instructions and related data to generate business proposals. The input is the user needs and dataset obtained in the previous step. The generative AI model (e.g., GPT-3 or ChatGPT) outputs optimal suggestions based on the large amount of data and user instructions. These suggestions might take the form of specific business proposals, such as a "product sales strategy using recycled materials."

[0270] Step 5:

[0271] The server sends the generated proposal to the user's terminal. The terminal displays the proposal, allowing the user to review it. The user evaluates the proposal on the terminal and, if appropriate, devises an action plan based on it.

[0272] Step 6:

[0273] Users input their evaluations and feedback on the proposals via their terminals and send them back to the server. The server analyzes this feedback and uses it to improve the generated AI model. It receives the user's feedback as input and adjusts the model's parameters based on it. This feedback process improves the accuracy of subsequent proposal generation, enabling better results.

[0274] (Application Example 1)

[0275] 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."

[0276] There is a problem in that companies and sole proprietors are unable to make the most of the data and resources they possess to effectively generate new business ideas. Furthermore, there is a challenge in formulating business strategies that fully consider local consumer trends.

[0277] 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.

[0278] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from a user and initiating a generation process, means for generating proposals based on database information and user instructions using a generation model, means for analyzing regional information and understanding consumer trends, and means for proposing new business ideas based on consumer trends. This makes it possible to integrate information from both inside and outside the company and quickly generate innovative and needs-based business ideas.

[0279] "Internal data" refers to available information and historical records held by a company or organization.

[0280] A "database" is a system that systematically stores information and allows for quick access and management as needed.

[0281] A "user" is a person or organization that intends to obtain specific information or results by using a system.

[0282] The "generation process" is the process of using data collected based on user instructions to execute a series of steps in order to generate new suggestions or results.

[0283] A "generative model" is an algorithm or machine learning system that generates new proposals or ideas based on input information.

[0284] "Regional information" refers to data related to a specific geographical region and includes consumer behavior patterns and market trends.

[0285] "Consumer trends" refer to identifying changes in consumer preferences, behaviors, and purchasing patterns in the market.

[0286] "Business ideas" refer to new business plans or concepts that can provide new value to existing businesses and markets.

[0287] To implement this invention, it is necessary to construct a system in which a server, a terminal, and a user operate in cooperation with each other. In this system, the server plays an important role, collecting specific data and efficiently storing it in a database. Software such as MySQL or PostgreSQL can be used as the database system for the server. The terminal functions as an interface for the user to input specific instructions.

[0288] The server receives instructions from the user and uses a natural language processing model such as OpenAI's GPT-3 or its successor model as a generation AI model to start the generation process. This model integrates the user's input content and the information in the database to generate new business proposals.

[0289] This new proposal is displayed to and evaluated by the user. After that, the server analyzes the evaluation information obtained from the user and further improves the generation model. Also, the system incorporates a data acquisition function using open data sources on the Internet and APIs for collecting regional information.

[0290] As a concrete example, when a store manager inputs instructions into the system to "come up with a limited-time summer product," one possible prompt message might be "Please propose a new limited-time summer product, taking into account past data of local consumers." Based on this prompt, the system extracts past purchase data from the database and uses an AI model to generate new suggestions.

[0291] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0292] Step 1:

[0293] The server collects necessary data from both internal and external sources and stores it in a database. Web scraping tools and APIs are used for data collection, and the collected data is organized and stored in a MySQL database. Input is raw data from various data sources, and output is a structured database.

[0294] Step 2:

[0295] The user logs into the system using a terminal and inputs specific business needs. This input consists of the conditions and requirements for the business proposal the user desires. This information forms part of the prompts for the generating AI model. As output, prompt sentences based on the input are generated and sent to the server.

[0296] Step 3:

[0297] The server receives a prompt from the user and extracts data from the database related to the conditions entered in the second step. This involves using SQL queries to search the database and filtering and retrieve relevant information. The input is the user prompt, and the output is the relevant dataset.

[0298] Step 4:

[0299] The server sends prompt messages to the generative AI model, which then generates business proposals based on information extracted from the database. This process involves the generative AI model analyzing data and creating new business ideas. The input consists of prompt messages and a dataset, while the output is a new business proposal.

[0300] Step 5:

[0301] The generated business proposal is sent from the server to the user's terminal and displayed there. The user reviews the results and enters an evaluation based on the validity and satisfaction level of the proposal. The input is the user's evaluation, and the output is feedback information.

[0302] Step 6:

[0303] The server collects evaluation feedback from users and analyzes it as an evaluation of the generative model. This analysis is used to refine the generative AI model and improve the accuracy of its suggestions. The input is the user's evaluation result, and the output is the adjustment data for the generation process. Actual operation includes retraining the model using the evaluation data.

[0304] 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.

[0305] This invention is a system that recognizes user emotions and adjusts and optimizes business proposals based on those emotions. The following describes a specific form for implementing this system.

[0306] This system consists of a server, terminals, and an emotion engine. The server collects publicly available data from within the company and stores it in a database. This provides the foundational data for generating business proposals. The data includes past project information, resource information, and research results.

[0307] The user sends specific instructions that match the business purpose to the system via the terminal. At this time, the emotion engine analyzes the user's emotional state in real time. The emotion engine analyzes the tone of the user's voice, expression, input style, etc., and identifies the emotion according to the situation.

[0308] Based on the emotion data received from the emotion engine, the server generates an optimal proposal based on the acquired data and the user's instructions. The generation model also takes into account the emotion information and adjusts the content and expression of the proposal. In this process, efforts are made to provide proposals that the user is interested in and can accept.

[0309] The generated proposal is displayed on the terminal. The user can confirm the proposal on the spot and provide feedback. The server collects the feedback and records it as evaluation data. Furthermore, the changes in emotions are recorded through the emotion engine and used to improve the generation process. It is reflected in the proposal generation for the next time and later, improving the accuracy of the system and the flexibility of user response.

[0310] As a specific example, when the user instructs the system to "come up with a plan for a new support service to improve the customer experience", the emotion engine analyzes the user's expectations and interests. Generate proposals that the user is interested in, for example, to propose a "personalized support system using an AI chatbot". In this way, the user can receive appropriate business proposals that take into account their emotional state.

[0311] This invention realizes an optimal advisory service that better meets user needs by incorporating emotion information into the business proposal generation process.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server collects publicly available data from within the company and stores it in a database. This data includes business details, available resources, and historical performance information, forming the basis for business proposals.

[0315] Step 2:

[0316] Users input specific instructions into the system via their terminal. For example, they might specify business needs such as, "Please propose a new energy efficiency improvement plan."

[0317] Step 3:

[0318] The emotion engine analyzes the user's emotional state at the time of input. It examines the user's voice intonation, input speed, and past emotional history to infer what kinds of suggestions the user might be interested in.

[0319] Step 4:

[0320] The server takes sentiment data into account and queries the database for information related to the user's instructions. It selects the most relevant data and prepares it as input for the generative model.

[0321] Step 5:

[0322] The generative model generates business proposals tailored to the user based on emotional information and data obtained through queries. The emotional information is used to adjust the tone of expression, resulting in content that resonates with the user's emotions.

[0323] Step 6:

[0324] The device displays the generated suggestions to the user. The user can review the suggestions and provide feedback on their satisfaction level, requests for additional information, etc.

[0325] Step 7:

[0326] The server collects feedback and emotion change data sent from the terminal and uses it as training material for the generative model. Based on the feedback, the model is improved to further enhance the accuracy of future suggestions.

[0327] (Example 2)

[0328] 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".

[0329] In recent years, there has been a growing need to respond quickly to the diversifying needs of consumers. However, conventional business proposal systems have struggled to generate proposals that take into account the emotional state of the user, making it difficult to quickly present the most suitable proposal to the user. A solution to this problem is needed.

[0330] 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.

[0331] In this invention, the server includes means for collecting internal and external information and storing data, means for using an emotion analysis device to analyze the user's emotional state, and means for generating suggestions based on information, user instructions, and emotional state using a generative model. This makes it possible to quickly and effectively generate and present optimal suggestions that are tailored to the user's emotions.

[0332] "Internal and external information" refers to information that includes publicly available data from within a company and related data obtained from external sources.

