system

Abstract

We provide the system. [Solution] An information processing device equipped with a function for inputting tasks, comprising means for storing task information, A means of making saved assignment information public to other users, A means of entering and saving solutions based on published challenges, A means for executing an algorithm that evaluates the input solution and generates a score, A means of displaying the solution based on the generated score, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It solves the problem that there is generally a lack of a system for efficiently solving the technical problems of enterprises by utilizing information technology. It also solves the difficulty of easily making an appropriate matching between new technologies and innovative approaches required by enterprises and solution providers capable of responding thereto.

Means for Solving the Problems

[0005] This invention uses an information processing device equipped with a problem input function to store a company's technical challenges and make that information publicly available to other users. Furthermore, it receives input from solution providers for the published challenges, evaluates this input, and runs an algorithm to generate a score, thereby enabling the display and selection of the optimal solution. This provides a system that efficiently matches companies with solution providers and promotes the resolution of challenges.

[0006] An "information processing device" refers to a computer system used for processing, storing, and managing data, and which provides specific functions to the user.

[0007] "Challenge information" refers to data that specifically describes the technical or operational problems and needs that a company faces, and includes the targets for which solutions are sought.

[0008] A "solution" refers to specific countermeasures or technical solutions provided in response to problem information, and includes proposals for solving the problem.

[0009] An "algorithm" refers to a set of steps or formulas designed to solve a specific problem, and is a mechanism for efficiently processing and evaluating data.

[0010] A "score" refers to a numerical value that quantitatively represents the evaluation results of a solution and is used to determine priorities in the selection process.

[0011] "Matching" refers to the process of aligning challenges and solutions between companies and solution providers based on specific criteria, and identifying partners who can meet the needs of both parties. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral 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.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The present invention provides a system that efficiently solves a company's technical challenges using an information processing device. This system has a mechanism that allows multiple users to input and make public challenge information. Corporate users input challenge information that reflects their company's technical needs from a terminal, and the information processing device stores this information in a database and makes it public to other users.

[0034] Users of the solution provider input solutions to publicly available challenges via a terminal and send them to the information processing device. In this system, the information processing device automatically evaluates the received solutions using an algorithm and generates a score for each solution. This allows corporate users to view the evaluation results of each proposal via the terminal, making it easy to select the optimal solution.

[0035] For example, if a manufacturing company is seeking to improve its production process and needs a solution, the company uses a terminal to post information about its challenges aimed at improving efficiency to the system. In response, a software company developing new technologies provides a solution via the terminal, such as automating the production line using image recognition. The information processing device evaluates the provided solution, and if it receives a high score, the manufacturing company can select that solution and use it to improve its actual process.

[0036] Thus, the system of the present invention enables effective matching between companies and solution providers, contributing to the resolution of companies' technical challenges.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user (company) accesses the issue information input screen using a terminal and enters their technical issue. The issue information includes the issue title, detailed description, and the requirements for the desired solution.

[0040] Step 2:

[0041] The terminal sends the entered assignment information to the server. The server receives the transmitted information and saves it to its database. This prepares the assignment information for public sharing with other users.

[0042] Step 3:

[0043] The user (solution provider) uses a terminal to view a list of published problem information and selects a problem of interest. They then devise a solution for the selected problem and input the solution via the terminal.

[0044] Step 4:

[0045] The terminal sends the entered solution to the server. The server receives the submitted solution and records it in a database. Then, it passes the solution to an AI algorithm for evaluation and scoring.

[0046] Step 5:

[0047] The server stores the scores of the evaluated solutions in a database and ranks the solutions based on those scores. Users (companies) can view the evaluation scores and solution details via their terminals.

[0048] Step 6:

[0049] The user (company) operates a terminal to select the most suitable solution. The selection result is notified to the server, which then saves the selection information to a database.

[0050] Step 7:

[0051] The server generates contract information based on the selected solution and provides it to both the user (company) and the user (solution provider). The contract is formally concluded when both parties review and agree to the terms and conditions.

[0052] Step 8:

[0053] After the contract is finalized, the server manages the process of paying the user (solution provider) a success fee. Payment information is recorded in a database, and the user can check it on their device.

[0054] (Example 1)

[0055] 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."

[0056] In solving technical challenges within companies, there is a need for a system that efficiently discloses problem information and provides appropriate countermeasures. Furthermore, it is necessary to automate the evaluation of disclosed countermeasures and quickly select the most suitable solution. In addition, there is a need to address the current lack of security measures to ensure the reliability of information and the sophisticated analytical capabilities required for the proposed solutions.

[0057] 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.

[0058] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for storing task information, means for analyzing proposals using natural language processing technology with a machine learning model, and means for security functions that analyze access data and monitor trends. This enables efficient publication of tasks, rapid and reliable selection of countermeasures, and advanced analysis of proposal content.

[0059] An "information processing device" is a combination of hardware and software for inputting, processing, storing, and outputting data.

[0060] "Issue information" refers to data that describes in detail the requirements and conditions that need to be resolved in a particular technical problem or situation.

[0061] "Users" refer to ordinary people, organizations, or companies that use the system, and are the entities that input problem information or provide solutions.

[0062] A "solution" refers to information that presents specific solutions or methods proposed based on publicly available problem information.

[0063] An "algorithm" refers to a set of calculation procedures or equations that evaluate a solution and score it based on certain criteria.

[0064] A "score" is a numerical value that quantifies the quality and suitability of the submitted solution, and is used as a criterion for evaluation.

[0065] A "machine learning model" refers to artificial intelligence technology that can learn patterns from data and perform natural language processing and other analytical tasks.

[0066] "Security features" refer to mechanisms that analyze access data and monitor for fraudulent activity and security threats in order to ensure the safety of information.

[0067] This invention is an information processing system that efficiently solves technical challenges for companies. This system has a function where users input challenge information from a terminal, a server stores that information in a database, and then makes it available to other users. The challenge information is data that describes in detail the specific technical needs of the company user, and solution providers can input countermeasures in response.

[0068] The server receives issue information submitted by users and stores it in relational databases such as MySQL® or PostgreSQL. This stored information is then provided to users as a RESTful API when it becomes publicly available. Furthermore, the countermeasures entered for the provided issues are first validated through text analysis, and then evaluated by a machine learning model. Libraries such as scikit-learn and TENSORFLOW® can be used for this machine learning model.

[0069] This evaluation process utilizes generative AI models and leverages natural language processing technology. This allows the server to assign scores to each countermeasure, enabling users to easily select the optimal solution through their terminals. Furthermore, the server continuously analyzes access data and monitors for unauthorized access through security features.

[0070] As a concrete example, consider a scenario where a manufacturing company submits a challenge requesting improvements to its production process. This company uses a terminal to input information about the challenges in improving production efficiency into the system. In response, another technology company can provide an automation solution for the production line using image recognition technology. The server evaluates this solution using a generative AI model and assigns an appropriate score. As a result, the manufacturing company can easily identify high-scoring solutions and implement them in practice.

[0071] An example of a prompt message is, "Please provide specific steps on how to improve labor efficiency in the production process by utilizing image recognition technology."

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user enters issue information using a terminal. The entered issue information is text data that describes the company's technical needs in detail. This input data is sent from the terminal to the server via HTTPS communication. The server initially validates the received issue information. This validation checks for SQL injection and inappropriate content to obtain clean data.

[0075] Step 2:

[0076] The server saves issue information that has passed validation to a database. Data with fields such as "Issue ID," "Company Name," and "Issue Details" is stored in a relational database (e.g., MySQL). The saved issue information is indexed so that other users can search it and access it via a RESTful API.

[0077] Step 3:

[0078] Users enter solutions based on published challenges using their devices. The entered solutions are sent as text from the device to the server. The server validates this data again and converts it into a format suitable for analysis by machine learning models.

[0079] Step 4:

[0080] The server evaluates received solutions using generative AI models and natural language processing techniques. It analyzes text data and generates relevant evaluation metrics, including criteria such as content quality, relevance, and originality. The model analyzes the input text and outputs a score. TensorFlow and scikit-learn may be used in this process.

[0081] Step 5:

[0082] The server stores the evaluation results and scores in a database. The score for each solution is stored along with its associated metadata. This allows users to later query the evaluation results of the solutions.

[0083] Step 6:

[0084] Users view the solution evaluation results through their terminals. Solutions are listed in order of score via a dashboard displayed on the terminal. Based on the displayed evaluation results, users can easily select the optimal solution. The server also analyzes user access logs and performs security monitoring. This detects unauthorized access and abnormal behavior, ensuring secure operation.

[0085] (Application Example 1)

[0086] 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."

[0087] To efficiently optimize business processes in industrial facilities, it is necessary to quickly identify and implement appropriate solutions to technical challenges. However, existing systems require manual identification of challenges and evaluation of solutions, which is time-consuming and labor-intensive. Furthermore, there is a lack of mechanisms to objectively evaluate the quality of solutions submitted by solution providers, making it difficult to select the optimal solution.

[0088] 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.

[0089] In this invention, the server is a data processing device equipped with a function for inputting problems, and includes means for storing problem information, means for making the stored problem information available to other users, and means for executing an algorithm that evaluates input solutions and generates a score. This enables the rapid identification of solutions to technical problems aimed at optimizing operations in industrial facilities and the objective evaluation of high-quality solutions.

[0090] A "challenge" refers to a specific problem or need that requires improvement in an industrial facility or business process.

[0091] A "data processing device" is a computer system that stores and processes information and performs necessary functions based on user input.

[0092] A "solution" is a set of improvements or technical suggestions offered to address a specific problem.

[0093] A "score" is a quantitative indicator given to evaluate the effectiveness and suitability of a proposed solution.

[0094] "Optimizing industrial facility operations" refers to initiatives aimed at improving production efficiency and business processes, thereby enhancing overall performance.

[0095] An "algorithm" refers to a set of computational procedures or rules designed for solving problems or processing data.

[0096] The system that realizes this invention uses a data processing device to automate the process of problem management and solution evaluation in industrial facilities.

[0097] The server receives issue information from users and stores it in a database. This issue information includes specific improvement needs and technical requirements for industrial facilities. Users can input this information from devices such as smartphones and tablets. Furthermore, this issue information is made public to other users, creating an environment where solution providers can propose appropriate solutions.

[0098] The server receives submitted solutions and evaluates each solution using a pre-configured algorithm. This algorithm utilizes a generative AI model to generate scores, quantifying the effectiveness and feasibility of each solution. This allows enterprise users to select the most effective solution through their terminals. Furthermore, solutions are ranked and visually displayed based on the generated scores, making it easier for users to make the optimal choice.

[0099] As a concrete example, let's consider a challenge related to improving the efficiency of machinery operating in a manufacturing plant. In this case, an engineer posts information about the challenge, "Development of a new control algorithm to improve machine efficiency," to the system. Solution providers can then propose a "control system utilizing real-time data analysis," and the server automatically assigns a score to each proposal. The user can then view the displayed score and select the most appropriate control algorithm.

[0100] An example of a prompt message might be, "Please propose a control system that utilizes real-time data analysis to improve machine efficiency."

[0101] This invention enables efficient and rapid resolution of technical challenges in industrial facilities.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users input issue information using a terminal. This input includes technical problems and processes requiring improvement within industrial facilities. The server receives the issue information submitted by the user and stores it in a database. This issue information includes text data and related metadata.

[0105] Step 2:

[0106] The server publishes the stored issue information to other solution providers. The published issue information is displayed through a user interface so that other users who can provide solutions can view it. This process includes access control to the issue information, ensuring that interested users can view the issue.

[0107] Step 3:

[0108] Solution providers review the published issues and enter solutions using their own devices. Once the solutions are sent from the device to the server, the server stores them in a database. The solutions include technical suggestions and are expressed in text format.

[0109] Step 4:

[0110] The server evaluates the input solutions using a generative AI model. The evaluation process analyzes the effectiveness and suitability of the solutions and generates scores based on the algorithm. It uses text data of the solutions as input and generates numerical scores as output.