[0333] "Means of accumulating data" refers to the function of organizing collected data and saving it to storage so that it can be centrally managed.

[0334] "User's emotional state" refers to the psychological and emotional state analyzed from the user's voice tone, facial expressions, input style, etc.

[0335] An "emotion analysis device" refers to a component that uses technologies such as voice analysis and image analysis to identify the user's emotions.

[0336] A "generative model" refers to a system that uses algorithms and artificial intelligence technologies to provide optimal suggestions to users, based on accumulated data and emotional information.

[0337] "Means for generating proposals based on information, user instructions, and emotional state" refers to a function that manages the process of generating optimal business proposals based on the results of the user's emotional analysis.

[0338] Specific embodiments for carrying out this invention are shown below.

[0339] This system is based on a server, terminals, and emotion analysis devices.

[0340] The server first collects internal and external information and stores it in a database. This information includes publicly available internal company data and related information obtained from external sources. The collected data is centrally managed and functions as the basis for generating proposals.

[0341] The user sends specific instructions aligned with business objectives to the system via a terminal. At this time, an emotion analysis device installed in the terminal is activated to analyze the user's emotional state. This utilizes voice analysis software and image analysis hardware, among other things. For example, the terminal's camera can capture the user's facial expressions, or the microphone can analyze the tone of their voice.

[0342] The server uses a generative model to generate optimal suggestions based on information stored in the database, as well as user instructions and emotional states. The generative model is enhanced by machine learning algorithms, and the suggestions are adjusted to match the user's expectations.

[0343] The generated suggestions are displayed on the device, and the user reviews them. The user can send feedback on the suggestions via the device. For example, feedback can include comments such as "This suggestion is very helpful" or "Please explain the suggestion in more detail."

[0344] For example, if a user inputs "I would like you to suggest a new marketing strategy," the emotion analysis device detects feelings of anticipation and excitement. As a result, the server generates a suggestion such as "an interactive advertising campaign using AR technology" and displays it on the device.

[0345] An example of a prompt might be, "Please propose a new marketing strategy. We are particularly interested in proposals that utilize the latest technologies." By incorporating emotional information in this way into the proposal generation process and providing business proposals that meet the user's needs, we aim to realize more effective advisory services.

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

[0347] Step 1:

[0348] The server collects internal and external information and stores it in a database. It uses publicly available information within the company and data obtained from external sources as input. Specifically, it retrieves data from each information source using APIs, checks the data's integrity, and then stores it in the database. This process prepares the basic data necessary for subsequent proposal generation. The output is a well-organized and saved database.

[0349] Step 2:

[0350] The user sends specific instructions to the system via a terminal. This input includes the user entering requests and prompts tailored to business objectives. Specifically, the user types text using a keyboard on the user interface and presses the send button. At this time, the sentiment analysis device is simultaneously activated. The output is the instruction information sent by the user.

[0351] Step 3:

[0352] An emotion analysis device identifies the user's emotional state. It collects the user's voice, facial expressions, and input style as input. Specifically, it records audio using the device's microphone, acquires video using the camera, and analyzes the speed and frequency of text input. Based on this data, an emotion recognition algorithm is executed to generate the user's emotional data. The output is emotional data based on the analyzed emotional state.

[0353] Step 4:

[0354] The server uses a generated AI model to process information from a database and user instructions and sentiment data. Inputs include database information, user instructions, and sentiment data. Specifically, a machine learning model analyzes this data and generates a list of potential suggestions. The model considers the emotional state and selects the most appropriate suggestion. The output is an optimized business proposal.

[0355] Step 5:

[0356] The terminal displays the generated business proposal. It receives optimal proposals sent from the server as input. Specifically, it performs a function to display text and images on the terminal display to visualize the proposal content. The user can review this proposal. The output is the display status of the proposal presented to the user.

[0357] Step 6:

[0358] Users provide feedback on suggestions and record their emotional changes. The system receives comments and ratings from users as input. Specifically, users enter text into a feedback form and submit it. At this point, the sentiment analysis system restarts to track emotional changes before and after viewing the suggestion. The output consists of collected feedback information and updated sentiment data.

[0359] Step 7:

[0360] The server uses feedback information and sentiment data to train the system. As input, it collects user feedback and data on changes in sentiment. Specifically, this involves analyzing this data and adjusting the parameters of the generative AI model. At this stage, the system's accuracy is improved, which is useful for the next proposal generation process. The output is the updated model state.

[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 the "server" and the smart glasses 214 as the "terminal".

[0363] In the field of mail-order sales, there is a challenge in optimizing the user experience and purchase intent due to a lack of product suggestions based on user emotions. Specifically, there is a need to respond quickly to changes in user interests and emotions and propose personalized products and promotions.

[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.

[0365] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from the user and initiating a generation process, and means for analyzing the user's visual and auditory data and recognizing their emotions. This enables appropriate product suggestions and promotional displays that correspond to the user's emotional state.

[0366] "Internal data" refers to data collected and stored within a company, such as past project information and resource information.

[0367] A "generative model" is an algorithm that generates optimal suggestions based on acquired information, user instructions, and sentiment data.

[0368] "Visual data" refers to image information such as facial expressions and movements obtained from the user.

[0369] "Voice data" refers to sound information such as voice tone and speaking style obtained from the user.

[0370] "Means of recognizing emotions" refers to technologies that analyze a user's visual and auditory data to identify their current emotional state.

[0371] "Means of generating proposals" refers to a method of generating proposals using a generative model based on database information and user instructions.

[0372] "Evaluation feedback" refers to information that includes user evaluations and opinions on the generated suggestions.

[0373] "Means of improving the generation process" refers to methods of analyzing evaluation feedback and adjusting the generation model so that future proposals better meet user needs.

[0374] To realize this invention, it is necessary to configure a system that uses a server, a terminal, and an emotion recognition engine. The server collects internal data from within the company and stores it in a database. This data consists of past project information and resource information, and is used as basic data for generating suggestions using a generative AI model.

[0375] The terminal functions as an interface for users to send specific business instructions to the system. The terminal is equipped with a camera to capture the user's facial expressions and a microphone to pick up audio, thereby acquiring visual and audio data in real time. An emotion recognition engine analyzes this data to identify the user's emotions. Existing software such as OpenCV and the Google Cloud Speech-to-Text API are used for the analysis.

[0376] The server generates suggestions using a generative AI model based on sentiment data and user instructions. An algorithm operates to extract relevant information from the database during this process. The generated suggestions are displayed on the terminal, and the user has the option to review them and provide feedback. This feedback is sent to the server and used to improve the generation process.

[0377] As a concrete example, when a user searches for new headphones, the emotion recognition engine detects from the user's facial expressions that they are showing a high level of interest. Based on this information, the server extracts appropriate product information from the database and presents it to the user in order to suggest related accessories and promotions.

[0378] Examples of prompt statements include the following:

[0379] "We analyze the user's facial expressions and voice to generate product recommendations based on their level of interest. Example: 'If the user shows interest in product X, please tell us how to provide related item Y and promotional information.'"

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server collects internal data from within the company and stores it in a database. Inputs include past project and resource information, which, through storage in the database, build an information base usable for subsequent proposal generation. Outputs are the foundational data necessary for proposal generation.

[0383] Step 2:

[0384] The user sends specific instructions to the system via a terminal. The input is the user's text or voice instructions, which the terminal passes to the emotion recognition engine. The output is user instruction data for analysis.

[0385] Step 3:

[0386] The device uses a camera and microphone to acquire the user's visual and auditory data in real time. The input consists of the user's facial expressions and voice, and the data is processed using OpenCV or the Google Cloud Speech-to-Text API. The output is emotion data used by the emotion recognition engine.

[0387] Step 4:

[0388] The server receives emotional data analyzed by the emotion recognition engine and user instructions, and generates suggestions using a generative AI model. Inputs include database information, user instructions, and emotional data. Output is the optimal business proposal presented to the user.

[0389] Step 5:

[0390] The generated proposals are displayed on the terminal, and the user reviews them and provides evaluation feedback. The input is the presented proposal, and the output is the evaluation feedback data sent to the server.

[0391] Step 6:

[0392] The server analyzes user feedback and adjusts the generative model. The input is feedback data, used for learning to optimize the generative process. The output is the improved proposal generation process.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is a system for generating new business ideas by combining resources and data available within a company. The following describes in detail the forms in which this system can be implemented.

[0410] The system according to this invention is implemented through the interaction of a server, a terminal, and a user. The server collects publicly available and useful data within the enterprise and organizes and stores it in a database. The data includes details of past business operations, activity records, and information on available resources.

[0411] Users access the system via a terminal and input specific instructions based on their requirements. This prepares the system to generate business proposals tailored to the company's needs.

[0412] The server analyzes the instructions received from the user and searches for and retrieves relevant data from the database. Next, it uses a generative model to integrate the user's instructions with the information in the database and create the optimal business proposal for the user. The generated proposal is then displayed to the user, and their feedback and evaluations are collected.

[0413] Users review the generated suggestions on their devices, consider them, and provide feedback. This feedback is sent from the device to the server, which analyzes the feedback to improve the generative model. This continuous feedback loop allows the system to gradually improve its accuracy and provide more appropriate business suggestions.

[0414] For example, if a user inputs a request for "proposals for new energy projects that are environmentally friendly," the server will use internal environmental data and information on past energy projects to generate a proposal such as "a localized clean energy plan utilizing renewable energy." The user can then use this as a basis to develop a concrete business plan.

[0415] This invention enables companies with vast amounts of data to make the most of their resources and quickly and efficiently acquire innovative business ideas.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The server collects publicly available data within the company and stores it in a database. This data includes records of past projects, resource information, and research results, all of which are useful for business development.

[0419] Step 2:

[0420] Users use a terminal to input specific instructions based on their business needs into the system. For example, they might input instructions such as "Please provide ideas for a new health management service" via the terminal.

[0421] Step 3:

[0422] The server analyzes the instructions received from the user and extracts relevant keywords and context. Based on this information, it queries the database for relevant data and prepares it for generation.

[0423] Step 4:

[0424] The server uses a generative model to integrate information retrieved from the database with user instructions to generate new business proposals. The generative model considers known patterns and business trends to construct proposals that best meet user needs.

[0425] Step 5:

[0426] The terminal displays the generated business proposal to the user. The user can review the proposal and input their evaluation and feedback on the content into the terminal.

[0427] Step 6:

[0428] The server receives evaluations and feedback submitted by users and uses them as training data for the generative model. This feedback is used to improve the accuracy of the generative model and refine it so that future suggestions are more precise.

[0429] (Example 1)

[0430] 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."

[0431] The goal is to provide a system that efficiently utilizes the vast amount of data and resources held by companies to rapidly generate new business ideas. Furthermore, it aims to implement a mechanism for continuously improving the quality of the generated proposals.

[0432] 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.

[0433] In this invention, the server includes means for collecting data and storing it on a recording medium, means for receiving requests from users and initiating a generation process, and means for generating proposals based on the information on the recording medium and the requests using a generation model. This makes it possible to maximize the potential within a company and provide high-quality business proposals.

[0434] "Means of collecting data and storing it on a recording medium" refers to technologies and methods for acquiring data from various sources both inside and outside a company, structuring it, and storing it on a storage medium.

[0435] "Means for receiving requests from users and initiating the generation process" refers to technologies and methods for receiving information and requests entered by users and triggering the generation process.

[0436] "Means of generating proposals based on information and requests in a recording medium using a generative model" refers to technologies and methods that utilize AI technology and algorithms to combine recorded data with user requests to output new proposals.

[0437] "A means of displaying generated proposals on a visual device and collecting feedback from users" refers to a function or method for displaying generated proposals on a display device and collecting feedback from users.

[0438] "Means for analyzing opinions and improving the generation process" refers to technologies and methods for analyzing feedback collected from users and reflecting it in generation models and techniques to improve the quality of proposals.

[0439] "Means for analyzing the request content and selecting relevant information when retrieving information from a recording medium" refers to technologies and methods for analyzing the user's request content and efficiently selecting and extracting information related to it.

[0440] "Learning methods for adjusting generative models using feedback" refer to learning algorithms and techniques that update and adjust generative models based on user feedback to improve their performance.

[0441] This invention provides a system for efficiently generating new business proposals using a generation AI model that utilizes a wide variety of information obtained from both inside and outside a company.

[0442] The server first collects data using a data management system (e.g., MySQL or PostgreSQL) to integrate internal company data and information acquired from external sources, and stores it on a storage medium. This data includes past business activities, activity records, resource information, and more.

[0443] Users access the system from their devices via a web browser or dedicated application. They can input business requirements based on their own needs and initiate the generation process. For example, they can provide instructions to the system by entering specific prompts such as, "I need ideas for a new mobile service to be deployed in a specific region."

[0444] The server analyzes the instructions received from the user using natural language processing technology. Based on this analysis, the server efficiently searches for highly relevant data from the storage medium and uses a generative AI model (e.g., GPT-3 or ChatGPT) to generate the optimal business proposal that matches the user's instructions.

[0445] The generated suggestions are sent from the server to the terminal, where the user can view them in real time. The user reviews the suggestions and provides feedback to the server, including their evaluation and opinions. This feedback is incorporated into the learning process of the generative AI model and used to improve the quality of suggestion generation.

[0446] This process allows the system to make the most of a company's data and quickly deliver high-quality business proposals. By repeating this cycle, the accuracy and relevance of the generated proposals continuously improve, supporting companies in developing businesses that leverage innovative ideas.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The server collects data from both within and outside the company. It receives information from various departments and related organizations as input. This data includes historical business records, current resource information, and external market data. The server stores this data in a data management system (e.g., MySQL or PostgreSQL) and saves it in a structured format. At this stage, the data becomes ready for analysis and retrieval.

[0450] Step 2:

[0451] The user accesses the system using a terminal and enters their business requirements. Here, the system provides specific needs and hints for business ideas as input. For example, the prompt might read, "Please suggest new marketing ideas for eco-friendly services." The terminal then sends this instruction to the server, proceeding to the next step.

[0452] Step 3:

[0453] The server receives instructions from the user and analyzes them using natural language processing techniques. The output of the analysis clarifies keywords and intentions related to the user's needs. Next, the server searches a database based on the analysis results and extracts highly relevant information. This process yields the dataset that best matches the user's instructions.

[0454] Step 4:

[0455] The server uses a generative AI model to integrate user instructions and related data to generate business proposals. The input is the user needs and dataset obtained in the previous step. The generative AI model (e.g., GPT-3 or ChatGPT) outputs optimal suggestions based on the large amount of data and user instructions. These suggestions might take the form of specific business proposals, such as a "product sales strategy using recycled materials."

[0456] Step 5:

[0457] The server sends the generated proposal to the user's terminal. The terminal displays the proposal, allowing the user to review it. The user evaluates the proposal on the terminal and, if appropriate, devises an action plan based on it.

[0458] Step 6:

[0459] Users input their evaluations and feedback on the proposals via their terminals and send them back to the server. The server analyzes this feedback and uses it to improve the generated AI model. It receives the user's feedback as input and adjusts the model's parameters based on it. This feedback process improves the accuracy of subsequent proposal generation, enabling better results.

[0460] (Application Example 1)

[0461] 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."

[0462] There is a problem in that companies and sole proprietors are unable to make the most of the data and resources they possess to effectively generate new business ideas. Furthermore, there is a challenge in formulating business strategies that fully consider local consumer trends.

[0463] 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.

[0464] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from a user and initiating a generation process, means for generating proposals based on database information and user instructions using a generation model, means for analyzing regional information and understanding consumer trends, and means for proposing new business ideas based on consumer trends. This makes it possible to integrate information from both inside and outside the company and quickly generate innovative and needs-based business ideas.

[0465] "Internal data" refers to available information and historical records held by a company or organization.

[0466] A "database" is a system that systematically stores information and allows for quick access and management as needed.

[0467] A "user" is a person or organization that intends to obtain specific information or results by using a system.

[0468] The "generation process" is the process of using data collected based on user instructions to execute a series of steps in order to generate new suggestions or results.

[0469] A "generative model" is an algorithm or machine learning system that generates new proposals or ideas based on input information.