[0111] Step 5:

[0112] The server ranks the solutions based on the generated scores and displays them visually to make it easier for the user to choose. The solutions and their scores are displayed on the terminal, allowing the user to make the best choice based on the scores. A ranked list of solutions is generated as output.

[0113] Step 6:

[0114] The user selects the most suitable solution from those displayed on the terminal and saves this selection information to the server. This selection information includes the ID of the selected solution and the reason for its selection. The selected solution is then used for subsequent decision-making and contract preparation.

[0115] 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.

[0116] The present invention combines an emotion engine with an information processing device to recognize the user's emotional state and apply it when inputting tasks and solutions. This system consists of terminals used by companies and solution providers, and a server that manages the data.

[0117] When a user (company) accesses the issue information input screen using a terminal, the system activates an emotion engine and acquires emotion data from the user's facial expressions, voice, etc. This emotion data is sent to the server along with the issue information and stored in the database as supplementary information to deepen understanding of the issue.

[0118] On the other hand, when users (solution providers) input the solutions they offer, their emotions are recognized by an emotion engine through their terminal. Based on this data, the server can be incorporated into a process to generate an emotion score in order to more accurately evaluate the quality of the solutions. Evaluation results that reflect the user's emotional state enable companies to make more reliable solution selections.

[0119] For example, when a company submits a technology-related challenge, the emotion engine can detect whether the user is experiencing stress. This information helps to indicate that the challenge is urgent. When solution providers submit proposals, the emotion engine captures emotions that indicate their confidence and passion, and this is taken into account when evaluating the proposal.

[0120] Thus, by using an emotion engine, the system of the present invention not only solves technical problems but also achieves advanced matching and evaluation that takes into account the user's emotional state.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user (company) accesses the problem information input screen using a terminal and describes the technical problem. Simultaneously with the input of the problem information, the terminal activates an emotion engine to analyze the user's facial expressions and voice to detect their current emotional state.

[0124] Step 2:

[0125] The terminal sends the detected emotion data along with the entered task information to the server. The server receives this information and stores it in a database as additional context for understanding the task.

[0126] Step 3:

[0127] The user (solution provider) views the published challenges through the terminal and inputs the corresponding solutions. The terminal utilizes an emotion engine during the input process to recognize the user's emotions in real time.

[0128] Step 4:

[0129] The terminal sends the user-inputted solution and corresponding sentiment data to the server. The server receives this data and performs a solution evaluation process that includes the sentiment data. Sentiment is considered as an indicator of the user's confidence and motivation.

[0130] Step 5:

[0131] The server generates evaluation scores, including sentiment data, and stores them in a database. Users (companies) then review these scores on their devices and select the optimal solution.

[0132] Step 6:

[0133] The user (company) notifies the server of their selected solution from their terminal, and the server records the selection results. Based on this selection, contract information is generated and shared with both users.

[0134] Step 7:

[0135] After the contract is approved, the server manages the payment of success fees to the user (solution provider) and notifies the user via the terminal. All payment information is recorded in the database.

[0136] (Example 2)

[0137] 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".

[0138] Conventional information processing systems evaluate issues and solutions without considering the user's emotional state, making it difficult to accurately determine the urgency of an issue or the applicability of a solution. This could lead to problems such as urgent issues being neglected or the optimal solution not being selected.

[0139] 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.

[0140] In this invention, the server includes means for acquiring and storing problem information and the user's emotional state; means for providing the problem information to other users using the acquired emotional state and storing the emotional information as supplementary data; and means for executing an algorithm that evaluates the input solution using the user's emotional data and generates an emotional score. This makes it possible to evaluate problems and select solutions that take the user's emotional state into consideration, thereby providing a more appropriate problem-solving process.

[0141] "Information processing equipment" is a general term for electronic devices used to input, process, store, and display data, and includes terminals used by users to input tasks and solutions.

[0142] "User emotional state" refers to data that indicates the user's psychological state, obtained by analyzing the user's facial expressions and voice.

[0143] An "emotion engine" is software or hardware that automatically acquires and quantifies a user's emotional state from their facial expressions and voice.

[0144] "Emotional data" refers to data that quantifies or expresses a user's emotional state as textual information, and is used to evaluate problems and solutions.

[0145] An "emotion score" is a numerical value generated based on acquired emotional data, and it serves as a criterion for evaluating the importance and applicability of issues and solutions.

[0146] An "evaluation algorithm" refers to a series of calculation steps that analyze input data and generate a score based on specific evaluation criteria.

[0147] A "solution" refers to a method or means proposed to solve a specific problem.

[0148] "Selection information" refers to data on the optimal solution selected within the server, including the reasons for that selection.

[0149] "Contract information" refers to data that describes transactions and terms of use based on agreements between users, and includes important elements when a contract is formed.

[0150] The system of this invention combines an information processing device with an emotion engine, recognizing the user's emotional state and applying it to the input of tasks and solutions. The system consists of terminals used by companies and solution providers, and a server that manages the data.

[0151] Users (companies) input issue information through a terminal. The terminal uses an emotion engine to acquire the user's facial expressions and voice, and recognize their emotional state. Specifically, libraries such as OpenCV are used for facial recognition, and Google's Cloud Speech-to-Text API is used for voice analysis. The acquired emotion data is transferred to the server along with the issue information.

[0152] The server organizes and stores the received data in a database management system (DBMS). Sentimental data is used as supplementary information to gain a deeper understanding of the issues. Furthermore, the server uses a generative AI model to generate sentimental scores based on the sentimental data. These sentimental scores are used to evaluate the issues and solutions.

[0153] When a user (solution provider) inputs a solution using a terminal, emotional data recognized by the emotion engine is sent to the server. Based on this data, the server runs an algorithm to evaluate the quality of the solution, enabling the selection of a reliable solution.

[0154] As a concrete example, when a company submits a challenge regarding the launch of a product into a new market, the emotion engine detects the user's stress level. This information serves as an indicator of the urgency of the challenge. Furthermore, emotional data is captured when solution providers enter their proposals, and their beliefs and passions are reflected in the evaluation. An example of a prompt might be, "Propose a solution that reflects the emotional score based on anxiety about the new product." This enables sophisticated solution matching that takes emotions into account.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The user (company) activates the information processing terminal and accesses the task information input screen. The input at this time includes the task details and supplementary materials. The terminal displays a user interface and activates the emotion engine. Facial expression and voice data are collected in real time and input into the terminal as emotion data. Specifically, video is captured from the webcam and audio is input from the microphone.

[0158] Step 2:

[0159] The device analyzes collected facial and audio data using an emotion engine. Here, OpenCV is used to extract facial feature points, and the Google Cloud Speech-to-Text API is used to convert the audio to text. From this data, the user's current emotional state (e.g., stress level) is quantified. The input is facial and audio data, and the output is emotion data.

[0160] Step 3:

[0161] The device sends the generated emotion data along with the task information to the server. The input is the task information and emotion data, and the output is the transmitted data sent to the server. Data encryption is performed during this process to ensure security.

[0162] Step 4:

[0163] The server stores the received data in a database management system (DBMS). The input is the transmitted data, and the output is structured data stored in the database. Within the database, the server associates and stores issue information with sentiment data. Specifically, this involves writing data using SQL queries.

[0164] Step 5:

[0165] When the user (solution provider) inputs the solution using the terminal, the emotion engine is activated again, and facial and voice data are collected. The input consists of the solution content and emotion data, and the output is the solution data sent to the server. Emotion analysis is performed again here, and a new emotional state is recognized.

[0166] Step 6:

[0167] The server executes an evaluation algorithm based on the received solution data. A generative AI model is used to calculate sentiment scores. The inputs are solution data and sentiment data, and the output is an evaluation result including sentiment scores. The evaluation results are quantified and used in subsequent processing.

[0168] Step 7:

[0169] The server provides feedback on the evaluation results to the user (company) or solution provider's terminal based on the generated sentiment score. The input is the evaluation result, and the output is the evaluation information displayed on the user's screen. This allows the user to select a problem solution that takes sentiment evaluation into account. Specifically, measures to reduce the emotional burden of candidate solutions are presented.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] Conventional information processing systems handled problems and solutions without considering the user's emotional state, resulting in a lack of intuitive emotional elements in solution selection and evaluation. Therefore, there is a need for reliable solution selection and evaluation that reflects the user's emotions.

[0173] 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.

[0174] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for acquiring the user's emotional state, means for executing an algorithm that evaluates solutions and generates an emotional score based on the acquired emotional data, and means for optimally displaying solutions based on the generated emotional score. This makes it possible to select and evaluate more appropriate and reliable solutions that reflect the user's emotions.

[0175] An "information processing device" is a machine or device used for collecting, processing, and managing data, and refers to computers and smart devices.

[0176] "User emotional state" refers to the type and intensity of emotions a user is currently experiencing, based on information obtained from facial expressions, voice, and other sources.

[0177] "Emotional data" refers to information that indicates a user's emotional state, and is a collection of data that includes numerical values ​​and indicators obtained through facial recognition and voice analysis.

[0178] An "algorithm" is a set of rules that define the procedures or calculation methods for solving a specific problem.

[0179] An "emotion score" is an index used to quantify and evaluate a user's emotional data, indicating the impact a user's emotional state has on a solution.

[0180] "Optimal display" means using the most effective or efficient arrangement and order when displaying information based on specific criteria.

[0181] "Contract information" refers to a collection of important information generated based on the selected solution, which is necessary to formally establish commercial transactions and business relationships.

[0182] The system for realizing this invention acquires the user's emotional state in real time and performs a series of processes to select and evaluate solutions based on that state.

[0183] The server uses computers or smart devices as information processing devices to receive input from user terminals. The terminals collect emotional data from the user's facial expressions and voice using cameras and microphones. Here, visual data is processed using Microsoft® Azure® Face API and Google Cloud Vision API, and voice data is analyzed using Google Cloud Speech-to-Text API.

[0184] The server stores the acquired sentiment data in a real-time database such as Firebase, and analyzes the sentiment data and user-entered issue information on the server side using Node.js. It then analyzes the generated sentiment score and displays the most suitable solution based on the user's emotional state.

[0185] For example, if a user is considering purchasing a relaxation product on an online shopping site, the system can prioritize displaying appropriate relaxation products if they experience stress during the purchase process. This flexible approach, tailored to the user's emotions, aims to improve the shopping experience.

[0186] Examples of prompt statements for a generative AI model are as follows:

[0187] "Devise a method to analyze the emotions users experience while browsing products and optimize product recommendations based on that analysis."

[0188] "Design a new approach that leverages emotion recognition capabilities to improve the user experience on e-commerce websites."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] When a user visits an online shopping site, the device activates its camera and microphone to collect the user's facial expressions and voice in real time. The data collected at this time becomes the input. Here, the image data from the camera is sent to Microsoft Azure's Face API, and the voice data is sent to Google Cloud Speech-to-Text API. This results in the output of numerical emotion data that indicates the user's emotional state.

[0192] Step 2:

[0193] The server receives emotion data sent from the terminal. The input at this time is numerical emotion data. The server stores this data in Firebase and uses Node.js to analyze the emotion data stored in the database and the product browsing information entered by the user. The analysis process outputs analysis results, including the user's emotion score.

[0194] Step 3:

[0195] The server uses a generative AI model based on the analysis results to optimize product recommendations, taking into account the current user sentiment. Here, prompt text is input into the generative AI model, and the model outputs an optimized product list. This output becomes the product suggestion list to be presented to the user.

[0196] Step 4:

[0197] The user receives an optimized product suggestion list sent from the server on their device screen. This list takes the user's emotional state into consideration and is displayed in a way that prioritizes products the user is looking for. The device then provides the user with the functionality to select or purchase products using this list.

[0198] Step 5:

[0199] The server combines the user's purchase history, list selection information, and sentiment score, and stores the new analytical data in the database to reflect the data and further optimize future suggestions. This data serves as input data to improve the accuracy of future suggestions, leading to an improved user experience.

[0200] 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.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] The present invention provides a system that efficiently solves a company's technical challenges using an information processing device. This system has a mechanism that allows multiple users to input and make public challenge information. Corporate users input challenge information that reflects their company's technical needs from a terminal, and the information processing device stores this information in a database and makes it public to other users.

[0217] Users of the solution provider input solutions to publicly available challenges via a terminal and send them to the information processing device. In this system, the information processing device automatically evaluates the received solutions using an algorithm and generates a score for each solution. This allows corporate users to view the evaluation results of each proposal via the terminal, making it easy to select the optimal solution.

[0218] For example, if a manufacturing company is seeking to improve its production process and needs a solution, the company uses a terminal to post information about its challenges aimed at improving efficiency to the system. In response, a software company developing new technologies provides a solution via the terminal, such as automating the production line using image recognition. The information processing device evaluates the provided solution, and if it receives a high score, the manufacturing company can select that solution and use it to improve its actual process.

[0219] Thus, the system of the present invention enables effective matching between companies and solution providers, contributing to the resolution of companies' technical challenges.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The user (company) accesses the issue information input screen using a terminal and enters their technical issue. The issue information includes the issue title, detailed description, and the requirements for the desired solution.

[0223] Step 2:

[0224] The terminal sends the entered assignment information to the server. The server receives the transmitted information and saves it to its database. This prepares the assignment information for public sharing with other users.

[0225] Step 3:

[0226] The user (solution provider) uses a terminal to view a list of published problem information and selects a problem of interest. They then devise a solution for the selected problem and input the solution via the terminal.

[0227] Step 4:

[0228] The terminal sends the entered solution to the server. The server receives the submitted solution and records it in a database. Then, it passes the solution to an AI algorithm for evaluation and scoring.

[0229] Step 5:

[0230] The server stores the scores of the evaluated solutions in a database and ranks the solutions based on those scores. Users (companies) can view the evaluation scores and solution details via their terminals.

[0231] Step 6:

[0232] The user (company) operates a terminal to select the most suitable solution. The selection result is notified to the server, which then saves the selection information to a database.

[0233] Step 7:

[0234] The server generates contract information based on the selected solution and provides it to both the user (company) and the user (solution provider). The contract is formally concluded when both parties review and agree to the terms and conditions.

[0235] Step 8:

[0236] After the contract is finalized, the server manages the process of paying the user (solution provider) a success fee. Payment information is recorded in a database, and the user can check it on their device.

[0237] (Example 1)

[0238] 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."

[0239] In solving technical challenges within companies, there is a need for a system that efficiently discloses problem information and provides appropriate countermeasures. Furthermore, it is necessary to automate the evaluation of disclosed countermeasures and quickly select the most suitable solution. In addition, there is a need to address the current lack of security measures to ensure the reliability of information and the sophisticated analytical capabilities required for the proposed solutions.

[0240] 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.

[0241] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for storing task information, means for analyzing proposals using natural language processing technology with a machine learning model, and means for security functions that analyze access data and monitor trends. This enables efficient publication of tasks, rapid and reliable selection of countermeasures, and advanced analysis of proposal content.

[0242] An "information processing device" is a combination of hardware and software for inputting, processing, storing, and outputting data.

[0243] "Issue information" refers to data that describes in detail the requirements and conditions that need to be resolved in a particular technical problem or situation.

[0244] "Users" refer to ordinary people, organizations, or companies that use the system, and are the entities that input problem information or provide solutions.

[0245] A "solution" refers to information that presents specific solutions or methods proposed based on publicly available problem information.

[0246] An "algorithm" refers to a set of calculation procedures or equations that evaluate a solution and score it based on certain criteria.

[0247] A "score" is a numerical value that quantifies the quality and suitability of the submitted solution, and is used as a criterion for evaluation.

[0248] A "machine learning model" refers to artificial intelligence technology that can learn patterns from data and perform natural language processing and other analytical tasks.

[0249] "Security features" refer to mechanisms that analyze access data and monitor for fraudulent activity and security threats in order to ensure the safety of information.

[0250] This invention is an information processing system that efficiently solves technical challenges for companies. This system has a function where users input challenge information from a terminal, a server stores that information in a database, and then makes it available to other users. The challenge information is data that describes in detail the specific technical needs of the company user, and solution providers can input countermeasures in response.

[0251] The server receives task information submitted by users and stores it in a relational database such as MySQL or PostgreSQL. This stored information is then provided to users as a RESTful API when it becomes publicly available. Furthermore, the submitted solutions to the tasks are first validated through text analysis, and then evaluated by a machine learning model. Libraries such as scikit-learn and TensorFlow could be used for this machine learning model.

[0252] This evaluation process utilizes generative AI models and leverages natural language processing technology. This allows the server to assign scores to each countermeasure, enabling users to easily select the optimal solution through their terminals. Furthermore, the server continuously analyzes access data and monitors for unauthorized access through security features.

[0253] As a concrete example, consider a scenario where a manufacturing company submits a challenge requesting improvements to its production process. This company uses a terminal to input information about the challenges in improving production efficiency into the system. In response, another technology company can provide an automation solution for the production line using image recognition technology. The server evaluates this solution using a generative AI model and assigns an appropriate score. As a result, the manufacturing company can easily identify high-scoring solutions and implement them in practice.

[0254] An example of a prompt message is, "Please provide specific steps on how to improve labor efficiency in the production process by utilizing image recognition technology."

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The user enters issue information using a terminal. The entered issue information is text data that describes the company's technical needs in detail. This input data is sent from the terminal to the server via HTTPS communication. The server initially validates the received issue information. This validation checks for SQL injection and inappropriate content to obtain clean data.

[0258] Step 2:

[0259] The server saves issue information that has passed validation to a database. Data with fields such as "Issue ID," "Company Name," and "Issue Details" is stored in a relational database (e.g., MySQL). The saved issue information is indexed so that other users can search it and access it via a RESTful API.

[0260] Step 3:

[0261] Users enter solutions based on published challenges using their devices. The entered solutions are sent as text from the device to the server. The server validates this data again and converts it into a format suitable for analysis by machine learning models.

[0262] Step 4:

[0263] The server evaluates received solutions using generative AI models and natural language processing techniques. It analyzes text data and generates relevant evaluation metrics, including criteria such as content quality, relevance, and originality. The model analyzes the input text and outputs a score. TensorFlow and scikit-learn may be used in this process.

[0264] Step 5:

[0265] The server stores the evaluation results and scores in a database. The score for each solution is stored along with its associated metadata. This allows users to later query the evaluation results of the solutions.

[0266] Step 6:

[0267] Users view the solution evaluation results through their terminals. Solutions are listed in order of score via a dashboard displayed on the terminal. Based on the displayed evaluation results, users can easily select the optimal solution. The server also analyzes user access logs and performs security monitoring. This detects unauthorized access and abnormal behavior, ensuring secure operation.

[0268] (Application Example 1)

[0269] 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."

[0270] To efficiently optimize business processes in industrial facilities, it is necessary to quickly identify and implement appropriate solutions to technical challenges. However, existing systems require manual identification of challenges and evaluation of solutions, which is time-consuming and labor-intensive. Furthermore, there is a lack of mechanisms to objectively evaluate the quality of solutions submitted by solution providers, making it difficult to select the optimal solution.

[0271] 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.

[0272] In this invention, the server is a data processing device equipped with a function for inputting problems, and includes means for storing problem information, means for making the stored problem information available to other users, and means for executing an algorithm that evaluates input solutions and generates a score. This enables the rapid identification of solutions to technical problems aimed at optimizing operations in industrial facilities and the objective evaluation of high-quality solutions.

[0273] A "challenge" refers to a specific problem or need that requires improvement in an industrial facility or business process.

[0274] A "data processing device" is a computer system that stores and processes information and performs necessary functions based on user input.

[0275] A "solution" is a set of improvements or technical suggestions offered to address a specific problem.

[0276] A "score" is a quantitative indicator given to evaluate the effectiveness and suitability of a proposed solution.

[0277] "Optimizing industrial facility operations" refers to initiatives aimed at improving production efficiency and business processes, thereby enhancing overall performance.

[0278] An "algorithm" refers to a set of computational procedures or rules designed for solving problems or processing data.

[0279] The system that realizes this invention uses a data processing device to automate the process of problem management and solution evaluation in industrial facilities.

[0280] The server receives task information from the user and stores it in the database. The task information includes specific improvement needs and technical requirements of industrial facilities. The user can input this information from terminals such as smartphones and tablets. Also, since this task information is made public to other users, an environment is created where solution providers can propose appropriate solutions.

[0281] The server receives the transmitted solutions and evaluates each solution using a pre-set algorithm. This algorithm utilizes a generative AI model to generate scores and quantifies the effectiveness and feasibility of the solutions. As a result, enterprise users can select the most effective solution through the terminal. Also, based on the generated scores, the solutions are ranked and visually displayed, enabling users to make an optimal choice easily.

[0282] As a specific example, assume a task related to improving the efficiency of a machine in operation at a manufacturing site. In this case, a technician posts task information such as "Development of a new control algorithm for improving the efficiency of the machine" to the system. A solution provider can propose "A control system utilizing real-time data analysis" for this, and the server automatically assigns a score to the proposal. The user can view the displayed scores and select the most appropriate control algorithm.

[0283] As an example of a prompt sentence, content such as "Please propose a control system that utilizes real-time data analysis to improve the efficiency of the machine" can be considered.

[0284] According to this invention, technical problems in industrial facilities can be solved efficiently and quickly.

[0285] The flow of a specific process in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] Users input issue information using a terminal. This input includes technical problems and processes requiring improvement within industrial facilities. The server receives the issue information submitted by the user and stores it in a database. This issue information includes text data and related metadata.

[0288] Step 2:

[0289] The server publishes the stored issue information to other solution providers. The published issue information is displayed through a user interface so that other users who can provide solutions can view it. This process includes access control to the issue information, ensuring that interested users can view the issue.

[0290] Step 3:

[0291] Solution providers review the published issues and enter solutions using their own devices. Once the solutions are sent from the device to the server, the server stores them in a database. The solutions include technical suggestions and are expressed in text format.

[0292] Step 4:

[0293] The server evaluates the input solutions using a generative AI model. The evaluation process analyzes the effectiveness and suitability of the solutions and generates scores based on the algorithm. It uses text data of the solutions as input and generates numerical scores as output.

[0294] Step 5:

[0295] The server ranks the solutions based on the generated scores and displays them visually to make it easier for the user to choose. The solutions and their scores are displayed on the terminal, allowing the user to make the best choice based on the scores. A ranked list of solutions is generated as output.

[0296] Step 6:

[0297] The user selects the most suitable solution from those displayed on the terminal and saves this selection information to the server. This selection information includes the ID of the selected solution and the reason for its selection. The selected solution is then used for subsequent decision-making and contract preparation.

[0298] 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.

[0299] The present invention combines an emotion engine with an information processing device to recognize the user's emotional state and apply it when inputting tasks and solutions. This system consists of terminals used by companies and solution providers, and a server that manages the data.

[0300] When a user (company) accesses the issue information input screen using a terminal, the system activates an emotion engine and acquires emotion data from the user's facial expressions, voice, etc. This emotion data is sent to the server along with the issue information and stored in the database as supplementary information to deepen understanding of the issue.

[0301] On the other hand, when users (solution providers) input the solutions they offer, their emotions are recognized by an emotion engine through their terminal. Based on this data, the server can be incorporated into a process to generate an emotion score in order to more accurately evaluate the quality of the solutions. Evaluation results that reflect the user's emotional state enable companies to make more reliable solution selections.