[0470] "Local information" refers to data relating to a specific geographical area, including consumer behavior patterns and market trends.

[0471] "Consumer trends" refer to identifying changes in consumer preferences, behavior, and purchasing patterns in the market.

[0472] A "business idea" is a new business plan or concept that has the potential to provide new value to the existing business or market.

[0473] To implement this invention, it is necessary to construct a system in which a server, a terminal, and a user work in cooperation with each other. In this system, the server plays a crucial role in collecting specific data and efficiently storing it in a database. The server can use software such as MySQL or PostgreSQL as its database system. The terminal functions as an interface for the user to input specific instructions.

[0474] The server receives instructions from the user and uses a natural language processing model as a generative AI model to initiate the generation process, such as OpenAI's GPT-3 or its successor. This model integrates the user's input with information in the database to generate new business proposals.

[0475] This new proposal is displayed to and evaluated by the user. The server then analyzes the evaluation information received from the user and further refines the generative model. The system also incorporates data acquisition capabilities via open data sources and APIs on the internet for collecting local information.

[0476] As a concrete example, when a store manager inputs instructions into the system to "come up with a limited-time summer product," one possible prompt message might be "Please propose a new limited-time summer product, taking into account past data of local consumers." Based on this prompt, the system extracts past purchase data from the database and uses an AI model to generate new suggestions.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The server collects necessary data from both internal and external sources and stores it in a database. Web scraping tools and APIs are used for data collection, and the collected data is organized and stored in a MySQL database. Input is raw data from various data sources, and output is a structured database.

[0480] Step 2:

[0481] The user logs into the system using a terminal and inputs specific business needs. This input consists of the conditions and requirements for the business proposal the user desires. This information forms part of the prompts for the generating AI model. As output, prompt sentences based on the input are generated and sent to the server.

[0482] Step 3:

[0483] The server receives a prompt from the user and extracts data from the database related to the conditions entered in the second step. This involves using SQL queries to search the database and filtering and retrieve relevant information. The input is the user prompt, and the output is the relevant dataset.

[0484] Step 4:

[0485] The server sends prompt messages to the generative AI model, which then generates business proposals based on information extracted from the database. This process involves the generative AI model analyzing data and creating new business ideas. The input consists of prompt messages and a dataset, while the output is a new business proposal.

[0486] Step 5:

[0487] The generated business proposal is sent from the server to the user's terminal and displayed there. The user reviews the results and enters an evaluation based on the validity and satisfaction level of the proposal. The input is the user's evaluation, and the output is feedback information.

[0488] Step 6:

[0489] The server collects evaluation feedback from users and analyzes it as an evaluation of the generative model. This analysis is used to refine the generative AI model and improve the accuracy of its suggestions. The input is the user's evaluation result, and the output is the adjustment data for the generation process. Actual operation includes retraining the model using the evaluation data.

[0490] 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.

[0491] This invention is a system that recognizes user emotions and adjusts and optimizes business proposals based on those emotions. The following describes a specific form for implementing this system.

[0492] This system consists of a server, terminals, and an emotion engine. The server collects publicly available data from within the company and stores it in a database. This provides the foundational data for generating business proposals. The data includes past project information, resource information, and research results.

[0493] The user sends specific instructions to the system via their device, tailored to their business objectives. During this process, the emotion engine analyzes the user's emotional state in real time. The emotion engine analyzes the user's tone of voice, facial expressions, and input style to identify the appropriate emotion for the situation.

[0494] The server generates optimal suggestions based on emotional data received from the emotion engine, along with acquired data and user instructions. The generative model also considers emotional information and adjusts the content and expression of the suggestions. In this process, efforts are made to provide suggestions that the user will find interesting and accept.

[0495] The generated suggestions are displayed on the terminal. Users can review the suggestions and provide feedback on the spot. The server collects the feedback and records it as evaluation data. Furthermore, it records changes in emotions through the emotion engine and uses this data to improve the generation process. This is reflected in subsequent suggestion generation, improving the system's accuracy and the flexibility of user response.

[0496] For example, if a user instructs the system to "provide ideas for new support services that will improve the customer experience," the emotion engine will analyze the user's expectations and interests. It will then generate suggestions that the user is interested in, such as "a personalized support system utilizing an AI chatbot." This allows the user to receive appropriate business suggestions that take their emotional state into consideration.

[0497] This invention aims to provide optimal advisory services that better meet user needs by incorporating emotional information into the business proposal generation process.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The server collects publicly available data from within the company and stores it in a database. This data includes business details, available resources, and historical performance information, forming the basis for business proposals.

[0501] Step 2:

[0502] Users input specific instructions into the system via their terminal. For example, they might specify business needs such as, "Please propose a new energy efficiency improvement plan."

[0503] Step 3:

[0504] The emotion engine analyzes the user's emotional state at the time of input. It examines the user's voice intonation, input speed, and past emotional history to infer what kinds of suggestions the user might be interested in.

[0505] Step 4:

[0506] The server takes sentiment data into account and queries the database for information related to the user's instructions. It selects the most relevant data and prepares it as input for the generative model.

[0507] Step 5:

[0508] The generative model generates business proposals tailored to the user based on emotional information and data obtained through queries. The emotional information is used to adjust the tone of expression, resulting in content that resonates with the user's emotions.

[0509] Step 6:

[0510] The device displays the generated suggestions to the user. The user can review the suggestions and provide feedback on their satisfaction level, requests for additional information, etc.

[0511] Step 7:

[0512] The server collects feedback and emotion change data sent from the terminal and uses it as training material for the generative model. Based on the feedback, the model is improved to further enhance the accuracy of future suggestions.

[0513] (Example 2)

[0514] 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."

[0515] In recent years, there has been a growing need to respond quickly to the diversifying needs of consumers. However, conventional business proposal systems have struggled to generate proposals that take into account the emotional state of the user, making it difficult to quickly present the most suitable proposal to the user. A solution to this problem is needed.

[0516] 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.

[0517] In this invention, the server includes means for collecting internal and external information and storing data, means for using an emotion analysis device to analyze the user's emotional state, and means for generating suggestions based on information, user instructions, and emotional state using a generative model. This makes it possible to quickly and effectively generate and present optimal suggestions that are tailored to the user's emotions.

[0518] "Internal and external information" refers to information that includes publicly available data from within a company and related data obtained from external sources.

[0519] "Means of accumulating data" refers to the function of organizing collected data and saving it to storage so that it can be centrally managed.

[0520] "User's emotional state" refers to the psychological and emotional state analyzed from the user's voice tone, facial expressions, input style, etc.

[0521] An "emotion analysis device" refers to a component that uses technologies such as voice analysis and image analysis to identify the user's emotions.

[0522] A "generative model" refers to a system that uses algorithms and artificial intelligence technologies to provide optimal suggestions to users, based on accumulated data and emotional information.

[0523] "Means for generating proposals based on information, user instructions, and emotional state" refers to a function that manages the process of generating optimal business proposals based on the results of the user's emotional analysis.

[0524] Specific embodiments for carrying out this invention are shown below.

[0525] This system is based on a server, terminals, and emotion analysis devices.

[0526] The server first collects internal and external information and stores it in a database. This information includes publicly available internal company data and related information obtained from external sources. The collected data is centrally managed and functions as the basis for generating proposals.

[0527] The user sends specific instructions aligned with business objectives to the system via a terminal. At this time, an emotion analysis device installed in the terminal is activated to analyze the user's emotional state. This utilizes voice analysis software and image analysis hardware, among other things. For example, the terminal's camera can capture the user's facial expressions, or the microphone can analyze the tone of their voice.

[0528] The server uses a generative model to generate optimal suggestions based on information stored in the database, as well as user instructions and emotional states. The generative model is enhanced by machine learning algorithms, and the suggestions are adjusted to match the user's expectations.

[0529] The generated suggestions are displayed on the device, and the user reviews them. The user can send feedback on the suggestions via the device. For example, feedback can include comments such as "This suggestion is very helpful" or "Please explain the suggestion in more detail."

[0530] For example, if a user inputs "I would like you to suggest a new marketing strategy," the emotion analysis device detects feelings of anticipation and excitement. As a result, the server generates a suggestion such as "an interactive advertising campaign using AR technology" and displays it on the device.

[0531] An example of a prompt might be, "Please propose a new marketing strategy. We are particularly interested in proposals that utilize the latest technologies." By incorporating emotional information in this way into the proposal generation process and providing business proposals that meet the user's needs, we aim to realize more effective advisory services.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The server collects internal and external information and stores it in a database. It uses publicly available information within the company and data obtained from external sources as input. Specifically, it retrieves data from each information source using APIs, checks the data's integrity, and then stores it in the database. This process prepares the basic data necessary for subsequent proposal generation. The output is a well-organized and saved database.

[0535] Step 2:

[0536] The user sends specific instructions to the system via a terminal. This input includes the user entering requests and prompts tailored to business objectives. Specifically, the user types text using a keyboard on the user interface and presses the send button. At this time, the sentiment analysis device is simultaneously activated. The output is the instruction information sent by the user.

[0537] Step 3:

[0538] An emotion analysis device identifies the user's emotional state. It collects the user's voice, facial expressions, and input style as input. Specifically, it records audio using the device's microphone, acquires video using the camera, and analyzes the speed and frequency of text input. Based on this data, an emotion recognition algorithm is executed to generate the user's emotional data. The output is emotional data based on the analyzed emotional state.

[0539] Step 4:

[0540] The server uses a generated AI model to process information from a database and user instructions and sentiment data. Inputs include database information, user instructions, and sentiment data. Specifically, a machine learning model analyzes this data and generates a list of potential suggestions. The model considers the emotional state and selects the most appropriate suggestion. The output is an optimized business proposal.

[0541] Step 5:

[0542] The terminal displays the generated business proposal. It receives optimal proposals sent from the server as input. Specifically, it performs a function to display text and images on the terminal display to visualize the proposal content. The user can review this proposal. The output is the display status of the proposal presented to the user.

[0543] Step 6:

[0544] Users provide feedback on suggestions and record their emotional changes. The system receives comments and ratings from users as input. Specifically, users enter text into a feedback form and submit it. At this point, the sentiment analysis system restarts to track emotional changes before and after viewing the suggestion. The output consists of collected feedback information and updated sentiment data.

[0545] Step 7:

[0546] The server uses feedback information and sentiment data to train the system. As input, it collects user feedback and data on changes in sentiment. Specifically, this involves analyzing this data and adjusting the parameters of the generative AI model. At this stage, the system's accuracy is improved, which is useful for the next proposal generation process. The output is the updated model state.

[0547] (Application Example 2)

[0548] 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."

[0549] In the field of mail-order sales, there is a challenge in optimizing the user experience and purchase intent due to a lack of product suggestions based on user emotions. Specifically, there is a need to respond quickly to changes in user interests and emotions and propose personalized products and promotions.

[0550] 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.

[0551] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from the user and initiating a generation process, and means for analyzing the user's visual and auditory data and recognizing their emotions. This enables appropriate product suggestions and promotional displays that correspond to the user's emotional state.

[0552] "Internal data" refers to data collected and stored within a company, such as past project information and resource information.

[0553] A "generative model" is an algorithm that generates optimal suggestions based on acquired information, user instructions, and sentiment data.

[0554] "Visual data" refers to image information such as facial expressions and movements obtained from the user.

[0555] "Voice data" refers to sound information such as voice tone and speaking style obtained from the user.

[0556] "Means of recognizing emotions" refers to technologies that analyze a user's visual and auditory data to identify their current emotional state.

[0557] "Means of generating proposals" refers to a method of generating proposals using a generative model based on database information and user instructions.

[0558] "Evaluation feedback" refers to information that includes user evaluations and opinions on the generated suggestions.

[0559] "Means of improving the generation process" refers to methods of analyzing evaluation feedback and adjusting the generation model so that future proposals better meet user needs.

[0560] To realize this invention, it is necessary to configure a system that uses a server, a terminal, and an emotion recognition engine. The server collects internal data from within the company and stores it in a database. This data consists of past project information and resource information, and is used as basic data for generating suggestions using a generative AI model.

[0561] The terminal functions as an interface for users to send specific business instructions to the system. The terminal is equipped with a camera to capture the user's facial expressions and a microphone to pick up audio, thereby acquiring visual and audio data in real time. An emotion recognition engine analyzes this data to identify the user's emotions. Existing software such as OpenCV and the Google Cloud Speech-to-Text API are used for the analysis.

[0562] The server generates suggestions using a generative AI model based on sentiment data and user instructions. An algorithm operates to extract relevant information from the database during this process. The generated suggestions are displayed on the terminal, and the user has the option to review them and provide feedback. This feedback is sent to the server and used to improve the generation process.

[0563] As a concrete example, when a user searches for new headphones, the emotion recognition engine detects from the user's facial expressions that they are showing a high level of interest. Based on this information, the server extracts appropriate product information from the database and presents it to the user in order to suggest related accessories and promotions.

[0564] Examples of prompt statements include the following:

[0565] "We analyze the user's facial expressions and voice to generate product recommendations based on their level of interest. Example: 'If the user shows interest in product X, please tell us how to provide related item Y and promotional information.'"

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server collects internal data from within the company and stores it in a database. Inputs include past project and resource information, which, through storage in the database, build an information base usable for subsequent proposal generation. Outputs are the foundational data necessary for proposal generation.

[0569] Step 2:

[0570] The user sends specific instructions to the system via a terminal. The input is the user's text or voice instructions, which the terminal passes to the emotion recognition engine. The output is user instruction data for analysis.

[0571] Step 3:

[0572] The device uses a camera and microphone to acquire the user's visual and auditory data in real time. The input consists of the user's facial expressions and voice, and the data is processed using OpenCV or the Google Cloud Speech-to-Text API. The output is emotion data used by the emotion recognition engine.

[0573] Step 4:

[0574] The server receives emotional data analyzed by the emotion recognition engine and user instructions, and generates suggestions using a generative AI model. Inputs include database information, user instructions, and emotional data. Output is the optimal business proposal presented to the user.

[0575] Step 5:

[0576] The generated proposals are displayed on the terminal, and the user reviews them and provides evaluation feedback. The input is the presented proposal, and the output is the evaluation feedback data sent to the server.

[0577] Step 6:

[0578] The server analyzes user feedback and adjusts the generative model. The input is feedback data, used for learning to optimize the generative process. The output is the improved proposal generation process.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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".

[0596] This invention is a system for generating new business ideas by combining resources and data available within a company. The following describes in detail the forms in which this system can be implemented.

[0597] The system according to this invention is implemented through the interaction of a server, a terminal, and a user. The server collects publicly available and useful data within the enterprise and organizes and stores it in a database. The data includes details of past business operations, activity records, and information on available resources.

[0598] Users access the system via a terminal and input specific instructions based on their requirements. This prepares the system to generate business proposals tailored to the company's needs.

[0599] The server analyzes the instructions received from the user and searches for and retrieves relevant data from the database. Next, it uses a generative model to integrate the user's instructions with the information in the database and create the optimal business proposal for the user. The generated proposal is then displayed to the user, and their feedback and evaluations are collected.

[0600] Users review the generated suggestions on their devices, consider them, and provide feedback. This feedback is sent from the device to the server, which analyzes the feedback to improve the generative model. This continuous feedback loop allows the system to gradually improve its accuracy and provide more appropriate business suggestions.

[0601] For example, if a user inputs a request for "proposals for new energy projects that are environmentally friendly," the server will use internal environmental data and information on past energy projects to generate a proposal such as "a localized clean energy plan utilizing renewable energy." The user can then use this as a basis to develop a concrete business plan.

[0602] This invention enables companies with vast amounts of data to make the most of their resources and quickly and efficiently acquire innovative business ideas.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The server collects publicly available data within the company and stores it in a database. This data includes records of past projects, resource information, and research results, all of which are useful for business development.

[0606] Step 2:

[0607] Users use a terminal to input specific instructions based on their business needs into the system. For example, they might input instructions such as "Please provide ideas for a new health management service" via the terminal.

[0608] Step 3:

[0609] The server analyzes the instructions received from the user and extracts relevant keywords and context. Based on this information, it queries the database for relevant data and prepares it for generation.