[0302] For example, when a company submits a technology-related issue, the emotion engine detects whether the user is feeling stressed. This information helps to indicate that the issue is urgent. When solution providers enter their proposals, the emotion engine captures the emotions that show their confidence and passion, which are taken into account in the evaluation of the proposals.

[0303] Thus, by using the emotion engine, the system of the present invention not only stays at simply solving technical problems, but also realizes advanced matching and evaluation considering the user's emotional state.

[0304] The following describes the process flow.

[0305] Step 1:

[0306] The user (company) accesses the input screen of issue information using a terminal and describes the technical issue. The terminal activates the emotion engine simultaneously with the input of the issue information, analyzes the user's facial expressions and voice, and detects the current emotional state.

[0307] Step 2:

[0308] The terminal sends the detected emotion data together with the input issue information to the server. The server receives this information and saves it in the database as additional context for understanding the issue.

[0309] Step 3:

[0310] The user (solution provider) browses the issues published through the terminal and enters the corresponding solutions. The terminal utilizes the emotion engine during the input process to recognize the user's emotions in real time.

[0311] Step 4:

[0312] The terminal sends the user-inputted solution and corresponding sentiment data to the server. The server receives this data and performs a solution evaluation process that includes the sentiment data. Sentiment is considered as an indicator of the user's confidence and motivation.

[0313] Step 5:

[0314] The server generates evaluation scores, including sentiment data, and stores them in a database. Users (companies) then review these scores on their devices and select the optimal solution.

[0315] Step 6:

[0316] The user (company) notifies the server of their selected solution from their terminal, and the server records the selection results. Based on this selection, contract information is generated and shared with both users.

[0317] Step 7:

[0318] After the contract is approved, the server manages the payment of success fees to the user (solution provider) and notifies the user via the terminal. All payment information is recorded in the database.

[0319] (Example 2)

[0320] 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".

[0321] Conventional information processing systems evaluate issues and solutions without considering the user's emotional state, making it difficult to accurately determine the urgency of an issue or the applicability of a solution. This could lead to problems such as urgent issues being neglected or the optimal solution not being selected.

[0322] 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.

[0323] In this invention, the server includes means for acquiring and storing problem information and the user's emotional state; means for providing the problem information to other users using the acquired emotional state and storing the emotional information as supplementary data; and means for executing an algorithm that evaluates the input solution using the user's emotional data and generates an emotional score. This makes it possible to evaluate problems and select solutions that take the user's emotional state into consideration, thereby providing a more appropriate problem-solving process.

[0324] "Information processing equipment" is a general term for electronic devices used to input, process, store, and display data, and includes terminals used by users to input tasks and solutions.

[0325] "User emotional state" refers to data that indicates the user's psychological state, obtained by analyzing the user's facial expressions and voice.

[0326] An "emotion engine" is software or hardware that automatically acquires and quantifies a user's emotional state from their facial expressions and voice.

[0327] "Emotional data" refers to data that quantifies or expresses a user's emotional state as textual information, and is used to evaluate problems and solutions.

[0328] An "emotion score" is a numerical value generated based on acquired emotional data, and it serves as a criterion for evaluating the importance and applicability of issues and solutions.

[0329] An "evaluation algorithm" refers to a series of calculation steps that analyze input data and generate a score based on specific evaluation criteria.

[0330] A "solution" refers to a method or means proposed to solve a specific problem.

[0331] "Selection information" refers to data on the optimal solution selected within the server, including the reasons for that selection.

[0332] "Contract information" refers to data that describes transactions and terms of use based on agreements between users, and includes important elements when a contract is formed.

[0333] The system of this invention combines an information processing device with an emotion engine, recognizing the user's emotional state and applying it to the input of tasks and solutions. The system consists of terminals used by companies and solution providers, and a server that manages the data.

[0334] Users (companies) input issue information through a terminal. The terminal uses an emotion engine to capture the user's facial expressions and voice, and recognize their emotional state. Specifically, libraries such as OpenCV are used for facial recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The acquired emotion data is transferred to the server along with the issue information.

[0335] The server organizes and stores the received data in a database management system (DBMS). Sentimental data is used as supplementary information to gain a deeper understanding of the issues. Furthermore, the server uses a generative AI model to generate sentimental scores based on the sentimental data. These sentimental scores are used to evaluate the issues and solutions.

[0336] When a user (solution provider) inputs a solution using a terminal, emotional data recognized by the emotion engine is sent to the server. Based on this data, the server runs an algorithm to evaluate the quality of the solution, enabling the selection of a reliable solution.

[0337] As a concrete example, when a company submits a challenge regarding the launch of a product into a new market, the emotion engine detects the user's stress level. This information serves as an indicator of the urgency of the challenge. Furthermore, emotional data is captured when solution providers enter their proposals, and their beliefs and passions are reflected in the evaluation. An example of a prompt might be, "Propose a solution that reflects the emotional score based on anxiety about the new product." This enables sophisticated solution matching that takes emotions into account.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user (company) activates the information processing terminal and accesses the task information input screen. The input at this time includes the task details and supplementary materials. The terminal displays a user interface and activates the emotion engine. Facial expression and voice data are collected in real time and input into the terminal as emotion data. Specifically, video is captured from the webcam and audio is input from the microphone.

[0341] Step 2:

[0342] The device analyzes collected facial and audio data using an emotion engine. Here, OpenCV is used to extract facial feature points, and the Google Cloud Speech-to-Text API is used to convert the audio to text. From this data, the user's current emotional state (e.g., stress level) is quantified. The input is facial and audio data, and the output is emotion data.

[0343] Step 3:

[0344] The device sends the generated emotion data along with the task information to the server. The input is the task information and emotion data, and the output is the transmitted data sent to the server. Data encryption is performed during this process to ensure security.

[0345] Step 4:

[0346] The server stores the received data in a database management system (DBMS). The input is the transmitted data, and the output is structured data stored in the database. Within the database, the server associates and stores issue information with sentiment data. Specifically, this involves writing data using SQL queries.

[0347] Step 5:

[0348] When the user (solution provider) inputs the solution using the terminal, the emotion engine is activated again, and facial and voice data are collected. The input consists of the solution content and emotion data, and the output is the solution data sent to the server. Emotion analysis is performed again here, and a new emotional state is recognized.

[0349] Step 6:

[0350] The server executes an evaluation algorithm based on the received solution data. A generative AI model is used to calculate sentiment scores. The inputs are solution data and sentiment data, and the output is an evaluation result including sentiment scores. The evaluation results are quantified and used in subsequent processing.

[0351] Step 7:

[0352] The server provides feedback on the evaluation results to the user (company) or solution provider's terminal based on the generated sentiment score. The input is the evaluation result, and the output is the evaluation information displayed on the user's screen. This allows the user to select a problem solution that takes sentiment evaluation into account. Specifically, measures to reduce the emotional burden of candidate solutions are presented.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0355] Conventional information processing systems handled problems and solutions without considering the user's emotional state, resulting in a lack of intuitive emotional elements in solution selection and evaluation. Therefore, there is a need for reliable solution selection and evaluation that reflects the user's emotions.

[0356] 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.

[0357] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for acquiring the user's emotional state, means for executing an algorithm that evaluates solutions and generates an emotional score based on the acquired emotional data, and means for optimally displaying solutions based on the generated emotional score. This makes it possible to select and evaluate more appropriate and reliable solutions that reflect the user's emotions.

[0358] An "information processing device" is a machine or device used for collecting, processing, and managing data, and refers to computers and smart devices.

[0359] "User emotional state" refers to the type and intensity of emotions a user is currently experiencing, based on information obtained from facial expressions, voice, and other sources.

[0360] "Emotional data" refers to information that indicates a user's emotional state, and is a collection of data that includes numerical values ​​and indicators obtained through facial recognition and voice analysis.

[0361] An "algorithm" is a set of rules that define the procedures or calculation methods for solving a specific problem.

[0362] An "emotion score" is an index used to quantify and evaluate a user's emotional data, indicating the impact a user's emotional state has on a solution.

[0363] "Optimal display" means using the most effective or efficient arrangement and order when displaying information based on specific criteria.

[0364] "Contract information" refers to a collection of important information generated based on the selected solution, which is necessary to formally establish commercial transactions and business relationships.

[0365] The system for realizing this invention acquires the user's emotional state in real time and performs a series of processes to select and evaluate solutions based on that state.

[0366] The server uses computers or smart devices as information processing devices to receive input from user terminals. The terminals collect emotional data from the user's facial expressions and voice using cameras and microphones. Here, visual data is processed using Microsoft Azure's Face API and Google Cloud's Vision API, and voice data is analyzed using Google Cloud Speech-to-Text API.

[0367] The server stores the acquired sentiment data in a real-time database such as Firebase, and analyzes the sentiment data and user-entered issue information on the server side using Node.js. It then analyzes the generated sentiment score and displays the most suitable solution based on the user's emotional state.

[0368] For example, if a user is considering purchasing a relaxation product on an online shopping site, the system can prioritize displaying appropriate relaxation products if they experience stress during the purchase process. This flexible approach, tailored to the user's emotions, aims to improve the shopping experience.

[0369] Examples of prompt statements for a generative AI model are as follows:

[0370] "Devise a method to analyze the emotions users experience while browsing products and optimize product recommendations based on that analysis."

[0371] "Design a new approach that leverages emotion recognition capabilities to improve the user experience on e-commerce websites."

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] When a user visits an online shopping site, the device activates its camera and microphone to collect the user's facial expressions and voice in real time. The data collected at this time becomes the input. Here, the image data from the camera is sent to Microsoft Azure's Face API, and the voice data is sent to Google Cloud Speech-to-Text API. This results in the output of numerical emotion data that indicates the user's emotional state.

[0375] Step 2:

[0376] The server receives emotion data sent from the terminal. The input at this time is numerical emotion data. The server stores this data in Firebase and uses Node.js to analyze the emotion data stored in the database and the product browsing information entered by the user. The analysis process outputs analysis results, including the user's emotion score.

[0377] Step 3:

[0378] The server uses a generative AI model based on the analysis results to optimize product recommendations, taking into account the current user sentiment. Here, prompt text is input into the generative AI model, and the model outputs an optimized product list. This output becomes the product suggestion list to be presented to the user.

[0379] Step 4:

[0380] The user receives an optimized product suggestion list sent from the server on their device screen. This list takes the user's emotional state into consideration and is displayed in a way that prioritizes products the user is looking for. The device then provides the user with the functionality to select or purchase products using this list.

[0381] Step 5:

[0382] The server combines the user's purchase history, list selection information, and sentiment score, and stores the new analytical data in the database to reflect the data and further optimize future suggestions. This data serves as input data to improve the accuracy of future suggestions, leading to an improved user experience.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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".

[0399] The present invention provides a system that efficiently solves a company's technical challenges using an information processing device. This system has a mechanism that allows multiple users to input and make public challenge information. Corporate users input challenge information that reflects their company's technical needs from a terminal, and the information processing device stores this information in a database and makes it public to other users.

[0400] Users of the solution provider input solutions to publicly available challenges via a terminal and send them to the information processing device. In this system, the information processing device automatically evaluates the received solutions using an algorithm and generates a score for each solution. This allows corporate users to view the evaluation results of each proposal via the terminal, making it easy to select the optimal solution.

[0401] For example, if a manufacturing company is seeking to improve its production process and needs a solution, the company uses a terminal to post information about its challenges aimed at improving efficiency to the system. In response, a software company developing new technologies provides a solution via the terminal, such as automating the production line using image recognition. The information processing device evaluates the provided solution, and if it receives a high score, the manufacturing company can select that solution and use it to improve its actual process.

[0402] Thus, the system of the present invention enables effective matching between companies and solution providers, contributing to the resolution of companies' technical challenges.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The user (company) accesses the issue information input screen using a terminal and enters their technical issue. The issue information includes the issue title, detailed description, and the requirements for the desired solution.

[0406] Step 2:

[0407] The terminal sends the entered assignment information to the server. The server receives the transmitted information and saves it to its database. This prepares the assignment information for public sharing with other users.

[0408] Step 3:

[0409] The user (solution provider) uses a terminal to view a list of published problem information and selects a problem of interest. They then devise a solution for the selected problem and input the solution via the terminal.

[0410] Step 4:

[0411] The terminal sends the entered solution to the server. The server receives the submitted solution and records it in a database. Then, it passes the solution to an AI algorithm for evaluation and scoring.

[0412] Step 5:

[0413] The server stores the scores of the evaluated solutions in a database and ranks the solutions based on those scores. Users (companies) can view the evaluation scores and solution details via their terminals.

[0414] Step 6:

[0415] The user (company) operates a terminal to select the most suitable solution. The selection result is notified to the server, which then saves the selection information to a database.

[0416] Step 7:

[0417] The server generates contract information based on the selected solution and provides it to both the user (company) and the user (solution provider). The contract is formally concluded when both parties review and agree to the terms and conditions.

[0418] Step 8:

[0419] After the contract is finalized, the server manages the process of paying the user (solution provider) a success fee. Payment information is recorded in a database, and the user can check it on their device.

[0420] (Example 1)

[0421] 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."

[0422] In solving technical challenges within companies, there is a need for a system that efficiently discloses problem information and provides appropriate countermeasures. Furthermore, it is necessary to automate the evaluation of disclosed countermeasures and quickly select the most suitable solution. In addition, there is a need to address the current lack of security measures to ensure the reliability of information and the sophisticated analytical capabilities required for the proposed solutions.

[0423] 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.

[0424] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for storing task information, means for analyzing proposals using natural language processing technology with a machine learning model, and means for security functions that analyze access data and monitor trends. This enables efficient publication of tasks, rapid and reliable selection of countermeasures, and advanced analysis of proposal content.

[0425] An "information processing device" is a combination of hardware and software for inputting, processing, storing, and outputting data.

[0426] "Issue information" refers to data that describes in detail the requirements and conditions that need to be resolved in a particular technical problem or situation.

[0427] "Users" refer to ordinary people, organizations, or companies that use the system, and are the entities that input problem information or provide solutions.

[0428] A "solution" refers to information that presents specific solutions or methods proposed based on publicly available problem information.

[0429] An "algorithm" refers to a set of calculation procedures or equations that evaluate a solution and score it based on certain criteria.

[0430] A "score" is a numerical value that quantifies the quality and suitability of the submitted solution, and is used as a criterion for evaluation.

[0431] A "machine learning model" refers to artificial intelligence technology that can learn patterns from data and perform natural language processing and other analytical tasks.

[0432] "Security features" refer to mechanisms that analyze access data and monitor for fraudulent activity and security threats in order to ensure the safety of information.

[0433] This invention is an information processing system that efficiently solves technical challenges for companies. This system has a function where users input challenge information from a terminal, a server stores that information in a database, and then makes it available to other users. The challenge information is data that describes in detail the specific technical needs of the company user, and solution providers can input countermeasures in response.

[0434] The server receives task information submitted by users and stores it in a relational database such as MySQL or PostgreSQL. This stored information is then provided to users as a RESTful API when it becomes publicly available. Furthermore, the submitted solutions to the tasks are first validated through text analysis, and then evaluated by a machine learning model. Libraries such as scikit-learn and TensorFlow could be used for this machine learning model.

[0435] This evaluation process utilizes generative AI models and leverages natural language processing technology. This allows the server to assign scores to each countermeasure, enabling users to easily select the optimal solution through their terminals. Furthermore, the server continuously analyzes access data and monitors for unauthorized access through security features.

[0436] As a concrete example, consider a scenario where a manufacturing company submits a challenge requesting improvements to its production process. This company uses a terminal to input information about the challenges in improving production efficiency into the system. In response, another technology company can provide an automation solution for the production line using image recognition technology. The server evaluates this solution using a generative AI model and assigns an appropriate score. As a result, the manufacturing company can easily identify high-scoring solutions and implement them in practice.

[0437] An example of a prompt message is, "Please provide specific steps on how to improve labor efficiency in the production process by utilizing image recognition technology."

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The user enters issue information using a terminal. The entered issue information is text data that describes the company's technical needs in detail. This input data is sent from the terminal to the server via HTTPS communication. The server initially validates the received issue information. This validation checks for SQL injection and inappropriate content to obtain clean data.

[0441] Step 2:

[0442] The server saves issue information that has passed validation to a database. Data with fields such as "Issue ID," "Company Name," and "Issue Details" is stored in a relational database (e.g., MySQL). The saved issue information is indexed so that other users can search it and access it via a RESTful API.

[0443] Step 3:

[0444] Users enter solutions based on published challenges using their devices. The entered solutions are sent as text from the device to the server. The server validates this data again and converts it into a format suitable for analysis by machine learning models.

[0445] Step 4:

[0446] The server evaluates received solutions using generative AI models and natural language processing techniques. It analyzes text data and generates relevant evaluation metrics, including criteria such as content quality, relevance, and originality. The model analyzes the input text and outputs a score. TensorFlow and scikit-learn may be used in this process.

[0447] Step 5:

[0448] The server stores the evaluation results and scores in a database. The score for each solution is stored along with its associated metadata. This allows users to later query the evaluation results of the solutions.

[0449] Step 6:

[0450] Users view the solution evaluation results through their terminals. Solutions are listed in order of score via a dashboard displayed on the terminal. Based on the displayed evaluation results, users can easily select the optimal solution. The server also analyzes user access logs and performs security monitoring. This detects unauthorized access and abnormal behavior, ensuring secure operation.

[0451] (Application Example 1)

[0452] 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."

[0453] To efficiently optimize business processes in industrial facilities, it is necessary to quickly identify and implement appropriate solutions to technical challenges. However, existing systems require manual identification of challenges and evaluation of solutions, which is time-consuming and labor-intensive. Furthermore, there is a lack of mechanisms to objectively evaluate the quality of solutions submitted by solution providers, making it difficult to select the optimal solution.

[0454] 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.

[0455] In this invention, the server is a data processing device equipped with a function for inputting problems, and includes means for storing problem information, means for making the stored problem information available to other users, and means for executing an algorithm that evaluates input solutions and generates a score. This enables the rapid identification of solutions to technical problems aimed at optimizing operations in industrial facilities and the objective evaluation of high-quality solutions.

[0456] A "challenge" refers to a specific problem or need that requires improvement in an industrial facility or business process.

[0457] A "data processing device" is a computer system that stores and processes information and performs necessary functions based on user input.

[0458] A "solution" is a set of improvements or technical suggestions offered to address a specific problem.

[0459] A "score" is a quantitative indicator given to evaluate the effectiveness and suitability of a proposed solution.

[0460] "Optimizing industrial facility operations" refers to initiatives aimed at improving production efficiency and business processes, thereby enhancing overall performance.

[0461] An "algorithm" refers to a set of computational procedures or rules designed for solving problems or processing data.

[0462] The system that realizes this invention uses a data processing device to automate the process of problem management and solution evaluation in industrial facilities.

[0463] The server receives issue information from users and stores it in a database. This issue information includes specific improvement needs and technical requirements for industrial facilities. Users can input this information from devices such as smartphones and tablets. Furthermore, this issue information is made public to other users, creating an environment where solution providers can propose appropriate solutions.

[0464] The server receives submitted solutions and evaluates each solution using a pre-configured algorithm. This algorithm utilizes a generative AI model to generate scores, quantifying the effectiveness and feasibility of each solution. This allows enterprise users to select the most effective solution through their terminals. Furthermore, solutions are ranked and visually displayed based on the generated scores, making it easier for users to make the optimal choice.

[0465] As a concrete example, let's consider a challenge related to improving the efficiency of machinery operating in a manufacturing plant. In this case, an engineer posts information about the challenge, "Development of a new control algorithm to improve machine efficiency," to the system. Solution providers can then propose a "control system utilizing real-time data analysis," and the server automatically assigns a score to each proposal. The user can then view the displayed score and select the most appropriate control algorithm.

[0466] An example of a prompt message might be, "Please propose a control system that utilizes real-time data analysis to improve machine efficiency."

[0467] This invention enables efficient and rapid resolution of technical challenges in industrial facilities.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] Users input issue information using a terminal. This input includes technical problems and processes requiring improvement within industrial facilities. The server receives the issue information submitted by the user and stores it in a database. This issue information includes text data and related metadata.

[0471] Step 2:

[0472] The server publishes the stored issue information to other solution providers. The published issue information is displayed through a user interface so that other users who can provide solutions can view it. This process includes access control to the issue information, ensuring that interested users can view the issue.

[0473] Step 3:

[0474] Solution providers review the published issues and enter solutions using their own devices. Once the solutions are sent from the device to the server, the server stores them in a database. The solutions include technical suggestions and are expressed in text format.

[0475] Step 4:

[0476] The server evaluates the input solutions using a generative AI model. The evaluation process analyzes the effectiveness and suitability of the solutions and generates scores based on the algorithm. It uses text data of the solutions as input and generates numerical scores as output.

[0477] Step 5:

[0478] The server ranks the solutions based on the generated scores and displays them visually to make it easier for the user to choose. The solutions and their scores are displayed on the terminal, allowing the user to make the best choice based on the scores. A ranked list of solutions is generated as output.

[0479] Step 6:

[0480] The user selects the most suitable solution from those displayed on the terminal and saves this selection information to the server. This selection information includes the ID of the selected solution and the reason for its selection. The selected solution is then used for subsequent decision-making and contract preparation.

[0481] 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.

[0482] The present invention combines an emotion engine with an information processing device to recognize the user's emotional state and apply it when inputting tasks and solutions. This system consists of terminals used by companies and solution providers, and a server that manages the data.

[0483] When a user (company) accesses the issue information input screen using a terminal, the system activates an emotion engine and acquires emotion data from the user's facial expressions, voice, etc. This emotion data is sent to the server along with the issue information and stored in the database as supplementary information to deepen understanding of the issue.

[0484] On the other hand, when users (solution providers) input the solutions they offer, their emotions are recognized by an emotion engine through their terminal. Based on this data, the server can be incorporated into a process to generate an emotion score in order to more accurately evaluate the quality of the solutions. Evaluation results that reflect the user's emotional state enable companies to make more reliable solution selections.

[0485] For example, when a company submits a technology-related challenge, the emotion engine can detect whether the user is experiencing stress. This information helps to indicate that the challenge is urgent. When solution providers submit proposals, the emotion engine captures emotions that indicate their confidence and passion, and this is taken into account when evaluating the proposal.

[0486] Thus, by using an emotion engine, the system of the present invention not only solves technical problems but also achieves advanced matching and evaluation that takes into account the user's emotional state.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The user (company) accesses the problem information input screen using a terminal and describes the technical problem. Simultaneously with the input of the problem information, the terminal activates an emotion engine to analyze the user's facial expressions and voice to detect their current emotional state.

[0490] Step 2:

[0491] The terminal sends the detected emotion data along with the entered task information to the server. The server receives this information and stores it in a database as additional context for understanding the task.

[0492] Step 3:

[0493] The user (solution provider) views the published challenges through the terminal and inputs the corresponding solutions. The terminal utilizes an emotion engine during the input process to recognize the user's emotions in real time.

[0494] Step 4:

[0495] The terminal sends the user-inputted solution and corresponding sentiment data to the server. The server receives this data and performs a solution evaluation process that includes the sentiment data. Sentiment is considered as an indicator of the user's confidence and motivation.

[0496] Step 5:

[0497] The server generates evaluation scores, including sentiment data, and stores them in a database. Users (companies) then review these scores on their devices and select the optimal solution.

[0498] Step 6:

[0499] The user (company) notifies the server of their selected solution from their terminal, and the server records the selection results. Based on this selection, contract information is generated and shared with both users.