[0610] Step 4:

[0611] The server uses a generative model to integrate information retrieved from the database with user instructions to generate new business proposals. The generative model considers known patterns and business trends to construct proposals that best meet user needs.

[0612] Step 5:

[0613] The terminal displays the generated business proposal to the user. The user can review the proposal and input their evaluation and feedback on the content into the terminal.

[0614] Step 6:

[0615] The server receives evaluations and feedback submitted by users and uses them as training data for the generative model. This feedback is used to improve the accuracy of the generative model and refine it so that future suggestions are more precise.

[0616] (Example 1)

[0617] 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".

[0618] The goal is to provide a system that efficiently utilizes the vast amount of data and resources held by companies to rapidly generate new business ideas. Furthermore, it aims to implement a mechanism for continuously improving the quality of the generated proposals.

[0619] 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.

[0620] In this invention, the server includes means for collecting data and storing it on a recording medium, means for receiving requests from users and initiating a generation process, and means for generating proposals based on the information on the recording medium and the requests using a generation model. This makes it possible to maximize the potential within a company and provide high-quality business proposals.

[0621] "Means of collecting data and storing it on a recording medium" refers to technologies and methods for acquiring data from various sources both inside and outside a company, structuring it, and storing it on a storage medium.

[0622] "Means for receiving requests from users and initiating the generation process" refers to technologies and methods for receiving information and requests entered by users and triggering the generation process.

[0623] "Means of generating proposals based on information and requests in a recording medium using a generative model" refers to technologies and methods that utilize AI technology and algorithms to combine recorded data with user requests to output new proposals.

[0624] "A means of displaying generated proposals on a visual device and collecting feedback from users" refers to a function or method for displaying generated proposals on a display device and collecting feedback from users.

[0625] "Means for analyzing opinions and improving the generation process" refers to technologies and methods for analyzing feedback collected from users and reflecting it in generation models and techniques to improve the quality of proposals.

[0626] "Means for analyzing the request content and selecting relevant information when retrieving information from a recording medium" refers to technologies and methods for analyzing the user's request content and efficiently selecting and extracting information related to it.

[0627] "Learning methods for adjusting generative models using feedback" refer to learning algorithms and techniques that update and adjust generative models based on user feedback to improve their performance.

[0628] This invention provides a system for efficiently generating new business proposals using a generation AI model that utilizes a wide variety of information obtained from both inside and outside a company.

[0629] The server first collects data using a data management system (e.g., MySQL or PostgreSQL) to integrate internal company data and information acquired from external sources, and stores it on a storage medium. This data includes past business activities, activity records, resource information, and more.

[0630] Users access the system from their devices via a web browser or dedicated application. They can input business requirements based on their own needs and initiate the generation process. For example, they can provide instructions to the system by entering specific prompts such as, "I need ideas for a new mobile service to be deployed in a specific region."

[0631] The server analyzes the instructions received from the user using natural language processing technology. Based on this analysis, the server efficiently searches for highly relevant data from the storage medium and uses a generative AI model (e.g., GPT-3 or ChatGPT) to generate the optimal business proposal that matches the user's instructions.

[0632] The generated suggestions are sent from the server to the terminal, where the user can view them in real time. The user reviews the suggestions and provides feedback to the server, including their evaluation and opinions. This feedback is incorporated into the learning process of the generative AI model and used to improve the quality of suggestion generation.

[0633] This process allows the system to make the most of a company's data and quickly deliver high-quality business proposals. By repeating this cycle, the accuracy and relevance of the generated proposals continuously improve, supporting companies in developing businesses that leverage innovative ideas.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The server collects data from both within and outside the company. It receives information from various departments and related organizations as input. This data includes historical business records, current resource information, and external market data. The server stores this data in a data management system (e.g., MySQL or PostgreSQL) and saves it in a structured format. At this stage, the data becomes ready for analysis and retrieval.

[0637] Step 2:

[0638] The user accesses the system using a terminal and enters their business requirements. Here, the system provides specific needs and hints for business ideas as input. For example, the prompt might read, "Please suggest new marketing ideas for eco-friendly services." The terminal then sends this instruction to the server, proceeding to the next step.

[0639] Step 3:

[0640] The server receives instructions from the user and analyzes them using natural language processing techniques. The output of the analysis clarifies keywords and intentions related to the user's needs. Next, the server searches a database based on the analysis results and extracts highly relevant information. This process yields the dataset that best matches the user's instructions.

[0641] Step 4:

[0642] The server uses a generative AI model to integrate user instructions and related data to generate business proposals. The input is the user needs and dataset obtained in the previous step. The generative AI model (e.g., GPT-3 or ChatGPT) outputs optimal suggestions based on the large amount of data and user instructions. These suggestions might take the form of specific business proposals, such as a "product sales strategy using recycled materials."

[0643] Step 5:

[0644] The server sends the generated proposal to the user's terminal. The terminal displays the proposal, allowing the user to review it. The user evaluates the proposal on the terminal and, if appropriate, devises an action plan based on it.

[0645] Step 6:

[0646] Users input their evaluations and feedback on the proposals via their terminals and send them back to the server. The server analyzes this feedback and uses it to improve the generated AI model. It receives the user's feedback as input and adjusts the model's parameters based on it. This feedback process improves the accuracy of subsequent proposal generation, enabling better results.

[0647] (Application Example 1)

[0648] 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".

[0649] There is a problem in that companies and sole proprietors are unable to make the most of the data and resources they possess to effectively generate new business ideas. Furthermore, there is a challenge in formulating business strategies that fully consider local consumer trends.

[0650] 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.

[0651] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from a user and initiating a generation process, means for generating proposals based on database information and user instructions using a generation model, means for analyzing regional information and understanding consumer trends, and means for proposing new business ideas based on consumer trends. This makes it possible to integrate information from both inside and outside the company and quickly generate innovative and needs-based business ideas.

[0652] "Internal data" refers to available information and historical records held by a company or organization.

[0653] A "database" is a system that systematically stores information and allows for quick access and management as needed.

[0654] A "user" is a person or organization that intends to obtain specific information or results by using a system.

[0655] The "generation process" is the process of using data collected based on user instructions to execute a series of steps in order to generate new suggestions or results.

[0656] A "generative model" is an algorithm or machine learning system that generates new proposals or ideas based on input information.

[0657] "Local information" refers to data relating to a specific geographical area, including consumer behavior patterns and market trends.

[0658] "Consumer trends" refer to identifying changes in consumer preferences, behavior, and purchasing patterns in the market.

[0659] A "business idea" is a new business plan or concept that has the potential to provide new value to the existing business or market.

[0660] To implement this invention, it is necessary to construct a system in which a server, a terminal, and a user work in cooperation with each other. In this system, the server plays a crucial role in collecting specific data and efficiently storing it in a database. The server can use software such as MySQL or PostgreSQL as its database system. The terminal functions as an interface for the user to input specific instructions.

[0661] The server receives instructions from the user and uses a natural language processing model as a generative AI model to initiate the generation process, such as OpenAI's GPT-3 or its successor. This model integrates the user's input with information in the database to generate new business proposals.

[0662] This new proposal is displayed to and evaluated by the user. The server then analyzes the evaluation information received from the user and further refines the generative model. The system also incorporates data acquisition capabilities via open data sources and APIs on the internet for collecting local information.

[0663] As a concrete example, when a store manager inputs instructions into the system to "come up with a limited-time summer product," one possible prompt message might be "Please propose a new limited-time summer product, taking into account past data of local consumers." Based on this prompt, the system extracts past purchase data from the database and uses an AI model to generate new suggestions.

[0664] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0665] Step 1:

[0666] The server collects necessary data from both internal and external sources and stores it in a database. Web scraping tools and APIs are used for data collection, and the collected data is organized and stored in a MySQL database. Input is raw data from various data sources, and output is a structured database.

[0667] Step 2:

[0668] The user logs into the system using a terminal and inputs specific business needs. This input consists of the conditions and requirements for the business proposal the user desires. This information forms part of the prompts for the generating AI model. As output, prompt sentences based on the input are generated and sent to the server.

[0669] Step 3:

[0670] The server receives a prompt from the user and extracts data from the database related to the conditions entered in the second step. This involves using SQL queries to search the database and filtering and retrieve relevant information. The input is the user prompt, and the output is the relevant dataset.

[0671] Step 4:

[0672] The server sends prompt messages to the generative AI model, which then generates business proposals based on information extracted from the database. This process involves the generative AI model analyzing data and creating new business ideas. The input consists of prompt messages and a dataset, while the output is a new business proposal.

[0673] Step 5:

[0674] The generated business proposal is sent from the server to the user's terminal and displayed there. The user reviews the results and enters an evaluation based on the validity and satisfaction level of the proposal. The input is the user's evaluation, and the output is feedback information.

[0675] Step 6:

[0676] The server collects evaluation feedback from users and analyzes it as an evaluation of the generative model. This analysis is used to refine the generative AI model and improve the accuracy of its suggestions. The input is the user's evaluation result, and the output is the adjustment data for the generation process. Actual operation includes retraining the model using the evaluation data.

[0677] 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.

[0678] This invention is a system that recognizes user emotions and adjusts and optimizes business proposals based on those emotions. The following describes a specific form for implementing this system.

[0679] This system consists of a server, terminals, and an emotion engine. The server collects publicly available data from within the company and stores it in a database. This provides the foundational data for generating business proposals. The data includes past project information, resource information, and research results.

[0680] The user sends specific instructions to the system via their device, tailored to their business objectives. During this process, the emotion engine analyzes the user's emotional state in real time. The emotion engine analyzes the user's tone of voice, facial expressions, and input style to identify the appropriate emotion for the situation.

[0681] The server generates optimal suggestions based on emotional data received from the emotion engine, along with acquired data and user instructions. The generative model also considers emotional information and adjusts the content and expression of the suggestions. In this process, efforts are made to provide suggestions that the user will find interesting and accept.

[0682] The generated suggestions are displayed on the terminal. Users can review the suggestions and provide feedback on the spot. The server collects the feedback and records it as evaluation data. Furthermore, it records changes in emotions through the emotion engine and uses this data to improve the generation process. This is reflected in subsequent suggestion generation, improving the system's accuracy and the flexibility of user response.

[0683] For example, if a user instructs the system to "provide ideas for new support services that will improve the customer experience," the emotion engine will analyze the user's expectations and interests. It will then generate suggestions that the user is interested in, such as "a personalized support system utilizing an AI chatbot." This allows the user to receive appropriate business suggestions that take their emotional state into consideration.

[0684] This invention aims to provide optimal advisory services that better meet user needs by incorporating emotional information into the business proposal generation process.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The server collects publicly available data from within the company and stores it in a database. This data includes business details, available resources, and historical performance information, forming the basis for business proposals.

[0688] Step 2:

[0689] Users input specific instructions into the system via their terminal. For example, they might specify business needs such as, "Please propose a new energy efficiency improvement plan."

[0690] Step 3:

[0691] The emotion engine analyzes the user's emotional state at the time of input. It examines the user's voice intonation, input speed, and past emotional history to infer what kinds of suggestions the user might be interested in.

[0692] Step 4:

[0693] The server takes sentiment data into account and queries the database for information related to the user's instructions. It selects the most relevant data and prepares it as input for the generative model.

[0694] Step 5:

[0695] The generative model generates business proposals tailored to the user based on emotional information and data obtained through queries. The emotional information is used to adjust the tone of expression, resulting in content that resonates with the user's emotions.

[0696] Step 6:

[0697] The device displays the generated suggestions to the user. The user can review the suggestions and provide feedback on their satisfaction level, requests for additional information, etc.

[0698] Step 7:

[0699] The server collects feedback and emotion change data sent from the terminal and uses it as training material for the generative model. Based on the feedback, the model is improved to further enhance the accuracy of future suggestions.

[0700] (Example 2)

[0701] 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".

[0702] In recent years, there has been a growing need to respond quickly to the diversifying needs of consumers. However, conventional business proposal systems have struggled to generate proposals that take into account the emotional state of the user, making it difficult to quickly present the most suitable proposal to the user. A solution to this problem is needed.

[0703] 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.

[0704] In this invention, the server includes means for collecting internal and external information and storing data, means for using an emotion analysis device to analyze the user's emotional state, and means for generating suggestions based on information, user instructions, and emotional state using a generative model. This makes it possible to quickly and effectively generate and present optimal suggestions that are tailored to the user's emotions.

[0705] "Internal and external information" refers to information that includes publicly available data from within a company and related data obtained from external sources.

[0706] "Means of accumulating data" refers to the function of organizing collected data and saving it to storage so that it can be centrally managed.

[0707] "User's emotional state" refers to the psychological and emotional state analyzed from the user's voice tone, facial expressions, input style, etc.

[0708] An "emotion analysis device" refers to a component that uses technologies such as voice analysis and image analysis to identify the user's emotions.

[0709] A "generative model" refers to a system that uses algorithms and artificial intelligence technologies to provide optimal suggestions to users, based on accumulated data and emotional information.

[0710] "Means for generating proposals based on information, user instructions, and emotional state" refers to a function that manages the process of generating optimal business proposals based on the results of the user's emotional analysis.

[0711] Specific embodiments for carrying out this invention are shown below.

[0712] This system is based on a server, terminals, and emotion analysis devices.

[0713] The server first collects internal and external information and stores it in a database. This information includes publicly available internal company data and related information obtained from external sources. The collected data is centrally managed and functions as the basis for generating proposals.

[0714] The user sends specific instructions aligned with business objectives to the system via a terminal. At this time, an emotion analysis device installed in the terminal is activated to analyze the user's emotional state. This utilizes voice analysis software and image analysis hardware, among other things. For example, the terminal's camera can capture the user's facial expressions, or the microphone can analyze the tone of their voice.

[0715] The server uses a generative model to generate optimal suggestions based on information stored in the database, as well as user instructions and emotional states. The generative model is enhanced by machine learning algorithms, and the suggestions are adjusted to match the user's expectations.

[0716] The generated suggestions are displayed on the device, and the user reviews them. The user can send feedback on the suggestions via the device. For example, feedback can include comments such as "This suggestion is very helpful" or "Please explain the suggestion in more detail."

[0717] For example, if a user inputs "I would like you to suggest a new marketing strategy," the emotion analysis device detects feelings of anticipation and excitement. As a result, the server generates a suggestion such as "an interactive advertising campaign using AR technology" and displays it on the device.

[0718] An example of a prompt might be, "Please propose a new marketing strategy. We are particularly interested in proposals that utilize the latest technologies." By incorporating emotional information in this way into the proposal generation process and providing business proposals that meet the user's needs, we aim to realize more effective advisory services.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The server collects internal and external information and stores it in a database. It uses publicly available information within the company and data obtained from external sources as input. Specifically, it retrieves data from each information source using APIs, checks the data's integrity, and then stores it in the database. This process prepares the basic data necessary for subsequent proposal generation. The output is a well-organized and saved database.

[0722] Step 2:

[0723] The user sends specific instructions to the system via a terminal. This input includes the user entering requests and prompts tailored to business objectives. Specifically, the user types text using a keyboard on the user interface and presses the send button. At this time, the sentiment analysis device is simultaneously activated. The output is the instruction information sent by the user.

[0724] Step 3:

[0725] An emotion analysis device identifies the user's emotional state. It collects the user's voice, facial expressions, and input style as input. Specifically, it records audio using the device's microphone, acquires video using the camera, and analyzes the speed and frequency of text input. Based on this data, an emotion recognition algorithm is executed to generate the user's emotional data. The output is emotional data based on the analyzed emotional state.

[0726] Step 4:

[0727] The server uses a generated AI model to process information from a database and user instructions and sentiment data. Inputs include database information, user instructions, and sentiment data. Specifically, a machine learning model analyzes this data and generates a list of potential suggestions. The model considers the emotional state and selects the most appropriate suggestion. The output is an optimized business proposal.

[0728] Step 5:

[0729] The terminal displays the generated business proposal. It receives optimal proposals sent from the server as input. Specifically, it performs a function to display text and images on the terminal display to visualize the proposal content. The user can review this proposal. The output is the display status of the proposal presented to the user.