[0500] Step 7:

[0501] After the contract is approved, the server manages the payment of success fees to the user (solution provider) and notifies the user via the terminal. All payment information is recorded in the database.

[0502] (Example 2)

[0503] 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."

[0504] Conventional information processing systems evaluate issues and solutions without considering the user's emotional state, making it difficult to accurately determine the urgency of an issue or the applicability of a solution. This could lead to problems such as urgent issues being neglected or the optimal solution not being selected.

[0505] 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.

[0506] In this invention, the server includes means for acquiring and storing problem information and the user's emotional state; means for providing the problem information to other users using the acquired emotional state and storing the emotional information as supplementary data; and means for executing an algorithm that evaluates the input solution using the user's emotional data and generates an emotional score. This makes it possible to evaluate problems and select solutions that take the user's emotional state into consideration, thereby providing a more appropriate problem-solving process.

[0507] "Information processing equipment" is a general term for electronic devices used to input, process, store, and display data, and includes terminals used by users to input tasks and solutions.

[0508] "User emotional state" refers to data that indicates the user's psychological state, obtained by analyzing the user's facial expressions and voice.

[0509] An "emotion engine" is software or hardware that automatically acquires and quantifies a user's emotional state from their facial expressions and voice.

[0510] "Emotional data" refers to data that quantifies or expresses a user's emotional state as textual information, and is used to evaluate problems and solutions.

[0511] An "emotion score" is a numerical value generated based on acquired emotional data, and it serves as a criterion for evaluating the importance and applicability of issues and solutions.

[0512] An "evaluation algorithm" refers to a series of calculation steps that analyze input data and generate a score based on specific evaluation criteria.

[0513] A "solution" refers to a method or means proposed to solve a specific problem.

[0514] "Selection information" refers to data on the optimal solution selected within the server, including the reasons for that selection.

[0515] "Contract information" refers to data that describes transactions and terms of use based on agreements between users, and includes important elements when a contract is formed.

[0516] The system of this invention combines an information processing device with an emotion engine, recognizing the user's emotional state and applying it to the input of tasks and solutions. The system consists of terminals used by companies and solution providers, and a server that manages the data.

[0517] Users (companies) input issue information through a terminal. The terminal uses an emotion engine to capture the user's facial expressions and voice, and recognize their emotional state. Specifically, libraries such as OpenCV are used for facial recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The acquired emotion data is transferred to the server along with the issue information.

[0518] The server organizes and stores the received data in a database management system (DBMS). Sentimental data is used as supplementary information to gain a deeper understanding of the issues. Furthermore, the server uses a generative AI model to generate sentimental scores based on the sentimental data. These sentimental scores are used to evaluate the issues and solutions.

[0519] When a user (solution provider) inputs a solution using a terminal, emotional data recognized by the emotion engine is sent to the server. Based on this data, the server runs an algorithm to evaluate the quality of the solution, enabling the selection of a reliable solution.

[0520] As a concrete example, when a company submits a challenge regarding the launch of a product into a new market, the emotion engine detects the user's stress level. This information serves as an indicator of the urgency of the challenge. Furthermore, emotional data is captured when solution providers enter their proposals, and their beliefs and passions are reflected in the evaluation. An example of a prompt might be, "Propose a solution that reflects the emotional score based on anxiety about the new product." This enables sophisticated solution matching that takes emotions into account.

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] The user (company) activates the information processing terminal and accesses the task information input screen. The input at this time includes the task details and supplementary materials. The terminal displays a user interface and activates the emotion engine. Facial expression and voice data are collected in real time and input into the terminal as emotion data. Specifically, video is captured from the webcam and audio is input from the microphone.

[0524] Step 2:

[0525] The device analyzes collected facial and audio data using an emotion engine. Here, OpenCV is used to extract facial feature points, and the Google Cloud Speech-to-Text API is used to convert the audio to text. From this data, the user's current emotional state (e.g., stress level) is quantified. The input is facial and audio data, and the output is emotion data.

[0526] Step 3:

[0527] The device sends the generated emotion data along with the task information to the server. The input is the task information and emotion data, and the output is the transmitted data sent to the server. Data encryption is performed during this process to ensure security.

[0528] Step 4:

[0529] The server stores the received data in a database management system (DBMS). The input is the transmitted data, and the output is structured data stored in the database. Within the database, the server associates and stores issue information with sentiment data. Specifically, this involves writing data using SQL queries.

[0530] Step 5:

[0531] When the user (solution provider) inputs the solution using the terminal, the emotion engine is activated again, and facial and voice data are collected. The input consists of the solution content and emotion data, and the output is the solution data sent to the server. Emotion analysis is performed again here, and a new emotional state is recognized.

[0532] Step 6:

[0533] The server executes an evaluation algorithm based on the received solution data. A generative AI model is used to calculate sentiment scores. The inputs are solution data and sentiment data, and the output is an evaluation result including sentiment scores. The evaluation results are quantified and used in subsequent processing.

[0534] Step 7:

[0535] The server provides feedback on the evaluation results to the user (company) or solution provider's terminal based on the generated sentiment score. The input is the evaluation result, and the output is the evaluation information displayed on the user's screen. This allows the user to select a problem solution that takes sentiment evaluation into account. Specifically, measures to reduce the emotional burden of candidate solutions are presented.

[0536] (Application Example 2)

[0537] 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."

[0538] Conventional information processing systems handled problems and solutions without considering the user's emotional state, resulting in a lack of intuitive emotional elements in solution selection and evaluation. Therefore, there is a need for reliable solution selection and evaluation that reflects the user's emotions.

[0539] 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.

[0540] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for acquiring the user's emotional state, means for executing an algorithm that evaluates solutions and generates an emotional score based on the acquired emotional data, and means for optimally displaying solutions based on the generated emotional score. This makes it possible to select and evaluate more appropriate and reliable solutions that reflect the user's emotions.

[0541] An "information processing device" is a machine or device used for collecting, processing, and managing data, and refers to computers and smart devices.

[0542] "User emotional state" refers to the type and intensity of emotions a user is currently experiencing, based on information obtained from facial expressions, voice, and other sources.

[0543] "Emotional data" refers to information that indicates a user's emotional state, and is a collection of data that includes numerical values ​​and indicators obtained through facial recognition and voice analysis.

[0544] An "algorithm" is a set of rules that define the procedures or calculation methods for solving a specific problem.

[0545] An "emotion score" is an index used to quantify and evaluate a user's emotional data, indicating the impact a user's emotional state has on a solution.

[0546] "Optimal display" means using the most effective or efficient arrangement and order when displaying information based on specific criteria.

[0547] "Contract information" refers to a collection of important information generated based on the selected solution, which is necessary to formally establish commercial transactions and business relationships.

[0548] The system for realizing this invention acquires the user's emotional state in real time and performs a series of processes to select and evaluate solutions based on that state.

[0549] The server uses computers or smart devices as information processing devices to receive input from user terminals. The terminals collect emotional data from the user's facial expressions and voice using cameras and microphones. Here, visual data is processed using Microsoft Azure's Face API and Google Cloud's Vision API, and voice data is analyzed using Google Cloud Speech-to-Text API.

[0550] The server stores the acquired sentiment data in a real-time database such as Firebase, and analyzes the sentiment data and user-entered issue information on the server side using Node.js. It then analyzes the generated sentiment score and displays the most suitable solution based on the user's emotional state.

[0551] For example, if a user is considering purchasing a relaxation product on an online shopping site, the system can prioritize displaying appropriate relaxation products if they experience stress during the purchase process. This flexible approach, tailored to the user's emotions, aims to improve the shopping experience.

[0552] Examples of prompt statements for a generative AI model are as follows:

[0553] "Devise a method to analyze the emotions users experience while browsing products and optimize product recommendations based on that analysis."

[0554] "Design a new approach that leverages emotion recognition capabilities to improve the user experience on e-commerce websites."

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] When a user visits an online shopping site, the device activates its camera and microphone to collect the user's facial expressions and voice in real time. The data collected at this time becomes the input. Here, the image data from the camera is sent to Microsoft Azure's Face API, and the voice data is sent to Google Cloud Speech-to-Text API. This results in the output of numerical emotion data that indicates the user's emotional state.

[0558] Step 2:

[0559] The server receives emotion data sent from the terminal. The input at this time is numerical emotion data. The server stores this data in Firebase and uses Node.js to analyze the emotion data stored in the database and the product browsing information entered by the user. The analysis process outputs analysis results, including the user's emotion score.

[0560] Step 3:

[0561] The server uses a generative AI model based on the analysis results to optimize product recommendations, taking into account the current user sentiment. Here, prompt text is input into the generative AI model, and the model outputs an optimized product list. This output becomes the product suggestion list to be presented to the user.

[0562] Step 4:

[0563] The user receives an optimized product suggestion list sent from the server on their device screen. This list takes the user's emotional state into consideration and is displayed in a way that prioritizes products the user is looking for. The device then provides the user with the functionality to select or purchase products using this list.

[0564] Step 5:

[0565] The server combines the user's purchase history, list selection information, and sentiment score, and stores the new analytical data in the database to reflect the data and further optimize future suggestions. This data serves as input data to improve the accuracy of future suggestions, leading to an improved user experience.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] [Fourth Embodiment]

[0570] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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".

[0583] The present invention provides a system that efficiently solves a company's technical challenges using an information processing device. This system has a mechanism that allows multiple users to input and make public challenge information. Corporate users input challenge information that reflects their company's technical needs from a terminal, and the information processing device stores this information in a database and makes it public to other users.

[0584] Users of the solution provider input solutions to publicly available challenges via a terminal and send them to the information processing device. In this system, the information processing device automatically evaluates the received solutions using an algorithm and generates a score for each solution. This allows corporate users to view the evaluation results of each proposal via the terminal, making it easy to select the optimal solution.

[0585] For example, if a manufacturing company is seeking to improve its production process and needs a solution, the company uses a terminal to post information about its challenges aimed at improving efficiency to the system. In response, a software company developing new technologies provides a solution via the terminal, such as automating the production line using image recognition. The information processing device evaluates the provided solution, and if it receives a high score, the manufacturing company can select that solution and use it to improve its actual process.

[0586] Thus, the system of the present invention enables effective matching between companies and solution providers, contributing to the resolution of companies' technical challenges.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The user (company) accesses the issue information input screen using a terminal and enters their technical issue. The issue information includes the issue title, detailed description, and the requirements for the desired solution.

[0590] Step 2:

[0591] The terminal sends the entered assignment information to the server. The server receives the transmitted information and saves it to its database. This prepares the assignment information for public sharing with other users.

[0592] Step 3:

[0593] The user (solution provider) uses a terminal to view a list of published problem information and selects a problem of interest. They then devise a solution for the selected problem and input the solution via the terminal.

[0594] Step 4:

[0595] The terminal sends the entered solution to the server. The server receives the submitted solution and records it in a database. Then, it passes the solution to an AI algorithm for evaluation and scoring.

[0596] Step 5:

[0597] The server stores the scores of the evaluated solutions in a database and ranks the solutions based on those scores. Users (companies) can view the evaluation scores and solution details via their terminals.

[0598] Step 6:

[0599] The user (company) operates a terminal to select the most suitable solution. The selection result is notified to the server, which then saves the selection information to a database.

[0600] Step 7:

[0601] The server generates contract information based on the selected solution and provides it to both the user (company) and the user (solution provider). The contract is formally concluded when both parties review and agree to the terms and conditions.

[0602] Step 8:

[0603] After the contract is finalized, the server manages the process of paying the user (solution provider) a success fee. Payment information is recorded in a database, and the user can check it on their device.

[0604] (Example 1)

[0605] 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".

[0606] In solving technical challenges within companies, there is a need for a system that efficiently discloses problem information and provides appropriate countermeasures. Furthermore, it is necessary to automate the evaluation of disclosed countermeasures and quickly select the most suitable solution. In addition, there is a need to address the current lack of security measures to ensure the reliability of information and the sophisticated analytical capabilities required for the proposed solutions.