[0730] Step 6:

[0731] Users provide feedback on suggestions and record their emotional changes. The system receives comments and ratings from users as input. Specifically, users enter text into a feedback form and submit it. At this point, the sentiment analysis system restarts to track emotional changes before and after viewing the suggestion. The output consists of collected feedback information and updated sentiment data.

[0732] Step 7:

[0733] The server uses feedback information and sentiment data to train the system. As input, it collects user feedback and data on changes in sentiment. Specifically, this involves analyzing this data and adjusting the parameters of the generative AI model. At this stage, the system's accuracy is improved, which is useful for the next proposal generation process. The output is the updated model state.

[0734] (Application Example 2)

[0735] 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".

[0736] In the field of mail-order sales, there is a challenge in optimizing the user experience and purchase intent due to a lack of product suggestions based on user emotions. Specifically, there is a need to respond quickly to changes in user interests and emotions and propose personalized products and promotions.

[0737] 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.

[0738] In this invention, the server includes means for collecting internal data and storing it in a database, means for receiving specific instructions from the user and initiating a generation process, and means for analyzing the user's visual and auditory data and recognizing their emotions. This enables appropriate product suggestions and promotional displays that correspond to the user's emotional state.

[0739] "Internal data" refers to data collected and stored within a company, such as past project information and resource information.

[0740] A "generative model" is an algorithm that generates optimal suggestions based on acquired information, user instructions, and sentiment data.

[0741] "Visual data" refers to image information such as facial expressions and movements obtained from the user.

[0742] "Voice data" refers to sound information such as voice tone and speaking style obtained from the user.

[0743] "Means of recognizing emotions" refers to technologies that analyze a user's visual and auditory data to identify their current emotional state.

[0744] "Means of generating proposals" refers to a method of generating proposals using a generative model based on database information and user instructions.

[0745] "Evaluation feedback" refers to information that includes user evaluations and opinions on the generated suggestions.

[0746] "Means of improving the generation process" refers to methods of analyzing evaluation feedback and adjusting the generation model so that future proposals better meet user needs.

[0747] To realize this invention, it is necessary to configure a system that uses a server, a terminal, and an emotion recognition engine. The server collects internal data from within the company and stores it in a database. This data consists of past project information and resource information, and is used as basic data for generating suggestions using a generative AI model.

[0748] The terminal functions as an interface for users to send specific business instructions to the system. The terminal is equipped with a camera to capture the user's facial expressions and a microphone to pick up audio, thereby acquiring visual and audio data in real time. An emotion recognition engine analyzes this data to identify the user's emotions. Existing software such as OpenCV and the Google Cloud Speech-to-Text API are used for the analysis.

[0749] The server generates suggestions using a generative AI model based on sentiment data and user instructions. An algorithm operates to extract relevant information from the database during this process. The generated suggestions are displayed on the terminal, and the user has the option to review them and provide feedback. This feedback is sent to the server and used to improve the generation process.

[0750] As a concrete example, when a user searches for new headphones, the emotion recognition engine detects from the user's facial expressions that they are showing a high level of interest. Based on this information, the server extracts appropriate product information from the database and presents it to the user in order to suggest related accessories and promotions.

[0751] Examples of prompt statements include the following:

[0752] "We analyze the user's facial expressions and voice to generate product recommendations based on their level of interest. Example: 'If the user shows interest in product X, please tell us how to provide related item Y and promotional information.'"

[0753] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0754] Step 1:

[0755] The server collects internal data from within the company and stores it in a database. Inputs include past project and resource information, which, through storage in the database, build an information base usable for subsequent proposal generation. Outputs are the foundational data necessary for proposal generation.

[0756] Step 2:

[0757] The user sends specific instructions to the system via a terminal. The input is the user's text or voice instructions, which the terminal passes to the emotion recognition engine. The output is user instruction data for analysis.

[0758] Step 3:

[0759] The device uses a camera and microphone to acquire the user's visual and auditory data in real time. The input consists of the user's facial expressions and voice, and the data is processed using OpenCV or the Google Cloud Speech-to-Text API. The output is emotion data used by the emotion recognition engine.

[0760] Step 4:

[0761] The server receives emotional data analyzed by the emotion recognition engine and user instructions, and generates suggestions using a generative AI model. Inputs include database information, user instructions, and emotional data. Output is the optimal business proposal presented to the user.

[0762] Step 5:

[0763] The generated proposals are displayed on the terminal, and the user reviews them and provides evaluation feedback. The input is the presented proposal, and the output is the evaluation feedback data sent to the server.

[0764] Step 6:

[0765] The server analyzes user feedback and adjusts the generative model. The input is feedback data, used for learning to optimize the generative process. The output is the improved proposal generation process.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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."

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] Means for collecting internal data and storing it in a database,

[0790] A means of receiving specific instructions from the user and starting the generation process,

[0791] A means of generating suggestions based on database information and user instructions using a generative model,

[0792] A means of displaying the generated suggestions and collecting user feedback,

[0793] A means of analyzing evaluation feedback and improving the generation process,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, comprising means for analyzing instructions and selecting relevant information when extracting information from a database.

[0797] (Claim 3)

[0798] The system according to claim 1, comprising adjustment means for training a generative model using evaluation feedback.

[0799] "Example 1"

[0800] (Claim 1)

[0801] A means for collecting data and storing it on a recording medium,

[0802] A means of receiving a request from a user and starting the generation process,

[0803] A means for generating proposals based on information and requirements of a recording medium using a generative model,

[0804] A means of displaying the generated proposals on a visual device and collecting feedback from users,

[0805] Means for analyzing opinions and improving the production process,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, comprising means for analyzing the request content and selecting relevant information when retrieving information from a recording medium.

[0809] (Claim 3)

[0810] The system according to claim 1, comprising a learning means for adjusting the generative model using feedback.

[0811] "Application Example 1"

[0812] (Claim 1)

[0813] Means for collecting internal data and storing it in a database,

[0814] A means of receiving specific instructions from the user and starting the generation process,

[0815] A means of generating suggestions based on database information and user instructions using a generative model,

[0816] A means of displaying the generated suggestions and collecting user feedback,

[0817] A means of analyzing evaluation feedback and improving the generation process,

[0818] A means of analyzing local information and understanding consumer trends,

[0819] A means of proposing new business ideas based on consumer trends,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, comprising means for analyzing instructions and selecting relevant information when extracting information from a database.

[0823] (Claim 3)

[0824] The system according to claim 1, comprising adjustment means for training a generative model using evaluation feedback.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] A means of collecting internal and external information and accumulating data,

[0828] A means of receiving specific instructions from the user and initiating the process,

[0829] A means of analyzing the user's emotional state using an emotion analysis device,

[0830] A means for generating suggestions based on information, user instructions, and emotional state using a generative model,

[0831] A means of displaying the generated suggestions and collecting feedback from users,

[0832] A means of analyzing evaluation information and improving the process,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, comprising means for analyzing the content of instructions and emotional state when extracting information and selecting relevant information.

[0836] (Claim 3)

[0837] The system according to claim 1, comprising adjustment means for training a generative model using evaluation information and emotion changes.

[0838] "Application example 2 of combining emotional engines"

[0839] (Claim 1)

[0840] Means for collecting internal data and storing it in a database,

[0841] A means of receiving specific instructions from the user and starting the generation process,

[0842] A means of analyzing the user's visual and audio data to recognize emotions,

[0843] A means of generating suggestions based on database information, user instructions, and emotions using a generative model,

[0844] A means of displaying the generated suggestions and collecting user feedback,

[0845] A means of analyzing evaluation feedback and improving the generation process,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising means for analyzing instructions and selecting relevant information when extracting information from a database.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising adjustment means for training a generative model using evaluation feedback. [Explanation of Symbols]

[0851] 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 internal data and storing it in a database, A means of receiving specific instructions from the user and starting the generation process, A means of generating suggestions based on database information and user instructions using a generative model, A means of displaying the generated suggestions and collecting user feedback, A means of analyzing evaluation feedback and improving the generation process, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing instructions and selecting relevant information when extracting information from a database.

3. The system according to claim 1, further comprising adjustment means for training a generative model using evaluation feedback.

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