[0607] 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.

[0608] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for storing task information, means for analyzing proposals using natural language processing technology with a machine learning model, and means for security functions that analyze access data and monitor trends. This enables efficient publication of tasks, rapid and reliable selection of countermeasures, and advanced analysis of proposal content.

[0609] An "information processing device" is a combination of hardware and software for inputting, processing, storing, and outputting data.

[0610] "Issue information" refers to data that describes in detail the requirements and conditions that need to be resolved in a particular technical problem or situation.

[0611] "Users" refer to ordinary people, organizations, or companies that use the system, and are the entities that input problem information or provide solutions.

[0612] A "solution" refers to information that presents specific solutions or methods proposed based on publicly available problem information.

[0613] An "algorithm" refers to a set of calculation procedures or equations that evaluate a solution and score it based on certain criteria.

[0614] A "score" is a numerical value that quantifies the quality and suitability of the submitted solution, and is used as a criterion for evaluation.

[0615] A "machine learning model" refers to artificial intelligence technology that can learn patterns from data and perform natural language processing and other analytical tasks.

[0616] "Security features" refer to mechanisms that analyze access data and monitor for fraudulent activity and security threats in order to ensure the safety of information.

[0617] This invention is an information processing system that efficiently solves technical challenges for companies. This system has a function where users input challenge information from a terminal, a server stores that information in a database, and then makes it available to other users. The challenge information is data that describes in detail the specific technical needs of the company user, and solution providers can input countermeasures in response.

[0618] The server receives task information submitted by users and stores it in a relational database such as MySQL or PostgreSQL. This stored information is then provided to users as a RESTful API when it becomes publicly available. Furthermore, the submitted solutions to the tasks are first validated through text analysis, and then evaluated by a machine learning model. Libraries such as scikit-learn and TensorFlow could be used for this machine learning model.

[0619] This evaluation process utilizes generative AI models and leverages natural language processing technology. This allows the server to assign scores to each countermeasure, enabling users to easily select the optimal solution through their terminals. Furthermore, the server continuously analyzes access data and monitors for unauthorized access through security features.

[0620] As a concrete example, consider a scenario where a manufacturing company submits a challenge requesting improvements to its production process. This company uses a terminal to input information about the challenges in improving production efficiency into the system. In response, another technology company can provide an automation solution for the production line using image recognition technology. The server evaluates this solution using a generative AI model and assigns an appropriate score. As a result, the manufacturing company can easily identify high-scoring solutions and implement them in practice.

[0621] An example of a prompt message is, "Please provide specific steps on how to improve labor efficiency in the production process by utilizing image recognition technology."

[0622] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0623] Step 1:

[0624] The user enters issue information using a terminal. The entered issue information is text data that describes the company's technical needs in detail. This input data is sent from the terminal to the server via HTTPS communication. The server initially validates the received issue information. This validation checks for SQL injection and inappropriate content to obtain clean data.

[0625] Step 2:

[0626] The server saves issue information that has passed validation to a database. Data with fields such as "Issue ID," "Company Name," and "Issue Details" is stored in a relational database (e.g., MySQL). The saved issue information is indexed so that other users can search it and access it via a RESTful API.

[0627] Step 3:

[0628] Users enter solutions based on published challenges using their devices. The entered solutions are sent as text from the device to the server. The server validates this data again and converts it into a format suitable for analysis by machine learning models.

[0629] Step 4:

[0630] The server evaluates received solutions using generative AI models and natural language processing techniques. It analyzes text data and generates relevant evaluation metrics, including criteria such as content quality, relevance, and originality. The model analyzes the input text and outputs a score. TensorFlow and scikit-learn may be used in this process.

[0631] Step 5:

[0632] The server stores the evaluation results and scores in a database. The score for each solution is stored along with its associated metadata. This allows users to later query the evaluation results of the solutions.

[0633] Step 6:

[0634] Users view the solution evaluation results through their terminals. Solutions are listed in order of score via a dashboard displayed on the terminal. Based on the displayed evaluation results, users can easily select the optimal solution. The server also analyzes user access logs and performs security monitoring. This detects unauthorized access and abnormal behavior, ensuring secure operation.

[0635] (Application Example 1)

[0636] 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".

[0637] To efficiently optimize business processes in industrial facilities, it is necessary to quickly identify and implement appropriate solutions to technical challenges. However, existing systems require manual identification of challenges and evaluation of solutions, which is time-consuming and labor-intensive. Furthermore, there is a lack of mechanisms to objectively evaluate the quality of solutions submitted by solution providers, making it difficult to select the optimal solution.

[0638] 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.

[0639] In this invention, the server is a data processing device equipped with a function for inputting problems, and includes means for storing problem information, means for making the stored problem information available to other users, and means for executing an algorithm that evaluates input solutions and generates a score. This enables the rapid identification of solutions to technical problems aimed at optimizing operations in industrial facilities and the objective evaluation of high-quality solutions.

[0640] A "challenge" refers to a specific problem or need that requires improvement in an industrial facility or business process.

[0641] A "data processing device" is a computer system that stores and processes information and performs necessary functions based on user input.

[0642] A "solution" is a set of improvements or technical suggestions offered to address a specific problem.

[0643] A "score" is a quantitative indicator given to evaluate the effectiveness and suitability of a proposed solution.

[0644] "Optimizing industrial facility operations" refers to initiatives aimed at improving production efficiency and business processes, thereby enhancing overall performance.

[0645] An "algorithm" refers to a set of computational procedures or rules designed for solving problems or processing data.

[0646] The system that realizes this invention uses a data processing device to automate the process of problem management and solution evaluation in industrial facilities.

[0647] The server receives issue information from users and stores it in a database. This issue information includes specific improvement needs and technical requirements for industrial facilities. Users can input this information from devices such as smartphones and tablets. Furthermore, this issue information is made public to other users, creating an environment where solution providers can propose appropriate solutions.

[0648] The server receives submitted solutions and evaluates each solution using a pre-configured algorithm. This algorithm utilizes a generative AI model to generate scores, quantifying the effectiveness and feasibility of each solution. This allows enterprise users to select the most effective solution through their terminals. Furthermore, solutions are ranked and visually displayed based on the generated scores, making it easier for users to make the optimal choice.

[0649] As a concrete example, let's consider a challenge related to improving the efficiency of machinery operating in a manufacturing plant. In this case, an engineer posts information about the challenge, "Development of a new control algorithm to improve machine efficiency," to the system. Solution providers can then propose a "control system utilizing real-time data analysis," and the server automatically assigns a score to each proposal. The user can then view the displayed score and select the most appropriate control algorithm.

[0650] An example of a prompt message might be, "Please propose a control system that utilizes real-time data analysis to improve machine efficiency."

[0651] This invention enables efficient and rapid resolution of technical challenges in industrial facilities.

[0652] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0653] Step 1:

[0654] Users input issue information using a terminal. This input includes technical problems and processes requiring improvement within industrial facilities. The server receives the issue information submitted by the user and stores it in a database. This issue information includes text data and related metadata.

[0655] Step 2:

[0656] The server publishes the stored issue information to other solution providers. The published issue information is displayed through a user interface so that other users who can provide solutions can view it. This process includes access control to the issue information, ensuring that interested users can view the issue.

[0657] Step 3:

[0658] Solution providers review the published issues and enter solutions using their own devices. Once the solutions are sent from the device to the server, the server stores them in a database. The solutions include technical suggestions and are expressed in text format.

[0659] Step 4:

[0660] The server evaluates the input solutions using a generative AI model. The evaluation process analyzes the effectiveness and suitability of the solutions and generates scores based on the algorithm. It uses text data of the solutions as input and generates numerical scores as output.

[0661] Step 5:

[0662] The server ranks the solutions based on the generated scores and displays them visually to make it easier for the user to choose. The solutions and their scores are displayed on the terminal, allowing the user to make the best choice based on the scores. A ranked list of solutions is generated as output.

[0663] Step 6:

[0664] The user selects the most suitable solution from those displayed on the terminal and saves this selection information to the server. This selection information includes the ID of the selected solution and the reason for its selection. The selected solution is then used for subsequent decision-making and contract preparation.

[0665] 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.

[0666] The present invention combines an emotion engine with an information processing device to recognize the user's emotional state and apply it when inputting tasks and solutions. This system consists of terminals used by companies and solution providers, and a server that manages the data.

[0667] When a user (company) accesses the issue information input screen using a terminal, the system activates an emotion engine and acquires emotion data from the user's facial expressions, voice, etc. This emotion data is sent to the server along with the issue information and stored in the database as supplementary information to deepen understanding of the issue.

[0668] On the other hand, when users (solution providers) input the solutions they offer, their emotions are recognized by an emotion engine through their terminal. Based on this data, the server can be incorporated into a process to generate an emotion score in order to more accurately evaluate the quality of the solutions. Evaluation results that reflect the user's emotional state enable companies to make more reliable solution selections.

[0669] For example, when a company submits a technology-related challenge, the emotion engine can detect whether the user is experiencing stress. This information helps to indicate that the challenge is urgent. When solution providers submit proposals, the emotion engine captures emotions that indicate their confidence and passion, and this is taken into account when evaluating the proposal.

[0670] Thus, by using an emotion engine, the system of the present invention not only solves technical problems but also achieves advanced matching and evaluation that takes into account the user's emotional state.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] The user (company) accesses the problem information input screen using a terminal and describes the technical problem. Simultaneously with the input of the problem information, the terminal activates an emotion engine to analyze the user's facial expressions and voice to detect their current emotional state.

[0674] Step 2:

[0675] The terminal sends the detected emotion data along with the entered task information to the server. The server receives this information and stores it in a database as additional context for understanding the task.

[0676] Step 3:

[0677] The user (solution provider) views the published challenges through the terminal and inputs the corresponding solutions. The terminal utilizes an emotion engine during the input process to recognize the user's emotions in real time.

[0678] Step 4:

[0679] The terminal sends the user-inputted solution and corresponding sentiment data to the server. The server receives this data and performs a solution evaluation process that includes the sentiment data. Sentiment is considered as an indicator of the user's confidence and motivation.

[0680] Step 5:

[0681] The server generates evaluation scores, including sentiment data, and stores them in a database. Users (companies) then review these scores on their devices and select the optimal solution.

[0682] Step 6:

[0683] The user (company) notifies the server of their selected solution from their terminal, and the server records the selection results. Based on this selection, contract information is generated and shared with both users.

[0684] Step 7:

[0685] After the contract is approved, the server manages the payment of success fees to the user (solution provider) and notifies the user via the terminal. All payment information is recorded in the database.

[0686] (Example 2)

[0687] 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".

[0688] Conventional information processing systems evaluate issues and solutions without considering the user's emotional state, making it difficult to accurately determine the urgency of an issue or the applicability of a solution. This could lead to problems such as urgent issues being neglected or the optimal solution not being selected.

[0689] 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.

[0690] In this invention, the server includes means for acquiring and storing problem information and the user's emotional state; means for providing the problem information to other users using the acquired emotional state and storing the emotional information as supplementary data; and means for executing an algorithm that evaluates the input solution using the user's emotional data and generates an emotional score. This makes it possible to evaluate problems and select solutions that take the user's emotional state into consideration, thereby providing a more appropriate problem-solving process.

[0691] "Information processing equipment" is a general term for electronic devices used to input, process, store, and display data, and includes terminals used by users to input tasks and solutions.

[0692] "User emotional state" refers to data that indicates the user's psychological state, obtained by analyzing the user's facial expressions and voice.

[0693] An "emotion engine" is software or hardware that automatically acquires and quantifies a user's emotional state from their facial expressions and voice.

[0694] "Emotional data" refers to data that quantifies or expresses a user's emotional state as textual information, and is used to evaluate problems and solutions.

[0695] An "emotion score" is a numerical value generated based on acquired emotional data, and it serves as a criterion for evaluating the importance and applicability of issues and solutions.

[0696] An "evaluation algorithm" refers to a series of calculation steps that analyze input data and generate a score based on specific evaluation criteria.

[0697] A "solution" refers to a method or means proposed to solve a specific problem.

[0698] "Selection information" refers to data on the optimal solution selected within the server, including the reasons for that selection.

[0699] "Contract information" refers to data that describes transactions and terms of use based on agreements between users, and includes important elements when a contract is formed.

[0700] The system of this invention combines an information processing device with an emotion engine, recognizing the user's emotional state and applying it to the input of tasks and solutions. The system consists of terminals used by companies and solution providers, and a server that manages the data.

[0701] Users (companies) input issue information through a terminal. The terminal uses an emotion engine to capture the user's facial expressions and voice, and recognize their emotional state. Specifically, libraries such as OpenCV are used for facial recognition, and the Google Cloud Speech-to-Text API is used for voice analysis. The acquired emotion data is transferred to the server along with the issue information.

[0702] The server organizes and stores the received data in a database management system (DBMS). Sentimental data is used as supplementary information to gain a deeper understanding of the issues. Furthermore, the server uses a generative AI model to generate sentimental scores based on the sentimental data. These sentimental scores are used to evaluate the issues and solutions.

[0703] When a user (solution provider) inputs a solution using a terminal, emotional data recognized by the emotion engine is sent to the server. Based on this data, the server runs an algorithm to evaluate the quality of the solution, enabling the selection of a reliable solution.

[0704] As a concrete example, when a company submits a challenge regarding the launch of a product into a new market, the emotion engine detects the user's stress level. This information serves as an indicator of the urgency of the challenge. Furthermore, emotional data is captured when solution providers enter their proposals, and their beliefs and passions are reflected in the evaluation. An example of a prompt might be, "Propose a solution that reflects the emotional score based on anxiety about the new product." This enables sophisticated solution matching that takes emotions into account.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The user (company) activates the information processing terminal and accesses the task information input screen. The input at this time includes the task details and supplementary materials. The terminal displays a user interface and activates the emotion engine. Facial expression and voice data are collected in real time and input into the terminal as emotion data. Specifically, video is captured from the webcam and audio is input from the microphone.

[0708] Step 2:

[0709] The device analyzes collected facial and audio data using an emotion engine. Here, OpenCV is used to extract facial feature points, and the Google Cloud Speech-to-Text API is used to convert the audio to text. From this data, the user's current emotional state (e.g., stress level) is quantified. The input is facial and audio data, and the output is emotion data.

[0710] Step 3:

[0711] The device sends the generated emotion data along with the task information to the server. The input is the task information and emotion data, and the output is the transmitted data sent to the server. Data encryption is performed during this process to ensure security.

[0712] Step 4:

[0713] The server stores the received data in a database management system (DBMS). The input is the transmitted data, and the output is structured data stored in the database. Within the database, the server associates and stores issue information with sentiment data. Specifically, this involves writing data using SQL queries.

[0714] Step 5:

[0715] When the user (solution provider) inputs the solution using the terminal, the emotion engine is activated again, and facial and voice data are collected. The input consists of the solution content and emotion data, and the output is the solution data sent to the server. Emotion analysis is performed again here, and a new emotional state is recognized.

[0716] Step 6:

[0717] The server executes an evaluation algorithm based on the received solution data. A generative AI model is used to calculate sentiment scores. The inputs are solution data and sentiment data, and the output is an evaluation result including sentiment scores. The evaluation results are quantified and used in subsequent processing.

[0718] Step 7:

[0719] The server provides feedback on the evaluation results to the user (company) or solution provider's terminal based on the generated sentiment score. The input is the evaluation result, and the output is the evaluation information displayed on the user's screen. This allows the user to select a problem solution that takes sentiment evaluation into account. Specifically, measures to reduce the emotional burden of candidate solutions are presented.

[0720] (Application Example 2)

[0721] 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".

[0722] Conventional information processing systems handled problems and solutions without considering the user's emotional state, resulting in a lack of intuitive emotional elements in solution selection and evaluation. Therefore, there is a need for reliable solution selection and evaluation that reflects the user's emotions.

[0723] 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.

[0724] In this invention, the server is an information processing device equipped with a function for inputting tasks, and includes means for acquiring the user's emotional state, means for executing an algorithm that evaluates solutions and generates an emotional score based on the acquired emotional data, and means for optimally displaying solutions based on the generated emotional score. This makes it possible to select and evaluate more appropriate and reliable solutions that reflect the user's emotions.

[0725] An "information processing device" is a machine or device used for collecting, processing, and managing data, and refers to computers and smart devices.

[0726] "User emotional state" refers to the type and intensity of emotions a user is currently experiencing, based on information obtained from facial expressions, voice, and other sources.

[0727] "Emotional data" refers to information that indicates a user's emotional state, and is a collection of data that includes numerical values ​​and indicators obtained through facial recognition and voice analysis.

[0728] An "algorithm" is a set of rules that define the procedures or calculation methods for solving a specific problem.

[0729] An "emotion score" is an index used to quantify and evaluate a user's emotional data, indicating the impact a user's emotional state has on a solution.

[0730] "Optimal display" means using the most effective or efficient arrangement and order when displaying information based on specific criteria.

[0731] "Contract information" refers to a collection of important information generated based on the selected solution, which is necessary to formally establish commercial transactions and business relationships.

[0732] The system for realizing this invention acquires the user's emotional state in real time and performs a series of processes to select and evaluate solutions based on that state.

[0733] The server uses computers or smart devices as information processing devices to receive input from user terminals. The terminals collect emotional data from the user's facial expressions and voice using cameras and microphones. Here, visual data is processed using Microsoft Azure's Face API and Google Cloud's Vision API, and voice data is analyzed using Google Cloud Speech-to-Text API.

[0734] The server stores the acquired sentiment data in a real-time database such as Firebase, and analyzes the sentiment data and user-entered issue information on the server side using Node.js. It then analyzes the generated sentiment score and displays the most suitable solution based on the user's emotional state.

[0735] For example, if a user is considering purchasing a relaxation product on an online shopping site, the system can prioritize displaying appropriate relaxation products if they experience stress during the purchase process. This flexible approach, tailored to the user's emotions, aims to improve the shopping experience.

[0736] Examples of prompt statements for a generative AI model are as follows:

[0737] "Devise a method to analyze the emotions users experience while browsing products and optimize product recommendations based on that analysis."

[0738] "Design a new approach that leverages emotion recognition capabilities to improve the user experience on e-commerce websites."

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] When a user visits an online shopping site, the device activates its camera and microphone to collect the user's facial expressions and voice in real time. The data collected at this time becomes the input. Here, the image data from the camera is sent to Microsoft Azure's Face API, and the voice data is sent to Google Cloud Speech-to-Text API. This results in the output of numerical emotion data that indicates the user's emotional state.

[0742] Step 2:

[0743] The server receives emotion data sent from the terminal. The input at this time is numerical emotion data. The server stores this data in Firebase and uses Node.js to analyze the emotion data stored in the database and the product browsing information entered by the user. The analysis process outputs analysis results, including the user's emotion score.

[0744] Step 3:

[0745] The server uses a generative AI model based on the analysis results to optimize product recommendations, taking into account the current user sentiment. Here, prompt text is input into the generative AI model, and the model outputs an optimized product list. This output becomes the product suggestion list to be presented to the user.

[0746] Step 4:

[0747] The user receives an optimized product suggestion list sent from the server on their device screen. This list takes the user's emotional state into consideration and is displayed in a way that prioritizes products the user is looking for. The device then provides the user with the functionality to select or purchase products using this list.

[0748] Step 5:

[0749] The server combines the user's purchase history, list selection information, and sentiment score, and stores the new analytical data in the database to reflect the data and further optimize future suggestions. This data serves as input data to improve the accuracy of future suggestions, leading to an improved user experience.

[0750] 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.

[0751] 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.

[0752] In the above embodiment, an example was given in which the 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] An information processing device equipped with a function for inputting tasks, comprising means for storing task information,

[0774] A means of making saved assignment information public to other users,

[0775] A means of entering and saving solutions based on published challenges,

[0776] A means for executing an algorithm that evaluates the input solution and generates a score,

[0777] A means of displaying the solution based on the generated score,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, which selects a solution suitable for solving a problem and stores the selection information.

[0781] (Claim 3)

[0782] The system according to claim 1, which generates contract information based on selected solutions and manages contracts.

[0783] "Example 1"

[0784] (Claim 1)

[0785] An information processing device equipped with a function for inputting tasks, comprising means for storing task information,

[0786] A means of providing saved assignment information to other users,

[0787] A means of inputting and saving countermeasures based on the provided issues,

[0788] A means for executing an algorithm that evaluates the input countermeasures and generates evaluation metrics,

[0789] A means of displaying countermeasures based on the generated evaluation indicators,

[0790] A method for analyzing proposals using natural language processing techniques with machine learning models,

[0791] A means of providing security functions that analyze access data and monitor trends,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, which selects a suitable countermeasure for solving a problem and stores the selection information.

[0795] (Claim 3)

[0796] The system according to claim 1, which generates contract information based on selected countermeasures and manages contracts.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A data processing device equipped with a function for inputting tasks, comprising means for storing task information,

[0800] A means of making saved assignment information available to other users,

[0801] A means of entering and saving solutions based on published issues,

[0802] A means for executing an algorithm that evaluates the input solution and generates a score,

[0803] A means of displaying solutions based on the generated score,

[0804] A means of combining challenges and solutions for optimizing operations in industrial facilities through data processing equipment,

[0805] A means of selecting the optimal business improvement measures based on the score of the solutions,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, which selects a solution suitable for solving a problem and stores the selection information.

[0809] (Claim 3)

[0810] The system according to claim 1, which generates contract information based on selected solutions and manages contracts.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] An information processing device equipped with a task input function, comprising means for acquiring and storing task information and the user's emotional state,

[0814] A means of providing task information to other users using the acquired emotional state, and storing the emotional information as supplementary data,

[0815] A means of inputting a solution based on the given challenge and acquiring user sentiment data,

[0816] A means for executing an algorithm that evaluates an input solution using user sentiment data and generates a sentiment score,

[0817] A means for displaying solutions based on the generated sentiment score and providing results that take sentiment evaluation into account,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, which selects a suitable solution based on emotional evaluation to solve a problem and stores the selection information.

[0821] (Claim 3)

[0822] The system according to claim 1, which generates contract information based on the selected solution and performs contract management including sentiment evaluation.

[0823] "Application example 2 when combining with an emotional engine"

[0824] (Claim 1)

[0825] An information processing device equipped with a function for inputting tasks, comprising means for acquiring the user's emotional state,

[0826] A means of saving emotional data when inputting tasks,

[0827] A means of making saved task information and sentiment data public to other users,

[0828] A means to input and save solutions based on publicly available challenges, and to capture the emotions at the time of input,

[0829] A means of executing an algorithm that evaluates the solution and generates an emotional score based on acquired emotional data,

[0830] A means of optimally displaying solutions based on the generated sentiment score,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, which selects a solution suitable for solving a problem based on emotional data and stores the selection information.

[0834] (Claim 3)

[0835] The system according to claim 1, which generates contract information that takes into account sentiment scores based on selected solutions and manages contracts. [Explanation of Symbols]

[0836] 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. An information processing device equipped with a function for inputting tasks, comprising means for storing task information, A means of making saved assignment information public to other users, A means of entering and saving solutions based on published challenges, A means for executing an algorithm that evaluates the input solution and generates a score, A means of displaying the solution based on the generated score, A system that includes this.

2. The system according to claim 1, which selects a solution suitable for solving a problem and stores the selection information.

3. The system according to claim 1, which generates contract information based on the selected solution and manages the contract.

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